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a4111641-4d18-4dbd-8371-8d2fbf3316e3	is-that-a-chair-imagining-affordances-using	1909.07572	null	https://arxiv.org/abs/1909.07572v2	https://arxiv.org/pdf/1909.07572v2.pdf	Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body	For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.	['Gregory S. Chirikjian', 'Hongtao Wu', 'Deven Misra']	2019-09-17	null	null	null	null	['physical-simulations']	['miscellaneous']	[ 1.15121566e-01 4.79079396e-01 2.48702168e-01 -4.39396143e-01 2.41370071e-02 -5.69388866e-01 6.16597950e-01 -1.62366480e-01 -1.86154664e-01 3.63115788e-01 -4.10847038e-01 -2.31309310e-01 -5.08861467e-02 -7.50798941e-01 -1.08736718e+00 -5.88602602e-01 4.85092252e-02 8.20482135e-01 1.95593163e-01 -1.69111609e-01 1.25558525e-01 9.58072960e-01 -1.66219366e+00 1.31465659e-01 6.93763196e-01 1.05749643e+00 8.28783989e-01 4.06408042e-01 2.53748864e-01 4.23649728e-01 -5.70111036e-01 -5.82694188e-02 4.11130130e-01 -1.35026380e-01 -9.24699962e-01 5.32792926e-01 3.32185566e-01 -4.94224459e-01 4.04650010e-02 9.88953412e-01 -1.08709015e-01 9.35302675e-02 1.11292815e+00 -1.29032946e+00 -5.90929449e-01 2.36260727e-01 -2.02747509e-01 -5.74759364e-01 7.42337584e-01 1.78133830e-01 9.71559763e-01 -8.46787930e-01 6.62306309e-01 1.38961732e+00 2.19821721e-01 6.77025259e-01 -1.22560525e+00 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5.56322753e-01 2.91553229e-01 -1.29561281e+00 -3.40307415e-01 1.16045602e-01]	[5.853400707244873, -0.8138607740402222]
ef5fe8f1-b8ea-4d35-a569-a87a1e83da4c	fast-vehicle-detection-algorithm-based-on	2304.06002	null	https://arxiv.org/abs/2304.06002v3	https://arxiv.org/pdf/2304.06002v3.pdf	Fast vehicle detection algorithm based on lightweight YOLO7-tiny	The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed; then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information; and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species; finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. The WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on KITTI and BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance.	['Fei Zhong', 'Hao Xu', 'Yihua Chen', 'Bo Li']	2023-04-12	null	null	null	null	['fast-vehicle-detection']	['computer-vision']	[-2.73329526e-01 -3.95474583e-01 2.11858302e-02 -2.06098974e-01 -1.59982517e-01 -2.02946946e-01 4.86364305e-01 -3.47598255e-01 -7.63538599e-01 2.58170724e-01 -2.77467340e-01 -2.72394240e-01 3.57017666e-01 -9.92863595e-01 -7.05321491e-01 -9.39454079e-01 -3.54179665e-02 -1.37288466e-01 9.79118049e-01 -1.39589399e-01 1.01117097e-01 5.69507480e-01 -1.69711423e+00 -1.61249399e-01 9.01182830e-01 1.13086367e+00 6.17768586e-01 7.02174425e-01 6.01675175e-02 4.05302703e-01 -4.02323991e-01 -1.21801712e-01 2.98143834e-01 3.80393982e-01 -3.81084532e-02 -3.50235820e-01 3.71887058e-01 -6.99268639e-01 -5.70831597e-01 1.06378961e+00 6.15610242e-01 9.45705920e-02 5.30213833e-01 -1.66950870e+00 -2.47277752e-01 1.66340739e-01 -1.00658083e+00 3.08131844e-01 -3.35598469e-01 4.18773979e-01 4.06881690e-01 -9.51982021e-01 3.22492719e-01 1.41892731e+00 1.00961924e+00 3.66329521e-01 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f36c9bc1-ec46-48fa-87c9-9bb75b7a116c	optimal-sizing-of-a-holdout-set-for-safe	2202.06374	null	https://arxiv.org/abs/2202.06374v3	https://arxiv.org/pdf/2202.06374v3.pdf	Optimal sizing of a holdout set for safe predictive model updating	Predictive risk scores are increasingly used to guide clinical or other interventions in complex settings, particularly healthcare. Directly updating a risk score used to guide interventions leads to biased risk estimates. We propose updating using a `holdout set' -- a subset of the population that does not receive risk-score-guided interventions -- to prevent this. Since samples in the holdout set do not benefit from risk predictions, its size must trade off performance of the updated risk score whilst minimising the number of held out samples. We prove that this approach outperforms simple alternatives, and by defining a general loss function describe conditions under which an optimal holdout size (OHS) can be readily identified. We introduce parametric and semi-parametric algorithms for OHS estimation and demonstrate their use on a recent risk score for pre-eclampsia. Based on these results, we argue that a holdout set is a safe, viable and easily implemented means to safely update predictive risk scores.	['James Liley', 'Louis J M Aslett', 'Samuel R Emerson', 'Sami Haidar-Wehbe']	2022-02-13	null	null	null	null	['holdout-set']	['computer-vision']	[ 4.70879465e-01 6.23617589e-01 -6.95481896e-01 -5.62319577e-01 -9.21843529e-01 -3.89438659e-01 2.70308852e-01 7.33720422e-01 -4.91847962e-01 8.94833446e-01 1.88399285e-01 -7.40749478e-01 -7.37790942e-01 -8.76008987e-01 -5.24202108e-01 -5.63782215e-01 -6.06547058e-01 9.82139051e-01 1.64581627e-01 2.56873757e-01 2.74740487e-01 4.76207018e-01 -1.10035431e+00 9.71052349e-02 1.29058313e+00 6.12864494e-01 -2.79683590e-01 7.40436018e-01 2.22779423e-01 6.77564383e-01 -4.17663574e-01 -5.21937907e-01 4.16226327e-01 -7.31769204e-01 -5.11883557e-01 -5.25983453e-01 1.48510918e-01 -6.11934662e-01 2.75827914e-01 6.72163129e-01 7.19989717e-01 1.40059695e-01 1.05194378e+00 -1.03985190e+00 1.03119880e-01 7.24883616e-01 -2.45877504e-01 2.51061797e-01 2.88766287e-02 -1.41651973e-01 7.49853790e-01 -2.41257995e-01 3.93232644e-01 1.11948276e+00 1.17156339e+00 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851dcb8c-4082-4976-b8e9-7c72edcd9f85	multi-view-response-selection-for-human	null	null	https://aclanthology.org/D16-1036	https://aclanthology.org/D16-1036.pdf	Multi-view Response Selection for Human-Computer Conversation	null	['dianhai yu', 'daxiang dong', 'Hua Wu', 'Hao Tian', 'Xuan Liu', 'Xiangyang Zhou', 'Shiqi Zhao', 'Rui Yan']	2016-11-01	null	null	null	emnlp-2016-11	['conversational-response-selection']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 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6b7f8476-738b-4605-bd1f-9c99e9d785b0	genie-show-me-the-data-for-quantization	2212.0478	null	https://arxiv.org/abs/2212.04780v2	https://arxiv.org/pdf/2212.04780v2.pdf	Genie: Show Me the Data for Quantization	Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters ($\mu$ and $\sigma$) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on generating synthetic data. Subsequently, they distill knowledge from the pre-trained model (teacher) to the quantized model (student) such that the quantized model can be optimized with the synthetic dataset. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours. Furthermore, we propose a framework called \genie~that generates data suited for quantization. With the data synthesized by Genie, we can produce robust quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach.	['Ho-young Kim', 'Chungman Lee', 'Yongkweon Jeon']	2022-12-09	null	http://openaccess.thecvf.com//content/CVPR2023/html/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023_paper.pdf	cvpr-2023-1	['data-free-quantization', 'data-free-quantization']	['computer-vision', 'methodology']	[ 1.43145248e-01 4.36129063e-01 -1.44350544e-01 -6.21169448e-01 -9.87501323e-01 -1.58216693e-02 4.63604510e-01 -8.05990323e-02 -8.12967002e-01 8.78229141e-01 -1.32455349e-01 -1.95094600e-01 3.24499398e-01 -1.06366169e+00 -9.80859101e-01 -5.99149764e-01 2.89399683e-01 1.67385250e-01 -1.13879256e-02 -1.94522068e-01 -4.51987311e-02 4.81071994e-02 -1.77958024e+00 1.70211017e-01 9.00156081e-01 1.32186735e+00 6.12676367e-02 5.16546071e-01 2.37714310e-04 5.45458198e-01 -8.19158256e-01 -8.75185490e-01 3.52134258e-01 -5.27083635e-01 -6.39711440e-01 -1.84880748e-01 6.11639678e-01 -7.94928730e-01 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f42054a2-3f54-472a-be20-147c5971ff07	siamese-based-neural-network-for-offline	2211.14443	null	https://arxiv.org/abs/2211.14443v1	https://arxiv.org/pdf/2211.14443v1.pdf	Siamese based Neural Network for Offline Writer Identification on word level data	Handwriting recognition is one of the desirable attributes of document comprehension and analysis. It is concerned with the documents writing style and characteristics that distinguish the authors. The diversity of text images, notably in images with varying handwriting, makes the process of learning good features difficult in cases where little data is available. In this paper, we propose a novel scheme to identify the author of a document based on the input word image. Our method is text independent and does not impose any constraint on the size of the input image under examination. To begin with, we detect crucial components in handwriting and extract regions surrounding them using Scale Invariant Feature Transform (SIFT). These patches are designed to capture individual writing features (including allographs, characters, or combinations of characters) that are likely to be unique for an individual writer. These features are then passed through a deep Convolutional Neural Network (CNN) in which the weights are learned by applying the concept of Similarity learning using Siamese network. Siamese network enhances the discrimination power of CNN by mapping similarity between different pairs of input image. Features learned at different scales of the extracted SIFT key-points are encoded using Sparse PCA, each components of the Sparse PCA is assigned a saliency score signifying its level of significance in discriminating different writers effectively. Finally, the weighted Sparse PCA corresponding to each SIFT key-points is combined to arrive at a final classification score for each writer. The proposed algorithm was evaluated on two publicly available databases (namely IAM and CVL) and is able to achieve promising result, when compared with other deep learning based algorithm.	['Suresh Sundaram', 'Vineet Kumar']	2022-11-17	null	null	null	null	['handwriting-recognition']	['computer-vision']	[ 4.1286069e-01 -5.7098991e-01 -2.1627247e-01 -3.8684238e-02 -2.8468826e-01 -6.8418050e-01 7.9183930e-01 2.0630789e-01 -2.8067163e-01 2.9907221e-01 3.4565401e-01 3.6467737e-01 -4.0265614e-01 -6.1442715e-01 -4.2794830e-01 -8.5966176e-01 2.7995232e-01 3.2065105e-01 2.1966466e-01 -2.0678252e-01 1.0066280e+00 1.0328031e+00 -1.6381106e+00 4.7446269e-01 7.6510483e-01 1.0124562e+00 5.9788525e-01 6.4869553e-01 -3.4927160e-01 6.1139613e-01 -7.3910648e-01 -1.6276935e-01 3.3130664e-01 -4.0500268e-01 -4.0547389e-01 4.2450646e-01 6.8901217e-01 -2.9045761e-02 -5.8406764e-01 1.2368029e+00 4.3337318e-01 2.5481933e-01 9.1932976e-01 -8.6865526e-01 -9.3759096e-01 4.0551010e-01 -8.0528837e-01 6.1427319e-01 3.6493349e-01 -1.2262422e-02 1.0462214e+00 -1.1213107e+00 7.6379406e-01 1.0330062e+00 4.3357259e-01 2.5060555e-02 -8.2078272e-01 -3.5868084e-01 -7.9527140e-02 4.4844407e-01 -1.2800789e+00 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5b9e983d-28d0-4013-b0b0-2469287f5856	nuwa-infinity-autoregressive-over	2207.09814	null	https://arxiv.org/abs/2207.09814v2	https://arxiv.org/pdf/2207.09814v2.pdf	NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis	In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos. An autoregressive over autoregressive generation mechanism is proposed to deal with this variable-size generation task, where a global patch-level autoregressive model considers the dependencies between patches, and a local token-level autoregressive model considers dependencies between visual tokens within each patch. A Nearby Context Pool (NCP) is introduced to cache-related patches already generated as the context for the current patch being generated, which can significantly save computation costs without sacrificing patch-level dependency modeling. An Arbitrary Direction Controller (ADC) is used to decide suitable generation orders for different visual synthesis tasks and learn order-aware positional embeddings. Compared to DALL-E, Imagen and Parti, NUWA-Infinity can generate high-resolution images with arbitrary sizes and support long-duration video generation additionally. Compared to NUWA, which also covers images and videos, NUWA-Infinity has superior visual synthesis capabilities in terms of resolution and variable-size generation. The GitHub link is https://github.com/microsoft/NUWA. The homepage link is https://nuwa-infinity.microsoft.com.	['Nan Duan', 'Yuejian Fang', 'Zicheng Liu', 'Lijuan Wang', 'JianFeng Wang', 'Zhe Gan', 'Xiaowei Hu', 'Jian Liang', 'Chenfei Wu']	2022-07-20	null	null	null	null	['image-outpainting', 'video-generation']	['computer-vision', 'computer-vision']	[-8.91575292e-02 -1.15018830e-01 -1.28043205e-01 9.20791477e-02 -8.20767641e-01 -3.88666987e-01 5.57227850e-01 -3.34485620e-01 5.52087091e-02 5.89954615e-01 3.53027582e-01 -2.20086932e-01 1.06316730e-01 -1.11576116e+00 -8.90212893e-01 -6.39918506e-01 -4.56619896e-02 1.92876339e-01 2.89751679e-01 -1.25520304e-01 4.85430099e-02 4.37140316e-01 -1.64757538e+00 5.43218017e-01 7.59879231e-01 9.90045667e-01 8.37264657e-01 1.11045551e+00 -4.69410084e-02 7.86547065e-01 -6.33580744e-01 -5.61201833e-02 3.64539742e-01 -5.81227362e-01 -3.24711889e-01 2.81429946e-01 6.22402668e-01 -5.12416899e-01 -4.89284426e-01 6.52975380e-01 8.16108525e-01 1.40673965e-01 5.19902945e-01 -1.28575850e+00 -1.21913242e+00 5.60409665e-01 -6.67750716e-01 3.44505697e-01 1.79375425e-01 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862cd549-e986-471e-b8bc-2fd8f730a6fa	a-non-linear-function-on-function-model-for	2011.12378	null	https://arxiv.org/abs/2011.12378v1	https://arxiv.org/pdf/2011.12378v1.pdf	A Non-linear Function-on-Function Model for Regression with Time Series Data	In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate time series regression problem. Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations. The first two types of models can only handle regularly observed time series. Besides, the conventional multivariate regression models tend to be biased and inefficient, as they are incapable of encoding the temporal dependencies among observations from the same time series. The sequential learning models explicitly use the same set of parameters along time, which has negative impacts on accuracy. The function-on-function linear model in functional data analysis (a branch of statistics) is insufficient to capture complex correlations among the considered time series and suffer from underfitting easily. In this paper, we propose a general functional mapping that embraces the function-on-function linear model as a special case. We then propose a non-linear function-on-function model using the fully connected neural network to learn the mapping from data, which addresses the aforementioned concerns in the existing approaches. For the proposed model, we describe in detail the corresponding numerical implementation procedures. The effectiveness of the proposed model is demonstrated through the application to two real-world problems.	['Hamed Khorasgani', 'Aniruddha Rajendra Rao', 'Chetan Gupta', 'HaiYan Wang', 'Qiyao Wang']	2020-11-24	null	null	null	null	['time-series-regression']	['time-series']	[ 2.17673838e-01 -6.57491088e-01 -3.33162487e-01 -2.65050411e-01 -6.09230757e-01 -2.82590896e-01 3.86711299e-01 3.70747715e-01 -5.77307701e-01 1.05459940e+00 -3.08229387e-01 -1.98221937e-01 -6.21562362e-01 -7.29220510e-01 -6.91123784e-01 -8.07344615e-01 -4.01360363e-01 -1.58033878e-01 -1.94134012e-01 -1.33839175e-01 2.46998802e-01 5.00352204e-01 -1.39221072e+00 -3.50689828e-01 9.49276865e-01 1.22229373e+00 1.36476099e-01 4.20094043e-01 -1.14622295e-01 7.11240947e-01 -6.63470685e-01 1.36142552e-01 1.96862593e-03 -4.36992377e-01 -8.91115516e-02 -1.16147630e-01 -8.63032639e-02 -1.37246534e-01 -3.05418193e-01 8.72513652e-01 3.10812384e-01 2.69598126e-01 7.16703057e-01 -1.51081562e+00 -5.04646838e-01 4.32130665e-01 -7.98221588e-01 3.52050573e-01 -1.47292912e-01 -1.61405563e-01 8.19838643e-01 -5.55972338e-01 1.27572462e-01 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ae68a171-e3ac-4023-94b6-4d5383fe4c23	text-based-person-search-via-attribute-aided	null	null	https://openaccess.thecvf.com/content_WACV_2020/html/Aggarwal_Text-based_Person_Search_via_Attribute-aided_Matching_WACV_2020_paper.html	https://openaccess.thecvf.com/content_WACV_2020/papers/Aggarwal_Text-based_Person_Search_via_Attribute-aided_Matching_WACV_2020_paper.pdf	Text-based Person Search via Attribute-aided Matching	Text-based person search aims to retrieve the pedestrian images that best match a given text query. Existing methods utilize class-id information to get discriminative and identity-preserving features. However, it is not wellexplored whether it is beneficial to explicitly ensure that the semantics of the data are retained. In the proposed work, we aim to create semantics-preserving embeddings through an additional task of attribute prediction. Since attribute annotation is typically unavailable in text-based person search, we first mine them from the text corpus. These attributes are then used as a means to bridge the modality gap between the image-text inputs, as well as to improve the representation learning. In summary, we propose an approach for textbased person search by learning an attribute-driven space along with a class-information driven space, and utilize both for obtaining the retrieval results. Our experiments on benchmark dataset, CUHK-PEDES, show that learning the attribute-space not only helps in improving performance, giving us state-of-the-art Rank-1 accuracy of 56.68%, but also yields humanly-interpretable features.	['Surbhi Aggarwal R.', 'Anirban Chakraborty', 'Venkatesh Babu']	2020-03-14	null	null	null	null	['nlp-based-person-retrival', 'person-search']	['computer-vision', 'computer-vision']	[ 2.00340256e-01 -3.62806648e-01 -4.45730865e-01 -7.36265898e-01 -8.67185414e-01 -3.32591355e-01 8.90094519e-01 2.46110588e-01 -7.19501972e-01 7.27534115e-01 5.38763762e-01 1.09243132e-01 -2.24405274e-01 -7.15617001e-01 -4.95730996e-01 -6.86966360e-01 3.39589357e-01 6.29705489e-01 6.38374016e-02 -3.87635222e-03 4.52705845e-02 2.47057661e-01 -1.94839942e+00 3.59420300e-01 9.09259617e-01 1.30474854e+00 -3.99948955e-02 2.34492660e-01 -1.52201569e-02 7.23889768e-01 -1.66407913e-01 -8.64394009e-01 2.17238531e-01 -3.38228583e-01 -9.54282284e-01 1.68770969e-01 6.65825009e-01 -2.43477881e-01 -6.69172406e-01 7.89166927e-01 5.51011682e-01 2.57603526e-01 9.57137585e-01 -1.37492239e+00 -9.01735544e-01 1.80547044e-01 -3.64106357e-01 2.66840886e-02 5.31890154e-01 -1.45151958e-01 1.37077069e+00 -1.14543116e+00 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29b15e2b-9d05-43b8-85b1-3261f9aa508a	sk-unet-model-with-fourier-domain-for-mitosis	2109.00957	null	https://arxiv.org/abs/2109.00957v3	https://arxiv.org/pdf/2109.00957v3.pdf	Sk-Unet Model with Fourier Domain for Mitosis Detection	Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F1 with 0.7456 on the preliminary test set.	['Xiyue Wang', 'Jun Zhang', 'Feng Luo', 'Sen yang']	2021-09-01	null	null	null	null	['mitosis-detection']	['medical']	[ 8.26527104e-02 2.24815514e-02 -2.94766992e-01 -3.03018391e-01 -9.59746122e-01 -2.28283405e-01 1.92798972e-01 3.07684834e-03 -6.39708817e-01 8.91891181e-01 -1.70411959e-01 -3.50473493e-01 6.63139522e-02 -8.92688155e-01 -3.19556236e-01 -1.12402165e+00 2.50322312e-01 2.97472686e-01 6.83890581e-01 7.71840215e-02 4.55130577e-01 3.68233681e-01 -9.48389471e-01 3.31233084e-01 8.49268854e-01 7.55172551e-01 2.60148495e-01 7.23196566e-01 -4.83777970e-01 4.04493697e-02 -9.20763016e-01 6.82836547e-02 5.46758659e-02 -5.50882578e-01 -8.60240877e-01 -1.99440829e-02 1.96517929e-01 2.41957773e-02 -3.33212435e-01 1.29727125e+00 7.85827935e-01 -4.53097582e-01 9.42918539e-01 -1.02320337e+00 -2.47466281e-01 4.45513904e-01 -1.22420478e+00 7.58210540e-01 -2.09649622e-01 -1.29496269e-02 1.66057333e-01 -2.80029744e-01 6.95321202e-01 6.03849649e-01 9.02827203e-01 6.31738782e-01 -9.52359676e-01 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c5eba12f-63db-486c-980f-4c3f40af6d85	oracle-teacher-towards-better-knowledge	2111.03664	null	https://arxiv.org/abs/2111.03664v3	https://arxiv.org/pdf/2111.03664v3.pdf	Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models	Knowledge distillation (KD), best known as an effective method for model compression, aims at transferring the knowledge of a bigger network (teacher) to a much smaller network (student). Conventional KD methods usually employ the teacher model trained in a supervised manner, where output labels are treated only as targets. Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input. Since the Oracle Teacher learns a more accurate CTC alignment by referring to the target information, it can provide the student with more optimal guidance. One potential risk for the proposed approach is a trivial solution that the model's output directly copies the target input. Based on a many-to-one mapping property of the CTC algorithm, we present a training strategy that can effectively prevent the trivial solution and thus enables utilizing both source and target inputs for model training. Extensive experiments are conducted on two sequence learning tasks: speech recognition and scene text recognition. From the experimental results, we empirically show that the proposed model improves the students across these tasks while achieving a considerable speed-up in the teacher model's training time.	['Nam Soo Kim', 'Sunghwan Ahn', 'Hyeonseung Lee', 'Hyung Yong Kim', 'Ji Won Yoon']	2021-11-05	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 7.77151406e-01 3.51496369e-01 -5.65638840e-01 -2.95098096e-01 -6.73974395e-01 -6.18147433e-01 5.36795795e-01 -3.88202332e-02 -3.84995371e-01 5.29070795e-01 -2.52589941e-01 -6.42568529e-01 1.11232474e-01 -6.89416349e-01 -9.15090740e-01 -1.01644933e+00 2.94245869e-01 4.80976313e-01 3.80609810e-01 4.64405864e-02 1.51728675e-01 3.07978779e-01 -1.39151096e+00 3.33407432e-01 1.09679568e+00 9.24063683e-01 6.07600093e-01 7.89028823e-01 -1.39009282e-01 1.26325607e+00 -6.53456628e-01 -4.29448515e-01 8.75596255e-02 -6.67922795e-01 -9.64471698e-01 -1.51189983e-01 1.91403642e-01 -3.89282644e-01 -5.85347235e-01 1.09414482e+00 4.55280334e-01 1.77285194e-01 6.72899961e-01 -1.03602767e+00 -4.13491040e-01 8.71743917e-01 -3.73847246e-01 2.75794417e-01 -1.33483037e-01 1.85040459e-01 7.99706936e-01 -6.18982017e-01 3.19186926e-01 9.78065729e-01 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ac5c723a-064a-4b95-806f-c94693dfc219	easnet-searching-elastic-and-accurate-network	2207.09796	null	https://arxiv.org/abs/2207.09796v1	https://arxiv.org/pdf/2207.09796v1.pdf	EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching	Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference speed. To ease the workload in network design, neural architecture search (NAS) has been applied with great success to various sparse prediction tasks, such as image classification and object detection. However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capabilities. To this end, we propose to train an elastic and accurate network for stereo matching (EASNet) that supports various 3D architectural settings on devices with different computing capabilities. Given the deployment latency constraint on the target device, we can quickly extract a sub-network from the full EASNet without additional training while the accuracy of the sub-network can still be maintained. Extensive experiments show that our EASNet outperforms both state-of-the-art human-designed and NAS-based architectures on Scene Flow and MPI Sintel datasets in terms of model accuracy and inference speed. Particularly, deployed on an inference GPU, EASNet achieves a new SOTA 0.73 EPE on the Scene Flow dataset with 100 ms, which is 4.5$\times$ faster than LEAStereo with a better quality model.	['Xiaowen Chu', 'Kaiyong Zhao', 'Shaohuai Shi', 'Qiang Wang']	2022-07-20	null	null	null	null	['stereo-matching-1']	['computer-vision']	[-2.27123827e-01 -4.91595179e-01 -3.72863740e-01 -5.48027694e-01 -1.12656973e-01 -2.02934265e-01 6.41997010e-02 -3.20281804e-01 -4.82264191e-01 4.05311227e-01 -8.05766657e-02 -5.64749658e-01 -8.47997069e-02 -8.29777300e-01 -7.17908204e-01 -3.51407498e-01 1.23487366e-02 5.70773184e-01 5.97188234e-01 1.89125855e-02 1.95744127e-01 7.08890438e-01 -1.84556496e+00 1.76894546e-01 6.64664567e-01 1.55731821e+00 2.84693182e-01 8.38355005e-01 -4.25252505e-03 1.04689956e+00 -3.94497126e-01 -3.25404972e-01 5.87911069e-01 3.50295842e-01 -7.02698827e-01 -5.49635351e-01 1.14217758e+00 -7.73220479e-01 -9.86774385e-01 9.07385647e-01 9.30978119e-01 1.07734270e-01 -1.22722656e-01 -1.20278430e+00 2.86584705e-01 3.63239110e-01 -3.10476273e-01 6.77079558e-01 -1.05915971e-01 4.05553907e-01 6.31515145e-01 -8.67136538e-01 3.72166365e-01 1.20664990e+00 8.78843665e-01 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bdc844ba-3c1d-42bc-8a6d-2776fe032e35	morphological-inflection-with-phonological	2306.12581	null	https://arxiv.org/abs/2306.12581v1	https://arxiv.org/pdf/2306.12581v1.pdf	Morphological Inflection with Phonological Features	Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis. Yet, such morphological tasks cannot be considered solved, especially when little training data is available or when generalizing to previously unseen lemmas. This work explores effects on performance obtained through various ways in which morphological models get access to subcharacter phonological features that are the targets of morphological processes. We design two methods to achieve this goal: one that leaves models as is but manipulates the data to include features instead of characters, and another that manipulates models to take phonological features into account when building representations for phonemes. We elicit phonemic data from standard graphemic data using language-specific grammars for languages with shallow grapheme-to-phoneme mapping, and we experiment with two reinflection models over eight languages. Our results show that our methods yield comparable results to the grapheme-based baseline overall, with minor improvements in some of the languages. All in all, we conclude that patterns in character distributions are likely to allow models to infer the underlying phonological characteristics, even when phonemes are not explicitly represented.	['Reut Tsarfaty', 'Omer Goldman', 'David Guriel']	2023-06-21	null	null	null	null	['morphological-inflection']	['natural-language-processing']	[ 2.39925221e-01 1.60844058e-01 3.99387814e-03 -4.10913259e-01 -6.58944786e-01 -1.02037776e+00 5.97607374e-01 4.77520674e-01 -7.15035796e-01 4.15086448e-01 6.25419319e-01 -6.92528129e-01 2.74331927e-01 -8.43204737e-01 -7.44080365e-01 -3.64774764e-01 5.50143309e-02 6.02003515e-01 2.45600015e-01 -3.72798771e-01 4.13943678e-01 4.73093420e-01 -1.30159605e+00 4.91342306e-01 9.78094459e-01 1.97230399e-01 4.99007791e-01 5.10691762e-01 -4.65213746e-01 4.83617276e-01 -6.83121622e-01 -6.31115198e-01 2.06231624e-01 -3.70972693e-01 -7.59208262e-01 -2.40498246e-03 5.47119677e-01 2.02814698e-01 7.71770403e-02 1.05371988e+00 1.97498456e-01 1.94258794e-01 8.00608814e-01 -4.79595423e-01 -1.13937318e+00 1.26369667e+00 -3.91659617e-01 5.76153636e-01 2.95860320e-01 2.29855999e-01 1.35703874e+00 -9.88643765e-01 5.70486605e-01 1.61528265e+00 5.07847965e-01 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-1.04517531e+00 -5.78852892e-01 4.11449015e-01 -6.36345148e-02 -4.55718309e-01 -3.83534133e-01 8.83105099e-02]	[10.648652076721191, 9.63415813446045]
