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J0nasW
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paperswithcode

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91de2389-e4fa-47a8-b1d8-e711c5f1c68f	neural-concept-formation-in-knowledge-graphs	null	null	https://openreview.net/forum?id=V61-62OS4mZ	https://openreview.net/pdf?id=V61-62OS4mZ	Neural Concept Formation in Knowledge Graphs	In this work, we investigate how to learn novel concepts in Knowledge Graphs (KGs) in a principled way, and how to effectively exploit them to produce more accurate neural link prediction models. Specifically, we show how concept membership relationships learned via unsupervised clustering of entities can be reified and used to augment a KG. In a thorough set of experiments, we confirm that neural link predictors trained on these augmented KGs, or in a joint Expectation-Maximization iterative scheme, can generalize better and produce more accurate predictions for infrequent relationships. For instance, our method yields relative improvements of up to 8.6% MRR on WN18RR for rare predicates, and up to 82% in small-data regimes, where the model has access to just a small subset of the training triples. Furthermore, our proposed models are able to learn meaningful concepts.	['Pasquale Minervini', 'Antonio Vergari', 'Agnieszka Dobrowolska']	2021-06-22	null	null	null	akbc-2021-10	['novel-concepts']	['reasoning']	[ 1.54296324e-01 8.67769837e-01 -6.80537403e-01 -6.45391524e-01 -2.36760721e-01 -3.32420886e-01 3.21055084e-01 4.21708882e-01 -3.44073683e-01 1.20781672e+00 1.37622237e-01 -2.46026158e-01 -3.62160653e-01 -1.14378023e+00 -1.16703248e+00 -1.55001879e-01 -5.35619795e-01 7.03670561e-01 2.03727037e-01 -1.64004818e-01 -3.04270357e-01 3.12875748e-01 -1.26758432e+00 1.52926981e-01 1.31287313e+00 7.20617175e-01 -1.09426551e-01 2.76628315e-01 -7.17689320e-02 8.86690259e-01 -1.94362357e-01 -7.91421711e-01 -3.12800333e-02 -6.36593476e-02 -1.04167628e+00 -3.78318071e-01 4.07629371e-01 -1.51912794e-01 -4.75584954e-01 9.76112962e-01 9.73426402e-02 4.17182267e-01 6.79234982e-01 -9.45565283e-01 -1.05270863e+00 1.45925033e+00 -5.18380880e-01 3.15076470e-01 1.30442619e-01 -4.22742695e-01 1.61547422e+00 -9.14651453e-01 9.03729916e-01 1.07008874e+00 7.71530569e-01 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5657ebcc-0485-4be6-9c31-8ca3fb8c111a	a-large-scale-study-of-language-models-for	1804.01849	null	http://arxiv.org/abs/1804.01849v1	http://arxiv.org/pdf/1804.01849v1.pdf	A Large-Scale Study of Language Models for Chord Prediction	We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically explore hyper-parameter settings for the recurrent neural networks---a crucial step in achieving good results with this model class. Our results show not only a quantitative difference between the models, but also a qualitative one: in contrast to static N-gram models, certain RNN configurations adapt to the songs at test time. This finding constitutes a further step towards the development of chord recognition systems that are more aware of local musical context than what was previously possible.	