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229654b5-bd4f-44cf-b087-4f0ee545015b	towards-a-foundation-model-for-generalist	2305.10455	null	https://arxiv.org/abs/2305.10455v2	https://arxiv.org/pdf/2305.10455v2.pdf	Towards Generalist Robots: A Promising Paradigm via Generative Simulation	This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots. The purpose of this document is to share the excitement of the authors with the community and highlight a promising research direction in robotics and AI. The authors believe the proposed paradigm is a feasible path towards accomplishing the long-standing goal of robotics research: deploying robots, or embodied AI agents more broadly, in various non-factory real-world settings to perform diverse tasks. This document presents a specific idea for mining knowledge in the latest large-scale foundation models for robotics research. Instead of directly using or adapting these models to produce low-level policies and actions, it advocates for a fully automated generative pipeline (termed as generative simulation), which uses these models to generate diversified tasks, scenes and training supervisions at scale, thereby scaling up low-level skill learning and ultimately leading to a foundation model for robotics that empowers generalist robots. The authors are actively pursuing this direction, but in the meantime, they recognize that the ambitious goal of building generalist robots with large-scale policy training demands significant resources such as computing power and hardware, and research groups in academia alone may face severe resource constraints in implementing the entire vision. Therefore, the authors believe sharing their thoughts at this early stage could foster discussions, attract interest towards the proposed pathway and related topics from industry groups, and potentially spur significant technical advancements in the field.	['Yian Wang', 'Tsun-Hsuan Wang', 'Yi-Ling Qiao', 'Zhenjia Xu', 'Theophile Gervet', 'Zhou Xian']	2023-05-17	null	null	null	null	['scene-generation']	['computer-vision']	[ 2.04644963e-01 5.92568398e-01 -2.07308188e-01 -1.40267044e-01 -6.94417953e-02 -5.73503017e-01 6.50865078e-01 -3.92093748e-01 -3.09361994e-01 5.76249063e-01 2.85641346e-02 -4.46837485e-01 -2.60427982e-01 -6.86941504e-01 -8.57577562e-01 -6.77584946e-01 1.77843109e-01 4.17840749e-01 -1.90708667e-01 -4.54656541e-01 6.15246773e-01 7.97295034e-01 -1.74942029e+00 -2.25294530e-01 9.38378751e-01 5.25764465e-01 8.58087301e-01 3.61857712e-01 4.18853074e-01 7.78965950e-01 -4.74177808e-01 -2.45613351e-01 4.43283558e-01 -2.04731598e-01 -6.62006259e-01 3.06768954e-01 2.46046130e-02 -2.52630502e-01 -1.88352674e-01 8.90552282e-01 4.02448833e-01 1.46490082e-01 3.66075903e-01 -1.40121746e+00 -7.53314137e-01 5.75493693e-01 -1.39845401e-01 -1.83196217e-01 4.43623066e-02 6.85358226e-01 8.04494917e-01 -4.57078159e-01 5.25461793e-01 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94ca3772-dc9b-47b6-8246-f3c04a65dc04	ltiatcmu-at-semeval-2020-task-11	2008.04820	null	https://arxiv.org/abs/2008.04820v2	https://arxiv.org/pdf/2008.04820v2.pdf	LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification	In this paper we describe our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda. The "multi-granular" model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant. To facilitate better representation learning, we also collect a corpus of 10k news articles, and use it for fine-tuning the model. The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge and attains $4^{th}$ position on the test leaderboard. Our final model and code is released at https://github.com/sopu/PropagandaSemEval2020.	['Alan W. black', 'Rishabh Joshi', 'Yulia Tsvetkov', 'Sopan Khosla', 'Ritam Dutt']	2020-08-11	null	https://aclanthology.org/2020.semeval-1.230	https://aclanthology.org/2020.semeval-1.230.pdf	semeval-2020	['propaganda-span-identification']	['natural-language-processing']	[-3.87092233e-02 3.91695388e-02 -6.08306170e-01 -4.00254935e-01 -8.88848364e-01 -5.54734528e-01 9.09949064e-01 4.75132108e-01 -4.31899399e-01 7.86963224e-01 9.55861032e-01 -5.11495709e-01 2.91023284e-01 -7.24153817e-01 -4.26860452e-01 -5.04001081e-01 1.11610321e-02 2.24033576e-02 -1.68055639e-01 -4.87134129e-01 6.70913994e-01 -2.96635777e-01 -9.03813064e-01 9.07704532e-01 8.99332047e-01 7.90681839e-01 -2.55406797e-01 6.47331119e-01 -2.08706886e-01 1.66436636e+00 -1.10420763e+00 -4.76317346e-01 -2.51954049e-01 -6.47379398e-01 -1.13575137e+00 -4.57750708e-01 3.27604115e-01 -1.73461527e-01 -1.21918589e-01 9.42371786e-01 5.57792366e-01 -4.54385988e-02 6.63100123e-01 -4.27075565e-01 -9.90917623e-01 1.60533726e+00 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11bd89ef-73d7-40cc-b01c-ca3ad5127661	learning-to-navigate-the-energy-landscape	1603.05772	null	http://arxiv.org/abs/1603.05772v1	http://arxiv.org/pdf/1603.05772v1.pdf	Learning to Navigate the Energy Landscape	In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimization of the reconstruction error which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization. To make the RGB camera relocalization problem particularly challenging, we introduce a new dataset of 3D environments which are significantly larger than those found in other publicly-available datasets. Our experiments reveal that the proposed method is able to achieve state-of-the-art camera relocalization results. We also demonstrate the generalizability of our approach on Hand Pose Estimation and Image Retrieval tasks.	['Shahram Izadi', 'Matthias Nießner', 'Julien Valentin', 'Cem Keskin', 'Philip Torr', 'Angela Dai', 'Pushmeet Kohli']	2016-03-18	null	null	null	null	['camera-relocalization']	['computer-vision']	[ 2.10450783e-01 -4.10964519e-01 -2.45312244e-01 -2.49411613e-01 -1.15715873e+00 -7.05216885e-01 4.66710120e-01 -3.97905111e-01 -6.46314502e-01 6.46314800e-01 -1.21394796e-02 -6.75293431e-02 -3.03534955e-01 -3.66479784e-01 -8.30295205e-01 -8.58066261e-01 3.55099589e-01 8.48379672e-01 2.48040155e-01 -1.83356434e-01 5.18232226e-01 8.47373009e-01 -1.61004186e+00 1.67719126e-01 4.49938983e-01 1.06263995e+00 5.29511452e-01 6.44398153e-01 1.89150020e-01 3.68862182e-01 -2.81406015e-01 -1.61404431e-01 4.94728804e-01 -6.82371929e-02 -9.35374498e-01 1.91698730e-01 1.03503942e+00 -3.44854087e-01 3.10528725e-02 9.01685417e-01 8.71277153e-01 2.28122428e-01 4.78266925e-01 -8.45593631e-01 -3.07053775e-01 5.42961210e-02 -5.95515430e-01 -2.57593766e-02 6.24540567e-01 1.69828266e-01 1.10284376e+00 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c20d3c48-cac1-4da1-85e2-ff459c05ed98	coordinating-policies-among-multiple-agents	2205.10607	null	https://arxiv.org/abs/2205.10607v2	https://arxiv.org/pdf/2205.10607v2.pdf	Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel	In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. In this paper, we propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents' collective performance. To ensure that this facilitator does not become a centralized controller, agents are incentivized to reduce their dependence on the messages it conveys, and the messages can only influence the selection of a policy from a fixed set, not instantaneous actions given the policy. We demonstrate the strength of this architecture over existing baselines on several cooperative MARL environments.	['Yoshua Bengio', 'Nicolas Heess', 'Michael Mozer', 'Tianmin Shu', 'Anirudh Goyal', 'Cristian Meo', 'Oussama Boussif', 'Vedant Shah', 'Dianbo Liu']	2022-05-21	null	null	null	null	['intelligent-communication']	['time-series']	[-2.37908766e-01 4.43129301e-01 -3.65883380e-01 -2.40413755e-01 -5.68860292e-01 -6.49496138e-01 8.43217552e-01 4.33723897e-01 -9.97530580e-01 1.17607188e+00 2.32494175e-01 8.60483050e-02 -8.86141788e-04 -9.25879240e-01 -7.49809444e-01 -8.10300231e-01 -5.64804375e-01 7.35488415e-01 3.21914047e-01 -6.04365051e-01 7.31998757e-02 1.63809508e-01 -9.97788727e-01 -7.31167197e-03 3.66734087e-01 4.89418775e-01 7.85894468e-02 9.82963979e-01 4.43955511e-01 1.48502409e+00 -1.10329115e+00 2.69710392e-01 2.24481180e-01 -5.23345351e-01 -5.39630949e-01 4.30878587e-02 -3.51154596e-01 -8.08430851e-01 -2.51325399e-01 6.77856445e-01 3.96731466e-01 1.41466796e-01 6.01445079e-01 -1.44619477e+00 -3.43521625e-01 1.15815735e+00 -6.11541867e-01 1.45530015e-01 3.44282657e-01 5.10548830e-01 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c5a9541b-367c-4465-973d-f48b69117f1c	adaptive-deep-learning-for-nonparametric-time	2207.02546	null	https://arxiv.org/abs/2207.02546v1	https://arxiv.org/pdf/2207.02546v1.pdf	Adaptive deep learning for nonparametric time series regression	In this paper, we develop a general theory for adaptive nonparametric estimation of mean functions of nonstationary and nonlinear time series using deep neural networks (DNNs). We first consider two types of DNN estimators, non-penalized and sparse-penalized DNN estimators, and establish their generalization error bounds for general nonstationary time series. We then derive minimax lower bounds for estimating mean functions belonging to a wide class of nonlinear autoregressive (AR) models that include nonlinear generalized additive AR, single index, and threshold AR models. Building upon the results, we show that the sparse-penalized DNN estimator is adaptive and attains the minimax optimal rates up to a poly-logarithmic factor for many nonlinear AR models. Through numerical simulations, we demonstrate the usefulness of the DNN methods for estimating nonlinear AR models with intrinsic low-dimensional structures and discontinuous or rough mean functions, which is consistent with our theory.	['Yuta Koike', 'Riku Fukami', 'Daisuke Kurisu']	2022-07-06	null	null	null	null	['time-series-regression']	['time-series']	[-1.75034508e-01 -4.29447740e-01 -2.13452727e-01 -4.93305445e-01 -7.25235462e-01 -4.87406880e-01 1.92863479e-01 -5.78366280e-01 -2.32784376e-01 9.81033385e-01 1.81117401e-01 -1.76480357e-02 -4.90647495e-01 -5.31061709e-01 -8.81695747e-01 -9.89266157e-01 -5.69167614e-01 7.55455673e-01 -2.77475089e-01 -2.95167584e-02 -3.89549173e-02 9.47077990e-01 -1.36603761e+00 -6.18083537e-01 8.40634048e-01 1.28705132e+00 -2.82634944e-01 9.18554723e-01 1.65413961e-01 4.25213516e-01 -5.64344704e-01 -8.76917597e-03 2.64210492e-01 -2.67394096e-01 1.94872484e-01 -3.42258215e-01 5.90925038e-01 -4.50193435e-01 -5.53549409e-01 1.21979940e+00 4.11721736e-01 6.89628363e-01 1.33685184e+00 -1.17888200e+00 -1.32389688e+00 5.59565008e-01 -5.87604225e-01 7.13347673e-01 -4.54981148e-01 -3.35762113e-01 5.37616909e-01 -9.26133692e-01 -7.56183043e-02 1.49637663e+00 1.21133983e+00 6.26850247e-01 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a9edd959-3bc3-400c-927f-e6acc72a71c8	graphcspn-geometry-aware-depth-completion-via	2210.10758	null	https://arxiv.org/abs/2210.10758v1	https://arxiv.org/pdf/2210.10758v1.pdf	GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs	Image guided depth completion aims to recover per-pixel dense depth maps from sparse depth measurements with the help of aligned color images, which has a wide range of applications from robotics to autonomous driving. However, the 3D nature of sparse-to-dense depth completion has not been fully explored by previous methods. In this work, we propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. In addition, the proposed networks explicitly incorporate learnable geometric constraints to regularize the propagation process performed in three-dimensional space rather than in two-dimensional plane. Furthermore, we construct the graph utilizing sequences of feature patches, and update it dynamically with an edge attention module during propagation, so as to better capture both the local neighboring features and global relationships over long distance. Extensive experiments on both indoor NYU-Depth-v2 and outdoor KITTI datasets demonstrate that our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps. Code and models are available at the project page.	['Shengjin Wang', 'YaLi Li', 'Bo wang', 'Xiaofei Shao', 'Xin Liu']	2022-10-19	null	null	null	null	['depth-completion']	['computer-vision']	[ 2.57237405e-01 2.97760814e-01 5.27644642e-02 -4.90537226e-01 -3.34468216e-01 -1.85493499e-01 4.38532084e-01 -1.30672604e-01 -4.04286832e-01 5.92655718e-01 2.04371780e-01 -3.37773860e-02 -1.76514283e-01 -1.03521478e+00 -7.56391287e-01 -5.55265188e-01 -6.63046986e-02 3.28896016e-01 2.15153053e-01 -6.09993301e-02 3.58636022e-01 5.57866514e-01 -1.37962389e+00 -2.21659333e-01 9.58972394e-01 1.03867853e+00 3.40337276e-01 5.95953345e-01 -2.26002753e-01 8.47744465e-01 3.22145186e-02 -1.43505633e-01 3.24721098e-01 -9.25379321e-02 -5.38223028e-01 2.56483316e-01 7.16432512e-01 -6.23140991e-01 -9.34450448e-01 1.03098953e+00 3.22596192e-01 3.52618188e-01 3.12699914e-01 -9.56028819e-01 -6.20547414e-01 1.29411474e-01 -7.51553178e-01 -1.45562381e-01 4.23699647e-01 2.19765559e-01 7.93547988e-01 -9.55515027e-01 6.78892434e-01 1.14018226e+00 5.05618632e-01 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a0a13299-2cbf-4cfb-9ade-f65ec3f26eea	how-does-pretraining-improve-discourse-aware	2305.19847	null	https://arxiv.org/abs/2305.19847v1	https://arxiv.org/pdf/2305.19847v1.pdf	How Does Pretraining Improve Discourse-Aware Translation?	Pretrained language models (PLMs) have produced substantial improvements in discourse-aware neural machine translation (NMT), for example, improved coherence in spoken language translation. However, the underlying reasons for their strong performance have not been well explained. To bridge this gap, we introduce a probing task to interpret the ability of PLMs to capture discourse relation knowledge. We validate three state-of-the-art PLMs across encoder-, decoder-, and encoder-decoder-based models. The analysis shows that (1) the ability of PLMs on discourse modelling varies from architecture and layer; (2) discourse elements in a text lead to different learning difficulties for PLMs. Besides, we investigate the effects of different PLMs on spoken language translation. Through experiments on IWSLT2017 Chinese-English dataset, we empirically reveal that NMT models initialized from different layers of PLMs exhibit the same trends with the probing task. Our findings are instructive to understand how and when discourse knowledge in PLMs should work for downstream tasks.	['Derek F. Wong', 'Siyou Liu', 'Longyue Wang', 'Zhihong Huang']	2023-05-31	null	null	null	null	['nmt']	['computer-code']	[ 3.25563908e-01 5.40167034e-01 -5.18647134e-01 -3.54618937e-01 -9.84042108e-01 -4.81234610e-01 1.06966913e+00 -1.39455900e-01 -2.43948415e-01 7.42017388e-01 9.29261267e-01 -7.54437506e-01 3.02143425e-01 -3.99387568e-01 -1.02946031e+00 -3.40025336e-01 3.35714370e-01 6.72685623e-01 -6.91214427e-02 -3.81997049e-01 7.33034685e-02 -2.92309463e-01 -7.64178336e-01 1.02597809e+00 1.01795316e+00 3.06909859e-01 4.37468827e-01 4.82852072e-01 -3.01118761e-01 1.22179508e+00 -6.93438172e-01 -4.03968692e-01 -2.11856231e-01 -1.03473723e+00 -1.11281586e+00 4.54854919e-03 2.26008341e-01 -3.23069125e-01 -2.24801376e-01 6.40906692e-01 3.77206445e-01 -3.19504499e-01 8.32805455e-01 -8.42230022e-01 -1.18996787e+00 1.46175408e+00 -2.43549794e-01 2.20002338e-01 1.95466250e-01 2.61412710e-01 1.17374372e+00 -1.07202005e+00 8.91298532e-01 1.55648553e+00 4.54494417e-01 7.98412621e-01 -1.32900262e+00 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383684e6-c5b3-4160-8994-57c5e0c82658	knowledge-enhanced-masked-language-model-for	null	null	https://aclanthology.org/2021.naacl-main.376	https://aclanthology.org/2021.naacl-main.376.pdf	Knowledge Enhanced Masked Language Model for Stance Detection	Detecting stance on Twitter is especially challenging because of the short length of each tweet, the continuous coinage of new terminology and hashtags, and the deviation of sentence structure from standard prose. Fine-tuned language models using large-scale in-domain data have been shown to be the new state-of-the-art for many NLP tasks, including stance detection. In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. Instead of random token masking, we propose using a weighted log-odds-ratio to identify words with high stance distinguishability and then model an attention mechanism that focuses on these words. We show that our proposed approach outperforms the state of the art for stance detection on Twitter data about the 2020 US Presidential election.	['Lisa Singh', 'Kornraphop Kawintiranon']	2021-05-26	null	null	null	naacl-2021-4	['stance-detection-us-election-2020-biden', 'stance-detection-us-election-2020-trump']	['natural-language-processing', 'natural-language-processing']	[-1.95476804e-02 1.93693087e-01 -8.18321526e-01 -4.93763715e-01 -1.20925295e+00 -7.67839730e-01 1.00383687e+00 6.15957201e-01 -7.25128531e-01 8.68004143e-01 8.66966188e-01 -5.01832247e-01 4.43486571e-01 -8.60157013e-01 -4.35758948e-01 -3.46110135e-01 1.99715078e-01 4.76255864e-01 5.59010766e-02 -5.87962747e-01 6.18983805e-01 -2.37059265e-01 -1.24268460e+00 5.55651426e-01 1.02701592e+00 6.11333251e-01 -2.45225891e-01 4.23988968e-01 -3.05251658e-01 6.34700835e-01 -7.47193873e-01 -4.20108825e-01 -5.17579168e-02 -2.97274679e-01 -6.21851742e-01 -3.31707895e-01 5.33321440e-01 -2.87249498e-02 -1.46995962e-01 9.80162382e-01 6.48060381e-01 -3.55468631e-01 4.26645070e-01 -6.86610758e-01 -6.28837526e-01 1.54648042e+00 -8.76910746e-01 5.39032102e-01 3.31683248e-01 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7a2ad700-ebc6-4460-9847-52a9f8cf66fe	text-independent-speaker-verification-using	1705.09422	null	http://arxiv.org/abs/1705.09422v7	http://arxiv.org/pdf/1705.09422v7.pdf	Text-Independent Speaker Verification Using 3D Convolutional Neural Networks	In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN) architecture has been proposed for speaker verification in the text-independent setting. One of the main challenges is the creation of the speaker models. Most of the previously-reported approaches create speaker models based on averaging the extracted features from utterances of the speaker, which is known as the d-vector system. In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speakers' utterances and creation of the speaker model. This leads to simultaneously capturing the speaker-related information and building a more robust system to cope with within-speaker variation. We demonstrate that the proposed method significantly outperforms the traditional d-vector verification system. Moreover, the proposed system can also be an alternative to the traditional d-vector system which is a one-shot speaker modeling system by utilizing 3D-CNNs.	['Nasser M. Nasrabadi', 'Jeremy Dawson', 'Amirsina Torfi']	2017-05-26	null	null	null	null	['text-independent-speaker-verification']	['speech']	[-1.05907932e-01 -1.86041355e-01 1.34567991e-01 -7.18496859e-01 -6.69026256e-01 -4.24682081e-01 7.50597298e-01 -7.67084509e-02 -2.77368486e-01 -1.01052068e-01 2.11030126e-01 -3.12522531e-01 2.14210764e-01 -3.23289424e-01 -3.20002913e-01 -6.85741723e-01 1.78631172e-01 3.15332055e-01 -2.47249663e-01 -2.30803654e-01 1.25077352e-01 7.88808525e-01 -1.91157627e+00 -3.19508582e-01 3.11357170e-01 1.02931273e+00 -5.56409918e-02 9.09703076e-01 -5.03103614e-01 2.68221259e-01 -8.71038258e-01 -1.51398271e-01 2.59786844e-01 -2.91015804e-01 -3.65294695e-01 4.42786127e-01 5.47164857e-01 -5.29345572e-01 -1.51700050e-01 7.57644653e-01 9.32940841e-01 2.69148707e-01 5.76262414e-01 -1.31450188e+00 -7.49671102e-01 8.02789688e-01 -3.21138382e-01 4.97511886e-02 2.72549868e-01 -1.71168819e-01 6.75254703e-01 -1.26087224e+00 2.37517059e-01 1.50862730e+00 5.29124379e-01 9.99417722e-01 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10771f26-3ba3-4a06-8ee6-7f1baafadc47	human-object-interaction-prediction-in-videos	2306.03597	null	https://arxiv.org/abs/2306.03597v1	https://arxiv.org/pdf/2306.03597v1.pdf	Human-Object Interaction Prediction in Videos through Gaze Following	Understanding the human-object interactions (HOIs) from a video is essential to fully comprehend a visual scene. This line of research has been addressed by detecting HOIs from images and lately from videos. However, the video-based HOI anticipation task in the third-person view remains understudied. In this paper, we design a framework to detect current HOIs and anticipate future HOIs in videos. We propose to leverage human gaze information since people often fixate on an object before interacting with it. These gaze features together with the scene contexts and the visual appearances of human-object pairs are fused through a spatio-temporal transformer. To evaluate the model in the HOI anticipation task in a multi-person scenario, we propose a set of person-wise multi-label metrics. Our model is trained and validated on the VidHOI dataset, which contains videos capturing daily life and is currently the largest video HOI dataset. Experimental results in the HOI detection task show that our approach improves the baseline by a great margin of 36.3% relatively. Moreover, we conduct an extensive ablation study to demonstrate the effectiveness of our modifications and extensions to the spatio-temporal transformer. Our code is publicly available on https://github.com/nizhf/hoi-prediction-gaze-transformer.	['Dongheui Lee', 'Hyemin Ahn', 'Esteve Valls Mascaró', 'Zhifan Ni']	2023-06-06	null	null	null	null	['human-object-interaction-detection']	['computer-vision']	[ 1.86990991e-01 -3.38389218e-01 -2.04096556e-01 -4.33460563e-01 -4.49181348e-01 -3.56967270e-01 4.40444797e-01 -3.20635825e-01 -2.23170340e-01 2.93153644e-01 4.06458676e-01 2.87800133e-01 6.35965765e-02 1.03788882e-01 -6.81432843e-01 -4.01434630e-01 -1.27885893e-01 -7.63217360e-02 1.97970480e-01 1.91070110e-01 1.68763265e-01 1.54446363e-02 -2.04487133e+00 4.57163513e-01 4.35527235e-01 8.99972260e-01 1.26536265e-01 8.45996678e-01 9.68500018e-01 1.02767348e+00 -2.68835038e-01 -2.26329744e-01 1.31468043e-01 -5.29188514e-01 -7.18229949e-01 3.70785117e-01 9.68824089e-01 -5.89900196e-01 -4.27718520e-01 6.92283094e-01 4.09247547e-01 1.09119453e-01 3.77990991e-01 -2.03145266e+00 -3.81983340e-01 9.58562791e-02 -7.64801502e-01 4.85910296e-01 9.89146113e-01 5.27459621e-01 1.00554967e+00 -9.81112003e-01 7.40086079e-01 1.24827659e+00 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3e974c55-bce7-49e0-b6c6-7452c530c18d	image-inpainting-via-conditional-texture-and	2108.09760	null	https://arxiv.org/abs/2108.09760v1	https://arxiv.org/pdf/2108.09760v1.pdf	Image Inpainting via Conditional Texture and Structure Dual Generation	Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors. Due to the lack of proper interaction with image texture during structure reconstruction, however, current solutions are incompetent in handling the cases with large corruptions, and they generally suffer from distorted results. In this paper, we propose a novel two-stream network for image inpainting, which models the structure-constrained texture synthesis and texture-guided structure reconstruction in a coupled manner so that they better leverage each other for more plausible generation. Furthermore, to enhance the global consistency, a Bi-directional Gated Feature Fusion (Bi-GFF) module is designed to exchange and combine the structure and texture information and a Contextual Feature Aggregation (CFA) module is developed to refine the generated contents by region affinity learning and multi-scale feature aggregation. Qualitative and quantitative experiments on the CelebA, Paris StreetView and Places2 datasets demonstrate the superiority of the proposed method. Our code is available at https://github.com/Xiefan-Guo/CTSDG.	['Di Huang', 'Hongyu Yang', 'Xiefan Guo']	2021-08-22	null	http://openaccess.thecvf.com//content/ICCV2021/html/Guo_Image_Inpainting_via_Conditional_Texture_and_Structure_Dual_Generation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_Image_Inpainting_via_Conditional_Texture_and_Structure_Dual_Generation_ICCV_2021_paper.pdf	iccv-2021-1	['texture-synthesis']	['computer-vision']	[ 1.69189468e-01 -1.53996632e-01 -1.15685746e-01 -2.94016570e-01 -8.98297966e-01 -1.81415096e-01 5.83560348e-01 -1.47792488e-01 1.28332347e-01 8.34447861e-01 5.30841172e-01 2.14617848e-01 1.44286025e-02 -9.58493888e-01 -7.19828665e-01 -9.76351142e-01 4.28297341e-01 1.94963440e-01 8.14756602e-02 -2.69919127e-01 2.25797072e-01 4.42694783e-01 -1.58994472e+00 5.06517649e-01 1.15023530e+00 1.12205720e+00 4.62725282e-01 3.06396455e-01 -2.79350840e-02 8.89357030e-01 -9.34228525e-02 -1.85183510e-01 3.64235163e-01 -5.64113677e-01 -6.07208967e-01 3.72763366e-01 3.52411121e-01 -5.16130805e-01 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104ca499-2159-4a3e-a461-1677a9fc0efb	principled-inference-of-hyperedges-and	2204.05646	null	https://arxiv.org/abs/2204.05646v2	https://arxiv.org/pdf/2204.05646v2.pdf	Inference of hyperedges and overlapping communities in hypergraphs	Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges. Our approach illustrates the fundamental advantages of a hypergraph probabilistic model when modeling relational systems with higher-order interactions.	['Caterina De Bacco', 'Federico Battiston', 'Martina Contisciani']	2022-04-12	null	null	null	null	['hyperedge-prediction']	['graphs']	[ 2.35788599e-01 3.06005567e-01 -2.21520923e-02 -5.70966825e-02 3.71388346e-02 -7.06663609e-01 7.32460082e-01 3.88427764e-01 2.24910881e-02 8.52524519e-01 1.57461062e-01 -1.32024378e-01 -8.52137625e-01 -1.15703773e+00 -7.75430560e-01 -7.83189893e-01 -8.40638936e-01 1.20896566e+00 5.84015071e-01 -4.48949523e-02 2.18021631e-01 6.08203828e-01 -1.28956091e+00 4.19657409e-01 4.56006020e-01 -4.82819900e-02 1.57285243e-01 8.55405629e-01 -7.01975834e-04 5.37795901e-01 -4.19486612e-01 -4.44528639e-01 2.67749075e-02 -1.81195959e-01 -7.56937504e-01 5.57788126e-02 1.66436151e-01 2.02865645e-01 -7.18447804e-01 7.88725436e-01 3.25670093e-01 -4.56825078e-01 7.41176069e-01 -1.46735144e+00 -4.93281424e-01 1.17849755e+00 -8.33368599e-01 1.42982379e-01 5.45109749e-01 -7.47063085e-02 1.48152506e+00 -4.59584057e-01 1.26710021e+00 1.45570731e+00 7.39957809e-01 -1.52179375e-01 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7608227b-2ee6-46e5-b0f1-4fd24c1beb77	a-survey-on-text-generation-using-generative	2212.11119	null	https://arxiv.org/abs/2212.11119v1	https://arxiv.org/pdf/2212.11119v1.pdf	A survey on text generation using generative adversarial networks	This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.	['João Paulo Papa', 'Gustavo Henrique de Rosa']	2022-12-20	null	null	null	null	['adversarial-text']	['adversarial']	[ 5.15534461e-01 4.35725242e-01 6.78570345e-02 -2.10422538e-02 -7.26495028e-01 -6.46676898e-01 1.06854260e+00 -2.05468044e-01 -3.08011621e-01 1.45930386e+00 6.31118566e-02 -1.82861969e-01 9.59675163e-02 -1.14181352e+00 -6.94329560e-01 -7.45497584e-01 2.35622287e-01 4.64300662e-01 -3.80258471e-01 -4.45231616e-01 4.30156082e-01 6.82644725e-01 -1.45193517e+00 4.66505326e-02 1.05227721e+00 7.67732739e-01 -2.64168292e-01 9.03377235e-01 -4.19189692e-01 7.33936727e-01 -1.38344717e+00 -8.31220686e-01 1.48612663e-01 -7.48871028e-01 -5.93104959e-01 -2.03377396e-01 2.02041820e-01 -1.67799294e-01 -3.80875021e-01 1.00107682e+00 7.89785326e-01 1.55212536e-01 1.09304810e+00 -1.54894817e+00 -1.01966155e+00 9.07436669e-01 4.46222071e-03 6.86555505e-02 5.37907660e-01 1.43863946e-01 6.31242216e-01 -7.06605315e-01 5.88543653e-01 1.27045250e+00 4.85121161e-01 9.03603077e-01 -9.67629492e-01 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-5.24917901e-01 3.42534393e-01 1.11066371e-01 -3.23837072e-01 -3.74516577e-01 3.78964573e-01]	[15.441539764404297, 6.041910648345947]