78a0721d-4004-4db7-8d3c-fb1ecb9d50bc	helping-domain-experts-build-speech	1510.01942	null	http://arxiv.org/abs/1510.01942v1	http://arxiv.org/pdf/1510.01942v1.pdf	Helping Domain Experts Build Speech Translation Systems	We present a new platform, "Regulus Lite", which supports rapid development and web deployment of several types of phrasal speech translation systems using a minimal formalism. A distinguishing feature is that most development work can be performed directly by domain experts. We motivate the need for platforms of this type and discuss three specific cases: medical speech translation, speech-to-sign-language translation and voice questionnaires. We briefly describe initial experiences in developing practical systems.	['Pierrette Bouillon', 'Manny Rayner', 'Sonia Halimi', 'Irene Strasly', 'Alejandro Armando', 'Johanna Gerlach', 'Sarah Ebling', 'Nikos Tsourakis']	2015-10-07	null	null	null	null	['sign-language-translation']	['computer-vision']	[-8.87190104e-02 5.22649527e-01 3.65146101e-02 -4.14911747e-01 -1.13886893e+00 -8.20530236e-01 4.23849195e-01 -2.29623675e-01 -2.57593870e-01 9.77846026e-01 4.66097355e-01 -7.99092591e-01 1.93590671e-01 -3.40255380e-01 -7.63340071e-02 -2.05290109e-01 6.12851977e-01 7.81771004e-01 5.94532967e-01 -7.78062165e-01 -2.80036360e-01 3.50156486e-01 -1.18249059e+00 6.37620747e-01 6.19391501e-01 2.96003580e-01 3.88328046e-01 6.53505981e-01 -6.18189991e-01 5.26459336e-01 -8.66016030e-01 -4.00192499e-01 3.01457854e-04 -7.74971485e-01 -1.08217978e+00 -1.02660990e-04 -3.33364397e-01 -1.39744520e-01 2.87115246e-01 9.05081630e-01 9.83128369e-01 -1.59890845e-01 1.92857653e-01 -9.86572862e-01 -6.82296157e-01 7.77346194e-01 5.47411203e-01 4.18645516e-02 1.31722593e+00 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7e638b28-f87d-4e1b-b374-b00d4b5f4f6e	keep-meeting-summaries-on-topic-abstractive	null	null	https://aclanthology.org/P19-1210	https://aclanthology.org/P19-1210.pdf	Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization	Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention across three levels: segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional text features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that the utterance is more important if the speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.	['Richard J. Radke', 'Manling Li', 'Lingyu Zhang', 'Heng Ji']	2019-07-01	null	null	null	acl-2019-7	['meeting-summarization']	['natural-language-processing']	[ 3.33460212e-01 2.94917524e-01 -1.44623086e-01 -3.69418651e-01 -1.33278656e+00 -6.13820791e-01 8.31491351e-01 5.90432286e-01 -1.06299303e-01 7.35747874e-01 1.30515599e+00 8.72719437e-02 2.40348667e-01 -2.31321216e-01 -5.07819712e-01 -4.06426221e-01 2.59819269e-01 2.34654397e-01 -7.64507577e-02 -4.22058776e-02 5.49833775e-01 -4.70079146e-02 -1.36148441e+00 8.43954861e-01 1.02125585e+00 4.96133149e-01 3.08319300e-01 1.05367541e+00 -2.51507044e-01 7.09509373e-01 -1.26580596e+00 -3.50227170e-02 -5.02946436e-01 -8.80994737e-01 -9.27032471e-01 5.51993847e-01 5.77736080e-01 -1.97677791e-01 -3.11761856e-01 8.19920480e-01 5.93239307e-01 4.06674117e-01 9.50047433e-01 -9.52987969e-01 -6.59617901e-01 1.03197098e+00 -7.51250088e-01 3.57392699e-01 8.17892253e-01 -1.82880074e-01 1.30883300e+00 -9.39761281e-01 6.06737912e-01 1.60969388e+00 2.25338548e-01 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-2.94551581e-01 -1.47380769e+00 -4.30497169e-01 1.16966523e-01 6.15573704e-01 -2.64998645e-01 -4.05018479e-01 4.36308980e-01]	[12.598539352416992, 9.37134838104248]
3353f4b7-00c8-41a4-bb8c-933719c04246	multi-path-region-based-convolutional-neural	1703.09145	null	http://arxiv.org/abs/1703.09145v1	http://arxiv.org/pdf/1703.09145v1.pdf	Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained "Hard Faces"	Large-scale variations still pose a challenge in unconstrained face detection. To the best of our knowledge, no current face detection algorithm can detect a face as large as 800 x 800 pixels while simultaneously detecting another one as small as 8 x 8 pixels within a single image with equally high accuracy. We propose a two-stage cascaded face detection framework, Multi-Path Region-based Convolutional Neural Network (MP-RCNN), that seamlessly combines a deep neural network with a classic learning strategy, to tackle this challenge. The first stage is a Multi-Path Region Proposal Network (MP-RPN) that proposes faces at three different scales. It simultaneously utilizes three parallel outputs of the convolutional feature maps to predict multi-scale candidate face regions. The "atrous" convolution trick (convolution with up-sampled filters) and a newly proposed sampling layer for "hard" examples are embedded in MP-RPN to further boost its performance. The second stage is a Boosted Forests classifier, which utilizes deep facial features pooled from inside the candidate face regions as well as deep contextual features pooled from a larger region surrounding the candidate face regions. This step is included to further remove hard negative samples. Experiments show that this approach achieves state-of-the-art face detection performance on the WIDER FACE dataset "hard" partition, outperforming the former best result by 9.6% for the Average Precision.	['Yuguang Liu', 'Martin D. Levine']	2017-03-27	null	null	null	null	['robust-face-recognition']	['computer-vision']	[ 3.15116227e-01 4.70829941e-02 -4.10295241e-02 -2.61367053e-01 -7.15679526e-01 -2.77666509e-01 3.92401189e-01 -5.47467411e-01 -3.63150030e-01 4.40365285e-01 -3.73164296e-01 -8.38776976e-02 2.92394668e-01 -8.33843827e-01 -7.13638544e-01 -7.43287802e-01 -1.22406706e-01 2.58154213e-01 4.87267792e-01 4.75989543e-02 7.96832517e-02 1.15367782e+00 -1.76923370e+00 6.86333418e-01 3.55854124e-01 1.34536982e+00 9.06707272e-02 6.27957702e-01 7.56259635e-02 2.46837050e-01 -5.01894116e-01 -4.10072833e-01 6.70615792e-01 -1.96741726e-02 -3.42715651e-01 5.62361255e-02 1.00197148e+00 -7.83266902e-01 -1.63161233e-01 9.40102339e-01 7.22286642e-01 -3.03719848e-01 4.85908985e-01 -1.02909601e+00 -3.46379876e-01 2.16001436e-01 -1.29121614e+00 2.38216862e-01 2.85167754e-01 3.74185950e-01 3.98597568e-01 -1.60436225e+00 5.43871522e-01 1.50253308e+00 9.41039920e-01 7.75573730e-01 -1.15812600e+00 -1.20110571e+00 4.43712771e-02 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d3b0c4e0-856b-4d5f-b957-6a5cbec0eacc	multi-cue-vehicle-detection-for-semantic	1907.01176	null	https://arxiv.org/abs/1907.01176v1	https://arxiv.org/pdf/1907.01176v1.pdf	Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos	Detection of moving objects such as vehicles in videos acquired from an airborne camera is very useful for video analytics applications. Using fast low power algorithms for onboard moving object detection would also provide region of interest-based semantic information for scene content aware image compression. This would enable more efficient and flexible communication link utilization in lowbandwidth airborne cloud computing networks. Despite recent advances in both UAV or drone platforms and imaging sensor technologies, vehicle detection from aerial video remains challenging due to small object sizes, platform motion and camera jitter, obscurations, scene complexity and degraded imaging conditions. This paper proposes an efficient moving vehicle detection pipeline which synergistically fuses both appearance and motion-based detections in a complementary manner using deep learning combined with flux tensor spatio-temporal filtering. Our proposed multi-cue pipeline is able to detect moving vehicles with high precision and recall, while filtering out false positives such as parked vehicles, through intelligent fusion. Experimental results show that incorporating contextual information of moving vehicles enables high semantic compression ratios of over 100:1 with high image fidelity, for better utilization of limited bandwidth air-to-ground network links.	['Guna Seetharaman', 'Noor Al-Shakarji', 'Kannappan Palaniappan', 'Hadi Aliakbarpour', 'Filiz Bunyak']	2019-07-02	null	null	null	null	['moving-object-detection']	['computer-vision']	[ 4.22422081e-01 -1.03010309e+00 -1.14650264e-01 -8.40507448e-03 -4.08510536e-01 -8.09089065e-01 3.41558307e-01 -1.16838329e-01 -4.52388197e-01 4.99461621e-01 -4.34474200e-01 -5.59842922e-02 -3.88716757e-01 -8.29614520e-01 -6.23641551e-01 -7.69307256e-01 -6.00442886e-01 -1.51321888e-01 7.27642953e-01 2.34502344e-03 -5.08020222e-02 7.54037559e-01 -1.88794541e+00 4.18129206e-01 5.08720517e-01 1.48632538e+00 8.20525467e-01 1.17920244e+00 4.13830549e-01 8.00720513e-01 -4.61437643e-01 -5.26575446e-02 6.00615025e-01 2.24379972e-01 -1.91258281e-01 3.97285998e-01 8.71606290e-01 -1.23298395e+00 -5.01732290e-01 1.06739497e+00 2.00596735e-01 2.76258290e-02 1.46234870e-01 -1.35632038e+00 -1.13704734e-01 -9.19378325e-02 -6.64441049e-01 8.37177575e-01 2.44157180e-01 5.59140623e-01 4.25298512e-01 -7.15354502e-01 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bc14ea7c-ce72-457c-9eda-ba997041a131	4d-attention-based-neural-network-for-eeg	2101.05484	null	https://arxiv.org/abs/2101.05484v1	https://arxiv.org/pdf/2101.05484v1.pdf	4D Attention-based Neural Network for EEG Emotion Recognition	Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the weights of different brain regions and frequency bands, and a convolutional neural network (CNN) is utilized to deal with the spectral and spatial information of the 4D representations. Moreover, a temporal attention mechanism is integrated into a bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of the 4D representations. Our model achieves state-of-the-art performance on the SEED dataset under intra-subject splitting. The experimental results have shown the effectiveness of the attention mechanisms in different domains for EEG emotion recognition.	['Quansheng Ren', 'Zhendi Chen', 'Bowen Xu', 'Mengwen Ye', 'Guowen Xiao']	2021-01-14	null	null	null	null	['eeg-emotion-recognition']	['miscellaneous']	[-1.33008420e-01 -5.42817473e-01 3.34515303e-01 -4.87817645e-01 -2.84862489e-01 1.32791206e-01 -2.66654771e-02 -1.49576277e-01 -4.06906188e-01 6.08360827e-01 1.83696568e-01 5.61353043e-02 -1.47008717e-01 -3.49347174e-01 -3.46553981e-01 -7.04240859e-01 -4.19360936e-01 -2.27336958e-01 -7.58825392e-02 -2.15132162e-02 3.26222658e-01 5.74255049e-01 -1.31086242e+00 3.56021613e-01 9.75834310e-01 1.58962071e+00 3.24231893e-01 3.72453213e-01 -2.07828015e-01 4.94624466e-01 -7.21231222e-01 1.92407370e-01 -3.34033936e-01 -5.55089355e-01 -5.64698219e-01 -9.10982043e-02 -4.67839003e-01 5.62525950e-02 -4.67783600e-01 1.07792401e+00 8.26353133e-01 2.81918138e-01 7.09924221e-01 -1.13189209e+00 -9.38642800e-01 7.79260546e-02 -8.38956475e-01 8.03040981e-01 2.48114407e-01 -1.58695549e-01 4.01907265e-01 -1.06934643e+00 -4.67079170e-02 8.02479982e-01 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969cfdae-2291-4196-8d19-ae7597614e9f	defocus-blur-detection-via-depth-distillation	2007.08113	null	https://arxiv.org/abs/2007.08113v1	https://arxiv.org/pdf/2007.08113v1.pdf	Defocus Blur Detection via Depth Distillation	Defocus Blur Detection(DBD) aims to separate in-focus and out-of-focus regions from a single image pixel-wisely. This task has been paid much attention since bokeh effects are widely used in digital cameras and smartphone photography. However, identifying obscure homogeneous regions and borderline transitions in partially defocus images is still challenging. To solve these problems, we introduce depth information into DBD for the first time. When the camera parameters are fixed, we argue that the accuracy of DBD is highly related to scene depth. Hence, we consider the depth information as the approximate soft label of DBD and propose a joint learning framework inspired by knowledge distillation. In detail, we learn the defocus blur from ground truth and the depth distilled from a well-trained depth estimation network at the same time. Thus, the sharp region will provide a strong prior for depth estimation while the blur detection also gains benefits from the distilled depth. Besides, we propose a novel decoder in the fully convolutional network(FCN) as our network structure. In each level of the decoder, we design the Selective Reception Field Block(SRFB) for merging multi-scale features efficiently and reuse the side outputs as Supervision-guided Attention Block(SAB). Unlike previous methods, the proposed decoder builds reception field pyramids and emphasizes salient regions simply and efficiently. Experiments show that our approach outperforms 11 other state-of-the-art methods on two popular datasets. Our method also runs at over 30 fps on a single GPU, which is 2x faster than previous works. The code is available at: https://github.com/vinthony/depth-distillation	['Chi-Man Pun', 'Xiaodong Cun']	2020-07-16	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2044_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580732.pdf	eccv-2020-8	['defocus-blur-detection']	['computer-vision']	[ 2.07915857e-01 -1.90002397e-01 -1.23115070e-01 -4.64338928e-01 -5.51213384e-01 -3.06928366e-01 2.48158842e-01 -1.64628431e-01 -4.02832150e-01 7.41503716e-01 4.25978154e-01 -1.85652375e-01 2.20646009e-01 -6.17558002e-01 -7.52592027e-01 -8.52954924e-01 4.17621315e-01 -2.43873656e-01 6.80206895e-01 2.47740760e-01 4.14740652e-01 2.75819659e-01 -1.33375967e+00 1.79162979e-01 1.26366687e+00 1.05101037e+00 8.29584479e-01 6.33429348e-01 1.52929083e-01 1.15024519e+00 -3.08505476e-01 1.43116945e-02 1.55492201e-01 -2.41886079e-01 -5.85796952e-01 1.15144067e-01 6.71247184e-01 -1.10481334e+00 -6.70248628e-01 1.25197351e+00 5.92102408e-01 -3.51921022e-02 2.71418750e-01 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79091156-dfe2-43c0-8ce8-2cce29a8da8a	robot-learning-with-sensorimotor-pre-training	2306.10007	null	https://arxiv.org/abs/2306.10007v1	https://arxiv.org/pdf/2306.10007v1.pdf	Robot Learning with Sensorimotor Pre-training	We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and past actions, we encode the interleaved sequence into tokens, mask out a random subset, and train a model to predict the masked-out content. We hypothesize that if the robot can predict the missing content it has acquired a good model of the physical world that can enable it to act. RPT is designed to operate on latent visual representations which makes prediction tractable, enables scaling to 10x larger models, and 10 Hz inference on a real robot. To evaluate our approach, we collect a dataset of 20,000 real-world trajectories over 9 months using a combination of motion planning and model-based grasping algorithms. We find that pre-training on this data consistently outperforms training from scratch, leads to 2x improvements in the block stacking task, and has favorable scaling properties.	['Jitendra Malik', 'Trevor Darrell', 'Ken Goldberg', 'Letian Fu', 'Baifeng Shi', 'Ilija Radosavovic']	2023-06-16	null	null	null	null	['motion-planning']	['robots']	[ 3.83561164e-01 2.15600193e-01 -4.62565601e-01 -9.75263715e-02 -5.85849822e-01 -4.51102942e-01 8.11068952e-01 -2.15555787e-01 -2.64501065e-01 7.38733351e-01 4.28520381e-01 -2.24510193e-01 -8.74879360e-02 -5.32800019e-01 -1.45906460e+00 -6.01845443e-01 -4.94654626e-01 7.99050927e-01 2.43322894e-01 1.02958225e-01 5.57886660e-01 3.61165464e-01 -1.71828759e+00 5.86778402e-01 3.26984316e-01 7.72404552e-01 1.15836966e+00 7.96603441e-01 2.63291270e-01 1.41511810e+00 1.20395720e-02 3.64746839e-01 3.36435318e-01 -4.82782274e-02 -1.04257298e+00 1.57834992e-01 -5.68606018e-04 -6.54019833e-01 -7.43112326e-01 6.25708342e-01 -1.48145258e-01 -2.35859752e-02 7.87433028e-01 -1.25472701e+00 -5.09564579e-01 7.66123652e-01 -7.65531957e-02 -2.37416342e-01 5.38390815e-01 7.48843431e-01 7.97989130e-01 -7.10319161e-01 9.78642821e-01 1.51696479e+00 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-7.90016830e-01 -1.26017404e+00 -1.26180023e-01 3.89436513e-01 5.34789085e-01 -1.02541435e+00 -1.76419884e-01 5.08121371e-01]	[4.568708896636963, 0.7653839588165283]
76036eb1-6eb6-4b42-ba0f-ef4aa1f24753	policy-entropy-for-out-of-distribution	2005.12069	null	https://arxiv.org/abs/2005.12069v1	https://arxiv.org/pdf/2005.12069v1.pdf	Policy Entropy for Out-of-Distribution Classification	One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent's policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.	['Claudia Linnhoff-Popien', 'Robert Müller', 'Andreas Sedlmeier', 'Steffen Illium']	2020-05-25	null	null	null	null	['one-class-classifier']	['methodology']	[ 7.38575161e-02 2.42672697e-01 -1.64839730e-01 -2.63920486e-01 -5.40335476e-01 -7.06983685e-01 9.90246594e-01 4.27349597e-01 -6.93882048e-01 9.89863992e-01 -3.15705985e-01 -6.07126236e-01 -5.13758548e-02 -8.71575654e-01 -8.51168334e-01 -5.58153629e-01 -3.42626840e-01 4.66623932e-01 5.90923548e-01 -1.14899330e-01 3.58168989e-01 6.30775094e-01 -1.93169975e+00 5.07101953e-01 6.03794456e-01 9.92891848e-01 -1.05419487e-01 7.48092175e-01 2.46352911e-01 1.37736082e+00 -1.15126169e+00 1.22180417e-01 1.85281426e-01 -4.10386324e-01 -7.77880728e-01 -4.69392389e-01 1.12795591e-01 -5.50798297e-01 1.73038065e-01 1.24260628e+00 2.09545225e-01 1.54474294e-02 7.14173853e-01 -1.68785262e+00 3.02196771e-01 6.46470129e-01 1.60063088e-01 1.78431440e-02 4.87933367e-01 7.13716090e-01 4.10417259e-01 6.40140399e-02 5.01021624e-01 1.12061715e+00 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-1.16381192e+00 -9.03094232e-01 -5.70528209e-01 4.39114124e-01 3.05039406e-01 -9.81508255e-01 -3.11794549e-01 -3.95070970e-01]	[4.497403621673584, 1.8888804912567139]
06657c7e-1ecc-4a18-bbdc-293aa41b1fe5	unbalanced-optimal-transport-a-unified-1	2307.02402	null	https://arxiv.org/abs/2307.02402v1	https://arxiv.org/pdf/2307.02402v1.pdf	Unbalanced Optimal Transport: A Unified Framework for Object Detection	During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the closest ground truth box (mostly used in combination with anchors), or matching via the Hungarian algorithm (mostly used in anchor-free methods). Each of these strategies comes with its own properties, underlying losses, and heuristics. We show how Unbalanced Optimal Transport unifies these different approaches and opens a whole continuum of methods in between. This allows for a finer selection of the desired properties. Experimentally, we show that training an object detection model with Unbalanced Optimal Transport is able to reach the state-of-the-art both in terms of Average Precision and Average Recall as well as to provide a faster initial convergence. The approach is well suited for GPU implementation, which proves to be an advantage for large-scale models.	['Luc van Gool', 'Tinne Tuytelaars', 'Marc Proesmans', 'Johan A. K. Suykens', 'Pierre-François De Plaen', 'Henri De Plaen']	2023-07-05	unbalanced-optimal-transport-a-unified	http://openaccess.thecvf.com//content/CVPR2023/html/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.pdf	cvpr-2023-1	['object-detection']	['computer-vision']	[ 3.10106903e-01 1.09602008e-02 -1.10522874e-01 -2.94143587e-01 -8.28601122e-01 -4.00569171e-01 6.35730267e-01 6.09431088e-01 -5.44684887e-01 5.96980929e-01 -5.38399994e-01 -3.50506268e-02 -1.26307443e-01 -9.92457628e-01 -5.41080952e-01 -8.20617199e-01 -1.84854358e-01 9.37373400e-01 1.11388719e+00 -1.04526728e-01 3.83671910e-01 8.01881015e-01 -1.98932886e+00 3.43617529e-01 5.03427267e-01 1.25042510e+00 3.08095962e-01 6.34421170e-01 -3.20386976e-01 2.82401145e-01 -2.59407669e-01 -8.24838519e-01 3.55803818e-01 -1.68776214e-01 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d1f6bba3-d37b-4742-9102-64f48ed9ccf4	road-damage-detection-using-deep-ensemble	2011.00728	null	https://arxiv.org/abs/2011.00728v1	https://arxiv.org/pdf/2011.00728v1.pdf	Road Damage Detection using Deep Ensemble Learning	Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, we present an ensemble model for efficient detection and classification of road damages, which we have submitted to the IEEE BigData Cup Challenge 2020. Our solution utilizes a state-of-the-art object detector known as You Only Look Once (YOLO-v4), which is trained on images of various types of road damages from Czech, Japan and India. Our ensemble approach was extensively tested with several different model versions and it was able to achieve an F1 score of 0.628 on the test 1 dataset and 0.6358 on the test 2 dataset.	['Yasin Yilmaz', 'Keval Doshi']	2020-10-30	null	null	null	null	['road-damage-detection']	['computer-vision']	[-9.43962187e-02 -2.41495416e-01 3.38702261e-01 -9.40710679e-03 -8.39946449e-01 -2.24722356e-01 4.94665414e-01 1.47955537e-01 -3.57608736e-01 5.39007366e-01 3.99060920e-02 -1.76964313e-01 -3.29876691e-01 -1.10004938e+00 -4.72279161e-01 -6.35030329e-01 -5.53950407e-02 1.59795210e-01 6.73177421e-01 -2.86033869e-01 3.03135395e-01 7.93654144e-01 -2.33687806e+00 1.31058753e-01 1.03642678e+00 1.07274449e+00 3.08256954e-01 9.95652020e-01 4.05834049e-01 6.06814504e-01 -7.13917434e-01 -2.77296156e-01 1.29021198e-01 2.72550642e-01 -6.68401420e-01 -4.90014777e-02 7.12674975e-01 -3.08257908e-01 -3.97153914e-01 7.33458817e-01 6.84608638e-01 6.54100906e-03 8.35216880e-01 -9.03171599e-01 -2.65365124e-01 -3.23910266e-02 -3.48305434e-01 4.65435654e-01 1.52334422e-01 2.13637426e-02 8.74031544e-01 -1.02502656e+00 3.87948304e-01 1.05412853e+00 6.10103965e-01 1.54582545e-01 -9.39148784e-01 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2.56765008e-01 2.73507476e-01 -9.83139694e-01 -4.27715570e-01 2.82481518e-02]	[7.399096488952637, 1.177903413772583]
f110e2d6-32aa-4215-80ed-66d4c82e18b5	resolving-camera-position-for-a-practical	2201.02946	null	https://arxiv.org/abs/2201.02946v2	https://arxiv.org/pdf/2201.02946v2.pdf	Resolving Camera Position for a Practical Application of Gaze Estimation on Edge Devices	Most Gaze estimation research only works on a setup condition that a camera perfectly captures eyes gaze. They have not literarily specified how to set up a camera correctly for a given position of a person. In this paper, we carry out a study on gaze estimation with a logical camera setup position. We further bring our research in a practical application by using inexpensive edge devices with a realistic scenario. That is, we first set up a shopping environment where we want to grasp customers gazing behaviors. This setup needs an optimal camera position in order to maintain estimation accuracy from existing gaze estimation research. We then apply the state-of-the-art of few-shot learning gaze estimation to reduce training sampling in the inference phase. In the experiment, we perform our implemented research on NVIDIA Jetson TX2 and achieve a reasonable speed, 12 FPS which is faster compared with our reference work, without much degradation of gaze estimation accuracy. The source code is released at https://github.com/linh-gist/GazeEstimationTX2.	['Moongu Jeon', 'Tin Trung Tran', 'Linh Van Ma']	2022-01-09	null	null	null	null	['gaze-estimation']	['computer-vision']	[-9.70811248e-02 -2.87339211e-01 -1.14846542e-01 -5.31404436e-01 -8.12750161e-02 -1.49416804e-01 -1.63152236e-02 -3.03884000e-01 -3.23279440e-01 3.90580922e-01 -2.92885482e-01 -3.10482144e-01 1.96873292e-01 -3.45363468e-01 -5.32903075e-01 -4.94841397e-01 3.64151478e-01 9.76727754e-02 2.05294043e-01 -5.79331070e-03 7.45671630e-01 1.47055173e-02 -2.01520848e+00 -9.70628411e-02 7.85441935e-01 8.07980120e-01 4.52767730e-01 8.65728915e-01 1.64279267e-01 7.77946532e-01 -4.60609734e-01 -4.35754299e-01 9.48758498e-02 -2.73272246e-01 -5.95182002e-01 3.10433924e-01 7.08802581e-01 -7.22507358e-01 2.46456981e-01 1.12396729e+00 6.13995016e-01 1.18425049e-01 2.19706222e-01 -1.66643834e+00 -5.53065717e-01 1.68566138e-01 -9.28178608e-01 4.84465212e-01 5.60075343e-01 3.31116617e-01 6.09493911e-01 -5.30533612e-01 2.98151284e-01 8.63779604e-01 4.34467077e-01 6.54929876e-01 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df261ae5-832c-4d97-aaf4-e539b63f5c45	image-quality-aware-diagnosis-via-meta	2303.15038	null	https://arxiv.org/abs/2303.15038v2	https://arxiv.org/pdf/2303.15038v2.pdf	Image Quality-aware Diagnosis via Meta-knowledge Co-embedding	Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while ignoring their potential value for models. Through effectively learning and leveraging the knowledge of degradations, models can better resist their adverse effects and avoid misdiagnosis. In this paper, we raise the problem of image quality-aware diagnosis, which aims to take advantage of low-quality images and image quality labels to achieve a more accurate and robust diagnosis. However, the diversity of degradations and superficially unrelated targets between image quality assessment and disease diagnosis makes it still quite challenging to effectively leverage quality labels to assist diagnosis. Thus, to tackle these issues, we propose a novel meta-knowledge co-embedding network, consisting of two subnets: Task Net and Meta Learner. Task Net constructs an explicit quality information utilization mechanism to enhance diagnosis via knowledge co-embedding features, while Meta Learner ensures the effectiveness and constrains the semantics of these features via meta-learning and joint-encoding masking. Superior performance on five datasets with four widely-used medical imaging modalities demonstrates the effectiveness and generalizability of our method.	['Hao Chen', 'Siyu Chen', 'Haoxuan Che']	2023-03-27	null	http://openaccess.thecvf.com//content/CVPR2023/html/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Che_Image_Quality-Aware_Diagnosis_via_Meta-Knowledge_Co-Embedding_CVPR_2023_paper.pdf	cvpr-2023-1	['image-quality-assessment']	['computer-vision']	[ 2.88456589e-01 1.05441205e-01 -4.50073063e-01 -3.98679674e-01 -7.44466424e-01 5.80991954e-02 2.06405699e-01 3.98106053e-02 -1.29289970e-01 4.50344533e-01 4.72001195e-01 -7.67238885e-02 -4.49907929e-01 -7.34268248e-01 -4.31117266e-01 -7.73742318e-01 -7.38288835e-03 -1.06281981e-01 4.51080613e-02 1.23257600e-01 7.25503545e-03 1.91169232e-01 -1.36402118e+00 5.79452395e-01 1.32103908e+00 1.06796837e+00 3.10158134e-01 4.54852134e-01 2.10366696e-02 1.06419492e+00 -5.88236809e-01 -3.32050622e-01 7.64645264e-02 -4.97762501e-01 -7.62041450e-01 2.73918033e-01 1.64754555e-01 -6.32504106e-01 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414aeda5-9d61-4f7c-8d5c-ac749e51d880	data-augmentation-with-manifold-exploring	1901.0442	null	http://arxiv.org/abs/1901.04420v1	http://arxiv.org/pdf/1901.04420v1.pdf	Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness	In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their robustness to random transformations, both affine and projective. Inspired by ManiFool, the augmentation is performed by a line-search manifold-exploration method that learns affine geometric transformations that lead to the misclassification on an image, while ensuring that it remains on the same manifold as the training data. This augmentation method populates any training dataset with images that lie on the border of the manifolds between two-classes and maximizes the variance the network is exposed to during training. Our method was thoroughly evaluated on the challenging tasks of fine-grained skin lesion classification from limited data, and breast tumor classification of mammograms. Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.	