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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
449e9e5c-b556-44e7-b7c4-34cb1773be86 | decentralized-motor-skill-learning-for | 2306.17411 | null | https://arxiv.org/abs/2306.17411v1 | https://arxiv.org/pdf/2306.17411v1.pdf | Decentralized Motor Skill Learning for Complex Robotic Systems | Reinforcement learning (RL) has achieved remarkable success in complex robotic systems (eg. quadruped locomotion). In previous works, the RL-based controller was typically implemented as a single neural network with concatenated observation input. However, the corresponding learned policy is highly task-specific. Since... | ['Jianyu Chen', 'Jingyue Gao', 'Yen-Jen Wang', 'Zheyuan Jiang', 'Yanjiang Guo'] | 2023-06-30 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [ 1.91659499e-02 5.10648131e-01 -3.30180019e-01 2.85466373e-01
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-2.94791996e-01 -5.49663186e-01 -1.01256204e+00 -1.27758420e+00
-2.12193757e-01 3.27657282e-01 4.07387614e-01 -4.64987248... | [4.380160808563232, 1.2929694652557373] |
9d80e25c-2597-4d84-86f7-2c3a7681a781 | task-robust-pre-training-for-worst-case | 2306.12070 | null | https://arxiv.org/abs/2306.12070v2 | https://arxiv.org/pdf/2306.12070v2.pdf | Task-Robust Pre-Training for Worst-Case Downstream Adaptation | Pre-training has achieved remarkable success when transferred to downstream tasks. In machine learning, we care about not only the good performance of a model but also its behavior under reasonable shifts of condition. The same philosophy holds when pre-training a foundation model. However, the foundation model may not... | ['Yang Chen', 'Zhouchen Lin', 'Cong Fang', 'Xingyu Xie', 'Jianghui Wang'] | 2023-06-21 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 4.85668659e-01 4.14833218e-01 2.76540760e-02 -4.29931641e-01
-1.14222968e+00 -4.98100907e-01 4.83632296e-01 1.67565614e-01
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-1.30059302e-01 2.23925769e-01 9.76272747e-02 -2.41913199... | [9.326844215393066, 3.4979746341705322] |
05fb7e8d-b9be-4015-b327-636e81f9ea30 | interactive-data-synthesis-for-systematic | 2305.12799 | null | https://arxiv.org/abs/2305.12799v1 | https://arxiv.org/pdf/2305.12799v1.pdf | Interactive Data Synthesis for Systematic Vision Adaptation via LLMs-AIGCs Collaboration | Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. In parallel, the problem of data scarcity has brought a growing interest in employing AIGC technology for high-quality data expansion. However, this paradigm requires well-designed prompt engineering ... | ['Yueting Zhuang', 'Siliang Tang', 'Wentao Ye', 'Juncheng Li', 'Qifan Yu'] | 2023-05-22 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.50808299e-01 2.59452552e-01 2.17288062e-01 -3.41698378e-01
-9.12916243e-01 -3.81299645e-01 7.33621597e-01 -2.69837409e-01
-2.23266751e-01 5.50932884e-01 3.30976665e-01 -3.32541257e-01
3.02446902e-01 -5.18211842e-01 -8.85311782e-01 -5.26442230e-01
3.26479673e-01 4.29753006e-01 -4.14285921e-02 -3.75727713... | [11.346965789794922, -0.26973533630371094] |
dab9b13b-13f3-4211-bf65-e0f804f98209 | monocular-3d-human-pose-estimation-by | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Nie_Monocular_3D_Human_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Nie_Monocular_3D_Human_ICCV_2017_paper.pdf | Monocular 3D Human Pose Estimation by Predicting Depth on Joints | This paper aims at estimating full-body 3D human poses from monocular images of which the biggest challenge is the inherent ambiguity introduced by lifting the 2D pose into 3D space. We propose a novel framework focusing on reducing this ambiguity by predicting the depth of human joints based on 2D human joint location... | ['Song-Chun Zhu', 'Bruce Xiaohan Nie', 'Ping Wei'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 5.13888747e-02 4.23042297e-01 -3.13174069e-01 -2.60993659e-01
-6.26366317e-01 3.49885434e-01 4.23066646e-01 -3.76986504e-01
-6.07317448e-01 4.38426554e-01 4.29980040e-01 3.99927318e-01
1.46698698e-01 -6.98032796e-01 -8.93167138e-01 -5.63296795e-01
-2.25502387e-01 7.41651237e-01 4.89682704e-01 -9.66835693... | [7.053112030029297, -0.8292876482009888] |
faa9368d-838d-44a0-99ec-35a1cce5ec07 | logical-entity-representation-in-knowledge | 2305.12738 | null | https://arxiv.org/abs/2305.12738v1 | https://arxiv.org/pdf/2305.12738v1.pdf | Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning | Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. However, previous efforts have largely been limited to only modeling chain-like Horn clauses such as... | ['Heng Ji', 'Hanghang Tong', 'Xinya Du', 'Charles Yu', 'Qizheng He', 'Chi Han'] | 2023-05-22 | null | null | null | null | ['knowledge-graph-completion'] | ['knowledge-base'] | [-8.25767815e-02 8.47780824e-01 -7.92355895e-01 -3.96170944e-01
-3.27576578e-01 -3.64927053e-01 3.58157009e-01 2.67721355e-01
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-7.58352876e-01 -1.48433185e+00 -1.05450141e+00 -2.68550158e-01
-4.82541800e-01 5.40476143e-01 1.75076723e-01 -2.45020851... | [8.843635559082031, 7.702932357788086] |
3d184742-6c48-4c9a-98cd-a350ee605fad | digitizing-handwriting-with-a-sensor-pen-a | 2107.03704 | null | https://arxiv.org/abs/2107.03704v1 | https://arxiv.org/pdf/2107.03704v1.pdf | Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer | Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system ... | ['Bjoern Eskofier', 'Jens Barth', 'Tim Hamann', 'Mohamad Wehbi'] | 2021-07-08 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 4.05074269e-01 -6.32908583e-01 -1.55009523e-01 -3.65002126e-01
1.43926069e-01 -7.73122489e-01 6.03799582e-01 -1.08931221e-01
-5.64943314e-01 4.24364775e-01 -4.17711765e-01 -4.52907056e-01
-2.93260962e-01 -8.51236343e-01 -6.63107038e-01 -5.71617186e-01
3.69173259e-01 5.94967723e-01 2.50018060e-01 -4.99236465... | [11.882561683654785, 2.5682590007781982] |
10eb52c1-b863-4c0f-9c19-333d13d2736a | muscaps-generating-captions-for-music-audio | 2104.11984 | null | https://arxiv.org/abs/2104.11984v1 | https://arxiv.org/pdf/2104.11984v1.pdf | MusCaps: Generating Captions for Music Audio | Content-based music information retrieval has seen rapid progress with the adoption of deep learning. Current approaches to high-level music description typically make use of classification models, such as in auto-tagging or genre and mood classification. In this work, we propose to address music description via audio ... | ['Gyorgy Fazekas', 'Elio Quinton', 'Emmanouil Benetos', 'Ilaria Manco'] | 2021-04-24 | null | null | null | null | ['audio-captioning', 'music-information-retrieval'] | ['audio', 'music'] | [ 5.34304142e-01 1.64394200e-01 -8.58461764e-03 -1.40618607e-01
-1.47737181e+00 -7.57748008e-01 7.14221358e-01 1.61701709e-01
-3.53954062e-02 2.06928685e-01 1.04453623e+00 2.74439812e-01
-2.98092276e-01 -2.40151078e-01 -6.49647713e-01 -3.15581381e-01
-4.54304926e-03 5.65003633e-01 -4.01184350e-01 -4.86818939... | [15.627087593078613, 5.170064926147461] |
ae7d114c-74d0-4471-8e95-7cfcdb0d3d14 | does-multimodality-help-human-and-machine-for | 1605.09186 | null | http://arxiv.org/abs/1605.09186v4 | http://arxiv.org/pdf/1605.09186v4.pdf | Does Multimodality Help Human and Machine for Translation and Image Captioning? | This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in orde... | ['Joost Van de Weijer', 'Loïc Barrault', 'Mercedes García-Martínez', 'Marc Masana', 'Walid Aransa', 'Ozan Caglayan', 'Yaxing Wang', 'Fethi Bougares'] | 2016-05-30 | does-multimodality-help-human-and-machine-for-1 | https://aclanthology.org/W16-2358 | https://aclanthology.org/W16-2358.pdf | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.17872882e-01 -1.00051969e-01 -3.72556061e-01 -5.06184064e-02
-1.36640942e+00 -4.87661123e-01 1.35865271e+00 -9.21796039e-02
-7.25441217e-01 9.13771570e-01 3.46138567e-01 -5.13445973e-01
2.88724065e-01 -1.61760580e-02 -4.99145836e-01 -4.32491273e-01
5.05144119e-01 1.03802061e+00 -1.71094805e-01 -4.03364658... | [11.513670921325684, 1.5288325548171997] |
102af934-8458-4433-9bb7-740713886f20 | align-rudder-learning-from-few-demonstrations | 2009.14108 | null | https://arxiv.org/abs/2009.14108v2 | https://arxiv.org/pdf/2009.14108v2.pdf | Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution | Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks. However, often only few ep... | ['Jose A. Arjona-Medina', 'Johannes Brandstetter', 'Patrick M. Blies', 'Matthias Dorfer', 'Marius-Constantin Dinu', 'Sepp Hochreiter', 'Markus Hofmarcher', 'Vihang P. Patil'] | 2020-09-29 | align-rudder-learning-from-few-demonstrations-1 | https://openreview.net/forum?id=8bZC3CyF-f7 | https://openreview.net/pdf?id=8bZC3CyF-f7 | null | ['multiple-sequence-alignment', 'safe-exploration'] | ['medical', 'robots'] | [-2.63792872e-01 3.78595898e-03 -3.63152981e-01 -1.78904384e-01
-9.59919095e-01 -6.04187965e-01 2.40128115e-01 8.45657215e-02
-6.13541067e-01 1.34060669e+00 -7.07364902e-02 -9.02110860e-02
-1.60089403e-01 -3.59398454e-01 -7.22187936e-01 -5.18950939e-01
-3.43765378e-01 4.93084788e-01 6.10918412e-03 -1.63172781... | [4.122997760772705, 1.6287682056427002] |
5aebde59-1208-4a54-b63c-f2545adf8f7d | dtw-at-quran-qa-2022-utilising-transfer | null | null | https://aclanthology.org/2022.osact-1.10 | https://aclanthology.org/2022.osact-1.10.pdf | DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain | The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understu... | ['Ruslan Mitkov', 'Wajdi Zaghouani', 'Tharindu Ranasinghe', 'Damith Premasiri'] | null | null | null | null | osact-lrec-2022-6 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.42231447e-01 3.96149099e-01 1.48915440e-01 -3.41275990e-01
-1.30532968e+00 -6.64958537e-01 7.34711289e-01 3.97485107e-01
-4.65021938e-01 9.62730587e-01 6.48382902e-01 -6.41685426e-01
-1.07811555e-01 -8.64701867e-01 -5.55956602e-01 -4.38878119e-01
1.02327749e-01 7.31609166e-01 2.45679095e-01 -9.17954266... | [11.376626014709473, 8.212153434753418] |
28701a37-f3a9-4f00-90cd-c8fd93b33e10 | a-deeper-look-at-saliency-feature-contrast | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Bruce_A_Deeper_Look_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Bruce_A_Deeper_Look_CVPR_2016_paper.pdf | A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond | In this paper we consider the problem of visual saliency modeling, including both human gaze prediction and salient object segmentation. The overarching goal of the paper is to identify high level considerations relevant to deriving more sophisticated visual saliency models. A deep learning model based on fully convolu... | ['Sasa Janjic', 'Christopher Catton', 'Neil D. B. Bruce'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['eye-tracking'] | ['computer-vision'] | [ 6.14720225e-01 3.44227344e-01 -4.94956732e-01 -4.87738967e-01
-2.96572089e-01 -1.42824277e-01 7.34255075e-01 1.16018273e-01
-4.04530287e-01 5.07171154e-01 3.52852166e-01 -1.72430903e-01
-2.02320382e-01 -1.38917521e-01 -7.65170276e-01 -3.96974981e-01
2.04441044e-02 -9.37996805e-02 4.45247293e-01 -3.04940015... | [10.041171073913574, 1.4305429458618164] |
4c1805dd-8c21-45c7-9d51-ceb8e112a273 | exploring-visual-context-for-weakly | 2106.10506 | null | https://arxiv.org/abs/2106.10506v2 | https://arxiv.org/pdf/2106.10506v2.pdf | Exploring Visual Context for Weakly Supervised Person Search | Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person re-identification. Existing approaches follow a fully supervised setting where both bounding box and identity annotations are available. However, annotating identities is labor-intensive, limiting the practic... | ['Ling Shao', 'Xiaokang Yang', 'Bingbing Ni', 'Jie Qin', 'Shengcai Liao', 'Jinpeng Li', 'Yichao Yan'] | 2021-06-19 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.63434619e-01 -3.68590653e-01 -1.81395113e-01 -2.95432895e-01
-7.98813522e-01 -6.77660584e-01 7.65798271e-01 1.05503753e-01
-9.04638231e-01 8.25404823e-01 2.84399629e-01 -8.05714168e-03
3.61189961e-01 -5.96162438e-01 -5.33892632e-01 -5.89002609e-01
2.11768478e-01 3.61518085e-01 4.03331101e-01 1.91206425... | [14.812477111816406, 0.8606293797492981] |