8bbcec31-a726-483e-be40-dd618c55f98d	d2conv3d-dynamic-dilated-convolutions-for	null	null	https://openaccess.thecvf.com/content/WACV2022/html/Schmidt_D2Conv3D_Dynamic_Dilated_Convolutions_for_Object_Segmentation_in_Videos_WACV_2022_paper.html	https://openaccess.thecvf.com/content/WACV2022/papers/Schmidt_D2Conv3D_Dynamic_Dilated_Convolutions_for_Object_Segmentation_in_Videos_WACV_2022_paper.pdf	D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos	Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the efficacy of dilated and deformable convolutions for various image-level segmentation tasks. This gives reason to believe that 3D extensions of such convolutions should also yield performance improvements for video-level segmentation tasks. However, this aspect has not yet been explored thoroughly in existing literature. In this paper, we propose Dynamic Dilated Convolutions (D2Conv3D): a novel type of convolution which draws inspiration from dilated and deformable convolutions and extends them to the 3D (spatio-temporal) domain. We experimentally show that D2Conv3D can be used to improve the performance of multiple 3D CNN architectures across multiple video segmentation related benchmarks by simply employing D2Conv3D as a drop-in replacement for standard convolutions. We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D. Lastly, we set a new state-of-the-art on the DAVIS 2016 Unsupervised Video Object Segmentation benchmark. Code is made publicly available at https://github.com/Schmiddo/d2conv3d.	['Bastian Leibe', 'Sabarinath Mahadevan', 'Ali Athar', 'Christian Schmidt']	2021-11-15	null	null	null	wacv-2021-11	['video-instance-segmentation', 'unsupervised-video-object-segmentation', 'multi-object-tracking-and-segmentation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 4.69441824e-02 -1.43289983e-01 -1.34790912e-01 -4.13524777e-01 -2.31708765e-01 -9.49476302e-01 4.96151090e-01 -4.79773313e-01 -5.94661951e-01 3.84212822e-01 -2.34558582e-02 -6.46651566e-01 2.68473834e-01 -5.10866463e-01 -8.92403305e-01 -4.58118945e-01 -1.38397768e-01 -1.06112827e-02 8.29360962e-01 7.48905865e-03 -4.31016386e-02 8.04947734e-01 -1.38770378e+00 1.66193485e-01 6.12111330e-01 9.19258475e-01 2.67305337e-02 8.44505429e-01 -5.45762964e-02 3.70455742e-01 -3.26595604e-01 -3.71019989e-01 7.96229303e-01 -1.92050889e-01 -1.25332212e+00 2.33257219e-01 9.02856052e-01 -7.11524308e-01 -4.62467968e-01 1.05908418e+00 3.78028959e-01 2.08471388e-01 4.26376462e-01 -1.24700296e+00 -7.49413133e-01 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9ae9e70d-0d67-4560-8232-df430bad987a	dick-preston-and-morbo-at-semeval-2019-task-4	null	null	https://aclanthology.org/S19-2160	https://aclanthology.org/S19-2160.pdf	Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection	In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important. We present a deep learning-based approach in which a pre-trained language model has been fine-tuned on domain-specific data and used for classification of news articles, as part of the SemEval-2019 task on hyperpartisan news detection. The suggested approach yields accuracy and F1-scores around 0.8 which places the best performing classifier among the top-5 systems in the competition.	['Tim Isbister', 'Fredrik Johansson']	2019-06-01	null	null	null	semeval-2019-6	['news-classification']	['natural-language-processing']	[-3.77884865e-01 5.39839745e-01 -6.84537113e-01 -7.64237866e-02 -9.31156397e-01 -8.86392951e-01 1.40213954e+00 7.93366373e-01 -5.29779971e-01 8.46329749e-01 6.66565061e-01 -7.41338015e-01 1.09740891e-01 -9.44736004e-01 -1.14340961e+00 -3.55827749e-01 2.25109637e-01 9.99732852e-01 2.28241339e-01 -3.94191802e-01 6.12985075e-01 3.29475611e-01 -9.22278106e-01 9.83376324e-01 4.56316680e-01 8.52560520e-01 -7.36349046e-01 5.61350107e-01 -3.12605798e-02 1.12384713e+00 -8.84119213e-01 -1.04257536e+00 1.12408869e-01 -2.90759265e-01 -9.44202423e-01 -4.64879423e-01 4.49797064e-01 -1.41159743e-01 -7.53416419e-01 1.12355387e+00 2.23528013e-01 -4.11473304e-01 9.01651382e-01 -6.05849802e-01 -7.54644394e-01 1.32810676e+00 -6.90194547e-01 7.26636946e-01 1.84073150e-01 -2.61701226e-01 1.23248267e+00 -5.84137201e-01 9.56600726e-01 1.31184053e+00 5.23217201e-01 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c8ca5532-4672-4828-897b-c80cb3e79ec3	simultaneous-denoising-and-dereverberation	2004.02420	null	https://arxiv.org/abs/2004.02420v1	https://arxiv.org/pdf/2004.02420v1.pdf	Simultaneous Denoising and Dereverberation Using Deep Embedding Features	Monaural speech dereverberation is a very challenging task because no spatial cues can be used. When the additive noises exist, this task becomes more challenging. In this paper, we propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features, which is based on the deep clustering (DC). DC is a state-of-the-art method for speech separation that includes embedding learning and K-means clustering. As for our proposed method, it contains two stages: denoising and dereverberation. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. These embedding features are generated from the anechoic speech and residual reverberation signals. They can represent the inferred spectral masking patterns of the desired signals, which are discriminative features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another supervised neural network is utilized to estimate the anechoic speech from these deep embedding features. Finally, the denoising stage and dereverberation stage are optimized by the joint training method. Experimental results show that the proposed method outperforms the WPE and BLSTM baselines, especially in the low SNR condition.	['Jian-Hua Tao', 'Cunhang Fan', 'Bin Liu', 'Zhengqi Wen', 'Jiangyan Yi']	2020-04-06	null	null	null	null	['speech-denoising', 'speech-dereverberation']	['speech', 'speech']	[-2.03614980e-01 -4.67484862e-01 7.02173114e-01 -1.10760339e-01 -1.13813531e+00 -3.22553188e-01 1.68728203e-01 -3.00724834e-01 -2.28479177e-01 3.39203805e-01 6.44758701e-01 -2.24565476e-01 -8.69347528e-03 -3.15185130e-01 -5.19779444e-01 -1.45847011e+00 2.21099719e-01 -3.22563052e-01 -1.43297791e-01 -9.00477730e-03 -1.87940910e-01 1.36319816e-01 -1.62441421e+00 2.42239729e-01 9.61080015e-01 1.27436578e+00 6.23334825e-01 1.00407076e+00 1.67781636e-01 5.57485104e-01 -8.28654706e-01 9.34941396e-02 7.53488466e-02 -5.38956225e-01 1.98324114e-01 -1.33105163e-02 2.67135501e-01 -3.12850475e-01 -8.58169973e-01 1.20123565e+00 1.23763978e+00 4.85749483e-01 3.63494188e-01 -7.16093838e-01 -4.25899148e-01 4.86304551e-01 -1.70404464e-01 2.95552254e-01 1.94230862e-02 2.62204021e-01 8.41684878e-01 -1.25511026e+00 -2.58319199e-01 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0c2947e7-88c4-463f-8e40-72583a8c2c09	meta-learning-extractors-for-music-source	2002.07016	null	https://arxiv.org/abs/2002.07016v1	https://arxiv.org/pdf/2002.07016v1.pdf	Meta-learning Extractors for Music Source Separation	We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-sharing, while still allowing for instrument-specific parameterization. Meta-TasNet is shown to be more effective than the models trained independently or in a multi-task setting, and achieve performance comparable with state-of-the-art methods. In comparison to the latter, our extractors contain fewer parameters and have faster run-time performance. We discuss important architectural considerations, and explore the costs and benefits of this approach.	['Jason Naradowsky', 'Aditya Ganeshan', 'David Samuel']	2020-02-17	null	null	null	null	['music-source-separation']	['music']	[ 1.84260085e-01 -1.83995180e-02 -2.76644558e-01 9.61144790e-02 -1.32177138e+00 -6.25898838e-01 3.44068974e-01 -3.81039351e-01 -2.62268811e-01 2.32269615e-01 5.58062136e-01 1.71823636e-01 -2.60926038e-01 -1.94347501e-01 -2.73290873e-01 -6.52651906e-01 -1.16227090e-01 4.55470473e-01 -1.37616038e-01 -1.71137854e-01 1.57974988e-01 6.91413581e-02 -1.32215190e+00 4.62200552e-01 6.86303318e-01 6.92192495e-01 2.74771929e-01 9.34681475e-01 1.66373961e-02 6.53783381e-01 -8.06111515e-01 -3.95561159e-01 3.01746905e-01 -7.74249971e-01 -7.77124345e-01 -1.73948303e-01 7.02048481e-01 -1.20288007e-01 -1.47820607e-01 8.26230764e-01 9.76885974e-01 3.58511209e-01 4.99571919e-01 -1.07140470e+00 -2.09233388e-01 1.15600622e+00 -4.13084656e-01 2.39124954e-01 -1.62887424e-02 -1.98815748e-01 1.55663002e+00 -7.80719042e-01 3.65088433e-01 9.57262337e-01 9.77393091e-01 4.87433434e-01 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96cd49c9-4cd1-4efd-bd58-1e63f682137b	blind-analysis-of-ct-image-noise-using	1605.07650	null	http://arxiv.org/abs/1605.07650v1	http://arxiv.org/pdf/1605.07650v1.pdf	Blind Analysis of CT Image Noise Using Residual Denoised Images	CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images. These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics. This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images. Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation. We implement and evaluate the noise estimation performance of six spatial- and frequency- based methods, derived from conventional image filtering algorithms. Algorithms were tested on patient data sets from whole-body repeat CT acquisitions performed with a higher and lower dose technique over the same scan region. Results: The proposed performance metrics can evaluate the relative tradeoff of filter parameters and noise estimation performance. The proposed automated methods tend to underestimate CT image noise at low-flux levels. Initial application of methodology suggests that anisotropic diffusion and Wavelet-transform based filters provide optimal estimates of noise. Furthermore, methodology does not provide accurate estimates of absolute noise levels, but can provide estimates of relative change and/or trends in noise levels.	['Adam Alessio', 'Sohini Roychowdhury', 'Nathan Hollraft']	2016-05-24	null	null	null	null	['noise-estimation']	['medical']	[ 2.34460905e-01 -4.12579924e-01 3.02568525e-01 -4.50635582e-01 -1.05217063e+00 -3.96600991e-01 4.08913255e-01 4.68694866e-01 -7.43786335e-01 6.33893549e-01 5.98166943e-01 -2.20805943e-01 -4.96785462e-01 -6.36869133e-01 1.04953945e-01 -8.47544968e-01 -3.34944725e-01 4.30400014e-01 6.03845239e-01 1.54627576e-01 9.50726792e-02 6.36324406e-01 -9.19676363e-01 5.82607090e-01 6.21629298e-01 7.98294723e-01 2.66911715e-01 1.11622906e+00 -1.09213963e-01 1.00460172e+00 -5.61160564e-01 1.13148436e-01 3.07272494e-01 -7.76050985e-01 -7.00440228e-01 -2.52116099e-02 2.42542833e-01 -4.21694249e-01 -2.24944383e-01 1.22909319e+00 9.78034854e-01 -2.08385326e-02 7.35529602e-01 -4.57496405e-01 -1.29460776e-02 7.16939270e-01 -3.27558368e-01 1.13172793e+00 3.58557403e-01 5.25412321e-01 -4.53220084e-02 -5.10274351e-01 3.92922491e-01 6.87052965e-01 1.04604018e+00 2.66112387e-01 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39316861-c14a-4897-b187-9d3ebf19b1e6	look-backward-and-forward-self-knowledge	2203.05248	null	https://arxiv.org/abs/2203.05248v2	https://arxiv.org/pdf/2203.05248v2.pdf	Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation	Neural Machine Translation(NMT) models are usually trained via unidirectional decoder which corresponds to optimizing one-step-ahead prediction. However, this kind of unidirectional decoding framework may incline to focus on local structure rather than global coherence. To alleviate this problem, we propose a novel method, Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation(SBD-NMT). We deploy a backward decoder which can act as an effective regularization method to the forward decoder. By leveraging the backward decoder's information about the longer-term future, distilling knowledge learned in the backward decoder can encourage auto-regressive NMT models to plan ahead. Experiments show that our method is significantly better than the strong Transformer baselines on multiple machine translation data sets.	['Yanjun Miao', 'Liang Wang', 'Disheng Pan', 'Libin Shen', 'Xuanwei Zhang']	2022-03-10	null	null	null	null	['self-knowledge-distillation']	['computer-vision']	[ 1.20166153e-01 6.16700292e-01 -6.16858661e-01 -5.19522667e-01 -8.83139014e-01 -3.35532606e-01 8.99946928e-01 -5.20326018e-01 -1.91412926e-01 9.06556368e-01 8.69789124e-01 -7.80961454e-01 6.50084615e-01 -6.63345158e-01 -1.23353112e+00 -4.06376779e-01 4.89891350e-01 6.48113370e-01 -2.44140014e-01 -4.36506331e-01 -9.88894701e-02 -2.51161665e-01 -2.47123495e-01 5.08926272e-01 9.59003270e-01 4.16523606e-01 4.37066823e-01 1.78311661e-01 -1.62725508e-01 1.13441718e+00 -1.25162169e-01 -8.30240488e-01 1.17435075e-01 -8.09706151e-01 -8.13476503e-01 -3.05511951e-01 -8.83304551e-02 -4.04853523e-01 -5.60150564e-01 9.02724326e-01 2.37309039e-01 -2.55583286e-01 5.57689190e-01 -5.24051666e-01 -1.24577272e+00 1.26481295e+00 -4.39909130e-01 1.02927603e-01 -6.62622675e-02 3.40463609e-01 1.16147697e+00 -1.14144325e+00 6.42205954e-01 1.35434449e+00 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1774fd76-73dd-4e3c-b8ae-5f9bdf754cf2	core-gpt-combining-open-access-research-and	2307.04683	null	https://arxiv.org/abs/2307.04683v1	https://arxiv.org/pdf/2307.04683v1.pdf	CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering	In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles from CORE. We first demonstrate that GPT3.5 and GPT4 cannot be relied upon to provide references or citations for generated text. We then introduce CORE-GPT which delivers evidence-based answers to questions, along with citations and links to the cited papers, greatly increasing the trustworthiness of the answers and reducing the risk of hallucinations. CORE-GPT's performance was evaluated on a dataset of 100 questions covering the top 20 scientific domains in CORE, resulting in 100 answers and links to 500 relevant articles. The quality of the provided answers and and relevance of the links were assessed by two annotators. Our results demonstrate that CORE-GPT can produce comprehensive and trustworthy answers across the majority of scientific domains, complete with links to genuine, relevant scientific articles.	['Petr Knoth', 'Matteo Cancellieri', 'David Pride']	2023-07-06	null	null	null	null	['question-answering']	['natural-language-processing']	[-7.09762156e-01 6.50663555e-01 -8.97881836e-02 1.92251727e-01 -1.50865424e+00 -1.00092697e+00 5.56432009e-01 5.71922123e-01 -2.06489384e-01 1.03835106e+00 5.15225887e-01 -6.26270473e-01 -4.49384570e-01 -5.83233297e-01 -6.49127066e-01 1.15646452e-01 5.24532497e-01 5.96611440e-01 2.92090207e-01 -2.44617268e-01 8.99246275e-01 -1.27935290e-01 -8.95267427e-01 4.19870853e-01 1.58572507e+00 7.13304460e-01 4.63243499e-02 5.11662126e-01 -7.56587565e-01 8.47356856e-01 -1.05825961e+00 -1.26780021e+00 -3.79008949e-01 -1.83398798e-01 -1.08728957e+00 -9.14221764e-01 5.00198960e-01 2.19289422e-01 -1.37493789e-01 7.90262163e-01 7.26218998e-01 -3.54516625e-01 4.25498426e-01 -1.23648190e+00 -1.56698358e+00 6.45807803e-01 -1.19391866e-01 3.65540415e-01 1.10966635e+00 9.04081911e-02 1.09171236e+00 -1.19147348e+00 1.08832002e+00 1.38381433e+00 6.91479743e-01 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545d3fbe-089d-416f-9410-01b33e464b9e	interpretable-eeg-seizure-prediction-using-a	null	null	https://www.nature.com/articles/s41598-022-08322-w	https://www.nature.com/articles/s41598-022-08322-w	Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm	Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 (≈ 38%) were solely validated by our methodology, while 24 (≈44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.	['César Teixeira', 'Pedro Martins', 'António Dourado', 'Fábio Lopes', 'Adriana Leal', 'Tiago Coelho', 'Mauro Pinto']	2022-03-15	null	null	null	scientific-reports-2022-3	['seizure-prediction']	['medical']	[ 2.03265995e-01 1.51414216e-01 2.27012888e-01 -2.75636047e-01 -6.70234561e-01 -5.73541939e-01 6.00142241e-01 4.63096559e-01 -3.71920019e-01 5.41428089e-01 1.81384444e-01 -4.44025755e-01 -6.42152071e-01 -4.67603445e-01 -2.21835151e-01 -7.99399078e-01 -8.26083601e-01 5.24346173e-01 3.41569744e-02 -1.41902128e-03 3.71120781e-01 5.47080815e-01 -1.49957991e+00 6.32838726e-01 7.71995544e-01 1.01305461e+00 2.97213852e-01 3.92402261e-01 2.30363831e-01 3.54216784e-01 -7.50714242e-01 2.28067145e-01 1.24770187e-01 -5.03350735e-01 -4.42019701e-01 -6.49290755e-02 -4.79229182e-01 3.28588411e-02 2.29785889e-01 5.99698901e-01 8.33078921e-01 -5.52304506e-01 5.66221714e-01 -1.04096115e+00 7.38822073e-02 6.65183067e-01 -1.66367814e-01 3.29344988e-01 5.26137888e-01 2.71314353e-01 4.42279458e-01 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59a4c687-df57-4f78-9d71-658fd5c01c09	rw-resnet-a-novel-speech-anti-spoofing-model	2108.05684	null	https://arxiv.org/abs/2108.05684v2	https://arxiv.org/pdf/2108.05684v2.pdf	RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform	In recent years, synthetic speech generated by advanced text-to-speech (TTS) and voice conversion (VC) systems has caused great harms to automatic speaker verification (ASV) systems, urging us to design a synthetic speech detection system to protect ASV systems. In this paper, we propose a new speech anti-spoofing model named ResWavegram-Resnet (RW-Resnet). The model contains two parts, Conv1D Resblocks and backbone Resnet34. The Conv1D Resblock is based on the Conv1D block with a residual connection. For the first part, we use the raw waveform as input and feed it to the stacked Conv1D Resblocks to get the ResWavegram. Compared with traditional methods, ResWavegram keeps all the information from the audio signal and has a stronger ability in extracting features. For the second part, the extracted features are fed to the backbone Resnet34 for the spoofed or bonafide decision. The ASVspoof2019 logical access (LA) corpus is used to evaluate our proposed RW-Resnet. Experimental results show that the RW-Resnet achieves better performance than other state-of-the-art anti-spoofing models, which illustrates its effectiveness in detecting synthetic speech attacks.	['Shugong Xu', 'Zongze Ren', 'Youxuan Ma']	2021-08-12	null	null	null	null	['synthetic-speech-detection']	['audio']	[-1.40762255e-02 -1.68322250e-01 6.04810799e-03 -3.49064134e-02 -4.15410489e-01 -1.79552883e-01 4.50370640e-01 -3.31809938e-01 -3.14219773e-01 4.50077057e-01 6.71474397e-01 -7.46076584e-01 5.20525515e-01 -5.32852471e-01 -2.05582976e-01 -7.42004156e-01 9.66666862e-02 -4.92597818e-01 5.90771735e-01 -5.42931437e-01 8.36509243e-02 4.89980072e-01 -1.29941511e+00 3.98514301e-01 7.36503541e-01 1.06942832e+00 2.73584276e-01 7.67735839e-01 -2.99473614e-01 6.83232963e-01 -1.06584227e+00 -1.38366982e-01 3.74033637e-02 -3.23410809e-01 -4.03419554e-01 -4.60556000e-01 -8.17761719e-02 -4.13007557e-01 -6.98085248e-01 1.27106416e+00 1.05698085e+00 5.83065394e-03 3.51420194e-02 -1.27810311e+00 -3.50937635e-01 9.91289318e-01 -3.05461913e-01 7.34114528e-01 3.69618326e-01 1.21279113e-01 6.04864657e-01 -1.12684369e+00 2.89426416e-01 1.68571353e+00 6.07524037e-01 7.90587544e-01 -6.88885927e-01 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1d95f548-94b5-4c88-b126-2a05187899e0	a-token-wise-cnn-based-method-for-sentence	2009.11260	null	https://arxiv.org/abs/2009.11260v1	https://arxiv.org/pdf/2009.11260v1.pdf	A Token-wise CNN-based Method for Sentence Compression	Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fields e.g. one can build tools for language education. However, current methods are largely based on Recurrent Neural Network (RNN) models which suffer from poor processing speed. To address this issue, in this paper, we propose a token-wise Convolutional Neural Network, a CNN-based model along with pre-trained Bidirectional Encoder Representations from Transformers (BERT) features for deletion-based sentence compression. We also compare our model with RNN-based models and fine-tuned BERT. Although one of the RNN-based models outperforms marginally other models given the same input, our CNN-based model was ten times faster than the RNN-based approach.	['Piotr Koniusz', 'Sabrina Caldwell', 'Tom Gedeon', 'Weiwei Hou', 'Hanna Suominen']	2020-09-23	null	null	null	null	['sentence-compression']	['natural-language-processing']	[ 4.58778232e-01 2.74957478e-01 4.21364903e-02 -2.55172819e-01 -7.27079690e-01 -1.56549320e-01 4.29848433e-01 3.62752169e-01 -6.50411308e-01 7.98035622e-01 8.35821211e-01 -6.68149412e-01 2.47429624e-01 -9.55756366e-01 -8.53518784e-01 -3.34052563e-01 2.67811030e-01 1.70128658e-01 6.31337985e-02 -6.28516018e-01 5.30731976e-01 3.31643403e-01 -1.32639146e+00 4.77707744e-01 9.52558458e-01 5.14020562e-01 4.53826070e-01 9.94218051e-01 -3.79170388e-01 1.25303233e+00 -7.59611726e-01 -5.16261160e-01 -9.05553699e-02 -5.13029814e-01 -1.08204901e+00 -7.27937520e-01 2.79868484e-01 -3.18740934e-01 -6.69430077e-01 1.03155887e+00 6.17774606e-01 2.81731546e-01 1.88499033e-01 -6.70714259e-01 -9.25423086e-01 1.05895233e+00 -2.01304704e-01 2.22927332e-01 3.15088511e-01 -6.98458627e-02 9.18375194e-01 -8.21948826e-01 6.55630112e-01 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6.61925077e-01 -7.54237711e-01 -5.54358244e-01 -1.26465690e+00 -3.35145116e-01 1.33748859e-01 6.60177529e-01 2.30503500e-01 -4.55775782e-02 -6.89176098e-02]	[12.05704116821289, 9.223612785339355]
a41adec2-6e6e-4e4e-b5ae-2a95d77f49a2	surveying-the-research-on-fake-news-in-social	2109.07909	null	https://arxiv.org/abs/2109.07909v2	https://arxiv.org/pdf/2109.07909v2.pdf	Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language	With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets' credibility. Therefore, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines struggle with an information overload problem. The aim of this survey is threefold: (1) we present the results of a network-based analysis of the existing multidisciplinary literature to support the search for relevant trends and central publications; (2) we describe the main results and necessary background to attack the problem under a computational perspective; (3) we review selected contributions using network science as a unifying framework and computational linguistics as the tool to make sense of the shared content. Despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that those interested in the area of fake news should be aware of crucial aspects of both disciplines.	['Paolo Rosso', 'Anastasia Giachanou', 'Alfonso Semeraro', 'Giancarlo Ruffo']	2021-09-13	null	null	null	null	['rumour-detection']	['natural-language-processing']	[ 6.91942172e-03 5.39590657e-01 -8.09310555e-01 3.66958231e-01 -7.14315251e-02 -5.73616326e-01 6.30555511e-01 9.11644280e-01 -5.88424861e-01 5.18292427e-01 6.27280712e-01 -6.53473616e-01 -2.46670097e-01 -8.75168681e-01 -3.26405942e-01 -1.03128918e-01 9.27910358e-02 1.63971066e-01 2.98714131e-01 -5.17287493e-01 8.71862888e-01 2.23457351e-01 -1.23487878e+00 -4.70531397e-02 9.30507421e-01 4.73818004e-01 -1.65496752e-01 -1.59862399e-01 -4.44595635e-01 7.87811100e-01 -8.87661397e-01 -8.22040677e-01 -3.23476583e-01 -6.35009289e-01 -9.12093043e-01 -1.22026026e-01 -1.59746021e-01 5.86943403e-02 -4.92208451e-01 1.43403530e+00 2.46091411e-01 -2.31001377e-01 1.37953401e-01 -1.03887546e+00 -8.85503292e-01 1.14306796e+00 -4.79052484e-01 4.76682752e-01 3.41208607e-01 -2.13994771e-01 9.84061301e-01 -3.64073634e-01 1.16653490e+00 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348b4bf9-0b47-46fd-b793-66f83d548e43	atp-amrize-then-parse-enhancing-amr-parsing	2204.08875	null	https://arxiv.org/abs/2204.08875v2	https://arxiv.org/pdf/2204.08875v2.pdf	ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs	As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g. MT and summarization in the text-to-AMR transition even with much less data. 2) To make a better fit for AMR, data from auxiliary tasks should be properly "AMRized" to PseudoAMR before training. Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform. 3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning. From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on different benchmarks especially in topology-related scores.	['Baobao Chang', 'Zhifang Sui', 'Tianyu Liu', 'Runxin Xu', 'Peiyi Wang', 'Liang Chen']	2022-04-19	null	https://aclanthology.org/2022.findings-naacl.190	https://aclanthology.org/2022.findings-naacl.190.pdf	findings-naacl-2022-7	['semantic-role-labeling']	['natural-language-processing']	[ 6.87951386e-01 8.48200619e-01 -5.19887090e-01 -5.82031250e-01 -1.20443058e+00 -5.91832280e-01 5.33098221e-01 2.60325551e-01 -2.38411680e-01 6.96043015e-01 9.22422528e-01 -5.37053704e-01 1.66334450e-01 -8.10808778e-01 -9.22976017e-01 -2.35550910e-01 2.86240220e-01 5.76967359e-01 2.12431014e-01 -4.93052363e-01 3.47836792e-01 2.89361048e-02 -9.66946483e-01 7.38675416e-01 9.29809809e-01 6.59928322e-01 5.95406234e-01 4.43412364e-01 -9.48900640e-01 1.01529777e+00 -8.15970659e-01 -4.94642735e-01 -5.89998700e-02 -3.07706118e-01 -1.57953572e+00 -4.78508472e-02 1.72145173e-01 8.51812139e-02 6.18192814e-02 8.63359094e-01 1.62772700e-01 2.15599179e-01 3.55043977e-01 -7.59552181e-01 -8.18345249e-01 1.39616489e+00 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ed9ad00f-cdb4-40e9-b1c2-80b72aaa9bb7	do-we-really-need-scene-specific-pose	2012.12014	null	https://arxiv.org/abs/2012.12014v1	https://arxiv.org/pdf/2012.12014v1.pdf	Do We Really Need Scene-specific Pose Encoders?	Visual pose regression models estimate the camera pose from a query image with a single forward pass. Current models learn pose encoding from an image using deep convolutional networks which are trained per scene. The resulting encoding is typically passed to a multi-layer perceptron in order to regress the pose. In this work, we propose that scene-specific pose encoders are not required for pose regression and that encodings trained for visual similarity can be used instead. In order to test our hypothesis, we take a shallow architecture of several fully connected layers and train it with pre-computed encodings from a generic image retrieval model. We find that these encodings are not only sufficient to regress the camera pose, but that, when provided to a branching fully connected architecture, a trained model can achieve competitive results and even surpass current \textit{state-of-the-art} pose regressors in some cases. Moreover, we show that for outdoor localization, the proposed architecture is the only pose regressor, to date, consistently localizing in under 2 meters and 5 degrees.	['Ron Ferens', 'Yoli Shavit']	2020-12-22	null	null	null	null	['outdoor-localization']	['robots']	[ 2.99195468e-01 2.26636559e-01 -3.29446271e-02 -6.54220819e-01 -9.49824870e-01 -6.67534530e-01 6.13861561e-01 2.12055683e-01 -7.55941927e-01 3.98275256e-01 -9.67019200e-02 -3.67430039e-02 1.30178362e-01 -5.15967727e-01 -1.41340852e+00 -4.14940834e-01 7.20556974e-02 7.78428495e-01 3.24181139e-01 -1.98014021e-01 1.78529024e-01 6.49568737e-01 -1.50403059e+00 1.68286145e-01 3.68175060e-01 1.16791272e+00 3.32906991e-01 9.59948719e-01 2.23748356e-01 7.99163222e-01 -4.86356556e-01 -4.59285885e-01 3.98103178e-01 -7.55247399e-02 -8.62361729e-01 2.71449555e-02 9.87884760e-01 -6.74601078e-01 -5.43482780e-01 8.01933944e-01 3.01095545e-01 -2.35539693e-02 6.77391291e-01 -8.00560057e-01 -3.38527888e-01 3.09227705e-01 -2.93410212e-01 1.37292503e-04 3.99690926e-01 1.43463418e-01 1.06611156e+00 -8.55575204e-01 7.14208126e-01 9.83548880e-01 7.28821874e-01 3.77884775e-01 -1.20890391e+00 -3.13035101e-01 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2.08495706e-01 5.92152297e-01 -5.63049376e-01 -1.64134994e-01 1.25956722e-02]	[7.720593452453613, -2.1309449672698975]