['Christian Wachinger', 'Rüdiger Göbl', 'Muhammad Ferjad Naeem', 'Walter Simson', 'Magdalini Paschali', 'Nassir Navab', 'Abhijit Guha Roy']	2019-01-14	null	null	null	null	['skin-lesion-classification']	['medical']	[ 5.79273582e-01 7.16558218e-01 -1.28466235e-02 -2.10010543e-01 -6.44914567e-01 -5.25881469e-01 5.42914331e-01 -1.75586149e-01 -1.87112153e-01 5.15380323e-01 -2.48092830e-01 -4.15449083e-01 -5.99906817e-02 -8.88062000e-01 -1.10371256e+00 -8.64879072e-01 -2.18854733e-02 3.97706211e-01 -6.11624680e-02 -1.39978379e-01 -2.24452049e-01 8.18157613e-01 -1.03960741e+00 1.44863352e-01 8.48913789e-01 9.34733212e-01 -5.33516467e-01 4.85543966e-01 6.36845920e-03 5.93165338e-01 -5.39164960e-01 -6.06082439e-01 3.68548751e-01 -3.60954076e-01 -8.58573139e-01 3.07085961e-01 8.76212955e-01 -4.98123430e-02 -7.55829930e-01 1.39174175e+00 2.07429647e-01 -1.13701142e-01 8.24060500e-01 -1.35559142e+00 -7.79820383e-01 3.75764728e-01 -4.71747249e-01 1.49024455e-02 -4.04847860e-01 1.37795404e-01 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8e3da1ed-d4b9-49a5-bdcc-9cf7ef619326	les-syst-emes-de-dialogue-orient-es-but-etat	null	null	https://aclanthology.org/2019.jeptalnrecital-recital.7	https://aclanthology.org/2019.jeptalnrecital-recital.7.pdf	Les syst\`emes de dialogue orient\'es-but : \'etat de l'art et perspectives d'am\'elioration (Goal-oriented dialog systems : a recent overview and research prospects )	La gestion et la s{\'e}lection des informations pertinentes pour un tour de parole donn{\'e} restent un probl{\`e}me pour les syst{\`e}mes de dialogue {\`a} domaine ouvert. Pour ces derniers, les interactions possibles entre un utilisateur et un agent sont a priori infinies et ind{\'e}finies. La possibilit{\'e} d{'}une r{\'e}ponse erron{\'e}e de l{'}agent {\`a} l{'}utilisateur demeure donc {\'e}lev{\'e}e. Pour les syst{\`e}mes orient{\'e}s-but, le probl{\`e}me est consid{\'e}r{\'e} comme r{\'e}solu, mais d{'}apr{\`e}s notre exp{\'e}rience aucun syst{\`e}me ne montre une robustesse remarquable lorsqu{'}il est {\'e}valu{\'e} en situation r{\'e}elle. Dans cet article, nous dressons un {\'e}tat de l{'}art des m{\'e}thodes d{'}apprentissage de l{'}agent et des diff{\'e}rents mod{\`e}les d{'}agent conversationnel. Selon nous, l{'}une des pistes d{'}am{\'e}lioration de l{'}agent r{\'e}side dans sa m{\'e}moire, car cette derni{\`e}re (souvent repr{\'e}sent{\'e}e par le triplet : tour de parole courant, historique du dialogue et base de connaissances) n{'}est pas encore mod{\'e}lis{\'e}e avec assez de pr{\'e}cision. En dotant l{'}agent d{'}un mod{\`e}le de m{\'e}moire d{'}inspiration cognitive, nous pensons pouvoir augmenter les performances d{'}un syst{\`e}me de dialogue orient{\'e}-but en situation r{\'e}elle, par l{'}emploi d{'}algorithmes d{'}apprentissage automatique avec une approche antagoniste en support d{'}un nouveau mod{\`e}le de m{\'e}moire pour l{'}agent.	["L{\\'e}on-Paul Schaub", 'Cyndel Vaudapiviz']	2019-07-01	null	null	null	jeptalnrecital-2019-7	['goal-oriented-dialog']	['natural-language-processing']	[ 6.32489622e-02 4.58816469e-01 5.69701552e-01 -3.38247538e-01 -4.08470035e-01 -1.23862052e+00 7.97195435e-01 3.69697273e-01 -7.74491370e-01 8.99567425e-01 -3.95102382e-01 -2.61646688e-01 -4.48808700e-01 -1.01600051e+00 -4.60342914e-01 -5.80443919e-01 -3.89622748e-01 6.52243853e-01 2.17405543e-01 -8.05316865e-01 8.46149623e-02 3.93978417e-01 -1.48281360e+00 1.83509931e-01 4.40061182e-01 1.27429831e+00 2.79170960e-01 6.51377797e-01 2.02633530e-01 1.00349069e+00 -8.53508413e-01 -6.13617539e-01 5.70679247e-01 -7.60985255e-01 -6.92271829e-01 -1.66073620e-01 -1.09207898e-01 2.93374598e-01 -1.95121661e-01 1.61151648e+00 -7.94336349e-02 4.61036950e-01 1.83397031e+00 -1.12244797e+00 -4.03693259e-01 6.50888324e-01 -3.23087990e-01 7.02884912e-01 5.31447172e-01 3.53068173e-01 1.12308431e+00 -6.63802147e-01 4.46618974e-01 6.02976143e-01 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2a761574-b535-4028-b16a-6c6f177fa5a5	active-learning-to-classify-macromolecular	2102.1204	null	https://arxiv.org/abs/2102.12040v2	https://arxiv.org/pdf/2102.12040v2.pdf	Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography	Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by cryo-ET are difficult due to high structural complexity and imaging limits. Deep learning based subtomogram classification have played critical roles for such tasks. As supervised approaches, however, their performance relies on sufficient and laborious annotation on a large training dataset. Results: To alleviate this major labeling burden, we proposed a Hybrid Active Learning (HAL) framework for querying subtomograms for labelling from a large unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select the subtomograms that have the most uncertain predictions. Moreover, to mitigate the sampling bias caused by such strategy, a discriminator is introduced to judge if a certain subtomogram is labeled or unlabeled and subsequently the model queries the subtomogram that have higher probabilities to be unlabeled. Additionally, HAL introduces a subset sampling strategy to improve the diversity of the query set, so that the information overlap is decreased between the queried batches and the algorithmic efficiency is improved. Our experiments on subtomogram classification tasks using both simulated and real data demonstrate that we can achieve comparable testing performance (on average only 3% accuracy drop) by using less than 30% of the labeled subtomograms, which shows a very promising result for subtomogram classification task with limited labeling resources.	['Min Xu', 'Jing Zhang', 'Yi-Wei Chang', 'Xiangrui Zeng', 'Zhenxi Zhu', 'Haohan Wang', 'Xuefeng Du']	2021-02-24	null	null	null	null	['electron-tomography']	['medical']	[ 1.20635070e-01 5.71141504e-02 -2.95678616e-01 -4.45726782e-01 -1.02367067e+00 -6.18627787e-01 2.57156044e-01 3.86810005e-01 -4.02753115e-01 1.07856607e+00 -2.70924211e-01 -2.58499265e-01 -2.21878923e-02 -7.58699000e-01 -7.65657723e-01 -1.30217731e+00 2.04534501e-01 9.52685952e-01 3.27295899e-01 3.94908100e-01 3.09196740e-01 6.71118021e-01 -1.12522423e+00 1.46081999e-01 1.07398617e+00 1.07524168e+00 6.37051165e-01 3.95933807e-01 -2.85772949e-01 5.90143561e-01 -5.48636317e-01 -5.98718598e-02 4.76947390e-02 -1.53790385e-01 -6.69761121e-01 2.00830221e-01 -1.27465660e-02 -1.24472575e-02 2.53640652e-01 9.13936853e-01 6.37240529e-01 -1.00202158e-01 8.44802797e-01 -6.81676209e-01 -2.08481953e-01 5.18883653e-02 -2.28309304e-01 2.96666443e-01 2.50366807e-01 1.96486160e-01 7.73885190e-01 -1.06800604e+00 7.44301438e-01 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9a1e8f74-ec58-4653-9784-38fbaf1e74cf	why-having-10000-parameters-in-your-camera	1912.02908	null	https://arxiv.org/abs/1912.02908v3	https://arxiv.org/pdf/1912.02908v3.pdf	Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve	Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone.	['Thomas Schöps', 'Viktor Larsson', 'Marc Pollefeys', 'Torsten Sattler']	2019-12-05	null	null	null	null	['stereo-depth-estimation']	['computer-vision']	[-1.38561383e-01 -2.88526993e-02 1.90560278e-02 -3.58105630e-01 -4.36213702e-01 -9.67857420e-01 5.06327569e-01 -2.06094012e-01 -3.39627236e-01 4.44922298e-01 8.08049738e-02 -2.81472892e-01 1.33579627e-01 -4.02975440e-01 -8.55733633e-01 -3.16092640e-01 6.61846399e-01 7.26737022e-01 3.57279003e-01 6.36105612e-02 4.57828432e-01 6.74374163e-01 -1.37549305e+00 -3.43693316e-01 6.48853481e-01 3.59900177e-01 3.84774953e-01 6.89337254e-01 9.32381153e-02 4.10232395e-01 -4.08562720e-01 -7.15708673e-01 5.29546857e-01 -2.00981632e-01 -6.16117656e-01 5.05748391e-01 6.36155188e-01 -7.66819060e-01 -1.94171995e-01 8.84075701e-01 5.20187914e-01 -2.52293348e-01 3.79657835e-01 -1.13338220e+00 -3.22522640e-01 1.76520392e-01 -5.71087480e-01 -1.85425162e-01 6.04070306e-01 1.33458123e-01 7.40842044e-01 -6.65958166e-01 8.18148911e-01 9.30537224e-01 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99bd4ba2-f62d-40ad-8b97-d89756812d63	fine-tuning-of-sign-language-recognition	2302.07693	null	https://arxiv.org/abs/2302.07693v2	https://arxiv.org/pdf/2302.07693v2.pdf	Fine-tuning of sign language recognition models: a technical report	Sign Language Recognition (SLR) is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. %Skeleton Aware Multi-modal Sign Language Recognition In this work, we focused on investigating two questions: how fine-tuning on datasets from other sign languages helps improve sign recognition quality, and whether sign recognition is possible in real-time without using GPU. Three different languages datasets (American sign language WLASL, Turkish - AUTSL, Russian - RSL) have been used to validate the models. The average speed of this system has reached 3 predictions per second, which meets the requirements for the real-time scenario. This model (prototype) will benefit speech or hearing impaired people talk with other trough internet. We also investigated how the additional training of the model in another sign language affects the quality of recognition. The results show that further training of the model on the data of another sign language almost always leads to an improvement in the quality of gesture recognition. We also provide code for reproducing model training experiments, converting models to ONNX format, and inference for real-time gesture recognition.	['Iuliia Zemtsova', 'Dmitriy Milevich', 'Ruslan Murtazin', 'Leonid Verkhovtsev', 'Maxim Novopoltsev']	2023-02-15	null	null	null	null	['sign-language-recognition', 'gesture-recognition']	['computer-vision', 'computer-vision']	[-7.18333796e-02 -3.07808578e-01 -7.98999220e-02 -3.48796546e-01 -5.59384108e-01 -3.05298239e-01 4.36197817e-01 -1.10195470e+00 -8.10505033e-01 3.36154640e-01 3.37233365e-01 -2.84131140e-01 1.50204256e-01 -3.22392166e-01 -2.66506076e-01 -6.96039736e-01 3.17003429e-02 6.41702235e-01 5.23315728e-01 -2.77672172e-01 1.07301444e-01 1.05475414e+00 -1.79380691e+00 3.99006903e-01 2.84882575e-01 5.45201063e-01 -9.07746777e-02 1.16521680e+00 -8.23891386e-02 6.57472968e-01 -4.78713304e-01 8.80392715e-02 4.52666104e-01 -3.45838308e-01 -4.60980296e-01 -1.68737203e-01 7.43158340e-01 -6.52451098e-01 -3.73815149e-01 5.47325671e-01 1.04763663e+00 -6.36355653e-02 5.94736040e-01 -1.03891027e+00 8.83515105e-02 3.01181376e-01 -2.56655425e-01 -3.12635899e-01 5.03320754e-01 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5d3edf72-9dce-46a9-9d49-6730a5f7a1d8	amr-parsing-with-instruction-fine-tuned-pre	2304.12272	null	https://arxiv.org/abs/2304.12272v1	https://arxiv.org/pdf/2304.12272v1.pdf	AMR Parsing with Instruction Fine-tuned Pre-trained Language Models	Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unseen tasks. However, a majority of standard parsing tasks including abstract meaning representation (AMR), universal dependency (UD), semantic role labeling (SRL) has been excluded from the FLAN collections for both model training and evaluations. In this paper, we take one of such instruction fine-tuned pre-trained language models, i.e. FLAN-T5, and fine-tune them for AMR parsing. Our extensive experiments on various AMR parsing tasks including AMR2.0, AMR3.0 and BioAMR indicate that FLAN-T5 fine-tuned models out-perform previous state-of-the-art models across all tasks. In addition, full fine-tuning followed by the parameter efficient fine-tuning, LoRA, further improves the model performances, setting new state-of-the-arts in Smatch on AMR2.0 (86.4), AMR3.0 (84.9) and BioAMR (82.3).	['Salim Roukos', 'Tahira Naseem', 'Radu Florian', 'Ramón Fernandez Astudillo', 'Young-suk Lee']	2023-04-24	null	null	null	null	['amr-parsing', 'semantic-role-labeling']	['natural-language-processing', 'natural-language-processing']	[ 8.67710561e-02 1.41001090e-01 -5.86592972e-01 -6.46496475e-01 -9.98076200e-01 -7.00098872e-01 3.84734601e-01 2.18382388e-01 -3.17630410e-01 6.90228403e-01 3.93453568e-01 -9.82446849e-01 3.50614935e-01 -7.03218460e-01 -8.82133245e-01 -3.98294516e-02 2.09494755e-01 1.99547485e-01 3.82391095e-01 -6.35674655e-01 2.81992137e-01 2.63019711e-01 -1.26207101e+00 6.16287291e-01 7.61511743e-01 5.14807761e-01 1.95927471e-01 8.50297511e-01 -4.44741547e-01 1.10551119e+00 -9.21963990e-01 -3.22925001e-01 -3.33435059e-01 6.74590915e-02 -1.01085579e+00 -5.88239551e-01 4.15623099e-01 -3.11451286e-01 -1.76333919e-01 6.40670836e-01 3.07734907e-01 2.97447294e-01 3.68056864e-01 -7.51384616e-01 -1.17423558e+00 1.28021550e+00 -7.85986722e-01 3.96986187e-01 2.95318931e-01 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9.16622937e-01 2.49332979e-01 1.24342740e+00 -8.48608278e-03 -2.68253058e-01 -8.04280996e-01 -1.26656902e+00 1.13619410e-01 3.95546138e-01 -3.53438966e-02 -4.65930164e-01 -6.59154877e-02 5.27031086e-02]	[10.419587135314941, 8.67853832244873]
e451b653-9007-4d05-8ae4-8fc2cc3e0515	fe-fusion-vpr-attention-based-multi-scale	2211.12244	null	https://arxiv.org/abs/2211.12244v2	https://arxiv.org/pdf/2211.12244v2.pdf	FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events	Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to failure in weakly textured or motionless scenes, while standard cameras can still provide appearance information in this case. Thus, exploiting the complementarity of standard cameras and event cameras can effectively improve the performance of VPR algorithms. In the paper, we propose FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by fusing frames and events. First, the intensity frame and event volume are fed into the two-stream feature extraction network for shallow feature fusion. Next, the three-scale features are obtained through the multi-scale fusion network and aggregated into three sub-descriptors using the VLAD layer. Finally, the weight of each sub-descriptor is learned through the descriptor re-weighting network to obtain the final refined descriptor. Experimental results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and NetVLAD. To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR.	['Zheng Fang', 'XinJie Huang', 'Hao Zhuang', 'Junjie Jiang', 'Delei Kong', 'Kuanxu Hou']	2022-11-22	null	null	null	null	['visual-place-recognition']	['computer-vision']	[-1.68745220e-01 -8.13002110e-01 -1.21364728e-01 -2.23269552e-01 -8.07361245e-01 -3.07547867e-01 6.38269544e-01 1.46270022e-01 -6.51589513e-01 5.07842541e-01 1.10882059e-01 2.61833161e-01 8.30622092e-02 -7.27687955e-01 -5.72487772e-01 -7.17055202e-01 3.77110355e-02 -2.07923383e-01 7.12711692e-01 -1.98898226e-01 4.10201550e-02 7.52276182e-01 -1.80467951e+00 3.43981504e-01 3.80884528e-01 1.35112047e+00 3.46440166e-01 5.82207799e-01 2.43965387e-02 9.16081309e-01 -4.17852134e-01 -5.79753369e-02 2.69120961e-01 -3.23687354e-03 -1.50853679e-01 6.17358508e-03 6.25622690e-01 -6.49349034e-01 -8.94903839e-01 8.49137604e-01 6.26238942e-01 6.12843394e-01 3.51374894e-01 -1.41938043e+00 -6.81292832e-01 -6.13796264e-02 -9.28717852e-01 4.73844379e-01 6.75891519e-01 2.96678215e-01 9.05515790e-01 -1.16012979e+00 7.08600819e-01 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dbe6346d-99a1-4e8c-ab2b-4314098bdf20	self-knowledge-distillation-in-natural	1908.01851	null	https://arxiv.org/abs/1908.01851v1	https://arxiv.org/pdf/1908.01851v1.pdf	Self-Knowledge Distillation in Natural Language Processing	Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans. Such high performance can be explained by efficient knowledge representation of deep learning models. While many methods have been proposed to learn more efficient representation, knowledge distillation from pretrained deep networks suggest that we can use more information from the soft target probability to train other neural networks. In this paper, we propose a new knowledge distillation method self-knowledge distillation, based on the soft target probabilities of the training model itself, where multimode information is distilled from the word embedding space right below the softmax layer. Due to the time complexity, our method approximates the soft target probabilities. In experiments, we applied the proposed method to two different and fundamental NLP tasks: language model and neural machine translation. The experiment results show that our proposed method improves performance on the tasks.	['Heeyoul Choi', 'Sangchul Hahn']	2019-08-02	self-knowledge-distillation-in-natural-1	https://aclanthology.org/R19-1050	https://aclanthology.org/R19-1050.pdf	ranlp-2019-9	['self-knowledge-distillation']	['computer-vision']	[ 6.65315986e-02 3.06434184e-01 -5.56261957e-01 -3.94002408e-01 -4.59623516e-01 -3.94185573e-01 8.18340838e-01 5.53843156e-02 -7.25239098e-01 8.88822377e-01 3.99860263e-01 -2.45245010e-01 1.11487828e-01 -8.37544084e-01 -7.01054931e-01 -6.57287300e-01 3.14356387e-01 5.80267251e-01 9.14950445e-02 -8.16695839e-02 2.19887234e-02 1.74249247e-01 -8.53791773e-01 2.78603464e-01 8.54880035e-01 9.15940166e-01 4.08402622e-01 3.55161846e-01 -5.16035318e-01 7.40216613e-01 -3.56742591e-01 -5.69044709e-01 2.67650992e-01 -2.85835803e-01 -8.06564271e-01 -7.46245742e-01 -1.68487862e-01 -2.30097190e-01 -4.37747687e-01 1.15715551e+00 4.56355363e-01 2.58695245e-01 7.85895109e-01 -7.43123770e-01 -1.22459996e+00 1.02628028e+00 -4.21322227e-01 1.78794667e-01 1.13680325e-01 7.33121186e-02 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41ede22c-6f0d-4e16-a656-cedabe163b76	diffeomorphic-temporal-alignment-nets	null	null	https://neurips.cc/Conferences/2019/Schedule?showEvent=13767	https://www.cs.bgu.ac.il/~orenfr/DTAN/ShapiraWeber_NeurIPS_2019.pdf	Diffeomorphic Temporal Alignment Nets	Time-series analysis is confounded by nonlinear time warping of the data. Traditional methods for joint alignment do not generalize: after aligning a given signal ensemble, they lack a mechanism, that does not require solving a new optimization problem, to align previously-unseen signals. In the multi-class case, they must also first classify the test data before aligning it. Here we propose the Diffeomorphic Temporal alignment Net (DTAN), a learning-based method for time-series joint alignment. Via flexible temporal transformer layers, DTAN learns and applies an input-dependent nonlinear time warping to its input signal. Once learned, DTAN easily aligns previously-unseen signals by its inexpensive forward pass. In a single-class case, the method is unsupervised: the ground-truth alignments are unknown. In the multi-class case, it is semi-supervised in the sense that class labels (but not the ground-truth alignments) are used during learning; in test time, however, the class labels are unknown. As we show, DTAN not only outperforms existing joint-alignment methods in aligning training data but also generalizes well to test data. Our code is available at https://github.com/BGU-CS-VIL/dtan.	['Oren Shriki', 'Ron Shapira Weber', 'Nicki Skafte', 'Matan Eyal', 'Oren Freifeld']	2019-12-10	diffeomorphic-temporal-alignment-nets-1	http://papers.nips.cc/paper/8884-diffeomorphic-temporal-alignment-nets	http://papers.nips.cc/paper/8884-diffeomorphic-temporal-alignment-nets.pdf	neurips-2019-12	['ecg-classification', 'electrocardiography-ecg', 'time-series-alignment', 'time-series-averaging']	['medical', 'methodology', 'time-series', 'time-series']	[ 1.91194758e-01 -8.50397050e-02 -1.47999153e-01 -4.48333323e-01 -9.85209167e-01 -9.51492906e-01 6.71559155e-01 -3.83345276e-01 -3.51248354e-01 6.25357151e-01 1.07803911e-01 -1.45735577e-01 -2.20827535e-01 -3.74368578e-01 -6.37688816e-01 -9.08206224e-01 -2.35909417e-01 7.14521229e-01 1.32027939e-02 -1.19644716e-01 -1.82705477e-01 2.95159668e-01 -9.71976399e-01 2.02223212e-01 4.64506567e-01 6.37320518e-01 -1.26839101e-01 5.77205181e-01 3.83602083e-01 3.79318923e-01 -3.67890149e-01 -2.92352121e-03 4.47510660e-01 -8.63177359e-01 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d5344734-958e-4816-b221-55b73580ccfd	large-scale-product-categorization-using	1903.04254	null	http://arxiv.org/abs/1903.04254v1	http://arxiv.org/pdf/1903.04254v1.pdf	Large Scale Product Categorization using Structured and Unstructured Attributes	Product categorization using text data for eCommerce is a very challenging extreme classification problem with several thousands of classes and several millions of products to classify. Even though multi-class text classification is a well studied problem both in academia and industry, most approaches either deal with treating product content as a single pile of text, or only consider a few product attributes for modelling purposes. Given the variety of products sold on popular eCommerce platforms, it is hard to consider all available product attributes as part of the modeling exercise, considering that products possess their own unique set of attributes based on category. In this paper, we compare hierarchical models to flat models and show that in specific cases, flat models perform better. We explore two Deep Learning based models that extract features from individual pieces of unstructured data from each product and then combine them to create a product signature. We also propose a novel idea of using structured attributes and their values together in an unstructured fashion along with convolutional filters such that the ordering of the attributes and the differing attributes by product categories no longer becomes a modelling challenge. This approach is also more robust to the presence of faulty product attribute names and values and can elegantly generalize to use both closed list and open list attributes.	['Abilash Amarthaluri', 'Abhinandan Krishnan']	2019-03-01	null	null	null	null	['product-categorization']	['miscellaneous']	[ 1.52492560e-02 -3.29871267e-01 -2.03704178e-01 -7.61432886e-01 -1.42459065e-01 -1.04134667e+00 5.20467401e-01 8.25848460e-01 -2.05007941e-01 3.92658919e-01 7.74445310e-02 -2.71195799e-01 -4.51604962e-01 -1.04467738e+00 -5.00980973e-01 -4.90409821e-01 2.89324299e-02 9.12079334e-01 -7.27897882e-02 -4.15602952e-01 2.02771455e-01 4.17148083e-01 -1.98589444e+00 7.87244201e-01 7.73815453e-01 1.53156435e+00 -1.74874365e-01 4.59793121e-01 -5.51353157e-01 7.15881050e-01 -6.86499059e-01 -7.51812577e-01 7.04253674e-01 -2.88900174e-02 -9.11771894e-01 4.07169133e-01 7.76630580e-01 -2.87762135e-01 1.23556897e-01 1.03215671e+00 9.44473147e-02 -9.27977860e-02 7.09568799e-01 -1.42571402e+00 -6.14502668e-01 8.42385173e-01 -4.65428203e-01 -2.25084215e-01 1.78307086e-01 -2.91616887e-01 1.55560493e+00 -5.59114337e-01 5.56684077e-01 1.10474980e+00 7.09491909e-01 -4.03152741e-02 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1f19e8d4-04f1-4029-b9fa-e26781dd044a	knowledge-augmented-graph-neural-networks	2301.10451	null	https://arxiv.org/abs/2301.10451v1	https://arxiv.org/pdf/2301.10451v1.pdf	Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection	Adverse drug events (ADEs) are an important aspect of drug safety. Various texts such as biomedical literature, drug reviews, and user posts on social media and medical forums contain a wealth of information about ADEs. Recent studies have applied word embedding and deep learning -based natural language processing to automate ADE detection from text. However, they did not explore incorporating explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. This paper adopts the heterogenous text graph which describes relationships between documents, words and concepts, augments it with medical knowledge from the Unified Medical Language System, and proposes a concept-aware attention mechanism which learns features differently for the different types of nodes in the graph. We further utilize contextualized embeddings from pretrained language models and convolutional graph neural networks for effective feature representation and relational learning. Experiments on four public datasets show that our model achieves performance competitive to the recent advances and the concept-aware attention consistently outperforms other attention mechanisms.	['Pekka Marttinen', 'Ya Gao', 'Shaoxiong Ji']	2023-01-25	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[-1.22487791e-01 3.17258574e-02 -7.55902648e-01 -1.40104562e-01 -5.25319934e-01 -3.19819331e-01 5.78454792e-01 1.07707870e+00 -5.37333250e-01 5.62120259e-01 8.23402405e-01 -5.54302633e-01 -2.39869639e-01 -1.12798214e+00 -4.14326012e-01 -3.09970737e-01 -3.44907403e-01 3.78968060e-01 -5.87315373e-02 -2.28440106e-01 1.23333968e-01 4.85787928e-01 -5.19761384e-01 2.19624624e-01 8.42013299e-01 8.09117556e-01 1.25361653e-02 3.82027268e-01 -5.96001685e-01 8.14194739e-01 -4.82979953e-01 -4.52894211e-01 -2.36926034e-01 -2.21846715e-01 -6.76427484e-01 -7.10782781e-02 -6.86217248e-02 -2.61625320e-01 -7.27365315e-01 1.21719182e+00 6.73244536e-01 -9.80859473e-02 7.53378570e-01 -8.20690691e-01 -1.66619349e+00 5.20879626e-01 -5.68140686e-01 4.78532821e-01 4.85763162e-01 1.64184421e-01 1.27749753e+00 -9.46287990e-01 6.38540745e-01 1.24295342e+00 5.09611487e-01 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656f4eef-86c0-4c80-b07d-6b2ea782b81f	if-only-we-had-better-counterfactual	2103.01035	null	https://arxiv.org/abs/2103.01035v1	https://arxiv.org/pdf/2103.01035v1.pdf	If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques	In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techniques. We survey 100 distinct counterfactual explanation methods reported in the literature. This survey addresses the extent to which these methods have been adequately evaluated, both psychologically and computationally, and quantifies the shortfalls occurring. For instance, only 21% of these methods have been user tested. Five key deficits in the evaluation of these methods are detailed and a roadmap, with standardised benchmark evaluations, is proposed to resolve the issues arising; issues, that currently effectively block scientific progress in this field.	['Barry Smyth', 'Eoin Delaney', 'Eoin M Kenny', 'Mark T Keane']	2021-02-26	null	null	null	null	['counterfactual-explanation']	['miscellaneous']	[ 4.71406162e-01 1.05658889e+00 -7.10643828e-01 -2.95020580e-01 -2.83264339e-01 -5.62540233e-01 1.03807402e+00 7.76166692e-02 -2.59726137e-01 1.21656215e+00 6.60158038e-01 -1.07488048e+00 -6.59102857e-01 -2.18435064e-01 -4.77089435e-01 -1.71988770e-01 -8.26195907e-03 6.30389988e-01 -4.59114492e-01 -9.53490958e-02 1.09878242e+00 2.50680178e-01 -1.77832115e+00 -3.52331661e-02 1.15392065e+00 3.93349409e-01 -4.06636357e-01 4.90271628e-01 -1.19374953e-01 6.56718016e-01 -6.50427997e-01 -9.48262095e-01 -5.87294586e-02 -6.03709400e-01 -1.20699847e+00 -1.89040676e-02 2.17788547e-01 -5.15546203e-01 -9.12502483e-02 7.76386797e-01 3.26980531e-01 -6.34181499e-02 7.24258780e-01 -1.75446761e+00 -9.87161636e-01 8.85962248e-01 -2.84081876e-01 4.34241056e-01 7.55724311e-01 3.88317913e-01 8.88206959e-01 -3.94430250e-01 5.67367733e-01 1.44994271e+00 3.44598770e-01 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8a3a6f0e-ddd4-4895-9c75-d833d27ce491	upi-net-semantic-contour-detection-in	1909.00229	null	https://arxiv.org/abs/1909.00229v2	https://arxiv.org/pdf/1909.00229v2.pdf	UPI-Net: Semantic Contour Detection in Placental Ultrasound	Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach, namely UPI-Net, aims to capture long-range dependencies in placenta geometry through lightweight global context modelling and effective multi-scale feature aggregation. We perform a subject-level 10-fold nested cross-validation on a placental ultrasound database (4,871 images with labelled UPI from 49 scans). Experimental results demonstrate that, without introducing considerable computational overhead, UPI-Net yields the highest performance in terms of standard contour detection metrics, compared to other competitive benchmarks.	