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40abf085-36aa-4d5c-b440-c6ea989bfa1a | an-in-depth-analysis-of-the-effect-of-lexical | null | null | https://aclanthology.org/D19-5515 | https://aclanthology.org/D19-5515.pdf | An In-depth Analysis of the Effect of Lexical Normalization on the Dependency Parsing of Social Media | Existing natural language processing systems have often been designed with standard texts in mind. However, when these tools are used on the substantially different texts from social media, their performance drops dramatically. One solution is to translate social media data to standard language before processing, this ... | ['Rob van der Goot'] | 2019-11-01 | null | null | null | ws-2019-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 2.31376782e-01 2.16346696e-01 -8.38779956e-02 -5.91334105e-01
-6.05377614e-01 -7.78031170e-01 6.00226581e-01 8.59153330e-01
-9.98087525e-01 6.43221319e-01 6.12212956e-01 -2.21498489e-01
1.35751531e-01 -6.23145640e-01 -3.49765569e-01 -3.64401639e-01
4.86773640e-01 5.01279235e-01 3.62047672e-01 -5.38128853... | [10.108417510986328, 9.939483642578125] |
b063a9df-9590-4ee5-98ed-b159cea73229 | linking-generative-semi-supervised-learning | 2303.11702 | null | https://arxiv.org/abs/2303.11702v3 | https://arxiv.org/pdf/2303.11702v3.pdf | On the link between generative semi-supervised learning and generative open-set recognition | This study investigates the relationship between semi-supervised learning (SSL) and open-set recognition (OSR) under the context of generative adversarial networks (GANs). Although no previous study has formally linked SSL and OSR, their respective methods share striking similarities. Specifically, SSL-GANs and OSR-GAN... | ['Johan du Preez', 'Emile Reyn Engelbrecht'] | 2023-03-21 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.39315367e-01 7.86561549e-01 -3.71799678e-01 -3.05161923e-01
-7.79791355e-01 -9.07852113e-01 8.85447800e-01 -5.19399762e-01
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3.58763039e-01 4.50694114e-01 -2.13698104e-01 -3.37296098... | [11.609551429748535, -0.12981708347797394] |
52955f74-f74b-4a97-8e90-1529bfc8b1ef | smm4h-shared-task-2020-a-hybrid-pipeline-for | null | null | https://aclanthology.org/2020.smm4h-1.9 | https://aclanthology.org/2020.smm4h-1.9.pdf | SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing | This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our tr... | ['Yindalon Aphinyanaphongs', 'William McMahon', 'Mark T. Rutledge', 'Rajat S. Chandra', 'Whitley M. Yi', 'Allison Black', 'Emir Y. Haskovic', 'Isabel Metzger'] | null | null | null | null | smm4h-coling-2020-12 | ['text-categorization'] | ['natural-language-processing'] | [ 2.65708059e-01 2.58699596e-01 -7.00383544e-01 -3.93217921e-01
-8.70734453e-01 -4.02838886e-01 9.75666583e-01 1.41257632e+00
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-1.69250876e-01 4.44557309e-01 -4.27756548e-01 -3.40876691... | [8.42078971862793, 8.92742919921875] |
8701b988-7f88-4103-aab7-43df88404d9d | predicting-pulmonary-hypertension-by | 2304.12447 | null | https://arxiv.org/abs/2304.12447v1 | https://arxiv.org/pdf/2304.12447v1.pdf | Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning | Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart (Mayo Clinic, 2017). A mean pulmonary artery pressure greater than 25 mmHg is defined as Pulmonary hypertension. The estimated 5-year survival rate from the time of diagnosis of pulmo... | ['Praveen Kumar Pandian Shanmuganathan', 'Eashan Kosaraju'] | 2023-04-24 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.35171711e-01 1.55338496e-01 -8.97856653e-02 -3.29921097e-02
-1.28220931e-01 -2.72341251e-01 -1.91473857e-01 1.92606553e-01
-2.25334242e-01 7.34374583e-01 1.66381374e-01 -8.21654916e-01
-3.66408825e-01 -8.96834731e-01 -6.63380921e-02 -4.35212463e-01
-4.49505776e-01 8.24191391e-01 1.45108914e-02 4.69155103... | [14.285680770874023, 3.2185983657836914] |