d4c424a7-0800-46a3-b4e0-2c96b90f024b	learning-quality-aware-dynamic-memory-for	2207.07922	null	https://arxiv.org/abs/2207.07922v1	https://arxiv.org/pdf/2207.07922v1.pdf	Learning Quality-aware Dynamic Memory for Video Object Segmentation	Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quality of the memory. Therefore, frames with poor segmentation masks are prone to be memorized, which leads to a segmentation mask error accumulation problem and further affect the segmentation performance. In addition, the linear increase of memory frames with the growth of frame number also limits the ability of the models to handle long videos. To this end, we propose a Quality-aware Dynamic Memory Network (QDMN) to evaluate the segmentation quality of each frame, allowing the memory bank to selectively store accurately segmented frames to prevent the error accumulation problem. Then, we combine the segmentation quality with temporal consistency to dynamically update the memory bank to improve the practicability of the models. Without any bells and whistles, our QDMN achieves new state-of-the-art performance on both DAVIS and YouTube-VOS benchmarks. Moreover, extensive experiments demonstrate that the proposed Quality Assessment Module (QAM) can be applied to memory-based methods as generic plugins and significantly improves performance. Our source code is available at https://github.com/workforai/QDMN.	['Yujiu Yang', 'Weihao Xia', 'Wei Zhao', 'Xinyuan Zhao', 'Fei Yin', 'Ran Yu', 'Yong liu']	2022-07-16	null	null	null	null	['semi-supervised-video-object-segmentation']	['computer-vision']	[-1.91044196e-01 -4.48204517e-01 -3.43038529e-01 -1.40841365e-01 -4.63786900e-01 -3.13861877e-01 9.23059210e-02 1.11660115e-01 -4.75914806e-01 3.77515465e-01 -1.72334418e-01 -1.05703451e-01 2.47987270e-01 -9.08005655e-01 -7.24832833e-01 -6.13962889e-01 9.34114829e-02 -2.63631362e-02 1.16810346e+00 1.94933221e-01 3.45389664e-01 3.75592411e-01 -1.61982250e+00 3.19862604e-01 8.98262799e-01 1.23606360e+00 5.98722756e-01 5.50847411e-01 -2.72525728e-01 8.67548227e-01 -6.72985613e-01 -2.08183303e-01 2.78955698e-01 -3.15170169e-01 -8.56523216e-01 1.06989853e-01 2.97607690e-01 -6.88778162e-01 -6.30601287e-01 1.05809605e+00 4.38597351e-01 2.81573147e-01 2.00917330e-02 -1.21844816e+00 -3.06335211e-01 4.13281262e-01 -5.48273444e-01 7.42521942e-01 7.02988654e-02 4.92688239e-01 4.71933484e-01 -8.64842117e-01 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dd4d8828-c6c1-4f29-88b0-cecf3662a65f	weakly-supervised-action-localization-by-2	2003.12424	null	https://arxiv.org/abs/2003.12424v2	https://arxiv.org/pdf/2003.12424v2.pdf	Weakly-Supervised Action Localization by Generative Attention Modeling	Weakly-supervised temporal action localization is a problem of learning an action localization model with only video-level action labeling available. The general framework largely relies on the classification activation, which employs an attention model to identify the action-related frames and then categorizes them into different classes. Such method results in the action-context confusion issue: context frames near action clips tend to be recognized as action frames themselves, since they are closely related to the specific classes. To solve the problem, in this paper we propose to model the class-agnostic frame-wise probability conditioned on the frame attention using conditional Variational Auto-Encoder (VAE). With the observation that the context exhibits notable difference from the action at representation level, a probabilistic model, i.e., conditional VAE, is learned to model the likelihood of each frame given the attention. By maximizing the conditional probability with respect to the attention, the action and non-action frames are well separated. Experiments on THUMOS14 and ActivityNet1.2 demonstrate advantage of our method and effectiveness in handling action-context confusion problem. Code is now available on GitHub.	['Qi Dai', 'Baifeng Shi', 'Yadong Mu', 'Jingdong Wang']	2020-03-27	weakly-supervised-action-localization-by-3	http://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Weakly-Supervised_Action_Localization_by_Generative_Attention_Modeling_CVPR_2020_paper.pdf	cvpr-2020-6	['weakly-supervised-action-localization', 'weakly-supervised-temporal-action']	['computer-vision', 'computer-vision']	[ 4.11493450e-01 1.73129991e-01 -5.54623246e-01 -3.20229799e-01 -7.64568508e-01 -2.07400039e-01 5.42922437e-01 -1.44767329e-01 -2.82720596e-01 6.60711884e-01 6.36019111e-01 2.79320508e-01 2.00962380e-01 -3.15489560e-01 -6.95120752e-01 -9.10353899e-01 4.94687147e-02 4.79806252e-02 5.06284952e-01 2.71935523e-01 2.51331031e-01 2.28410047e-02 -1.45011485e+00 7.29786396e-01 4.11956787e-01 1.01361775e+00 4.38069552e-01 5.45325875e-01 -9.20480117e-02 1.54433894e+00 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5411f983-da0a-4e64-9f3d-18b0ea552b72	innovations-in-neural-data-to-text-generation	2207.12571	null	https://arxiv.org/abs/2207.12571v2	https://arxiv.org/pdf/2207.12571v2.pdf	Innovations in Neural Data-to-text Generation: A Survey	The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating DTG from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for DTG research that not only focus on the design of linguistically capable systems but also systems that exhibit fairness and accountability.	['Naren Ramakrishnan', 'Ajay Gogineni', 'Mandar Sharma']	2022-07-25	null	null	null	null	['data-to-text-generation']	['natural-language-processing']	[ 2.80118614e-01 8.03140223e-01 -2.95498639e-01 -5.13696074e-01 -5.36190629e-01 -6.35202527e-01 1.10445893e+00 2.12880582e-01 -3.12151790e-01 8.72764587e-01 1.07843900e+00 -5.21779597e-01 1.34833828e-01 -9.23126996e-01 -1.95513979e-01 -2.75358856e-01 2.55027175e-01 2.12465629e-01 -9.04530227e-01 -4.37970877e-01 2.90045619e-01 3.68652731e-01 -1.50002837e+00 5.20820737e-01 1.11566484e+00 9.84833539e-01 -4.37240690e-01 5.28372765e-01 -4.32899237e-01 8.38547945e-01 -9.65234876e-01 -7.55306363e-01 2.85960346e-01 -6.10474169e-01 -9.62290823e-01 -5.17907739e-01 5.39618313e-01 -1.46223441e-01 -2.27984995e-01 7.47220337e-01 1.06586134e+00 2.16970667e-01 4.93259639e-01 -1.26297331e+00 -1.62518060e+00 8.83518994e-01 -1.14469528e-02 1.03403211e-01 3.65769178e-01 4.49261606e-01 1.10653138e+00 -7.17246890e-01 7.52895057e-01 1.44568694e+00 6.03343964e-01 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7387e99a-812f-47de-ac2a-e64567b251c8	learning-to-super-resolve-blurry-images-with	2302.13766	null	https://arxiv.org/abs/2302.13766v1	https://arxiv.org/pdf/2302.13766v1.pdf	Learning to Super-Resolve Blurry Images with Events	Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this paper, we employ events to alleviate the burden of SRB and propose an Event-enhanced SRB (E-SRB) algorithm, which can generate a sequence of sharp and clear images with High Resolution (HR) from a single blurry image with Low Resolution (LR). To achieve this end, we formulate an event-enhanced degeneration model to consider the low spatial resolution, motion blurs, and event noises simultaneously. We then build an event-enhanced Sparse Learning Network (eSL-Net++) upon a dual sparse learning scheme where both events and intensity frames are modeled with sparse representations. Furthermore, we propose an event shuffle-and-merge scheme to extend the single-frame SRB to the sequence-frame SRB without any additional training process. Experimental results on synthetic and real-world datasets show that the proposed eSL-Net++ outperforms state-of-the-art methods by a large margin. Datasets, codes, and more results are available at https://github.com/ShinyWang33/eSL-Net-Plusplus.	['Gui-Song Xia', 'Jianzhuang Liu', 'Wen Yang', 'Haijian Zhang', 'Xiang Zhang', 'Bishan Wang', 'Lei Yu']	2023-02-27	null	null	null	null	['sparse-learning']	['methodology']	[ 4.24481273e-01 -6.46959543e-01 6.15434013e-02 -2.95919269e-01 -8.84551048e-01 1.26180887e-01 3.37936491e-01 -7.70050883e-01 -6.51873648e-02 1.22992790e+00 6.36421740e-01 1.82598859e-01 -1.80044189e-01 -4.12151605e-01 -7.64059365e-01 -8.51697445e-01 1.41839221e-01 -3.71855170e-01 4.27367389e-01 3.17396000e-02 7.08158240e-02 2.26626828e-01 -1.32691813e+00 5.15181124e-01 1.05473900e+00 8.50656390e-01 8.14929068e-01 6.49250090e-01 2.36460924e-01 1.47527230e+00 -2.01997638e-01 2.08904058e-01 2.52134353e-01 -7.12037683e-01 -5.71627378e-01 2.78260440e-01 3.62773716e-01 -7.92158723e-01 -1.00064242e+00 1.13833261e+00 6.63276553e-01 3.71674359e-01 1.33454293e-01 -8.89384449e-01 -1.00494778e+00 3.59414339e-01 -1.21717870e+00 8.73723388e-01 3.60335261e-01 3.81001502e-01 2.40715936e-01 -1.26086569e+00 5.56220710e-01 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4869a839-0dea-4f26-a31d-c40fcc6330dd	bi-directional-dermoscopic-feature-learning	2002.08694	null	https://arxiv.org/abs/2002.08694v1	https://arxiv.org/pdf/2002.08694v1.pdf	Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation	Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.	['Jun Liu', 'Henghui Ding', 'Xudong Jiang', 'Xiaohong Wang']	2020-02-20	null	null	null	null	['skin-lesion-segmentation']	['medical']	[ 5.18712938e-01 -9.31243226e-02 -5.35088778e-01 -5.17292678e-01 -8.11191738e-01 -5.05597532e-01 3.14031810e-01 3.37880015e-01 -2.59419411e-01 3.25184196e-01 -1.21259876e-02 -2.31759608e-01 -2.95450896e-01 -8.01267564e-01 -3.25247556e-01 -9.16740835e-01 3.80344719e-01 5.98387979e-02 4.38627571e-01 1.12788007e-01 2.00379252e-01 5.08788943e-01 -1.21728861e+00 5.60009420e-01 1.44079995e+00 1.19247651e+00 3.13073844e-01 5.65344691e-01 -3.61342013e-01 5.28797984e-01 -3.38590294e-01 -6.03154659e-01 -9.63050779e-03 -2.93233901e-01 -8.56310070e-01 3.92782420e-01 6.64926589e-01 -2.45315164e-01 1.42014995e-01 1.35909402e+00 3.36430907e-01 -4.76909459e-01 7.52355814e-01 -4.76754457e-01 -3.36474717e-01 2.19205424e-01 -8.78923476e-01 1.93626523e-01 -7.41835684e-02 1.60496622e-01 9.58947778e-01 -3.99995625e-01 7.27429509e-01 7.45641351e-01 5.41528106e-01 5.43152332e-01 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d476fb90-cdb3-48e1-a7a3-afcbdc4047bb	sliced-wasserstein-on-symmetric-positive	2303.05798	null	https://arxiv.org/abs/2303.05798v2	https://arxiv.org/pdf/2303.05798v2.pdf	Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals	When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with these matrices requires using Riemanian geometry to account for their structure. In this paper, we propose a new method to deal with distributions of covariance matrices and demonstrate its computational efficiency on M/EEG multivariate time series. More specifically, we define a Sliced-Wasserstein distance between measures of symmetric positive definite matrices that comes with strong theoretical guarantees. Then, we take advantage of its properties and kernel methods to apply this distance to brain-age prediction from MEG data and compare it to state-of-the-art algorithms based on Riemannian geometry. Finally, we show that it is an efficient surrogate to the Wasserstein distance in domain adaptation for Brain Computer Interface applications.	['Nicolas Courty', 'Matthieu Kowalski', 'Thomas Moreau', 'Lucas Drumetz', 'Alain Rakotomamonjy', 'Benoît Malézieux', 'Clément Bonet']	2023-03-10	null	null	null	null	['eeg', 'eeg']	['methodology', 'time-series']	[ 4.23349738e-02 1.52829945e-01 4.70037133e-01 -5.22117555e-01 -4.31373090e-01 -1.06344447e-01 2.84744322e-01 -9.75797549e-02 -6.69056296e-01 6.01442218e-01 -2.04362422e-02 -2.73437440e-01 -7.31995344e-01 -3.16716939e-01 -6.42937660e-01 -8.22061479e-01 -7.68944979e-01 5.07702827e-01 -1.62167147e-01 6.57168180e-02 3.93757969e-01 4.51450616e-01 -1.06723952e+00 -2.64691949e-01 1.13110542e+00 8.71237874e-01 2.31017739e-01 5.56607902e-01 3.36331248e-01 4.24258292e-01 -6.08442761e-02 -3.28285813e-01 1.88636124e-01 -2.71869510e-01 -9.81728137e-01 -7.50455037e-02 3.06154400e-01 5.73798455e-02 -2.74599254e-01 1.21783602e+00 3.72586340e-01 3.38270873e-01 1.39408123e+00 -1.22350740e+00 -7.93774128e-01 6.49893701e-01 -5.76404452e-01 3.63546401e-01 -1.89410336e-03 -4.23140556e-01 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d3367fc5-6615-4bd6-aa3e-38b81ae744c7	trier-template-guided-neural-networks-for	2009.05407	null	https://arxiv.org/abs/2009.05407v1	https://arxiv.org/pdf/2009.05407v1.pdf	TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings	Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we propose a pre-training technique that handles this challenge in sleep staging tasks. Inspired by conventional methods that experienced physicians have used to classify sleep states from the existence of characteristic waveform shapes, or template patterns, our method introduces a cosine similarity based convolutional neural network to extract representative waveforms from training data. Afterwards, these features guide a model to construct representations based on template patterns. Through extensive experiments, we demonstrated that guiding a neural network with template patterns is an effective approach for sleep staging, since (1) classification performances are significantly enhanced and (2) robustness in several aspects are improved. Last but not least, interpretations on models showed that notable features exploited by trained experts are correctly addressed during prediction in the proposed method.	['Jeonghwan Hwang', 'Taeheon Lee', 'Honggu Lee']	2020-09-10	null	null	null	null	['sleep-staging']	['medical']	[ 2.75067508e-01 -6.33125659e-03 -7.68974274e-02 -3.49795252e-01 -2.61043370e-01 -2.80923456e-01 2.17832610e-01 3.15226644e-01 -3.38399589e-01 7.25870728e-01 7.23724812e-02 -1.24068528e-01 -4.65299755e-01 -5.89617670e-01 -3.36068004e-01 -7.49170065e-01 -2.41454303e-01 5.23138717e-02 1.11671619e-01 -2.87671924e-01 4.64695841e-01 6.86766207e-01 -1.62884700e+00 2.48156816e-01 1.15686357e+00 1.26551044e+00 1.26766011e-01 3.07317734e-01 -1.04146175e-01 6.30898893e-01 -9.74098563e-01 -1.67502329e-01 -1.28833771e-01 -4.32421565e-01 -4.59787279e-01 -1.48538947e-01 -1.80673257e-01 1.80438608e-01 -1.87574685e-01 9.60722685e-01 6.11062706e-01 9.11960155e-02 6.53557003e-01 -8.73908818e-01 -3.19190890e-01 3.12153488e-01 -3.26931030e-02 5.78741014e-01 1.63734525e-01 1.68111652e-01 5.75760484e-01 -4.83669251e-01 1.00161880e-01 5.57548821e-01 1.12701786e+00 5.25512099e-01 -1.12787938e+00 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-4.12107378e-01 3.81271511e-01 4.97258425e-01 -9.08351898e-01 -3.40126216e-01 5.89045808e-02]	[13.68143367767334, 3.450352191925049]
341a172a-b4da-4be4-ba2a-d17ce5928689	trainable-time-warping-aligning-time-series	1903.09245	null	http://arxiv.org/abs/1903.09245v1	http://arxiv.org/pdf/1903.09245v1.pdf	Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain	DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuous-time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on 85 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks.	['Soheil Khorram', 'Melvin G McInnis', 'Emily Mower Provost']	2019-03-21	null	null	null	null	['time-series-averaging']	['time-series']	[ 3.79442275e-01 -7.22749889e-01 4.14531901e-02 -4.14847642e-01 -1.21394563e+00 -7.67489195e-01 3.86003256e-01 -3.49676274e-02 -6.18794799e-01 4.36704636e-01 7.89229423e-02 -2.04485461e-01 -2.89587945e-01 -3.77180636e-01 -4.50426847e-01 -7.73978591e-01 -7.18561709e-01 1.76979348e-01 2.70222753e-01 -3.08256745e-01 1.42588422e-01 4.10690993e-01 -9.60159600e-01 -1.17048882e-02 6.11636639e-01 8.99130046e-01 -7.94043690e-02 8.81549716e-01 2.20847502e-01 2.59125829e-01 -5.87751150e-01 -6.27978146e-02 4.27847415e-01 -7.25032628e-01 -6.14613712e-01 -3.63651663e-01 3.72899413e-01 9.41124856e-02 -3.47905517e-01 6.57000780e-01 8.26474011e-01 4.40551251e-01 1.74147725e-01 -1.36271751e+00 -3.56936097e-01 4.06359673e-01 -5.88244617e-01 6.25011981e-01 1.99888155e-01 -8.94853100e-02 8.91584635e-01 -5.52113712e-01 6.98003829e-01 8.94699454e-01 1.14298451e+00 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5a37cd89-c540-464e-987c-f7faf0a0c8b2	trajectory-modeling-and-prediction-with	1811.08576	null	https://arxiv.org/abs/1811.08576v4	https://arxiv.org/pdf/1811.08576v4.pdf	CM Modeling of Trajectory	Information about the waypoints of a moving object, e.g., an airliner in an air traffic control (ATC) problem, should be considered in trajectory modeling and prediction. Due to the ATC regulations, trajectory design criteria, and restricted motion capability of airliners there are long-range dependencies in trajectories of airliners. Waypoint information can be used for modeling such dependencies in trajectories. This paper proposes a conditionally Markov (CM) sequence for modeling trajectories passing by waypoints. A dynamic model governing the proposed sequence is obtained. Filtering and trajectory prediction formulations are presented. The use of the proposed sequence for modeling trajectories with waypoints is justified.	['X. Rong Li', 'Reza Rezaie']	2018-11-21	null	null	null	null	['trajectory-modeling']	['time-series']	[-2.43602723e-01 -7.00126886e-01 -6.89111531e-01 -2.41637230e-01 1.33817539e-01 -7.16573060e-01 5.23799658e-01 2.77835280e-02 -3.18809658e-01 6.13052130e-01 2.75201887e-01 -1.11242807e+00 -9.74573255e-01 -7.46386468e-01 -3.11453998e-01 -5.45954883e-01 -8.86519477e-02 2.48174101e-01 3.39626729e-01 -1.83851764e-01 2.00370669e-01 1.48271787e+00 -1.23404264e+00 3.40012312e-01 6.07082367e-01 4.45864946e-01 4.25997704e-01 1.19051838e+00 -2.02300683e-01 5.64326108e-01 -1.88379064e-01 -1.63236871e-01 2.27301210e-01 -1.95675492e-01 -6.09350920e-01 1.83711067e-01 -3.57827157e-01 -3.11542064e-01 -7.30965137e-01 6.47450626e-01 -4.15490508e-01 1.06754982e+00 8.45194697e-01 -1.55406594e+00 1.42116040e-01 9.37614217e-02 2.40977481e-01 5.15816987e-01 2.50911117e-01 1.15470745e-01 5.81186652e-01 -6.98492169e-01 4.49305505e-01 1.01895082e+00 4.57725257e-01 2.93808907e-01 -6.19310617e-01 -4.71680582e-01 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-4.95367870e-03 3.80652130e-01 -6.57518685e-01 -8.40872526e-03 4.76609208e-02]	[5.769467830657959, 1.2200042009353638]
f139f321-8f5e-4b0d-b9b8-e1010d54ad90	terminology-extraction-using-co-occurrence	null	null	https://aclanthology.org/2022.term-1.5	https://aclanthology.org/2022.term-1.5.pdf	Terminology extraction using co-occurrence patterns as predictors of semantic relevance	We propose a method for automatic term extraction based on a statistical measure that ranks term candidates according to their semantic relevance to a specialised domain. As a measure of relevance we use term co-occurrence, defined as the repeated instantiation of two terms in the same sentences, in indifferent order and at variable distances. In this way, term candidates are ranked higher if they show a tendency to co-occur with a selected group of other units, as opposed to those showing more uniform distributions. No external resources are needed for the application of the method, but performance improves when provided with a pre-existing term list. We present results of the application of this method to a Spanish-English Linguistics corpus, and the evaluation compares favourably with a standard method based on reference corpora.	['David Lindemann', 'Rogelio Nazar']	null	null	null	null	term-lrec-2022-6	['term-extraction']	['natural-language-processing']	[ 2.92384565e-01 5.77441789e-02 -6.84229806e-02 -4.10983294e-01 -7.98438370e-01 -6.59064353e-01 9.54621315e-01 8.36138785e-01 -1.00744116e+00 9.66240108e-01 3.96381557e-01 -3.46257180e-01 -5.62775910e-01 -8.10109437e-01 1.75764353e-03 -5.98923922e-01 -8.33365023e-02 7.87356377e-01 4.71275032e-01 -5.71579933e-01 5.89225888e-01 3.19106609e-01 -1.82726693e+00 2.97595859e-01 8.03112686e-01 5.16699255e-01 5.95765054e-01 -4.37656231e-02 -3.68826807e-01 1.19514346e-01 -9.98407423e-01 -2.91518003e-01 -2.04126015e-01 -3.80723745e-01 -1.11047244e+00 -1.19208068e-01 3.27684805e-02 4.19007421e-01 4.26323920e-01 1.18622994e+00 1.91475913e-01 3.28671187e-01 9.73866463e-01 -4.44312662e-01 -2.01369852e-01 8.12680662e-01 -1.11320511e-01 3.68002087e-01 8.81888270e-01 -7.33424664e-01 1.29369795e+00 -7.22174227e-01 9.59213436e-01 1.05568087e+00 4.69501972e-01 2.11334527e-01 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833a29ac-39cc-4637-9062-91b02a66cf25	energy-flows-towards-determinant-free	2206.06672	null	https://arxiv.org/abs/2206.06672v2	https://arxiv.org/pdf/2206.06672v2.pdf	Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows	Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an alternative sample-based loss based on proper scoring rules. The energy objective is determinant-free and supports flexible model architectures that are not easily compatible with maximum likelihood training, including semi-autoregressive energy flows, a novel model family that interpolates between fully autoregressive and non-autoregressive models. Energy flows feature competitive sample quality, posterior inference, and generation speed relative to likelihood-based flows; this performance is decorrelated from the quality of log-likelihood estimates, which are generally very poor. Our findings question the use of maximum likelihood as an objective or a metric, and contribute to a scientific study of its role in generative modeling.	['Volodymyr Kuleshov', 'Yair Schiff', 'Subham Sekhar Sahoo', 'Zeyi Chen', 'Phillip Si']	2022-06-14	null	null	null	null	['hypothesis-testing', 'hypothesis-testing']	['methodology', 'miscellaneous']	[-1.13165438e-01 9.31444019e-02 -2.27136552e-01 -2.33782142e-01 -8.78371656e-01 -6.05732262e-01 9.42360163e-01 -4.33205605e-01 -3.82206410e-01 1.02841115e+00 3.70729446e-01 -4.09169316e-01 -3.45064998e-01 -8.18168581e-01 -4.72640067e-01 -5.24985015e-01 -5.60661890e-02 6.13719523e-01 -8.51770211e-03 1.44819498e-01 2.28237957e-01 7.34680653e-01 -1.20508063e+00 -3.73151988e-01 1.06168771e+00 7.74122238e-01 -1.89160272e-01 8.96958649e-01 -1.21093638e-01 6.65484369e-01 -4.39846367e-01 -7.00367272e-01 1.68978795e-01 -9.39173520e-01 -4.35921073e-01 -3.30425501e-01 5.13393819e-01 -2.58026212e-01 -1.65467545e-01 7.11875021e-01 4.71661419e-01 2.61558533e-01 1.17899597e+00 -1.30855441e+00 -7.07988322e-01 2.73798972e-01 1.47612035e-01 4.45531458e-01 1.59354851e-01 4.31852967e-01 1.01100683e+00 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4.09000695e-01 3.35160434e-01 4.41354901e-01 -8.03179801e-01 -6.07516646e-01 -7.76542962e-01 -3.71191472e-01 3.36778946e-02 2.30601549e-01 -6.14670277e-01 -6.87178969e-01 3.75381000e-02]	[7.151242733001709, 3.7968366146087646]