['Sally Collins', 'J. Alison Noble', 'Huan Qi']	2019-08-31	null	null	null	null	['contour-detection']	['computer-vision']	[ 9.07685101e-01 6.54991090e-01 1.93645120e-01 -5.95797598e-01 -9.31631267e-01 -5.29545426e-01 4.79799479e-01 6.24348760e-01 7.90014490e-02 2.33863890e-01 3.96920554e-02 -4.21431214e-01 -2.50226021e-01 -7.66550899e-01 -7.11924732e-01 -5.18966496e-01 -6.96055114e-01 6.64945483e-01 7.63616204e-01 1.84248403e-01 1.47468582e-01 5.50609767e-01 -1.05964983e+00 4.14340228e-01 6.80829942e-01 1.13089621e+00 -1.29199460e-01 1.04787052e+00 -1.68697983e-01 4.92750496e-01 -2.90336907e-01 -7.51677692e-01 4.48306911e-02 -6.30522907e-01 -8.66121054e-01 6.01879954e-02 5.17161071e-01 1.05548784e-01 3.69929314e-01 9.05411541e-01 7.56726265e-01 -3.98801118e-01 1.00707936e+00 -7.19025254e-01 9.92224291e-02 3.97650003e-01 -9.04583037e-01 5.63222885e-01 2.32172534e-01 -5.22115111e-01 8.07377756e-01 -8.53001833e-01 8.95182908e-01 1.15516961e+00 1.11853743e+00 1.83075532e-01 -1.22408736e+00 -3.59774292e-01 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9ffff2ca-3664-4a59-89e0-df7f806779d0	instance-dependent-partial-label-learning	2110.12911	null	https://arxiv.org/abs/2110.12911v2	https://arxiv.org/pdf/2110.12911v2.pdf	Instance-Dependent Partial Label Learning	Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, this assumption is not realistic since the candidate labels are always instance-dependent. In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature. The incorrect label with a high degree is more likely to be annotated as the candidate label. Therefore, the latent label distribution is the essential labeling information in partially labeled examples and worth being leveraged for predictive model training. Motivated by this consideration, we propose a novel PLL method that recovers the label distribution as a label enhancement (LE) process and trains the predictive model iteratively in every epoch. Specifically, we assume the true posterior density of the latent label distribution takes on the variational approximate Dirichlet density parameterized by an inference model. Then the evidence lower bound is deduced for optimizing the inference model and the label distributions generated from the variational posterior are utilized for training the predictive model. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method. Source code is available at https://github.com/palm-ml/valen.	['Min-Ling Zhang', 'Xin Geng', 'Congyu Qiao', 'Ning Xu']	2021-10-25	null	http://proceedings.neurips.cc/paper/2021/hash/e38e37a99f7de1f45d169efcdb288dd1-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/e38e37a99f7de1f45d169efcdb288dd1-Paper.pdf	neurips-2021-12	['partial-label-learning']	['methodology']	[ 3.07632148e-01 4.24453437e-01 -9.51659918e-01 -6.34367526e-01 -8.46538365e-01 -4.06594992e-01 4.76560891e-01 2.71098316e-01 -2.13295951e-01 8.87784421e-01 -1.72276080e-01 1.02589093e-01 2.37878338e-02 -7.55035996e-01 -7.85520017e-01 -1.07703114e+00 5.83488047e-01 7.67651618e-01 8.13283473e-02 6.14567280e-01 1.28145128e-01 -9.44757685e-02 -1.56143796e+00 3.35212320e-01 8.77708256e-01 1.00743628e+00 2.71132141e-01 1.30840421e-01 -3.34215671e-01 8.10108364e-01 -5.41250110e-01 -2.35221520e-01 -5.07330224e-02 -4.44164246e-01 -6.41189814e-01 2.82778442e-01 1.28566995e-01 -1.46441534e-01 2.03354001e-01 1.39745927e+00 1.67690516e-01 -1.08474866e-02 9.91055906e-01 -1.41763186e+00 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87605565-0628-42e6-95ff-8cf30b362ced	panda-llm-training-data-and-evaluation-for	2305.03025	null	https://arxiv.org/abs/2305.03025v1	https://arxiv.org/pdf/2305.03025v1.pdf	Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models	This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various training data factors, such as quantity, quality, and linguistic distribution, influence the performance of instruction-tuned models trained on publicly accessible high-quality instruction datasets for both English and Chinese languages. Our goal is to supplement evaluation with quantitative analyses, providing valuable insights for the continued advancement of open-source chat models. Our model, data, and code are publicly available for others to use and build upon.	['Zhanfeng Mo', 'Tianze Luo', 'Bosheng Ding', 'Fangkai Jiao']	2023-05-04	null	null	null	null	['instruction-following']	['natural-language-processing']	[-6.05144799e-01 -2.48756036e-01 -7.62452304e-01 -5.43478727e-01 -1.06709397e+00 -7.07613468e-01 2.70975351e-01 2.16660142e-01 -6.54236972e-01 8.25045228e-01 5.16911149e-01 -8.02996874e-01 5.51553726e-01 -5.37679315e-01 -7.02826500e-01 4.68970202e-02 9.73620787e-02 1.64012805e-01 1.88440621e-01 -5.26553214e-01 4.33090031e-01 2.25518830e-02 -1.32689631e+00 3.06664288e-01 1.14394486e+00 2.20389351e-01 2.80056417e-01 7.47946322e-01 -4.45811003e-01 1.21918511e+00 -7.48661578e-01 -4.74239469e-01 -2.81465977e-01 -2.82726854e-01 -7.64296293e-01 -5.24045944e-01 4.24224705e-01 -5.29206812e-01 -2.01420367e-01 7.07426786e-01 3.34076405e-01 3.54490012e-01 5.56662261e-01 -9.40475881e-01 -1.16906512e+00 1.12885594e+00 6.35352433e-02 4.55865949e-01 2.81419307e-01 5.34984052e-01 1.28260028e+00 -7.09863007e-01 7.68273532e-01 8.95411313e-01 6.08109295e-01 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e43603fd-bb62-41b7-94d7-fe3ea5895be5	type-to-track-retrieve-any-object-via-prompt	2305.13495	null	https://arxiv.org/abs/2305.13495v1	https://arxiv.org/pdf/2305.13495v1.pdf	Type-to-Track: Retrieve Any Object via Prompt-based Tracking	One of the recent trends in vision problems is to use natural language captions to describe the objects of interest. This approach can overcome some limitations of traditional methods that rely on bounding boxes or category annotations. This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-Track, which allows users to track objects in videos by typing natural language descriptions. We present a new dataset for that Grounded Multiple Object Tracking task, called GroOT, that contains videos with various types of objects and their corresponding textual captions describing their appearance and action in detail. Additionally, we introduce two new evaluation protocols and formulate evaluation metrics specifically for this task. We develop a new efficient method that models a transformer-based eMbed-ENcoDE-extRact framework (MENDER) using the third-order tensor decomposition. The experiments in five scenarios show that our MENDER approach outperforms another two-stage design in terms of accuracy and efficiency, up to 14.7% accuracy and 4$\times$ speed faster.	['Khoa Luu', 'Kris Kitani', 'Kha Gia Quach', 'Pha Nguyen']	2023-05-22	null	null	null	null	['multiple-object-tracking']	['computer-vision']	[ 7.17059076e-02 -4.71636027e-01 -2.97832251e-01 -4.32702214e-01 -8.20513844e-01 -6.99197829e-01 5.16953111e-01 1.12339243e-01 -3.47687811e-01 3.86167914e-01 6.33992031e-02 5.55656664e-02 1.58964649e-01 -2.46130943e-01 -8.68086755e-01 -5.23363113e-01 -8.55292156e-02 3.80446941e-01 7.98759282e-01 9.81214717e-02 1.62726671e-01 3.64853591e-01 -1.68581665e+00 6.46303713e-01 2.13396817e-01 1.18252659e+00 2.27304339e-01 7.24764109e-01 -1.71264648e-01 1.10993564e+00 -4.23735738e-01 -7.14065611e-01 3.98039401e-01 9.32146981e-03 -6.20335817e-01 2.76334494e-01 9.89027143e-01 -7.27874756e-01 -2.80959427e-01 9.84194875e-01 2.55309284e-01 8.53034556e-02 3.59926909e-01 -1.80768633e+00 -6.55416965e-01 5.32568872e-01 -8.21740031e-01 1.82640925e-01 5.54171562e-01 8.29823017e-02 9.62113678e-01 -9.35456097e-01 6.45634949e-01 1.49041283e+00 7.95851290e-01 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b3104e85-9d31-481a-97c8-3e4707cd69e9	authorship-attribution-using-text-distortion	null	null	https://aclanthology.org/E17-1107	https://aclanthology.org/E17-1107.pdf	Authorship Attribution Using Text Distortion	Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topic-specific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.	['Efstathios Stamatatos']	2017-04-01	null	null	null	eacl-2017-4	['authorship-verification']	['natural-language-processing']	[ 1.39636740e-01 -1.46012217e-01 -1.46517679e-01 -1.45925850e-01 -3.07935774e-01 -8.28416646e-01 7.85753787e-01 4.62786436e-01 -5.62467754e-01 7.62456536e-01 1.77630916e-01 -7.59812221e-02 -1.82882801e-01 -3.90217632e-01 -1.76267758e-01 -3.42048585e-01 5.22115469e-01 4.92876023e-01 8.79463404e-02 1.64163515e-01 1.12532771e+00 7.80224562e-01 -1.20534503e+00 -7.73696154e-02 9.38467085e-01 4.66336221e-01 -1.40706420e-01 5.66614568e-01 -4.18551058e-01 5.68293273e-01 -1.35268331e+00 -9.89462435e-01 1.99630275e-01 -4.40832973e-01 -7.02782273e-01 2.56308198e-01 6.29981816e-01 -5.82969002e-02 -7.25187883e-02 1.19825101e+00 3.88001174e-01 -8.47051665e-02 1.10508728e+00 -1.17476261e+00 -7.32146978e-01 6.24407887e-01 -7.70278692e-01 4.88271266e-01 1.56155571e-01 -2.49903455e-01 7.33905554e-01 -4.56770450e-01 6.60493791e-01 1.42966843e+00 6.16092980e-01 2.77266443e-01 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e5bdeb74-8ef9-4e6b-9513-0558186a2a3c	multi-view-convolutional-neural-networks-for	1505.0088	null	http://arxiv.org/abs/1505.00880v3	http://arxiv.org/pdf/1505.00880v3.pdf	Multi-view Convolutional Neural Networks for 3D Shape Recognition	A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.	['Erik Learned-Miller', 'Subhransu Maji', 'Hang Su', 'Evangelos Kalogerakis']	2015-05-05	multi-view-convolutional-neural-networks-for-1	http://openaccess.thecvf.com/content_iccv_2015/html/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.html	http://openaccess.thecvf.com/content_iccv_2015/papers/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.pdf	iccv-2015-12	['3d-shape-recognition']	['computer-vision']	[ 1.98134892e-02 -1.13876797e-01 1.13311917e-01 -6.28444612e-01 -5.20658731e-01 -1.02755928e+00 8.79363239e-01 -3.84352840e-02 9.70781744e-02 -2.63770670e-01 1.99605301e-01 -1.85811058e-01 -1.66072138e-03 -9.49665129e-01 -5.29596746e-01 -2.98882186e-01 5.44451832e-05 8.06080043e-01 1.99926242e-01 -2.71068066e-01 2.35263795e-01 1.62678373e+00 -1.73685706e+00 4.32365596e-01 -6.76037073e-02 1.38194120e+00 -3.13372046e-01 6.79849565e-01 -4.31554347e-01 -8.22549313e-02 -5.69821656e-01 -2.62936622e-01 6.80553198e-01 2.72636235e-01 -4.60384339e-01 3.98270041e-01 1.37207913e+00 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c1f59a42-5695-436b-b338-b7941a2c27cf	exploring-the-boundaries-of-semi-supervised	2306.01229	null	https://arxiv.org/abs/2306.01229v1	https://arxiv.org/pdf/2306.01229v1.pdf	Exploring the Boundaries of Semi-Supervised Facial Expression Recognition: Learning from In-Distribution, Out-of-Distribution, and Unconstrained Data	Deep learning-based methods have been the key driving force behind much of the recent success of facial expression recognition (FER) systems. However, the need for large amounts of labelled data remains a challenge. Semi-supervised learning offers a way to overcome this limitation, allowing models to learn from a small amount of labelled data along with a large unlabelled dataset. While semi-supervised learning has shown promise in FER, most current methods from general computer vision literature have not been explored in the context of FER. In this work, we present a comprehensive study on 11 of the most recent semi-supervised methods, in the context of FER, namely Pi-model, Pseudo-label, Mean Teacher, VAT, UDA, MixMatch, ReMixMatch, FlexMatch, CoMatch, and CCSSL. Our investigation covers semi-supervised learning from in-distribution, out-of-distribution, unconstrained, and very small unlabelled data. Our evaluation includes five FER datasets plus one large face dataset for unconstrained learning. Our results demonstrate that FixMatch consistently achieves better performance on in-distribution unlabelled data, while ReMixMatch stands out among all methods for out-of-distribution, unconstrained, and scarce unlabelled data scenarios. Another significant observation is that semi-supervised learning produces a reasonable improvement over supervised learning, regardless of whether in-distribution, out-of-distribution, or unconstrained data is utilized as the unlabelled set. We also conduct sensitivity analyses on critical hyper-parameters for the two best methods of each setting.	['Ali Etemad', 'Shuvendu Roy']	2023-06-02	null	null	null	null	['facial-expression-recognition', 'pseudo-label']	['computer-vision', 'miscellaneous']	[ 3.11345667e-01 1.75698042e-01 -3.36306274e-01 -7.86174715e-01 -7.57717133e-01 -2.61357218e-01 6.73008919e-01 -3.53745997e-01 -4.32673573e-01 8.48723650e-01 -1.16929531e-01 -4.46148366e-02 -1.87461644e-01 -2.97479242e-01 -4.79207367e-01 -9.16624784e-01 -2.91354302e-02 6.34882748e-01 -2.02759907e-01 -1.40378997e-01 -2.81124830e-01 5.41862369e-01 -1.77010500e+00 2.50476748e-01 4.54812557e-01 1.21306074e+00 -3.02902460e-01 2.45189756e-01 -1.45149678e-01 9.75388706e-01 -6.03569329e-01 -6.24832928e-01 2.61053920e-01 -5.05678117e-01 -7.25909829e-01 3.81083906e-01 6.79566324e-01 -2.58733958e-01 -5.25488481e-02 7.95823991e-01 9.93388534e-01 -9.61805955e-02 9.79548454e-01 -1.54730868e+00 -2.25615263e-01 3.57913941e-01 -9.70482707e-01 8.24701488e-02 3.08899432e-01 5.37941931e-03 6.08871877e-01 -9.28705812e-01 5.86112142e-01 1.42858458e+00 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fa4d015b-9ae3-494c-a191-387e03556c16	hard-regularization-to-prevent-collapse-in	2303.16521	null	https://arxiv.org/abs/2303.16521v1	https://arxiv.org/pdf/2303.16521v1.pdf	Hard Regularization to Prevent Collapse in Online Deep Clustering without Data Augmentation	Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. Successful existing models have employed various techniques to avoid this problem, most of which require data augmentation or which aim to make the average soft assignment across the dataset the same for each cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments. Using a Bayesian framework, we derive an intuitive optimization objective that can be straightforwardly included in the training of the encoder network. Tested on four image datasets, we show that it consistently avoids collapse more robustly than other methods and that it leads to more accurate clustering. We also conduct further experiments and analyses justifying our choice to regularize the hard cluster assignments.	['Thomas Lukasiewicz', 'Louis Mahon']	2023-03-29	null	null	null	null	['online-clustering', 'deep-clustering', 'deep-clustering']	['computer-vision', 'miscellaneous', 'natural-language-processing']	[-1.08124450e-01 1.66147575e-01 -1.58580381e-03 -7.21021593e-01 -6.45089984e-01 -7.78531551e-01 6.30460680e-01 3.19892794e-01 -5.45774817e-01 1.97603345e-01 -4.00716476e-02 -2.19885319e-01 -3.77421170e-01 -4.76315737e-01 -8.62223685e-01 -8.14121008e-01 -1.09672643e-01 8.95934463e-01 1.11328810e-01 4.60495442e-01 1.75482392e-01 3.59151691e-01 -1.50478184e+00 1.86278507e-01 6.21540129e-01 9.59091008e-01 2.70175159e-01 5.31483471e-01 3.89210470e-02 6.59107566e-01 -5.47272325e-01 -4.40653324e-01 5.59570134e-01 -2.44543761e-01 -9.12765980e-01 4.92538542e-01 1.39053866e-01 -1.27863601e-01 -2.53778338e-01 8.22726548e-01 2.58511275e-01 3.02990228e-01 5.70348561e-01 -1.20433211e+00 -5.28770149e-01 9.71758664e-01 -6.96305394e-01 -1.78535879e-01 -1.38817400e-01 -1.09957539e-01 9.43847060e-01 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54ab371e-0e69-4246-bfc6-2f85227c8a39	new-constraints-on-radiative-seesaw-models	2103.06881	null	https://arxiv.org/abs/2103.06881v1	https://arxiv.org/pdf/2103.06881v1.pdf	New constraints on radiative seesaw models from IceCube and other neutrino detectors	Dark matter (DM) scattering and its subsequent capture in the Sun can boost the local relic density, leading to an enhanced neutrino flux from DM annihilations that is in principle detectable at neutrino telescopes. We calculate the event rates expected for a radiative seesaw model containing both scalar triplet and singlet-doublet fermion DM candidates. In the case of scalar DM, the absence of a spin dependent scattering on nuclei results in a low capture rate in the Sun, which is reflected in an event rate of less than one per year in the current IceCube configuration with 86 strings. For singlet-doublet fermion DM, there is a spin dependent scattering process next to the spin independent one, which significantly boosts the event rate and thus makes indirect detection competitive with respect to the direct detection limits imposed by PICO-60. Due to a correlation between both scattering processes, the limits on the spin independent cross section set by XENON1T exclude also parts of the parameter space that can be probed at IceCube. Previously obtained limits by ANTARES, IceCube and Super-Kamiokande from the Sun and the Galactic Center are shown to be much weaker.	['S. Zeinstra', 'M. Klasen', 'A. Kappes', 'R. Busse', 'T. de Boer']	2021-03-11	null	null	null	null	['pico']	['natural-language-processing']	[ 8.35428089e-02 2.41590336e-01 -1.26645535e-01 4.35291566e-02 1.76894724e-01 -4.35619414e-01 1.37239182e+00 -4.38644320e-01 -5.71285367e-01 1.20500696e+00 -6.74774870e-02 -6.79075241e-01 5.33795595e-01 -1.04973471e+00 -2.64091402e-01 -1.32076466e+00 3.49751323e-01 9.32311773e-01 7.26219773e-01 -2.51332909e-01 -8.62645656e-02 5.52098691e-01 -1.54573178e+00 1.10733900e-02 2.21949562e-01 7.26922333e-01 -3.15981656e-02 3.76244783e-01 5.53862676e-02 5.15554011e-01 -6.58699349e-02 1.02077045e-01 6.98091447e-01 -1.02879155e+00 -3.25605035e-01 -4.54916447e-01 -5.85718602e-02 -2.57168055e-01 -4.11353379e-01 1.22921503e+00 1.24303810e-01 -2.08223257e-02 6.03850126e-01 -7.45781064e-01 -2.54526198e-01 8.38178575e-01 -5.45930803e-01 4.50556666e-01 -5.16894124e-02 2.94123739e-01 6.91393971e-01 -8.54433000e-01 1.07274830e+00 1.21458018e+00 -1.79191217e-01 6.20299101e-01 -1.16359174e+00 -7.83813775e-01 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-1.83762401e-01 3.28640193e-02 -8.10561001e-01 -1.19355989e+00 -6.95990324e-01]	[7.081794738769531, 3.401240348815918]
30381288-818a-467b-8187-53194e26b71b	improved-code-summarization-via-a-graph	2004.02843	null	https://arxiv.org/abs/2004.02843v2	https://arxiv.org/pdf/2004.02843v2.pdf	Improved Code Summarization via a Graph Neural Network	Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greater advantage of advances in neural network and AI technologies. In general, source code summarization techniques use the source code as input and outputs a natural language description. Yet a strong consensus is developing that using structural information as input leads to improved performance. The first approaches to use structural information flattened the AST into a sequence. Recently, more complex approaches based on random AST paths or graph neural networks have improved on the models using flattened ASTs. However, the literature still does not describe the using a graph neural network together with source code sequence as separate inputs to a model. Therefore, in this paper, we present an approach that uses a graph-based neural architecture that better matches the default structure of the AST to generate these summaries. We evaluate our technique using a data set of 2.1 million Java method-comment pairs and show improvement over four baseline techniques, two from the software engineering literature, and two from machine learning literature.	['Sakib Haque', 'Lingfei Wu', 'Alexander LeClair', 'Collin McMillan']	2020-04-06	null	null	null	null	['code-summarization']	['computer-code']	[ 3.59208375e-01 5.79808652e-01 -3.19193274e-01 -3.25439602e-01 -8.85102987e-01 -5.21814585e-01 3.34125608e-01 8.30414653e-01 7.69200623e-02 3.87393594e-01 6.57646060e-01 -4.86319959e-01 3.38626236e-01 -5.95252275e-01 -5.89496017e-01 -1.19426459e-01 -6.57971352e-02 2.54316092e-01 3.65608513e-01 -3.22309613e-01 9.05419052e-01 -2.70264372e-02 -1.39141381e+00 6.33099496e-01 1.10125697e+00 2.01133475e-01 6.04263358e-02 8.95328879e-01 -8.30536366e-01 1.32921469e+00 -9.33090627e-01 -5.00969231e-01 7.86665007e-02 -9.24213767e-01 -1.00130224e+00 -3.57987165e-01 7.12026477e-01 -6.01397306e-02 -3.83094773e-02 1.22339034e+00 3.23477834e-01 -9.45724323e-02 3.83039206e-01 -1.23783255e+00 -8.38950276e-01 1.40215194e+00 -7.10266411e-01 -4.30063084e-02 5.81397116e-01 -4.19510640e-02 1.19215786e+00 -4.59116369e-01 9.92443264e-01 9.36157167e-01 1.01802468e+00 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-5.37260532e-01 -9.84348476e-01 3.15967165e-02 2.20703021e-01 4.85311091e-01 2.87707094e-02 -2.83265114e-01 5.58016419e-01]	[7.635158061981201, 7.924810886383057]
4429ccf3-8657-4a5e-bd50-235d8e902f87	a-framework-for-the-automated	2303.08858	null	https://arxiv.org/abs/2303.08858v1	https://arxiv.org/pdf/2303.08858v1.pdf	A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline	This study proposes a framework for the automated hyperparameter optimization of a bearing fault detection pipeline for permanent magnet synchronous motors (PMSMs) without the need of external sensors. A automated machine learning (AutoML) pipeline search is performed by means of a genetic optimization to reduce human induced bias due to inappropriate parameterizations. For this purpose, a search space is defined, which includes general methods of signal processing and manipulation as well as methods tailored to the respective task and domain. The proposed framework is evaluated on the bearing fault detection use case under real world conditions. Considerations on the generalization of the deployed fault detection pipelines are also taken into account. Likewise, attention was paid to experimental studies for evaluations of the robustness of the fault detection pipeline to variations of the motors working condition parameters between the training and test domain. The present work contributes to the research of fault detection on rotating machinery in the following terms: (1) Reduction of the human induced bias to the data science process, while still considering expert and task related knowledge, ending in a generic search approach (2) tackling the bearing fault detection task without the need for external sensors (sensorless) (3) learning a domain robust fault detection pipeline applicable to varying motor operating parameters without the need of re-parameterizations or fine-tuning (4) investigations on working condition discrepancies with an excessive degree to determine the pipeline limitations regarding the abstraction of the motor parameters and the pipeline hyperparameters	['Elmar Engels', 'Alexander Gepperth', 'Tobias Wagner']	2023-03-15	null	null	null	null	['automl', 'fault-detection']	['methodology', 'miscellaneous']	[ 3.96439195e-01 1.62836194e-01 2.67662823e-01 8.63098577e-02 -1.36409670e-01 -3.09837043e-01 5.56083858e-01 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d4865297-51f5-4eb9-910f-8f718b51ae7f	on-the-security-vulnerabilities-of-text-to	2211.15363	null	https://arxiv.org/abs/2211.15363v2	https://arxiv.org/pdf/2211.15363v2.pdf	On the Security Vulnerabilities of Text-to-SQL Models	Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL systems that are commonly used to create natural language interfaces to databases. We showed that the Text-to-SQL modules within six commercial applications can be manipulated to produce malicious code, potentially leading to data breaches and Denial of Service attacks. This is the first demonstration that NLP models can be exploited as attack vectors in the wild. In addition, experiments using four open-source language models verified that straightforward backdoor attacks on Text-to-SQL systems achieve a 100% success rate without affecting their performance. The aim of this work is to draw the community's attention to potential software security issues associated with NLP algorithms and encourage exploration of methods to mitigate against them.	['Mark Stevenson', 'Jingfeng Yang', 'YiPeng Zhang', 'Xutan Peng']	2022-11-28	null	null	null	null	['text-to-sql']	['computer-code']	[ 1.80458531e-01 1.95390046e-01 -2.28662044e-01 -2.21081570e-01 -7.91958630e-01 -1.29429364e+00 5.86413145e-01 5.55706203e-01 -4.41638194e-03 9.68258157e-02 -1.28922611e-01 -1.37615609e+00 3.68694961e-01 -9.77701962e-01 -8.13189566e-01 1.10646427e-01 -1.95124716e-01 -1.58857480e-01 5.60146272e-01 -9.77189913e-02 6.42505229e-01 7.09691942e-01 -1.31308401e+00 5.67169011e-01 5.45257092e-01 3.06828022e-01 -7.61886537e-01 7.40093172e-01 -3.75066876e-01 8.51912498e-01 -9.53001618e-01 -7.84366250e-01 4.03245956e-01 1.74656540e-01 -8.51324499e-01 -5.30543804e-01 2.64986336e-01 -2.17221692e-01 2.40574956e-01 1.32225430e+00 1.53517097e-01 -6.56130850e-01 -1.37508297e-02 -1.84310961e+00 -2.59160370e-01 6.42996788e-01 -6.18707657e-01 -4.33300659e-02 8.15173090e-01 7.32897878e-01 7.41168618e-01 -6.08180881e-01 4.47676629e-01 1.29381275e+00 5.37389219e-01 5.27604938e-01 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-2.65298545e-01 4.01676923e-01 -4.47844751e-02 -7.00265728e-03 -5.47401726e-01 1.41242415e-01]	[6.190718173980713, 7.817381381988525]
95aedf75-daff-4d1c-9d54-997efc9d1846	promptbench-towards-evaluating-the-robustness	2306.04528	null	https://arxiv.org/abs/2306.04528v2	https://arxiv.org/pdf/2306.04528v2.pdf	PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts	The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic. These prompts are then employed in diverse tasks, such as sentiment analysis, natural language inference, reading comprehension, machine translation, and math problem-solving. Our study generates 4,032 adversarial prompts, meticulously evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our findings demonstrate that contemporary LLMs are vulnerable to adversarial prompts. Furthermore, we present comprehensive analysis to understand the mystery behind prompt robustness and its transferability. We then offer insightful robustness analysis and pragmatic recommendations for prompt composition, beneficial to both researchers and everyday users. We make our code, prompts, and methodologies to generate adversarial prompts publicly accessible, thereby enabling and encouraging collaborative exploration in this pivotal field: https://github.com/microsoft/promptbench.	['Xing Xie', 'Yue Zhang', 'Neil Zhenqiang Gong', 'Wei Ye', 'Linyi Yang', 'Yidong Wang', 'Hao Chen', 'Zichen Wang', 'Jiaheng Zhou', 'Jindong Wang', 'Kaijie Zhu']	2023-06-07	null	null	null	null	['natural-language-inference', 'sentiment-analysis', 'reading-comprehension']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.19375962e-01 -2.06291184e-01 6.71163350e-02 -1.84277281e-01 -1.19591916e+00 -1.35102999e+00 7.33338594e-01 5.06023645e-01 -3.75698745e-01 5.47396719e-01 6.87579095e-01 -7.66590476e-01 7.33551010e-02 -5.63082874e-01 -7.25068748e-01 -9.36730877e-02 2.25981906e-01 -1.31715029e-01 -1.69680834e-01 -6.21689320e-01 5.72193503e-01 4.09296751e-01 -9.48048949e-01 4.26900744e-01 8.61035407e-01 6.58273399e-01 -3.89001608e-01 1.07955790e+00 3.01603545e-02 1.22642148e+00 -9.48859155e-01 -1.07094753e+00 1.36706054e-01 1.96495205e-02 -1.00356913e+00 -6.63477957e-01 6.75970972e-01 -4.50834572e-01 -5.40830970e-01 1.04846382e+00 9.11374152e-01 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1496d5e9-ff29-4fdc-a3d6-abc2c89946c2	improving-the-quality-of-mt-output-using	1310.0573	null	http://arxiv.org/abs/1310.0573v1	http://arxiv.org/pdf/1310.0573v1.pdf	Improving the Quality of MT Output using Novel Name Entity Translation Scheme	This paper presents a novel approach to machine translation by combining the state of art name entity translation scheme. Improper translation of name entities lapse the quality of machine translated output. In this work, name entities are transliterated by using statistical rule based approach. This paper describes the translation and transliteration of name entities from English to Punjabi. We have experimented on four types of name entities which are: Proper names, Location names, Organization names and miscellaneous. Various rules for the purpose of syllabification have been constructed. Transliteration of name entities is accomplished with the help of Probability calculation. N-Gram probabilities for the extracted syllables have been calculated using statistical machine translation toolkit MOSES.	['Nisheeth Joshi', 'Iti Mathur', 'Deepti Bhalla']	2013-10-02	null	null	null	null	['miscellaneous']	['miscellaneous']	[-2.99681500e-02 -7.21743032e-02 -7.63760507e-02 -4.04729664e-01 -7.88554072e-01 -9.93917406e-01 7.36822963e-01 -9.35312286e-02 -4.75347131e-01 1.47603726e+00 4.88204598e-01 -8.44103456e-01 8.15146491e-02 -7.88686872e-01 -3.04950088e-01 -2.62601256e-01 5.80761135e-01 9.77060616e-01 -2.80429780e-01 -4.37667847e-01 5.74443281e-01 7.09499300e-01 -8.47083390e-01 1.11566111e-02 9.91465092e-01 -3.36067349e-01 -8.36116225e-02 7.69178867e-01 -4.37696755e-01 7.52363503e-02 -8.06103766e-01 -7.97834754e-01 3.09343338e-01 -7.49459565e-01 -1.01169407e+00 -1.25074476e-01 1.69093743e-01 6.48742020e-02 5.63564561e-02 9.72867787e-01 5.55090070e-01 -1.01706227e-02 9.30849969e-01 -7.81119406e-01 -1.19431639e+00 1.01696384e+00 -1.93489015e-01 4.58848506e-01 6.38360262e-01 -2.31089324e-01 7.76652217e-01 -1.15400720e+00 7.06420422e-01 1.17455781e+00 4.32417512e-01 4.21221942e-01 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b2321d3f-c81e-45fc-be7e-871d3938bc33	realization-rgbd-image-stylization	2305.06565	null	https://arxiv.org/abs/2305.06565v1	https://arxiv.org/pdf/2305.06565v1.pdf	Realization RGBD Image Stylization	This research paper explores the application of style transfer in computer vision using RGB images and their corresponding depth maps. We propose a novel method that incorporates the depth map and a heatmap of the RGB image to generate more realistic style transfer results. We compare our method to the traditional neural style transfer approach and find that our method outperforms it in terms of producing more realistic color and style. The proposed method can be applied to various computer vision applications, such as image editing and virtual reality, to improve the realism of generated images. Overall, our findings demonstrate the potential of incorporating depth information and heatmap of RGB images in style transfer for more realistic results.	['Aparna Mendu', 'Vaishnavi Mendu', 'Bhavya Sehgal']	2023-05-11	null	null	null	null	['style-transfer', 'image-stylization']	['computer-vision', 'computer-vision']	[ 5.06558359e-01 -6.59283996e-02 4.75742221e-01 -5.26608586e-01 -1.53475374e-01 -6.76343381e-01 5.63873768e-01 -6.48245394e-01 -2.76110321e-01 7.35018909e-01 3.30667645e-02 -2.79871583e-01 4.40595448e-01 -1.15114164e+00 -6.91236138e-01 -3.29819500e-01 6.88565254e-01 -5.80177009e-02 2.87076503e-01 -4.18030322e-01 4.41255957e-01 7.15824425e-01 -1.34423220e+00 3.87600482e-01 6.00976586e-01 9.26519752e-01 2.65471727e-01 7.85925031e-01 -3.77835900e-01 3.97767067e-01 -7.11044133e-01 -3.75563174e-01 5.60124993e-01 -5.87681830e-01 -6.80648267e-01 7.57373795e-02 6.40446544e-01 -7.15137064e-01 -3.44911665e-01 9.09721971e-01 7.31587648e-01 2.29608893e-01 5.50745070e-01 -1.23292661e+00 -9.57213879e-01 -2.46989325e-01 -8.86666179e-01 -1.29211575e-01 6.45818591e-01 5.32786325e-02 1.81224689e-01 -7.50651121e-01 7.93806195e-01 1.45987296e+00 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-1.51925170e+00 3.50131154e-01 -8.61185908e-01 -5.14498889e-01 6.74321711e-01 1.88198015e-01 -6.30988061e-01 3.37182358e-02 2.85046279e-01]	[10.0615234375, -2.4155783653259277]