48756609-0520-404d-89c4-dceb20c94cb7 | accidental-turntables-learning-3d-pose-by | 2212.06300 | null | https://arxiv.org/abs/2212.06300v1 | https://arxiv.org/pdf/2212.06300v1.pdf | Accidental Turntables: Learning 3D Pose by Watching Objects Turn | We propose a technique for learning single-view 3D object pose estimation models by utilizing a new source of data -- in-the-wild videos where objects turn. Such videos are prevalent in practice (e.g., cars in roundabouts, airplanes near runways) and easy to collect. We show that classical structure-from-motion algorit... | ['Subhransu Maji', 'Matheus Gadelha', 'Zezhou Cheng'] | 2022-12-13 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.30261853e-01 -2.56746352e-01 -2.66743034e-01 -5.54254234e-01
-1.07457864e+00 -8.78203630e-01 6.19313419e-01 -4.76974368e-01
-2.76818424e-01 2.57402599e-01 1.65398508e-01 4.41221260e-02
2.51544863e-01 -2.62214959e-01 -1.38825560e+00 -6.62288666e-01
-1.19028710e-01 8.60955000e-01 5.10437787e-01 -1.68213561... | [7.590003490447998, -2.6413393020629883] |
67d47309-6e2e-412a-94c8-fe37d360bca3 | dropout-training-of-matrix-factorization-and | 1512.04483 | null | http://arxiv.org/abs/1512.04483v1 | http://arxiv.org/pdf/1512.04483v1.pdf | Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs | Matrix factorization (MF) and Autoencoder (AE) are among the most successful
approaches of unsupervised learning. While MF based models have been
extensively exploited in the graph modeling and link prediction literature, the
AE family has not gained much attention. In this paper we investigate both MF
and AE's applica... | ['Zhongfei Zhang', 'Shuangfei Zhai'] | 2015-12-14 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-1.33092687e-01 3.14310223e-01 -4.71352428e-01 -2.03040078e-01
-1.19525477e-01 -1.55838534e-01 4.84007120e-01 3.19861710e-01
6.68949336e-02 5.67240596e-01 4.54802692e-01 -2.02701345e-01
-4.31341618e-01 -8.39152753e-01 -8.65386426e-01 -5.12873590e-01
-4.44382310e-01 4.88758236e-01 -4.13363501e-02 -8.24993476... | [7.109405040740967, 6.1716461181640625] |
fffe11ee-1acc-4491-9354-b6005faa6db3 | detecting-stance-of-authorities-towards | 2301.05863 | null | https://arxiv.org/abs/2301.05863v1 | https://arxiv.org/pdf/2301.05863v1.pdf | Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study | A myriad of studies addressed the problem of rumor verification in Twitter by either utilizing evidence from the propagation networks or external evidence from the Web. However, none of these studies exploited evidence from trusted authorities. In this paper, we define the task of detecting the stance of authorities to... | ['Tamer Elsayed', 'Fatima Haouari'] | 2023-01-14 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-2.30277002e-01 3.69730562e-01 -4.90174204e-01 -3.83890212e-01
-4.66566145e-01 -8.77985477e-01 1.26778853e+00 5.93692124e-01
-4.00310338e-01 9.22296047e-01 6.88655794e-01 -5.07155597e-01
3.34433585e-01 -7.03350246e-01 -4.65489775e-01 -3.75773787e-01
2.42141277e-01 5.46944678e-01 6.24119818e-01 -8.38649511... | [8.327198028564453, 10.06649398803711] |
97fd39e3-4c3a-42f3-ba17-b53821aeab39 | episodic-camn-contextual-attention-based | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Abdulnabi_Episodic_CAMN_Contextual_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Abdulnabi_Episodic_CAMN_Contextual_CVPR_2017_paper.pdf | Episodic CAMN: Contextual Attention-Based Memory Networks With Iterative Feedback for Scene Labeling | Scene labeling can be seen as a sequence-sequence prediction task (pixels-labels), and it is quite important to leverage relevant context to enhance the performance of pixel classification. In this paper, we introduce an episodic attention-based memory network to achieve the goal. We present a unified framework that ma... | ['Stefan Winkler', 'Abrar H. Abdulnabi', 'Gang Wang', 'Bing Shuai'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['scene-labeling'] | ['computer-vision'] | [ 6.69310689e-01 -2.79190123e-01 -3.16546440e-01 -6.49744868e-01
-3.72370541e-01 -1.89982355e-01 6.09609067e-01 2.00615469e-02
-4.73898441e-01 6.29003763e-01 4.41787660e-01 -1.91016823e-01
5.24395049e-01 -8.00306559e-01 -7.68516362e-01 -6.70574367e-01
1.78483933e-01 -1.66307062e-01 5.89793563e-01 -4.40899171... | [9.572493553161621, 0.4084725081920624] |
5e67b718-259f-4190-9183-ac4cecbf67b3 | mlanet-multi-level-attention-network-with-sub | 2303.01396 | null | https://arxiv.org/abs/2303.01396v1 | https://arxiv.org/pdf/2303.01396v1.pdf | MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation | Vision-and-Language Navigation (VLN) aims to develop intelligent agents to navigate in unseen environments only through language and vision supervision. In the recently proposed continuous settings (continuous VLN), the agent must act in a free 3D space and faces tougher challenges like real-time execution, complex ins... | ['Qijun Chen', 'Chengju Liu', 'Qingqing Yan', 'Shu Li', 'Liuyi Wang', 'Zongtao He'] | 2023-03-02 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [ 2.44989574e-01 -1.93976924e-01 -6.47396073e-02 -4.63596374e-01
-3.84978473e-01 -3.64557683e-01 6.20451987e-01 -1.43860117e-01
-6.64509535e-01 1.74385235e-01 1.72709659e-01 -6.05449557e-01
2.10323796e-01 -8.42981160e-01 -9.94495451e-01 -5.35784066e-01
2.19467551e-01 6.25999629e-01 4.05742735e-01 -4.65203851... | [4.466393947601318, 0.5192986130714417] |
568877d9-0df0-4c82-aaf0-e20c27c46904 | deep-reinforcement-learning-with-explicitly | 1911.08756 | null | https://arxiv.org/abs/1911.08756v5 | https://arxiv.org/pdf/1911.08756v5.pdf | Hierarchical Multiple-Instance Data Classification with Costly Features | We motivate our research with a real-world problem of classifying malicious web domains using a remote service that provides various information. Crucially, some of the information can be further analyzed into a certain depth and this process sequentially creates a tree of hierarchically structured multiple-instance da... | ['Viliam Lisý', 'Tomáš Pevný', 'Jaromír Janisch'] | 2019-11-20 | null | null | null | null | ['classification-with-costly-features'] | ['miscellaneous'] | [ 4.38104719e-01 1.34258151e-01 -5.33501506e-01 -5.90080142e-01
-8.91345799e-01 -4.99292195e-01 8.65125060e-01 1.59018010e-01
-3.28602999e-01 7.33939886e-01 -3.38816494e-01 -1.88294947e-01
-2.90485591e-01 -1.11633027e+00 -7.78316021e-01 -7.97438443e-01
-3.18628818e-01 1.17573786e+00 6.46594763e-01 1.68157205... | [4.156050682067871, 2.0414702892303467] |
ff0e4262-32cb-4ec3-98b1-0c6e73175d7a | contactpose-a-dataset-of-grasps-with-object | 2007.09545 | null | https://arxiv.org/abs/2007.09545v1 | https://arxiv.org/pdf/2007.09545v1.pdf | ContactPose: A Dataset of Grasps with Object Contact and Hand Pose | Grasping is natural for humans. However, it involves complex hand configurations and soft tissue deformation that can result in complicated regions of contact between the hand and the object. Understanding and modeling this contact can potentially improve hand models, AR/VR experiences, and robotic grasping. Yet, we cu... | ['Christopher D. Twigg', 'Samarth Brahmbhatt', 'James Hays', 'Chengcheng Tang', 'Charles C. Kemp'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1889_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580358.pdf | eccv-2020-8 | ['human-grasp-contact-prediction'] | ['miscellaneous'] | [-6.82826638e-02 -1.55762240e-01 -1.92745760e-01 -3.50998640e-01
-3.93739045e-01 -8.15185130e-01 2.69123644e-01 -2.34245598e-01
1.59649566e-01 2.04340488e-01 2.60427505e-01 -3.11546102e-02
-2.34487146e-01 -4.39854383e-01 -8.57247353e-01 -3.29914063e-01
-2.56467223e-01 9.51532304e-01 1.30049393e-01 -4.10042070... | [5.978238105773926, -0.9090684652328491] |
096c91bd-3c2a-434d-a451-9bef27f48a55 | joint-modelling-of-spoken-language | 2305.00926 | null | https://arxiv.org/abs/2305.00926v1 | https://arxiv.org/pdf/2305.00926v1.pdf | Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History | Most human interactions occur in the form of spoken conversations where the semantic meaning of a given utterance depends on the context. Each utterance in spoken conversation can be represented by many semantic and speaker attributes, and there has been an interest in building Spoken Language Understanding (SLU) syste... | ['Shinji Watanabe', 'Brian Yan', 'Emiru Tsunoo', 'Hayato Futami', 'Siddhant Arora'] | 2023-05-01 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.64666578e-01 2.95826018e-01 -9.17176083e-02 -1.14052546e+00
-5.17024934e-01 -5.36389351e-01 7.71682203e-01 1.50994122e-01
-2.27686048e-01 6.11823142e-01 8.77457440e-01 -3.13728869e-01
4.18931812e-01 -5.19491076e-01 -2.14511514e-01 -2.02860415e-01
1.34948999e-01 5.97624719e-01 1.57405674e-01 -4.90996987... | [12.7003755569458, 7.706036567687988] |
b7f23b73-93a3-4954-ad7e-a730d202138b | deploying-a-retrieval-based-response-model | 2210.14379 | null | https://arxiv.org/abs/2210.14379v1 | https://arxiv.org/pdf/2210.14379v1.pdf | Deploying a Retrieval based Response Model for Task Oriented Dialogues | Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a lar... | ['Patrick Ernst', 'Pavel Danchenko', 'Jorge Balazs', 'Cheng Wang', 'György Szarvas', 'Lahari Poddar'] | 2022-10-25 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.71184146e-01 5.31314194e-01 -3.00470255e-02 -1.02360654e+00
-8.94092441e-01 -7.18779087e-01 6.02855444e-01 -2.15984941e-01
-1.90346241e-01 9.21903372e-01 4.38316762e-01 -2.96661168e-01
-1.10376082e-01 -4.25938994e-01 -1.19566247e-02 7.09533468e-02
-5.99829145e-02 1.29399550e+00 1.20662220e-01 -6.54263020... | [12.86872673034668, 7.96024751663208] |
548c6b6e-51ee-4311-952e-0d21dd2f5c16 | distinguishing-unseen-from-seen-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.pdf | Distinguishing Unseen From Seen for Generalized Zero-Shot Learning | Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Recognizing unseen classes as seen ones or vice versa often leads to poor performance in GZSL. Therefore, distinguishing seen and unseen domains is naturally an effective yet challenging solution for... | ['Ke Lu', 'Lei Zhu', 'Zhi Chen', 'Jingjing Li', 'Hongzu Su'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.75996745e-01 -6.54346496e-02 -2.82364070e-01 -3.99137020e-01
-8.78080845e-01 -5.75189948e-01 7.50821173e-01 -1.32093847e-01
-6.73243329e-02 5.20975471e-01 1.45565122e-02 1.14871450e-01
5.85973402e-03 -8.03780615e-01 -4.70595151e-01 -9.52947676e-01
6.05403602e-01 4.51178104e-01 2.62186974e-01 1.88750342... | [9.902298927307129, 2.3995718955993652] |
b45338e3-8e1f-4ce6-adfc-24cc87e929af | is-attention-always-needed-a-case-study-on | 2110.03427 | null | https://arxiv.org/abs/2110.03427v2 | https://arxiv.org/pdf/2110.03427v2.pdf | Is Attention always needed? A Case Study on Language Identification from Speech | Language Identification (LID), a recommended initial step to Automatic Speech Recognition (ASR), is used to detect a spoken language from audio specimens. In state-of-the-art systems capable of multilingual speech processing, however, users have to explicitly set one or more languages before using them. LID, therefore,... | ['Sudip Kumar Naskar', 'Mahidas Bhattacharya', 'Indranil Dutta', 'Santanu Pal', 'Atanu Mandal'] | 2021-10-05 | null | null | null | null | ['classification', 'spoken-language-identification'] | ['methodology', 'speech'] | [-2.90302392e-02 -2.89425343e-01 8.67572278e-02 -1.29753992e-01
-1.10161734e+00 -4.46127504e-01 2.72157162e-01 -1.50130808e-01
-6.87844396e-01 3.92378360e-01 4.35765594e-01 -7.54778922e-01
3.00498337e-01 -3.78855824e-01 -3.48314226e-01 -4.77059484e-01
6.98453039e-02 3.10478151e-01 -9.00643691e-02 -4.50208932... | [14.220562934875488, 6.624608516693115] |
1b2c51e5-3beb-43ce-8b7b-1e4a8eb7b1ee | lighting-the-darkness-in-the-deep-learning | 2104.10729 | null | https://arxiv.org/abs/2104.10729v3 | https://arxiv.org/pdf/2104.10729v3.pdf | Low-Light Image and Video Enhancement Using Deep Learning: A Survey | Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions, where many learning strategies, network structures, loss functions, training data, etc. have... | ['Chen Change Loy', 'Jinwei Gu', 'Ming-Ming Cheng', 'Jun Jiang', 'Linghao Han', 'Chunle Guo', 'Chongyi Li'] | 2021-04-21 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 4.22294766e-01 -5.37890375e-01 -1.11481465e-01 -4.18973505e-01
-3.69651645e-01 -2.63628870e-01 2.62378424e-01 -1.89005628e-01