85022017-3be2-4858-a003-320e9670e59b	revisiting-spectral-graph-clustering-with	1709.04594	null	http://arxiv.org/abs/1709.04594v2	http://arxiv.org/pdf/1709.04594v2.pdf	Revisiting Spectral Graph Clustering with Generative Community Models	The methodology of community detection can be divided into two principles: imposing a network model on a given graph, or optimizing a designed objective function. The former provides guarantees on theoretical detectability but falls short when the graph is inconsistent with the underlying model. The latter is model-free but fails to provide quality assurance for the detected communities. In this paper, we propose a novel unified framework to combine the advantages of these two principles. The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs). SGC-GEN incorporates the predictability on correct community detection with a measure of community fitness to GCMs. It resembles the formulation of supervised learning problems by enabling various community detection loss functions and model mismatch metrics. We further establish a theoretical condition for correct community detection using the normalized graph Laplacian matrix under a GCM, which provides a novel data-driven loss function for SGC-GEN. In addition, we present an effective algorithm to implement SGC-GEN, and show that the computational complexity of SGC-GEN is comparable to the baseline methods. Our experiments on 18 real-world datasets demonstrate that SGC-GEN possesses superior and robust performance compared to 6 baseline methods under 7 representative clustering metrics.	['Pin-Yu Chen', 'Lingfei Wu']	2017-09-14	null	null	null	null	['spectral-graph-clustering']	['graphs']	[ 3.52520704e-01 2.30358597e-02 1.03315048e-01 1.47347495e-01 -5.75246274e-01 -5.73041975e-01 4.70757306e-01 4.58251506e-01 3.68990228e-02 3.00158083e-01 -3.76571208e-01 -2.14379877e-01 -4.17198360e-01 -9.59980905e-01 -4.15879101e-01 -8.79101276e-01 -4.59558785e-01 5.95293462e-01 4.75769162e-01 1.29549205e-01 3.91877860e-01 3.31491441e-01 -1.09558129e+00 -5.84120564e-02 1.17532194e+00 3.46298873e-01 2.77604192e-01 6.04348838e-01 1.09913051e-01 2.71835953e-01 -4.18986827e-01 -3.48499835e-01 1.81131467e-01 -7.79207051e-01 -6.66789412e-01 5.63454032e-01 4.81911711e-02 4.69876111e-01 -1.19443707e-01 1.48956013e+00 4.13623214e-01 -4.59069580e-01 9.41211641e-01 -1.72083974e+00 -4.63982910e-01 7.30865657e-01 -7.60083497e-01 3.86873484e-02 3.60382527e-01 -5.45597002e-02 1.11413264e+00 -8.10616374e-01 7.05838144e-01 1.24061668e+00 9.04168427e-01 1.65251240e-01 -1.72617733e+00 -5.01125574e-01 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578a1625-26e5-429e-9d8a-7ece9e315dfa	non-asymptotic-analysis-of-langevin-type	2303.12407	null	https://arxiv.org/abs/2303.12407v4	https://arxiv.org/pdf/2303.12407v4.pdf	Non-asymptotic analysis of Langevin-type Monte Carlo algorithms	We study Langevin-type algorithms for sampling from Gibbs distributions such that the potentials are dissipative and their weak gradients have finite moduli of continuity not necessarily convergent to zero. Our main result is a non-asymptotic upper bound of the 2-Wasserstein distance between a Gibbs distribution and the law of general Langevin-type algorithms based on the Liptser--Shiryaev theory and Poincar\'{e} inequalities. We apply this bound to show that the Langevin Monte Carlo algorithm can approximate Gibbs distributions with arbitrary accuracy if the potentials are dissipative and their gradients are uniformly continuous. We also propose Langevin-type algorithms with spherical smoothing for distributions whose potentials are not convex or continuously differentiable.	['Shogo Nakakita']	2023-03-22	null	null	null	null	['type']	['speech']	[-1.33940116e-01 3.74054015e-01 1.96971193e-01 -2.40749940e-01 -8.01310241e-01 -4.47164118e-01 4.91409153e-01 -9.47313681e-02 -6.32430315e-01 1.20156014e+00 -2.37538651e-01 -7.98312128e-02 9.47348587e-03 -9.74758983e-01 -7.28683710e-01 -1.20279396e+00 -3.18902582e-01 9.08298492e-01 2.35717878e-01 1.54849058e-02 2.12067127e-01 4.38176841e-01 -1.21957457e+00 -5.37847936e-01 1.22773111e+00 6.23288333e-01 -1.95394740e-01 1.24010968e+00 4.57430147e-02 5.82608938e-01 -2.01621518e-01 -1.27800196e-01 -5.03554493e-02 -6.75620735e-01 -8.90438318e-01 -4.87037987e-01 1.32203087e-01 -1.82952374e-01 -5.89774698e-02 1.59079599e+00 4.21847552e-01 4.71940994e-01 1.40239036e+00 -7.82749176e-01 -8.52955699e-01 5.38838267e-01 -4.58584428e-01 3.42765870e-03 -2.98338803e-03 -2.93517590e-01 6.96100533e-01 -7.41813302e-01 7.24653006e-01 1.07073736e+00 1.25275552e+00 9.17611003e-01 -1.63631082e+00 -1.63697109e-01 -4.67560977e-01 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5d310e25-6c25-4c9f-b559-9edd0d2a13ad	referring-image-segmentation-by-generative	null	null	https://www.semanticscholar.org/paper/Referring-Image-Segmentation-by-Generative-Learning-Qiu-Zhao/863ce99910dad0efa42385e8a3bcbeb1f400acfb	https://jianbojiao.com/pdfs/TMM.pdf	Referring Image Segmentation by Generative Adversarial Learning	Referring expression is a kind of language expression being used for referring to particular objects. In this paper, we focus on the problem of image segmentation from natural language referring expressions. Existing works tackle this problem by augmenting the convolutional semantic segmentation networks with an LSTM sentence encoder, which is optimized by a pixel-wise classification loss. We argue that the distribution similarity between the inference and ground truth plays an important role in referring image segmentation. Therefore we introduce a complementary loss considering the consistency between the two distributions. To this end, we propose to train the referring image segmentation model in a generative adversarial fashion, which well addresses the distribution similarity problem. In particular, the proposed adversarial semantic guidance network (ASGN) includes the following advantages: a) more detailed visual information is incorporated by the detail enhancement; b) semantic information counteracts the word embedding impact; c) the proposed adversarial learning approach relieves the distribution inconsistencies. Experimental results on four standard datasets show significant improvements over all the compared baseline models, demonstrating the effectiveness of our method	['Shuang Qiu', 'Shikui Wei', 'Jianbo Jiao', 'Yunchao Wei', 'Yao Zhao']	2020-04-20	null	null	null	ieee-2020-4	['referring-expression-segmentation']	['computer-vision']	[ 4.11147714e-01 4.35942292e-01 -3.14303786e-02 -5.15866160e-01 -8.05503786e-01 -2.93100685e-01 4.47681457e-01 -3.74095351e-01 -3.36506754e-01 6.63659930e-01 1.99554995e-01 -9.19572264e-02 7.34955147e-02 -9.79643047e-01 -1.03880262e+00 -7.83292353e-01 6.95270002e-01 1.89836711e-01 2.55260974e-01 -2.99533427e-01 1.52301386e-01 3.44247043e-01 -9.43487167e-01 -4.62972745e-02 1.18738377e+00 1.18062210e+00 1.69851184e-01 1.33135691e-01 -5.01420975e-01 1.06185353e+00 -6.91867292e-01 -6.64361715e-01 1.37809798e-01 -7.08831489e-01 -1.00263429e+00 3.30930084e-01 3.17134440e-01 -2.15757251e-01 -2.67150819e-01 1.55243623e+00 4.28186417e-01 1.62425280e-01 4.98524070e-01 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95eb1565-836a-4590-a7c8-55ddc1db8292	task-oriented-human-object-interactions	2303.13129	null	https://arxiv.org/abs/2303.13129v1	https://arxiv.org/pdf/2303.13129v1.pdf	Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations	Digital human motion synthesis is a vibrant research field with applications in movies, AR/VR, and video games. Whereas methods were proposed to generate natural and realistic human motions, most only focus on modeling humans and largely ignore object movements. Generating task-oriented human-object interaction motions in simulation is challenging. For different intents of using the objects, humans conduct various motions, which requires the human first to approach the objects and then make them move consistently with the human instead of staying still. Also, to deploy in downstream applications, the synthesized motions are desired to be flexible in length, providing options to personalize the predicted motions for various purposes. To this end, we propose TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations, which generates full human-object interaction motions to conduct specific tasks, given only the task type, the object, and a starting human status. TOHO generates human-object motions in three steps: 1) it first estimates the keyframe poses of conducting a task given the task type and object information; 2) then, it infills the keyframes and generates continuous motions; 3) finally, it applies a compact closed-form object motion estimation to generate the object motion. Our method generates continuous motions that are parameterized only by the temporal coordinate, which allows for upsampling or downsampling of the sequence to arbitrary frames and adjusting the motion speeds by designing the temporal coordinate vector. We demonstrate the effectiveness of our method, both qualitatively and quantitatively. This work takes a step further toward general human-scene interaction simulation.	['Bo Dai', 'Chen Change Loy', 'Jingbo Wang', 'Quanzhou Li']	2023-03-23	null	null	null	null	['human-object-interaction-detection', 'motion-estimation']	['computer-vision', 'computer-vision']	[ 1.87170953e-02 -3.77760716e-02 -9.70176980e-02 9.85018983e-02 -2.41646975e-01 -6.29263759e-01 5.64243972e-01 -4.11264241e-01 -2.74138570e-01 4.49882686e-01 3.91997576e-01 -5.73221780e-02 1.34849906e-01 -4.83413070e-01 -4.34064060e-01 -5.12930572e-01 1.39773130e-01 3.70410681e-01 3.97539318e-01 -2.24374160e-01 1.66199192e-01 6.91823006e-01 -1.41880524e+00 1.68058679e-01 4.99265075e-01 4.31010753e-01 5.61292648e-01 1.18302929e+00 2.32913226e-01 1.01119256e+00 -6.16974652e-01 1.20468077e-03 2.82792151e-01 -6.55195355e-01 -7.64515638e-01 5.27265191e-01 1.18087558e-03 -7.55144060e-01 -5.42006195e-01 6.20033622e-01 5.70377290e-01 7.20858037e-01 5.87449074e-01 -1.32090986e+00 -4.67698067e-01 2.24699184e-01 -4.02072459e-01 -1.33123308e-01 6.00791335e-01 6.42220318e-01 5.92083931e-01 -7.60182321e-01 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fc8c906b-ca52-42d2-a47e-62a3b7690783	rethinking-eye-blink-assessing-task	2102.06690	null	https://arxiv.org/abs/2102.06690v1	https://arxiv.org/pdf/2102.06690v1.pdf	Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking	Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.	['Youngjun Cho']	2021-02-12	null	null	null	null	['mental-workload-estimation']	['computer-vision']	[ 1.99295431e-01 -3.67311567e-01 1.72595292e-01 -1.03075139e-01 -3.38105977e-01 -3.73535305e-01 1.37468487e-01 1.55718327e-01 -3.24702531e-01 6.15508676e-01 -4.50221933e-02 -4.22748655e-01 -4.57984746e-01 -1.11414321e-01 -1.93066821e-01 -4.59464073e-01 3.31237227e-01 -4.63384509e-01 1.64591298e-01 -2.52141446e-01 5.74536800e-01 4.08170998e-01 -2.16919637e+00 3.03667426e-01 7.33871937e-01 1.23247373e+00 4.17034656e-01 1.17379093e+00 -3.22429766e-03 6.61072314e-01 -8.55867863e-01 2.60203153e-01 1.82365682e-02 -3.65909725e-01 -3.96187514e-01 -5.98321296e-02 3.62214059e-01 -1.04980394e-01 5.39918691e-02 6.31900430e-01 7.74867415e-01 4.01567528e-03 4.51834410e-01 -1.14728510e+00 -5.02399623e-01 -2.99723595e-01 -1.43211380e-01 6.68558300e-01 7.98478842e-01 2.65324354e-01 5.97688735e-01 -6.11516654e-01 8.62479582e-02 5.55884242e-01 7.55236268e-01 3.60432357e-01 -1.28999662e+00 -3.62430543e-01 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489a2683-3434-4058-8c72-7fe02b1b5638	beyond-known-reality-exploiting	2307.02131	null	https://arxiv.org/abs/2307.02131v1	https://arxiv.org/pdf/2307.02131v1.pdf	Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research	This study employs counterfactual explanations to explore "what if?" scenarios in medical research, with the aim of expanding our understanding beyond existing boundaries. Specifically, we focus on utilizing MRI features for diagnosing pediatric posterior fossa brain tumors as a case study. The field of artificial intelligence and explainability has witnessed a growing number of studies and increasing scholarly interest. However, the lack of human-friendly interpretations in explaining the outcomes of machine learning algorithms has significantly hindered the acceptance of these methods by clinicians in their clinical practice. To address this, our approach incorporates counterfactual explanations, providing a novel way to examine alternative decision-making scenarios. These explanations offer personalized and context-specific insights, enabling the validation of predictions and clarification of variations under diverse circumstances. Importantly, our approach maintains both statistical and clinical fidelity, allowing for the examination of distinct tumor features through alternative realities. Additionally, we explore the potential use of counterfactuals for data augmentation and evaluate their feasibility as an alternative approach in medical research. The results demonstrate the promising potential of counterfactual explanations to enhance trust and acceptance of AI-driven methods in clinical settings.	['Bilgin Keserci', 'Serkan Ayvaz', 'Toygar Tanyel']	2023-07-05	null	null	null	null	['decision-making']	['reasoning']	[ 4.99072641e-01 8.40891540e-01 -6.61690056e-01 -5.52290142e-01 -4.18051839e-01 -9.28622484e-02 6.97625816e-01 4.21296299e-01 -4.52600867e-01 1.02844214e+00 7.76289701e-01 -9.09036636e-01 -5.36732316e-01 -2.69966125e-01 -5.69314361e-01 -5.96632004e-01 -1.63479671e-01 4.61690813e-01 -7.02262640e-01 1.92997098e-01 3.00508618e-01 4.58914131e-01 -1.34102929e+00 4.11844730e-01 1.15017307e+00 5.48209846e-01 2.04790905e-02 1.45087659e-01 1.67041734e-01 6.55020893e-01 -3.39625478e-01 -7.31556833e-01 7.56107122e-02 -2.95442343e-01 -6.52380109e-01 1.18793763e-01 -2.33625285e-02 -4.41618085e-01 -1.34743243e-01 6.29398167e-01 4.90716875e-01 -9.65572074e-02 4.36510444e-01 -1.41048896e+00 -8.55964005e-01 1.00846076e+00 -3.76780093e-01 1.71645746e-01 3.87870580e-01 6.43727958e-01 7.93755233e-01 -4.07636970e-01 4.91147161e-01 9.20745313e-01 5.79760194e-01 8.94420624e-01 -1.26973224e+00 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4.21719223e-01 3.48405480e-01 -7.40910888e-01 -2.31640413e-01 -3.01510304e-01]	[8.477760314941406, 5.671731472015381]
a4fecac1-ef4d-4d3a-9b64-507e0acc573e	blind-mask-to-improve-intelligibility-of-non	2008.09175	null	https://arxiv.org/abs/2008.09175v1	https://arxiv.org/pdf/2008.09175v1.pdf	Blind Mask to Improve Intelligibility of Non-Stationary Noisy Speech	This letter proposes a novel blind acoustic mask (BAM) designed to adaptively detect noise components and preserve target speech segments in time-domain. A robust standard deviation estimator is applied to the non-stationary noisy speech to identify noise masking elements. The main contribution of the proposed solution is the use of this noise statistics to derive an adaptive information to define and select samples with lower noise proportion. Thus, preserving speech intelligibility. Additionally, no information of the target speech and noise signals statistics is previously required to this non-ideal mask. The BAM and three competitive methods, Ideal Binary Mask (IBM), Target Binary Mask (TBM), and Non-stationary Noise Estimation for Speech Enhancement (NNESE), are evaluated considering speech signals corrupted by three non-stationary acoustic noises and six values of signal-to-noise ratio (SNR). Results demonstrate that the BAM technique achieves intelligibility gains comparable to ideal masks while maintaining good speech quality.	['R. Coelho', 'F. Farias']	2020-08-20	null	null	null	null	['noise-estimation']	['medical']	[ 5.25471807e-01 -3.41360301e-01 3.15537125e-01 -9.92727503e-02 -7.98042357e-01 -4.35727358e-01 4.08805251e-01 -1.19974464e-01 -5.87982595e-01 6.26678407e-01 4.83914584e-01 -4.70374912e-01 -3.43785703e-01 -2.23947346e-01 -3.70559394e-02 -9.86230016e-01 -1.02273777e-01 -3.64276201e-01 2.94707060e-01 -1.32457316e-01 2.47900665e-01 6.15873337e-01 -1.85826850e+00 1.88949872e-02 9.27832782e-01 1.00871825e+00 8.38486493e-01 1.14059460e+00 9.96727422e-02 9.86639932e-02 -1.23520529e+00 2.97958180e-02 3.79247487e-01 -6.92760289e-01 -8.18991587e-02 3.14034224e-01 -5.04603721e-02 -4.68003005e-02 -2.33320355e-01 1.54614305e+00 1.10925889e+00 7.38029957e-01 7.03410685e-01 -5.57375133e-01 -3.98179710e-01 5.96191823e-01 -1.58264488e-01 7.44000554e-01 1.17737822e-01 1.21522784e-01 2.57792145e-01 -8.09417725e-01 -9.51988716e-03 1.12576675e+00 5.57324648e-01 4.74551916e-01 -1.06541765e+00 -5.67694187e-01 -3.48159999e-01 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afc9581d-c5c5-44c3-a8a2-97f3e096be71	neurar-neural-uncertainty-for-autonomous-3d	2207.10985	null	https://arxiv.org/abs/2207.10985v2	https://arxiv.org/pdf/2207.10985v2.pdf	NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations	Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality; secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://kingteeloki-ran.github.io/NeurAR/	['Qi Ye', 'Jiming Chen', 'Gimhee Lee', 'Yingfeng Chen', 'Lincheng Li', 'Shibo He', 'Jing Zeng', 'Yunlong Ran']	2022-07-22	null	null	null	null	['3d-scene-reconstruction']	['computer-vision']	[ 1.55684203e-01 3.71381164e-01 1.21838711e-01 -4.84453559e-01 -7.59230912e-01 -3.98970693e-01 4.97095674e-01 -1.85949564e-01 -2.75353462e-01 4.96694177e-01 1.70793816e-01 -6.70728311e-02 -3.48435968e-01 -6.32798374e-01 -8.75910223e-01 -6.88529253e-01 -1.30749808e-03 6.07321441e-01 -1.63980961e-01 -1.96259871e-01 3.29718947e-01 8.60871196e-01 -1.61282313e+00 -1.55856526e-02 6.42787278e-01 1.11186552e+00 6.77596331e-01 4.26051259e-01 3.02788705e-01 5.34397781e-01 -2.33497679e-01 -2.47732718e-02 5.95914066e-01 -1.91769674e-01 -6.75538480e-01 2.00491264e-01 2.38593936e-01 -6.27965331e-01 -3.84211093e-01 1.16730738e+00 4.79729623e-01 2.36066461e-01 6.72672689e-01 -1.01727819e+00 -3.33541155e-01 1.77789882e-01 -1.77492514e-01 -4.91294190e-02 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9141d692-dcdc-4bc4-8c37-9e353bc662d5	context-aware-unsupervised-clustering-for	2110.01341	null	https://arxiv.org/abs/2110.01341v1	https://arxiv.org/pdf/2110.01341v1.pdf	Context-Aware Unsupervised Clustering for Person Search	The existing person search methods use the annotated labels of person identities to train deep networks in a supervised manner that requires a huge amount of time and effort for human labeling. In this paper, we first introduce a novel framework of person search that is able to train the network in the absence of the person identity labels, and propose efficient unsupervised clustering methods to substitute the supervision process using annotated person identity labels. Specifically, we propose a hard negative mining scheme based on the uniqueness property that only a single person has the same identity to a given query person in each image. We also propose a hard positive mining scheme by using the contextual information of co-appearance that neighboring persons in one image tend to appear simultaneously in other images. The experimental results show that the proposed method achieves comparable performance to that of the state-of-the-art supervised person search methods, and furthermore outperforms the extended unsupervised person re-identification methods on the benchmark person search datasets.	['Jae-Young Sim', 'Kuhyeun Ko', 'Byeong-Ju Han']	2021-10-04	null	null	null	null	['person-search', 'unsupervised-person-re-identification']	['computer-vision', 'computer-vision']	[ 1.17395036e-01 -1.17945649e-01 7.41372108e-02 -6.05771244e-01 -1.16811179e-01 -3.78942102e-01 7.74720788e-01 -2.06668470e-02 -1.05036938e+00 6.47876978e-01 -2.12579757e-01 2.28295505e-01 -2.77007103e-01 -7.50874817e-01 -3.56044054e-01 -6.87622607e-01 2.57253259e-01 1.08771789e+00 8.02804977e-02 1.08427957e-01 -1.18245222e-01 1.21596389e-01 -1.91374373e+00 -2.85290539e-01 8.68154764e-01 9.00041223e-01 1.22081749e-01 5.20880073e-02 1.53411418e-01 5.01256466e-01 -6.19473159e-01 -6.54355347e-01 4.96263862e-01 -3.61702412e-01 -1.08761907e+00 1.93282917e-01 7.73564756e-01 -1.90938190e-01 -3.73003036e-01 1.42858756e+00 7.47780621e-01 4.41652000e-01 6.79987133e-01 -1.23228776e+00 -7.52869487e-01 4.75815237e-01 -7.92058468e-01 1.84189051e-01 2.55474627e-01 -1.89121470e-01 7.90723622e-01 -7.76414096e-01 4.18835521e-01 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0412240b-ee74-4da7-a001-26db26479215	deep-image-prior	1711.10925	null	https://arxiv.org/abs/1711.10925v4	https://arxiv.org/pdf/1711.10925v4.pdf	Deep Image Prior	Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity. Code and supplementary material are available at https://dmitryulyanov.github.io/deep_image_prior .	['Dmitry Ulyanov', 'Andrea Vedaldi', 'Victor Lempitsky']	2017-11-29	deep-image-prior-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Ulyanov_Deep_Image_Prior_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Ulyanov_Deep_Image_Prior_CVPR_2018_paper.pdf	cvpr-2018-6	['jpeg-compression-artifact-reduction']	['computer-vision']	[ 5.35793662e-01 1.20653465e-01 8.44370797e-02 -2.00353757e-01 -6.73515975e-01 -3.56554091e-01 6.26676798e-01 -3.63753676e-01 -2.68293530e-01 9.05609012e-01 2.17848167e-01 -9.09477100e-02 -1.17183834e-01 -8.31279099e-01 -9.34738696e-01 -9.24239337e-01 4.13196385e-01 1.75264105e-01 8.01844075e-02 -4.99018669e-01 1.77201807e-01 4.74590749e-01 -1.61090994e+00 1.82699516e-01 9.66152906e-01 9.08515513e-01 5.29512823e-01 4.07945752e-01 1.64093107e-01 1.01512432e+00 -4.42791820e-01 -2.86238223e-01 2.21566364e-01 -7.47278988e-01 -7.25530744e-01 -5.75029850e-03 3.25420380e-01 -5.56735694e-01 -7.73355663e-01 1.05403912e+00 5.86800218e-01 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a9a5f2d3-4b5b-4522-a8ed-73c578c3b584	skeleton-aware-multi-scale-heatmap-regression	2105.10904	null	https://arxiv.org/abs/2105.10904v1	https://arxiv.org/pdf/2105.10904v1.pdf	Skeleton-aware multi-scale heatmap regression for 2D hand pose estimation	Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of two main modules. The former presents a segmentation-based approach to detect the hand skeleton and localize the hand bounding box. The second module regresses the 2D joint locations through a multi-scale heatmap regression approach that exploits the predicted hand skeleton as a constraint to guide the model. Furthermore, we construct a new dataset that is suitable for both hand detection and pose estimation. We qualitatively and quantitatively validate our method on two datasets. Results demonstrate that the proposed method outperforms state-of-the-art and can recover the pose even in cluttered images and complex poses.	['Yakup Genc', 'Ikram Kourbane']	2021-05-23	null	null	null	null	['hand-detection']	['computer-vision']	[-1.50524914e-01 -3.82551700e-01 -1.75380155e-01 3.70667316e-02 -6.77908182e-01 -4.59734291e-01 1.83443680e-01 -3.79200518e-01 -6.00874305e-01 4.77504849e-01 9.59363058e-02 1.42475322e-01 9.48875099e-02 -5.61035752e-01 -4.59230930e-01 -5.70808470e-01 3.19814980e-01 8.84142101e-01 6.17130280e-01 4.51482758e-02 2.42501259e-01 9.50164616e-01 -1.30451536e+00 -9.48891640e-02 5.03208637e-01 8.42814028e-01 3.83694798e-01 5.92058718e-01 1.03280805e-01 2.91029662e-01 -5.12614191e-01 -1.75135687e-01 5.24557114e-01 -1.64429665e-01 -8.03309560e-01 2.73238122e-01 5.86279631e-01 -6.91304564e-01 -5.31111956e-01 6.99786186e-01 9.41133380e-01 6.83990493e-02 7.31663764e-01 -9.33329344e-01 2.20287174e-01 3.32737900e-02 -9.81930912e-01 -1.84385195e-01 6.80023968e-01 2.65693963e-01 8.61624837e-01 -9.04497027e-01 7.47423410e-01 1.35228169e+00 6.85563147e-01 4.76045251e-01 -1.21019447e+00 -4.56402123e-01 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b98363f3-f4c9-40ee-9539-5a5e9d0d0b7f	improving-temporal-action-proposal-generation	1906.06496	null	https://arxiv.org/abs/1906.06496v4	https://arxiv.org/pdf/1906.06496v4.pdf	Accelerating temporal action proposal generation via high performance computing	Temporal action recognition always depends on temporal action proposal generation to hypothesize actions and algorithms usually need to process very long video sequences and output the starting and ending times of each potential action in each video suffering from high computation cost. To address this, based on boundary sensitive network we propose a new temporal convolution network called Multipath Temporal ConvNet (MTN), which consists of two parts i.e. Multipath DenseNet and SE-ConvNet. In this work, one novel high performance ring parallel architecture based on Message Passing Interface (MPI) is further introduced into temporal action proposal generation, which is a reliable communication protocol, in order to respond to the requirements of large memory occupation and a large number of videos. Remarkably, the total data transmission is reduced by adding a connection between multiple computing load in the newly developed architecture. It is found that, compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection task with multiple GPUs, which is suitable for dealing with the tasks of temporal action proposal generation, especially for large datasets of millions of videos. We conduct experiments on ActivityNet-1.3 and THUMOS14, where our method outperforms other state-of-art temporal action detection methods with high recall and high temporal precision. In addition, a time metric is further proposed here to evaluate the speed performance in the distributed training process.	['Youyou Jiang', 'Choi Chang', 'Tian Wang', 'Hichem Snoussi', 'Guangcun Shan', 'Shiye Lei']	2019-06-15	null	null	null	null	['temporal-action-proposal-generation']	['computer-vision']	[ 1.97377652e-01 -5.25387406e-01 -1.34127870e-01 -9.06231701e-02 -2.63133943e-01 -1.65098682e-02 4.89716023e-01 -2.57072449e-01 -7.63204455e-01 5.89572728e-01 1.81138426e-01 1.42800119e-02 -7.64790699e-02 -8.40846181e-01 -5.30967891e-01 -9.90469754e-01 -3.39704365e-01 9.95984748e-02 8.99568975e-01 5.01098372e-02 1.42300293e-01 3.55616689e-01 -1.60880995e+00 5.09418190e-01 4.77110595e-01 1.27096844e+00 3.15045804e-01 5.35962343e-01 -6.54448494e-02 1.02632141e+00 -4.54623610e-01 -4.76838881e-03 2.11153954e-01 -4.02682155e-01 -6.01137042e-01 1.71663314e-01 3.54800490e-03 -7.48305798e-01 -4.46356177e-01 7.28196859e-01 6.11930847e-01 4.01745170e-01 7.13134035e-02 -1.37259805e+00 3.53302926e-01 6.18022144e-01 -5.81646681e-01 5.05917490e-01 1.82800710e-01 5.14391124e-01 5.83446383e-01 -5.74482203e-01 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3.58285010e-01 -8.13679457e-01 -3.42015147e-01 -1.06306171e+00 -5.23310244e-01 3.71390283e-01 -2.62138903e-01 -5.89622617e-01 -7.52952844e-02 -2.74638645e-02]	[8.37679672241211, 0.4050038456916809]