d6c2aea6-f913-4000-aedd-37ffa65037d0	efficient-neural-network-compression-via	null	null	https://openreview.net/forum?id=S1g5uBnusm	https://openreview.net/pdf?id=S1g5uBnusm	Efﬁcient Neural Network Compression via Transfer Learning for Industrial Optical Inspection	In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing. Our preliminary result has shown the stunning performance improvement by transfer learning from the completely dissimilar source domain: ImageNet. Further study for demystifying this improvement shows that the transfer learning produces a highly compressible network, which was not the case for the network learned from scratch. The experimental result shows that there is a negligible accuracy drop in the network learned by transfer learning until it is compressed to 1/128 reduction of the number of convolution ﬁlters. This result is contrary to the compression without transfer learning which loses more than 5% accuracy at the same compression rate.	['Frank C. Park', 'Yung-Kyun Noh', 'Seunghyeon Kim']	2018-10-20	null	null	null	null	['neural-network-compression', 'neural-network-compression']	['methodology', 'miscellaneous']	[ 2.24903777e-01 5.31261206e-01 5.91793954e-01 -4.22029823e-01 -1.70941055e-01 -1.07780099e-01 1.78615630e-01 -1.68364853e-01 -5.95957756e-01 8.71203542e-01 -2.82197297e-01 -4.57668215e-01 -4.36430454e-01 -8.79880190e-01 -1.04804623e+00 -8.58193099e-01 -1.73838854e-01 4.54748064e-01 8.84744152e-02 -3.40552539e-01 1.14487708e-01 7.34115481e-01 -1.23717880e+00 6.47657216e-01 3.86975765e-01 1.13645101e+00 3.68261307e-01 8.35388303e-01 3.18529248e-01 1.05724752e+00 -1.02104974e+00 -2.77434617e-01 6.96425378e-01 -3.86906475e-01 -1.16197360e+00 5.67597784e-02 8.43904912e-01 -9.42343473e-01 -6.36808038e-01 1.19750321e+00 3.22583288e-01 8.18024352e-02 5.42401671e-01 -8.55222881e-01 -5.91334999e-01 6.41203225e-01 -1.43301383e-01 2.30498165e-01 -3.62027228e-01 1.94544464e-01 4.88482565e-01 -3.03151369e-01 7.16963828e-01 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9e9b01da-b8dd-4995-980f-882f3d16ccaf	increasing-behavioral-complexity-for-evolved	1510.07957	null	http://arxiv.org/abs/1510.07957v1	http://arxiv.org/pdf/1510.07957v1.pdf	Increasing Behavioral Complexity for Evolved Virtual Creatures with the ESP Method	Since their introduction in 1994 (Sims), evolved virtual creatures (EVCs) have employed the coevolution of morphology and control to produce high-impact work in multiple fields, including graphics, evolutionary computation, robotics, and artificial life. However, in contrast to fixed-morphology creatures, there has been no clear increase in the behavioral complexity of EVCs in those two decades. This paper describes a method for moving beyond this limit, making use of high-level human input in the form of a syllabus of intermediate learning tasks--along with mechanisms for preservation, reuse, and combination of previously learned tasks. This method--named ESP for its three components: encapsulation, syllabus, and pandemonium--is presented in two complementary versions: Fast ESP, which constrains later morphological changes to achieve linear growth in computation time as behavioral complexity is added, and General ESP, which allows this restriction to be removed when sufficient computational resources are available. Experiments demonstrate that the ESP method allows evolved virtual creatures to reach new levels of behavioral complexity in the co-evolution of morphology and control, approximately doubling the previous state of the art.	['Sebastian Risi', 'Risto Miikkulainen', 'Dan Lessin', 'Don Fussell']	2015-10-27	null	null	null	null	['artificial-life']	['miscellaneous']	[ 5.63470833e-02 1.13735430e-01 5.91156125e-01 1.50809869e-01 2.11180061e-01 -6.90718055e-01 5.89688301e-01 2.57646769e-01 -5.56865990e-01 6.96314573e-01 -2.46530548e-01 -1.74239531e-01 -6.25543222e-02 -8.10210168e-01 -4.68634814e-01 -4.70469356e-01 -2.59487271e-01 2.53119737e-01 5.35545170e-01 -6.16894722e-01 4.15672928e-01 6.24352813e-01 -2.12163830e+00 -4.59359325e-02 1.06302500e+00 3.32508802e-01 3.89912963e-01 6.73486471e-01 -1.04295656e-01 1.28164798e-01 -5.26382923e-01 -4.42580849e-01 2.59571105e-01 -6.78299963e-01 -3.24850231e-01 4.44801711e-03 1.32308617e-01 3.50219995e-01 9.98315960e-02 6.46833241e-01 6.76559448e-01 1.92595899e-01 6.13123000e-01 -1.10165119e+00 -8.03472996e-01 2.98573941e-01 -4.25166070e-01 -9.81518328e-02 2.09545463e-01 2.67809480e-01 7.05439329e-01 -5.44983685e-01 9.51268196e-01 1.23563349e+00 8.13687682e-01 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d923fb20-5be6-4a52-9e0c-41e936107259	resolving-language-and-vision-ambiguities-1	null	null	https://aclanthology.org/D16-1156	https://aclanthology.org/D16-1156.pdf	Resolving Language and Vision Ambiguities Together: Joint Segmentation \& Prepositional Attachment Resolution in Captioned Scenes	null	['Kevin Kochersberger', 'Aishwarya Agrawal', 'Yash Goyal', 'Stanislaw Antol', 'Dhruv Batra', 'Ankit Laddha', 'Gordon Christie']	2016-11-01	null	null	null	emnlp-2016-11	['prepositional-phrase-attachment']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 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e592411a-ac4c-4715-9f81-782e8c4cf975	deep-implicit-distribution-alignment-networks	2302.08921	null	https://arxiv.org/abs/2302.08921v1	https://arxiv.org/pdf/2302.08921v1.pdf	Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition	In this paper, we propose a novel deep transfer learning method called deep implicit distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion recognition (SER) problem, in which the labeled training (source) and unlabeled testing (target) speech signals come from different corpora. Specifically, DIDAN first adopts a simple deep regression network consisting of a set of convolutional and fully connected layers to directly regress the source speech spectrums into the emotional labels such that the proposed DIDAN can own the emotion discriminative ability. Then, such ability is transferred to be also applicable to the target speech samples regardless of corpus variance by resorting to a well-designed regularization term called implicit distribution alignment (IDA). Unlike widely-used maximum mean discrepancy (MMD) and its variants, the proposed IDA absorbs the idea of sample reconstruction to implicitly align the distribution gap, which enables DIDAN to learn both emotion discriminative and corpus invariant features from speech spectrums. To evaluate the proposed DIDAN, extensive cross-corpus SER experiments on widely-used speech emotion corpora are carried out. Experimental results show that the proposed DIDAN can outperform lots of recent state-of-the-art methods in coping with the cross-corpus SER tasks.	['Li Zhao', 'Hailun Lian', 'Wenming Zheng', 'Yuan Zong', 'Jincen Wang', 'Yan Zhao']	2023-02-17	null	null	null	null	['cross-corpus', 'speech-emotion-recognition']	['computer-vision', 'speech']	[ 4.40467708e-02 -9.37353373e-02 1.78493083e-01 -7.23915219e-01 -1.00101447e+00 -1.73379213e-01 3.83832693e-01 -3.29987735e-01 -3.88711363e-01 5.99938095e-01 -8.26558918e-02 -1.08222686e-01 2.48994470e-01 -2.10423425e-01 -5.33967435e-01 -9.46052372e-01 7.01232776e-02 2.17742994e-01 -2.74015307e-01 -3.36517036e-01 -2.10079387e-01 2.03064486e-01 -1.38266003e+00 2.04165265e-01 1.02883780e+00 1.46671140e+00 3.07713568e-01 3.93450707e-01 -3.03230107e-01 8.15149009e-01 -8.57815266e-01 -3.88496876e-01 -6.45252541e-02 -7.39473522e-01 -5.38014233e-01 2.76271760e-01 -1.47138014e-01 1.90993324e-02 -1.61526620e-01 1.25994241e+00 9.08881009e-01 4.22266662e-01 7.02288628e-01 -1.33764851e+00 -8.06995690e-01 5.37282407e-01 -5.45670569e-01 2.38203660e-01 -3.95132191e-02 -1.50226489e-01 8.36376250e-01 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5897e751-9107-491e-a1f9-2287be0c1ecc	knowledge-informed-machine-learning-using-a	2201.01769	null	https://arxiv.org/abs/2201.01769v1	https://arxiv.org/pdf/2201.01769v1.pdf	Knowledge Informed Machine Learning using a Weibull-based Loss Function	Machine learning can be enhanced through the integration of external knowledge. This method, called knowledge informed machine learning, is also applicable within the field of Prognostics and Health Management (PHM). In this paper, the various methods of knowledge informed machine learning, from a PHM context, are reviewed with the goal of helping the reader understand the domain. In addition, a knowledge informed machine learning technique is demonstrated, using the common IMS and PRONOSTIA bearing data sets, for remaining useful life (RUL) prediction. Specifically, knowledge is garnered from the field of reliability engineering which is represented through the Weibull distribution. The knowledge is then integrated into a neural network through a novel Weibull-based loss function. A thorough statistical analysis of the Weibull-based loss function is conducted, demonstrating the effectiveness of the method on the PRONOSTIA data set. However, the Weibull-based loss function is less effective on the IMS data set. The results, shortcomings, and benefits of the approach are discussed in length. Finally, all the code is publicly available for the benefit of other researchers.	['Chris K Mechefske', 'Tim von Hahn']	2022-01-04	null	null	null	null	['time-series-regression', 'remaining-useful-lifetime-estimation']	['time-series', 'time-series']	[ 1.23752020e-01 3.52875233e-01 -2.98347533e-01 -5.17097414e-01 -7.06756294e-01 1.96696535e-01 1.54714808e-01 5.41983426e-01 -2.76570380e-01 1.27689767e+00 -2.06568792e-01 -4.26894486e-01 -1.03459918e+00 -8.38554502e-01 -4.73110944e-01 -1.00150764e+00 -3.20809275e-01 3.94425809e-01 -2.06433415e-01 -1.45515174e-01 4.70707953e-01 7.41948843e-01 -1.52820182e+00 4.38382030e-02 9.17769730e-01 1.14902544e+00 4.24479060e-02 2.92223096e-01 2.93427169e-01 1.15678430e+00 -6.17523015e-01 -3.45602036e-01 -3.58622253e-01 6.05769549e-03 -9.28533554e-01 -3.00056130e-01 -5.38728833e-01 -3.32187474e-01 -8.59333798e-02 2.62151241e-01 5.74407518e-01 3.92220676e-01 1.09588194e+00 -1.32296419e+00 -3.43332440e-01 5.64345837e-01 -1.96598414e-02 1.28024835e-02 1.78384006e-01 -3.71034712e-01 6.59737408e-01 -9.10908878e-01 6.54414669e-02 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e351002c-5318-42c0-84ed-8798ff6afe10	intention-based-lane-changing-and-lane	2011.07424	null	https://arxiv.org/abs/2011.07424v1	https://arxiv.org/pdf/2011.07424v1.pdf	Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System	Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver lane change intention. By using a deep learning-based method to model a driver decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.	['Kimihiko Nakano', 'Tsutomu Kaizuka', 'Bo Yang', 'Zheng Wang', 'Kaiming Yang', 'Zhanhong Yan']	2020-11-15	null	null	null	null	['steering-control']	['computer-vision']	[-2.29389265e-01 4.95071113e-01 -1.21618554e-01 -5.74840426e-01 7.46958926e-02 -7.07157999e-02 5.37215590e-01 -5.03336668e-01 -4.45100814e-01 4.11473900e-01 -1.69113725e-02 -8.06553960e-01 -5.96794784e-02 -5.78182995e-01 -3.45903933e-01 -5.95872700e-01 6.19188026e-02 -1.85664326e-01 7.19200611e-01 -8.15064907e-01 5.93759477e-01 3.82782280e-01 -2.22877645e+00 7.66060352e-02 1.16538405e+00 7.81695485e-01 9.19247866e-01 5.40107369e-01 1.34779155e-01 3.85006785e-01 2.44998336e-01 1.60341173e-01 1.69717446e-01 1.06551293e-02 3.89105454e-02 -3.31090987e-01 9.73755643e-02 -7.76519358e-01 -6.19681656e-01 5.72507679e-01 7.58623600e-01 1.73568845e-01 4.36527848e-01 -1.98913443e+00 1.54541343e-01 -1.86713785e-01 1.70548901e-01 -4.31512773e-01 4.28609699e-01 6.13820672e-01 2.35427245e-01 -7.69932330e-01 6.11166179e-01 1.09174466e+00 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ba20a2b6-6876-4e0c-861f-4bb803514fca	weakly-and-semi-supervised-human-body-part	1805.0431	null	http://arxiv.org/abs/1805.04310v1	http://arxiv.org/pdf/1805.04310v1.pdf	Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer	Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated impressive results, their performance highly depends on the quantity and quality of training data. In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations. Our key idea is to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose. Using these estimated results as additional training data, our semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU on the PASCAL-Person-Part dataset, and we achieve state-of-the-art human parsing results. Our approach is general and can be readily extended to other object/animal parsing task assuming that their anatomical similarity can be annotated by keypoints. The proposed model and accompanying source code are available at https://github.com/MVIG-SJTU/WSHP	['Yu-Wing Tai', 'Jianwen Xie', 'Guansong Lu', 'Hao-Shu Fang', 'Xiaolin Fang', 'Cewu Lu']	2018-05-11	weakly-and-semi-supervised-human-body-part-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Fang_Weakly_and_Semi_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Fang_Weakly_and_Semi_CVPR_2018_paper.pdf	cvpr-2018-6	['human-part-segmentation', 'human-parsing']	['computer-vision', 'computer-vision']	[ 3.24907780e-01 6.30414903e-01 -2.42223531e-01 -6.43213749e-01 -1.03563046e+00 -5.31269431e-01 3.21477234e-01 -2.36020871e-02 -4.47107792e-01 7.11107194e-01 -1.98572248e-01 1.87421978e-01 3.99283141e-01 -5.45507669e-01 -9.25382555e-01 -3.65597576e-01 3.42880636e-01 9.00406420e-01 6.71558559e-01 -2.01810062e-01 -1.85545906e-01 2.39133954e-01 -1.09408665e+00 9.36413631e-02 8.19713354e-01 8.38767290e-01 2.35862270e-01 7.48417974e-01 -4.59402651e-02 2.99242109e-01 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12351321-ddc1-406c-a77d-f356596c7198	generating-visual-spatial-description-via	2305.11768	null	https://arxiv.org/abs/2305.11768v2	https://arxiv.org/pdf/2305.11768v2.pdf	Generating Visual Spatial Description via Holistic 3D Scene Understanding	Visual spatial description (VSD) aims to generate texts that describe the spatial relations of the given objects within images. Existing VSD work merely models the 2D geometrical vision features, thus inevitably falling prey to the problem of skewed spatial understanding of target objects. In this work, we investigate the incorporation of 3D scene features for VSD. With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes. Besides, we propose a scene subgraph selecting mechanism, sampling topologically-diverse subgraphs from Go3D-S2G, where the diverse local structure features are navigated to yield spatially-diversified text generation. Experimental results on two VSD datasets demonstrate that our framework outperforms the baselines significantly, especially improving on the cases with complex visual spatial relations. Meanwhile, our method can produce more spatially-diversified generation. Code is available at https://github.com/zhaoyucs/VSD.	['Tat-Seng Chua', 'Min Zhang', 'Meishan Zhang', 'Jianguo Wei', 'Wei Ji', 'Hao Fei', 'Yu Zhao']	2023-05-19	null	null	null	null	['scene-understanding']	['computer-vision']	[-7.39437388e-03 2.52600629e-02 -2.02844739e-02 -3.11359286e-01 -3.60141635e-01 -6.66975319e-01 8.25931489e-01 -8.34696442e-02 3.05113554e-01 2.14061543e-01 7.73028553e-01 -2.00509563e-01 -1.75136387e-01 -1.02284575e+00 -5.73638380e-01 -5.25520802e-01 3.92215997e-01 3.34143221e-01 4.03275430e-01 -3.06262732e-01 5.13602018e-01 7.70469487e-01 -1.46793032e+00 3.95545036e-01 9.09709215e-01 7.97458649e-01 6.12409890e-01 4.60734040e-01 -4.17139560e-01 3.45559925e-01 -4.58568275e-01 -7.07536936e-02 3.21806073e-01 -4.89096701e-01 -7.51598597e-01 4.90684360e-01 4.51137573e-01 -2.89646268e-01 -5.64345658e-01 8.38764310e-01 4.34970528e-01 3.05370003e-01 8.41447115e-01 -1.22464323e+00 -1.03412902e+00 3.95085633e-01 -7.68768430e-01 1.84460636e-02 6.68543220e-01 3.90304893e-01 8.90146375e-01 -1.34414995e+00 8.49472940e-01 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8fb89175-ca46-45e3-a2a6-24cbeba49ac8	elasticvit-conflict-aware-supernet-training	2303.0973	null	https://arxiv.org/abs/2303.09730v2	https://arxiv.org/pdf/2303.09730v2.pdf	ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices	Neural Architecture Search (NAS) has shown promising performance in the automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing lightweight and low-latency ViT models for diverse mobile devices remains a big challenge. In this work, we propose ElasticViT, a two-stage NAS approach that trains a high-quality ViT supernet over a very large search space that supports a wide range of mobile devices, and then searches an optimal sub-network (subnet) for direct deployment. However, prior supernet training methods that rely on uniform sampling suffer from the gradient conflict issue: the sampled subnets can have vastly different model sizes (e.g., 50M vs. 2G FLOPs), leading to different optimization directions and inferior performance. To address this challenge, we propose two novel sampling techniques: complexity-aware sampling and performance-aware sampling. Complexity-aware sampling limits the FLOPs difference among the subnets sampled across adjacent training steps, while covering different-sized subnets in the search space. Performance-aware sampling further selects subnets that have good accuracy, which can reduce gradient conflicts and improve supernet quality. Our discovered models, ElasticViT models, achieve top-1 accuracy from 67.2% to 80.0% on ImageNet from 60M to 800M FLOPs without extra retraining, outperforming all prior CNNs and ViTs in terms of accuracy and latency. Our tiny and small models are also the first ViT models that surpass state-of-the-art CNNs with significantly lower latency on mobile devices. For instance, ElasticViT-S1 runs 2.62x faster than EfficientNet-B0 with 0.1% higher accuracy.	['Mao Yang', 'Zhi Wang', 'Yuqing Yang', 'Quanlu Zhang', 'Ting Cao', 'Jiahang Xu', 'Huiqiang Jiang', 'Li Lyna Zhang', 'Chen Tang']	2023-03-17	null	null	null	null	['architecture-search']	['methodology']	[-1.58673510e-01 -3.11375111e-01 -5.08007169e-01 -1.89022124e-01 -3.43256533e-01 -5.13159990e-01 5.45383096e-02 -6.53482497e-01 -7.35930622e-01 5.60253978e-01 -3.40554208e-01 -6.02714777e-01 -1.76422894e-01 -6.44696176e-01 -8.21700871e-01 -4.81191099e-01 3.05755645e-01 5.98237574e-01 7.84825981e-01 5.47192581e-02 4.02616560e-02 2.72176206e-01 -1.66796267e+00 1.07810602e-01 8.14148843e-01 1.48568761e+00 8.11362624e-01 6.73497021e-01 -2.05663964e-01 5.57885349e-01 -9.32848334e-01 -3.97110999e-01 3.05157453e-01 1.04331672e-01 -6.73250556e-01 -4.91417497e-01 1.04079580e+00 -3.88888210e-01 -3.41043055e-01 8.70365560e-01 6.95338964e-01 -3.53960097e-01 -6.64635003e-03 -1.37362039e+00 -3.64522368e-01 8.90476823e-01 -5.44347107e-01 3.94425273e-01 -4.91936266e-01 4.23381239e-01 8.30745757e-01 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9.16689277e-01 6.81365073e-01 3.16064388e-01 -7.74491906e-01 -5.77558041e-01 -6.50049388e-01 1.08090905e-03 -4.72084820e-01 3.49039972e-01 -3.75576049e-01 1.68256536e-02 -3.88027638e-01]	[8.588897705078125, 2.952136278152466]
eee5437e-0750-4668-bab8-83470898b604	evaluating-the-performance-of-stylegan2-ada	2210.03786	null	https://arxiv.org/abs/2210.03786v1	https://arxiv.org/pdf/2210.03786v1.pdf	Evaluating the Performance of StyleGAN2-ADA on Medical Images	Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impeded their utility: computational cost, data requirements, reliable evaluation measures, and training complexity. Our work investigates each of these obstacles in a novel application of StyleGAN2-ADA to high-resolution medical imaging datasets. Our dataset is comprised of liver-containing axial slices from non-contrast and contrast-enhanced computed tomography (CT) scans. Additionally, we utilized four public datasets composed of various imaging modalities. We trained a StyleGAN2 network with transfer learning (from the Flickr-Faces-HQ dataset) and data augmentation (horizontal flipping and adaptive discriminator augmentation). The network's generative quality was measured quantitatively with the Fr\'echet Inception Distance (FID) and qualitatively with a visual Turing test given to seven radiologists and radiation oncologists. The StyleGAN2-ADA network achieved a FID of 5.22 ($\pm$ 0.17) on our liver CT dataset. It also set new record FIDs of 10.78, 3.52, 21.17, and 5.39 on the publicly available SLIVER07, ChestX-ray14, ACDC, and Medical Segmentation Decathlon (brain tumors) datasets. In the visual Turing test, the clinicians rated generated images as real 42% of the time, approaching random guessing. Our computational ablation study revealed that transfer learning and data augmentation stabilize training and improve the perceptual quality of the generated images. We observed the FID to be consistent with human perceptual evaluation of medical images. Finally, our work found that StyleGAN2-ADA consistently produces high-quality results without hyperparameter searches or retraining.	['Kristy K. Brock', 'Ankit B. Patel', 'Yuan-Mao Lin', 'Brian De', 'Sireesha Yedururi', 'Aradhana M. Venkatesan', 'Hyunseon Christine Kang', 'Caroline Chung', 'Bruno Odisio', 'Eugene Koay', 'Ethan Lin', 'Suprateek Kundu', 'Brian M. Anderson', 'John Wood', 'McKell Woodland']	2022-10-07	null	null	null	null	['medical-image-generation']	['medical']	[ 5.24378002e-01 2.89071649e-01 8.18043202e-02 -2.86300957e-01 -1.39920259e+00 -8.33848596e-01 3.06609660e-01 -4.58543569e-01 -5.57108879e-01 7.55567729e-01 6.95103332e-02 -6.84569240e-01 6.59559220e-02 -5.06196797e-01 -6.24443769e-01 -8.44690919e-01 -4.05534506e-01 4.68471676e-01 -3.04034531e-01 1.44316390e-01 -4.21628535e-01 3.45572412e-01 -4.70779330e-01 4.98002052e-01 7.38281608e-01 9.74591434e-01 -6.60638660e-02 1.29319191e+00 6.70599043e-01 1.03688550e+00 -8.30244064e-01 -4.71824765e-01 5.09103417e-01 -8.16634536e-01 -9.33884799e-01 1.16866261e-01 4.18276459e-01 -6.48174524e-01 -5.49050093e-01 9.02558446e-01 1.01439023e+00 -1.78955615e-01 6.21020317e-01 -1.13151598e+00 -8.34835231e-01 5.98014414e-01 -4.17185307e-01 4.62160558e-01 8.39726031e-02 7.89864182e-01 3.65881532e-01 -6.04327857e-01 8.09636772e-01 7.13088393e-01 8.91979814e-01 7.56312609e-01 -1.25591063e+00 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21b65332-1fc6-4086-9e2d-949bf9b8c70e	graph-neural-network-on-electronic-health	1912.03761	null	https://arxiv.org/abs/1912.03761v2	https://arxiv.org/pdf/1912.03761v2.pdf	Variationally Regularized Graph-based Representation Learning for Electronic Health Records	Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of diseases and lab-disease correlations can be informative when only a subset of these variables is documented by the clinician. A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections. Existing medical ontologies can be the reference for EHR structures, but they place numerous constraints on the data source. Recent progress on graph neural networks (GNN) enables end-to-end learning of topological structures for non-grid or non-sequential data. However, there are problems to be addressed on how to learn the medical graph adaptively and how to understand the effect of the medical graph on representation learning. In this paper, we propose a variationally regularized encoder-decoder graph network that achieves more robustness in graph structure learning by regularizing node representations. Our model outperforms the existing graph and non-graph based methods in various EHR predictive tasks based on both public data and real-world clinical data. Besides the improvements in empirical experiment performances, we provide an interpretation of the effect of variational regularization compared to standard graph neural network, using singular value analysis.	