['Filip Korzeniowski', 'David R. W. Sears', 'Gerhard Widmer']	2018-04-05	null	null	null	null	['chord-recognition']	['audio']	[ 1.31269753e-01 -1.49378553e-01 -1.12563297e-02 -5.72638437e-02 -6.83169484e-01 -1.00801635e+00 3.60530078e-01 -4.51352932e-02 -5.17974138e-01 4.59509373e-01 5.72915971e-01 -2.67934382e-01 -1.70638099e-01 -6.52473748e-01 -2.88768828e-01 -5.65615356e-01 -3.11165273e-01 6.61789775e-01 5.04404008e-01 -7.02310681e-01 4.10270900e-01 5.75588405e-01 -1.48328602e+00 4.05647784e-01 -7.73190930e-02 5.12066960e-01 1.17409259e-01 1.04943848e+00 2.16723934e-01 8.93398046e-01 -5.56805134e-01 -2.02781036e-01 2.07842022e-01 -7.14278162e-01 -9.40511882e-01 -4.52200830e-01 1.72453314e-01 2.57465690e-01 -3.03477645e-01 7.09769487e-01 7.43931890e-01 4.58440155e-01 5.56505919e-01 -4.78914171e-01 -3.54028970e-01 1.25539529e+00 -1.11388443e-02 3.21812689e-01 1.89961195e-01 2.43172765e-01 1.46057296e+00 -5.38937986e-01 7.32476175e-01 8.33171487e-01 1.01066327e+00 6.32754445e-01 -1.08668840e+00 -6.31997168e-01 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1.90137908e-01 1.03192382e-01 -1.49010435e-01 -1.97172523e-01 -1.08706973e-01]	[15.893318176269531, 5.330941200256348]
6bb64aa4-f278-433d-8d44-b75d3ffadc49	consistent-and-symmetry-preserving-data	2104.11578	null	https://arxiv.org/abs/2104.11578v1	https://arxiv.org/pdf/2104.11578v1.pdf	Consistent and symmetry preserving data-driven interface reconstruction for the level-set method	Recently, machine learning has been used to substitute parts of conventional computational fluid dynamics, e.g. the cell-face reconstruction in finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF) method. The latter showed improvements in terms of accuracy for coarsely resolved interfaces, however at the expense of convergence and symmetry. In this work, a combined approach is proposed, adressing the aforementioned shortcomings. We focus on interface reconstruction (IR) in the level-set method, i.e. the computation of the volume fraction and apertures. The combined model consists of a classification neural network, that chooses between the conventional (linear) IR and the neural network IR depending on the local interface resolution. The proposed approach improves accuracy for coarsely resolved interfaces and recovers the conventional IR for high resolutions, yielding first order overall convergence. Symmetry is preserved by mirroring and rotating the input level-set grid and subsequently averaging the predictions. The combined model is implemented into a CFD solver and demonstrated for two-phase flows. Furthermore, we provide details of floating point symmetric implementation and computational efficiency.	['Nikolaus Adams', 'Deniz A. Bezgin', 'Aaron B. Buhendwa']	2021-04-23	null	null	null	null	['face-reconstruction']	['computer-vision']	[ 1.12194330e-01 -1.17852084e-01 4.91536885e-01 2.18671620e-01 -6.04186475e-01 -1.84181243e-01 6.49611294e-01 3.24491858e-01 -3.71626735e-01 9.88221288e-01 -2.43738443e-01 -3.24923217e-01 -3.40482146e-01 -9.80118155e-01 -4.43558455e-01 -1.04268193e+00 1.76164676e-02 7.28078723e-01 1.59679070e-01 -2.62490511e-01 3.82438898e-01 8.94627273e-01 -1.89849782e+00 3.27333897e-01 1.10450327e+00 1.22653198e+00 -2.15366483e-01 6.90595329e-01 -3.64773512e-01 6.21974707e-01 -8.50257427e-02 1.42021403e-01 2.05133572e-01 -2.13170886e-01 -6.50380492e-01 -1.56142637e-01 4.37544197e-01 -1.95397720e-01 2.90798634e-01 5.66363811e-01 4.33526844e-01 3.89648944e-01 9.61053610e-01 -7.73768306e-01 1.29567146e-01 -1.65334642e-02 -3.96267146e-01 -9.27667171e-02 3.99685472e-01 -3.48245562e-03 5.72766006e-01 -1.27787817e+00 5.43397427e-01 8.96344304e-01 9.03654635e-01 2.14743987e-01 -1.34385800e+00 -1.31554723e-01 -3.64412695e-01 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3963eb53-5252-41fe-a220-3e7e72c7c72f	resources-and-evaluations-for-multi	2306.12601	null	