5a50f288-a54a-4573-9ee3-5eaf07685383 | facial-expression-recognition-using-vanilla | 2207.11081 | null | https://arxiv.org/abs/2207.11081v3 | https://arxiv.org/pdf/2207.11081v3.pdf | Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers | Representation learning and feature disentanglement have recently attracted much research interests in facial expression recognition. The ubiquitous ambiguity of emotion labels is detrimental to those methods based on conventional supervised representation learning. Meanwhile, directly learning the mapping from a facia... | ['Meng Wang', 'Richang Hong', 'Dan Guo', 'Jiantao Nie', 'Jia Li'] | 2022-07-22 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.59881574e-01 4.59855407e-01 6.28560930e-02 -3.78091484e-01
-5.78367651e-01 -4.58890855e-01 4.95816112e-01 -1.16587162e+00
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1.62494689e-01 -9.83873196e-03 -6.95903361e-01 -3.76271278... | [12.956404685974121, 0.2759247124195099] |
39295310-ee79-4753-9beb-af15363e3f0a | vision-language-pre-training-with-object | 2305.10714 | null | https://arxiv.org/abs/2305.10714v1 | https://arxiv.org/pdf/2305.10714v1.pdf | Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding | In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks. However, when extended to point cloud data, existing works mainly focus on building task-specific models, ... | ['Shu-Tao Xia', 'Zhi Wang', 'Bin Chen', 'Dai Tao', 'Sunan He', 'Taolin Zhang'] | 2023-05-18 | null | null | null | null | ['visual-grounding', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 2.45765075e-02 -1.41310379e-01 -1.32310614e-01 -5.80376804e-01
-5.27577460e-01 -5.41212380e-01 9.70397770e-01 2.73048095e-02
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4.62712169e-01 4.91230428e-01 3.78379256e-01 -1.52552590... | [8.149785995483398, -3.345304250717163] |
0a89680a-7736-4e16-b234-4f0d9f4dc392 | coqar-question-rewriting-on-coqa | 2207.03240 | null | https://arxiv.org/abs/2207.03240v1 | https://arxiv.org/pdf/2207.03240v1.pdf | CoQAR: Question Rewriting on CoQA | Questions asked by humans during a conversation often contain contextual dependencies, i.e., explicit or implicit references to previous dialogue turns. These dependencies take the form of coreferences (e.g., via pronoun use) or ellipses, and can make the understanding difficult for automated systems. One way to facili... | ['Lina M. Rojas-Barahona', 'Gwenole Lecorve', 'Quentin Brabant'] | 2022-07-07 | null | https://aclanthology.org/2022.lrec-1.13 | https://aclanthology.org/2022.lrec-1.13.pdf | lrec-2022-6 | ['question-rewriting'] | ['natural-language-processing'] | [ 2.15321794e-01 6.82122171e-01 2.08199099e-01 -8.10427606e-01
-8.79631281e-01 -1.13800335e+00 7.86074877e-01 2.85463274e-01
-3.13569397e-01 8.72609198e-01 5.35019815e-01 -8.12772334e-01
8.86589512e-02 -8.12352955e-01 -5.05716801e-01 -2.57517658e-02
3.80353421e-01 7.09104121e-01 3.17677230e-01 -8.70075345... | [11.948186874389648, 8.01573371887207] |
bde7d2e1-dae3-4a57-b3b7-2a3f786da28b | scene-graph-expansion-for-semantics-guided | 2205.02958 | null | https://arxiv.org/abs/2205.02958v1 | https://arxiv.org/pdf/2205.02958v1.pdf | Scene Graph Expansion for Semantics-Guided Image Outpainting | In this paper, we address the task of semantics-guided image outpainting, which is to complete an image by generating semantically practical content. Different from most existing image outpainting works, we approach the above task by understanding and completing image semantics at the scene graph level. In particular, ... | ['Yu-Chiang Frank Wang', 'Meng-Lin Wu', 'Cheng-Fu Yang', 'Wan-Cyuan Fan', 'Cheng-Yo Tan', 'Chiao-An Yang'] | 2022-05-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Scene_Graph_Expansion_for_Semantics-Guided_Image_Outpainting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Scene_Graph_Expansion_for_Semantics-Guided_Image_Outpainting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-outpainting'] | ['computer-vision'] | [ 6.82713628e-01 4.54811364e-01 -7.96133280e-02 -2.84727007e-01