['Narges Razavian', 'Weicheng Zhu']	2019-12-08	null	null	null	null	['graph-structure-learning']	['graphs']	[ 1.62644520e-01 6.68472230e-01 -4.13056582e-01 -3.44532728e-01 -5.58905125e-01 -1.45136461e-01 1.32204950e-01 9.19414222e-01 -7.33686686e-02 7.31417894e-01 6.31405354e-01 -5.37824869e-01 -4.31742698e-01 -8.86448264e-01 -8.41417074e-01 -4.60381061e-01 -7.75648057e-01 8.68199527e-01 -1.26496479e-01 -6.82515725e-02 -2.96635151e-01 1.65801913e-01 -6.46418631e-01 3.67242247e-01 7.39380956e-01 7.25004137e-01 1.37737542e-01 6.12562478e-01 -2.68798143e-01 9.57004666e-01 -2.85296351e-01 -3.16412628e-01 -2.11300924e-01 -3.82375121e-01 -8.49127352e-01 -1.20866753e-01 3.47129077e-01 8.34489688e-02 -5.75637937e-01 1.12297869e+00 5.71038604e-01 -1.35066107e-01 7.93866754e-01 -9.84850109e-01 -1.17092550e+00 1.04053652e+00 -5.10615170e-01 4.20085579e-01 3.33215505e-01 -2.34254315e-01 1.28315103e+00 -4.73264575e-01 1.01020598e+00 1.16531873e+00 9.92108285e-01 4.51543659e-01 -1.36159623e+00 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5fdaccfd-cc10-4d83-aae3-10c9148fabd7	italian-language-and-dialect-identification	null	null	https://aclanthology.org/2022.vardial-1.13	https://aclanthology.org/2022.vardial-1.13.pdf	Italian Language and Dialect Identification and Regional French Variety Detection using Adaptive Naive Bayes	This article describes the language identification approach used by the SUKI team in the Identification of Languages and Dialects of Italy and the French Cross-Domain Dialect Identification shared tasks organized as part of the VarDial workshop 2022. We describe some experiments and the preprocessing techniques we used for the training data in preparation for the shared task submissions, which are also discussed. Our Naive Bayes-based adaptive system reached the first position in Italian language identification and came second in the French variety identification task.	['Krister Lindén', 'Heidi Jauhiainen', 'Tommi Jauhiainen']	null	null	null	null	vardial-coling-2022-10	['dialect-identification']	['natural-language-processing']	[-5.76070249e-01 -5.14668167e-01 -7.16383830e-02 -7.21899092e-01 -1.03312528e+00 -1.15078723e+00 8.18460524e-01 6.01588041e-02 -7.65880346e-01 7.41849601e-01 5.32029271e-01 -5.23428917e-01 -1.45238936e-01 -2.02672660e-01 9.50317383e-02 -3.66100997e-01 1.44606471e-01 1.16221178e+00 1.08460560e-02 -4.74717557e-01 1.77499861e-01 4.03583884e-01 -1.12146091e+00 6.31336033e-01 8.13269973e-01 2.90415108e-01 -1.36366561e-02 7.07950413e-01 -4.02341604e-01 5.51661730e-01 -3.34243208e-01 -6.14024758e-01 4.00264561e-01 -2.04558801e-02 -1.39228928e+00 -3.53631288e-01 5.40629447e-01 -2.70446688e-01 -1.14183575e-01 1.02015483e+00 7.08386362e-01 3.26448679e-03 8.81148696e-01 -7.58128941e-01 -1.43511668e-01 1.61449277e+00 -1.69420779e-01 4.56050783e-01 9.05196369e-01 -1.78545430e-01 9.69584882e-01 -9.76080716e-01 7.38977969e-01 1.59185457e+00 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80419917-83a0-4d63-8144-419200014bbc	plex-towards-reliability-using-pretrained	2207.07411	null	https://arxiv.org/abs/2207.07411v1	https://arxiv.org/pdf/2207.07411v1.pdf	Plex: Towards Reliability using Pretrained Large Model Extensions	A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, where we define a reliable model as one that not only achieves strong predictive performance but also performs well consistently over many decision-making tasks involving uncertainty (e.g., selective prediction, open set recognition), robust generalization (e.g., accuracy and proper scoring rules such as log-likelihood on in- and out-of-distribution datasets), and adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of tasks over 40 datasets in order to evaluate different aspects of reliability on both vision and language domains. To improve reliability, we developed ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol as it improves the out-of-the-box performance and does not require designing scores or tuning the model for each task. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples. We also demonstrate Plex's capabilities on challenging tasks including zero-shot open set recognition, active learning, and uncertainty in conversational language understanding.	['Balaji Lakshminarayanan', 'Jasper Snoek', 'Zoubin Ghahramani', 'Yarin Gal', 'D. Sculley', 'Kevin Murphy', 'Kelly Buchanan', 'Honglin Yuan', 'Nithum Thain', 'Rodolphe Jenatton', 'Andreas Kirsch', 'Joost van Amersfoort', 'Zachary Nado', 'Karan Singhal', 'Tim G. J. Rudner', 'Neil Band', 'Huiyi Hu', 'Zelda Mariet', 'Zi Wang', 'Kehang Han', 'Jie Ren', 'Mark Collier', 'Du Phan', 'Michael W. Dusenberry', 'Jeremiah Liu', 'Dustin Tran']	2022-07-15	null	null	null	null	['open-set-learning']	['miscellaneous']	[-1.44852251e-01 9.16946307e-02 -7.32995197e-02 -7.44165540e-01 -1.32567847e+00 -4.62367207e-01 7.37743199e-01 2.86651012e-02 -5.44337869e-01 7.44311810e-01 -1.07780974e-02 -2.20554069e-01 -2.35693499e-01 -3.35695922e-01 -6.95898712e-01 -5.52027464e-01 -1.51761025e-01 9.68868792e-01 2.24870667e-01 5.18118106e-02 1.36640593e-01 8.44824538e-02 -1.70766044e+00 2.12007210e-01 1.05760443e+00 1.29936516e+00 1.89713538e-01 6.74136460e-01 -1.89933941e-01 5.99687815e-01 -4.28800792e-01 -5.23329318e-01 -6.57802671e-02 2.03289956e-01 -7.43043602e-01 -1.20428726e-01 4.40548688e-01 -2.18029797e-01 -1.43160760e-01 8.29551816e-01 6.33634984e-01 2.85270482e-01 9.69113946e-01 -1.23939633e+00 -8.39029074e-01 6.96468413e-01 -4.39425796e-01 2.57407129e-01 1.96748003e-01 5.11731625e-01 1.05767643e+00 -1.13318753e+00 3.34157586e-01 1.48841584e+00 6.99495435e-01 8.57733190e-01 -1.59754694e+00 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-3.92361343e-01 2.01641768e-01 2.49537468e-01 -9.61432159e-01 -6.10180974e-01 -3.79102468e-01]	[9.914098739624023, 2.509899616241455]
44b83714-7eeb-441d-9dc7-a3153901a018	to-bert-or-not-to-bert-comparing-speech-and	2008.01551	null	https://arxiv.org/abs/2008.01551v1	https://arxiv.org/pdf/2008.01551v1.pdf	To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection	Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.	['Frank Rudzicz', 'Jekaterina Novikova', 'Aparna Balagopalan', 'Benjamin Eyre']	2020-07-26	null	null	null	null	['alzheimer-s-disease-detection']	['medical']	[ 3.97083521e-01 -1.13265350e-01 8.76052007e-02 -4.81102675e-01 -1.48511827e+00 -3.40573817e-01 7.19415128e-01 3.74078125e-01 -8.37590575e-01 5.16776264e-01 7.89585292e-01 -2.12764159e-01 -1.07808590e-01 -5.01758039e-01 -2.96688497e-01 -1.21973366e-01 -3.14040601e-01 5.82089186e-01 2.64791876e-01 -1.84967086e-01 -4.54365537e-02 2.30369091e-01 -1.42317820e+00 7.35448182e-01 7.25620985e-01 1.10132372e+00 3.41059715e-01 8.58780384e-01 -1.23588368e-01 7.30056822e-01 -4.57333803e-01 -2.53442466e-01 -2.58659005e-01 -4.21524227e-01 -9.14353311e-01 -1.63517207e-01 4.74423647e-01 -4.95993257e-01 -4.34178561e-01 8.93546045e-01 7.72893250e-01 -2.56679505e-01 7.05970883e-01 -7.05849171e-01 -7.13225663e-01 4.28620458e-01 -1.13696717e-01 8.72030795e-01 5.56460142e-01 1.04634956e-01 1.32841361e+00 -6.25810623e-01 5.62803984e-01 1.49904060e+00 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a4297c20-35d3-40b2-b95a-f995a8155e2e	source-free-domain-adaptation-for-question	2212.09563	null	https://arxiv.org/abs/2212.09563v1	https://arxiv.org/pdf/2212.09563v1.pdf	Source-Free Domain Adaptation for Question Answering with Masked Self-training	Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source domain data may, however, contain sensitive information and may be restricted. In this study, we investigate a more challenging setting, source-free UDA, in which we have only the pretrained source model and target domain data, without access to source domain data. We propose a novel self-training approach to QA models that integrates a unique mask module for domain adaptation. The mask is auto-adjusted to extract key domain knowledge while trained on the source domain. To maintain previously learned domain knowledge, certain mask weights are frozen during adaptation, while other weights are adjusted to mitigate domain shifts with pseudo-labeled samples generated in the target domain. %As part of the self-training process, we generate pseudo-labeled samples in the target domain based on models trained in the source domain. Our empirical results on four benchmark datasets suggest that our approach significantly enhances the performance of pretrained QA models on the target domain, and even outperforms models that have access to the source data during adaptation.	['C. Ling', 'Y. Dong', 'B. Wang', 'M. Yin']	2022-12-19	null	null	null	null	['source-free-domain-adaptation']	['computer-vision']	[ 5.77803612e-01 3.54897827e-01 -1.67195693e-01 -8.23096991e-01 -1.17015231e+00 -9.46647227e-01 4.37612265e-01 1.91252708e-01 -5.25620282e-01 8.56110394e-01 1.88070580e-01 -1.39839038e-01 2.15577424e-01 -9.34487104e-01 -8.24129879e-01 -3.74439180e-01 5.26566505e-01 9.88285363e-01 6.67525291e-01 -7.56668270e-01 -1.01298347e-01 4.44667526e-02 -1.33455205e+00 5.05400300e-01 1.45766354e+00 9.86157238e-01 2.36270532e-01 4.82443601e-01 -4.69203413e-01 7.36160100e-01 -9.46746707e-01 -5.13415158e-01 2.22262457e-01 -6.20291948e-01 -1.18354869e+00 -2.28427574e-02 4.14973021e-01 -6.86470866e-02 -1.26479775e-01 8.88149261e-01 5.63871324e-01 3.44235569e-01 5.95886827e-01 -9.25161660e-01 -8.46989393e-01 3.49060535e-01 3.33403908e-02 1.65355667e-01 3.38108122e-01 1.69428766e-01 7.81478703e-01 -7.18996644e-01 7.77726412e-01 9.88261819e-01 3.70956898e-01 1.13175511e+00 -1.31071866e+00 -4.73902404e-01 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68645924-4d12-416b-a855-3a256ab779c2	free-bits-latency-optimization-of-mixed	2307.02894	null	https://arxiv.org/abs/2307.02894v1	https://arxiv.org/pdf/2307.02894v1.pdf	Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge	Mixed-precision quantization, where a deep neural network's layers are quantized to different precisions, offers the opportunity to optimize the trade-offs between model size, latency, and statistical accuracy beyond what can be achieved with homogeneous-bit-width quantization. To navigate the intractable search space of mixed-precision configurations for a given network, this paper proposes a hybrid search methodology. It consists of a hardware-agnostic differentiable search algorithm followed by a hardware-aware heuristic optimization to find mixed-precision configurations latency-optimized for a specific hardware target. We evaluate our algorithm on MobileNetV1 and MobileNetV2 and deploy the resulting networks on a family of multi-core RISC-V microcontroller platforms with different hardware characteristics. We achieve up to 28.6% reduction of end-to-end latency compared to an 8-bit model at a negligible accuracy drop from a full-precision baseline on the 1000-class ImageNet dataset. We demonstrate speedups relative to an 8-bit baseline, even on systems with no hardware support for sub-byte arithmetic at negligible accuracy drop. Furthermore, we show the superiority of our approach with respect to differentiable search targeting reduced binary operation counts as a proxy for latency.	['Luca Benini', 'Francesco Conti', 'Georg Rutishauser']	2023-07-06	null	null	null	null	['quantization', 'navigate']	['methodology', 'reasoning']	[ 9.97624919e-02 -2.39072412e-01 -3.87161255e-01 -5.85510075e-01 -8.57468903e-01 -4.10548210e-01 2.31288210e-01 3.74288648e-01 -1.18009412e+00 4.54147458e-01 -4.94831979e-01 -8.32094669e-01 -3.16458046e-02 -6.89467907e-01 -8.50492299e-01 -4.00093198e-01 -3.15039247e-01 2.80576378e-01 3.87427032e-01 -9.12717432e-02 2.95144439e-01 5.89915216e-01 -1.58933938e+00 1.44193256e-02 4.57163215e-01 1.62560856e+00 1.56170884e-02 1.05709696e+00 5.79641461e-02 5.78955650e-01 -8.89104426e-01 -4.19437468e-01 6.28057003e-01 4.08149838e-01 -5.17421186e-01 -6.23237669e-01 1.11244452e+00 -6.88468397e-01 -4.32758480e-01 1.39955950e+00 5.90460241e-01 -4.94005889e-01 1.95188999e-01 -1.34863579e+00 1.88721299e-01 8.19179356e-01 -3.33464652e-01 5.34397423e-01 -4.15277988e-01 4.66582298e-01 7.88418412e-01 -2.08606169e-01 1.66535959e-01 1.37201142e+00 1.01487052e+00 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d018e066-e638-4407-bb0a-696bca48f281	a-holistic-approach-for-rapid-development-of	2201.13243	null	https://arxiv.org/abs/2201.13243v2	https://arxiv.org/pdf/2201.13243v2.pdf	FastIoT -- A framework and holistic approach for rapid development of IIoT systems	While lots of research has been conducted on the architecture of Industrial Internet of Things (IIoT) systems, concepts of structuring their development processes are missing. Therefore, we propose a holistic approach supporting organizations in rapid development of IIoT systems. It includes the structuring of the development process into multiple projects sharing project conventions. Utilizing a single configurable build script for all projects, our goal is to make the integration of code from various projects into broader IIoT systems easy and the systems decomposable with minimal effort.	['Tilman Klaeger', 'Konstantin Merker']	2022-01-31	null	null	null	null	['miscellaneous']	['miscellaneous']	[-5.23635745e-01 7.64671415e-02 1.39479637e-01 -5.62107623e-01 3.23319077e-01 -6.67700768e-01 2.40085781e-01 -2.27162406e-01 4.30018216e-01 3.05020481e-01 -2.98378646e-01 -6.17407024e-01 -3.91967773e-01 -8.14274669e-01 1.17993457e-02 -1.05088271e-01 6.46456838e-01 4.31851387e-01 5.82668483e-01 -1.87104046e-01 3.00160348e-01 4.22916830e-01 -1.27339685e+00 2.29186360e-02 5.85597456e-01 8.71991873e-01 4.77448851e-01 4.87662673e-01 -4.19006079e-01 8.61272275e-01 -6.46331906e-01 -2.62316853e-01 5.84937036e-01 -3.85274768e-01 -9.45963383e-01 4.40693766e-01 -2.88703740e-01 -2.98458666e-01 3.14934731e-01 7.99302936e-01 2.54302502e-01 -5.39270222e-01 7.02356324e-02 -1.63161874e+00 -4.02767003e-01 8.90408695e-01 -9.23090801e-02 -4.73701596e-01 1.65312052e-01 1.05619200e-01 5.67172348e-01 -7.13050485e-01 6.50940955e-01 7.96379030e-01 7.60506570e-01 7.76865426e-03 -1.25652838e+00 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ea9ac861-a6e9-4d71-9396-41f6d7875a7a	seeking-an-optimal-approach-for-computer	2109.07029	null	https://arxiv.org/abs/2109.07029v1	https://arxiv.org/pdf/2109.07029v1.pdf	Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection	Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE. However, numerous competing methods for a given task in the deep learning literature exist, causing great confusion regarding the development of a CAD PE system. To address this confusion, we present a comprehensive analysis of competing deep learning methods applicable to PE diagnosis using CTPA at the both image and exam levels. At the image level, we compare convolutional neural networks (CNNs) with vision transformers, and contrast self-supervised learning (SSL) with supervised learning, followed by an evaluation of transfer learning compared with training from scratch. At the exam level, we focus on comparing conventional classification (CC) with multiple instance learning (MIL). Our extensive experiments consistently show: (1) transfer learning consistently boosts performance despite differences between natural images and CT scans, (2) transfer learning with SSL surpasses its supervised counterparts; (3) CNNs outperform vision transformers, which otherwise show satisfactory performance; and (4) CC is, surprisingly, superior to MIL. Compared with the state of the art, our optimal approach provides an AUC gain of 0.2\% and 1.05\% for image-level and exam-level, respectively.	['Jianming Liang', 'Michael B Gotway', 'Zongwei Zhou', 'Shiv Gehlot', 'Nahid Ul Islam']	2021-09-15	null	null	null	null	['pulmonary-embolism-detection']	['medical']	[ 5.35691194e-02 -1.04874253e-01 -1.51667252e-01 3.40404660e-01 -8.79060984e-01 -4.58317816e-01 4.76954430e-01 1.76452935e-01 -2.37400293e-01 6.82656229e-01 4.28183191e-02 -7.47686625e-01 -1.14958189e-01 -6.95723534e-01 -5.65925360e-01 -6.35789275e-01 -1.26440167e-01 7.40214944e-01 6.21844411e-01 4.18559551e-01 -2.23825097e-01 8.69225085e-01 -8.48002434e-01 4.45190728e-01 7.72655725e-01 1.29041648e+00 -2.35219765e-02 9.11597371e-01 -2.18498304e-01 1.49339163e+00 -5.39470494e-01 -4.90985155e-01 1.64998800e-01 -6.01367414e-01 -1.06848598e+00 -1.86876684e-01 5.21769822e-01 -3.81859004e-01 -4.34389681e-01 5.62920630e-01 7.01666176e-01 -4.09787595e-01 9.25567806e-01 -1.08131576e+00 -4.23820436e-01 4.27350737e-02 -5.11184692e-01 7.97670245e-01 -9.33106244e-02 4.58211571e-01 6.67300045e-01 -9.67882454e-01 1.51564792e-01 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b92d415a-363f-420c-9e43-75e6ab43ab06	continual-learning-for-seq2seq-generations	null	null	https://openreview.net/forum?id=yd7uyR9_0iU	https://openreview.net/pdf?id=yd7uyR9_0iU	Continual Learning for Seq2Seq Generations with Transformer Calibration	Conventional NLP generation models are trained offline with a given dataset for a particular task, which is referred to as isolated learning. Research on sequence-to-sequence language generation aims to study continual learning model to constantly learning from sequentially encountered tasks. However, continual learning studies often suffer from catastrophic forgetting, a persistent challenge for lifelong learning. In this paper, we present a novel NLP transformer model which attempts to mitigate catastrophic forgetting in online continual learning from a new perspective, i.e., attention calibration. We model the attention in the transformer as a calibrated unit in a general formulation, where the attention calibration could give benefits to balance the stability and plasticity of continual learning algorithms through influencing both their forward inference path and backward optimization path. Our experiments, paraphrase generation, show that this work outperforms SOTA models by a considerable margin and remedy the forgetting greatly.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['paraphrase-generation', 'paraphrase-generation']	['computer-code', 'natural-language-processing']	[ 3.06017876e-01 2.70734340e-01 -1.82795927e-01 -6.70548826e-02 -8.00373435e-01 -4.70844448e-01 7.31100559e-01 -6.97128922e-02 -3.16748261e-01 1.29718459e+00 2.64305681e-01 -3.60264868e-01 9.16583538e-02 -5.69975734e-01 -1.13626909e+00 -6.35236740e-01 5.94512463e-01 4.97718066e-01 4.41060551e-02 -3.36806148e-01 2.87994385e-01 1.75500855e-01 -1.43963468e+00 1.96285665e-01 1.29733992e+00 3.60814989e-01 4.62290198e-01 6.39791071e-01 -4.35706466e-01 9.54254627e-01 -7.62457013e-01 -6.16156280e-01 -1.18953688e-02 -8.41252327e-01 -8.12503517e-01 -1.62412837e-01 3.73020381e-01 -1.52013302e-01 -1.73606306e-01 7.88418591e-01 7.08200157e-01 2.27241322e-01 5.47356069e-01 -1.42155039e+00 -1.28494191e+00 1.06648052e+00 -4.66047317e-01 3.38657439e-01 3.42653304e-01 5.60667574e-01 7.59264588e-01 -1.21391988e+00 5.13559103e-01 1.06916845e+00 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7f69f41a-7120-45f5-bcc6-ac88ed30e183	autotamp-autoregressive-task-and-motion	2306.06531	null	https://arxiv.org/abs/2306.06531v1	https://arxiv.org/pdf/2306.06531v1.pdf	AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers	For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. The recent and remarkable advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, many existing approaches either translate the natural language directly into robot trajectories, or factor the inference process by decomposing language into task sub-goals, then relying on a motion planner to execute each sub-goal. When complex environmental and temporal constraints are involved, inference over planning tasks must be performed jointly with motion plans using traditional task-and-motion planning (TAMP) algorithms, making such factorization untenable. Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediary task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan. To improve translation, we automatically detect and correct both syntactic and semantic errors via autoregressive re-prompting, resulting in significant improvements in task completion. We show that our approach outperforms several methods using LLMs as planners in complex task domains.	['Chuchu Fan', 'Nicholas Roy', 'Yang Zhang', 'Jacob Arkin', 'Yongchao Chen']	2023-06-10	null	null	null	null	['motion-planning']	['robots']	[ 5.19162536e-01 5.90746701e-01 9.12740733e-03 -2.98274070e-01 -1.02346194e+00 -7.32633948e-01 8.27217519e-01 4.60870713e-02 -5.23449838e-01 6.89009488e-01 5.81055939e-01 -4.85646784e-01 9.87179577e-02 -4.97240931e-01 -7.05480635e-01 -1.50806129e-01 7.49817267e-02 1.04149973e+00 2.40646198e-01 -2.05400348e-01 3.81717712e-01 3.63809615e-01 -1.16482282e+00 2.50173450e-01 7.47477770e-01 2.34955102e-01 9.61592674e-01 9.69902515e-01 -1.96849838e-01 1.28715825e+00 -1.41599596e-01 2.49492154e-01 1.47639185e-01 -6.27751410e-01 -1.42158711e+00 2.74370581e-01 -4.30210143e-01 -3.56153727e-01 -1.96241885e-01 8.89174104e-01 -5.41672558e-02 5.06112993e-01 7.60187626e-01 -1.38285255e+00 -2.64573663e-01 5.76792061e-01 -4.93424200e-02 -5.94188869e-01 9.44064379e-01 4.07536179e-01 8.44868362e-01 -6.22232854e-01 6.88447654e-01 1.73534954e+00 4.55993980e-01 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8274c756-8385-47d7-9b29-6e68f8729fdb	naturalprover-grounded-mathematical-proof	2205.1291	null	https://arxiv.org/abs/2205.12910v2	https://arxiv.org/pdf/2205.12910v2.pdf	NaturalProver: Grounded Mathematical Proof Generation with Language Models	Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of reasoning that are core to intelligence. Yet it has remained underexplored with modern generative models. We study large-scale language models on two new generation tasks: suggesting the next step in a mathematical proof, and full proof generation. We develop NaturalProver, a language model that generates proofs by conditioning on background references (e.g. theorems and definitions that are either retrieved or human-provided), and optionally enforces their presence with constrained decoding. On theorems from the NaturalProofs benchmark, NaturalProver improves the quality of next-step suggestions and generated proofs over fine-tuned GPT-3, according to human evaluations from university-level mathematics students. NaturalProver is capable of proving some theorems that require short (2-6 step) proofs, and providing next-step suggestions that are rated as correct and useful over 40% of the time, which is to our knowledge the first demonstration of these capabilities using neural language models.	['Yejin Choi', 'Hannaneh Hajishirzi', 'Ximing Lu', 'Jiacheng Liu', 'Sean Welleck']	2022-05-25	null	null	null	null	['automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'reasoning']	[ 9.86876786e-02 5.51437318e-01 -6.30061775e-02 -1.98497042e-01 -7.60173202e-01 -1.01036131e+00 1.09829783e+00 5.93099475e-01 -7.05861226e-02 1.01382363e+00 -1.45476520e-01 -1.35011339e+00 -3.53064001e-01 -1.11227965e+00 -1.12511182e+00 1.07580841e-01 -3.52409273e-01 6.81732476e-01 1.20275766e-01 -3.74919772e-01 5.52212536e-01 1.85886621e-01 -1.48355627e+00 4.38664734e-01 1.31812310e+00 1.49792194e-01 2.92214990e-01 1.25421917e+00 -3.48333806e-01 1.31965888e+00 -6.87233031e-01 -7.92526245e-01 -3.24535728e-01 -4.41326320e-01 -9.71634746e-01 -7.00884044e-01 8.59084725e-01 -6.12069070e-01 -1.71259820e-01 1.13038599e+00 3.13892439e-02 -1.26887083e-01 7.10689843e-01 -1.61393583e+00 -9.42717135e-01 1.42681885e+00 8.76362920e-02 3.45429420e-01 8.14197481e-01 2.72694349e-01 1.13364041e+00 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22a19542-50c7-4392-9466-73d9de2b6fa0	ecg-based-heart-arrhythmia-diagnosis-through	2108.10226	null	https://arxiv.org/abs/2108.10226v2	https://arxiv.org/pdf/2108.10226v2.pdf	ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks	Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG signals and automatically extract the informative dependencies for accurate arrhythmia detection. To evaluate the proposed approach, we conduct extensive experiments over a benchmark ECG dataset. Our main task is to find the arrhythmia from normal heartbeats and, at the meantime, accurately recognize the heart diseases from five arrhythmia types. We also provide convergence analysis of ABCNN and intuitively show the meaningfulness of extracted representation through visualization. The experimental results show that the proposed ABCNN outperforms the widely used baselines, which puts one step closer to intelligent heart disease diagnosis system.	['Xiang Zhang', 'Ziyu Liu']	2021-08-18	null	null	null	null	['arrhythmia-detection', 'electrocardiography-ecg']	['medical', 'methodology']	[ 2.48743027e-01 -1.02879040e-01 2.84727335e-01 -5.08919299e-01 -6.93947434e-01 -2.04566121e-01 -1.26256064e-01 1.62977278e-01 -5.68936504e-02 5.85895896e-01 1.52624846e-01 -4.64110970e-01 -3.57607901e-01 -3.42488289e-01 -2.07557663e-01 -6.52541459e-01 -3.45219404e-01 2.14906961e-01 -6.16443932e-01 1.53316319e-01 2.16711238e-02 4.94835526e-01 -8.20448458e-01 -1.27723336e-01 8.96494508e-01 1.15231812e+00 -3.68689239e-01 8.26422393e-01 4.09150392e-01 7.70851314e-01 -8.08446229e-01 -2.07835972e-01 -1.89112991e-01 -9.20454443e-01 -7.31395721e-01 6.17170110e-02 -2.30867609e-01 -2.81547338e-01 -3.10256984e-02 8.30991328e-01 1.25455773e+00 -3.22250575e-01 4.43747282e-01 -9.21775579e-01 -5.36105752e-01 5.78720510e-01 -3.97620767e-01 7.10485756e-01 6.06314130e-02 2.51939684e-01 9.83180881e-01 -1.01073897e+00 1.35900423e-01 5.99231839e-01 1.02054536e+00 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457af7a1-2d86-4cfb-a974-d54b93c0cb47	gui-at-mixmt-2022-english-hinglish-an-mt	2210.12215	null	https://arxiv.org/abs/2210.12215v1	https://arxiv.org/pdf/2210.12215v1.pdf	Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data	Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of WMT 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script. To our knowledge, we achieved one of the top ROUGE-L and WER scores for the first task of Monolingual to Code-Mixed machine translation. In this paper, we discuss the use of mBART with some special pre-processing and post-processing (transliteration from Devanagari to Roman) for the first task in detail and the experiments that we performed for the second task of translating code-mixed Hinglish to monolingual English.	['Vasudeva Varma', 'Dipti Misra Sharma', 'Tanvi Kamble', 'Saransh Rajput', 'Shivam Mangale', 'Anshul Padhi', 'Jayant Duneja', 'Akshat Gahoi']	2022-10-21	null	null	null	null	['transliteration']	['natural-language-processing']	[-2.06409901e-01 -1.09462015e-01 -2.15524770e-02 -2.78878689e-01 -1.50234091e+00 -9.37402725e-01 8.25390875e-01 -3.56545508e-01 -6.07811451e-01 1.17724276e+00 4.52305414e-02 -8.24848056e-01 4.05171365e-01 -1.94802970e-01 -7.42740870e-01 -3.43876958e-01 2.67792702e-01 8.70562196e-01 -1.00646891e-01 -6.91637039e-01 -1.06009834e-01 1.67990968e-01 -8.48957062e-01 6.84058785e-01 1.21325243e+00 5.06393611e-02 5.49118638e-01 7.61376619e-01 -1.97815016e-01 9.62014318e-01 -2.94257879e-01 -8.01816821e-01 4.92427796e-01 -8.65590632e-01 -1.04827094e+00 -2.64331281e-01 4.47712034e-01 2.12072551e-01 -6.06476031e-02 1.09483111e+00 4.61694092e-01 -4.06121045e-01 4.42207128e-01 -1.17179108e+00 -5.64345002e-01 1.18550825e+00 -5.11261225e-01 1.77964628e-01 6.32863224e-01 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60b87f6c-430c-48e8-95e2-17e018b7bdcb	sudo-rm-rf-efficient-networks-for-universal	2007.06833	null	https://arxiv.org/abs/2007.06833v1	https://arxiv.org/pdf/2007.06833v1.pdf	Sudo rm -rf: Efficient Networks for Universal Audio Source Separation	In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRMRF) as well as their aggregation which is performed through simple one-dimensional convolutions. In this way, we are able to obtain high quality audio source separation with limited number of floating point operations, memory requirements, number of parameters and latency. Our experiments on both speech and environmental sound separation datasets show that SuDoRMRF performs comparably and even surpasses various state-of-the-art approaches with significantly higher computational resource requirements.	['Paris Smaragdis', 'Zhepei Wang', 'Efthymios Tzinis']	2020-07-14	null	null	null	null	['audio-source-separation']	['audio']	[-8.46061576e-03 -8.25862825e-01 5.46543896e-01 -1.90057769e-01 -8.88205051e-01 -4.67534363e-01 2.59315282e-01 1.45071536e-01 -6.62474036e-01 5.56712925e-01 1.81984365e-01 -1.78394899e-01 -2.76824921e-01 -6.60040021e-01 -4.91956294e-01 -6.31343544e-01 -2.80804485e-01 -2.02509031e-01 3.14326316e-01 -3.32236588e-01 -1.01077341e-01 7.00800896e-01 -1.87423778e+00 3.13325942e-01 5.62131763e-01 1.30958796e+00 -6.26870766e-02 1.18212068e+00 1.29375488e-01 6.76004946e-01 -7.94806600e-01 -1.12297215e-01 2.11582154e-01 -5.23444526e-02 -6.94872320e-01 -3.74269128e-01 6.25989497e-01 -3.86803269e-01 -4.07576025e-01 9.81178880e-01 1.17039168e+00 5.78239024e-01 2.73706019e-01 -9.87231374e-01 -1.98337361e-01 7.01711059e-01 -3.37756515e-01 7.12391555e-01 1.45492643e-01 -2.26160616e-01 8.35185230e-01 -1.00962710e+00 -1.13554232e-01 1.04770029e+00 9.77886796e-01 1.50770172e-01 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4eacf385-ef25-48f3-9748-8dd18fce7492	automated-pavement-crack-segmentation-using	2001.01912	null	https://arxiv.org/abs/2001.01912v4	https://arxiv.org/pdf/2001.01912v4.pdf	Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network	Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.	['Stephen L. H. Lau', 'Edwin K. P. Chong', 'Xu Yang', 'Xin Wang']	2020-01-07	null	null	null	null	['crack-segmentation']	['computer-vision']	[ 4.02452022e-01 -9.44911391e-02 4.27837104e-01 -3.30490589e-01 -6.67118669e-01 -3.78217727e-01 2.02648550e-01 -9.33827534e-02 -6.22080922e-01 2.32865214e-01 -4.25506949e-01 -5.38038552e-01 2.71261454e-01 -1.04471374e+00 -9.27756071e-01 -5.34797668e-01 6.39397278e-02 9.37884971e-02 5.95284224e-01 -3.19437146e-01 6.71066046e-01 5.22891283e-01 -1.44547105e+00 4.30531353e-01 8.40910494e-01 9.60932374e-01 1.05301313e-01 8.97130728e-01 -6.43045604e-02 7.64723599e-01 -6.86999261e-01 -1.00028507e-01 3.17819417e-01 -2.91542113e-01 -9.53471482e-01 -4.18223627e-02 5.36849201e-01 -4.60341215e-01 -3.07362527e-01 6.19898498e-01 5.78668535e-01 1.17913075e-01 5.12537479e-01 -7.24346042e-01 -5.25361955e-01 5.77390730e-01 -8.79037261e-01 3.51882368e-01 -1.08863428e-01 3.13849837e-01 7.74426222e-01 -9.02809441e-01 3.56144756e-01 9.23609018e-01 1.18510377e+00 3.87927413e-01 -1.23994851e+00 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1.67769402e-01 3.51871014e-01 -6.27886832e-01 -2.59462416e-01 2.45857581e-01]	[7.4985270500183105, 1.6102079153060913]