-4.48918521e-01 7.87878036e-01 -1.97045892e-01 -1.84393907e-03
-7.14396983e-02 -7.08737850e-01 -4.15729672e-01 -9.09328878e-01
3.97313386e-02 -3.72100800e-01 -7.60247037e-02 -9.79997888... | [10.874138832092285, -2.3174889087677] |
0e462ce7-6e2c-4cbc-a046-3ede895505af | cost-volume-pyramid-network-with-multi | 2207.12032 | null | https://arxiv.org/abs/2207.12032v1 | https://arxiv.org/pdf/2207.12032v1.pdf | Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo | Multi-view stereo is an important research task in computer vision while still keeping challenging. In recent years, deep learning-based methods have shown superior performance on this task. Cost volume pyramid network-based methods which progressively refine depth map in coarse-to-fine manner, have yielded promising r... | ['Zhaoqi Wang', 'Zhaoxin Li', 'Shiyu Gao'] | 2022-07-25 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 2.41068929e-01 -3.89466107e-01 9.74819437e-02 -3.02873105e-01
-5.35421312e-01 -3.08208048e-01 5.08529961e-01 -2.14488223e-01
-5.12094557e-01 8.32323909e-01 2.06990466e-01 2.64518827e-01
-3.24827790e-01 -1.09654307e+00 -5.63145936e-01 -6.34311199e-01
2.77003884e-01 5.29526711e-01 7.83624172e-01 -8.20646137... | [9.065885543823242, -2.5014808177948] |
73a4dd4e-dd18-4317-ab0c-2494f7fc2e45 | comment-reflections-on-the-deconfounder | 1910.08042 | null | https://arxiv.org/abs/1910.08042v1 | https://arxiv.org/pdf/1910.08042v1.pdf | Comment: Reflections on the Deconfounder | The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the deconfounder method proposed in "The Blessings of Multiple Causes" [arXiv:1805.06826]. I will make three points here. First, in my role as th... | ["Alexander D'Amour"] | 2019-10-17 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 2.70729780e-01 4.30157334e-01 -4.02995944e-01 -3.11177284e-01
-4.32707220e-01 -4.65924472e-01 6.20398879e-01 3.01352143e-01
-4.47317123e-01 1.02446401e+00 5.50615072e-01 -8.97110045e-01
-9.43469822e-01 -6.12474620e-01 -8.44385028e-01 -5.34655690e-01
-2.51202881e-01 -1.22972265e-01 -1.53340504e-01 -2.41215900... | [8.057106018066406, 5.297098159790039] |
3e6c1f23-e44d-44cb-b24b-f6b3afa12126 | shall-we-pretrain-autoregressive-language | 2304.06762 | null | https://arxiv.org/abs/2304.06762v1 | https://arxiv.org/pdf/2304.06762v1.pdf | Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study | Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task accuracy is unclear. Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval? To answer it, we perform a... | ['Bryan Catanzaro', 'Anima Anandkumar', 'Chaowei Xiao', 'Bo Li', 'Oleksii Kuchaiev', 'Yi Dong', 'Mohammad Shoeybi', 'Zihan Liu', 'Lawrence McAfee', 'Peng Xu', 'Wei Ping', 'Boxin Wang'] | 2023-04-13 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 8.07390511e-02 6.18434884e-02 -5.53543605e-02 -3.03833038e-02
-1.72633648e+00 -8.12253773e-01 7.27086306e-01 -2.52072662e-01
-6.35280430e-01 9.19493496e-01 4.42764044e-01 -6.52915001e-01
3.57161202e-02 -5.64924061e-01 -9.84009087e-01 -5.13223648e-01
4.58607793e-01 7.26348519e-01 8.29137489e-02 -4.98856127... | [11.630195617675781, 8.803160667419434] |
56382222-a8e8-4e2b-83ee-2bdb9ddc3128 | estimating-metric-poses-of-dynamic-objects | 1808.06753 | null | http://arxiv.org/abs/1808.06753v1 | http://arxiv.org/pdf/1808.06753v1.pdf | Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion | A monocular 3D object tracking system generally has only up-to-scale pose
estimation results without any prior knowledge of the tracked object. In this
paper, we propose a novel idea to recover the metric scale of an arbitrary
dynamic object by optimizing the trajectory of the objects in the world frame,
without motion... | ['Tong Qin', 'Kejie Qiu', 'Hongwen Xie', 'Shaojie Shen'] | 2018-08-21 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-5.11516809e-01 -3.11256737e-01 2.11127624e-02 7.05525279e-02
-1.42759353e-01 -9.08971369e-01 5.11377752e-01 -5.44165254e-01
-4.53854948e-01 4.36834574e-01 -4.37569797e-01 -1.04768880e-01
2.00940192e-01 -3.54349852e-01 -8.67305875e-01 -6.53039932e-01
2.65136331e-01 5.78336239e-01 4.17149186e-01 9.38486010... | [7.285168170928955, -2.1327545642852783] |
06ff675b-806c-4b31-bdf9-19f2d38a0ce2 | unlocking-temporal-question-answering-for | 2305.15014 | null | https://arxiv.org/abs/2305.15014v1 | https://arxiv.org/pdf/2305.15014v1.pdf | Unlocking Temporal Question Answering for Large Language Models Using Code Execution | Large language models (LLMs) have made significant progress in natural language processing (NLP), and are utilized extensively in various applications. Recent works, such as chain-of-thought (CoT), have shown that intermediate reasoning steps can improve the performance of LLMs for complex reasoning tasks, such as math... | ['Lidong Bing', 'Shafiq Joty', 'Hwee Tou Ng', 'Qingyu Tan', 'Liying Cheng', 'Xingxuan Li'] | 2023-05-24 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-2.36201912e-01 2.35067204e-01 -2.09113583e-02 -3.53275120e-01
-4.94394660e-01 -5.99432647e-01 6.91957831e-01 3.91284466e-01
-2.40960270e-01 5.52033305e-01 -2.89373398e-02 -9.57022607e-01
-1.90231353e-01 -1.18191278e+00 -5.74588835e-01 1.47959724e-01
-2.42990181e-01 4.88431007e-01 9.40254509e-01 -3.93799484... | [9.244367599487305, 7.314091682434082] |
93201fef-23ea-4bc5-b5c6-de23396c8341 | performance-analysis-of-empirical-open-2 | 2306.16542 | null | https://arxiv.org/abs/2306.16542v1 | https://arxiv.org/pdf/2306.16542v1.pdf | Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-1: Performance Measures | The open circuit voltage to the state of charge (OCVSOC) characteristic is crucial for battery management systems. Using the OCV-SOC curve, the SOC and the battery capacity can be estimated in real-time. Accurate SOC and capacity information are important to carry out the majority of battery management functionalities ... | ['Balakumar Balasingam', 'James Nguyen', 'Prarthana Pillai'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [-3.52977008e-01 -9.68690574e-01 -1.91739723e-01 -2.90386289e-01
-3.87079865e-01 -5.27012169e-01 3.07653487e-01 7.32257009e-01
-1.91502720e-01 1.38410306e+00 -3.07903856e-01 -3.05593848e-01
-5.58595546e-02 -7.47782171e-01 -6.50769532e-01 -7.70473301e-01
1.65627062e-01 1.07487716e-01 5.67152262e-01 -1.20931432... | [6.30073881149292, 2.7510159015655518] |
b0cecc5b-3d35-4c05-98d7-b1b5fbfdc62b | from-inscription-to-semi-automatic-annotation | null | null | https://aclanthology.org/2022.lt4hala-1.16 | https://aclanthology.org/2022.lt4hala-1.16.pdf | From Inscription to Semi-automatic Annotation of Maya Hieroglyphic Texts | The Maya script is the only readable autochthonous writing system of the Americas and consists of more than 1000 word signs and syllables. It is only partially deciphered and is the subject of the project “Text Database and Dictionary of the Classic Maya” . Texts are recorded in TEI XML and on the basis of a digital si... | ['Christian Prager', 'Cristina Vertan'] | null | null | null | null | lt4hala-lrec-2022-6 | ['decipherment', 'transliteration'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.05137464e-02 -1.25312403e-01 2.69183517e-02 -5.80598488e-02
-3.15824449e-01 -1.03263986e+00 7.38347173e-01 7.76661187e-03
-2.97138631e-01 4.03545678e-01 4.11625355e-01 -7.34449029e-01
-2.54956931e-01 -6.00292146e-01 -5.23095131e-02 -1.19609468e-01
8.01285923e-01 9.78392303e-01 -1.10981045e-02 -5.43958724... | [10.413895606994629, 10.381119728088379] |
66c659d9-759d-40f3-bc01-e45bd273bf6c | earthquake-magnitude-prediction-in-hindukush | null | null | https://www.researchgate.net/publication/307951466_Earthquake_magnitude_prediction_in_Hindukush_region_using_machine_learning_techniques | https://www.researchgate.net/publication/307951466_Earthquake_magnitude_prediction_in_Hindukush_region_using_machine_learning_techniques | Earthquake magnitude prediction in Hindukush region using machine learning techniques | Earthquake magnitude prediction for Hindukush region has been carried out in this research using the temporal sequence of historic seismic activities in combination with the machine learning classifiers. Prediction has been made on the basis of mathematically calculated eight seismic indicators using the earthquake cat... | ['Francisco Martínez-Álvarez', 'Khawaja Asim', 'Abdul Basit', 'Talat Iqbal'] | 2016-09-08 | null | null | null | springer-2016-9 | ['earthquake-prediction'] | ['computer-vision'] | [ 2.32351869e-01 -7.68219978e-02 1.34508371e-01 -2.05922902e-01
-6.02763593e-01 -1.29573658e-01 4.90202874e-01 4.46978897e-01
-4.84091491e-01 9.81505156e-01 2.78233171e-01 -6.34087980e-01
-5.47894955e-01 -1.06707680e+00 -1.52845666e-01 -9.57226515e-01
-6.27321184e-01 4.51415002e-01 2.55310535e-01 -3.50751817... | [6.433723449707031, 3.001070022583008] |
daeabb89-5a9e-4e0e-8c65-16147b2f9380 | medical-image-registration-using-unsupervised | 2208.01825 | null | https://arxiv.org/abs/2208.01825v1 | https://arxiv.org/pdf/2208.01825v1.pdf | Medical image registration using unsupervised deep neural network: A scoping literature review | In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration i... | ['Alireza Mehdizadeh', 'Reza Javidan', 'Hedieh Khorasani', 'Raouf Khayami', 'Mohammad Amin Mosleh Shirazi', 'Hamid Reza Boveiri', 'Meysam Tavakoli', 'Samaneh Abbasi'] | 2022-08-03 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 4.10205752e-01 2.35946074e-01 -7.27243781e-01 -1.61827937e-01
-6.45998657e-01 7.68425921e-03 2.41355926e-01 5.71489990e-01
-7.67158270e-01 4.12435889e-01 2.53863305e-01 -2.10758850e-01
-5.36223412e-01 -6.00991011e-01 -1.94788173e-01 -1.13497734e+00
-3.79437625e-01 6.12651110e-01 -1.65600955e-01 -1.92875102... | [14.469731330871582, -2.5662286281585693] |
0a566926-eaea-4af3-80e8-9d337363c573 | simplifying-sparse-expert-recommendation-by | 2208.02438 | null | https://arxiv.org/abs/2208.02438v1 | https://arxiv.org/pdf/2208.02438v1.pdf | Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion | Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high migration of users in and out of communities, a key challenge is to design effective strategies for recom... | ['Nino Antulov-Fantulin', 'Vaibhav Krishna'] | 2022-08-04 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-6.19338274e-01 -2.26951301e-01 -5.56499884e-02 -1.73997834e-01
-4.60915267e-01 -7.33476281e-01 6.01196408e-01 5.06437123e-01
-4.36465681e-01 3.60434949e-01 6.68339014e-01 -1.95041060e-01
-4.22174156e-01 -9.39964175e-01 -1.75044850e-01 1.10425517e-01
-1.93537459e-01 7.44525313e-01 7.48707056e-01 -5.76027572... | [11.446191787719727, 7.925364017486572] |
31f012a8-4f7c-486c-a73e-b92ff2471957 | bn-drishti-bangla-document-recognition | 2306.09351 | null | https://arxiv.org/abs/2306.09351v1 | https://arxiv.org/pdf/2306.09351v1.pdf | BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images | Handwriting recognition remains challenging for some of the most spoken languages, like Bangla, due to the complexity of line and word segmentation brought by the curvilinear nature of writing and lack of quality datasets. This paper solves the segmentation problem by introducing a state-of-the-art method (BN-DRISHTI) ... | ['Mohammad Khairul Islam', 'Md. Ataur Rahman', 'Nazifa Tabassum', 'Sheikh Mohammad Jubaer'] | 2023-05-31 | null | null | null | null | ['handwriting-recognition', 'handwritten-line-segmentation', 'handwritten-word-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.00858800e-01 -2.70412862e-01 8.05286244e-02 -4.94324952e-01
-8.47608984e-01 -8.14788580e-01 4.63622808e-01 -1.96957797e-01
-7.11883426e-01 5.85275531e-01 -2.23151371e-01 -4.13122803e-01
3.99688073e-02 -6.71084046e-01 -5.27631283e-01 -7.93633342e-01
4.43010390e-01 8.10392082e-01 2.96814561e-01 -2.26090610... | [11.854950904846191, 2.575085163116455] |
3eda8985-f3c4-4577-9e2c-7acacef602d9 | how-do-languages-influence-each-other | 2305.13286 | null | https://arxiv.org/abs/2305.13286v1 | https://arxiv.org/pdf/2305.13286v1.pdf | How do languages influence each other? Studying cross-lingual data sharing during LLM fine-tuning | Multilingual large language models (MLLMs) are jointly trained on data from many different languages such that representation of individual languages can benefit from other languages' data. Impressive performance on zero-shot cross-lingual transfer shows that these models are capable of exploiting data from other langu... | ['Ekaterina Shutova', 'Dan Garrette', 'Rochelle Choenni'] | 2023-05-22 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-3.74247879e-01 -1.19813956e-01 -4.75928426e-01 -3.88082355e-01