-2.15841070e-01 -3.87104958e-01 4.88256454e-01 1.85785741e-01
6.28513619e-02 2.96706617e-01 2.97620863e-01 -2.29218572e-01
2.11714476e-01 -9.09263372e-01 -1.16853738e+00 -2.12929085e-01
4.26824003e-01 2.64248520e-01 -4.53575365e-02 -2.20834866... | [11.408853530883789, -0.48772168159484863] |
5b397907-a5f8-49e2-bf82-bec0c7b77e01 | unpaired-photo-to-caricature-translation-on | 1711.10735 | null | http://arxiv.org/abs/1711.10735v2 | http://arxiv.org/pdf/1711.10735v2.pdf | Unpaired Photo-to-Caricature Translation on Faces in the Wild | Recently, image-to-image translation has been made much progress owing to the
success of conditional Generative Adversarial Networks (cGANs). And some
unpaired methods based on cycle consistency loss such as DualGAN, CycleGAN and
DiscoGAN are really popular. However, it's still very challenging for
translation tasks wi... | ['Haiyong Zheng', 'Wang Chao', 'Nan Wang', 'Ziqiang Zheng', 'Zhibin Yu', 'Bing Zheng'] | 2017-11-29 | null | null | null | null | ['photo-to-caricature-translation', 'caricature'] | ['computer-vision', 'computer-vision'] | [ 5.01240671e-01 7.58468509e-02 1.12226591e-01 -3.07505786e-01
-6.23300970e-01 -6.09103143e-01 7.40823090e-01 -8.05881262e-01
4.77976389e-02 9.69290793e-01 4.15274454e-03 7.16830269e-02
4.44742292e-01 -9.28425491e-01 -1.00393093e+00 -9.12400663e-01
4.47921634e-01 2.80490607e-01 -1.23593494e-01 -3.48496228... | [12.0967435836792, -0.2946988642215729] |
9bfcefc7-bbe6-4f66-a49d-31aafa262bca | source-free-domain-adaptation-of-a-dnn-for | 2305.17403 | null | https://arxiv.org/abs/2305.17403v1 | https://arxiv.org/pdf/2305.17403v1.pdf | Source Free Domain Adaptation of a DNN for SSVEP-based Brain-Computer Interfaces | This paper presents a source free domain adaptation method for steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) spellers. SSVEP-based BCI spellers help individuals experiencing speech difficulties, enabling them to communicate at a fast rate. However, achieving a high information tran... | ['Huseyin Ozkan', 'Deniz Kucukahmetler', 'Osman Berke Guney'] | 2023-05-27 | null | null | null | null | ['source-free-domain-adaptation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 3.14437747e-01 -2.20102862e-01 4.32189517e-02 -3.00034612e-01
-8.72242570e-01 -3.47618014e-01 2.44298846e-01 -1.65573046e-01
-7.29750693e-01 1.26590705e+00 8.03610831e-02 -8.38252530e-02
-7.89301023e-02 -2.34872431e-01 -4.46872264e-01 -8.67729783e-01
-2.92449705e-02 7.44759142e-02 8.43782946e-02 -4.67861183... | [13.139204978942871, 3.4308183193206787] |
9a651c0c-3dcb-4a4b-a293-b32594319ddf | a-multi-task-selected-learning-approach-for | 1804.06896 | null | http://arxiv.org/abs/1804.06896v3 | http://arxiv.org/pdf/1804.06896v3.pdf | A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem | A 3D flexible bin packing problem (3D-FBPP) arises from the process of
warehouse packing in e-commerce. An online customer's order usually contains
several items and needs to be packed as a whole before shipping. In particular,
5% of tens of millions of packages are using plastic wrapping as outer
packaging every day, ... | ['Jiangwen Wei', 'Yu Qian', 'Yinghui Xu', 'Haoyuan Hu', 'Yu Gong', 'Xiaodong Zhang', 'Lu Duan'] | 2018-04-17 | null | null | null | null | ['3d-bin-packing'] | ['miscellaneous'] | [-2.99641848e-01 6.61458969e-02 -3.89413238e-01 -2.97664374e-01
-6.31606698e-01 -5.96344888e-01 -4.54927504e-01 3.82642806e-01
-2.59241402e-01 7.50316441e-01 -2.06669748e-01 -6.24955952e-01
-4.99875128e-01 -8.85095358e-01 -1.29877019e+00 -7.49032676e-01
-6.39810622e-01 1.33067775e+00 2.78208584e-01 -3.23368609... | [4.978424549102783, 2.697413444519043] |