ea6fef51-cc8c-4b1f-802d-0e815e88c556	optical-flow-based-real-time-moving-object	1807.0489	null	http://arxiv.org/abs/1807.04890v1	http://arxiv.org/pdf/1807.04890v1.pdf	Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes	Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. In this paper, an optical flow based moving object detection framework is proposed to address this problem. We utilize homography matrixes to online construct a background model in the form of optical flow. When judging out moving foregrounds from scenes, a dual-mode judge mechanism is designed to heighten the system's adaptation to challenging situations. In experiment part, two evaluation metrics are redefined for more properly reflecting the performance of methods. We quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art methods, indicating the advantages of optical flow based methods.	['Zheng Zhu', 'Wei Zou', 'Junjie Huang', 'Jiagang Zhu']	2018-07-13	null	null	null	null	['moving-object-detection']	['computer-vision']	[ 1.28525257e-01 -8.72915089e-01 -1.32945627e-01 -7.00252056e-02 2.30297986e-02 -4.78460133e-01 4.03121591e-01 -8.43560517e-01 -3.78899336e-01 6.88373864e-01 -1.31834999e-01 -2.42967874e-01 1.22144334e-01 -5.08313656e-01 -2.43595809e-01 -7.31932461e-01 3.02072358e-03 -3.18767101e-01 9.55106616e-01 1.65490031e-01 5.00979722e-01 4.90831703e-01 -1.51678371e+00 2.57981736e-02 8.77905369e-01 8.12432230e-01 5.04471719e-01 8.37911248e-01 -1.81752503e-01 1.18600440e+00 -5.69802821e-01 -1.96668983e-01 5.20732760e-01 -4.87183332e-01 -5.86233914e-01 6.97392523e-01 5.93844056e-01 -8.98687005e-01 -6.49505913e-01 1.26215589e+00 2.49147013e-01 4.56544816e-01 2.49660790e-01 -1.25853610e+00 -5.03514647e-01 5.23519069e-02 -6.25484586e-01 1.01401150e+00 4.75919902e-01 4.95772779e-01 5.37182689e-01 -1.09341884e+00 5.60887575e-01 1.42913210e+00 1.95892379e-01 5.36415577e-01 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f3e880b8-5871-43f6-8301-9cd0af97bdd6	automatic-debiased-learning-from-positive	2303.04797	null	https://arxiv.org/abs/2303.04797v1	https://arxiv.org/pdf/2303.04797v1.pdf	Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data	We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a sample is exposed to a user, (ii) the user then returns the label for the exposed sample, and (iii) we however can only observe the positive samples. Therefore, the positive labels that we observe are a combination of both the exposure and the labeling, which creates a selection bias problem for the observed positive samples. This scenario represents a conceptual framework for many practical applications, such as recommender systems, which we refer to as ``learning from positive, unlabeled, and exposure data'' (PUE classification). To tackle this problem, we initially assume access to data with exposure labels. Then, we propose a method to identify the function of interest using a strong ignorability assumption and develop an ``Automatic Debiased PUE'' (ADPUE) learning method. This algorithm directly debiases the selection bias without requiring intermediate estimates, such as the propensity score, which is necessary for other learning methods. Through experiments, we demonstrate that our approach outperforms traditional PU learning methods on various semi-synthetic datasets.	['Shota Yasui', 'Kodai Kureishi', 'Shuting Wu', 'Masahiro Kato']	2023-03-08	null	null	null	null	['selection-bias']	['natural-language-processing']	[ 4.60732102e-01 7.48469755e-02 -8.95122409e-01 -5.07665873e-01 -9.60334599e-01 -6.09844267e-01 5.12396753e-01 2.58642614e-01 -4.30578738e-01 9.19847369e-01 -1.86430976e-01 -4.91577893e-01 -9.20018777e-02 -1.05122364e+00 -9.39376473e-01 -9.72359538e-01 1.67469367e-01 5.79462826e-01 -5.47069535e-02 3.29564482e-01 1.40032649e-01 4.30828892e-03 -1.27789283e+00 3.72615419e-02 1.11449313e+00 1.15057647e+00 -9.31397006e-02 1.71414778e-01 6.09460995e-02 9.33473945e-01 -4.26921487e-01 -3.63005728e-01 3.25226516e-01 -4.87413287e-01 -5.96385479e-01 1.88547894e-01 2.91453540e-01 -1.70902252e-01 2.63389260e-01 1.31602764e+00 1.56269088e-01 2.62104750e-01 1.18671882e+00 -1.53096449e+00 -6.76211655e-01 5.51988959e-01 -9.16521132e-01 1.78002995e-02 1.73801526e-01 -1.66761473e-01 1.15100074e+00 -8.58880043e-01 4.45108443e-01 1.09012926e+00 6.55406594e-01 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07818e3f-5834-4c13-a210-05abf0276653	histogram-of-oriented-principal-components	1409.6813	null	http://arxiv.org/abs/1409.6813v2	http://arxiv.org/pdf/1409.6813v2.pdf	Histogram of Oriented Principal Components for Cross-View Action Recognition	Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The Experimental results show that our techniques provide significant improvement over state-of-the-art methods.	['Arif Mahmood', 'Ajmal Mian', 'Hossein Rahmani', 'Du Huynh']	2014-09-24	null	null	null	null	['3d-human-action-recognition']	['computer-vision']	[ 1.08744271e-01 -8.02983046e-01 -1.76179111e-01 5.98492287e-02 -6.59861803e-01 -5.89360178e-01 6.66288972e-01 3.27889398e-02 -2.54875273e-01 1.09153621e-01 3.79343063e-01 3.35646480e-01 -1.99623734e-01 -3.93331915e-01 -2.96020538e-01 -8.52197826e-01 -1.69958353e-01 2.74043232e-01 8.84544075e-01 -5.39065301e-02 7.53072739e-01 1.16976237e+00 -1.68224835e+00 4.73029673e-01 7.16894045e-02 1.08873558e+00 -3.51060927e-01 7.33017206e-01 2.96676397e-01 5.61393440e-01 -7.08868444e-01 9.97960642e-02 5.85666895e-01 -1.97350591e-01 -4.36602741e-01 6.05975986e-01 5.36130250e-01 -3.94407511e-01 -4.46714193e-01 7.25248754e-01 3.44083399e-01 2.88446665e-01 6.65766537e-01 -1.51501870e+00 -2.46538863e-01 -6.44688189e-01 -9.70101893e-01 4.58629102e-01 1.02748883e+00 8.49383920e-02 5.12975633e-01 -1.17104435e+00 7.99990058e-01 1.30335379e+00 5.28181672e-01 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c07bdcb0-7d48-4c2f-8c00-3cf4e0dca8d6	on-the-trajectory-of-stochastic-gradient	null	null	https://openreview.net/forum?id=SkMON20ctX	https://openreview.net/pdf?id=SkMON20ctX	On the Trajectory of Stochastic Gradient Descent in the Information Plane	Studying the evolution of information theoretic quantities during Stochastic Gradient Descent (SGD) learning of Artificial Neural Networks (ANNs) has gained popularity in recent years. Nevertheless, these type of experiments require estimating mutual information and entropy which becomes intractable for moderately large problems. In this work we propose a framework for understanding SGD learning in the information plane which consists of observing entropy and conditional entropy of the output labels of ANN. Through experimental results and theoretical justifications it is shown that, under some assumptions, the SGD learning trajectories appear to be similar for different ANN architectures. First, the SGD learning is modeled as a Hidden Markov Process (HMP) whose entropy tends to increase to the maximum. Then, it is shown that the SGD learning trajectory appears to move close to the shortest path between the initial and final joint distributions in the space of probability measures equipped with the total variation metric. Furthermore, it is shown that the trajectory of learning in the information plane can provide an alternative for observing the learning process, with potentially richer information about the learning than the trajectories in training and test error.	['Rudolf Mathar', 'Arash Behboodi', 'Emilio Rafael Balda']	2019-05-01	null	null	null	iclr-2019-5	['information-plane']	['methodology']	[-1.21353768e-01 3.20332915e-01 9.29048583e-02 -3.51331800e-01 -9.99091715e-02 -4.86719698e-01 6.57749176e-01 4.13104117e-01 -5.11621118e-01 5.90151966e-01 -3.62237275e-01 -3.43453407e-01 -4.92641360e-01 -5.86570978e-01 -5.34537137e-01 -1.04200256e+00 -5.10898411e-01 4.19529736e-01 -1.60718977e-01 1.81121245e-01 2.09055126e-01 5.77297032e-01 -1.37825227e+00 -3.38315725e-01 6.13555789e-01 9.98777747e-01 9.70217735e-02 9.56635892e-01 -1.77955195e-01 6.11300230e-01 -4.30506736e-01 -5.29917300e-01 4.43906248e-01 -6.53931141e-01 -1.00715077e+00 -2.23174602e-01 8.00404549e-02 9.73282009e-02 -1.00878485e-01 1.50551081e+00 8.53805616e-02 2.71425545e-01 1.13040936e+00 -1.25713849e+00 -2.22152635e-01 5.17612994e-01 -2.27782786e-01 1.98641092e-01 -1.01220675e-01 -1.74343750e-01 1.04541004e+00 -7.06627548e-01 5.15917718e-01 1.05209398e+00 6.71568096e-01 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de8618fc-4cc9-44f1-b604-ac715b92149a	capot-creating-robust-dense-query-encoders	2304.03401	null	https://arxiv.org/abs/2304.03401v2	https://arxiv.org/pdf/2304.03401v2.pdf	Noise-Robust Dense Retrieval via Contrastive Alignment Post Training	The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking. While effective and efficient, dual-encoders are brittle to variations in query distributions and noisy queries. Data augmentation can make models more robust but introduces overhead to training set generation and requires retraining and index regeneration. We present Contrastive Alignment POst Training (CAPOT), a highly efficient finetuning method that improves model robustness without requiring index regeneration, the training set optimization, or alteration. CAPOT enables robust retrieval by freezing the document encoder while the query encoder learns to align noisy queries with their unaltered root. We evaluate CAPOT noisy variants of MSMARCO, Natural Questions, and Trivia QA passage retrieval, finding CAPOT has a similar impact as data augmentation with none of its overhead.	['Alessandro Magnani', 'ChengXiang Zhai', 'Daniel Campos']	2023-04-06	null	null	null	null	['natural-questions', 'document-ranking', 'passage-retrieval']	['miscellaneous', 'natural-language-processing', 'natural-language-processing']	[ 2.47576565e-01 -2.52064615e-01 -2.71439403e-01 -1.84666991e-01 -1.81834555e+00 -8.62283587e-01 9.48120475e-01 5.21539509e-01 -8.23598027e-01 8.20948482e-01 7.62573242e-01 -2.63385803e-01 -1.86388239e-01 -4.91280675e-01 -8.03113520e-01 -3.99040043e-01 -2.97142025e-02 9.14325297e-01 1.55222028e-01 -6.74790025e-01 3.34942907e-01 2.81663984e-01 -1.41608226e+00 5.42595983e-01 5.31863451e-01 5.71196973e-01 2.01615274e-01 1.12235475e+00 -2.44750306e-01 7.05986083e-01 -1.05200458e+00 -3.86482209e-01 1.89016983e-01 -2.60550618e-01 -1.24048150e+00 -5.62288523e-01 7.84364581e-01 -4.75290775e-01 -7.46562183e-01 4.05262262e-01 1.01336992e+00 4.44642812e-01 6.15830779e-01 -8.00092399e-01 -1.18238139e+00 6.95632279e-01 -8.58489349e-02 6.23785615e-01 5.59938192e-01 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dec04177-bdbb-485e-b895-df5881e801a2	panoramic-annular-slam-with-loop-closure-and	2102.134	null	https://arxiv.org/abs/2102.13400v2	https://arxiv.org/pdf/2102.13400v2.pdf	Panoramic annular SLAM with loop closure and global optimization	In this paper, we propose panoramic annular simultaneous localization and mapping (PA-SLAM), a visual SLAM system based on panoramic annular lens. A hybrid point selection strategy is put forward in the tracking front-end, which ensures repeatability of keypoints and enables loop closure detection based on the bag-of-words approach. Every detected loop candidate is verified geometrically and the $Sim(3)$ relative pose constraint is estimated to perform pose graph optimization and global bundle adjustment in the back-end. A comprehensive set of experiments on real-world datasets demonstrates that the hybrid point selection strategy allows reliable loop closure detection, and the accumulated error and scale drift have been significantly reduced via global optimization, enabling PA-SLAM to reach state-of-the-art accuracy while maintaining high robustness and efficiency.	['Kaiwei Wang', 'Jian Bai', 'Kailun Yang', 'Weijian Hu', 'Hao Chen']	2021-02-26	null	null	null	null	['loop-closure-detection']	['computer-vision']	[-1.90653697e-01 -2.24598497e-01 -1.05943277e-01 -1.69366732e-01 -5.29995143e-01 -6.22587621e-01 7.14236677e-01 1.51066855e-01 -5.86345851e-01 4.87309903e-01 -4.97739047e-01 -2.17660785e-01 -2.26396188e-01 -6.01177275e-01 -8.40073466e-01 -1.68449238e-01 -4.54316556e-01 3.95160139e-01 4.48584646e-01 -1.61879435e-01 4.44033831e-01 8.70964348e-01 -1.30411100e+00 -9.98855948e-01 9.31449592e-01 1.00440729e+00 5.71945071e-01 5.91756761e-01 3.70869011e-01 1.36917204e-01 -1.86253026e-01 -1.39757961e-01 5.47210395e-01 2.61466000e-02 -1.67194575e-01 1.15016624e-02 1.01090479e+00 -2.57091880e-01 -3.19060355e-01 1.18335426e+00 3.91803861e-01 -4.59137633e-02 1.94750596e-02 -1.22888315e+00 2.85853952e-01 -4.16317433e-01 -7.97206104e-01 -3.64488959e-01 7.69572914e-01 9.39012542e-02 7.87909329e-01 -1.12443519e+00 8.74247372e-01 8.51315916e-01 1.13944483e+00 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499a5c6d-7706-4a1e-bf67-e0a44ab27ef2	haca3-a-unified-approach-for-multi-site-mr	2212.06065	null	https://arxiv.org/abs/2212.06065v2	https://arxiv.org/pdf/2212.06065v2.pdf	HACA3: A Unified Approach for Multi-site MR Image Harmonization	The lack of standardization is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. Despite the success of existing methods, we argue that three major improvements can be made. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art performance under multiple image quality metrics. We also demonstrate the applicability and versatility of HACA3 on downstream tasks including white matter lesion segmentation and longitudinal volumetric analyses.	['Murat Bilgel', 'Savannah P. Hays', 'Samuel W. Remedios', 'Aaron Carass', 'Jerry L. Prince', 'Susan M. Resnick', 'Peter A. Calabresi', 'Scott D. Newsome', 'Ellen M. Mowry', 'Blake E. Dewey', 'Yuan Xue', 'Yihao Liu', 'Lianrui Zuo']	2022-12-12	null	null	null	null	['image-harmonization']	['computer-vision']	[ 3.11727881e-01 -3.63720983e-01 -4.92601432e-02 -3.22257578e-01 -8.79222274e-01 -4.20845330e-01 3.39502573e-01 2.56996959e-01 -4.38264787e-01 6.03278518e-01 1.86900139e-01 -1.34529233e-01 -3.18186343e-01 -3.58793288e-01 -3.77321422e-01 -5.80868244e-01 -3.53752315e-01 2.68607467e-01 5.24186432e-01 -6.64699823e-02 1.34684518e-01 5.43837607e-01 -1.05644977e+00 1.82506949e-01 1.29274261e+00 7.39641130e-01 6.17880642e-01 3.35327089e-01 5.66363633e-02 5.42435586e-01 -4.68156189e-01 -8.53371024e-02 3.06582242e-01 -6.18777454e-01 -9.97096360e-01 8.40053931e-02 5.43975830e-01 -2.56471574e-01 -1.69548556e-01 1.28134692e+00 8.59745920e-01 1.18906409e-01 3.28596324e-01 -8.80408347e-01 -4.77717876e-01 6.44336581e-01 -8.57351363e-01 1.00096881e+00 -2.64882475e-01 3.36291134e-01 4.65098768e-01 -7.24141896e-01 5.81291437e-01 6.85149312e-01 7.26671994e-01 3.15338790e-01 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951a4f8b-db72-443a-9e4f-09bc373bf4f7	chain-of-thought-imitation-with-procedure	2205.10816	null	https://arxiv.org/abs/2205.10816v1	https://arxiv.org/pdf/2205.10816v1.pdf	Chain of Thought Imitation with Procedure Cloning	Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning problem in which one fits a function approximator to the input-output mapping exhibited by the logged demonstrations (input observations to output actions). While the framing of imitation learning as a supervised input-output learning problem allows for applicability in a wide variety of settings, it is also an overly simplistic view of the problem in situations where the expert demonstrations provide much richer insight into expert behavior. For example, applications such as path navigation, robot manipulation, and strategy games acquire expert demonstrations via planning, search, or some other multi-step algorithm, revealing not just the output action to be imitated but also the procedure for how to determine this action. While these intermediate computations may use tools not available to the agent during inference (e.g., environment simulators), they are nevertheless informative as a way to explain an expert's mapping of state to actions. To properly leverage expert procedure information without relying on the privileged tools the expert may have used to perform the procedure, we propose procedure cloning, which applies supervised sequence prediction to imitate the series of expert computations. This way, procedure cloning learns not only what to do (i.e., the output action), but how and why to do it (i.e., the procedure). Through empirical analysis on navigation, simulated robotic manipulation, and game-playing environments, we show that imitating the intermediate computations of an expert's behavior enables procedure cloning to learn policies exhibiting significant generalization to unseen environment configurations, including those configurations for which running the expert's procedure directly is infeasible.	['Ofir Nachum', 'Pieter Abbeel', 'Dale Schuurmans', 'Mengjiao Yang']	2022-05-22	null	null	null	null	['robot-manipulation']	['robots']	[ 3.42218161e-01 3.60301942e-01 -2.23152146e-01 -1.86057128e-02 -5.24616957e-01 -1.18824065e+00 6.78000093e-01 -1.92527547e-01 -4.28012609e-01 7.60623336e-01 -3.69154543e-01 -1.00720739e+00 -6.37157708e-02 -6.59311473e-01 -1.15025103e+00 -5.75838804e-01 -1.66472912e-01 4.89912808e-01 2.08581984e-01 -2.04609036e-01 2.81586796e-01 6.84420884e-01 -1.50620151e+00 -4.13883626e-02 8.21823001e-01 3.56028289e-01 3.55207741e-01 1.18840647e+00 3.08630258e-01 1.09155238e+00 -5.88045299e-01 3.34254056e-01 2.53124893e-01 -7.07147002e-01 -8.48189294e-01 7.17649758e-02 -2.67548472e-01 -8.02858353e-01 -3.08951795e-01 1.03945386e+00 -2.16690108e-01 3.12803000e-01 7.31297374e-01 -1.45767033e+00 6.19345717e-02 4.23592806e-01 2.95588702e-01 -2.43116185e-01 7.10557640e-01 1.02874839e+00 6.56993091e-01 -8.11917558e-02 7.09385455e-01 1.08309662e+00 1.32608548e-01 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e65b15ed-66ca-40c2-8e27-2e6937e775df	open-unmix-a-reference-implementation-for	null	null	https://joss.theoj.org/papers/10.21105/joss.01667	https://www.theoj.org/joss-papers/joss.01667/10.21105.joss.01667.pdf	Open-Unmix - A Reference Implementation for Music Source Separation	Music source separation is the task of decomposing music into its constitutive components,e.g., yielding separated stems for the vocals, bass, and drums. Such a separation has manyapplications ranging from rearranging/repurposing the stems (remixing, repanning, upmixing)to full extraction (karaoke, sample creation, audio restoration). Music separation has a longhistory of scientific activity as it is known to be a very challenging problem. In recent years,deep learning-based systems - for the first time - yielded high-quality separations that alsolead to increased commercial interest. However, until now, no open-source implementationthat achieves state-of-the-art results is available.Open-Unmixcloses this gap by providinga reference implementation based on deep neural networks. It serves two main purposes.Firstly, to accelerate academic research asOpen-Unmixprovides implementations for themost popular deep learning frameworks, giving researchers a flexible way to reproduce results.Secondly, we provide a pre-trained model for end users and even artists to try and use sourceseparation. Furthermore, we designedOpen-Unmixto be one core component in an openecosystem on music separation, where we already provide open datasets, software utilities,and open evaluation to foster reproducible research as the basis of future development.	['and YukiMitsufuji', 'Fabian-Robert Stöter', 'Stefan Uhlich', 'Antoine Liutkus']	2019-09-08	null	null	null	the-journal-of-open-source-software-2019-9	['music-source-separation']	['music']	[-1.70231074e-01 -4.30732161e-01 -2.38828212e-01 3.41021836e-01 -9.23081696e-01 -9.38354075e-01 3.86660606e-01 -3.47753048e-01 -6.50707334e-02 5.00337362e-01 2.06729650e-01 -1.04931198e-01 -1.96179494e-01 -4.22166824e-01 -5.16444564e-01 -8.12274277e-01 -1.72295347e-01 2.89658904e-01 -9.18415189e-02 -2.30701044e-01 5.98083213e-02 5.77704549e-01 -1.80786657e+00 4.05003399e-01 8.65371287e-01 8.01151395e-01 8.45382959e-02 7.57928133e-01 -5.17494865e-02 4.17640924e-01 -7.73506463e-01 -5.11058092e-01 3.39112192e-01 -6.86156154e-01 -5.84354043e-01 -2.03549564e-01 4.15400147e-01 -1.61567003e-01 1.48706347e-01 1.01845121e+00 7.34426081e-01 -6.96167722e-02 4.40707028e-01 -1.25491667e+00 -4.64684039e-01 1.17402041e+00 -6.74792171e-01 -4.11661975e-02 6.05217852e-02 -2.20338479e-01 1.24388814e+00 -8.43031704e-01 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dcd76bb9-5efc-41cd-8c59-3af9753be579	global-convergence-using-policy-gradient	2111.15228	null	https://arxiv.org/abs/2111.15228v1	https://arxiv.org/pdf/2111.15228v1.pdf	Global Convergence Using Policy Gradient Methods for Model-free Markovian Jump Linear Quadratic Control	Owing to the growth of interest in Reinforcement Learning in the last few years, gradient based policy control methods have been gaining popularity for Control problems as well. And rightly so, since gradient policy methods have the advantage of optimizing a metric of interest in an end-to-end manner, along with being relatively easy to implement without complete knowledge of the underlying system. In this paper, we study the global convergence of gradient-based policy optimization methods for quadratic control of discrete-time and model-free Markovian jump linear systems (MJLS). We surmount myriad challenges that arise because of more than one states coupled with lack of knowledge of the system dynamics and show global convergence of the policy using gradient descent and natural policy gradient methods. We also provide simulation studies to corroborate our claims.	['Abir De', 'Manoj Bhadu', 'Santanu Rathod']	2021-11-30	null	null	null	null	['policy-gradient-methods']	['methodology']	[-2.02896968e-01 -5.23308180e-02 -3.90952349e-01 1.87508196e-01 -6.57096744e-01 -4.96701807e-01 5.23093104e-01 1.69824943e-01 -7.18654513e-01 1.32163393e+00 5.85001940e-03 -7.17571378e-01 -2.54085809e-01 -2.97616303e-01 -5.89992642e-01 -6.80112660e-01 -3.62626165e-01 3.31624448e-01 -5.59423119e-02 -4.77836996e-01 2.70457149e-01 3.95285517e-01 -8.80584061e-01 -6.44955814e-01 7.45620131e-01 8.79619181e-01 -1.02759702e-02 8.19962084e-01 4.19973403e-01 1.00822377e+00 -1.35994598e-01 1.78798467e-01 3.23719025e-01 -4.66891974e-01 -4.96926188e-01 -1.20629156e-02 1.67484477e-01 -4.93704706e-01 -5.42091310e-01 1.04743171e+00 7.28405893e-01 4.94200826e-01 3.61627042e-01 -1.11348927e+00 -1.04103468e-01 1.27272561e-01 -5.05266607e-01 3.84618044e-01 8.06806386e-02 5.82383275e-01 8.48118901e-01 -4.67089206e-01 4.84133124e-01 1.33194399e+00 5.22485077e-01 6.00631356e-01 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9bd9973c-d4a2-4123-b555-8ceeaa5df760	consensus-based-networked-tracking-in	2302.07511	null	https://arxiv.org/abs/2302.07511v1	https://arxiv.org/pdf/2302.07511v1.pdf	Consensus-based Networked Tracking in Presence of Heterogeneous Time-Delays	We propose a distributed (single) target tracking scheme based on networked estimation and consensus algorithms over static sensor networks. The tracking part is based on linear time-difference-of-arrival (TDOA) measurement proposed in our previous works. This paper, in particular, develops delay-tolerant distributed filtering solutions over sparse data-transmission networks. We assume general arbitrary heterogeneous delays at different links. This may occur in many realistic large-scale applications where the data-sharing between different nodes is subject to latency due to communication-resource constraints or large spatially distributed sensor networks. The solution we propose in this work shows improved performance (verified by both theory and simulations) in such scenarios. Another privilege of such distributed schemes is the possibility to add localized fault-detection and isolation (FDI) strategies along with survivable graph-theoretic design, which opens many follow-up venues to this research. To our best knowledge no such delay-tolerant distributed linear algorithm is given in the existing distributed tracking literature.	['Usman A. Khan', 'Mohammad Pirani', 'Mohammadreza Doostmohammadian']	2023-02-15	null	null	null	null	['fault-detection']	['miscellaneous']	[-4.65241894e-02 1.61405742e-01 9.06863809e-02 -8.34784806e-02 -2.66412228e-01 -8.99891376e-01 2.02715605e-01 4.11831141e-01 -6.62398487e-02 9.32640672e-01 -3.12269956e-01 -1.15940072e-01 -9.63743567e-01 -9.53974128e-01 -6.23190939e-01 -1.01330411e+00 -1.07986903e+00 3.83641213e-01 7.32519090e-01 -2.27284044e-01 -7.66662806e-02 7.87869930e-01 -1.21154284e+00 -7.06193924e-01 7.85459518e-01 1.20327926e+00 7.60985538e-02 5.91884553e-01 2.75631338e-01 2.04023823e-01 -7.47590244e-01 -1.77601397e-01 5.14804602e-01 -1.87474072e-01 6.70365021e-02 1.69467717e-01 1.47309959e-01 -1.30935460e-01 -4.13187176e-01 1.23294735e+00 6.52495503e-01 -2.35627562e-01 3.15423995e-01 -1.76334441e+00 -5.09735703e-01 7.58669138e-01 -5.30962229e-01 5.27693704e-02 1.53364882e-01 -1.13756396e-01 1.70381159e-01 -2.31684878e-01 6.02527976e-01 1.02885997e+00 8.53070855e-01 3.62562597e-01 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b64626d7-77d0-4505-a3e8-654a37f0d66e	freegaze-resource-efficient-gaze-estimation	2209.06692	null	https://arxiv.org/abs/2209.06692v1	https://arxiv.org/pdf/2209.06692v1.pdf	FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning	Gaze estimation is of great importance to many scientific fields and daily applications, ranging from fundamental research in cognitive psychology to attention-aware mobile systems. While recent advancements in deep learning have yielded remarkable successes in building highly accurate gaze estimation systems, the associated high computational cost and the reliance on large-scale labeled gaze data for supervised learning place challenges on the practical use of existing solutions. To move beyond these limitations, we present FreeGaze, a resource-efficient framework for unsupervised gaze representation learning. FreeGaze incorporates the frequency domain gaze estimation and the contrastive gaze representation learning in its design. The former significantly alleviates the computational burden in both system calibration and gaze estimation, and dramatically reduces the system latency; while the latter overcomes the data labeling hurdle of existing supervised learning-based counterparts, and ensures efficient gaze representation learning in the absence of gaze label. Our evaluation on two gaze estimation datasets shows that FreeGaze can achieve comparable gaze estimation accuracy with existing supervised learning-based approach, while enabling up to 6.81 and 1.67 times speedup in system calibration and gaze estimation, respectively.	