07168c5e-1124-4982-a7a0-611440b118e7	leveraging-gans-for-data-scarcity-of-covid-19	2304.03536	null	https://arxiv.org/abs/2304.03536v1	https://arxiv.org/pdf/2304.03536v1.pdf	Leveraging GANs for data scarcity of COVID-19: Beyond the hype	Artificial Intelligence (AI)-based models can help in diagnosing COVID-19 from lung CT scans and X-ray images; however, these models require large amounts of data for training and validation. Many researchers studied Generative Adversarial Networks (GANs) for producing synthetic lung CT scans and X-Ray images to improve the performance of AI-based models. It is not well explored how good GAN-based methods performed to generate reliable synthetic data. This work analyzes 43 published studies that reported GANs for synthetic data generation. Many of these studies suffered data bias, lack of reproducibility, and lack of feedback from the radiologists or other domain experts. A common issue in these studies is the unavailability of the source code, hindering reproducibility. The included studies reported rescaling of the input images to train the existing GANs architecture without providing clinical insights on how the rescaling was motivated. Finally, even though GAN-based methods have the potential for data augmentation and improving the training of AI-based models, these methods fall short in terms of their use in clinical practice. This paper highlights research hotspots in countering the data scarcity problem, identifies various issues as well as potentials, and provides recommendations to guide future research. These recommendations might be useful to improve acceptability for the GAN-based approaches for data augmentation as GANs for data augmentation are increasingly becoming popular in the AI and medical imaging research community.	['Zubair Shah', 'Christer Gronlund', 'Hazrat Ali']	2023-04-07	null	null	null	null	['synthetic-data-generation', 'synthetic-data-generation']	['medical', 'miscellaneous']	[ 4.31268722e-01 6.69405341e-01 -2.90741086e-01 -3.06426078e-01 -9.82382238e-01 -3.87715846e-01 2.52801359e-01 -8.67748708e-02 -2.89242178e-01 8.18601370e-01 4.04576033e-01 -6.13178492e-01 2.01442003e-01 -8.85062337e-01 -7.10213363e-01 -8.24842930e-01 2.53825128e-01 7.64482856e-01 -1.67104736e-01 7.89266229e-02 -2.38261700e-01 4.53324169e-01 -9.19526219e-01 5.04832923e-01 8.75333250e-01 5.51014841e-01 -1.81093533e-02 8.45861971e-01 -1.07906893e-01 1.05921078e+00 -9.82399940e-01 -3.90307903e-01 2.38817945e-01 -1.16053724e+00 -6.52754962e-01 6.26445143e-03 1.46809697e-01 -6.12685800e-01 -2.35026404e-01 4.54873085e-01 8.88170362e-01 -2.83792853e-01 7.11137235e-01 -1.20530117e+00 -8.07924807e-01 4.83872145e-01 -2.69525260e-01 3.57342184e-01 -5.58898784e-02 6.14736021e-01 2.70658523e-01 -4.15589571e-01 6.96900070e-01 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2fe51e68-407a-4721-88db-5d6375c433ef	learning-image-adaptive-codebooks-for-class	2306.06513	null	https://arxiv.org/abs/2306.06513v2	https://arxiv.org/pdf/2306.06513v2.pdf	Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration	Recent work on discrete generative priors, in the form of codebooks, has shown exciting performance for image reconstruction and restoration, as the discrete prior space spanned by the codebooks increases the robustness against diverse image degradations. Nevertheless, these methods require separate training of codebooks for different image categories, which limits their use to specific image categories only (e.g. face, architecture, etc.), and fail to handle arbitrary natural images. In this paper, we propose AdaCode for learning image-adaptive codebooks for class-agnostic image restoration. Instead of learning a single codebook for each image category, we learn a set of basis codebooks. For a given input image, AdaCode learns a weight map with which we compute a weighted combination of these basis codebooks for adaptive image restoration. Intuitively, AdaCode is a more flexible and expressive discrete generative prior than previous work. Experimental results demonstrate that AdaCode achieves state-of-the-art performance on image reconstruction and restoration tasks, including image super-resolution and inpainting.	['Jinwei Gu', 'Inchang Choi', 'Yitong Jiang', 'Kechun Liu']	2023-06-10	null	null	null	null	['image-super-resolution', 'image-reconstruction', 'super-resolution', 'image-restoration']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 5.12849927e-01 -2.21529216e-01 -1.26603236e-02 -3.39501113e-01 -7.40013897e-01 -3.13872755e-01 7.23559022e-01 -4.01200026e-01 -6.93700463e-02 4.70127136e-01 4.99582559e-01 1.74035639e-01 1.94813702e-02 -6.85099304e-01 -6.83661699e-01 -9.11086380e-01 2.44975671e-01 4.09525484e-01 1.70673542e-02 -2.01056346e-01 1.62211940e-01 7.28292882e-01 -1.87047911e+00 5.14898241e-01 7.45457053e-01 7.10146546e-01 6.39606178e-01 6.46599770e-01 -3.06968689e-02 5.41673064e-01 -4.96967763e-01 -1.62854031e-01 2.34872580e-01 -6.72732830e-01 -5.42893767e-01 8.57069254e-01 7.30749190e-01 -6.22396886e-01 -6.09766364e-01 1.30316746e+00 4.60436136e-01 1.62625715e-01 8.46851647e-01 -1.04728401e+00 -1.23351908e+00 1.46176562e-01 -4.54021871e-01 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0f499947-87ad-4616-965d-4784afa58681	do-gpts-produce-less-literal-translations	2305.16806	null	https://arxiv.org/abs/2305.16806v4	https://arxiv.org/pdf/2305.16806v4.pdf	Do GPTs Produce Less Literal Translations?	Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little investigation on how such translations differ qualitatively from the translations generated by standard Neural Machine Translation (NMT) models. In this work, we investigate these differences in terms of the literalness of translations produced by the two systems. Using literalness measures involving word alignment and monotonicity, we find that translations out of English (E-X) from GPTs tend to be less literal, while exhibiting similar or better scores on MT quality metrics. We demonstrate that this finding is borne out in human evaluations as well. We then show that these differences are especially pronounced when translating sentences that contain idiomatic expressions.	['Hany Hassan Awadalla', 'Matt Post', 'Arul Menezes', 'Vikas Raunak']	2023-05-26	null	null	null	null	['nmt', 'word-alignment']	['computer-code', 'natural-language-processing']	[ 4.21884030e-01 4.59460258e-01 -5.94111621e-01 -4.02866960e-01 -1.05058825e+00 -6.86286986e-01 1.07033980e+00 -2.98129115e-03 -2.38131106e-01 1.07899320e+00 5.26511550e-01 -5.61010242e-01 1.61309958e-01 -6.35391235e-01 -8.09850037e-01 -2.40802437e-01 3.26440871e-01 8.61695826e-01 -4.07735497e-01 -7.40176201e-01 3.30009401e-01 1.75780714e-01 -8.74829292e-01 6.61623776e-01 1.01106405e+00 2.89750159e-01 6.74208850e-02 3.35824490e-01 -1.84522122e-01 4.91700619e-01 -5.26117504e-01 -7.79368997e-01 2.67637402e-01 -1.00827992e+00 -1.16258228e+00 -2.20381588e-01 4.25898701e-01 -9.15637016e-02 1.75488919e-01 1.01035464e+00 4.36259210e-01 -1.43263429e-01 7.34372079e-01 -9.51384544e-01 -1.15137219e+00 9.81096148e-01 -3.38835359e-01 3.50366443e-01 5.67319274e-01 2.33185366e-01 1.31723011e+00 -1.14192522e+00 1.15054953e+00 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3.12031180e-01 -5.68896472e-01 -9.96759236e-01 -1.27330756e+00 1.09446645e-01 5.04247546e-01 -6.53196201e-02 -4.54527698e-02 -3.42785493e-02 1.15125470e-01]	[11.595191955566406, 10.141671180725098]
2f98b2eb-ca60-42b9-a849-691ff72cabc2	q-diffusion-quantizing-diffusion-models	2302.04304	null	https://arxiv.org/abs/2302.04304v3	https://arxiv.org/pdf/2302.04304v3.pdf	Q-Diffusion: Quantizing Diffusion Models	Diffusion models have achieved great success in image synthesis through iterative noise estimation using deep neural networks. However, the slow inference, high memory consumption, and computation intensity of the noise estimation model hinder the efficient adoption of diffusion models. Although post-training quantization (PTQ) is considered a go-to compression method for other tasks, it does not work out-of-the-box on diffusion models. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture of the diffusion models, which compresses the noise estimation network to accelerate the generation process. We identify the key difficulty of diffusion model quantization as the changing output distributions of noise estimation networks over multiple time steps and the bimodal activation distribution of the shortcut layers within the noise estimation network. We tackle these challenges with timestep-aware calibration and split shortcut quantization in this work. Experimental results show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance (small FID change of at most 2.34 compared to >100 for traditional PTQ) in a training-free manner. Our approach can also be applied to text-guided image generation, where we can run stable diffusion in 4-bit weights with high generation quality for the first time.	['Long Lian', 'Kurt Keutzer', 'Shanghang Zhang', 'Daniel Kang', 'Zhen Dong', 'Huanrui Yang', 'Yijiang Liu', 'Xiuyu Li']	2023-02-08	null	null	null	null	['noise-estimation']	['medical']	[ 3.60348374e-01 -1.17360383e-01 -6.62982315e-02 -8.78411904e-02 -8.46211135e-01 -3.53745610e-01 6.78264916e-01 3.33860442e-02 -6.49189472e-01 4.09649849e-01 -5.29831387e-02 -4.16425318e-01 1.45024983e-02 -9.76586759e-01 -7.55594969e-01 -7.94560969e-01 2.02482179e-01 3.70969623e-01 4.54452813e-01 -2.06712976e-01 2.10441142e-01 3.82452101e-01 -1.20466387e+00 3.57090831e-01 7.57991016e-01 1.05682814e+00 4.24696952e-01 1.04862428e+00 -2.18519524e-01 8.66322517e-01 -7.90851116e-01 -5.70812106e-01 3.86194408e-01 -5.82256496e-01 -3.91910106e-01 -2.53381908e-01 5.05037248e-01 -5.78532994e-01 -5.29642880e-01 1.25660884e+00 7.99030483e-01 -9.37948897e-02 5.15739024e-01 -8.32501411e-01 -5.12194455e-01 9.07987535e-01 -6.42640710e-01 1.72640339e-01 -4.56091851e-01 7.00325906e-01 6.21683419e-01 -7.31222510e-01 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5.11035264e-01 3.82922828e-01 -1.00001144e+00 -8.10594678e-01 -8.20618987e-01 -3.54925096e-01 5.75542450e-02 4.34105337e-01 -4.70685437e-02 -2.62953371e-01 7.81053752e-02]	[11.212867736816406, -0.4453328549861908]
58bf8704-048f-40e1-a1ea-ea02a31772ad	unsupervised-learning-of-neurosymbolic	2107.13132	null	https://arxiv.org/abs/2107.13132v2	https://arxiv.org/pdf/2107.13132v2.pdf	Unsupervised Learning of Neurosymbolic Encoders	We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specific language. Our framework naturally incorporates symbolic expert knowledge into the learning process, which leads to more interpretable and factorized latent representations compared to fully neural encoders. We integrate modern program synthesis techniques with the variational autoencoding (VAE) framework, in order to learn a neurosymbolic encoder in conjunction with a standard decoder. The programmatic descriptions from our encoders can benefit many analysis workflows, such as in behavior modeling where interpreting agent actions and movements is important. We evaluate our method on learning latent representations for real-world trajectory data from animal biology and sports analytics. We show that our approach offers significantly better separation of meaningful categories than standard VAEs and leads to practical gains on downstream analysis tasks, such as for behavior classification.	['Swarat Chaudhuri', 'Yisong Yue', 'Ann Kennedy', 'Jennifer J. Sun', 'Eric Zhan']	2021-07-28	unsupervised-learning-of-neurosymbolic-1	https://openreview.net/forum?id=aJ_GcB4vcT0	https://openreview.net/pdf?id=aJ_GcB4vcT0	null	['sports-analytics']	['computer-vision']	[ 5.30214846e-01 4.36767370e-01 -3.90967309e-01 -3.90599012e-01 -2.89231032e-01 -5.85990012e-01 8.77257288e-01 2.16481119e-01 -2.78985947e-02 5.02583444e-01 5.59984922e-01 -3.82527024e-01 -1.44478589e-01 -9.43570554e-01 -1.40252852e+00 -4.57395107e-01 -1.13151759e-01 5.42994916e-01 -1.05641358e-01 -3.65747362e-02 -3.65539715e-02 2.06379950e-01 -1.64573491e+00 4.15431529e-01 7.97385752e-01 2.77945906e-01 6.50281459e-02 8.22798491e-01 1.55875431e-02 1.47291696e+00 -1.54576689e-01 -3.05187821e-01 -1.34673566e-01 -4.27545995e-01 -8.06118488e-01 -1.00672230e-01 1.12257682e-01 -4.27267730e-01 -4.52559412e-01 9.49099720e-01 -2.75949448e-01 1.71475291e-01 9.57998574e-01 -1.32613754e+00 -9.51517105e-01 1.12236929e+00 -1.45617679e-01 -1.25321612e-01 1.42010808e-01 4.87440914e-01 1.32101226e+00 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d18dc00d-9127-49f8-a2da-20c9254b20da	multimodal-deep-learning-to-differentiate	2302.14124	null	https://arxiv.org/abs/2302.14124v1	https://arxiv.org/pdf/2302.14124v1.pdf	Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma	Differentiating tumor progression (TP) from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for $35$ brain tumors from $26$ subjects in the PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN. The average test accuracy across all leave-one-out cross-validation iterations adjusting for class weights was $0.56$ using only the MR, $0.65$ using only the SUV, and $0.71$ using only the Ki voxels. Combining SUV and MR voxels increased the test accuracy to $0.62$. On the other hand, MR and Ki voxels increased the test accuracy to $0.74$. Thus, dPET features alone or with MR features in deep learning models would enhance prediction accuracy in differentiating TP vs TN in GBM.	['Bijoy Kundu', 'Miaomiao Zhang', 'David Schiff', 'Sohil Patel', 'Thomas Eluvathingal Muttikkal', 'Nivetha Jayakumar', 'Zoraiz Qureshi', 'Tonmoy Hossain']	2023-02-27	null	null	null	null	['multimodal-deep-learning']	['natural-language-processing']	[-2.62314510e-02 -2.46639922e-01 -2.78299451e-01 -3.78806353e-01 -1.00612617e+00 -2.64399707e-01 3.91781300e-01 4.71193880e-01 -1.04342341e+00 1.10931599e+00 -1.52342003e-02 -6.72805250e-01 6.01451397e-02 -8.72385621e-01 -2.59197623e-01 -9.96491730e-01 -1.60193801e-01 5.17035663e-01 2.06874490e-01 2.20544681e-01 -4.17145714e-02 7.72392511e-01 -5.84554672e-01 4.44774866e-01 7.46532023e-01 1.29405403e+00 4.80120599e-01 6.66301191e-01 -1.39449582e-01 1.10444713e+00 -3.28930795e-01 1.50910720e-01 6.09013960e-02 -4.25467372e-01 -7.11909235e-01 -6.71447337e-01 1.56256482e-01 -5.77195585e-01 -5.39458871e-01 8.19523811e-01 7.33118176e-01 1.20056085e-01 7.84079313e-01 -8.95326018e-01 -1.76241547e-01 6.65113568e-01 -5.10492802e-01 9.54517484e-01 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5f590126-d7b5-4365-baf3-184d24eb395f	balancing-fairness-and-accuracy-in-sentiment	2204.10940	null	https://arxiv.org/abs/2204.10940v1	https://arxiv.org/pdf/2204.10940v1.pdf	Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models	Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying detection, and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now available for sentiment detection. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasing fairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications.	['Vivek K. Singh', 'Abdulaziz A. Almuzaini']	2022-04-22	null	null	null	null	['product-recommendation']	['miscellaneous']	[ 1.02528512e-01 -1.64228886e-01 -5.98967135e-01 -7.30232120e-01 -9.78116453e-01 -6.14404380e-01 5.89420199e-01 5.72302520e-01 -4.07169342e-01 2.56916374e-01 1.76191986e-01 -2.51047075e-01 3.25998664e-01 -4.68062460e-01 -5.64455390e-01 -3.10383856e-01 3.29047024e-01 -1.80358440e-01 5.36266156e-02 -2.20943362e-01 4.37550694e-01 9.02871192e-02 -1.62335932e+00 6.76606238e-01 4.93255347e-01 1.20220518e+00 -6.52461886e-01 7.43376017e-01 2.39872001e-02 9.79205549e-01 -6.64951801e-01 -1.26080000e+00 2.18954489e-01 -1.00678876e-01 -4.55062062e-01 -2.79090166e-01 1.05442286e+00 -5.57790458e-01 8.69034380e-02 1.11507094e+00 5.94521403e-01 -2.19831109e-01 5.19945264e-01 -1.46291900e+00 -5.26994050e-01 7.09224403e-01 -1.02879369e+00 2.21346900e-01 5.95361352e-01 -1.37399495e-01 1.04870176e+00 -5.57942927e-01 4.69699413e-01 1.57087219e+00 4.23992872e-01 6.42738461e-01 -9.58448589e-01 -1.09979606e+00 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8.16067815e-01 1.55608401e-01 -4.01602596e-01 -3.60931039e-01 4.22964990e-02]	[13.015795707702637, 1.37960684299469]
c4db4746-ac62-4e8c-908b-af318cf0d75f	freezing-of-gait-prediction-from	2307.03475	null	https://arxiv.org/abs/2307.03475v1	https://arxiv.org/pdf/2307.03475v1.pdf	Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition	Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentially classify this data. The combination therefore holds the potential to detect FOG in real-time. In this work I present a simple 1-D convolutional neural network that was trained to detect FOG events in accelerometer data. Model performance was assessed by measuring the success of the model to discriminate normal movement from FOG episodes and resulted in a mean average precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction competition. The results underscore the potential of Deep Learning-based solutions in advancing the field of FOG detection, contributing to improved interventions and management strategies for PD patients.	['Jan Brederecke']	2023-07-07	null	null	null	null	['management']	['miscellaneous']	[-7.65734017e-02 -1.79325882e-02 -1.32305980e-01 -1.20684236e-01 -7.94085085e-01 -1.15182111e-02 -5.07086180e-02 -1.34904087e-01 -5.99668562e-01 7.57703185e-01 6.76695347e-01 3.69670689e-02 1.32112443e-01 -5.40825069e-01 -1.93007663e-01 -3.33607554e-01 -5.22456765e-01 6.59451246e-01 2.61896223e-01 -2.52704799e-01 -7.65567049e-02 2.36462593e-01 -1.17497051e+00 5.35924494e-01 7.77807415e-01 7.43194878e-01 5.19724786e-01 7.37478316e-01 5.86493015e-01 1.10234845e+00 -8.18110943e-01 2.96939194e-01 -1.87055051e-01 -3.36386830e-01 -5.71226418e-01 3.48014683e-02 1.26243636e-01 -8.85916352e-01 -3.05937678e-01 5.74311137e-01 7.79625177e-01 -4.15345371e-01 2.04550520e-01 -1.04733646e+00 -8.60610232e-02 -1.71440303e-01 -2.40909364e-02 9.10404503e-01 4.34667140e-01 9.10824895e-01 8.98843527e-01 -5.55248678e-01 6.47424638e-01 7.75235057e-01 1.16189158e+00 5.89864433e-01 -9.29062784e-01 -1.74083143e-01 -1.58953741e-01 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a018fee3-7be9-4c11-a57a-363e472b10fd	video-graph-transformer-for-video-question	2207.05342	null	https://arxiv.org/abs/2207.05342v3	https://arxiv.org/pdf/2207.05342v3.pdf	Video Graph Transformer for Video Question Answering	This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it exploits disentangled video and text Transformers for relevance comparison between the video and text to perform QA, instead of entangled cross-modal Transformer for answer classification. Vision-text communication is done by additional cross-modal interaction modules. With more reasonable video encoding and QA solution, we show that VGT can achieve much better performances on VideoQA tasks that challenge dynamic relation reasoning than prior arts in the pretraining-free scenario. Its performances even surpass those models that are pretrained with millions of external data. We further show that VGT can also benefit a lot from self-supervised cross-modal pretraining, yet with orders of magnitude smaller data. These results clearly demonstrate the effectiveness and superiority of VGT, and reveal its potential for more data-efficient pretraining. With comprehensive analyses and some heuristic observations, we hope that VGT can promote VQA research beyond coarse recognition/description towards fine-grained relation reasoning in realistic videos. Our code is available at https://github.com/sail-sg/VGT.	['Shuicheng Yan', 'Tat-Seng Chua', 'Pan Zhou', 'Junbin Xiao']	2022-07-12	null	null	null	null	['video-question-answering']	['computer-vision']	[-6.52494356e-02 3.31605487e-02 -2.41523087e-01 -1.77052930e-01 -8.53477657e-01 -7.51838982e-01 6.67897284e-01 -3.08723688e-01 -1.74442623e-02 3.06000918e-01 5.24050713e-01 -4.51351553e-01 -2.60398686e-01 -6.20016754e-01 -9.97371495e-01 -4.29109335e-01 -7.36403018e-02 7.30404377e-01 1.96740046e-01 -4.87428725e-01 -2.45954141e-01 3.52950804e-02 -1.48026252e+00 9.21333075e-01 4.93595988e-01 1.14956832e+00 -4.03383672e-02 1.15373576e+00 2.34193653e-02 1.71035647e+00 -2.02943623e-01 -1.05030751e+00 1.04967676e-01 -4.32119668e-01 -1.12419248e+00 9.54502821e-02 8.80381405e-01 -6.48564875e-01 -1.08175302e+00 7.75444806e-01 6.35289401e-02 1.55249268e-01 5.00878811e-01 -1.43358743e+00 -9.71739829e-01 5.45662403e-01 -3.14548850e-01 3.54169786e-01 7.32122958e-01 5.59423506e-01 1.54769468e+00 -7.79429734e-01 9.11951661e-01 1.53820217e+00 4.15439934e-01 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8b083318-6775-45d5-9e00-c73f71c5dc76	generative-probabilistic-image-colorization	2109.14518	null	https://arxiv.org/abs/2109.14518v1	https://arxiv.org/pdf/2109.14518v1.pdf	Generative Probabilistic Image Colorization	We propose Generative Probabilistic Image Colorization, a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, our method suggests multiple candidate colorized images. Therefore, our method accounts for the ill-posed nature of the colorization problem. We conducted comprehensive experiments investigating the colorization of line-drawing images, report the influence of a score-based MCMC approach that corrects the marginal distribution of estimated samples, and further compare different combinations of models and the similarity of their generated images. Despite using only a relatively small training dataset, we experimentally develop a method to generate multiple diverse colorization candidates which avoids mode collapse and does not require any additional constraints, losses, or re-training with alternative training conditions. Our proposed approach performed well not only on color-conditional image generation tasks using biased initial values, but also on some practical image completion and inpainting tasks.	['Yuri Odagiri', 'Michael Li', 'Shinya Kitaoka', 'Chie Furusawa']	2021-09-29	null	null	null	null	['conditional-image-generation']	['computer-vision']	[ 5.84849656e-01 -1.31564379e-01 4.43779558e-01 -1.07465029e-01 -1.00818038e+00 -6.40829861e-01 8.66302669e-01 -3.03561896e-01 -4.43057537e-01 8.06021214e-01 -9.97966751e-02 -1.87677369e-01 1.02154970e-01 -8.00628364e-01 -7.95934498e-01 -7.95455515e-01 5.50801396e-01 6.17621005e-01 1.05587982e-01 1.95749089e-01 5.83934307e-01 5.70869327e-01 -1.36906004e+00 2.75429398e-01 9.29422557e-01 4.49115306e-01 3.01781744e-01 1.16023815e+00 -1.10000290e-01 6.54854357e-01 -7.06793487e-01 -5.14198363e-01 2.68043995e-01 -9.75810468e-01 -4.99181151e-01 6.47813976e-01 4.00291592e-01 -3.33604485e-01 2.40992233e-02 1.23017836e+00 5.29030859e-01 2.15078637e-01 9.71384227e-01 -1.24754560e+00 -1.12531018e+00 3.23327512e-01 -8.96812916e-01 -2.80178219e-01 3.74876261e-01 3.22663456e-01 5.39432228e-01 -1.03915489e+00 7.92893410e-01 1.30076456e+00 4.65816289e-01 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-1.72155261e-01 -7.13051617e-01 -4.79570717e-01 5.98577678e-01 3.51578034e-02 -4.95434284e-01 -3.05654973e-01 4.89253134e-01]	[11.413546562194824, -0.4228103458881378]
150025dc-2c2e-4109-8eaa-a61199bd07ad	ai-audit-a-card-game-to-reflect-on-everyday	2305.17910	null	https://arxiv.org/abs/2305.17910v1	https://arxiv.org/pdf/2305.17910v1.pdf	AI Audit: A Card Game to Reflect on Everyday AI Systems	An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. There is little work in using game-based learning methods in AI literacy. Games are known to be compelling media to teach children about complex STEM concepts. In this work, we developed a competitive card game for middle and high school students called "AI Audit" where they play as AI start-up founders building novel AI-powered technology. Players can challenge other players with potential harms of their technology or defend their own businesses by features that mitigate these harms. The game mechanics reward systems that are ethically developed or that take steps to mitigate potential harms. In this paper, we present the game design, teacher resources for classroom deployment and early playtesting results. We discuss our reflections about using games as teaching tools for AI literacy in K-12 classrooms.	['Cynthia Breazeal', 'Vishesh Kumar', 'Safinah Ali']	2023-05-29	null	null	null	null	['ethics']	['miscellaneous']	[ 1.50356710e-01 1.12712169e+00 7.84710944e-02 -3.09129916e-02 -1.62009612e-01 -6.02566779e-01 2.61839718e-01 9.86184999e-02 -3.71500164e-01 3.46014202e-01 5.23958564e-01 -7.27303684e-01 -2.15090349e-01 -7.72127151e-01 -8.30938220e-01 -2.73627251e-01 5.57456732e-01 1.92530394e-01 1.96989104e-01 -7.62617171e-01 8.31080139e-01 1.98496088e-01 -1.84605074e+00 -3.12404513e-01 1.35380650e+00 1.20532475e-02 -1.32071584e-01 9.52347815e-01 1.35169953e-01 1.95074904e+00 -8.72848272e-01 -8.37641001e-01 1.35609150e-01 -1.09533703e+00 -9.68735278e-01 -8.62680972e-01 4.99719352e-01 -9.61960495e-01 2.60390460e-01 1.16135275e+00 6.81042314e-01 -7.31795747e-03 3.14387202e-01 -1.45596504e+00 -5.10027647e-01 9.99459445e-01 -2.25463167e-01 1.88693747e-01 5.65270603e-01 5.16276538e-01 4.48245496e-01 1.75651819e-01 4.22555745e-01 6.45086527e-01 5.69814563e-01 1.13705099e+00 -6.74862206e-01 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cd477be3-7b37-4b53-92d1-a701682f02d5	continual-segment-towards-a-single-unified	2302.00162	null	https://arxiv.org/abs/2302.00162v3	https://arxiv.org/pdf/2302.00162v3.pdf	Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans	Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added. In real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually-trained then frozen encoder coupled with incrementally-added decoders can extract and preserve sufficiently representative image features for new classes to be subsequently and validly segmented. To maintain a single network model complexity, we trim each decoder progressively using neural architecture search and teacher-student based knowledge distillation. To incorporate with both healthy and pathological organs appearing in different datasets, a novel anomaly-aware and confidence learning module is proposed to merge the overlapped organ predictions, originated from different decoders. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task).	['Dakai Jin', 'Qifeng Wang', 'Mingchen Gao', 'Le Lu', 'Jingren Zhou', 'Minfeng Xu', 'Xianghua Ye', 'Jia Ge', 'Ke Yan', 'Puyang Wang', 'Dazhou Guo', 'Zhanghexuan Ji']	2023-02-01	null	null	null	null	['continual-semantic-segmentation']	['computer-vision']	[ 5.41394353e-01 5.65445662e-01 -2.75914073e-01 -5.28584838e-01 -9.26288188e-01 -7.23761916e-01 8.97387713e-02 3.66071612e-01 -4.89490002e-01 7.24552453e-01 -2.77734309e-01 -4.64091569e-01 9.29275528e-02 -5.96699178e-01 -8.22235405e-01 -5.31362414e-01 2.35943198e-02 9.46713924e-01 5.91671348e-01 2.44105488e-01 -1.91082761e-01 5.32965362e-01 -9.65192914e-01 3.20098311e-01 1.04712403e+00 1.00552726e+00 3.40090036e-01 7.80764043e-01 -2.50685841e-01 5.81705153e-01 -3.75870794e-01 -4.85102922e-01 4.18954492e-01 -4.26532716e-01 -1.22641885e+00 2.27603927e-01 3.69061857e-01 -3.58662635e-01 -1.88395604e-01 8.94156039e-01 4.05539006e-01 -2.01537415e-01 4.10105735e-01 -7.69920707e-01 -3.93252730e-01 6.89765990e-01 -3.05181712e-01 2.19614729e-01 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426ec053-4c0f-4fd8-a01f-1a5c1aa191b4	yedrouj-net-an-efficient-cnn-for-spatial	1803.00407	null	http://arxiv.org/abs/1803.00407v1	http://arxiv.org/pdf/1803.00407v1.pdf	Yedrouj-Net: An efficient CNN for spatial steganalysis	For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN. Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.	['Marc Chaumont', 'Mehdi Yedroudj', 'Frederic Comby']	2018-02-26	null	null	null	null	['steganalysis']	['computer-vision']	[ 4.27068532e-01 2.18945533e-01 3.11088301e-02 -1.75275147e-01 -2.67896622e-01 -1.19463973e-01 8.14911187e-01 -3.95835899e-02 -6.18466675e-01 6.35306954e-01 -7.74102286e-02 -4.78682131e-01 1.41921386e-01 -8.99437129e-01 -5.96289158e-01 -9.90464211e-01 -2.79855907e-01 -2.48245850e-01 4.94457543e-01 -7.32561350e-01 3.59583050e-01 5.75471580e-01 -1.60266900e+00 4.22459900e-01 4.55848485e-01 1.39419830e+00 -3.00561249e-01 7.82925606e-01 2.02390850e-01 8.71930003e-01 -8.19332898e-01 -6.23699069e-01 3.08170587e-01 -4.07069981e-01 -4.00363266e-01 -2.08217278e-01 1.62643775e-01 -3.79848063e-01 -4.41981435e-01 8.70935917e-01 6.72755003e-01 -3.46224815e-01 4.18370008e-01 -1.04318750e+00 -5.38697779e-01 6.01876259e-01 -1.85542330e-01 1.50866196e-01 2.97202449e-02 -6.87747598e-02 6.48885250e-01 -7.40820408e-01 5.27401865e-01 7.79884398e-01 9.40349221e-01 4.30441558e-01 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-1.20758557e+00 -7.18774736e-01 6.52341694e-02 7.67075181e-01 -2.23635197e-01 -8.25304568e-01 4.46988530e-02]	[4.326910972595215, 8.044705390930176]
dcc707ae-8d73-4991-bb35-d37c6cbc4b03	satellite-image-forgery-detection-and	1802.04881	null	http://arxiv.org/abs/1802.04881v1	http://arxiv.org/pdf/1802.04881v1.pdf	Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier	Current satellite imaging technology enables shooting high-resolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity. In this paper, we propose an algorithm for satellite image forgery detection and localization. Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene. Our algorithm works under the assumption that no forged images are available for training. Using a generative adversarial network (GAN), we learn a feature representation of pristine satellite images. A one-class support vector machine (SVM) is trained on these features to determine their distribution. Finally, image forgeries are detected as anomalies. The proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape.	['David Güera', 'Sri Kalyan Yarlagadda', 'Fengqing Maggie Zhu', 'Edward J. Delp', 'Paolo Bestagini', 'Stefano Tubaro']	2018-02-13	null	null	null	null	['one-class-classifier']	['methodology']	[ 5.93956470e-01 -3.07676971e-01 3.98133814e-01 -1.55981943e-01 -6.13335192e-01 -9.29623783e-01 4.78902727e-01 -4.71605994e-02 -3.53964895e-01 7.53074706e-01 -5.25307953e-01 -3.49329650e-01 1.02569163e-01 -1.09403658e+00 -8.72713447e-01 -1.10854411e+00 4.92642932e-02 8.78769811e-03 2.21858472e-01 -8.51041526e-02 4.06859219e-01 8.62103462e-01 -1.57773161e+00 1.19129412e-01 9.47312593e-01 8.31693292e-01 1.97555363e-01 6.65727973e-01 1.51937485e-01 5.82017899e-01 -9.15106475e-01 -5.22007585e-01 5.35952508e-01 -3.35794210e-01 -5.69981635e-01 6.46873057e-01 3.26626033e-01 -8.61904204e-01 -7.39617527e-01 1.69797814e+00 1.18672334e-01 -5.23704253e-02 4.48737919e-01 -1.45321298e+00 -5.78048706e-01 2.44182825e-01 -6.42659068e-01 4.47483599e-01 -1.20126186e-02 5.14579713e-01 2.82083988e-01 -7.69379199e-01 3.83783549e-01 9.10044491e-01 5.33617139e-01 2.11080313e-01 -9.33016717e-01 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5.75839281e-01 2.80317396e-01 -6.15985930e-01 -8.52899730e-01 4.00472254e-01]	[12.403716087341309, 0.9604350924491882]