29c388cb-d027-4d81-b09f-04c0ef91d952 | short-term-aggregated-residential-load | 2302.05033 | null | https://arxiv.org/abs/2302.05033v1 | https://arxiv.org/pdf/2302.05033v1.pdf | Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM | Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential ... | ['Xingpeng Li', 'Vysali Gollapudi', 'Raymond I. Fernandez', 'Bharat Bohara'] | 2023-02-10 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-6.23423338e-01 -2.16463238e-01 1.51971415e-01 -4.40485388e-01
-6.54212534e-01 -2.21050516e-01 3.90096575e-01 -1.97147846e-01
3.20224017e-01 1.19074893e+00 4.21971470e-01 -5.10392487e-01
6.45145448e-03 -1.07875669e+00 -2.84463674e-01 -9.30191457e-01
-3.82535785e-01 8.05160329e-02 -5.71301818e-01 -3.21156114... | [6.177830219268799, 2.8071658611297607] |
92adc197-c823-4ac2-a29a-2f9bdce670f6 | automated-audio-captioning-using-transfer | 2108.04692 | null | https://arxiv.org/abs/2108.04692v1 | https://arxiv.org/pdf/2108.04692v1.pdf | Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization | In this paper, we examine the use of Transfer Learning using Pretrained Audio Neural Networks (PANNs), and propose an architecture that is able to better leverage the acoustic features provided by PANNs for the Automated Audio Captioning Task. We also introduce a novel self-supervised objective, Reconstruction Latent S... | ['Eng Siong Chng', 'Fuzhao Xue', 'Andrew Koh'] | 2021-08-10 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.65801501e-01 2.55063146e-01 1.53723925e-01 -3.70870203e-01
-1.18159699e+00 -3.75909507e-01 6.33004904e-01 -1.00292824e-01
-2.57018715e-01 3.56373042e-01 6.64381325e-01 1.66280016e-01
-2.62573478e-03 -4.16216046e-01 -9.17127788e-01 -3.81800711e-01
-1.70843259e-01 2.05972970e-01 -7.26355910e-02 -2.48910546... | [15.252867698669434, 5.146806716918945] |
03dc1807-cabe-4786-91f9-23013f00e9a1 | a-unifying-framework-for-spectrum-preserving | null | null | http://papers.nips.cc/paper/8989-a-unifying-framework-for-spectrum-preserving-graph-sparsification-and-coarsening | http://papers.nips.cc/paper/8989-a-unifying-framework-for-spectrum-preserving-graph-sparsification-and-coarsening.pdf | A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening | How might one ``reduce'' a graph?
That is, generate a smaller graph that preserves the global structure at the expense of discarding local details?
There has been extensive work on both graph sparsification (removing edges) and graph coarsening (merging nodes, often by edge contraction); however, these operations ar... | ['Gecia Bravo Hermsdorff', 'Lee Gunderson'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['graph-similarity'] | ['graphs'] | [ 4.85768735e-01 5.89982033e-01 -2.17769686e-02 1.48792714e-01
-1.95849568e-01 -8.73323381e-01 3.62710118e-01 3.30362618e-01
-4.05292213e-02 6.41633928e-01 5.08775301e-02 -1.39643431e-01
-4.29356396e-01 -1.20973563e+00 -7.63426065e-01 -1.08029306e+00
-4.40496236e-01 4.04824048e-01 2.15009451e-01 -1.72322601... | [7.083838939666748, 5.128429412841797] |
4ccbe705-61ec-4b7d-830a-54f0c3dc7aed | noise-estimation-using-density-estimation-for | 2003.03186 | null | https://arxiv.org/abs/2003.03186v3 | https://arxiv.org/pdf/2003.03186v3.pdf | Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning | One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is challenging and expensive. Recently, self-supervised multimodal methods that combine vision and language were proposed to learn multimodal r... | ['Rami Ben-Ari', 'Daniel Rotman', 'Alex Bronstein', 'Elad Amrani'] | 2020-03-06 | null | null | null | null | ['noise-estimation'] | ['medical'] | [-4.40271720e-02 -1.70791730e-01 -1.24193318e-01 -4.29087311e-01
-1.56337190e+00 -7.02889323e-01 6.58587992e-01 1.97092474e-01
-4.62611765e-01 6.01859212e-01 1.79788738e-01 -1.41148522e-01
-6.16898350e-02 -2.57905513e-01 -7.59177864e-01 -7.01920271e-01
2.64745444e-01 5.11433601e-01 -1.05099224e-01 1.13008671... | [10.686805725097656, 1.5791171789169312] |