https://arxiv.org/abs/2306.12601v1	https://arxiv.org/pdf/2306.12601v1.pdf	Resources and Evaluations for Multi-Distribution Dense Information Retrieval	We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate methods for multi-distribution retrieval, we design three benchmarks for this task from existing single-distribution datasets, namely, a dataset based on question answering and two based on entity matching. We propose simple methods for this task which allocate the fixed retrieval budget (top-k passages) strategically across domains to prevent the known domains from consuming most of the budget. We show that our methods lead to an average of 3.8+ and up to 8.0 points improvements in Recall@100 across the datasets and that improvements are consistent when fine-tuning different base retrieval models. Our benchmarks are made publicly available.	['Simran Arora', 'Omar Khattab', 'Soumya Chatterjee']	2023-06-21	null	null	null	null	['retrieval', 'question-answering', 'information-retrieval']	['methodology', 'natural-language-processing', 'natural-language-processing']	[-2.10464269e-01 -5.10327697e-01 -5.26404142e-01 -2.33647972e-01 -1.98825192e+00 -1.10904813e+00 6.78387940e-01 5.10692894e-01 -5.34735560e-01 9.06216025e-01 1.99299380e-01 -8.69033709e-02 -6.29670799e-01 -7.66481400e-01 -6.29947305e-01 -3.76926154e-01 1.20171323e-01 1.33626032e+00 6.35550082e-01 -3.41625720e-01 4.74821061e-01 5.02526760e-01 -1.44645298e+00 5.30076087e-01 6.28048897e-01 9.49928164e-01 3.17754522e-02 8.14154923e-01 -3.53760511e-01 6.21657252e-01 -1.09526861e+00 -2.37133235e-01 4.58187640e-01 -4.30445597e-02 -1.02265215e+00 -5.20315230e-01 6.89613879e-01 -5.95242500e-01 -6.05933547e-01 7.11037576e-01 6.96214318e-01 4.30305600e-01 1.08636510e+00 -1.09436989e+00 -9.92352009e-01 3.66921127e-01 -6.86262429e-01 5.66915810e-01 6.70047998e-01 -4.42831308e-01 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b328b38b-0cbc-44e0-b008-7896e324eaa0	chili-pepper-disease-diagnosis-via-image	2306.12057	null	https://arxiv.org/abs/2306.12057v1	https://arxiv.org/pdf/2306.12057v1.pdf	Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder	With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on CNN, but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through Grabcut. It shows high performance through image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images except for the actual input as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed higher performance than previous researches, and image-based scores showed the best performanc	['Sungyoung Kim', 'Jongwook Si']	2023-06-21	null	null	null	null	['image-reconstruction', 'anomaly-detection']	['computer-vision', 'methodology']	[ 3.03958982e-01 -3.16793501e-01 1.62073508e-01 -1.66590855e-01 -6.73410371e-02 -2.61246473e-01 1.68067962e-01 -1.79791704e-01 2.76662922e-03 4.86118883e-01 -4.02705550e-01 6.00404851e-02 -1.01132356e-01 -1.41629064e+00 -4.16858345e-01 -1.01171851e+00 3.96085948e-01 1.16422191e-01 6.36012927e-02 -2.81823158e-01 1.10325798e-01 4.16846305e-01 -1.43413019e+00 3.09709191e-01 1.06688607e+00 1.09913659e+00 4.15244251e-01 4.76284802e-01 -4.45358843e-01 6.64755881e-01 -1.15306151e+00 -2.56817043e-01 3.49206924e-01 -1.20515418e+00 -3.62644255e-01 1.76843092e-01 -4.52688336e-02 -6.44359231e-01 3.48820090e-02 1.43946350e+00 4.71086919e-01 -3.73298347e-01 6.93641007e-01 -1.46339977e+00 -1.11075723e+00 4.51759696e-01 -6.32218838e-01 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c13f7897-3384-4256-85c6-222f39ed7c89	