-1.08445442e+00 -8.40538561e-01 1.02322030e+00 1.31823927e-01
-7.58147120e-01 9.27237093e-01 3.65422130e-01 -2.50808328e-01
1.06372327e-01 -5.86451888e-01 -9.16263998e-01 -4.48580444e-01
2.03956366e-01 7.99510181e-01 2.66127288e-01 -5.96152127... | [10.910711288452148, 9.982070922851562] |
59efb44a-0599-4a8d-8516-52a2201761f8 | a-survey-on-causal-discovery-theory-and | 2305.10032 | null | https://arxiv.org/abs/2305.10032v1 | https://arxiv.org/pdf/2305.10032v1.pdf | A Survey on Causal Discovery: Theory and Practice | Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its... | ['Fabio Stella', 'Alessio Zanga'] | 2023-05-17 | null | null | null | null | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [ 4.20556724e-01 4.10777656e-03 -8.58801186e-01 -1.17333427e-01
2.07971409e-02 -7.42885530e-01 9.44435358e-01 4.34606493e-01
9.66134295e-02 9.16693568e-01 5.80436230e-01 -5.75529635e-01
-9.00305748e-01 -9.28674757e-01 -7.35672593e-01 -7.28880227e-01
-7.05745161e-01 2.62339443e-01 -3.83298192e-03 -1.45397499... | [7.899211883544922, 5.38633394241333] |
3c421086-f757-4166-bd58-77a010a0a4ee | multi-scale-representation-learning-on-1 | 2204.02337 | null | https://arxiv.org/abs/2204.02337v1 | https://arxiv.org/pdf/2204.02337v1.pdf | Multi-Scale Representation Learning on Proteins | Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence. The surface captures coarser details of the protein, while sequence as primary component and... | ['Andreas Krause', 'Charlotte Bunne', 'Vignesh Ram Somnath'] | 2022-04-04 | multi-scale-representation-learning-on | http://proceedings.neurips.cc/paper/2021/hash/d494020ff8ec181ef98ed97ac3f25453-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d494020ff8ec181ef98ed97ac3f25453-Paper.pdf | neurips-2021-12 | ['protein-function-prediction'] | ['medical'] | [ 5.22198737e-01 4.09025162e-01 -3.13200235e-01 -3.51575047e-01
-8.47704232e-01 -5.49451888e-01 3.09107274e-01 5.87995112e-01
-3.54671687e-01 1.04866254e+00 2.34215766e-01 -3.91248375e-01
1.55132771e-01 -5.62041402e-01 -1.10495496e+00 -7.81220973e-01
-1.86769977e-01 5.08734286e-01 3.16542178e-01 -6.03304841... | [4.849228858947754, 5.667567253112793] |
9225d7cb-0401-4fdb-9b1d-952f08e08c5a | spikecodec-an-end-to-end-learned-compression | 2306.14108 | null | https://arxiv.org/abs/2306.14108v1 | https://arxiv.org/pdf/2306.14108v1.pdf | SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera | Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challe... | ['Wen Gao', 'Siwei Ma', 'Chuanmin Jia', 'Kexiang Feng'] | 2023-06-25 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.80522883e-01 -5.56121647e-01 2.43226856e-01 -2.07442671e-01
-1.00721955e+00 -2.40400523e-01 4.24016416e-01 -6.66183457e-02
-5.01183510e-01 9.21314418e-01 3.48603725e-01 4.62979645e-01
-2.81086445e-01 -4.42629367e-01 -8.24933410e-01 -9.90729690e-01
5.72212562e-02 3.69758636e-01 3.20952445e-01 1.57474399... | [8.830697059631348, -1.2198576927185059] |
4b7e8da4-1297-48a6-89fd-8f30aa279be3 | deep-multimodal-subspace-clustering-networks | 1804.06498 | null | http://arxiv.org/abs/1804.06498v3 | http://arxiv.org/pdf/1804.06498v3.pdf | Deep Multimodal Subspace Clustering Networks | We present convolutional neural network (CNN) based approaches for
unsupervised multimodal subspace clustering. The proposed framework consists of
three main stages - multimodal encoder, self-expressive layer, and multimodal
decoder. The encoder takes multimodal data as input and fuses them to a latent
space representa... | ['Vishal M. Patel', 'Mahdi Abavisani'] | 2018-04-17 | null | null | null | null | ['multi-view-subspace-clustering', 'multiview-learning', 'multi-modal-subspace-clustering'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-8.59250082e-04 -5.22546060e-02 -1.19139351e-01 -5.04757822e-01
-9.87612247e-01 -5.73044538e-01 6.30108178e-01 -9.65162367e-02
-4.84099984e-01 3.89022827e-01 6.34659886e-01 2.21320242e-01
-7.78077543e-02 -4.60396379e-01 -8.74909222e-01 -1.10685372e+00
3.35577995e-01 5.64906061e-01 3.90358232e-02 5.91467209... | [13.128105163574219, 5.003395080566406] |
550c41e0-836e-4fe8-891b-38ecc7c4416a | acnlp-at-semeval-2020-task-6-a-supervised | null | null | https://aclanthology.org/2020.semeval-1.58 | https://aclanthology.org/2020.semeval-1.58.pdf | ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction | We describe our contribution to two of the subtasks of SemEval 2020 Task 6, DeftEval: Extracting term-definition pairs in free text. The system for Subtask 1: Sentence Classification is based on a transformer architecture where we use transfer learning to fine-tune a pretrained model on the downstream task, and the one... | ['Mhamed Hajaiej', 'Mathis Linger', 'Pirashanth Ratnamogan', 'Fabien Caspani'] | 2020-12-01 | null | null | null | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 4.83597130e-01 6.87374353e-01 -4.02178228e-01 -4.98912126e-01
-1.15892899e+00 -7.56730795e-01 8.17755878e-01 3.42049658e-01
-5.78488410e-01 1.22910833e+00 2.85212040e-01 -7.45664597e-01
-2.63552129e-01 -6.66469574e-01 -5.76590180e-01 -2.70167470e-01
-1.44846305e-01 5.62562525e-01 1.90002263e-01 -7.08856523... | [9.693426132202148, 8.932903289794922] |
19302f70-3e14-442f-859a-74a15575cf62 | ace-net-fine-level-face-alignment-through | 2012.01461 | null | https://arxiv.org/abs/2012.01461v2 | https://arxiv.org/pdf/2012.01461v2.pdf | ACE-Net: Fine-Level Face Alignment through Anchors and Contours Estimation | We propose a novel facial Anchors and Contours Estimation framework, ACE-Net, for fine-level face alignment tasks. ACE-Net predicts facial anchors and contours that are richer than traditional facial landmarks while overcoming ambiguities and inconsistencies in their definitions. We introduce a weakly supervised loss e... | ['Amir Tamrakar', 'Jihua Huang'] | 2020-12-02 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-4.57280055e-02 8.87157381e-01 -2.49904260e-01 -8.96752298e-01
-1.06962514e+00 -4.08052295e-01 6.02118850e-01 -4.34460104e-01
-2.50823885e-01 6.32631719e-01 3.57659370e-01 2.75065213e-01
2.57364780e-01 -5.12054443e-01 -6.89676821e-01 -3.03743899e-01
-5.74276932e-02 7.03271687e-01 -1.99888870e-01 -2.77328283... | [13.39801025390625, 0.2623896300792694] |
b8fcae79-53ee-4c5c-97b5-d8b2ace6d958 | motion-attentive-transition-for-zero-shot | 2003.04253 | null | https://arxiv.org/abs/2003.04253v3 | https://arxiv.org/pdf/2003.04253v3.pdf | Motion-Attentive Transition for Zero-Shot Video Object Segmentation | In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed withi... | ['Tianfei Zhou', 'Yazhou Yao', 'Shunzhou Wang', 'Jianwu Li', 'Yi Zhou', 'Ling Shao'] | 2020-03-09 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.33002141e-01 -1.36687979e-01 -4.87065226e-01 -4.64824021e-01
-5.25680721e-01 -2.22349405e-01 3.74685168e-01 -3.15310180e-01
-4.47158694e-01 3.78402710e-01 2.72504926e-01 6.24695383e-02
4.60564196e-01 -6.01402938e-01 -9.29498494e-01 -6.45898998e-01
-1.24838009e-01 -1.68339871e-02 8.80506516e-01 1.10242201... | [9.25650405883789, -0.10987801104784012] |
f7796008-ea6e-4358-a327-de6f8c6a96b8 | enhancing-local-feature-learning-using | 2207.01174 | null | https://arxiv.org/abs/2207.01174v1 | https://arxiv.org/pdf/2207.01174v1.pdf | Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding | Learning point clouds is challenging due to the lack of connectivity information, i.e., edges. Although existing edge-aware methods can improve the performance by modeling edges, how edges contribute to the improvement is unclear. In this study, we propose a method that automatically learns to enhance/suppress edges wh... | ['Masashi Matsuoka', 'Qiong Chang', 'Takayuki Shinohara', 'Kyoung-Sook Kim', 'Weimin WANG', 'Xin Liu', 'Haoyi Xiu'] | 2022-07-04 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 6.72488380e-03 1.14234842e-01 -2.31949449e-01 -1.99300319e-01
-3.45291018e-01 -3.46421421e-01 4.20475528e-02 2.72846729e-01
-1.41154662e-01 4.70079482e-01 -1.54654205e-01 -1.47482634e-01
-1.31411701e-01 -9.82182801e-01 -1.06866193e+00 -4.62547779e-01
-2.90606171e-01 2.37429872e-01 5.68765461e-01 3.57363820... | [8.002401351928711, -3.218546152114868] |
4738fe9a-4806-4171-b9c6-6246365c0fe6 | disentangling-object-motion-and-occlusion-for | 2203.15174 | null | https://arxiv.org/abs/2203.15174v2 | https://arxiv.org/pdf/2203.15174v2.pdf | Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth | Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem ... | ['Bing Li', 'YingLi Tian', 'HaiYan Wang', 'Longlong Jing', 'Liang Yang', 'Ziyue Feng'] | 2022-03-29 | null | null | null | null | ['motion-disentanglement'] | ['computer-vision'] | [-1.23593852e-01 -2.36230373e-01 -4.94870931e-01 -4.11694437e-01
-4.89502460e-01 -2.30359957e-01 5.26079118e-01 -5.40435374e-01
-9.14077908e-02 6.35653436e-01 3.01214606e-01 2.21465588e-01
1.56511039e-01 -6.29574180e-01 -4.94084328e-01 -8.06834161e-01
5.02816677e-01 3.87109607e-01 6.22317076e-01 1.47506922... | [8.66174030303955, -2.2848546504974365] |
a169790a-c0cb-4690-a133-51d1f8c9ae76 | unsupervised-domain-adaptation-without-source | 2010.12427 | null | https://arxiv.org/abs/2010.12427v5 | https://arxiv.org/pdf/2010.12427v5.pdf | Casting a BAIT for Offline and Online Source-free Domain Adaptation | We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online settin... | ['Shangling Jui', 'Luis Herranz', 'Joost Van de Weijer', 'Yaxing Wang', 'Shiqi Yang'] | 2020-10-23 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.18632096e-01 -1.73375830e-01 -4.18496221e-01 -4.17857856e-01
-7.90257812e-01 -7.09320664e-01 2.61211336e-01 2.73295432e-01
-3.71978015e-01 9.62433577e-01 -4.44901884e-01 -1.42035827e-01
3.05509157e-02 -7.18436241e-01 -6.25073969e-01 -1.01800740e+00
-1.13042600e-01 9.91283596e-01 6.22869253e-01 -3.98938395... | [10.360246658325195, 3.145914316177368] |
aac4c998-c36b-44bb-9b95-fda819ce1ad7 | concise-answers-to-complex-questions | 2305.19271 | null | https://arxiv.org/abs/2305.19271v1 | https://arxiv.org/pdf/2305.19271v1.pdf | Concise Answers to Complex Questions: Summarization of Long-form Answers | Long-form question answering systems provide rich information by presenting paragraph-level answers, often containing optional background or auxiliary information. While such comprehensive answers are helpful, not all information is required to answer the question (e.g. users with domain knowledge do not need an explan... | ['Eunsol Choi', 'Fangyuan Xu', 'Abhilash Potluri'] | 2023-05-30 | null | null | null | null | ['long-form-question-answering', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.23093376e-01 7.99661815e-01 -1.07180163e-01 -3.27946007e-01
-1.60367012e+00 -9.50952411e-01 5.50086260e-01 8.47405910e-01
-4.55881238e-01 1.03312564e+00 1.07318044e+00 -4.03849214e-01
-2.73296982e-01 -5.76678813e-01 -5.92475414e-01 9.48825106e-02
6.18294418e-01 9.33276772e-01 4.51059550e-01 -7.83962667... | [11.601612091064453, 8.186164855957031] |
6de79ec9-4838-4c73-bfbc-4d987b8f1929 | negation-scope-delimitation-in-clinical-text | null | null | https://aclanthology.org/W13-5635 | https://aclanthology.org/W13-5635.pdf | Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx, PyConTextNLP and SynNeg | null | ['Sumithra Velupillai', 'Hideyuki Tanushi', 'Hercules Dalianis', 'Martin Duneld', 'Maria Skeppstedt', 'Maria Kvist'] | 2013-05-01 | null | null | null | ws-2013-5 | ['negation-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.395808696746826, 3.7034409046173096] |
cad5ef65-ddf8-4fc4-ba26-d85fb9ae74de | formal-verification-of-piece-wise-linear-feed | 1705.01320 | null | http://arxiv.org/abs/1705.01320v3 | http://arxiv.org/pdf/1705.01320v3.pdf | Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks | We present an approach for the verification of feed-forward neural networks
in which all nodes have a piece-wise linear activation function. Such networks
are often used in deep learning and have been shown to be hard to verify for
modern satisfiability modulo theory (SMT) and integer linear programming (ILP)
solvers.