656c1993-6c19-4f1a-b98a-02d04012a8f1 | collfren-rich-bilingual-english-french | null | null | https://aclanthology.org/2020.mwe-1.1 | https://aclanthology.org/2020.mwe-1.1.pdf | CollFrEn: Rich Bilingual English–French Collocation Resource | Collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words traditionally pose a challenge to language learners and many Natural Language Processing (NLP) applications alike. Reliable ground truth (i.e., ideally manually compiled) resources are thus of high value. We present a man... | ['Leo Wanner', 'Joan Codina-Filbá', 'Luis Espinosa Anke', 'Beatriz Fisas'] | null | null | null | null | coling-mwe-2020-12 | ['relation-classification'] | ['natural-language-processing'] | [-3.04480225e-01 -3.98796760e-02 -4.60888326e-01 -5.74509725e-02
-8.14784467e-01 -1.12034667e+00 7.08927393e-01 1.03503573e+00
-5.77242672e-01 1.29978788e+00 5.29478967e-01 -5.15794098e-01
1.25017300e-01 -8.58062923e-01 -4.89079833e-01 -5.98528266e-01
7.45262429e-02 7.60428309e-01 -1.06083639e-01 -5.02052903... | [10.41144847869873, 9.503273963928223] |
a8cb6d46-7e72-40c9-a74e-d7c45e87b7a9 | 3d-molecular-geometry-analysis-with-2d-graphs | 2305.13315 | null | https://arxiv.org/abs/2305.13315v1 | https://arxiv.org/pdf/2305.13315v1.pdf | 3D Molecular Geometry Analysis with 2D Graphs | Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. Currently, an efficient alternative to computing ground-state 3D molecular geometries from 2D graphs is lacking. Here, we pro... | ['Shuiwang Ji', 'Maho Nakata', 'Cheng Deng', 'Kaleb Dickerson', 'Meng Liu', 'Xinyi Xu', 'Xuan Zhang', 'Youzhi Luo', 'Yaochen Xie', 'Zhao Xu'] | 2023-05-01 | null | null | null | null | ['property-prediction'] | ['medical'] | [-1.00897104e-01 -4.42922205e-01 -4.43567961e-01 -5.32642305e-01
-8.37408960e-01 -4.77969766e-01 1.77746773e-01 8.13549936e-01
-1.53679579e-01 1.06663275e+00 -1.72023565e-01 -9.49471891e-01
6.02213889e-02 -1.16671133e+00 -1.00769877e+00 -7.24045098e-01
-6.12885416e-01 3.61742765e-01 -4.77147758e-01 -6.98364079... | [5.147913932800293, 5.763134002685547] |
4d7f4898-8a63-49a8-816c-8b66eda88440 | face-alignment-assisted-by-head-pose | 1507.03148 | null | http://arxiv.org/abs/1507.03148v2 | http://arxiv.org/pdf/1507.03148v2.pdf | Face Alignment Assisted by Head Pose Estimation | In this paper we propose a supervised initialization scheme for cascaded face
alignment based on explicit head pose estimation. We first investigate the
failure cases of most state of the art face alignment approaches and observe
that these failures often share one common global property, i.e. the head pose
variation i... | ['Hatice Gunes', 'Yichi Zhang', 'Wenxuan Mou', 'Heng Yang', 'Ioannis Patras', 'Peter Robinson'] | 2015-07-11 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-9.88391340e-02 3.80016178e-01 9.47301984e-02 -6.18893445e-01
-5.33508003e-01 -3.45980316e-01 5.84642649e-01 -2.45557889e-01
-3.71633589e-01 3.63122076e-01 2.75220066e-01 2.45446667e-01
-4.70879041e-02 -4.52520698e-01 -8.39915335e-01 -8.95605445e-01
1.87241629e-01 9.34007406e-01 -1.57628432e-01 -3.18226755... | [13.477272033691406, 0.3072497546672821] |
6afae744-4229-4769-9f45-7fa407f9ca38 | scaffold-induced-molecular-graph-simg | 2109.05012 | null | https://arxiv.org/abs/2109.05012v1 | https://arxiv.org/pdf/2109.05012v1.pdf | Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery | Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoods. We formalize scaffold based drug discovery into a network design. Utilizing docking data from SA... | ['Rick Stevens', 'Arvind Ramanathan', 'Max Zvyagin', 'Ashka Shah', 'Austin Clyde'] | 2021-09-10 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.30563039e-01 2.27162614e-01 -9.97106373e-01 1.05536133e-01
-1.73629999e-01 -1.03359592e+00 4.19659734e-01 4.42621201e-01
7.87985623e-02 1.45983732e+00 4.59794760e-01 -1.23269928e+00
-5.78575969e-01 -9.12947237e-01 -8.18037450e-01 -4.18250412e-01