['Guohao Lan', 'Lingyu Du']	2022-09-14	null	null	null	null	['gaze-estimation']	['computer-vision']	[ 2.11853787e-01 -5.42567968e-02 -2.31244266e-01 -4.37971592e-01 -5.96532583e-01 -2.20093802e-01 7.02586770e-02 -6.46761619e-03 -4.46479023e-01 5.29701114e-01 -1.95351407e-01 -4.57625926e-01 -2.33624846e-01 -1.79271445e-01 -4.12703574e-01 -7.52816021e-01 2.67337024e-01 -1.15546539e-01 1.54420108e-01 -4.57323194e-02 6.58301651e-01 5.40560018e-03 -2.46824169e+00 -4.06136215e-01 1.04632604e+00 1.35738742e+00 2.19233632e-01 6.55273914e-01 -1.50718302e-01 8.53974462e-01 -5.75663567e-01 -2.44548067e-01 -2.95221567e-01 -1.92868114e-01 -6.64715290e-01 -1.45312667e-01 8.92221928e-01 -3.58753413e-01 2.36734867e-01 8.11180353e-01 6.53091609e-01 1.66754544e-01 4.86768335e-01 -1.71674931e+00 -7.57809103e-01 -2.14039162e-02 -1.13343871e+00 4.01475102e-01 4.32573467e-01 -1.11435927e-01 8.57876241e-01 -7.31150866e-01 1.44052789e-01 8.74965489e-01 5.42281628e-01 7.70387113e-01 -9.40160990e-01 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319b38be-6904-463b-ad6d-99dda12b2094	foreground-action-consistency-network-for	2108.06524	null	https://arxiv.org/abs/2108.06524v1	https://arxiv.org/pdf/2108.06524v1.pdf	Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization	As a challenging task of high-level video understanding, weakly supervised temporal action localization has been attracting increasing attention. With only video annotations, most existing methods seek to handle this task with a localization-by-classification framework, which generally adopts a selector to select snippets of high probabilities of actions or namely the foreground. Nevertheless, the existing foreground selection strategies have a major limitation of only considering the unilateral relation from foreground to actions, which cannot guarantee the foreground-action consistency. In this paper, we present a framework named FAC-Net based on the I3D backbone, on which three branches are appended, named class-wise foreground classification branch, class-agnostic attention branch and multiple instance learning branch. First, our class-wise foreground classification branch regularizes the relation between actions and foreground to maximize the foreground-background separation. Besides, the class-agnostic attention branch and multiple instance learning branch are adopted to regularize the foreground-action consistency and help to learn a meaningful foreground classifier. Within each branch, we introduce a hybrid attention mechanism, which calculates multiple attention scores for each snippet, to focus on both discriminative and less-discriminative snippets to capture the full action boundaries. Experimental results on THUMOS14 and ActivityNet1.3 demonstrate the state-of-the-art performance of our method. Our code is available at https://github.com/LeonHLJ/FAC-Net.	['Hongsheng Li', 'Liang Wang', 'Linjiang Huang']	2021-08-14	null	http://openaccess.thecvf.com//content/ICCV2021/html/Huang_Foreground-Action_Consistency_Network_for_Weakly_Supervised_Temporal_Action_Localization_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_Foreground-Action_Consistency_Network_for_Weakly_Supervised_Temporal_Action_Localization_ICCV_2021_paper.pdf	iccv-2021-1	['weakly-supervised-temporal-action']	['computer-vision']	[ 3.91625911e-01 -1.69771925e-01 -7.05762982e-01 -2.18011811e-01 -5.18994570e-01 -1.38504073e-01 5.84866822e-01 -3.46408695e-01 -1.49985656e-01 4.24831808e-01 2.14671388e-01 -6.72747344e-02 4.01740335e-03 -6.08385205e-01 -6.04133546e-01 -1.02256131e+00 1.04280837e-01 8.20639282e-02 1.08557320e+00 3.07787180e-01 6.39453679e-02 1.77084640e-01 -1.47473347e+00 5.96498430e-01 8.82209182e-01 1.29230499e+00 3.56958896e-01 2.61647731e-01 -3.14024031e-01 1.19675195e+00 -4.53488976e-01 8.49333256e-02 1.51783362e-01 -7.19528019e-01 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d64241bc-5bf3-49f1-bdad-e907ec6fce28	high-dimensional-clustering-onto-hamiltonian	2304.14531	null	https://arxiv.org/abs/2304.14531v2	https://arxiv.org/pdf/2304.14531v2.pdf	High-dimensional Clustering onto Hamiltonian Cycle	Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outliers. This paper proposes a new framework called High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above problems. First, HCHC combines global structure with local structure in one objective function for deep clustering, improving the labels as relative probabilities, to mine the similarities between different clusters while keeping the local structure in each cluster. Then, the anchors of different clusters are sorted on the optimal Hamiltonian cycle generated by the cluster similarities and mapped on the circumference of a circle. Finally, a sample with a higher probability of a cluster will be mapped closer to the corresponding anchor. In this way, our framework allows us to appreciate three aspects visually and simultaneously - clusters (formed by samples with high probabilities), cluster similarities (represented as circular distances), and outliers (recognized as dots far away from all clusters). The experiments illustrate the superiority of HCHC.	['Zhengjun Zhang', 'Stan Z. Li', 'Shenghui Cheng', 'Tianyi Huang']	2023-04-27	null	null	null	null	['deep-clustering', 'deep-clustering']	['miscellaneous', 'natural-language-processing']	[-3.42008471e-01 1.02347367e-01 8.00528973e-02 -3.25476408e-01 -5.12998939e-01 -4.06937152e-01 3.87520134e-01 5.37241936e-01 -1.34812027e-01 2.69436419e-01 5.63350953e-02 2.60210335e-01 -3.25624377e-01 -7.65160203e-01 -2.08766907e-01 -1.20559633e+00 -2.25089014e-01 6.60533249e-01 2.56573796e-01 3.17166448e-01 2.83493549e-01 6.01801932e-01 -1.67658079e+00 2.48198286e-01 9.87610042e-01 4.55524892e-01 2.25496665e-01 1.06362298e-01 -1.70936704e-01 1.02510624e-01 -7.31756032e-01 8.35425481e-02 9.75447297e-02 -7.20349848e-01 -4.09266651e-01 3.21453691e-01 -2.05993518e-01 2.99084216e-01 6.67953640e-02 1.33184814e+00 4.12642479e-01 5.81544861e-02 7.76622117e-01 -1.54749429e+00 -5.02612233e-01 6.15481496e-01 -9.57290530e-01 -2.92627394e-01 1.95260003e-01 -6.69655725e-02 7.67766654e-01 -9.78552461e-01 5.49622297e-01 1.35405552e+00 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de2cac35-5cd6-4a45-a7da-44ee6293f192	enhancing-local-feature-learning-for-3d-point	2203.00172	null	https://arxiv.org/abs/2203.00172v2	https://arxiv.org/pdf/2203.00172v2.pdf	Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention	We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis that the standard self-attention (SA) that operates globally tends to produce almost the same attention maps for different query positions, revealing difficulties for learning query-independent and query-dependent information jointly. Therefore, we reformulate the SA and propose query-independent (Unary) and query-dependent (Pairwise) components to facilitate the learning of both terms. In contrast to the SA, the UPA ensures query dependence via operating locally. Extensive experiments show that the UPA outperforms the SA consistently on various point cloud understanding tasks including shape classification, part segmentation, and scene segmentation. Moreover, simply equipping the popular PointNet++ method with the UPA even outperforms or is on par with the state-of-the-art attention-based approaches. In addition, the UPA systematically boosts the performance of both standard and modern networks when it is integrated into them as a compositional module.	['Weimin WANG', 'Masashi Matsuoka', 'Qiong Chang', 'Takayuki Shinohara', 'Kyoung-Sook Kim', 'Xin Liu', 'Haoyi Xiu']	2022-03-01	null	null	null	null	['scene-segmentation']	['computer-vision']	[-1.42813623e-01 -2.89705954e-02 -1.77877340e-02 -3.45037878e-01 -8.54359388e-01 -5.77039838e-01 6.21024430e-01 2.03869283e-01 -1.29436195e-01 4.65377010e-02 -4.58377860e-02 -3.07519525e-01 -2.43960127e-01 -7.18229055e-01 -1.26921082e+00 -4.52891737e-01 1.66566864e-01 8.81443143e-01 5.64275384e-01 -3.26208681e-01 2.21617356e-01 8.50561976e-01 -1.64471006e+00 2.91674174e-02 9.44371462e-01 9.86107647e-01 4.69278872e-01 4.55355257e-01 -5.22800326e-01 5.63034177e-01 -2.78270304e-01 -7.24919498e-01 3.18681747e-01 1.48784846e-01 -1.10527205e+00 -1.07048988e-01 7.84461141e-01 -1.95200935e-01 -2.31353849e-01 1.00378323e+00 3.38025421e-01 7.86537230e-02 3.84522319e-01 -1.43933535e+00 -1.03507829e+00 5.10536432e-01 -6.58938348e-01 1.59559473e-01 1.65754065e-01 5.79265133e-02 1.42392302e+00 -1.17163885e+00 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1a540a51-0274-43f2-a1cc-dd24cae21890	a-decade-survey-of-content-based-image	2012.00641	null	https://arxiv.org/abs/2012.00641v2	https://arxiv.org/pdf/2012.00641v2.pdf	A Decade Survey of Content Based Image Retrieval using Deep Learning	The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.	['Shiv Ram Dubey']	2020-11-23	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[-1.36871636e-01 -7.59256005e-01 -3.81924599e-01 -5.65479875e-01 -9.14121270e-01 -3.58104050e-01 7.48457551e-01 3.34282041e-01 -3.68607342e-01 1.27282232e-01 -2.28002053e-02 5.81510067e-01 -7.67950177e-01 -7.14213312e-01 -2.62051105e-01 -8.91225874e-01 -1.11846484e-01 2.87449241e-01 1.71274003e-02 -3.44652653e-01 8.08774590e-01 8.69864345e-01 -2.23781228e+00 5.04301190e-01 9.81134251e-02 1.52851021e+00 3.47161055e-01 4.15937841e-01 -2.69301713e-01 5.66663563e-01 -6.95531189e-01 -6.45557567e-02 3.47150594e-01 -2.47313932e-01 -7.35684514e-01 1.23539511e-02 8.38190734e-01 -4.19902056e-01 -7.08046019e-01 9.98702109e-01 8.02579880e-01 1.52211800e-01 9.15058613e-01 -1.39447200e+00 -1.02167380e+00 -9.10819918e-02 -3.20268840e-01 3.96572769e-01 2.83814371e-01 -4.32682097e-01 1.13946950e+00 -1.03964400e+00 7.98057079e-01 1.23394620e+00 1.14505038e-01 3.36455762e-01 -4.55619246e-01 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65b219bb-fb6d-4236-9521-07e1a45a29ed	intra-template-entity-compatibility-based	null	null	https://aclanthology.org/2022.bionlp-1.18	https://aclanthology.org/2022.bionlp-1.18.pdf	Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction	We present a deep learning based information extraction system that can extract the design and results of a published abstract describing a Randomized Controlled Trial (RCT). In contrast to other approaches, our system does not regard the PICO elements as flat objects or labels but as structured objects. We thus model the task as the one of filling a set of templates and slots; our two-step approach recognizes relevant slot candidates as a first step and assigns them to a corresponding template as second step, relying on a learned pairwise scoring function that models the compatibility of the different slot values. We evaluate the approach on a dataset of 211 manually annotated abstracts for type 2 Diabetes and Glaucoma, showing the positive impact of modelling intra-template entity compatibility. As main benefit, our approach yields a structured object for every RCT abstract that supports the aggregation and summarization of clinical trial results across published studies and can facilitate the task of creating a systematic review or meta-analysis.	['Philipp Cimiano', 'Christian Witte']	null	null	null	null	bionlp-acl-2022-5	['pico', 'slot-filling']	['natural-language-processing', 'natural-language-processing']	[ 4.67214137e-01 5.49520075e-01 -7.35336959e-01 -5.51308393e-01 -1.17549133e+00 -5.45827985e-01 4.29325014e-01 9.00595486e-01 -5.74392617e-01 8.47697139e-01 4.78085309e-01 -7.07564592e-01 -5.53534091e-01 -5.51606297e-01 -7.63990521e-01 -4.06675130e-01 -2.53881495e-02 8.67295921e-01 1.08315185e-01 4.58599091e-01 3.93296540e-01 4.44817454e-01 -1.39729452e+00 7.13205218e-01 7.99727738e-01 8.30742300e-01 1.15143768e-01 1.82032093e-01 -3.55702013e-01 3.73344541e-01 -8.09540927e-01 -5.24544418e-01 3.47772352e-02 -2.77677417e-01 -7.33620822e-01 -3.52845907e-01 3.73909980e-01 1.26832910e-02 4.58574504e-01 7.04166651e-01 7.94635057e-01 -4.84582931e-01 8.88218522e-01 -9.62089717e-01 -7.30429441e-02 1.11340570e+00 -4.21389997e-01 -3.13068330e-01 3.87613147e-01 -2.86159903e-01 1.16790533e+00 -9.00994718e-01 1.02561092e+00 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af10dea8-0619-4d3c-8a20-ab07c81da55f	a-differentiable-gaussian-prototype-layer-for	2306.14361	null	https://arxiv.org/abs/2306.14361v1	https://arxiv.org/pdf/2306.14361v1.pdf	A differentiable Gaussian Prototype Layer for explainable Segmentation	We introduce a Gaussian Prototype Layer for gradient-based prototype learning and demonstrate two novel network architectures for explainable segmentation one of which relies on region proposals. Both models are evaluated on agricultural datasets. While Gaussian Mixture Models (GMMs) have been used to model latent distributions of neural networks before, they are typically fitted using the EM algorithm. Instead, the proposed prototype layer relies on gradient-based optimization and hence allows for end-to-end training. This facilitates development and allows to use the full potential of a trainable deep feature extractor. We show that it can be used as a novel building block for explainable neural networks. We employ our Gaussian Prototype Layer in (1) a model where prototypes are detected in the latent grid and (2) a model inspired by Fast-RCNN with SLIC superpixels as region proposals. The earlier achieves a similar performance as compared to the state-of-the art while the latter has the benefit of a more precise prototype localization that comes at the cost of slightly lower accuracies. By introducing a gradient-based GMM layer we combine the benefits of end-to-end training with the simplicity and theoretical foundation of GMMs which will allow to adapt existing semi-supervised learning strategies for prototypical part models in future.	['Sebastian Bosse', 'Peter Eisert', 'Steffen Maaß', 'Michael Gerstenberger']	2023-06-25	null	null	null	null	['superpixels']	['computer-vision']	[ 4.86291870e-02 7.58323967e-01 -2.32640684e-01 -5.03723502e-01 -3.24066162e-01 -4.32588458e-01 6.37603521e-01 1.00909904e-01 -3.26655626e-01 3.41709256e-01 -4.31857377e-01 -4.64176863e-01 -4.30346467e-02 -8.84330511e-01 -9.28752422e-01 -7.38622665e-01 -1.40628472e-01 8.23472619e-01 6.67440295e-01 -3.10243331e-02 -8.70150700e-02 7.34991193e-01 -1.61019087e+00 3.51052642e-01 9.00484085e-01 1.08543575e+00 6.27786040e-01 5.14838338e-01 -3.38148266e-01 3.79509151e-01 -4.42239195e-01 -1.56155989e-01 2.82573223e-01 -1.94072038e-01 -5.44117510e-01 3.62561435e-01 6.05698526e-01 -2.25513250e-01 1.60178095e-01 6.39772058e-01 2.25108445e-01 -5.82771152e-02 7.92163253e-01 -1.08715510e+00 -5.85528612e-01 8.03882360e-01 -5.01121581e-01 -5.10162592e-01 -3.90575558e-01 -1.21233344e-01 6.53511643e-01 -6.16902590e-01 7.21427917e-01 1.14400089e+00 9.49999809e-01 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4be4e6d2-fc96-440a-8ce5-d96f7227590d	unseen-object-instance-segmentation-for	2007.08073	null	https://arxiv.org/abs/2007.08073v2	https://arxiv.org/pdf/2007.08073v2.pdf	Unseen Object Instance Segmentation for Robotic Environments	In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset required for this task typically does not exist for most robotic settings, which motivates the use of synthetic data. Our proposed method, UOIS-Net, separately leverages synthetic RGB and synthetic depth for unseen object instance segmentation. UOIS-Net is comprised of two stages: first, it operates only on depth to produce object instance center votes in 2D or 3D and assembles them into rough initial masks. Secondly, these initial masks are refined using RGB. Surprisingly, our framework is able to learn from synthetic RGB-D data where the RGB is non-photorealistic. To train our method, we introduce a large-scale synthetic dataset of random objects on tabletops. We show that our method can produce sharp and accurate segmentation masks, outperforming state-of-the-art methods on unseen object instance segmentation. We also show that our method can segment unseen objects for robot grasping.	['Yu Xiang', 'Dieter Fox', 'Christopher Xie', 'Arsalan Mousavian']	2020-07-16	null	null	null	null	['unseen-object-instance-segmentation']	['computer-vision']	[ 6.86536133e-01 5.28936803e-01 2.36729130e-01 -3.07333678e-01 -6.53481305e-01 -1.00662243e+00 3.39854717e-01 -1.50738463e-01 -2.43168563e-01 4.17637646e-01 -5.25708199e-01 -1.09030604e-01 5.75808324e-02 -6.47529662e-01 -1.15634239e+00 -4.86834854e-01 2.34591156e-01 9.20738935e-01 5.72333157e-01 -3.26774180e-01 2.58296847e-01 5.89189231e-01 -1.66868937e+00 3.99285555e-01 7.99184561e-01 1.12627220e+00 4.83100086e-01 7.88007021e-01 6.00877851e-02 4.00958270e-01 -4.72380519e-01 -3.12152296e-01 1.01188827e+00 -4.81975414e-02 -9.83664036e-01 6.08948886e-01 5.06550431e-01 -7.68962801e-01 -1.89245537e-01 9.89065945e-01 1.37871847e-01 2.88238246e-02 7.40834117e-01 -1.37191713e+00 -6.69558227e-01 4.84417200e-01 -3.93737644e-01 -3.58280957e-01 3.19234043e-01 4.61980790e-01 7.71419764e-01 -8.99088323e-01 7.82954454e-01 1.39739490e+00 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d1c87cf7-9cc3-4ade-aaed-40c0c1c2801a	provably-efficient-gauss-newton-temporal	2302.13087	null	https://arxiv.org/abs/2302.13087v1	https://arxiv.org/pdf/2302.13087v1.pdf	Provably Efficient Gauss-Newton Temporal Difference Learning Method with Function Approximation	In this paper, based on the spirit of Fitted Q-Iteration (FQI), we propose a Gauss-Newton Temporal Difference (GNTD) method to solve the Q-value estimation problem with function approximation. In each iteration, unlike the original FQI that solves a nonlinear least square subproblem to fit the Q-iteration, the GNTD method can be viewed as an \emph{inexact} FQI that takes only one Gauss-Newton step to optimize this subproblem, which is much cheaper in computation. Compared to the popular Temporal Difference (TD) learning, which can be viewed as taking a single gradient descent step to FQI's subproblem per iteration, the Gauss-Newton step of GNTD better retains the structure of FQI and hence leads to better convergence. In our work, we derive the finite-sample non-asymptotic convergence of GNTD under linear, neural network, and general smooth function approximations. In particular, recent works on neural TD only guarantee a suboptimal $\mathcal{\mathcal{O}}(\epsilon^{-4})$ sample complexity, while GNTD obtains an improved complexity of $\tilde{\mathcal{O}}(\epsilon^{-2})$. Finally, we validate our method via extensive experiments in both online and offline RL problems. Our method exhibits both higher rewards and faster convergence than TD-type methods, including DQN.	['Junyu Zhang', 'Zaiwen Wen', 'Zhifa Ke']	2023-02-25	null	null	null	null	['offline-rl']	['playing-games']	[-1.35441869e-01 1.64239019e-01 -1.02051422e-01 -9.49884802e-02 -1.09528005e+00 -3.39245319e-01 -2.29290336e-01 1.16675552e-02 -7.80062795e-01 1.07881176e+00 -5.05929708e-01 -6.49264038e-01 -4.22734678e-01 -6.44805968e-01 -1.05421770e+00 -7.09300697e-01 -2.47240156e-01 1.55793205e-01 -1.02104686e-01 -2.41045445e-01 1.91702202e-01 2.67883688e-02 -1.02388835e+00 -4.55300570e-01 1.38262594e+00 1.63680911e+00 2.79785395e-02 4.54905301e-01 5.59869930e-02 7.54535437e-01 -4.53829348e-01 -3.71032894e-01 5.44308007e-01 -6.16492808e-01 -6.88336849e-01 -2.22195312e-01 3.18932652e-01 -6.09770834e-01 -3.99284452e-01 1.31377339e+00 6.10323012e-01 5.80378413e-01 4.06607389e-01 -1.11185086e+00 -6.12276018e-01 2.89033592e-01 -7.39038229e-01 5.21381050e-02 8.84394646e-02 1.77011728e-01 9.62847412e-01 -9.48734343e-01 2.89982911e-02 1.07615447e+00 8.76341522e-01 3.30563664e-01 -1.23710823e+00 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2.42846191e-01 -4.99015808e-01 -3.39821249e-01 -5.77445090e-01]	[4.23995304107666, 2.645007371902466]
8443b899-c349-49a7-866e-a087b60716a8	functional-space-variational-inference-for	2005.11797	null	https://arxiv.org/abs/2005.11797v2	https://arxiv.org/pdf/2005.11797v2.pdf	Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis	Deep neural networks have revolutionized medical image analysis and disease diagnosis. Despite their impressive performance, it is difficult to generate well-calibrated probabilistic outputs for such networks, which makes them uninterpretable black boxes. Bayesian neural networks provide a principled approach for modelling uncertainty and increasing patient safety, but they have a large computational overhead and provide limited improvement in calibration. In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.	['Hrushikesh Loya', 'Pranav Poduval', 'Amit Sethi']	2020-05-24	null	https://openreview.net/forum?id=eLL-c_Xc0B	https://openreview.net/pdf?id=eLL-c_Xc0B	midl-2019-7	['skin-lesion-classification']	['medical']	[ 2.06322700e-01 4.45440978e-01 -3.90677273e-01 -6.38413727e-01 -1.02005041e+00 -3.00644040e-01 2.55976140e-01 1.14340961e-01 -5.37266672e-01 9.90956545e-01 3.50985862e-02 -4.42708611e-01 -3.81925821e-01 -6.96147263e-01 -6.50212944e-01 -8.22288454e-01 1.29487365e-01 5.33376694e-01 2.91619990e-02 4.97311234e-01 -2.23759130e-01 4.34638232e-01 -7.65231669e-01 1.49973691e-01 7.77784944e-01 9.36687648e-01 -2.76204526e-01 5.36949694e-01 3.24526906e-01 6.64278686e-01 -6.89769030e-01 -5.14813364e-01 -1.17022581e-01 -1.93730667e-01 -6.29707336e-01 -1.57232866e-01 1.93534166e-01 -6.70519948e-01 -4.92188781e-01 1.29380667e+00 5.76074660e-01 -2.34917104e-01 9.51294839e-01 -8.05932641e-01 -6.29117906e-01 9.26905215e-01 -5.18135369e-01 -1.74836576e-01 -1.81627527e-01 1.51633635e-01 6.82132781e-01 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1.04618430e-01 -5.71217895e-01 -1.10690427e+00 -9.89253044e-01 -4.93367940e-01 1.51229938e-02 3.08184057e-01 -7.26373494e-01 -1.23835690e-01 2.04294529e-02]	[14.153109550476074, -2.086202383041382]
3e8b715e-e8fb-4831-a7c3-088c374479f6	confronting-abusive-language-online-a-survey	2012.12305	null	https://arxiv.org/abs/2012.12305v2	https://arxiv.org/pdf/2012.12305v2.pdf	Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective	The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been devoted to addressing this problem through abusive content detection and related sub-areas, such as the detection of hate speech, toxicity, cyberbullying, etc. Although current technologies achieve high classification performance in research studies, it has been observed that the real-life application of this technology can cause unintended harms, such as the silencing of under-represented groups. We review a large body of NLP research on automatic abuse detection with a new focus on ethical challenges, organized around eight established ethical principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. In many cases, these principles relate not only to situational ethical codes, which may be context-dependent, but are in fact connected to universal human rights, such as the right to privacy, freedom from discrimination, and freedom of expression. We highlight the need to examine the broad social impacts of this technology, and to bring ethical and human rights considerations to every stage of the application life-cycle, from task formulation and dataset design, to model training and evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including `nudging', `quarantining', value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.	['Kathleen C. Fraser', 'Isar Nejadgholi', 'Svetlana Kiritchenko']	2020-12-22	null	null	null	null	['abuse-detection']	['natural-language-processing']	[ 2.81120241e-01 2.75709685e-02 -1.79281920e-01 -2.83757418e-01 -1.51918635e-01 -8.79133880e-01 3.96551192e-01 3.50063324e-01 -4.53608632e-01 7.82626987e-01 5.61536074e-01 -3.55992585e-01 -3.63030583e-01 -3.58780801e-01 -2.13318601e-01 -2.79496253e-01 1.18032463e-01 -2.74100929e-01 -2.75615633e-01 -2.84004152e-01 5.41567445e-01 6.76296651e-01 -1.25555718e+00 2.69578993e-01 9.71053362e-01 7.51105666e-01 -7.70509005e-01 3.49637121e-01 9.91836041e-02 1.04474604e+00 -9.06294882e-01 -1.06851113e+00 1.30109668e-01 -2.39456475e-01 -8.40853155e-01 -1.71464622e-01 2.30052635e-01 -8.33639026e-01 9.55157802e-02 1.37372088e+00 4.59363908e-01 -1.04023613e-01 4.03301150e-01 -1.68609202e+00 -9.39175487e-01 3.09507251e-01 -7.37577677e-01 2.81367421e-01 7.99037874e-01 4.54547405e-01 6.70117140e-01 -1.74910799e-01 8.30079138e-01 1.25315011e+00 7.49654830e-01 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49b4eada-e72a-440c-a95a-5ff9a77eee16	magicvo-end-to-end-monocular-visual-odometry	1811.10964	null	http://arxiv.org/abs/1811.10964v2	http://arxiv.org/pdf/1811.10964v2.pdf	MagicVO: End-to-End Monocular Visual Odometry through Deep Bi-directional Recurrent Convolutional Neural Network	This paper proposes a new framework to solve the problem of monocular visual odometry, called MagicVO . Based on Convolutional Neural Network (CNN) and Bi-directional LSTM (Bi-LSTM), MagicVO outputs a 6-DoF absolute-scale pose at each position of the camera with a sequence of continuous monocular images as input. It not only utilizes the outstanding performance of CNN in image feature processing to extract the rich features of image frames fully but also learns the geometric relationship from image sequences pre and post through Bi-LSTM to get a more accurate prediction. A pipeline of the MagicVO is shown in Fig. 1. The MagicVO system is end-to-end, and the results of experiments on the KITTI dataset and the ETH-asl cla dataset show that MagicVO has a better performance than traditional visual odometry (VO) systems in the accuracy of pose and the generalization ability.	['Zhongliang Deng', 'Yaokai Mo', 'Weilun Liu', 'Jian Jiao', 'Jichao Jiao']	2018-11-27	null	null	null	null	['monocular-visual-odometry']	['robots']	[-6.47583485e-01 -2.54888654e-01 -3.45408887e-01 -4.10840780e-01 2.06944883e-01 -1.09532379e-01 4.09746885e-01 -8.51292074e-01 -5.77527702e-01 4.97270584e-01 -9.21236649e-02 4.01436947e-02 3.63820642e-01 -4.85131413e-01 -1.02302313e+00 -2.75662094e-01 -6.22873157e-02 5.81544220e-01 2.80785739e-01 -2.41920426e-01 9.14697722e-02 5.32824576e-01 -1.56471097e+00 -1.57460526e-01 2.88128078e-01 1.19935763e+00 5.89458346e-01 7.54211485e-01 2.13496923e-01 1.12983584e+00 -2.02712074e-01 5.08456305e-02 3.73084992e-01 -4.88176718e-02 -8.06289613e-01 -6.44729361e-02 8.15991163e-01 -6.35524988e-01 -1.01186502e+00 1.03097320e+00 5.81188560e-01 -2.22236197e-02 3.53411436e-01 -1.31956780e+00 -7.41580069e-01 -7.35405087e-02 -3.70735586e-01 2.61182040e-01 4.48297352e-01 3.76790583e-01 7.27572858e-01 -8.94695580e-01 1.13905990e+00 1.41450739e+00 7.58425951e-01 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