... | ['Ruediger Ehlers'] | 2017-05-03 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.55174387e-01 8.47346544e-01 -1.64846718e-01 -5.87474346e-01
-2.46090263e-01 -6.45668983e-01 1.86009318e-01 2.39950851e-01
-1.82006344e-01 9.83688295e-01 -6.77142859e-01 -1.04774261e+00
-5.30117691e-01 -9.33627665e-01 -1.43599021e+00 -5.90191007e-01
-5.21316588e-01 7.38237679e-01 3.76440287e-01 -2.29621485... | [8.72257137298584, 6.931527614593506] |
411e0dca-d224-4154-9296-94814d534e3b | fast-graph-sampling-for-short-video | 2110.11420 | null | https://arxiv.org/abs/2110.11420v2 | https://arxiv.org/pdf/2110.11420v2.pdf | Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment | We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similarity path graph (SPG) $\mathcal{G}$, represented by graph Laplacian matrix $\mathbf{L}$, where the similarities between adjacent frames are en... | ['Chia-Wen Lin', 'Gene Cheung', 'Sadid Sahami'] | 2021-10-21 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.15721178e-01 4.65277284e-01 -8.12589601e-02 1.26579953e-02
-9.61072624e-01 -7.19919503e-01 -3.74591023e-01 2.56217390e-01
-2.17811465e-01 5.71664631e-01 -2.62009710e-01 -7.23264217e-01
-6.04516685e-01 -1.01216257e+00 -8.86845112e-01 -6.24576151e-01
-1.04987776e+00 -1.31533578e-01 9.30317864e-02 -2.80296147... | [6.5999627113342285, 4.765214920043945] |
308b2af1-e415-46ed-9fc8-cd455e9a29d1 | low-resource-named-entity-recognition-via | null | null | https://aclanthology.org/W18-6125 | https://aclanthology.org/W18-6125.pdf | Low-resource named entity recognition via multi-source projection: Not quite there yet? | Projecting linguistic annotations through word alignments is one of the most prevalent approaches to cross-lingual transfer learning. Conventional wisdom suggests that annotation projection {``}just works{''} regardless of the task at hand. We carefully consider multi-source projection for named entity recognition. Our... | ["{\\v{Z}}eljko Agi{\\'c}", 'S{\\o}ren Harrison', 'Jan Vium Enghoff'] | 2018-11-01 | null | null | null | ws-2018-11 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-5.72781935e-02 2.87304699e-01 -6.15067542e-01 -5.92188179e-01
-1.27116168e+00 -8.85458171e-01 4.58054662e-01 -2.36320153e-01
-8.33171725e-01 1.09022737e+00 6.10630810e-01 -7.56303549e-01
3.38528454e-01 -4.99831110e-01 -6.77425921e-01 -3.36808443e-01
3.71543407e-01 6.18779480e-01 8.48193914e-02 -2.86540926... | [10.339458465576172, 9.852641105651855] |
0ea5394a-a891-4a37-a5af-281144469afc | hierarchical-conditional-flow-a-unified | 2108.05301 | null | https://arxiv.org/abs/2108.05301v1 | https://arxiv.org/pdf/2108.05301v1.pdf | Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling | Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolution (HR) images from the low-resolution (LR) image rather than learning a deterministic mapping. For image rescaling, it achieves high accu... | ['Radu Timofte', 'Luc van Gool', 'Martin Danelljan', 'Kai Zhang', 'Andreas Lugmayr', 'Jingyun Liang'] | 2021-08-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liang_Hierarchical_Conditional_Flow_A_Unified_Framework_for_Image_Super-Resolution_and_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liang_Hierarchical_Conditional_Flow_A_Unified_Framework_for_Image_Super-Resolution_and_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-super-resolution'] | ['computer-vision'] | [ 7.11965144e-01 4.45962325e-02 -1.51562408e-01 -6.17878258e-01
-9.71337080e-01 4.93923128e-02 4.96236116e-01 -5.37505984e-01
-2.55678862e-01 7.39389002e-01 3.30872834e-01 1.61053568e-01
2.64278408e-02 -7.21915901e-01 -8.24199855e-01 -8.04194152e-01
3.75327826e-01 -4.68298681e-02 1.12368064e-02 -1.13026798... | [11.081486701965332, -2.0184104442596436] |
6d849a17-55fd-41a4-b4df-525a8dd5df0c | s-convnet-a-shallow-convolutional-neural | 1906.03381 | null | https://arxiv.org/abs/1906.03381v1 | https://arxiv.org/pdf/1906.03381v1.pdf | S-ConvNet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images | The concept of neuromuscular activity recognition using instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep convolutional neural network (ConvNet) a... | ['Wei-Ping Zhu', 'Philippe Massicotte', 'Francois Nougarou', 'Daniel Massicotte', 'Md. Rabiul Islam'] | 2019-06-08 | null | null | null | null | ['gesture-recognition', 'muscle-computer-interfaces-mcis', 'low-latency-processing'] | ['computer-vision', 'robots', 'robots'] | [ 2.35561237e-01 -2.68503010e-01 -1.08912900e-01 -8.80890042e-02
-3.86713505e-01 1.57881156e-01 8.70057270e-02 -8.09693038e-01
-8.14504921e-01 8.09780896e-01 -1.26401354e-02 2.17410743e-01
-2.21129581e-01 -5.47772527e-01 -7.66738594e-01 -8.94200742e-01
-2.24762589e-01 3.25704724e-01 2.44346246e-01 -8.59662443... | [6.894866943359375, 0.15377454459667206] |
a6cec09b-e85a-4900-8814-afa45014121f | tribeflow-mining-predicting-user-trajectories | 1511.01032 | null | http://arxiv.org/abs/1511.01032v2 | http://arxiv.org/pdf/1511.01032v2.pdf | TribeFlow: Mining & Predicting User Trajectories | Which song will Smith listen to next? Which restaurant will Alice go to
tomorrow? Which product will John click next? These applications have in common
the prediction of user trajectories that are in a constant state of flux over a
hidden network (e.g. website links, geographic location). What users are doing
now may b... | ['Christos Faloutsos', 'Jussara Almeida', 'Flavio Figueiredo', 'Bruno Ribeiro'] | 2015-11-03 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-2.32074738e-01 -2.78097004e-01 -8.34618390e-01 -3.21207285e-01
-3.47501814e-01 -7.53523231e-01 2.72664875e-01 3.15470606e-01
-4.05053422e-02 7.76668131e-01 4.88465935e-01 -5.54644167e-01
-6.08721256e-01 -9.48654175e-01 -5.62101662e-01 -3.45461786e-01
-6.23838603e-01 7.64477551e-01 4.46531892e-01 -2.78906643... | [7.124205112457275, 3.089689254760742] |
2fb2c55f-f65a-4dd0-bac9-15c88575e67d | hierarchical-reinforcement-learning-for-7 | 2210.10431 | null | https://arxiv.org/abs/2210.10431v1 | https://arxiv.org/pdf/2210.10431v1.pdf | Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes | In real life, the decoration of 3D indoor scenes through designing furniture layout provides a rich experience for people. In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL). The goal is to produce a pro... | ['Pengqian Yu', 'Xinhan Di'] | 2022-10-19 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-3.92425567e-01 -1.38064250e-01 1.92308649e-01 -1.88308969e-01
-3.52082998e-01 -3.37386906e-01 -5.73767163e-02 -1.90762773e-01
1.00838639e-01 8.55962038e-01 1.45137966e-01 -4.96053189e-01
-5.87581098e-01 -7.71240056e-01 -6.58091128e-01 -6.84092939e-01
-1.18786357e-01 3.26827675e-01 -3.57090116e-01 -2.47713953... | [5.025123596191406, 0.7876526117324829] |
af949eef-3231-4a03-9a68-f0d230747e03 | unpaired-point-cloud-completion-on-real-scans | 1904.00069 | null | https://arxiv.org/abs/1904.00069v3 | https://arxiv.org/pdf/1904.00069v3.pdf | Unpaired Point Cloud Completion on Real Scans using Adversarial Training | As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i.e., plausibly filling in regions there were missed in the raw scans. These methods, however, largely rely on supervision in the form of paired training data, i.e., partia... | ['Niloy J. Mitra', 'Xuelin Chen', 'Baoquan Chen'] | 2019-03-29 | null | https://openreview.net/forum?id=HkgrZ0EYwB | https://openreview.net/pdf?id=HkgrZ0EYwB | iclr-2020-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 4.10731405e-01 3.98629248e-01 1.45043835e-01 -5.33581257e-01
-9.34735060e-01 -5.74649811e-01 6.25953138e-01 -1.23717897e-01
-1.20738834e-01 4.63585287e-01 -9.82977003e-02 -4.09666210e-01
-2.11644322e-01 -9.83382523e-01 -1.15973580e+00 -6.80418909e-02
-3.82565297e-02 1.22581184e+00 2.36321002e-01 -2.00964153... | [8.417257308959961, -3.2561070919036865] |
77c20c87-59b3-4b1e-b94c-973f2c17af10 | gatector-a-unified-framework-for-gaze-object | 2112.03549 | null | https://arxiv.org/abs/2112.03549v3 | https://arxiv.org/pdf/2112.03549v3.pdf | GaTector: A Unified Framework for Gaze Object Prediction | Gaze object prediction is a newly proposed task that aims to discover the objects being stared at by humans. It is of great application significance but still lacks a unified solution framework. An intuitive solution is to incorporate an object detection branch into an existing gaze prediction method. However, previous... | ['Zhijie Zhang', 'Xiaojuan Chen', 'Baoshan Li', 'Tao Hu', 'Binglu Wang'] | 2021-12-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_GaTector_A_Unified_Framework_for_Gaze_Object_Prediction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_GaTector_A_Unified_Framework_for_Gaze_Object_Prediction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['gaze-estimation', 'eye-tracking'] | ['computer-vision', 'computer-vision'] | [ 2.97738731e-01 -8.09818283e-02 -5.03794029e-02 -6.36920333e-01
-3.05815730e-02 -1.70835685e-02 1.45788550e-01 -2.72062421e-01
-2.78025270e-01 4.20889348e-01 -1.49790933e-02 9.90300626e-02
-1.93474576e-01 -3.78301084e-01 -6.61253631e-01 -1.07987380e+00
5.47918260e-01 -2.78170913e-01 5.10884523e-01 -5.21251708... | [9.832725524902344, -0.40920644998550415] |
f230dc0f-6d16-46c0-ab11-f8765a794be8 | est-ce-que-tu-me-suis-une-revue-du-suivi-de | null | null | https://aclanthology.org/2022.jeptalnrecital-recital.1 | https://aclanthology.org/2022.jeptalnrecital-recital.1.pdf | « Est-ce que tu me suis ? » : une revue du suivi de l’état du dialogue (“Do you follow me ?" : a review of dialogue state tracking ) | Tout en communiquant avec un utilisateur, un système de dialogue orienté tâche doit suivre les besoins de l’utilisateur à chaque étape selon l’historique de la conversation. Ce procédé appelé suivi de l’état du dialogue est primordial car il informe directement les actions du système. Cet article présente dans un premi... | ['Léo Jacqmin'] | null | null | null | null | jep-taln-recital-2022-6 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 7.95102268e-02 2.56087273e-01 3.11221004e-01 -4.72122014e-01
-1.07066929e-01 -1.01379919e+00 1.34378076e+00 8.59585702e-01
-4.84243482e-01 9.21589136e-01 4.15636450e-01 -1.19994819e-01
3.54545861e-01 -9.81921732e-01 -5.99635005e-01 -5.12190796e-02
3.53563279e-01 2.10935831e-01 -1.76463723e-01 -1.10477626... | [14.102492332458496, 13.31672191619873] |
ebb3a513-9916-4e5f-82f4-e94b0cd21a01 | online-robust-mpc-based-emergency-maneuvering | 2109.11959 | null | https://arxiv.org/abs/2109.11959v2 | https://arxiv.org/pdf/2109.11959v2.pdf | Online Robust MPC based Emergency Maneuvering System for Autonomous Vehicles | Nonlinear Robust Model Predictive Control (RMPC) provides a very promising solution to the problem of automatic emergency maneuvering, which is capable of handling multiple possibly conflicting objectives of robustness and performance. Even though RMPC gives a suboptimal solution, the key challenge in real-time impleme... | ['Vivek Bithar', 'Shawn Midlam-Mohler', 'Punit Tulpule'] | 2021-09-24 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 3.58357638e-01 2.67630160e-01 7.57699609e-02 6.78289682e-02
-7.26550937e-01 -5.87528288e-01 8.32313657e-01 -1.00786239e-01
-4.73259985e-01 1.14092028e+00 -3.07702690e-01 -8.14432859e-01
-7.53131211e-01 -6.79420829e-01 -4.18128014e-01 -1.04526246e+00
-2.89431512e-01 7.39993334e-01 5.11572361e-01 -9.96316433... | [5.257544040679932, 2.088841676712036] |
27d7be43-ff22-4fe4-b35c-a0f90f405da9 | did-you-mean-confidence-based-trade-offs-in | 2303.16857 | null | https://arxiv.org/abs/2303.16857v2 | https://arxiv.org/pdf/2303.16857v2.pdf | Did You Mean...? Confidence-based Trade-offs in Semantic Parsing | We illustrate how a calibrated model can help balance common trade-offs in task-oriented parsing. In a simulated annotator-in-the-loop experiment, we show that well-calibrated confidence scores allow us to balance cost with annotator load, improving accuracy with a small number of interactions. We then examine how conf... | ['Benjamin Van Durme', 'Elias Stengel-Eskin'] | 2023-03-29 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-1.92988571e-02 5.14010489e-01 -4.09922078e-02 -7.76958704e-01
-1.26998353e+00 -9.81728733e-01 8.72623525e-04 6.15903080e-01
-6.89798295e-01 5.43428779e-01 4.90016770e-03 -6.68078125e-01
2.15300456e-01 -3.93829286e-01 -6.98996902e-01 -3.93785127e-02
2.22467273e-01 3.67390037e-01 5.78800440e-01 -1.72049664... | [10.675456047058105, 8.727056503295898] |
930d6df9-82f1-44fa-acb4-a1d5fa257ab7 | is-more-data-better-using-transformers-based | null | null | https://openreview.net/forum?id=f0KsTiVPZWZ | https://openreview.net/pdf?id=f0KsTiVPZWZ | Is More Data Better? Using Transformers-Based Active Learning for Efficient and Effective Detection of Abusive Language | Annotating abusive language content can cause psychological harm; yet, most machine learning research has prioritized efficacy (i.e., F1 or accuracy scores) while little research has analyzed data efficiency (i.e., how to minimize annotation requirements).In this paper, we use a series of simulated experiments over two... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['abusive-language'] | ['natural-language-processing'] | [ 6.28267378e-02 4.15693372e-01 -9.88586366e-01 -4.11066294e-01
-8.44546676e-01 -6.28637493e-01 2.73501247e-01 8.03409457e-01