channel-recurrent-attention-networks-for	2010.03108	null	https://arxiv.org/abs/2010.03108v1	https://arxiv.org/pdf/2010.03108v1.pdf	Channel Recurrent Attention Networks for Video Pedestrian Retrieval	Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed {\it channel recurrent attention network}, for the task of video pedestrian retrieval. The main attention unit, \textit{channel recurrent attention}, identifies attention maps at the frame level by jointly leveraging spatial and channel patterns via a recurrent neural network. This channel recurrent attention is designed to build a global receptive field by recurrently receiving and learning the spatial vectors. Then, a \textit{set aggregation} cell is employed to generate a compact video representation. Empirical experimental results demonstrate the superior performance of the proposed deep network, outperforming current state-of-the-art results across standard video person retrieval benchmarks, and a thorough ablation study shows the effectiveness of the proposed units.	['Mehrtash Harandi', 'Lars Petersson', 'Jieming Zhou', 'Pan Ji', 'Pengfei Fang']	2020-10-07	null	null	null	null	['person-retrieval']	['computer-vision']	[ 3.31758559e-01 -4.68979299e-01 -1.05405629e-01 -6.46921322e-02 -6.49204075e-01 -2.54998449e-02 6.67910695e-01 -3.15437496e-01 -3.57983410e-01 5.90136170e-01 6.06874585e-01 1.86678290e-01 1.51827484e-01 -5.35718501e-01 -8.61878991e-01 -7.90900230e-01 -8.10200050e-02 -2.36137047e-01 9.76259857e-02 5.31656183e-02 1.66045710e-01 1.85503915e-01 -1.78323710e+00 6.54671311e-01 5.30960917e-01 1.37397957e+00 4.62384909e-01 7.65930533e-01 1.72042683e-01 1.27195692e+00 -5.97364604e-01 -2.40849674e-01 1.72969699e-02 -2.99013674e-01 -5.15770674e-01 -1.24579743e-01 4.61369187e-01 -7.20947623e-01 -1.12005746e+00 7.20463574e-01 7.06781924e-01 4.39777046e-01 8.03683400e-01 -8.37787747e-01 -1.27053511e+00 4.55318987e-01 -5.71735203e-01 8.28846335e-01 5.04759789e-01 2.21892580e-01 1.13395238e+00 -1.27254629e+00 4.42217529e-01 1.35545611e+00 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1.00181259e-01 -9.06694949e-01 7.54621476e-02 4.60671633e-01 4.31188345e-01 -9.65963542e-01 2.87881851e-01 2.42348164e-01]	[9.441946983337402, 0.6835160255432129]
07343c04-d472-4117-93fd-aaedd6793ec2	can-neural-networks-do-arithmetic-a-survey-on	2303.07735	null	https://arxiv.org/abs/2303.07735v1	https://arxiv.org/pdf/2303.07735v1.pdf	Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models	Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, data sets, and benchmarks specifically designed to tackle mathematical problems, reporting notable success in disparate fields such as automated theorem proving, numerical integration, and discovery of new conjectures or matrix multiplication algorithms. However, despite these impressive achievements it is still unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. In this survey we critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.	['Alberto Testolin']	2023-03-14	null	null	null	null	['numerical-integration', 'automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'miscellaneous', 'reasoning']	[-1.94684893e-01 -1.03545956e-01 -2.23597452e-01 -2.23596275e-01 -2.01922163e-01 -6.41880989e-01 7.34340131e-01 5.51803887e-01 -4.35138553e-01 8.27689946e-01 -2.87558585e-01 -9.73234534e-01 -4.46500242e-01 -1.21110022e+00 -7.15499222e-01 -2.55844891e-01 -3.75246882e-01 5.26835799e-01 -2.19003975e-01 -5.23171842e-01 5.66715717e-01 7.35024750e-01 -1.34258640e+00 8.90057236e-02 