6cd8ba9e-5fef-4772-a64e-ae1c57b8ff9c	image-to-video-person-re-identification-by	1810.03989	null	http://arxiv.org/abs/1810.03989v2	http://arxiv.org/pdf/1810.03989v2.pdf	Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings	Image-to-video person re-identification identifies a target person by a probe image from quantities of pedestrian videos captured by non-overlapping cameras. Despite the great progress achieved,it's still challenging to match in the multimodal scenario,i.e. between image and video. Currently,state-of-the-art approaches mainly focus on the task-specific data,neglecting the extra information on the different but related tasks. In this paper,we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information.Concretely speaking,cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space,where similarity can be directly computed. Besides,training steps from fixed model reuse approach are integrated into our framework,which can incorporate beneficial information and eventually make the target networks independent of existing models. Apart from that,our proposed framework resorts to CNNs and LSTMs for extracting visual and spatiotemporal features,and combines the strengths of identification and verification model to improve the discriminative ability of the learned feature. The experimental results demonstrate the effectiveness of our framework on narrowing down the gap between heterogeneous data and obtaining observable improvement in image-to-video person re-identification.	['Zhongwei Xie', 'Luo Zhong', 'Xian Zhong', 'Lin Li']	2018-10-04	null	null	null	null	['image-to-video-person-re-identification']	['computer-vision']	[ 1.12847961e-01 -4.31786895e-01 -1.97819307e-01 -4.06391084e-01 -8.48834097e-01 -5.65259039e-01 6.79819763e-01 -1.46594793e-01 -6.56499028e-01 5.32314241e-01 3.92053992e-01 3.45487773e-01 1.10727139e-02 -3.13416719e-01 -7.54792750e-01 -6.00483179e-01 2.08326206e-01 1.44887313e-01 -4.62969840e-02 1.67730004e-01 6.95673451e-02 2.31334135e-01 -1.67622256e+00 2.59098262e-01 5.22588253e-01 9.67407227e-01 2.68726498e-02 4.54209507e-01 2.25258395e-01 5.89951694e-01 -2.30941549e-01 -8.71371329e-01 2.73783356e-01 -1.80837065e-01 -5.74035347e-01 3.22190404e-01 8.18245888e-01 -6.33222520e-01 -8.88579071e-01 1.10020387e+00 6.23202920e-01 3.16773862e-01 3.74380559e-01 -1.54916203e+00 -1.13269770e+00 1.48239687e-01 -5.68498552e-01 1.90764278e-01 6.25914216e-01 3.59224617e-01 6.68226540e-01 -1.07066536e+00 4.93639529e-01 1.36221433e+00 7.16805816e-01 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-4.64060664e-01 -8.95138085e-01 -3.40981752e-01 8.22824895e-01 4.56185877e-01 -8.97616923e-01 -1.27141193e-01 2.42138922e-01]	[14.637727737426758, 0.9495022892951965]
6ffe1a82-af8d-4539-9cd7-43cd9ce09c54	on-distances-paths-and-connections-for	1603.08497	null	http://arxiv.org/abs/1603.08497v1	http://arxiv.org/pdf/1603.08497v1.pdf	On distances, paths and connections for hyperspectral image segmentation	The present paper introduces the $\eta$ and {\eta} connections in order to add regional information on $\lambda$-flat zones, which only take into account a local information. A top-down approach is considered. First $\lambda$-flat zones are built in a way leading to a sub-segmentation. Then a finer segmentation is obtained by computing $\eta$-bounded regions and $\mu$-geodesic balls inside the $\lambda$-flat zones. The proposed algorithms for the construction of new partitions are based on queues with an ordered selection of seeds using the cumulative distance. $\eta$-bounded regions offers a control on the variations of amplitude in the class from a point, called center, and $\mu$-geodesic balls controls the "size" of the class. These results are applied to hyperspectral images.	['Guillaume Noyel', 'Jesus Angulo', 'Dominique Jeulin']	2016-02-02	null	null	null	null	['hyperspectral-image-segmentation']	['computer-vision']	[ 9.56045836e-02 6.97533041e-02 2.52796650e-01 -2.32102826e-01 -2.30867758e-01 -3.48971575e-01 2.21352544e-04 6.84933186e-01 -5.26358664e-01 6.67407393e-01 -5.34228265e-01 -4.08638835e-01 -8.56502652e-01 -1.61400998e+00 -3.18431169e-01 -9.41516757e-01 -4.18779075e-01 5.16260207e-01 4.13136363e-01 -1.57845467e-01 4.84203488e-01 9.31061387e-01 -1.70126426e+00 1.63217336e-01 9.57668602e-01 1.15192187e+00 7.94529393e-02 3.40964466e-01 -3.96612525e-01 -1.44143105e-01 -6.23112857e-01 1.30031705e-01 4.00302351e-01 -5.38422108e-01 -6.37062311e-01 2.11484715e-01 -2.03186885e-01 2.61542439e-01 5.19159734e-01 1.22470427e+00 1.72263682e-01 3.07525635e-01 9.05611634e-01 -7.88305104e-01 1.10092267e-01 6.64347708e-01 -8.65604460e-01 2.42722690e-01 1.84654444e-01 6.18498735e-02 9.37712610e-01 -6.25329852e-01 5.79741180e-01 7.62143135e-01 3.63865644e-01 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8.78443643e-02 -7.57870853e-01 -1.21253204e+00 3.45019624e-02 -1.35400310e-01 -5.02712071e-01 -5.08890808e-01 -2.03251183e-01]	[7.557278633117676, 4.56078577041626]
822fda65-5116-447c-b7a7-ee1d5d2c5644	adversarial-attacks-on-multi-task-visual	2107.07449	null	https://arxiv.org/abs/2107.07449v2	https://arxiv.org/pdf/2107.07449v2.pdf	Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving	Deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks, in recent years. On the other hand, current deep neural networks are easily fooled by adversarial attacks. This vulnerability raises significant concerns, particularly in safety-critical applications. As a result, research into attacking and defending DNNs has gained much coverage. In this work, detailed adversarial attacks are applied on a diverse multi-task visual perception deep network across distance estimation, semantic segmentation, motion detection, and object detection. The experiments consider both white and black box attacks for targeted and un-targeted cases, while attacking a task and inspecting the effect on all the others, in addition to inspecting the effect of applying a simple defense method. We conclude this paper by comparing and discussing the experimental results, proposing insights and future work. The visualizations of the attacks are available at https://youtu.be/6AixN90budY.	['Senthil Yogamani', 'Varun Ravi Kumar', 'Ahmed Hamed', 'Ibrahim Sobh']	2021-07-15	null	null	null	null	['motion-detection']	['computer-vision']	[ 2.50384331e-01 1.96457714e-01 2.08976626e-01 -3.20939362e-01 -2.88506299e-01 -1.15664601e+00 7.49482095e-01 -1.65567175e-02 -7.49160171e-01 5.27010202e-01 -1.69154823e-01 -6.04140341e-01 2.20307574e-01 -5.69767058e-01 -7.65496016e-01 -8.88353705e-01 -1.13459677e-01 -1.42430604e-01 5.46265543e-01 -2.37165168e-01 3.71418715e-01 8.04178178e-01 -1.14009929e+00 -1.07634872e-01 6.23460174e-01 1.06179249e+00 -2.64743328e-01 8.41518044e-01 3.95394295e-01 9.10038173e-01 -1.15612912e+00 -7.85353541e-01 4.47412103e-01 -4.34457697e-02 -6.75439298e-01 -4.44602102e-01 7.08453655e-01 -6.68459773e-01 -8.04004371e-01 1.66451144e+00 6.82996392e-01 2.46156156e-01 2.91606605e-01 -1.92563725e+00 -5.20746648e-01 4.33790207e-01 -5.01277804e-01 5.03286123e-01 -1.38582975e-01 5.01424193e-01 4.52216327e-01 -2.65196383e-01 1.88410267e-01 1.60240114e+00 3.05744022e-01 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8d38b851-b55c-4d2a-a665-c1ac1ef8418a	joint-entity-and-relation-extraction-from	null	null	http://ceur-ws.org/Vol-3004/paper2.pdf	http://ceur-ws.org/Vol-3004/paper2.pdf	Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types	Scientific articles contain various types of domain-specific entities and relations between them. The entities and their relations succinctly capture important information about the topic of the document and hence, they are crucial to the understanding and automatic analysis of the documents. In this paper, we aim to automatically extract entities and relations from a scientific abstract using a deep neural model. Given an input sentence, we use a pretrained transformer to produce contextual embeddings of the tokens which are then enriched with embeddings of their part-of-speech (POS) tags. A sequence of enriched token representations forms a span, and entities and relations are jointly learned over spans. Entity logits predicted by the entity classifier are used as features in the relation classifier. Our proposed model improves upon competitive baselines in the literature for entity and relation extraction on SciERC and ADE datasets.	['Partha Pratim Das', 'Debarshi Kumar Sanyal', 'Sudakshina Dutta', 'Prantika Chakraborty', 'T Y S S Santosh']	2021-09-30	null	null	null	extraction-and-evaluation-of-knowledge	['joint-entity-and-relation-extraction-on', 'joint-entity-and-relation-extraction']	['natural-language-processing', 'natural-language-processing']	[ 2.66835815e-03 4.22794938e-01 -4.50952172e-01 -4.72666055e-01 -5.46797276e-01 -7.11312175e-01 7.99462140e-01 8.73397529e-01 -6.62627995e-01 8.78080487e-01 5.81459045e-01 -3.67503822e-01 -1.04397386e-01 -1.16926014e+00 -8.74942601e-01 -2.79203117e-01 -2.31154531e-01 4.70869541e-01 -1.96526438e-01 1.73031569e-01 2.64206886e-01 5.45447111e-01 -1.06626201e+00 3.56486320e-01 8.50377202e-01 1.16801286e+00 2.01864094e-02 4.63757366e-01 -6.63928032e-01 7.59497941e-01 -7.31074154e-01 -7.30936110e-01 -2.72972733e-01 1.37726516e-01 -1.14350355e+00 -2.90800661e-01 6.02362752e-02 -9.94704897e-04 -5.69956779e-01 8.49578559e-01 1.97077915e-01 2.40801707e-01 1.01977086e+00 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a1754114-e7e4-4ef3-8a50-aef99feb2046	cheap-translation-for-cross-lingual-named	null	null	https://aclanthology.org/D17-1269	https://aclanthology.org/D17-1269.pdf	Cheap Translation for Cross-Lingual Named Entity Recognition	Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with \textit{very} minimal resources. Our approach makes use of a lexicon to {``}translate{''} annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5{\%} F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.	['Chen-Tse Tsai', 'Dan Roth', 'Stephen Mayhew']	2017-09-01	null	null	null	emnlp-2017-9	['cross-lingual-ner']	['natural-language-processing']	[-4.67894673e-01 4.71383408e-02 -3.12462598e-01 -1.16307624e-01 -1.40468526e+00 -9.72414911e-01 5.71952760e-01 1.64038718e-01 -1.18039501e+00 1.20406735e+00 2.03740224e-01 -3.38391930e-01 2.18061149e-01 -7.04045832e-01 -8.54071319e-01 -3.69228609e-02 1.08490616e-01 6.85491860e-01 1.71811044e-01 -5.71597040e-01 -8.02665129e-02 4.76165384e-01 -1.12502360e+00 9.35205296e-02 1.12583768e+00 2.45310172e-01 3.47149193e-01 4.31892991e-01 -5.74631870e-01 7.63336241e-01 -5.03942490e-01 -8.36470187e-01 3.05524051e-01 -1.66913211e-01 -1.14925420e+00 -4.90341574e-01 5.01605988e-01 9.82098579e-02 -1.07008688e-01 1.06190395e+00 6.04431629e-01 1.96055114e-01 4.39208776e-01 -5.48421860e-01 -7.76959419e-01 9.48643804e-01 -2.59467512e-01 2.09638804e-01 4.18338269e-01 -1.00551151e-01 8.82580519e-01 -9.54718530e-01 1.04787004e+00 7.27809489e-01 1.03594279e+00 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fb2a0ed8-fda2-404a-9b59-78b1cb7abd83	does-order-matter-an-empirical-study-on	1909.03590	null	https://arxiv.org/abs/1909.03590v4	https://arxiv.org/pdf/1909.03590v4.pdf	Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence	Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation. Existing studies concatenate target keyphrases in different orders but no study has examined the effects of ordering on models' behavior. In this paper, we propose several orderings for concatenation and inspect the important factors for training a successful keyphrase generation model. By running comprehensive comparisons, we observe one preferable ordering and summarize a number of empirical findings and challenges, which can shed light on future research on this line of work.	['Peter Brusilovsky', 'Xingdi Yuan', 'Tong Wang', 'Rui Meng', 'Daqing He', 'Adam Trischler']	2019-09-09	null	null	null	null	['keyphrase-generation']	['natural-language-processing']	[ 2.86932915e-01 -2.99675196e-01 -7.62165964e-01 1.00048386e-01 -7.33530402e-01 -1.05779719e+00 1.21973753e+00 4.41903532e-01 -5.32969356e-01 9.23065960e-01 6.56399190e-01 -6.98421776e-01 -4.74404246e-02 -6.03446364e-01 -6.77470565e-01 -4.99615282e-01 -7.86394328e-02 4.36199866e-02 1.18328765e-01 -3.84404749e-01 6.77941799e-01 5.42067230e-01 -1.43111229e+00 5.59802711e-01 6.31741524e-01 4.45065886e-01 2.95120925e-01 1.00630891e+00 -7.93632120e-02 9.44609940e-01 -1.07467926e+00 -5.00050545e-01 4.35348868e-01 -5.53095758e-01 -8.19714248e-01 -1.89548463e-01 8.78266513e-01 -5.78652501e-01 -7.46600628e-01 7.05210984e-01 3.91780078e-01 2.29245454e-01 6.44305527e-01 -1.39235508e+00 -6.72295213e-01 1.34681118e+00 -8.29405934e-02 5.51671505e-01 6.03903234e-01 -2.91094407e-02 1.54035771e+00 -6.33216560e-01 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275af6aa-61ae-4119-b7fb-e466f9dd65f8	crosssplit-mitigating-label-noise	2212.01674	null	https://arxiv.org/abs/2212.01674v2	https://arxiv.org/pdf/2212.01674v2.pdf	CrossSplit: Mitigating Label Noise Memorization through Data Splitting	We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labelled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios.	['Simon Lacoste-Julien', 'Yan Zhang', 'Aristide Baratin', 'JiHye Kim']	2022-12-03	null	null	null	null	['memorization']	['natural-language-processing']	[ 1.76570415e-01 1.83952272e-01 4.94533032e-02 -6.27178073e-01 -7.97193527e-01 -4.54517961e-01 3.15803587e-01 3.38137895e-01 -6.53359711e-01 7.74642825e-01 -1.54900566e-01 -1.18731540e-02 4.98711132e-02 -5.98124146e-01 -1.02341831e+00 -7.70182490e-01 2.87365973e-01 6.56870306e-01 6.07213974e-01 3.12556401e-02 2.04731245e-02 2.04638258e-01 -1.59246647e+00 4.94462311e-01 7.15901613e-01 1.20412421e+00 -1.17878757e-01 4.46612656e-01 -7.39418715e-02 1.32757926e+00 -5.80483317e-01 -5.32509029e-01 3.84528518e-01 -4.01891500e-01 -9.99649644e-01 -6.67910725e-02 8.10793519e-01 -1.64130479e-01 -2.83635259e-01 1.28812993e+00 5.56131959e-01 2.90019184e-01 4.54453260e-01 -1.16128325e+00 -6.32502615e-01 1.04668272e+00 -5.53765059e-01 1.36365905e-01 -4.33444023e-01 -9.20761526e-02 6.43994331e-01 -8.62677991e-01 5.68495929e-01 1.22986782e+00 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9688d16b-b28c-4eaa-8fa0-0a61be80f232	a-trainable-multiplication-layer-for-auto	1905.12871	null	https://arxiv.org/abs/1905.12871v1	https://arxiv.org/pdf/1905.12871v1.pdf	A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction	In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel value to the power of a weight and then multiplies them, thereby extracting the higher-order local auto-correlation from the input image. The TML can also be used to extract co-occurrence from the feature map of a convolutional network. The training of the TML is formulated based on backpropagation with constraints to the weights, enabling us to learn discriminative multiplication patterns in an end-to-end manner. In the experiments, the characteristics of the TML are investigated by visualizing learned kernels and the corresponding output features. The applicability of the TML for classification and neural network interpretation is also evaluated using public datasets.	['Seiichi Uchida', 'Hideaki Hayashi']	2019-05-30	null	null	null	null	['network-interpretation']	['computer-vision']	[ 3.92196834e-01 4.68663909e-02 5.36780097e-02 -6.53949678e-01 -1.07396487e-02 -3.53009999e-01 4.78561312e-01 2.30768755e-01 -6.97983682e-01 2.10539967e-01 -3.53154182e-01 -3.52161527e-01 -3.12071741e-01 -8.38347316e-01 -8.50146353e-01 -8.55806053e-01 -3.36658269e-01 -1.31602466e-01 1.59247130e-01 1.19822219e-01 1.74256682e-01 7.11607814e-01 -1.57215214e+00 8.69385123e-01 6.44280970e-01 1.47138286e+00 1.94093242e-01 6.44413710e-01 -6.93585873e-02 5.75305760e-01 -5.18643498e-01 -2.46583745e-01 2.59237915e-01 -1.07341945e-01 -4.62873101e-01 -2.28206199e-02 5.42206585e-01 5.87911643e-02 -1.16834871e-01 9.93071437e-01 -4.27790135e-02 -5.53253945e-03 8.81606102e-01 -1.10690999e+00 -4.76958930e-01 4.67960149e-01 -3.20436448e-01 2.31498733e-01 4.50316817e-02 1.33169413e-01 1.13317037e+00 -1.16651142e+00 4.29101378e-01 1.07382631e+00 4.46725905e-01 -7.19725490e-02 -1.34435701e+00 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6.69759989e-01 3.25183362e-01 -3.85310173e-01 -2.34374151e-01 2.08261281e-01]	[9.189428329467773, 2.2937657833099365]
c50e659a-af1a-4e27-8b9c-5b72787cea0a	an-analysis-of-massively-multilingual-neural	null	null	https://aclanthology.org/2020.lrec-1.458	https://aclanthology.org/2020.lrec-1.458.pdf	An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages	In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Using these, we investigate the best ways to use this many-way aligned resource for multilingual machine translation. Our experiments employ a grammatically and phylogenetically diverse set of source languages during testing for more representative evaluations. We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance{---}the best number depends on the source language. Furthermore, training on related languages can improve or degrade performance, depending on the language. As there is no one-size-fits-most answer, we find that it is critical to tailor one{'}s approach to the source language and its typology.	['David Yarowsky', 'Winston Wu', 'Garrett Nicolai', 'Arya D. McCarthy', 'Dylan Lewis', 'Aaron Mueller']	2020-05-01	null	null	null	lrec-2020-5	['low-resource-neural-machine-translation']	['natural-language-processing']	[-1.03338756e-01 -5.71590662e-01 -4.93710935e-01 -3.71393830e-01 -1.13579047e+00 -1.04508495e+00 5.52762449e-01 2.64641028e-02 -8.53191614e-01 1.17276299e+00 4.31787461e-01 -9.15858090e-01 2.73396224e-01 -5.34875453e-01 -8.40764523e-01 -4.48522836e-01 2.46317208e-01 9.17218208e-01 6.50032461e-02 -7.15607643e-01 2.73463339e-01 2.29788974e-01 -1.07518458e+00 3.17642063e-01 9.76179242e-01 -5.46396822e-02 2.67101705e-01 6.07129991e-01 -2.69544303e-01 3.78754109e-01 -6.55246198e-01 -7.00825214e-01 3.48766208e-01 -7.89108574e-01 -1.03590906e+00 -3.97820413e-01 3.92855704e-01 3.54030170e-02 1.35107875e-01 9.30344105e-01 4.97530460e-01 -4.41999614e-01 5.80716729e-01 -7.66707480e-01 -6.53244555e-01 8.67168188e-01 -4.48232412e-01 5.85932732e-01 1.72375470e-01 2.65901566e-01 1.06114030e+00 -8.57069433e-01 9.95836675e-01 1.28149199e+00 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bbcdbaa6-6bbd-4456-8630-8047b13873c9	humset-dataset-of-multilingual-information	2210.04573	null	https://arxiv.org/abs/2210.04573v3	https://arxiv.org/pdf/2210.04573v3.pdf	HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response	Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.	['Nicolò Tamagnone', 'Navid Rekabsaz', 'Ewan Oglethorpe', 'Ximena Contla', 'Ranjan Shrestha', 'Benjamin Minixhofer', 'Selim Fekih']	2022-10-10	null	null	null	null	['text-annotation', 'multilingual-nlp']	['natural-language-processing', 'natural-language-processing']	[ 4.57897503e-03 8.12946260e-02 -5.40071547e-01 -2.92383224e-01 -1.53891027e+00 -9.49662149e-01 9.33071494e-01 6.94609940e-01 -1.06110966e+00 1.01108253e+00 1.34839511e+00 -6.22265100e-01 2.74648294e-02 -2.12313071e-01 3.91368987e-03 -2.82565653e-01 -3.53603363e-02 1.40833020e+00 -4.13217813e-01 -5.60569406e-01 2.17723057e-01 3.90202940e-01 -4.53536183e-01 7.80123949e-01 6.91813052e-01 3.69034082e-01 -6.27367198e-02 5.95408022e-01 -7.62673244e-02 1.25692308e+00 -4.71068591e-01 -4.58205163e-01 2.97798842e-01 -1.63008749e-01 -1.39352703e+00 -4.99507874e-01 -2.13316247e-01 -2.55912036e-01 -2.12710693e-01 5.21852553e-01 1.12335122e+00 -5.74800372e-02 9.38066244e-01 -8.88549268e-01 -4.32393253e-01 8.51048231e-01 -4.70323145e-01 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4cb79e9d-bb11-41a3-a805-64944fd05a07	aggregated-semantic-matching-for-short-text	null	null	https://aclanthology.org/K18-1046	https://aclanthology.org/K18-1046.pdf	Aggregated Semantic Matching for Short Text Entity Linking	The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work. However, short text entity linking is more challenging since the text are noisy and less coherent. To better utilize the local information provided in short texts, we propose a novel neural network framework, Aggregated Semantic Matching (ASM), in which two different aspects of semantic information between the local context and the candidate entity are captured via representation-based and interaction-based neural semantic matching models, and then two matching signals work jointly for disambiguation with a rank aggregation mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on public tweet datasets.	['Rong pan', 'Chin-Yew Lin', 'Feng Nie', 'Jinpeng Wang', 'Jing Liu', 'Shuyan Zhou']	2018-10-01	null	null	null	conll-2018-10	['card-games']	['playing-games']	[-1.89174697e-01 1.63870096e-01 -6.69429183e-01 -2.73294389e-01 -8.73866379e-01 -2.37877116e-01 7.82164991e-01 8.32956553e-01 -6.76616609e-01 8.72311413e-01 7.28838563e-01 4.45066124e-01 -3.88711840e-01 -1.02663314e+00 -5.84038019e-01 -1.45993337e-01 5.91351837e-02 7.87527502e-01 4.09441829e-01 -5.07783890e-01 1.18228141e-03 -6.07514977e-02 -1.42603183e+00 2.98648715e-01 1.13559079e+00 7.86889493e-01 4.85580973e-02 -3.24219018e-02 -7.32559204e-01 6.17337227e-01 -4.05793160e-01 -6.36548460e-01 -1.92614287e-01 -1.56266212e-01 -1.06559241e+00 -5.69390893e-01 5.61903894e-01 1.21177070e-01 -5.89644670e-01 1.26595414e+00 5.88496447e-01 6.26481116e-01 5.54464996e-01 -1.16977620e+00 -9.94259894e-01 1.23746419e+00 -4.44839984e-01 1.06667057e-01 4.15289253e-01 -8.32582712e-01 1.60500944e+00 -9.67583179e-01 9.07504559e-01 1.60675371e+00 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08c49add-939b-4f7e-be46-8c43274b7971	pointcnn-convolution-on-mathcalx-transformed	1801.07791	null	http://arxiv.org/abs/1801.07791v5	http://arxiv.org/pdf/1801.07791v5.pdf	PointCNN: Convolution On $\mathcal{X}$-Transformed Points	We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point clouds are irregular and unordered, thus directly convolving kernels against features associated with the points, will result in desertion of shape information and variance to point ordering. To address these problems, we propose to learn an $\mathcal{X}$-transformation from the input points, to simultaneously promote two causes. The first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order. Element-wise product and sum operations of the typical convolution operator are subsequently applied on the $\mathcal{X}$-transformed features. The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.	['Rui Bu', 'Yangyan Li', 'Xinhan Di', 'Wei Wu', 'Mingchao Sun', 'Baoquan Chen']	2018-01-23	null	null	null	neurips-2018	['3d-instance-segmentation-1', '3d-part-segmentation', 'few-shot-3d-point-cloud-classification']	['computer-vision', 'computer-vision', 'computer-vision']	[-1.41651509e-02 -4.18814987e-01 3.62750947e-01 -4.43572998e-01 -2.35611960e-01 -4.10413384e-01 6.34323597e-01 2.98989564e-01 -4.71640110e-01 4.35665935e-01 -2.35324413e-01 -8.57382715e-02 -4.70143586e-01 -1.14322662e+00 -1.13098192e+00 -8.32600594e-01 -2.72108495e-01 3.12865436e-01 1.61841154e-01 -1.32818207e-01 5.24305284e-01 1.06217349e+00 -1.84551370e+00 1.59641147e-01 8.00901890e-01 1.42318368e+00 3.14952046e-01 1.51359618e-01 -5.17057121e-01 2.00648308e-01 -2.66411692e-01 -1.12725988e-01 4.37378138e-01 1.76292926e-01 -6.02032840e-01 1.48913432e-02 6.48235261e-01 1.03141695e-01 -2.26316035e-01 1.21790075e+00 6.41726404e-02 3.88765723e-01 8.24088156e-01 -1.23086357e+00 -9.88525450e-01 6.44226894e-02 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20fc6b09-44a1-4886-9d10-1af667ca063e	mc-hands-1m-a-glove-wearing-hand-dataset-for	2210.10428	null	https://arxiv.org/abs/2210.10428v1	https://arxiv.org/pdf/2210.10428v1.pdf	MC-hands-1M: A glove-wearing hand dataset for pose estimation	Nowadays, the need for large amounts of carefully and complexly annotated data for the training of computer vision modules continues to grow. Furthermore, although the research community presents state of the art solutions to many problems, there exist special cases, such as the pose estimation and tracking of a glove-wearing hand, where the general approaches tend to be unable to provide an accurate solution or fail completely. In this work, we are presenting a synthetic dataset1 for 3D pose estimation of glove-wearing hands, in order to depict the value of data synthesis in computer vision. The dataset is used to fine-tune a public hand joint detection model, achieving significant performance in both synthetic and real images of glove-wearing hands.	['Petros Daras', 'Anastasios Dimou', 'Konstantinos Konstantoudakis', 'Zisis Batzos', 'Prodromos Boutis']	2022-10-19	null	null	null	null	['3d-pose-estimation']	['computer-vision']	[-2.62466669e-02 -1.94815502e-01 1.39169365e-01 -1.93349466e-01 -3.49359393e-01 -7.73632228e-01 3.92083585e-01 -4.81311291e-01 -2.96247751e-01 3.25927943e-01 -2.13744864e-02 -1.00045249e-01 -1.26498505e-01 -1.14659578e-01 -4.71787006e-01 -6.14428878e-01 2.01990455e-01 6.35061681e-01 1.21850982e-01 -2.72191733e-01 -6.79704845e-02 8.04779828e-01 -1.57580805e+00 -8.12329501e-02 1.69761583e-01 8.18219543e-01 3.76983911e-01 8.60685825e-01 4.61600482e-01 3.28961909e-02 -8.16119075e-01 -7.11423814e-01 5.28147280e-01 -1.46316707e-01 -4.17802870e-01 5.12114584e-01 9.71931696e-01 -4.22689050e-01 -3.33083808e-01 8.19149733e-01 9.89668250e-01 -2.66024321e-01 5.17153978e-01 -1.04359603e+00 -6.36080950e-02 -6.39283210e-02 -3.18096250e-01 -7.63417482e-02 5.41684091e-01 3.55614960e-01 8.81677151e-01 -8.55731785e-01 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b97a66f2-d1e6-4084-a52f-ef3700906a4a	when-and-how-to-fool-explainable-models-and	2107.01943	null	https://arxiv.org/abs/2107.01943v2	https://arxiv.org/pdf/2107.01943v2.pdf	When and How to Fool Explainable Models (and Humans) with Adversarial Examples	Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out-of-distribution inputs. In this exploratory review, we explore the possibilities and limits of adversarial attacks for explainable machine learning models. First, we extend the notion of adversarial examples to fit in explainable machine learning scenarios, in which the inputs, the output classifications and the explanations of the model's decisions are assessed by humans. Next, we propose a comprehensive framework to study whether (and how) adversarial examples can be generated for explainable models under human assessment, introducing and illustrating novel attack paradigms. In particular, our framework considers a wide range of relevant yet often ignored factors such as the type of problem, the user expertise or the objective of the explanations, in order to identify the attack strategies that should be adopted in each scenario to successfully deceive the model (and the human). The intention of these contributions is to serve as a basis for a more rigorous and realistic study of adversarial examples in the field of explainable machine learning.	