-9.78281796e-01 7.92245865e-01 2.35970795e-01 -4.41087276e-01
-2.20493138e-01 -3.35873187e-01 -2.88653314e-01 -2.71489948e-01
-1.60996258e-01 2.57420003e-01 3.61062624e-02 1.08910382... | [8.709134101867676, 10.452536582946777] |
6397ac78-cdcd-42cb-894e-0eb2f200d830 | graph-attention-networks | 1710.10903 | null | http://arxiv.org/abs/1710.10903v3 | http://arxiv.org/pdf/1710.10903v3.pdf | Graph Attention Networks | We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over thei... | ['Pietro Liò', 'Arantxa Casanova', 'Petar Veličković', 'Yoshua Bengio', 'Guillem Cucurull', 'Adriana Romero'] | 2017-10-30 | graph-attention-networks-1 | https://openreview.net/forum?id=rJXMpikCZ | https://openreview.net/pdf?id=rJXMpikCZ | iclr-2018-1 | ['node-classification-on-non-homophilic', 'graph-regression'] | ['graphs', 'graphs'] | [ 5.70019305e-01 5.06777585e-01 -2.70349473e-01 -3.56839269e-01
-2.07632303e-01 -4.96313602e-01 5.90490699e-01 6.60726845e-01
-3.46184283e-01 7.67944038e-01 3.50372553e-01 -7.69435644e-01
-9.89673361e-02 -1.16936064e+00 -1.19487286e+00 -5.43128669e-01
-4.59176749e-01 6.64400935e-01 -9.50215757e-02 -3.05534214... | [6.869557857513428, 6.286044597625732] |
4d0ce8f0-71b6-4db7-9597-3c669cad1dc5 | attacking-pre-trained-recommendation | 2305.03995 | null | https://arxiv.org/abs/2305.03995v1 | https://arxiv.org/pdf/2305.03995v1.pdf | Attacking Pre-trained Recommendation | Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. Despite these advancements, the vulnerabilities of classical recommende... | ['Qing He', 'Yongjun Xu', 'Jie zhou', 'Fuzhen Zhuang', 'Yongchun Zhu', 'Zhao Zhang', 'Ruobing Xie', 'Yiqing Wu'] | 2023-05-06 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 1.48915350e-01 -3.59615028e-01 -4.51051861e-01 -5.26500382e-02
-2.45483294e-01 -1.24110532e+00 4.53209519e-01 -3.03608268e-01
5.66182323e-02 2.98790783e-01 2.35320166e-01 -9.82713044e-01
-4.16234940e-01 -8.89830887e-01 -7.63707697e-01 -5.94083965e-01
-3.29023093e-01 -2.01948792e-01 2.85452276e-01 -5.96525848... | [5.862672328948975, 7.1338725090026855] |
617a2d63-9b1f-4dd7-a9e2-0e2308a19d95 | dior-cvae-diffusion-priors-in-variational | 2305.15025 | null | https://arxiv.org/abs/2305.15025v1 | https://arxiv.org/pdf/2305.15025v1.pdf | Dior-CVAE: Diffusion Priors in Variational Dialog Generation | Conditional variational autoencoders (CVAEs) have been used recently for diverse response generation, by introducing latent variables to represent the relationship between a dialog context and its potential responses. However, the diversity of the generated responses brought by a CVAE model is limited due to the oversi... | ['Iryna Gurevych', 'Thy Thy Tran', 'Tianyu Yang'] | 2023-05-24 | null | null | null | null | ['response-generation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-2.33814865e-01 4.50693697e-01 1.28473714e-01 -5.89257360e-01
-7.89773166e-01 -6.52925551e-01 1.01581478e+00 -4.63476002e-01
-1.74638376e-01 8.49384308e-01 8.46690893e-01 2.44258679e-02
3.82946044e-01 -7.30573356e-01 -4.35337424e-01 -7.01015651e-01
6.55227900e-01 9.37729597e-01 4.43801172e-02 -4.47493941... | [12.560476303100586, 8.385916709899902] |
7b51c1c2-71ac-48eb-9062-b76356b6314d | food-ingredients-recognition-through-multi-1 | 2210.14147 | null | https://arxiv.org/abs/2210.14147v1 | https://arxiv.org/pdf/2210.14147v1.pdf | Food Ingredients Recognition through Multi-label Learning | The ability to recognize various food-items in a generic food plate is a key determinant for an automated diet assessment system. This study motivates the need for automated diet assessment and proposes a framework to achieve this. Within this framework, we focus on one of the core functionalities to visually recognize... | ['Zhaorui Yuan', 'Rameez Ismail'] | 2022-10-24 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.76666135e-01 -1.79978400e-01 -1.34209663e-01 -3.57994437e-01
-5.33280134e-01 -7.30377555e-01 3.46700013e-01 8.48742306e-01
-4.20810044e-01 1.84950948e-01 2.69222409e-01 4.98140976e-02
1.07389733e-01 -8.29491854e-01 -8.57305348e-01 -5.89156985e-01
-2.87981331e-01 1.47524923e-01 -1.44800395e-01 -9.95603651... | [11.5648832321167, 4.397433757781982] |
61381627-c841-40ca-b2af-a784f61fab3d | privacy-preserving-deep-learning-via-weight | 1809.03272 | null | http://arxiv.org/abs/1809.03272v3 | http://arxiv.org/pdf/1809.03272v3.pdf | Privacy-Preserving Deep Learning via Weight Transmission | This paper considers the scenario that multiple data owners wish to apply a
machine learning method over the combined dataset of all owners to obtain the
best possible learning output but do not want to share the local datasets owing
to privacy concerns. We design systems for the scenario that the stochastic
gradient d... | ['Tran Thi Phuong', 'Le Trieu Phong'] | 2018-09-10 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-4.63221669e-02 2.58495867e-01 -1.46594375e-01 -6.78100109e-01
-7.18510747e-01 -8.38423193e-01 3.54336232e-01 1.82658896e-01
-1.02185929e+00 9.83835399e-01 -2.36289218e-01 -4.80631769e-01
-9.19094309e-02 -8.83602858e-01 -1.08318770e+00 -1.20906365e+00
-2.47195587e-01 9.02684480e-02 -2.57739544e-01 1.21731617... | [5.87021017074585, 6.714230537414551] |
04fb5b6e-5ab0-425a-a827-1bfb4aba60da | 4dsr-gcn-4d-video-point-cloud-upsampling | 2306.01081 | null | https://arxiv.org/abs/2306.01081v1 | https://arxiv.org/pdf/2306.01081v1.pdf | 4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks | Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e.g., LiDAR in autonomous or assisted driving). In many cases, such volume of data is transmitted, thus requiring that proper compression tools are applied to either reduce the resolution... | ['Alberto del Bimbo', 'Marco Bertini', 'Stefano Berretti', 'Lorenzo Berlincioni'] | 2023-06-01 | null | null | null | null | ['graph-attention', 'edge-computing'] | ['graphs', 'time-series'] | [ 1.82669953e-01 1.58365861e-01 3.43180120e-01 1.10992370e-02
-4.24958348e-01 -5.09710550e-01 7.07701683e-01 2.14808449e-01
-4.16071862e-01 4.38108563e-01 -2.66914397e-01 -5.97611964e-01
3.95280942e-02 -1.25503349e+00 -1.12080991e+00 -3.83867294e-01
-2.41906047e-01 6.73397958e-01 3.34953040e-01 -2.40975887... | [8.487367630004883, -3.6283373832702637] |
5c205e60-caae-4e4d-bd13-a1e4d4218a52 | towards-open-set-3d-learning-a-benchmark-on | 2207.11554 | null | https://arxiv.org/abs/2207.11554v3 | https://arxiv.org/pdf/2207.11554v3.pdf | 3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds | In recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilities of robots and auto... | ['Tatiana Tommasi', 'Francesco Cappio Borlino', 'Antonio Alliegro'] | 2022-07-23 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.24603698e-01 1.24438696e-01 2.67220922e-02 -2.20746726e-01
-4.77428973e-01 -8.73433650e-01 6.69377267e-01 1.72158971e-01
-1.98128864e-01 4.76140440e-01 -3.18664938e-01 -8.15334246e-02
-3.65100265e-01 -5.46403289e-01 -6.75575495e-01 -7.27391779e-01
-5.25463343e-01 6.27823114e-01 5.21828532e-01 -4.43573296... | [8.117250442504883, -2.980351686477661] |
33830dff-3266-43bd-9fb3-2c5ac885ad5e | robust-tensor-recovery-using-low-rank-tensor | 1904.00435 | null | https://arxiv.org/abs/1904.00435v3 | https://arxiv.org/pdf/1904.00435v3.pdf | Robust Low-Rank Tensor Ring Completion | Low-rank tensor completion recovers missing entries based on different tensor decompositions. Due to its outstanding performance in exploiting some higher-order data structure, low rank tensor ring has been applied in tensor completion. To further deal with its sensitivity to sparse component as it does in tensor princ... | ['Yipeng Liu', 'Ce Zhu', 'Huyan Huang'] | 2019-03-31 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 1.17737658e-01 -4.69834507e-01 -4.56897207e-02 6.47542328e-02
-4.31356490e-01 -3.49091113e-01 1.64040431e-01 -4.72510964e-01
-1.40545100e-01 4.52109486e-01 3.37085515e-01 -3.71560194e-02
-4.72202510e-01 -1.45217374e-01 -3.94865960e-01 -1.15489697e+00
-2.67017186e-01 4.53261435e-02 -2.15673074e-01 -2.42930427... | [7.434358596801758, 4.4486775398254395] |
7a73e60f-0c03-4512-925a-356ad43a60b1 | spleeter-a-fast-and-state-of-the-art-music | null | null | https://archives.ismir.net/ismir2019/latebreaking/ | https://archives.ismir.net/ismir2019/latebreaking/000036.pdf | Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models | We present and release a new tool for music source separation with pre-trained models called Spleeter.Spleeter was designed with ease of use, separation performance and speed in mind. Spleeter is based onTensorflow [1] and makes it possible to:•separate audio files into2,4or5stems with a single command ... | ['Manuel Moussallam', 'Romain Hennequin', 'Felix Voituret', 'Anis Khlif'] | 2019-11-04 | null | null | null | ismir-2019-late-breaking-demo-2019-11 | ['music-source-separation'] | ['music'] | [-8.81820768e-02 -2.96953201e-01 -2.20163584e-01 9.47827846e-02
-1.04847193e+00 -7.81030834e-01 2.54863381e-01 2.27685392e-01
-2.66725898e-01 2.21733898e-01 4.58073080e-01 7.18201175e-02
-3.19579303e-01 -2.64802396e-01 -5.56319714e-01 -3.31238568e-01
-1.61955550e-01 4.42237675e-01 3.47437024e-01 -8.83096531... | [15.644262313842773, 5.4791951179504395] |
b341c6c5-f012-4434-8876-fdf45f5c6a1f | transfer-learning-with-jukebox-for-music | 2111.14200 | null | https://arxiv.org/abs/2111.14200v3 | https://arxiv.org/pdf/2111.14200v3.pdf | Transfer Learning with Jukebox for Music Source Separation | In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is using transfer learning, is quick to train and the results demonstrate performance comparable to other s... | ['A. Melnik', 'H. Ritter', 'O. Tautz', 'W. Zai El Amri'] | 2021-11-28 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [-1.83049470e-01 -4.30090427e-01 6.46709725e-02 -2.55090028e-01
-1.52783334e+00 -5.88835299e-01 1.30286708e-01 -5.71009934e-01
-9.31752399e-02 4.54287052e-01 3.50246787e-01 -2.99670935e-01
1.20652407e-01 -2.95293897e-01 -7.55378008e-01 -4.77849066e-01
-3.33440870e-01 1.63682446e-01 1.60164252e-01 -2.98384368... | [15.370015144348145, 5.428928375244141] |
349c93c6-6939-4592-85c1-c533f11bafb2 | unsupervised-one-class-learning-for-automatic | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Liu_Unsupervised_One-Class_Learning_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Liu_Unsupervised_One-Class_Learning_2014_CVPR_paper.pdf | Unsupervised One-Class Learning for Automatic Outlier Removal | Outliers are pervasive in many computer vision and pattern recognition problems. Automatically eliminating outliers scattering among practical data collections becomes increasingly important, especially for Internet inspired vision applications. In this paper, we propose a novel one-class learning approach which is rob... | ['Wei Liu', 'John R. Smith', 'Gang Hua'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['one-class-classifier'] | ['methodology'] | [ 3.01253170e-01 3.49193290e-02 8.13956261e-02 -4.09452230e-01
-9.86485064e-01 -2.68830836e-01 5.08358777e-01 5.36659777e-01
-6.19600952e-01 5.34080982e-01 -3.85135919e-01 -5.98983392e-02
-1.74386173e-01 -2.75337011e-01 -8.97904098e-01 -8.38239670e-01
-5.62786385e-02 4.11606640e-01 3.04811001e-01 3.97459000... | [7.687137603759766, 2.3307273387908936] |
e59700cf-336a-4fd5-930a-55d7b0f45952 | impact-of-business-analytics-and-decision | 2212.00016 | null | https://arxiv.org/abs/2212.00016v1 | https://arxiv.org/pdf/2212.00016v1.pdf | Impact of Business Analytics and Decision Support Systems on e-commerce in SMEs | With the advancement in the marketing channel, the use of e-commerce has increased tremendously therefore the basic objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises. Small and medium enterprises are becoming a priority for ... | ['Shah J Miah'] | 2022-11-30 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-9.23234046e-01 5.41267283e-02 -5.04357815e-01 -1.72544405e-01
4.26928014e-01 -5.51758647e-01 5.78141689e-01 5.19559622e-01
-3.18064272e-01 -1.71198100e-01 1.17483236e-01 -1.00441122e+00
-4.60520923e-01 -1.06322348e+00 -3.96153241e-01 -3.79919708e-01
1.43916160e-01 1.43540218e-01 -4.68254872e-02 -5.64182937... | [9.05168342590332, 6.154146194458008] |
a8449aff-e2ce-4eb6-8bf4-f689ce47599c | epistemic-uncertainty-weighted-loss-for | 2204.09389 | null | https://arxiv.org/abs/2204.09389v1 | https://arxiv.org/pdf/2204.09389v1.pdf | Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation | Deep neural networks are highly susceptible to learning biases in visual data. While various methods have been proposed to mitigate such bias, the majority require explicit knowledge of the biases present in the training data in order to mitigate. We argue the relevance of exploring methods which are completely ignoran... | ['David C Hogg', 'Andrew J Bulpitt', 'Nishant Ravikumar', 'Rebecca S Stone'] | 2022-04-20 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 3.42568189e-01 5.15007794e-01 -5.64427860e-02 -7.41388083e-01
-4.64129567e-01 -4.03477401e-01 9.39206481e-01 -1.99524779e-02
-7.41118371e-01 8.32062721e-01 2.01440632e-01 -2.49145269e-01
-3.55152011e-01 -8.69188070e-01 -1.07916760e+00 -7.43445218e-01
1.22770973e-01 2.64422297e-01 1.22361489e-01 1.87654495... | [8.826953887939453, 4.782235145568848] |