8.95370960e-01 1.14068627e+00 -5.63145936e-01 7.82502770e-01 -3.47819000e-01 1.35976529e+00 -5.87597668e-01 -6.86285436e-01 2.14038014e-01 -3.53685528e-01 -9.71815825e-01 -8.58779728e-01 5.89076579e-01 -3.99743825e-01 -5.18593788e-01 1.15479875e+00 8.26887861e-02 6.68514818e-02 3.96888673e-01 -1.49970329e+00 -8.68238091e-01 7.50834882e-01 -2.18655810e-01 5.37041664e-01 1.54518858e-01 2.58585483e-01 1.23982167e+00 -4.63477015e-01 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372e1a7f-7a3a-4cdf-af48-6fd0413ca8d8	pac-assisted-value-factorisation-with	2206.11420	null	https://arxiv.org/abs/2206.11420v3	https://arxiv.org/pdf/2206.11420v3.pdf	PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning	Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value function through the maximization of factorized per-agent utilities due to monotonicity. In this paper, we show that in partially observable MARL problems, an agent's ordering over its own actions could impose concurrent constraints (across different states) on the representable function class, causing significant estimation error during training. We tackle this limitation and propose PAC, a new framework leveraging Assistive information generated from Counterfactual Predictions of optimal joint action selection, which enable explicit assistance to value function factorization through a novel counterfactual loss. A variational inference-based information encoding method is developed to collect and encode the counterfactual predictions from an estimated baseline. To enable decentralized execution, we also derive factorized per-agent policies inspired by a maximum-entropy MARL framework. We evaluate the proposed PAC on multi-agent predator-prey and a set of StarCraft II micromanagement tasks. Empirical results demonstrate improved results of PAC over state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms on all benchmarks.	['Vaneet Aggarwal', 'Tian Lan', 'Hanhan Zhou']	2022-06-22	null	null	null	null	['starcraft-ii']	['playing-games']	[ 9.33378178e-04 3.37637067e-01 -7.03318775e-01 -7.28595704e-02 -1.04379749e+00 -4.86216396e-01 7.58728504e-01 1.25225976e-01 -8.62132728e-01 1.56500614e+00 4.47000980e-01 -1.93858057e-01 -4.48424280e-01 -7.45422244e-01 -8.85571718e-01 -9.10247803e-01 -6.83379650e-01 7.03703523e-01 -2.49959201e-01 -3.14699680e-01 1.89516563e-02 -3.92066725e-02 -1.31687689e+00 3.90387863e-01 7.54361808e-01 9.52060044e-01 8.90948176e-02 6.61737859e-01 3.20014536e-01 1.33430743e+00 -6.44056678e-01 -4.94902045e-01 5.52560925e-01 -3.72807205e-01 -5.86950481e-01 -1.54029906e-01 -1.58371627e-01 -9.40099418e-01 -2.42287472e-01 9.04352486e-01 4.39851731e-01 4.73658800e-01 6.73738301e-01 -1.83149004e+00 -3.37780595e-01 1.17881715e+00 -5.31198561e-01 -1.75321057e-01 2.00210989e-01 6.36239350e-01 1.39208126e+00 4.43545468e-02 5.32073319e-01 1.66264749e+00 2.60085315e-01 8.45506847e-01 -1.45269680e+00 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86976613-7bf1-456b-82c5-d500533d2921	monocular-3d-object-detection-using-multi	2212.11804	null	https://arxiv.org/abs/2212.11804v1	https://arxiv.org/pdf/2212.11804v1.pdf	Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference	3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple images. Monocular 3D object detection is an important yet challenging task. Beyond the significant progress in image-based 2D object detection, 3D understanding of real-world objects is an open challenge that has not been explored extensively thus far. In addition to the most closely related studies.	