['Jose A. Lozano', 'Roberto Santana', 'Jon Vadillo']	2021-07-05	null	null	null	null	['explainable-models']	['computer-vision']	[ 4.85947460e-01 8.60850453e-01 1.65156975e-01 -4.17155117e-01 -2.97858030e-01 -1.05559552e+00 5.97600460e-01 -9.52345654e-02 1.18810639e-01 7.42753625e-01 -2.42675096e-01 -7.64062345e-01 -3.98862690e-01 -5.83482921e-01 -7.58417010e-01 -5.03902256e-01 2.51667034e-02 4.45968956e-01 -2.94662088e-01 -3.22627366e-01 1.58011496e-01 8.34909379e-01 -1.34412289e+00 3.61685663e-01 9.55458760e-01 6.69241011e-01 -5.76126099e-01 7.78733969e-01 1.51456580e-01 9.74987209e-01 -1.13447428e+00 -9.69837487e-01 2.74994403e-01 -4.77130502e-01 -8.11209679e-01 1.92904752e-02 4.72855002e-01 -4.84202623e-01 -2.36594975e-01 1.08286095e+00 2.30723530e-01 -1.07186034e-01 8.44802797e-01 -1.84056556e+00 -9.14762080e-01 8.18081379e-01 3.73626858e-01 1.48653267e-02 1.66706517e-01 5.27216554e-01 5.86084306e-01 -4.33156371e-01 2.98766404e-01 1.35063934e+00 4.45285857e-01 1.17368436e+00 -1.04972088e+00 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2c4ed654-d094-4697-9f4a-5bbe39b6c625	real-time-speech-emotion-recognition-based-on	2204.11382	null	https://arxiv.org/abs/2204.11382v3	https://arxiv.org/pdf/2204.11382v3.pdf	Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction	Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to increase feature processing speed and tolerance to adversity. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to achieve the real-time latency and lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives real-time latency while predicting with state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.	['Cheng-Shan Jiang', 'Wei-Hua Cao', 'Min Wu', 'Zhen-Tao Liu', 'Abdul Rehman']	2022-04-25	null	null	null	null	['cross-corpus']	['computer-vision']	[ 1.41285688e-01 9.01716109e-03 5.03988564e-01 -5.10608435e-01 -9.25552666e-01 -1.39754072e-01 7.83969462e-03 2.07656741e-01 -6.88993514e-01 5.24616599e-01 1.19678028e-01 -2.28874519e-01 1.79596215e-01 -4.14305836e-01 -3.31511378e-01 -5.92961252e-01 -2.11672366e-01 -1.93506703e-02 -7.95511678e-02 -3.29793602e-01 9.54042748e-02 3.55024397e-01 -1.80620086e+00 5.03737450e-01 6.88361943e-01 1.57135034e+00 3.41450833e-02 1.07583356e+00 1.94757115e-02 7.78050005e-01 -8.21080625e-01 -3.67494017e-01 1.28691848e-02 -3.01986992e-01 -5.67892492e-01 -1.31754786e-01 -2.15566814e-01 -7.84557909e-02 -1.03972025e-01 8.98383439e-01 8.96700859e-01 6.36821210e-01 4.66345519e-01 -1.34341955e+00 -2.95535564e-01 4.99446869e-01 -1.66156366e-01 3.04117411e-01 3.61687213e-01 -2.87007272e-01 8.59891772e-01 -1.10333395e+00 1.19917996e-01 1.04185617e+00 7.81131208e-01 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6e1f1c44-2c7f-457e-a38b-4029cd949c03	dp-hypo-an-adaptive-private-hyperparameter	2306.05734	null	https://arxiv.org/abs/2306.05734v1	https://arxiv.org/pdf/2306.05734v1.pdf	DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework	Hyperparameter optimization, also known as hyperparameter tuning, is a widely recognized technique for improving model performance. Regrettably, when training private ML models, many practitioners often overlook the privacy risks associated with hyperparameter optimization, which could potentially expose sensitive information about the underlying dataset. Currently, the sole existing approach to allow privacy-preserving hyperparameter optimization is to uniformly and randomly select hyperparameters for a number of runs, subsequently reporting the best-performing hyperparameter. In contrast, in non-private settings, practitioners commonly utilize "adaptive" hyperparameter optimization methods such as Gaussian process-based optimization, which select the next candidate based on information gathered from previous outputs. This substantial contrast between private and non-private hyperparameter optimization underscores a critical concern. In our paper, we introduce DP-HyPO, a pioneering framework for "adaptive" private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization. To accomplish this, we provide a comprehensive differential privacy analysis of our framework. Furthermore, we empirically demonstrate the effectiveness of DP-HyPO on a diverse set of real-world and synthetic datasets.	['Milan Shen', 'Weijie J. Su', 'Huanyu Zhang', 'Sheng Gao', 'Hua Wang']	2023-06-09	null	null	null	null	['hyperparameter-optimization']	['methodology']	[ 1.07242569e-01 -3.60683352e-02 -3.19642961e-01 -3.50838363e-01 -1.11784315e+00 -1.08255923e+00 2.04986975e-01 2.52783567e-01 -4.66428488e-01 8.63539398e-01 -1.07962517e-02 -2.96825647e-01 -1.09396927e-01 -8.10958445e-01 -7.16620803e-01 -1.23265707e+00 1.09401487e-01 3.24869990e-01 -5.73107123e-01 3.37628275e-01 2.28532061e-01 5.13142288e-01 -1.08152819e+00 -9.74890515e-02 6.56376600e-01 8.44784439e-01 -6.24610841e-01 2.88086563e-01 3.73117119e-01 -1.99611634e-01 -5.14353096e-01 -9.53671396e-01 6.05673194e-01 -1.90020382e-01 -3.90503526e-01 -2.04015017e-01 7.73038119e-02 -4.65827256e-01 -3.93915296e-01 1.39069211e+00 5.95486581e-01 2.73748338e-01 3.38228852e-01 -1.28571248e+00 -3.99749964e-01 7.72231996e-01 -5.63571811e-01 -9.85140353e-02 1.51327744e-01 5.05325317e-01 1.24274182e+00 -5.26418447e-01 4.02935624e-01 1.01938796e+00 5.25473893e-01 3.76064658e-01 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ec850c2e-f899-4a34-9f1a-6725bdbec182	t-former-an-efficient-transformer-for-image	2305.07239	null	https://arxiv.org/abs/2305.07239v2	https://arxiv.org/pdf/2305.07239v2.pdf	T-former: An Efficient Transformer for Image Inpainting	Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of CNNs (e.g. local prior, spatially shared parameters) limit the performance in the face of broken images with diverse and complex forms. Recently, a class of attention-based network architectures, called transformer, has shown significant performance on natural language processing fields and high-level vision tasks. Compared with CNNs, attention operators are better at long-range modeling and have dynamic weights, but their computational complexity is quadratic in spatial resolution, and thus less suitable for applications involving higher resolution images, such as image inpainting. In this paper, we design a novel attention linearly related to the resolution according to Taylor expansion. And based on this attention, a network called $T$-former is designed for image inpainting. Experiments on several benchmark datasets demonstrate that our proposed method achieves state-of-the-art accuracy while maintaining a relatively low number of parameters and computational complexity. The code can be found at \href{https://github.com/dengyecode/T-former_image_inpainting}{github.com/dengyecode/T-former\_image\_inpainting}	['Jinjun Wang', 'Deyu Meng', 'Sanping Zhou', 'Siqi Hui', 'Ye Deng']	2023-05-12	null	null	null	null	['image-inpainting', 'long-range-modeling']	['computer-vision', 'natural-language-processing']	[ 3.44608328e-03 -2.53434777e-01 -1.94209307e-01 -2.23969400e-01 -7.16669083e-01 7.06123188e-02 1.68703079e-01 -2.17033550e-01 -3.30594748e-01 5.42685449e-01 8.31063688e-02 -2.16503382e-01 3.08323968e-02 -8.26230168e-01 -8.40313792e-01 -5.06463528e-01 2.76605844e-01 -8.81964862e-02 2.18991369e-01 -2.47187287e-01 2.56160170e-01 3.54835749e-01 -1.22884893e+00 2.36328259e-01 1.11293972e+00 1.21998918e+00 5.22848010e-01 3.75002801e-01 -2.53122360e-01 8.08225751e-01 -4.06432897e-01 -3.24460238e-01 2.53198236e-01 -5.84827960e-01 -4.92978483e-01 -1.20118015e-01 2.09188372e-01 -3.62320215e-01 -7.94816971e-01 1.23322642e+00 5.17386496e-01 5.08961827e-02 2.84869254e-01 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1ea7e928-9aa4-41f1-8eb8-f65bef6fbcdc	advances-in-very-deep-convolutional-neural	1604.01792	null	http://arxiv.org/abs/1604.01792v2	http://arxiv.org/pdf/1604.01792v2.pdf	Advances in Very Deep Convolutional Neural Networks for LVCSR	Very deep CNNs with small 3x3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.	['Vaibhava Goel', 'Tom Sercu']	2016-04-06	null	null	null	null	['set-matching']	['computer-vision']	[ 6.53129816e-02 -5.89817725e-02 1.98888406e-01 -5.12973070e-01 -9.31600988e-01 -5.30407250e-01 4.14747179e-01 -2.52314150e-01 -1.08937705e+00 3.44551027e-01 1.02644570e-01 -8.23257208e-01 2.20030114e-01 -2.34995246e-01 -6.93515182e-01 -7.15062618e-01 -8.64422917e-02 2.05430791e-01 4.06052440e-01 -1.43020660e-01 -2.60241866e-01 5.59766352e-01 -1.46606219e+00 1.85584307e-01 2.24530995e-01 1.01975203e+00 3.74955356e-01 1.23243904e+00 1.58188060e-01 7.48074412e-01 -9.50574160e-01 -5.19954801e-01 1.46880895e-01 -6.39486387e-02 -6.38473928e-01 -1.87141612e-01 5.70051610e-01 -7.32592702e-01 -6.50671542e-01 6.28169775e-01 8.82965326e-01 2.76550740e-01 4.38405350e-02 -1.02776790e+00 -3.83729279e-01 7.02543497e-01 2.04708099e-01 4.21649963e-01 -2.41539463e-01 4.52284187e-01 9.95734692e-01 -6.28970146e-01 3.68779600e-01 1.07466900e+00 7.44735599e-01 5.90495050e-01 -1.01850390e+00 -6.32067323e-01 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1.15871973e-01 7.30397522e-01 -6.01611733e-02 -3.44101608e-01 -4.09118012e-02]	[14.402082443237305, 6.402997970581055]
829498ee-7bee-478e-8e99-76c97dcb63fe	a-novel-dataset-towards-extracting-virus-host	2305.13317	null	https://arxiv.org/abs/2305.13317v1	https://arxiv.org/pdf/2305.13317v1.pdf	A Novel Dataset Towards Extracting Virus-Host Interactions	We describe a novel dataset for the automated recognition of named taxonomic and other entities relevant to the association of viruses with their hosts. We further describe some initial results using pre-trained models on the named-entity recognition (NER) task on this novel dataset. We propose that our dataset of manually annotated abstracts now offers a Gold Standard Corpus for training future NER models in the automated extraction of host-pathogen detection methods from scientific publications, and further explain how our work makes first steps towards predicting the important human health-related concept of viral spillover risk automatically from the scientific literature.	['Beckett Sterner', 'Nathan S. Upham', 'Atriya Sen', 'Rasha Alshawi']	2023-05-11	null	null	null	null	['named-entity-recognition-ner']	['natural-language-processing']	[ 6.78222626e-02 2.16700807e-01 -3.40802670e-01 -2.63472855e-01 -4.70948130e-01 -5.16695321e-01 8.44347179e-01 7.81467915e-01 -7.28585839e-01 1.22878075e+00 3.62006843e-01 -5.96177697e-01 -1.74243413e-02 -7.41678119e-01 -5.75344265e-01 -3.74560505e-01 -4.08176720e-01 1.00275743e+00 7.46319909e-03 1.59566298e-01 6.61300004e-01 9.56147552e-01 -9.73347187e-01 3.88875812e-01 8.48035157e-01 -1.73621681e-02 3.31874818e-01 8.42272401e-01 -6.25858724e-01 7.91156113e-01 -1.23007786e+00 -4.81439680e-01 -4.10887152e-01 -2.63736665e-01 -1.05693638e+00 -7.58918643e-01 -1.49503246e-01 1.50523797e-01 4.60348949e-02 5.81605315e-01 4.33923215e-01 -2.26868138e-01 1.24837947e+00 -1.07639194e+00 -8.26525390e-01 4.49323565e-01 -3.40004675e-02 5.26690722e-01 3.79020691e-01 -1.42338187e-01 7.89166272e-01 -7.50483930e-01 1.96718907e+00 9.00884688e-01 1.01253724e+00 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88c9a115-4678-43a5-8f9e-7bd3feff7f54	deepfont-identify-your-font-from-an-image	1507.03196	null	http://arxiv.org/abs/1507.03196v1	http://arxiv.org/pdf/1507.03196v1.pdf	DeepFont: Identify Your Font from An Image	As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers. We study the Visual Font Recognition (VFR) problem, and advance the state-of-the-art remarkably by developing the DeepFont system. First of all, we build up the first available large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data. Next, to combat the domain mismatch between available training and testing data, we introduce a Convolutional Neural Network (CNN) decomposition approach, using a domain adaptation technique based on a Stacked Convolutional Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text images combined with synthetic data preprocessed in a specific way. Moreover, we study a novel learning-based model compression approach, in order to reduce the DeepFont model size without sacrificing its performance. The DeepFont system achieves an accuracy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion. We also achieve around 6 times compression of the model without any visible loss of recognition accuracy.	['Aseem Agarwala', 'Zhangyang Wang', 'Jianchao Yang', 'Thomas S. Huang', 'Hailin Jin', 'Eli Shechtman', 'Jonathan Brandt']	2015-07-12	null	null	null	null	['font-recognition']	['computer-vision']	[ 4.87668425e-01 -5.22891164e-01 7.45057315e-02 -4.63796347e-01 -4.47275311e-01 -8.02708745e-01 4.85770345e-01 -2.38816857e-01 -7.62841552e-02 4.57436621e-01 5.36701530e-02 -5.11867225e-01 1.50974661e-01 -5.11306107e-01 -7.79197276e-01 -4.33124185e-01 6.26156092e-01 2.47055218e-01 2.24531159e-01 -3.38047534e-01 5.09562671e-01 4.61279184e-01 -1.87805581e+00 7.02569306e-01 1.12941670e+00 1.40742886e+00 5.44346154e-01 8.29795182e-01 -3.52841586e-01 8.98270845e-01 -7.46747851e-01 -8.89369845e-01 5.21202087e-01 -3.35536115e-02 -6.37674391e-01 5.66370845e-01 8.38366628e-01 -5.26431203e-01 -5.73028803e-01 8.65485251e-01 6.80869639e-01 -3.26126814e-01 7.31213808e-01 -8.66646886e-01 -1.28860688e+00 4.01018500e-01 -5.18815637e-01 -2.88136989e-01 1.75100207e-01 4.68091816e-01 6.07055664e-01 -1.07018220e+00 5.17082632e-01 8.95482838e-01 4.37987030e-01 6.42157674e-01 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-8.61356854e-01 -6.69709086e-01 4.09211636e-01 3.17132138e-02 -2.79409796e-01 1.43059239e-01 2.95443296e-01]	[11.949542999267578, 1.9488672018051147]
019d7bff-af3d-4dc1-abb6-4ec5b32e488b	calibration-of-multiple-fish-eye-cameras	1407.1267	null	http://arxiv.org/abs/1407.1267v1	http://arxiv.org/pdf/1407.1267v1.pdf	Calibration of Multiple Fish-Eye Cameras Using a Wand	Fish-eye cameras are becoming increasingly popular in computer vision, but their use for 3D measurement is limited partly due to the lack of an accurate, efficient and user-friendly calibration procedure. For such a purpose, we propose a method to calibrate the intrinsic and extrinsic parameters (including radial distortion parameters) of two/multiple fish-eye cameras simultaneously by using a wand under general motions. Thanks to the generic camera model used, the proposed calibration method is also suitable for two/multiple conventional cameras and mixed cameras (e.g. two conventional cameras and a fish-eye camera). Simulation and real experiments demonstrate the effectiveness of the proposed method. Moreover, we develop the camera calibration toolbox, which is available online.	['Kai-Yuan Cai', 'Qiang Fu', 'Quan Quan']	2014-07-04	null	null	null	null	['camera-auto-calibration']	['computer-vision']	[-2.61674792e-01 -3.61022115e-01 2.11450011e-01 -1.47184581e-01 -1.04694314e-01 -6.56552553e-01 2.73522228e-01 -4.65725511e-01 -6.13824487e-01 3.34624827e-01 -4.37050790e-01 -1.34207964e-01 1.15281366e-01 -6.87599853e-02 -4.40413892e-01 -8.05870295e-01 6.10123873e-01 -5.37206754e-02 3.04558128e-01 1.46973342e-01 2.99670517e-01 5.17931163e-01 -1.39342511e+00 -8.77567053e-01 6.36354089e-01 7.93271840e-01 5.26326835e-01 6.81756735e-01 2.98188329e-01 2.00350896e-01 -3.05422693e-01 -5.10374248e-01 4.74605352e-01 -4.36235487e-01 -1.35546148e-01 6.16739154e-01 3.04775774e-01 -6.23027742e-01 -1.96880087e-01 1.19656181e+00 3.37664187e-01 1.62683219e-01 3.37303519e-01 -1.20323408e+00 -2.62914717e-01 -1.09888628e-01 -9.12040412e-01 -2.64808419e-03 4.51622993e-01 5.99959269e-02 6.02625728e-01 -9.21585321e-01 3.93258601e-01 9.75310445e-01 4.37345207e-01 5.32537282e-01 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de1bef8d-a4ca-43ba-a89a-6c19033ce44b	tlnets-transformation-learning-networks-for	2305.15770	null	https://arxiv.org/abs/2305.15770v1	https://arxiv.org/pdf/2305.15770v1.pdf	TLNets: Transformation Learning Networks for long-range time-series prediction	Time series prediction is a prevalent issue across various disciplines, such as meteorology, traffic surveillance, investment, and energy production and consumption. Many statistical and machine-learning strategies have been developed to tackle this problem. However, these approaches either lack explainability or exhibit less satisfactory performance when the prediction horizon increases. To this end, we propose a novel plan for the designing of networks' architecture based on transformations, possessing the potential to achieve an enhanced receptive field in learning which brings benefits to fuse features across scales. In this context, we introduce four different transformation mechanisms as bases to construct the learning model including Fourier Transform (FT), Singular Value Decomposition (SVD), matrix multiplication and Conv block. Hence, we develop four learning models based on the above building blocks, namely, FT-Matrix, FT-SVD, FT-Conv, and Conv-SVD. Note that the FT and SVD blocks are capable of learning global information, while the Conv blocks focus on learning local information. The matrix block is sparsely designed to learn both global and local information simultaneously. The above Transformation Learning Networks (TLNets) have been extensively tested and compared with multiple baseline models based on several real-world datasets and showed clear potential in long-range time-series forecasting.	['Hao Sun', 'Yang Liu', 'Wei Wang']	2023-05-25	null	null	null	null	['time-series-prediction']	['time-series']	[-6.21244125e-02 -4.23940629e-01 -1.51476219e-01 -4.92409915e-01 -1.92889869e-01 -1.56130716e-01 8.56775343e-01 -1.44765764e-01 -9.04674977e-02 5.88932395e-01 3.48047853e-01 -3.56527537e-01 -3.49437505e-01 -8.46068859e-01 -5.63475370e-01 -8.87993515e-01 -1.80371657e-01 -2.38678187e-01 6.43185675e-02 -4.46515948e-01 1.17628761e-02 6.88042581e-01 -1.44789338e+00 3.88194889e-01 8.86154294e-01 1.28453457e+00 -1.60076506e-02 1.88093483e-01 -3.40085834e-01 8.91378224e-01 -4.06797320e-01 3.51893716e-02 3.58910263e-01 -3.12601656e-01 -1.76826328e-01 -1.28989026e-01 -6.61499575e-02 -1.43632323e-01 -5.35822511e-01 8.02525580e-01 3.06510091e-01 3.30816716e-01 6.00549757e-01 -1.47543406e+00 -8.13679636e-01 5.57961881e-01 -6.06256366e-01 3.68645549e-01 -2.02646464e-01 3.33304219e-02 8.04823637e-01 -1.02703083e+00 2.72139221e-01 1.10335732e+00 7.79864728e-01 2.39616081e-01 -9.10634220e-01 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1.83519632e-01 4.77377743e-01 -5.56475282e-01 -3.31738919e-01 4.82315272e-02]	[6.8760271072387695, 2.865030288696289]
59469a8f-0b14-4b28-b356-235f21fc0013	partially-observable-mean-field-multi-agent	2304.12653	null	https://arxiv.org/abs/2304.12653v1	https://arxiv.org/pdf/2304.12653v1.pdf	Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention	Traditional multi-agent reinforcement learning algorithms are difficultly applied in a large-scale multi-agent environment. The introduction of mean field theory has enhanced the scalability of multi-agent reinforcement learning in recent years. This paper considers partially observable multi-agent reinforcement learning (MARL), where each agent can only observe other agents within a fixed range. This partial observability affects the agent's ability to assess the quality of the actions of surrounding agents. This paper focuses on developing a method to capture more effective information from local observations in order to select more effective actions. Previous work in this field employs probability distributions or weighted mean field to update the average actions of neighborhood agents, but it does not fully consider the feature information of surrounding neighbors and leads to a local optimum. In this paper, we propose a novel multi-agent reinforcement learning algorithm, Partially Observable Mean Field Multi-Agent Reinforcement Learning based on Graph--Attention (GAMFQ) to remedy this flaw. GAMFQ uses a graph attention module and a mean field module to describe how an agent is influenced by the actions of other agents at each time step. This graph attention module consists of a graph attention encoder and a differentiable attention mechanism, and this mechanism outputs a dynamic graph to represent the effectiveness of neighborhood agents against central agents. The mean--field module approximates the effect of a neighborhood agent on a central agent as the average effect of effective neighborhood agents. We evaluate GAMFQ on three challenging tasks in the MAgents framework. Experiments show that GAMFQ outperforms baselines including the state-of-the-art partially observable mean-field reinforcement learning algorithms.	['Ziyuan Zhou', 'Guanjun Liu', 'Min Yang']	2023-04-25	null	null	null	null	['multi-agent-reinforcement-learning']	['methodology']	[-2.21870705e-01 6.41978607e-02 -3.30429167e-01 1.40158996e-01 -6.32527947e-01 -1.99359596e-01 7.79369295e-01 4.10103083e-01 -7.56860673e-01 1.02216744e+00 3.46960455e-01 1.75735548e-01 -3.15022200e-01 -1.04354024e+00 -9.22538638e-01 -9.77203190e-01 -6.03076577e-01 7.37666905e-01 3.90251070e-01 -4.06198442e-01 3.04198414e-01 2.94155866e-01 -1.12293971e+00 3.70294638e-02 7.30184019e-01 2.67616034e-01 4.16790992e-01 9.64787185e-01 3.66197407e-01 1.50188315e+00 -9.62655962e-01 2.15547383e-01 5.17181233e-02 -6.54877782e-01 -5.60339689e-01 1.99602962e-01 1.66778550e-01 -7.32541442e-01 -5.60886800e-01 1.06402719e+00 6.04641497e-01 5.88272214e-01 6.94518924e-01 -1.62967157e+00 -9.54686046e-01 8.41400027e-01 -6.19607329e-01 4.56396699e-01 2.84343690e-01 6.75953925e-01 1.17615783e+00 -1.44501016e-01 5.33210218e-01 1.80809450e+00 8.60316083e-02 4.42298532e-01 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47095f12-f326-4754-9b33-097a47e21ad7	gliding-vertex-on-the-horizontal-bounding-box	1911.09358	null	https://arxiv.org/abs/1911.09358v2	https://arxiv.org/pdf/1911.09358v2.pdf	Gliding vertex on the horizontal bounding box for multi-oriented object detection	Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.	['Gui-Song Xia', 'Yongchao Xu', 'Kai Chen', 'Qimeng Wang', 'Yukang Wang', 'Xiang Bai', 'Mingtao Fu']	2019-11-21	null	null	null	null	['object-detection-in-aerial-images']	['computer-vision']	[ 6.65208371e-03 -3.88068467e-01 -5.78320250e-02 -2.43923873e-01 -2.89006680e-01 -5.07078290e-01 2.23161384e-01 1.24974124e-01 -5.56991816e-01 2.45626584e-01 -3.21615547e-01 -4.17054176e-01 1.18436985e-01 -7.21650958e-01 -6.75031722e-01 -9.18344736e-01 4.27632369e-02 3.12341545e-02 7.55029976e-01 -1.08228855e-01 3.88660103e-01 5.38865030e-01 -1.36377442e+00 1.96174815e-01 6.42612219e-01 1.14252710e+00 3.33430886e-01 6.52692735e-01 1.02581911e-01 4.41683471e-01 -6.00301087e-01 -1.76433161e-01 4.96148288e-01 2.25829538e-02 -4.06579375e-01 5.09862065e-01 5.72301984e-01 -7.32112050e-01 -5.55807464e-02 1.12831414e+00 5.27246594e-01 -3.42906788e-02 8.12128007e-01 -1.21434486e+00 -6.69620097e-01 4.97619003e-01 -1.44220161e+00 4.63880897e-01 9.95667838e-03 2.30148569e-01 9.85177517e-01 -8.75529885e-01 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5253296c-245a-40c6-9cf9-6d91b8ce3f32	on-generalisability-of-machine-learning-based	2205.04112	null	https://arxiv.org/abs/2205.04112v1	https://arxiv.org/pdf/2205.04112v1.pdf	On Generalisability of Machine Learning-based Network Intrusion Detection Systems	Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datasets. Though, there is no record of if and how these results generalise to other network scenarios, in particular to real-world networks. In this paper, we investigate the generalisability property of ML-based NIDSs by extensively evaluating seven supervised and unsupervised learning models on four recently published benchmark NIDS datasets. Our investigation indicates that none of the considered models is able to generalise over all studied datasets. Interestingly, our results also indicate that the generalisability has a high degree of asymmetry, i.e., swapping the source and target domains can significantly change the classification performance. Our investigation also indicates that overall, unsupervised learning methods generalise better than supervised learning models in our considered scenarios. Using SHAP values to explain these results indicates that the lack of generalisability is mainly due to the presence of strong correspondence between the values of one or more features and Attack/Benign classes in one dataset-model combination and its absence in other datasets that have different feature distributions.	['Marius Portmann', 'Siamak Layeghy']	2022-05-09	null	null	null	null	['network-intrusion-detection']	['miscellaneous']	[ 2.60035396e-01 -8.83711502e-02 -6.89908490e-02 -3.47851813e-01 1.73411518e-01 -5.19998550e-01 1.06115150e+00 5.40481448e-01 -5.53714991e-01 9.15014446e-01 -6.08040869e-01 -6.24428928e-01 -7.99639940e-01 -8.45675170e-01 -2.87783444e-01 -7.85781920e-01 -4.07539606e-01 6.90699220e-01 5.86084723e-01 -3.22704524e-01 2.76542902e-01 9.76399720e-01 -1.45160532e+00 6.66683242e-02 3.50427985e-01 7.30635166e-01 -4.49072629e-01 4.25144672e-01 -1.55744955e-01 6.11349523e-01 -1.22437274e+00 -3.29892606e-01 3.20506811e-01 -2.88240135e-01 -6.57212853e-01 -2.06213593e-01 9.19981971e-02 1.32861376e-01 6.51358292e-02 8.49613011e-01 2.58823305e-01 -3.25143009e-01 9.76470053e-01 -1.54670036e+00 1.56231001e-01 3.74282897e-01 -2.30231807e-01 4.87318814e-01 1.39284462e-01 3.67864996e-01 8.44749272e-01 -1.88695565e-01 5.92190623e-01 1.09188378e+00 5.07949889e-01 1.35426924e-01 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-7.06253231e-01 5.30534983e-01 1.90945670e-01 -8.29162419e-01 -8.12806427e-01 -5.32392621e-01]	[5.238846778869629, 7.2058563232421875]