496359b5-cfec-40ed-a8a0-06517443a1c2 | noise2noise-learning-image-restoration | 1803.04189 | null | http://arxiv.org/abs/1803.04189v3 | http://arxiv.org/pdf/1803.04189v3.pdf | Noise2Noise: Learning Image Restoration without Clean Data | We apply basic statistical reasoning to signal reconstruction by machine
learning -- learning to map corrupted observations to clean signals -- with a
simple and powerful conclusion: it is possible to learn to restore images by
only looking at corrupted examples, at performance at and sometimes exceeding
training using... | ['Timo Aila', 'Tero Karras', 'Jacob Munkberg', 'Jaakko Lehtinen', 'Miika Aittala', 'Samuli Laine', 'Jon Hasselgren'] | 2018-03-12 | noise2noise-learning-image-restoration-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2014 | http://proceedings.mlr.press/v80/lehtinen18a/lehtinen18a.pdf | icml-2018-7 | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 8.56007099e-01 3.78123671e-01 3.91184151e-01 -4.32152390e-01
-1.43403804e+00 -1.17063880e-01 4.96553630e-01 -4.09213424e-01
-4.38568324e-01 1.06006765e+00 4.14607793e-01 -2.18294561e-01
-2.33683258e-01 -4.07216370e-01 -8.82946372e-01 -1.17182708e+00
-3.26419473e-01 2.37040326e-01 -2.86669344e-01 1.67837769... | [11.808302879333496, -2.3925271034240723] |
ad4d1149-5dd2-456f-b4bc-2630b9871742 | weakly-supervised-learning-of-human-dynamics | 2007.08969 | null | https://arxiv.org/abs/2007.08969v2 | https://arxiv.org/pdf/2007.08969v2.pdf | Weakly-supervised Learning of Human Dynamics | This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion. Although there are many solutions to capture pure human motion readily available, their data is not sufficient to analyze quality and efficiency of movements. Instead, the forces and moments driving human motion (the d... | ['Petrissa Zell', 'Bastian Wandt', 'Bodo Rosenhahn'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5301_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710069.pdf | eccv-2020-8 | ['human-dynamics'] | ['computer-vision'] | [-1.52027041e-01 5.82046434e-02 -6.71223581e-01 -6.33431301e-02
-5.48819780e-01 -3.50065082e-01 5.43649375e-01 -2.11368397e-01
-7.70679832e-01 8.37598205e-01 7.15913326e-02 3.66823487e-02
1.66461561e-02 -7.11293101e-01 -9.91247892e-01 -7.73714542e-01
-2.68825948e-01 6.05838180e-01 1.79629937e-01 -4.45790350... | [7.271344184875488, -0.3582984209060669] |
131f0269-46db-42ea-a824-99eaec2c775a | versatile-speech-databases-for-high-quality | null | null | https://aclanthology.org/L12-1014 | https://aclanthology.org/L12-1014.pdf | Versatile Speech Databases for High Quality Synthesis for Basque | This paper presents three new speech databases for standard Basque. They are designed primarily for corpus-based synthesis but each database has its specific purpose: 1) AhoSyn: high quality speech synthesis (recorded also in Spanish), 2) AhoSpeakers: voice conversion and 3) AhoEmo3: emotional speech synthesis. The who... | ["Inma Hern{\\'a}ez", 'I{\\~n}aki Sainz', 'Jon Sanchez', 'Daniel Erro', 'Ibon Saratxaga', 'Eva Navas', 'Igor Odriozola'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['emotional-speech-synthesis'] | ['speech'] | [-2.59434104e-01 4.58169341e-01 5.43407314e-02 -2.71185964e-01
-9.48498189e-01 -4.57723141e-01 7.28781641e-01 -1.00293141e-02
-2.06240103e-01 1.12488866e+00 4.33247864e-01 -3.12555403e-01
2.91274190e-01 -2.72026271e-01 -8.51873532e-02 -6.94792688e-01
3.99929494e-01 6.58096910e-01 2.51628429e-01 -6.02915168... | [14.709670066833496, 6.585424900054932] |
053436f3-96ac-4092-bda4-1db0e8e36bfd | multi-agent-reinforcement-learning-methods | 2305.10091 | null | https://arxiv.org/abs/2305.10091v1 | https://arxiv.org/pdf/2305.10091v1.pdf | Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges | Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. This paper aims to review methods and applications and point out research trends and visionary prospects fo... | ['Ying Tang', 'Guanjun Liu', 'Ziyuan Zhou'] | 2023-05-17 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.10267478e-01 2.33041532e-02 -5.50315201e-01 4.43937480e-02
1.26659378e-01 -3.57424259e-01 6.66478872e-01 9.91320014e-02
-5.41980803e-01 1.38899243e+00 -4.62510616e-01 -5.95250428e-02
-3.62703562e-01 -7.92079449e-01 -2.80746460e-01 -8.74132037e-01
-2.58778900e-01 1.99402332e-01 -7.79821500e-02 -5.45626163... | [4.036454200744629, 1.9121806621551514] |
e71d14ad-545b-44f0-b5a8-6e490b7d750c | a-hierarchical-residual-network-with-compact | 2109.13536 | null | https://arxiv.org/abs/2109.13536v1 | https://arxiv.org/pdf/2109.13536v1.pdf | A hierarchical residual network with compact triplet-center loss for sketch recognition | With the widespread use of touch-screen devices, it is more and more convenient for people to draw sketches on screen. This results in the demand for automatically understanding the sketches. Thus, the sketch recognition task becomes more significant than before. To accomplish this task, it is necessary to solve the cr... | ['Yu Sang', 'Xiaoxiao Zhang', 'Huan He', 'Shihui Zhang', 'Lei Wang'] | 2021-09-28 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 5.23812361e-02 -4.77728516e-01 -2.10907698e-01 -3.22560281e-01
-3.99783850e-01 -2.29354918e-01 5.08835614e-01 -2.24196628e-01
-3.07793051e-01 4.19144988e-01 1.46556288e-01 -6.07454181e-02
-1.80820376e-01 -7.80520439e-01 -3.70343596e-01 -7.09480584e-01
4.17224050e-01 8.73179063e-02 3.32415998e-01 -2.20879927... | [11.623538970947266, 0.6550179719924927] |
be96cb02-0cf0-4fcd-854e-18d2349cf8d2 | deep-spatio-temporal-forecasting-of | 2106.10940 | null | https://arxiv.org/abs/2106.10940v1 | https://arxiv.org/pdf/2106.10940v1.pdf | Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand | Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better opt... | ['Francisco C. Pereira', 'Filipe Rodrigues', 'Inon Peled', 'Frederik Boe Hüttel'] | 2021-06-21 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-4.82802480e-01 -2.80120105e-01 -2.37408176e-01 -4.82847810e-01
6.19823076e-02 -5.56343079e-01 7.59668827e-01 -3.67866084e-02
1.81060538e-01 7.55186915e-01 1.29844129e-01 -8.56034517e-01
-5.29738963e-01 -1.44694507e+00 -2.79895753e-01 -7.21424818e-01
-3.18151504e-01 4.37752932e-01 -2.11094365e-01 -5.43868244... | [6.25131893157959, 2.682584762573242] |
10e6ff4f-82d2-41ec-a2cf-fc21d7abbfe6 | investigating-modulation-spectrum | null | null | https://aclanthology.org/O15-3005 | https://aclanthology.org/O15-3005.pdf | 調變頻譜分解技術於強健語音辨識之研究 (Investigating Modulation Spectrum Factorization Techniques for Robust Speech Recognition) [In Chinese] | null | ['Hsin-Min Wang', 'Kuan-Yu Chen', 'Hsiao-Tsung Hung', 'Ting-Hao Chang', 'Berlin Chen'] | 2015-12-01 | investigating-modulation-spectrum-1 | https://aclanthology.org/O15-3005 | https://aclanthology.org/O15-3005.pdf | roclingijclclp-2015-12 | ['robust-speech-recognition'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.223554611206055, 3.64066743850708] |
37fc6a65-a18d-41fb-9922-77bc07653d4c | object-semantics-give-us-the-depth-we-need | 2304.12542 | null | https://arxiv.org/abs/2304.12542v1 | https://arxiv.org/pdf/2304.12542v1.pdf | Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion | Depth completion and object detection are two crucial tasks often used for aerial 3D mapping, path planning, and collision avoidance of Uncrewed Aerial Vehicles (UAVs). Common solutions include using measurements from a LiDAR sensor; however, the generated point cloud is often sparse and irregular and limits the system... | ['Homayoun Najjaran', 'Iraj Mantegh', 'Miodrag Bolic', 'Fardad Dadboud', 'Sara Hatami Gazani'] | 2023-04-25 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 5.01616955e-01 -1.13360379e-02 8.18084851e-02 -2.18061790e-01
-5.61384678e-01 -6.09732330e-01 4.15830761e-01 2.41977319e-01
-4.35304344e-01 3.26123178e-01 -4.31907505e-01 -1.88045427e-01
-1.96452171e-01 -9.03573275e-01 -7.89363265e-01 -5.40840924e-01
-3.25654633e-02 5.68788826e-01 4.24661934e-01 -9.59803835... | [7.8760986328125, -2.575617551803589] |
41cab8b3-33e0-48ad-9160-64f508eea672 | using-statistical-and-semantic-models-for | 1805.04579 | null | http://arxiv.org/abs/1805.04579v2 | http://arxiv.org/pdf/1805.04579v2.pdf | Using Statistical and Semantic Models for Multi-Document Summarization | We report a series of experiments with different semantic models on top of
various statistical models for extractive text summarization. Though
statistical models may better capture word co-occurrences and distribution
around the text, they fail to detect the context and the sense of sentences
/words as a whole. Semant... | ['Anukarsh Singh', 'Divyanshu Daiya', 'Mukesh Jadon'] | 2018-05-11 | using-statistical-and-semantic-models-for-2 | https://aclanthology.org/O18-1018 | https://aclanthology.org/O18-1018.pdf | roclingijclclp-2018-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.65391767e-01 1.11786254e-01 1.63719233e-03 -4.99800384e-01
-7.27336824e-01 -5.65396369e-01 9.63130951e-01 9.77090120e-01
-5.96575499e-01 1.12198925e+00 1.45401859e+00 7.94016868e-02
-2.14570850e-01 -7.66907573e-01 -2.69313276e-01 -2.89141268e-01
2.76862495e-02 5.68811357e-01 5.69600642e-01 -7.12756872... | [12.380186080932617, 9.495598793029785] |
043a10f3-bb6f-495a-814a-e593a4fc72f8 | effective-handwritten-digit-recognition-using | null | null | https://www.researchgate.net/publication/341068954_Effective_Handwritten_Digit_Recognition_using_Deep_Convolution_Neural_Network | http://www.warse.org/IJATCSE/static/pdf/file/ijatcse66922020.pdf | Effective Handwritten Digit Recognition using Deep Convolution Neural Network | This paper proposed a simple neural network approach towards handwritten digit recognition using convolution. With machine learning algorithms like KNN, SVM/SOM, recognizing digits is considered as one of the unsolvable tasks due to its distinctiveness in the style of writing. In this paper, Convolutio... | ['Sriram V.P', 'Yellapragada SS Bharadwaj', 'Rajaram P', 'Sudhakar S', 'Kolla Bhanu Prakash'] | 2020-04-30 | null | null | null | international-journal-of-advanced-trends-in | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.36172310e-01 -2.89029002e-01 -2.88573429e-02 -4.04419780e-01
5.23409069e-01 -7.42360115e-01 6.85955882e-01 -7.88815841e-02
-6.30416751e-01 1.08752179e+00 -1.30346432e-01 -4.66884702e-01
-2.98253655e-01 -7.88638175e-01 -2.86108673e-01 -4.14652020e-01
2.49950305e-01 3.52024913e-01 -5.18368669e-02 -1.15726940... | [11.829987525939941, 2.6613848209381104] |
62d974ca-d290-41eb-b671-e903db9b5d3f | semi-perspective-decoupled-heatmaps-for-3d | 2207.02519 | null | https://arxiv.org/abs/2207.02519v1 | https://arxiv.org/pdf/2207.02519v1.pdf | Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps | Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications, such as the detection of unsafe situations or the study of mutual interactions for statistical and social purposes. In this paper, we propose a non-invasive and light-invariant framework based on depth ... | ['Roberto Vezzani', 'Guido Borghi', 'Stefano Pini', 'Alessandro Simoni'] | 2022-07-06 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 9.01476070e-02 2.51069665e-01 4.74980801e-01 -3.05997938e-01
-1.45647705e-01 -3.75028610e-01 6.04032636e-01 -9.38290805e-02
-8.36824596e-01 4.86927480e-01 -3.49838912e-01 1.03431486e-01
-2.35722110e-01 -7.15925932e-01 -9.04999673e-01 -6.71459198e-01
5.44069894e-02 9.44820702e-01 1.61713228e-01 -1.71092764... | [6.923571586608887, -1.2598028182983398] |
ce9eb72c-9c39-4e5d-a094-d9c3084a1f6d | super-resolution-of-license-plate-images | 2305.17313 | null | https://arxiv.org/abs/2305.17313v1 | https://arxiv.org/pdf/2305.17313v1.pdf | Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers | Recent years have seen significant developments in the field of License Plate Recognition (LPR) through the integration of deep learning techniques and the increasing availability of training data. Nevertheless, reconstructing license plates (LPs) from low-resolution (LR) surveillance footage remains challenging. To ad... | ['David Menotti', 'William Robson Schwartz', 'Jorge de A. Lambert', 'Rayson Laroca', 'Valfride Nascimento'] | 2023-05-27 | null | null | null | null | ['optical-character-recognition', 'image-super-resolution', 'license-plate-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.19399464e-01 -3.73198569e-01 1.57769382e-01 -2.45570809e-01
-1.32633460e+00 -6.60419643e-01 5.81382394e-01 -6.31891847e-01
-2.55413026e-01 7.12408185e-01 1.04918592e-01 1.16261523e-02
2.32652783e-01 -9.14344609e-01 -1.15291214e+00 -5.85739434e-01
4.84426022e-01 -3.54780070e-02 4.64694649e-01 -3.02409261... | [11.106231689453125, -2.1841297149658203] |
5a23e2f3-7977-4a0b-92d9-6dae0d9f016e | signnet-single-channel-sign-generation-using | 2212.02848 | null | https://arxiv.org/abs/2212.02848v1 | https://arxiv.org/pdf/2212.02848v1.pdf | SignNet: Single Channel Sign Generation using Metric Embedded Learning | A true interpreting agent not only understands sign language and translates to text, but also understands text and translates to signs. Much of the AI work in sign language translation to date has focused mainly on translating from signs to text. Towards the latter goal, we propose a text-to-sign translation model, Sig... | ['Ifeoma Nwogu', 'Lipisha Chaudhary', 'Tejaswini Ananthanarayana'] | 2022-12-06 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 4.42846656e-01 -9.66297686e-02 -1.28480509e-01 -5.20103395e-01