['Ashish Patel', 'Abonia Sojasingarayar']	2022-12-22	null	null	null	null	['monocular-3d-object-detection']	['computer-vision']	[ 9.89828184e-02 -1.63990825e-01 2.02761710e-01 -2.35028028e-01 9.24237967e-02 -7.84826398e-01 4.96305585e-01 -2.50436049e-02 -4.58533257e-01 2.91924417e-01 -5.47745168e-01 -4.77302819e-01 4.83384699e-01 -6.86647594e-01 -6.26309335e-01 -3.28630507e-01 1.65992066e-01 5.87148786e-01 9.19364214e-01 -2.90166825e-01 3.80129874e-01 1.23715281e+00 -1.79464543e+00 -2.30604485e-01 1.47022709e-01 1.24738967e+00 5.61934471e-01 9.15202320e-01 -6.19707219e-02 2.75320232e-01 -3.07790279e-01 -1.01208396e-01 4.97306108e-01 2.72824485e-02 -1.29995018e-01 3.38278443e-01 4.68838543e-01 -6.34278953e-01 -3.99475634e-01 1.03968060e+00 1.94326997e-01 -9.45656840e-03 4.10432816e-01 -1.28548002e+00 -3.42275441e-01 -4.74266976e-01 -7.74313807e-01 6.98203743e-02 8.45731854e-01 4.99429330e-02 2.40629956e-01 -9.33380306e-01 6.13104343e-01 1.38215435e+00 2.00562581e-01 4.14714187e-01 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252dfae8-3c79-4859-8731-65362d70fa17	towards-a-better-understanding-of	2305.18491	null	https://arxiv.org/abs/2305.18491v1	https://arxiv.org/pdf/2305.18491v1.pdf	Towards a Better Understanding of Representation Dynamics under TD-learning	TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. Critical to the accuracy of value predictions is the quality of state representations. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? Complementary to prior work, we provide a set of analysis that sheds further light on the representation dynamics under TD-learning. We first show that when the environments are reversible, end-to-end TD-learning strictly decreases the value approximation error over time. Under further assumptions on the environments, we can connect the representation dynamics with spectral decomposition over the transition matrix. This latter finding establishes fitting multiple value functions from randomly generated rewards as a useful auxiliary task for representation learning, as we empirically validate on both tabular and Atari game suites.	['Rémi Munos', 'Yunhao Tang']	2023-05-29	null	null	null	null	['value-prediction']	['computer-code']	[ 3.80673148e-02 2.57456988e-01 -7.59773910e-01 -1.18984714e-01 -8.37758243e-01 -7.46941507e-01 5.37485898e-01 3.18429887e-01 -4.77282286e-01 1.03467464e+00 3.78732532e-01 -5.25980055e-01 -5.67838490e-01 -5.89721203e-01 -7.18707085e-01 -5.01070082e-01 -6.04621530e-01 4.44654077e-01 -6.87963970e-04 -5.53710878e-01 3.91249061e-01 2.79594153e-01 -1.45029783e+00 1.58292890e-01 6.62259221e-01 8.60214949e-01 -1.50202572e-01 7.26712406e-01 1.90846041e-01 1.40294504e+00 -5.05558014e-01 -4.22862284e-02 4.02510583e-01 -4.99859959e-01 -8.95516455e-01 -1.01493932e-02 -2.99152702e-01 -6.48896754e-01 -5.93281746e-01 7.77311206e-01 4.19166237e-01 6.81637347e-01 7.72844434e-01 -1.27633119e+00 -3.55051637e-01 8.64426851e-01 -2.72892684e-01 2.79180557e-01 4.01785046e-01 5.51923931e-01 1.16538286e+00 -1.65450647e-01 6.82727873e-01 1.19862902e+00 6.75402522e-01 6.14339888e-01 -1.57255006e+00 -3.23363513e-01 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a4d27304-421c-46e7-8f4e-b6cd0beaa69a	multi-modal-page-stream-segmentation-with	null	null	https://link.springer.com/article/10.1007/s10579-019-09476-2	https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemann-lre-pss.pdf	Multi-modal Page Stream Segmentation with Convolutional Neural Networks	In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page documents, the preservation of document contexts is a major requirement. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into coherent multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach for PSS based on convolutional neural networks (CNN). As a first project, we combine visual information from scanned images with semantic information from OCR-ed texts for this task. The multi-modal combination of features in a single classification architecture allows for major improvements towards optimal document separation. Further to multimodality, our PSS approach profits from transfer-learning and sequential page modeling. We achieve accuracy up to 95% on multi-page documents on our in-house dataset and up to 93% on a publicly available dataset.	['Gerhard Heyer', 'Gregor Wiedemann']	2019-09-27	null	null	null	lang-resources-evaluation-2019-9	['page-stream-segmentation']	['natural-language-processing']	[ 6.70916975e-01 -1.95391372e-01 1.54130861e-01 -2.95114279e-01 -1.16281581e+00 -8.68752360e-01 5.69551170e-01 4.09916759e-01 -4.00897682e-01 3.82297307e-01 -6.19797818e-02 -3.81673992e-01 -2.39493340e-01 -6.03458226e-01 -6.65960968e-01 -2.46021464e-01 3.54760557e-01 8.67896736e-01 3.68163407e-01 1.07595190e-01 5.19239783e-01 1.10770488e+00 -1.40636790e+00 9.17419136e-01 7.19632030e-01 8.65691960e-01 6.16234779e-01 1.15006709e+00 -8.91650319e-01 3.25680375e-01 -6.82069123e-01 -4.61209953e-01 1.03707209e-01 -2.37383887e-01 -1.15249753e+00 6.73698127e-01 7.37189949e-01 -2.41813689e-01 -1.31252892e-02 7.99045146e-01 2.35415891e-01 -1.32422656e-01 6.04295015e-01 -8.07358027e-01 -5.24065316e-01 3.69088590e-01 -4.41428304e-01 -2.79318020e-02 1.53390259e-01 -1.13259152e-01 1.09169221e+00 -6.29075646e-01 7.20682383e-01 1.03781760e+00 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2.44239941e-01 -9.37988043e-01 -3.87596071e-01 -6.95858300e-01 -8.53534162e-01 3.03733498e-01 1.35111846e-02 -9.33669433e-02 -1.35943651e-01 4.04616207e-01]	[11.750812530517578, 2.6896584033966064]
31bf57ad-19aa-4903-96d6-71fe643559c7	video-face-clustering-with-unknown-number-of	1908.03381	null	https://arxiv.org/abs/1908.03381v2	https://arxiv.org/pdf/1908.03381v2.pdf	Video Face Clustering with Unknown Number of Clusters	Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing. We address the challenging problem of clustering face tracks based on their identity. Different from previous work in this area, we choose to operate in a realistic and difficult setting where: (i) the number of characters is not known a priori; and (ii) face tracks belonging to minor or background characters are not discarded. To this end, we propose Ball Cluster Learning (BCL), a supervised approach to carve the embedding space into balls of equal size, one for each cluster. The learned ball radius is easily translated to a stopping criterion for iterative merging algorithms. This gives BCL the ability to estimate the number of clusters as well as their assignment, achieving promising results on commonly used datasets. We also present a thorough discussion of how existing metric learning literature can be adapted for this task.	