124c0ab3-a540-4db4-badd-074a6f18f0a2	a-feature-embedding-strategy-for-high-level	1705.04301	null	http://arxiv.org/abs/1705.04301v1	http://arxiv.org/pdf/1705.04301v1.pdf	A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets	Following the rapidly growing digital image usage, automatic image categorization has become preeminent research area. It has broaden and adopted many algorithms from time to time, whereby multi-feature (generally, hand-engineered features) based image characterization comes handy to improve accuracy. Recently, in machine learning, pre-trained deep convolutional neural networks (DCNNs or ConvNets) have been that the features extracted through such DCNN can improve classification accuracy. Thence, in this paper, we further investigate a feature embedding strategy to exploit cues from multiple DCNNs. We derive a generalized feature space by embedding three different DCNN bottleneck features with weights respect to their Softmax cross-entropy loss. Test outcomes on six different object classification data-sets and an action classification data-set show that regardless of variation in image statistics and tasks the proposed multi-DCNN bottleneck feature fusion is well suited to image classification tasks and an effective complement of DCNN. The comparisons to existing fusion-based image classification approaches prove that the proposed method surmounts the state-of-the-art methods and produces competitive results with fully trained DCNNs as well.	['Wei Jiang', 'Thangarajah Akilan', 'Q. M. Jonathan Wu']	2017-05-11	null	null	null	null	['image-categorization']	['computer-vision']	[ 5.58764279e-01 -3.03484797e-01 -2.65039861e-01 -5.15272260e-01 -6.41175687e-01 -1.57026842e-01 1.04530096e+00 2.69906223e-01 -8.45243335e-01 6.70923352e-01 8.30069259e-02 1.00873308e-02 -6.13065004e-01 -5.59105158e-01 -3.40058774e-01 -9.62109745e-01 -7.92694688e-02 -1.55271113e-01 5.49743958e-02 -9.64440107e-02 3.96996439e-01 7.17809200e-01 -2.17987037e+00 3.78195912e-01 5.68781316e-01 1.75201952e+00 1.25110209e-01 6.89164519e-01 -1.41781978e-02 6.15026057e-01 -5.75600684e-01 -3.10756236e-01 3.61371845e-01 -1.87943593e-01 -6.81344211e-01 7.32587576e-02 6.46778941e-01 -7.64025077e-02 -4.38215584e-01 1.08534408e+00 6.38202131e-01 4.29428406e-02 7.82091260e-01 -1.39347851e+00 -8.87561858e-01 2.98537463e-01 -2.96785206e-01 3.33185822e-01 7.34386519e-02 -2.59016100e-02 8.49543333e-01 -7.34203935e-01 5.48778355e-01 1.13835478e+00 7.62894273e-01 3.22359353e-01 -1.27565861e+00 -4.21097726e-01 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5edc7a3f-e207-4b09-81d9-b045e70d7f27	metagross-meta-gated-recursive-controller	null	null	https://openreview.net/forum?id=Sygn20VtwH	https://openreview.net/pdf?id=Sygn20VtwH	Metagross: Meta Gated Recursive Controller Units for Sequence Modeling	This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit. Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion. This can be interpreted as a form of meta-gating and recursively parameterizing a recurrent model. We postulate that our proposed inductive bias provides modeling benefits pertaining to learning with inherently hierarchically-structured sequence data (e.g., language, logical or music tasks). To this end, we conduct extensive experiments on recursive logic tasks (sorting, tree traversal, logical inference), sequential pixel-by-pixel classification, semantic parsing, code generation, machine translation and polyphonic music modeling, demonstrating the widespread utility of the proposed approach, i.e., achieving state-of-the-art (or close) performance on all tasks.	['Yew Soon Ong', 'Alvin Chan', 'Yikang Shen', 'Yi Tay']	2020-01-01	null	null	null	iclr-2020-1	['music-modeling']	['music']	[ 7.54900515e-01 1.67140946e-01 -1.26295507e-01 -2.23552108e-01 -4.52487439e-01 -4.87398416e-01 4.95266259e-01 -6.36714473e-02 -1.47112936e-01 5.95319271e-01 1.02720231e-01 -5.88755131e-01 3.41947883e-01 -9.66456413e-01 -9.90489721e-01 -8.11707795e-01 -6.71681985e-02 2.48941898e-01 1.51868880e-01 -1.04261465e-01 4.90354061e-01 2.21886933e-01 -1.72791219e+00 9.53934491e-01 6.21529222e-01 7.76378572e-01 7.15772867e-01 1.06113720e+00 -4.49064374e-01 1.44541824e+00 -3.87981445e-01 -1.65909246e-01 -4.44219559e-01 -8.08325112e-01 -8.05896759e-01 -1.60840064e-01 -4.22616638e-02 7.66958892e-02 1.53470591e-01 9.74980831e-01 2.29228571e-01 1.01941302e-01 5.26822567e-01 -8.52522373e-01 -6.83252037e-01 1.17380691e+00 -2.78453618e-01 3.13078053e-02 3.97600561e-01 1.32636294e-01 1.55014217e+00 -8.54460478e-01 4.96995866e-01 1.39047921e+00 5.27986765e-01 8.86686206e-01 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54f23ed7-b4b9-4d52-8b20-b0b7618082f8	bayesbeat-a-bayesian-deep-learning-approach	2011.00753	null	https://arxiv.org/abs/2011.00753v2	https://arxiv.org/pdf/2011.00753v2.pdf	BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data	Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provides an uncertainty estimate of the prediction. Extensive experiments on two publicly available dataset reveal that our proposed method BayesBeat outperforms the existing state-of-the-art methods. Moreover, BayesBeat is substantially more efficient having 40-200X fewer parameters than state-of-the-art baseline approaches making it suitable for deployment in resource constrained wearable devices.	['Mohammed Eunus Ali', 'Mohammad Mehedy Masud', 'Atif Rahman', 'Md. Saiful Islam', 'Masum Rahman', 'Subangkar Karmaker Shanto', 'Sarkar Snigdha Sarathi Das']	2020-11-02	null	null	null	null	['photoplethysmography-ppg', 'atrial-fibrillation-detection']	['medical', 'medical']	[-1.14490673e-01 -1.75174624e-01 -4.17028189e-01 -3.81822675e-01 -9.06382024e-01 -3.03954840e-01 -9.00583565e-02 -1.37120724e-01 -7.23986402e-02 8.88002634e-01 4.75660652e-01 -3.31842184e-01 8.43636319e-02 -6.60287976e-01 -2.07466364e-01 -7.07053602e-01 -1.37018129e-01 8.65068063e-02 -2.26906985e-01 5.43105960e-01 -3.81178439e-01 -6.53112531e-02 -8.01394522e-01 -6.85109943e-02 8.52205515e-01 1.47321534e+00 -4.90480930e-01 4.71329093e-01 5.06469607e-01 1.53878987e-01 -6.11332834e-01 -1.78605214e-01 1.58667192e-01 -4.68877226e-01 -3.51294428e-02 -6.12887859e-01 3.51382464e-01 -6.43225372e-01 -4.58471656e-01 6.88526392e-01 1.17346823e+00 -4.32213008e-01 1.31878465e-01 -8.89785409e-01 -2.86575973e-01 5.67325234e-01 -4.30373520e-01 4.46979672e-01 4.40870672e-01 3.69340390e-01 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4516632f-0750-4943-89c8-b6e81d7bb869	distill-the-image-to-nowhere-inversion	2210.04468	null	https://arxiv.org/abs/2210.04468v2	https://arxiv.org/pdf/2210.04468v2.pdf	Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation	Past works on multimodal machine translation (MMT) elevate bilingual setup by incorporating additional aligned vision information. However, an image-must requirement of the multimodal dataset largely hinders MMT's development -- namely that it demands an aligned form of [image, source text, target text]. This limitation is generally troublesome during the inference phase especially when the aligned image is not provided as in the normal NMT setup. Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme. In particular, a multimodal feature generator is executed with a knowledge distillation module, which directly generates the multimodal feature from (only) source texts as the input. While there have been a few prior works entertaining the possibility to support image-free inference for machine translation, their performances have yet to rival the image-must translation. In our experiments, we identify our method as the first image-free approach to comprehensively rival or even surpass (almost) all image-must frameworks, and achieved the state-of-the-art result on the often-used Multi30k benchmark. Our code and data are available at: https://github.com/pengr/IKD-mmt/tree/master..	['Junbo Zhao', 'Yawen Zeng', 'Ru Peng']	2022-10-10	null	null	null	null	['multimodal-machine-translation']	['natural-language-processing']	[ 5.49462914e-01 9.42373574e-02 -8.58260319e-02 -2.74850965e-01 -1.13146794e+00 -7.49140739e-01 1.03697932e+00 -2.68323988e-01 -5.88813424e-01 7.17178464e-01 -2.16437310e-01 -7.33484626e-01 2.19779998e-01 -5.15682399e-01 -1.00592244e+00 -7.01551378e-01 7.80467093e-01 9.44730461e-01 -3.16986531e-01 -3.10189158e-01 1.74939409e-02 1.11723267e-01 -1.44945049e+00 5.26202202e-01 9.66496110e-01 8.19934249e-01 4.92342412e-01 7.02476740e-01 -9.62725654e-02 5.08743942e-01 -2.86075413e-01 -1.01931977e+00 4.09029812e-01 -6.67896628e-01 -8.43510628e-01 1.95482031e-01 7.39847958e-01 -2.77247995e-01 -2.46158928e-01 1.07002246e+00 5.55132449e-01 -2.18270615e-01 6.12095177e-01 -1.40236449e+00 -8.27282667e-01 5.93439400e-01 -5.94739079e-01 -1.02162339e-01 4.27091509e-01 3.53736877e-01 9.42551434e-01 -1.33904588e+00 8.47509265e-01 1.04867041e+00 6.85986280e-02 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3bc46bd6-4db9-480e-a84a-bc3054005d49	cogvideo-large-scale-pretraining-for-text-to	2205.15868	null	https://arxiv.org/abs/2205.15868v1	https://arxiv.org/pdf/2205.15868v1.pdf	CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers	Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computation cost makes the training from scratch unaffordable; The scarcity and weak relevance of text-video datasets hinder the model understanding complex movement semantics. In this work, we present 9B-parameter transformer CogVideo, trained by inheriting a pretrained text-to-image model, CogView2. We also propose multi-frame-rate hierarchical training strategy to better align text and video clips. As (probably) the first open-source large-scale pretrained text-to-video model, CogVideo outperforms all publicly available models at a large margin in machine and human evaluations.	['Jie Tang', 'Xinghan Liu', 'Wendi Zheng', 'Ming Ding', 'Wenyi Hong']	2022-05-29	null	null	null	null	['text-to-video-generation']	['natural-language-processing']	[ 3.66184235e-01 1.42570600e-01 -3.58578116e-01 1.76065285e-02 -9.16482866e-01 -4.43972260e-01 8.98696482e-01 -7.73739100e-01 -9.54518616e-02 6.71891332e-01 5.90780497e-01 -2.85832703e-01 2.09616289e-01 -5.61919153e-01 -1.25311363e+00 -4.61949736e-01 2.59506702e-01 5.09639084e-01 3.13625842e-01 -2.42418349e-01 4.04864512e-02 -6.64117187e-02 -1.64904463e+00 7.03426123e-01 7.11892605e-01 1.01468682e+00 5.53099155e-01 9.26936924e-01 1.49546385e-01 1.02950478e+00 -4.75434870e-01 -5.08602560e-01 2.59379953e-01 -7.11789429e-01 -8.41784477e-01 2.83407867e-01 9.79315221e-01 -5.56901097e-01 -4.82883483e-01 4.78177309e-01 5.98044753e-01 -1.13278069e-02 7.64477730e-01 -1.47054207e+00 -6.95603669e-01 6.94227457e-01 -3.42442602e-01 -2.42990186e-03 5.19139528e-01 1.92207381e-01 1.08837545e+00 -1.01866663e+00 1.18952906e+00 1.13202584e+00 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ea62dee8-9e73-491b-85a6-511f218a5c70	peano-learning-formal-mathematical-reasoning	2211.15864	null	https://arxiv.org/abs/2211.15864v1	https://arxiv.org/pdf/2211.15864v1.pdf	Peano: Learning Formal Mathematical Reasoning	General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzzles is the structure of procedural abstractions underlying mathematics. We explore this idea in a case study on 5 sections of beginning algebra on the Khan Academy platform. To define a computational foundation, we introduce Peano, a theorem-proving environment where the set of valid actions at any point is finite. We use Peano to formalize introductory algebra problems and axioms, obtaining well-defined search problems. We observe existing reinforcement learning methods for symbolic reasoning to be insufficient to solve harder problems. Adding the ability to induce reusable abstractions ("tactics") from its own solutions allows an agent to make steady progress, solving all problems. Furthermore, these abstractions induce an order to the problems, seen at random during training. The recovered order has significant agreement with the expert-designed Khan Academy curriculum, and second-generation agents trained on the recovered curriculum learn significantly faster. These results illustrate the synergistic role of abstractions and curricula in the cultural transmission of mathematics.	['Noah D. Goodman', 'Gabriel Poesia']	2022-11-29	null	null	null	null	['automated-theorem-proving', 'mathematical-reasoning', 'automated-theorem-proving']	['miscellaneous', 'natural-language-processing', 'reasoning']	[-1.32352859e-01 4.31119055e-01 2.68985406e-02 2.00997353e-01 -2.67232060e-01 -1.10868430e+00 4.98934627e-01 9.05571058e-02 -1.05814815e-01 8.36143196e-01 1.73655245e-02 -9.30232465e-01 -7.64526725e-01 -1.35876715e+00 -9.94842887e-01 -3.32271665e-01 -5.73184133e-01 7.40508795e-01 2.19694644e-01 -8.63916159e-01 4.99741167e-01 4.99136001e-01 -1.60196316e+00 3.49166870e-01 1.23457670e+00 4.59622666e-02 2.40295961e-01 9.03824031e-01 -2.05959290e-01 1.35239172e+00 -6.56642377e-01 -5.67939937e-01 4.63852286e-01 -5.27770638e-01 -1.42344058e+00 -1.27523676e-01 3.59727353e-01 -4.04373586e-01 -3.42854947e-01 9.16855931e-01 -2.96228200e-01 1.63107086e-02 4.32220310e-01 -1.48775923e+00 -6.71270013e-01 1.20009494e+00 6.76698536e-02 -3.73593643e-02 7.23923147e-01 4.84909624e-01 1.17802346e+00 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cb5a210c-1246-417d-8638-b86f409b8eb5	hierarchical-adaptive-voxel-guided-sampling	2305.14306	null	https://arxiv.org/abs/2305.14306v1	https://arxiv.org/pdf/2305.14306v1.pdf	Hierarchical Adaptive Voxel-guided Sampling for Real-time Applications in Large-scale Point Clouds	While point-based neural architectures have demonstrated their efficacy, the time-consuming sampler currently prevents them from performing real-time reasoning on scene-level point clouds. Existing methods attempt to overcome this issue by using random sampling strategy instead of the commonly-adopted farthest point sampling~(FPS), but at the expense of lower performance. So the effectiveness/efficiency trade-off remains under-explored. In this paper, we reveal the key to high-quality sampling is ensuring an even spacing between points in the subset, which can be naturally obtained through a grid. Based on this insight, we propose a hierarchical adaptive voxel-guided point sampler with linear complexity and high parallelization for real-time applications. Extensive experiments on large-scale point cloud detection and segmentation tasks demonstrate that our method achieves competitive performance with the most powerful FPS, at an amazing speed that is more than 100 times faster. This breakthrough in efficiency addresses the bottleneck of the sampling step when handling scene-level point clouds. Furthermore, our sampler can be easily integrated into existing models and achieves a 20$\sim$80\% reduction in runtime with minimal effort. The code will be available at https://github.com/OuyangJunyuan/pointcloud-3d-detector-tensorrt	['Haoyao Chen', 'Xiao Liu', 'Junyuan Ouyang']	2023-05-23	null	null	null	null	['cloud-detection']	['computer-vision']	[-1.51265010e-01 -2.11987734e-01 8.19019042e-03 -1.96392953e-01 -8.97964120e-01 -4.19358641e-01 2.59183347e-01 3.03007308e-02 -2.80958623e-01 3.93234074e-01 -5.34032226e-01 -4.07072753e-01 1.01438046e-01 -1.29208767e+00 -9.73594129e-01 -7.41817415e-01 -1.32408105e-02 7.05888748e-01 7.09245145e-01 9.85428109e-04 3.22041035e-01 8.04407597e-01 -1.84157705e+00 9.48902965e-02 9.57415700e-01 1.12404561e+00 5.36460400e-01 5.45429111e-01 -5.94675541e-01 1.20308727e-01 -3.28933924e-01 -1.56448379e-01 4.34682041e-01 8.94472599e-02 -5.92201591e-01 6.49230182e-03 5.20897329e-01 -4.95465577e-01 2.37417985e-02 1.13926876e+00 3.04730296e-01 -1.91999935e-02 3.86231214e-01 -1.22709727e+00 -7.50009492e-02 2.03555688e-01 -8.90632808e-01 5.84427454e-02 -4.81230840e-02 2.94183314e-01 9.20850396e-01 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53f14f5a-da3a-4539-9935-129dd6b915b4	learning-to-generate-better-than-your-llm	2306.11816	null	https://arxiv.org/abs/2306.11816v1	https://arxiv.org/pdf/2306.11816v1.pdf	Learning to Generate Better Than Your LLM	Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for conditional text generation. In particular, recent LLMs such as ChatGPT and GPT-4 can engage in fluent conversations with users by incorporating RL and feedback from humans. Inspired by learning-to-search algorithms and capitalizing on key properties of text generation, we seek to investigate reinforcement learning algorithms beyond general purpose algorithms such as Proximal policy optimization (PPO). In particular, we extend RL algorithms to allow them to interact with a dynamic black-box guide LLM such as GPT-3 and propose RL with guided feedback (RLGF), a suite of RL algorithms for LLM fine-tuning. We experiment on the IMDB positive review and CommonGen text generation task from the GRUE benchmark. We show that our RL algorithms achieve higher performance than supervised learning (SL) and default PPO baselines, demonstrating the benefit of interaction with the guide LLM. On CommonGen, we not only outperform our SL baselines but also improve beyond PPO across a variety of lexical and semantic metrics beyond the one we optimized for. Notably, on the IMDB dataset, we show that our GPT-2 based policy outperforms the zero-shot GPT-3 oracle, indicating that our algorithms can learn from a powerful, black-box GPT-3 oracle with a simpler, cheaper, and publicly available GPT-2 model while gaining performance.	['Wen Sun', 'Dipendra Misra', 'Rajkumar Ramamurthy', 'Kiante Brantley', 'Jonathan D. Chang']	2023-06-20	null	null	null	null	['text-generation', 'conditional-text-generation']	['natural-language-processing', 'natural-language-processing']	[ 1.03957891e-01 5.20652056e-01 -5.56363344e-01 5.74628357e-03 -1.44891560e+00 -7.01166630e-01 1.04996979e+00 -1.01316236e-01 -4.44619268e-01 1.06222141e+00 6.51483297e-01 -7.09447205e-01 2.53937542e-01 -7.81610668e-01 -7.22448349e-01 -2.95191705e-01 1.60232738e-01 1.00762188e+00 -2.88750470e-01 -6.37452304e-01 1.13695100e-01 -2.15672497e-02 -1.11506665e+00 2.81046212e-01 1.06316900e+00 4.16889578e-01 3.97618383e-01 9.33153510e-01 -1.96923852e-01 8.06461096e-01 -6.51772857e-01 -5.14138043e-01 8.13543610e-03 -5.23699939e-01 -1.26139152e+00 -2.02979058e-01 1.45476401e-01 -4.07905340e-01 -1.13408007e-01 4.11372244e-01 8.56736898e-01 6.04373097e-01 5.94563305e-01 -1.12070072e+00 -7.70381629e-01 1.27337325e+00 -2.51933396e-01 -2.03270853e-01 6.68147862e-01 6.72166109e-01 1.48308885e+00 -6.37383342e-01 7.85796583e-01 1.76014543e+00 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b6544a0a-a796-4bca-bea2-53ba7e0270f1	knowledge-driven-self-supervised	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.pdf	Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition	Facial action unit (AU) recognition is formulated as a supervised learning problem by recent works. However, the complex labeling process makes it challenging to provide AU annotations for large amounts of facial images. To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) to design a novel knowledge-driven self-supervised representation learning framework for AU recognition. The representation encoder is trained using large amounts of facial images without AU annotations. AU labeling rules are summarized from FACS to design facial partition manners and determine correlations between facial regions. The method utilizes a backbone network to extract local facial area representations and a project head to map the representations into a low-dimensional latent space. In the latent space, a contrastive learning component leverages the inter-area difference to learn AU-related local representations while maintaining intra-area instance discrimination. Correlations between facial regions summarized from AU labeling rules are also explored to further learn representations using a predicting learning component. Evaluation on two benchmark databases demonstrates that the learned representation is powerful and data-efficient for AU recognition.	['Shangfei Wang', 'Yanan Chang']	2022-01-01	null	null	null	cvpr-2022-1	['facial-action-unit-detection']	['computer-vision']	[ 6.67370081e-01 4.80975091e-01 -7.71570325e-01 -8.17139268e-01 -8.35703015e-01 -2.92808741e-01 4.95968908e-01 -5.01734197e-01 2.26478562e-01 2.28274211e-01 4.49625224e-01 3.91329497e-01 2.35154018e-01 -7.88321555e-01 -6.76933885e-01 -9.01703537e-01 2.26403028e-02 1.05174743e-01 -2.91787654e-01 5.16482592e-02 -4.74033430e-02 8.39643717e-01 -1.92966032e+00 9.24035251e-01 3.04486245e-01 1.69680166e+00 -4.49828893e-01 1.18700370e-01 -9.04312879e-02 1.00132906e+00 -1.21526226e-01 -3.73085663e-02 7.31051564e-01 -5.37398279e-01 -9.68459606e-01 6.61034644e-01 8.93158793e-01 -5.25150001e-01 -3.00472558e-01 8.66038918e-01 2.02344164e-01 9.06636417e-02 1.10366392e+00 -1.39025390e+00 -6.46389842e-01 1.02506012e-01 -6.99322462e-01 -1.31084561e-01 3.22966754e-01 -4.42897044e-02 1.23580313e+00 -9.79725182e-01 6.49832547e-01 1.38670838e+00 3.99148464e-01 8.30246389e-01 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e01d9f05-9401-44ba-948e-d264f05ad472	multi-evidence-filtering-and-fusion-for-multi	1802.09129	null	http://arxiv.org/abs/1802.09129v1	http://arxiv.org/pdf/1802.09129v1.pdf	Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning	Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation. In this pipeline, we first obtain intermediate object localization and pixel labeling results for the training images, and then use such results to train task-specific deep networks in a fully supervised manner. The entire process consists of four stages, including object localization in the training images, filtering and fusing object instances, pixel labeling for the training images, and task-specific network training. To obtain clean object instances in the training images, we propose a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms. In this algorithm, we incorporate both metric learning and density-based clustering to filter detected object instances. Experiments show that our weakly supervised pipeline achieves state-of-the-art results in multi-label image classification as well as weakly supervised object detection and very competitive results in weakly supervised semantic segmentation on MS-COCO, PASCAL VOC 2007 and PASCAL VOC 2012.	['Weifeng Ge', 'Yizhou Yu', 'Sibei Yang']	2018-02-26	multi-evidence-filtering-and-fusion-for-multi-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Ge_Multi-Evidence_Filtering_and_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Ge_Multi-Evidence_Filtering_and_CVPR_2018_paper.pdf	cvpr-2018-6	['multi-label-image-classification']	['computer-vision']	[ 6.64179265e-01 -2.81468406e-02 -4.04267460e-01 -6.78950548e-01 -1.17735255e+00 -7.95444429e-01 2.33994484e-01 2.59085029e-01 -6.99361622e-01 5.12748301e-01 -5.92940390e-01 -3.77182811e-02 2.92367309e-01 -5.83801091e-01 -1.09057212e+00 -8.39879155e-01 4.70968932e-01 8.74347866e-01 7.96735227e-01 5.58328748e-01 1.28782764e-01 2.90234536e-01 -1.70040631e+00 7.32774079e-01 6.77167118e-01 1.31151319e+00 5.24108112e-01 8.16917121e-01 -4.53343749e-01 9.46539998e-01 -5.25623560e-01 -8.83137360e-02 4.48464043e-02 -9.35239345e-02 -1.18138790e+00 5.73230863e-01 7.82640815e-01 -1.67788163e-01 5.09184480e-01 1.30976558e+00 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a90cb0ef-d629-43bd-978c-3051029b9261	patchaugment-local-neighborhood-augmentation	null	null	https://openaccess.thecvf.com/content/ICCV2021W/DLGC/html/Sheshappanavar_PatchAugment_Local_Neighborhood_Augmentation_in_Point_Cloud_Classification_ICCVW_2021_paper.html	https://openaccess.thecvf.com/content/ICCV2021W/DLGC/papers/Sheshappanavar_PatchAugment_Local_Neighborhood_Augmentation_in_Point_Cloud_Classification_ICCVW_2021_paper.pdf	PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification	Recent deep neural network models trained on smaller and less diverse datasets use data augmentation to alleviate limitations such as overfitting, reduced robustness, and lower generalization. Methods using 3D datasets are among the most common to use data augmentation techniques such as random point drop, scaling, translation, rotations, and jittering. However, these data augmentation techniques are fixed and are often applied to the entire object, ignoring the object’s local geometry. Different local neighborhoods on the object surface hold a different amount of geometric complexity. Applying the same data augmentation techniques at the object level is less effective in augmenting local neighborhoods with complex structures. This paper presents PatchAugment, a data augmentation framework to apply different augmentation techniques to the local neighborhoods. Our experimental studies on PointNet++ and DGCNN models demonstrate the effectiveness of PatchAugment on the task of 3D Point Cloud Classification. We evaluated our technique against these models using four benchmark datasets, ModelNet40 (synthetic), ModelNet10 (synthetic), SHREC’16 (synthetic) and ScanObjectNN (real-world).	['Chandra Kambhamettu', 'Vinit Veerendraveer Singh', 'Shivanand Venkanna Sheshappanavar']	2021-10-19	null	null	null	iccv-workshops-2021-10	['3d-point-cloud-classification', 'point-cloud-classification']	['computer-vision', 'computer-vision']	[-1.78082108e-01 4.89627756e-02 1.35584120e-02 -3.71009499e-01 -1.31871058e-02 -4.10006493e-01 5.69285512e-01 2.87203074e-01 -3.14575613e-01 2.06578404e-01 -1.40511289e-01 -3.72249573e-01 2.72206128e-01 -1.07173800e+00 -1.02240622e+00 -3.27145159e-01 -1.60185516e-01 7.19026446e-01 3.87263149e-01 -4.62866783e-01 1.80120245e-01 1.39428198e+00 -1.55786216e+00 6.73626317e-03 6.03931665e-01 9.58635926e-01 -1.02947935e-01 1.67892694e-01 -4.38210487e-01 2.57943213e-01 -6.31898165e-01 1.13694824e-01 7.49654412e-01 3.48378628e-01 -5.17973602e-01 5.18625267e-02 8.25781405e-01 -3.28973323e-01 -3.71810794e-01 8.08096766e-01 4.56310451e-01 2.37863421e-01 5.27784944e-01 -1.46136737e+00 -7.03120351e-01 1.70877710e-01 -8.81230056e-01 3.69037203e-02 -9.94148627e-02 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aaa06808-d77a-4168-a061-21e718e478a5	development-of-a-rule-based-lemmatization	2210.16006	null	https://arxiv.org/abs/2210.16006v1	https://arxiv.org/pdf/2210.16006v1.pdf	Development of a rule-based lemmatization algorithm through Finite State Machine for Uzbek language	Lemmatization is one of the core concepts in natural language processing, thus creating a lemmatization tool is an important task. This paper discusses the construction of a lemmatization algorithm for the Uzbek language. The main purpose of the work is to remove affixes of words in the Uzbek language by means of the finite state machine and to identify a lemma (a word that can be found in the dictionary) of the word. The process of removing affixes uses a database of affixes and part of speech knowledge. This lemmatization consists of the general rules and a part of speech data of the Uzbek language, affixes, classification of affixes, removing affixes on the basis of the finite state machine for each class, as well as a definition of this word lemma.	['Ogabek Sobirov', 'Maksud Sharipov']	2022-10-28	null	null	null	null	['lemmatization']	['natural-language-processing']	[ 1.52874649e-01 2.50602990e-01 -7.99954087e-02 -3.16620231e-01 3.24700736e-02 -8.07075858e-01 7.72159278e-01 4.24990773e-01 -6.44809842e-01 7.04937935e-01 4.97354835e-01 -7.53049016e-01 -1.59783915e-01 -1.06953287e+00 -3.23283821e-01 -4.90212709e-01 1.79102853e-01 7.31944919e-01 2.62948661e-03 -5.65685272e-01 3.89583588e-01 8.18648815e-01 -1.47594512e+00 3.00551474e-01 4.99390751e-01 2.06010312e-01 2.29312494e-01 4.48515534e-01 -6.90838873e-01 2.97793984e-01 -7.51497209e-01 -3.32850128e-01 4.23431873e-01 -7.22713828e-01 -1.54351938e+00 5.70069589e-02 3.16627681e-01 3.85062397e-02 8.28479379e-02 1.42370558e+00 5.90869784e-02 3.13855380e-01 8.78527939e-01 -5.36862493e-01 -8.31421390e-02 1.14291310e+00 4.49399531e-01 4.70450699e-01 1.06085427e-01 -3.40935856e-01 1.06937814e+00 -5.22121608e-01 9.19876814e-01 1.33706236e+00 2.27370560e-01 2.88582265e-01 -8.48322451e-01 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