-1.03665972e+00 -7.36406326e-01 9.07501221e-01 -6.55536890e-01
-5.12923956e-01 5.73060036e-01 6.39728785e-01 -1.03401802e-01
4.49263901e-02 -2.22906411e-01 -8.47675741e-01 -7.49221742e-01
1.02368094e-01 7.19517112e-01 -5.21841720e-02 -3.54043096... | [9.210980415344238, -6.5356621742248535] |
9637cb78-b8ad-4103-9695-9ab87da79264 | where-are-we-in-discourse-relation | null | null | https://aclanthology.org/2021.sigdial-1.34 | https://aclanthology.org/2021.sigdial-1.34.pdf | Where Are We in Discourse Relation Recognition? | Discourse parsers recognize the intentional and inferential relationships that organize extended texts. They have had a great influence on a variety of NLP tasks as well as theoretical studies in linguistics and cognitive science. However it is often difficult to achieve good results from current discourse models, larg... | ['Malihe Alikhani', 'Junyi Jessy Li', 'Katherine Atwell'] | null | null | null | null | sigdial-acl-2021-7 | ['discourse-parsing', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.99752593e-01 9.34676886e-01 -3.72160614e-01 -5.06325245e-01
-5.70673525e-01 -7.09335148e-01 1.18429065e+00 5.17780960e-01
-2.44805947e-01 9.24289703e-01 1.03142989e+00 -7.80940711e-01
-2.07776606e-01 -7.80471981e-01 -1.26033038e-01 -2.69392610e-01
-9.02563557e-02 7.20199764e-01 7.01659441e-01 -5.60875237... | [10.748438835144043, 9.449780464172363] |
2eab4122-8d3f-4f05-b45c-341a6df838f7 | amr-to-text-generation-with-graph-structure | null | null | https://openreview.net/forum?id=6zZEZD2LsxW | https://openreview.net/pdf?id=6zZEZD2LsxW | AMR-to-text Generation with Graph Structure Reconstruction and Coverage | Generating text from semantic representations such as AMR is a challenging task. Previous research formalizes this task as a graph-to-sequence learning problem and uses various graph neural networks to model the graph structure. Recently, methods based on pre-trained models improve the performance significantly due to ... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 4.65469152e-01 6.97410583e-01 -4.36764866e-01 -1.64265856e-01
-5.56949437e-01 -2.17848793e-01 5.11167169e-01 9.11381245e-02
1.90496415e-01 9.14751470e-01 4.74757344e-01 -4.08184260e-01
2.41827726e-01 -1.30422342e+00 -7.88270533e-01 -1.55256033e-01
3.79640192e-01 6.60363197e-01 1.73379898e-01 -5.02658546... | [10.352855682373047, 8.321170806884766] |
c80327ba-a2ea-4e59-99e8-e96fd4467ada | convolutional-and-deep-learning-based | 2306.10084 | null | https://arxiv.org/abs/2306.10084v1 | https://arxiv.org/pdf/2306.10084v1.pdf | Convolutional and Deep Learning based techniques for Time Series Ordinal Classification | Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into a... | ['César Hervás-Martínez', 'Anthony Bagnall', 'Pedro Antonio Gutiérrez', 'David Guijo-Rubio', 'Rafael Ayllón-Gavilán'] | 2023-06-16 | null | null | null | null | ['benchmarking', 'benchmarking', 'time-series-classification'] | ['miscellaneous', 'robots', 'time-series'] | [ 2.52460003e-01 -4.32208717e-01 -4.65670675e-01 -4.98277575e-01
-4.41834450e-01 -6.67442083e-01 1.02286923e+00 8.01392555e-01
-5.65080047e-01 7.11615324e-01 -9.72842723e-02 -2.35793710e-01
-8.07717085e-01 -6.10911429e-01 -3.87752444e-01 -7.61697471e-01
-9.84789312e-01 5.53679168e-01 -6.20238371e-02 -3.42618972... | [7.257309913635254, 3.300638437271118] |
c4a5a19f-96dc-461a-9ac0-80ef7db6d073 | a-survey-and-implementation-of-performance | 2011.05847 | null | https://arxiv.org/abs/2011.05847v1 | https://arxiv.org/pdf/2011.05847v1.pdf | A Survey and Implementation of Performance Metrics for Self-Organized Maps | Self-Organizing Map algorithms have been used for almost 40 years across various application domains such as biology, geology, healthcare, industry and humanities as an interpretable tool to explore, cluster and visualize high-dimensional data sets. In every application, practitioners need to know whether they can \tex... | ['Jérôme Lacaille', 'Hanane Azzag', 'Mustapha Lebbah', 'Florent Forest'] | 2020-11-11 | null | null | null | null | ['self-organized-clustering'] | ['miscellaneous'] | [-9.50371325e-02 -6.62362799e-02 1.06655531e-01 -2.76985705e-01
-1.11151129e-01 -9.30540085e-01 6.45909011e-01 5.90393841e-01
-4.73335177e-01 5.83145320e-01 7.73573071e-02 -1.55443892e-01
-8.25483859e-01 -8.47785652e-01 -2.35099122e-01 -9.85332072e-01
-2.85136104e-01 8.33310068e-01 4.82160389e-01 -2.69722700... | [7.670138359069824, 4.505418300628662] |
0b4ef7a4-c11e-46b7-8a83-fb16c66da3c5 | pointgpt-auto-regressively-generative-pre | 2305.11487 | null | https://arxiv.org/abs/2305.11487v2 | https://arxiv.org/pdf/2305.11487v2.pdf | PointGPT: Auto-regressively Generative Pre-training from Point Clouds | Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challe... | ['Yufeng Yue', 'Li Yuan', 'Kai Yu', 'Yi Yang', 'Meiling Wang', 'Guangyan Chen'] | 2023-05-19 | null | null | null | null | ['3d-point-cloud-classification', '3d-part-segmentation', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.79486865e-01 9.96960029e-02 -3.53101157e-02 -1.18829548e-01
-1.34629357e+00 -3.38969231e-01 9.61381257e-01 3.49935405e-02
5.00907451e-02 4.94593263e-01 1.02872699e-01 -2.59033054e-01
2.33836211e-02 -1.16573739e+00 -1.09690118e+00 -8.88868093e-01
1.37635171e-01 8.82931888e-01 4.00932491e-01 -1.36111379... | [8.12630558013916, -3.405186414718628] |
e226137d-0047-48cc-8eec-367ad5f338de | 3d-lip-event-detection-via-interframe-motion | 2111.09485 | null | https://arxiv.org/abs/2111.09485v1 | https://arxiv.org/pdf/2111.09485v1.pdf | 3D Lip Event Detection via Interframe Motion Divergence at Multiple Temporal Resolutions | The lip is a dominant dynamic facial unit when a person is speaking. Detecting lip events is beneficial to speech analysis and support for the hearing impaired. This paper proposes a 3D lip event detection pipeline that automatically determines the lip events from a 3D speaking lip sequence. We define a motion divergen... | ['Robert B. Fisher', 'Jie Zhang'] | 2021-11-18 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-3.08269173e-01 -4.78135943e-01 -3.64069849e-01 -7.62521625e-02
-9.88253832e-01 -5.78586459e-01 6.47926629e-01 -2.56950974e-01
-3.05266351e-01 -2.02541798e-02 6.12953305e-01 7.05679655e-02
2.62586772e-01 -2.27882430e-01 -1.65668517e-01 -7.59224594e-01
-5.05471416e-02 4.61917259e-02 5.09706020e-01 1.64454430... | [14.339941024780273, 5.006660461425781] |
a5c87ef4-e54f-48ab-a8fa-460d29eb6aaf | an-application-of-a-runtime-epistemic | 2209.13043 | null | https://arxiv.org/abs/2209.13043v1 | https://arxiv.org/pdf/2209.13043v1.pdf | An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems | We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The ... | ['Silvia Rossi', 'Marco Grazioso', 'Salim Malek', 'Luca Raggioli', "Fabio Aurelio D'Asaro"] | 2022-09-26 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 1.14925854e-01 6.26929045e-01 -1.62299007e-01 -4.41075057e-01
-3.35521966e-01 -3.41878474e-01 3.46551418e-01 4.96492445e-01
-6.70217395e-01 6.23674035e-01 1.11504748e-01 3.78851593e-02
-6.13469839e-01 -7.97617972e-01 -6.79715753e-01 -6.81661785e-01
-2.04008422e-03 5.60073435e-01 4.96127337e-01 -6.92221895... | [13.146673202514648, 3.106675148010254] |
8d7b9c7a-1ebd-4bea-8cfa-ae1e21231fb6 | meta-learning-for-vision-and-language-cross | 2305.14843 | null | https://arxiv.org/abs/2305.14843v1 | https://arxiv.org/pdf/2305.14843v1.pdf | Meta-Learning For Vision-and-Language Cross-lingual Transfer | Current pre-trained vison-language models (PVLMs) achieve excellent performance on a range of multi-modal datasets. Recent work has aimed at building multilingual models, and a range of novel multilingual multi-modal datasets have been proposed. Current PVLMs typically perform poorly on these datasets when used for mul... | ['Frank Keller', 'Hanxu Hu'] | 2023-05-24 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.18797803e-01 -4.58043188e-01 -5.02817631e-01 -4.70531017e-01
-1.52212250e+00 -4.93671924e-01 1.05382490e+00 -3.91772389e-01
-7.83442557e-01 5.74597180e-01 1.72094330e-01 -3.04533720e-01
6.06688797e-01 -4.35661316e-01 -9.14626896e-01 -3.83553386e-01
4.85582888e-01 8.30302298e-01 2.05608562e-01 -3.83231670... | [11.164384841918945, 1.5667983293533325] |
10ff6ee7-43bc-4e06-9059-0832ced71910 | accuracy-of-automatic-cross-corpus-emotion | null | null | https://aclanthology.org/L16-1634 | https://aclanthology.org/L16-1634.pdf | Accuracy of Automatic Cross-Corpus Emotion Labeling for Conversational Speech Corpus Commonization | There exists a major incompatibility in emotion labeling framework among emotional speech corpora, that is, category-based and dimension-based. Commonizing these requires inter-corpus emotion labeling according to both frameworks, but doing this by human annotators is too costly for most cases. This paper examines the ... | ['Atsushi Nagaoka', 'Yoshiko Arimoto', 'Hiroki Mori'] | 2016-05-01 | accuracy-of-automatic-cross-corpus-emotion-1 | https://aclanthology.org/L16-1634 | https://aclanthology.org/L16-1634.pdf | lrec-2016-5 | ['cross-corpus'] | ['computer-vision'] | [-2.64297724e-01 3.83112907e-01 1.74888179e-01 -6.38273239e-01
-4.21551198e-01 -5.96592009e-01 6.11102104e-01 2.74935722e-01
-4.32178050e-01 6.94197774e-01 3.34283829e-01 -6.82015298e-03
-1.31823659e-01 -2.28522554e-01 2.60718703e-01 -6.27169549e-01
1.71383649e-01 5.62194288e-01 -2.01214254e-01 -3.30515504... | [13.005096435546875, 6.164022922515869] |
7097374d-824a-4726-8e16-782149839824 | transformers-meet-stochastic-block-models | 2210.15541 | null | https://arxiv.org/abs/2210.15541v1 | https://arxiv.org/pdf/2210.15541v1.pdf | Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost | To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under a hand-crafted patterns and 2) full attention followed by a sparse variant of softmax such as $\alpha$-entmax. Unfortunately, the first gro... | ['Seunghoon Hong', 'Honglak Lee', 'Moontae Lee', 'Jinwoo Kim', 'Seonwoo Min', 'Sungjun Cho'] | 2022-10-27 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.21937656e-01 2.36272112e-01 -2.37656862e-01 -3.58631968e-01
-7.03718603e-01 -2.56291628e-01 2.45134622e-01 -1.07226864e-01
-4.32014525e-01 5.87366581e-01 -1.89087056e-02 -3.67747962e-01
-3.70909576e-03 -9.00974214e-01 -1.13523996e+00 -7.10303128e-01
-8.76844674e-02 6.43627763e-01 1.76120862e-01 -1.65348321... | [8.674904823303223, 4.669475078582764] |
2c37b16f-c332-48f3-9997-4545faf10d0d | efficient-approximate-quantum-state | 2009.07601 | null | https://arxiv.org/abs/2009.07601v3 | https://arxiv.org/pdf/2009.07601v3.pdf | Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks | We use a meta-learning neural-network approach to analyse data from a measured quantum state. Once our neural network has been trained it can be used to efficiently sample measurements of the state in measurement bases not contained in the training data. These samples can be used calculate expectation values and other ... | ['Alistair W. R. Smith', 'M. S. Kim', 'Johnnie Gray'] | 2020-09-16 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.63943624e-01 4.19279262e-02 2.32997134e-01 -5.68821311e-01
-1.30327511e+00 -5.10914028e-01 6.99627340e-01 -9.37550701e-03
-8.72354090e-01 1.22043860e+00 -2.44898811e-01 -8.33860397e-01
-1.08958788e-01 -1.13899148e+00 -6.46005094e-01 -1.17214906e+00
7.55558610e-02 8.19913566e-01 -2.36172393e-01 -6.66419715... | [5.5823469161987305, 4.9362077713012695] |
4cb5e7f6-d827-4707-9081-a7fc5a7a16f9 | active-learning-on-a-programmable-photonic | 2208.02104 | null | https://arxiv.org/abs/2208.02104v1 | https://arxiv.org/pdf/2208.02104v1.pdf | Active Learning on a Programmable Photonic Quantum Processor | Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the original dataset to learn while maintaining the trained model's perform... | ['He-Liang Huang', 'Wan-su Bao', 'Shuo Zhang', 'Yun-Fei Niu', 'Xiao-Yue Xu', 'Chen Ding'] | 2022-08-03 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 4.24170494e-01 1.26233190e-01 -4.22153145e-01 -3.58301401e-01
-1.11677206e+00 -4.81195331e-01 3.24722469e-01 -1.47783486e-02
-6.77155077e-01 8.72559249e-01 -6.01441443e-01 -4.97078866e-01
1.09748796e-01 -1.32575488e+00 -6.91433311e-01 -1.35689592e+00
1.98605910e-01 5.36968112e-01 7.75186718e-02 -1.29157543... | [5.5508012771606445, 4.9886298179626465] |
aa23a0f1-fdea-4e6d-80b5-594b78ce3899 | remote-sensing-scene-classification-with | 2302.14256 | null | https://arxiv.org/abs/2302.14256v2 | https://arxiv.org/pdf/2302.14256v2.pdf | Remote Sensing Scene Classification with Masked Image Modeling (MIM) | Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the Machine Learning (ML) methods for performing the task mainly used the backbones pre... | ['Alex Tien', 'Liya Wang'] | 2023-02-28 | null | null | null | null | ['scene-classification', 'earthquake-prediction'] | ['computer-vision', 'computer-vision'] | [ 4.29324657e-01 -1.39266238e-01 -9.38057601e-02 -3.79141837e-01
-8.68841708e-01 -1.24932647e-01 8.42930496e-01 -1.22965708e-01
-4.14232582e-01 4.77130532e-01 8.46386515e-03 -6.50914729e-01
-5.38528487e-02 -9.30694699e-01 -6.07322574e-01 -9.06036615e-01
-4.57789987e-01 1.57328665e-01 2.94980019e-01 -3.00964117... | [9.64865493774414, -1.3016871213912964] |
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