['Sanja Fidler', 'Marc T. Law', 'Makarand Tapaswi']	2019-08-09	null	null	null	iccv-2019-10	['face-clustering']	['computer-vision']	[ 6.90754205e-02 -6.53285980e-02 -2.68651247e-01 -3.28140646e-01 -5.03036916e-01 -8.36068809e-01 5.81524193e-01 2.08750412e-01 -4.23652768e-01 3.41593415e-01 -1.22059703e-01 -7.69765377e-02 -1.24244347e-01 -5.36969364e-01 -5.45166552e-01 -7.11296618e-01 -1.84486344e-01 8.21935058e-01 1.97814584e-01 1.63019478e-01 2.17853025e-01 6.68225884e-01 -1.63628864e+00 -9.54759249e-04 4.91583139e-01 8.11958075e-01 -1.15332134e-01 6.89902425e-01 7.82606527e-02 6.90635562e-01 -3.14777851e-01 -7.44517744e-01 3.62934977e-01 -6.88437700e-01 -8.27109456e-01 4.73708838e-01 5.31078994e-01 -1.20429300e-01 -9.25364345e-02 1.11080432e+00 2.00998172e-01 1.17579386e-01 8.84958744e-01 -1.49367893e+00 -1.59282044e-01 4.51136440e-01 -8.74767005e-01 2.53641754e-01 2.90704668e-01 -5.05403697e-01 1.16798413e+00 -9.55850124e-01 6.36828244e-01 1.10986066e+00 6.19185269e-01 6.15397274e-01 -1.46934223e+00 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1fd2aa26-8c5c-4b6d-bea3-fb247190d80c	multi-view-subspace-clustering-via-partition	1912.01201	null	https://arxiv.org/abs/1912.01201v1	https://arxiv.org/pdf/1912.01201v1.pdf	Multi-view Subspace Clustering via Partition Fusion	Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. However, its performance may degrade due to noises existing in each individual view or inconsistency between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.	['Zenglin Xu', 'Boyu Wang', 'Zhao Kang', 'Juncheng Lv', 'Luping Ji']	2019-12-03	null	null	null	null	['multi-view-subspace-clustering']	['computer-vision']	[-1.57365635e-01 -5.27896643e-01 -1.45767763e-01 -5.70858158e-02 -5.12539804e-01 -7.55040526e-01 3.75528932e-01 7.99397826e-02 1.44503817e-01 2.03171283e-01 4.52410221e-01 1.74532682e-01 -4.09128547e-01 -7.21879840e-01 -1.36038601e-01 -9.43844259e-01 2.30492398e-01 2.18531981e-01 3.64398628e-01 1.33844435e-01 2.96466410e-01 1.69014886e-01 -1.37958312e+00 3.36331189e-01 1.04537046e+00 4.93329942e-01 1.54781461e-01 3.51318508e-01 8.63310508e-03 5.63896537e-01 -1.23103879e-01 -2.02345952e-01 1.27893493e-01 -4.28036213e-01 -5.78272879e-01 6.33885801e-01 -3.17890197e-01 2.20500290e-01 -7.89122954e-02 1.17281795e+00 6.05324626e-01 5.61744012e-02 6.88735545e-01 -1.39036250e+00 -3.46076429e-01 4.51104224e-01 -1.06953609e+00 5.72663322e-02 2.96456158e-01 -1.44384146e-01 1.06956291e+00 -8.87486994e-01 5.64091027e-01 1.24959540e+00 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A cleaned dataset from paperswithcode.com

Last dataset update: July 2023

This is a cleaned up dataset optained from paperswithcode.com through their API service. It represents a set of around 56K carefully categorized papers into 3K tasks and 16 areas. The papers contain arXiv and NIPS IDs as well as title, abstract and other meta information. It can be used for training text classifiers that concentrate on the use of specific AI and ML methods and frameworks.

It contains the following tables:

papers.csv (around 56K)
papers_train.csv (80% from 56K)
papers_test.csv (20% from 56K)
tasks.csv
areas.csv

Specials

UUIDs were added to the dataset since the PapersWithCode IDs (pwc_ids) are not distinct enough. These UUIDs may change in the future with new versions of the dataset. Also, embeddings were calculated for all of the 56K papers using the brilliant model SciNCL as well as dimensionality-redused 2D coordinates using UMAP.

There is also a simple Python Notebook which was used to optain and refactor the dataset.

Downloads last month: 81

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Size of downloaded dataset files:

788 MB

Size of the auto-converted Parquet files:

500 MB

Number of rows:

55,760

A cleaned dataset from paperswithcode.com

Contents

Specials