-3.44269156e-01 1.55536890e-01 1.15527004e-01 -2.74528384... | [5.007588863372803, 5.773229598999023] |
c3e67ea2-b108-4525-8274-26c854c2e113 | tab-vcr-tags-and-attributes-based-vcr | 1910.14671 | null | https://arxiv.org/abs/1910.14671v2 | https://arxiv.org/pdf/1910.14671v2.pdf | TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines | Reasoning is an important ability that we learn from a very early age. Yet, reasoning is extremely hard for algorithms. Despite impressive recent progress that has been reported on tasks that necessitate reasoning, such as visual question answering and visual dialog, models often exploit biases in datasets. To develop ... | ['Alexander G. Schwing', 'Jingxiang Lin', 'Unnat Jain'] | 2019-10-31 | null | null | null | neurips-2019-12 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 2.55042404e-01 5.70514619e-01 4.01375480e-02 -3.23064655e-01
-6.51669443e-01 -5.28735518e-01 8.32081318e-01 1.86093435e-01
-5.45496523e-01 6.35741293e-01 4.13421243e-01 -6.84411108e-01
1.20939091e-01 -7.44874060e-01 -8.93153310e-01 -1.17183745e-01
5.72074354e-01 6.85584784e-01 2.87981272e-01 -3.07294458... | [10.842796325683594, 1.921000599861145] |
b310aa06-709e-454a-bb3b-33e5e9287f6c | hide-and-seek-forcing-a-network-to-be | 1704.04232 | null | http://arxiv.org/abs/1704.04232v2 | http://arxiv.org/pdf/1704.04232v2.pdf | Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization | We propose `Hide-and-Seek', a weakly-supervised framework that aims to
improve object localization in images and action localization in videos. Most
existing weakly-supervised methods localize only the most discriminative parts
of an object rather than all relevant parts, which leads to suboptimal
performance. Our key ... | ['Yong Jae Lee', 'Krishna Kumar Singh'] | 2017-04-13 | hide-and-seek-forcing-a-network-to-be-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Singh_Hide-And-Seek_Forcing_a_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Singh_Hide-And-Seek_Forcing_a_ICCV_2017_paper.pdf | iccv-2017-10 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 1.80253953e-01 1.62928522e-01 -6.74088478e-01 -2.14916304e-01
-9.32208300e-01 -7.62497127e-01 1.24204099e-01 -1.22549385e-01
-6.08232558e-01 5.65251350e-01 2.31432065e-01 6.49546310e-02
1.28106505e-01 -2.89627373e-01 -1.09153426e+00 -7.89207876e-01
-3.07057232e-01 3.59600902e-01 7.89468884e-01 1.57773092... | [8.502265930175781, 0.5204090476036072] |
323c4b98-f8fe-4a3a-b1b4-91239cb281cb | self-supervised-character-to-character | 2211.00288 | null | https://arxiv.org/abs/2211.00288v3 | https://arxiv.org/pdf/2211.00288v3.pdf | Self-supervised Character-to-Character Distillation for Text Recognition | When handling complicated text images (e.g., irregular structures, low resolution, heavy occlusion, and uneven illumination), existing supervised text recognition methods are data-hungry. Although these methods employ large-scale synthetic text images to reduce the dependence on annotated real images, the domain gap st... | ['Zekun Jiang', 'Qi Feng', 'Xue Yang', 'Wei Shen', 'Tongkun Guan'] | 2022-11-01 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 7.98504353e-01 -2.94555038e-01 -2.52287418e-01 -4.83055592e-01
-6.90825045e-01 -6.57754183e-01 3.64823371e-01 -3.84409577e-01
-2.28155524e-01 5.75267494e-01 4.63857055e-02 -2.80357040e-02
4.20685738e-01 -5.12027204e-01 -5.88446558e-01 -8.60854268e-01
7.63231516e-01 3.32151771e-01 3.15568238e-01 -5.29488400... | [11.805229187011719, 2.0041048526763916] |
c6e48d52-dc90-4a88-8d99-17ae2110cfc2 | open-surgery-tool-classification-and-hand | 2111.06098 | null | https://arxiv.org/abs/2111.06098v1 | https://arxiv.org/pdf/2111.06098v1.pdf | Open surgery tool classification and hand utilization using a multi-camera system | Purpose: The goal of this work is to use multi-camera video to classify open surgery tools as well as identify which tool is held in each hand. Multi-camera systems help prevent occlusions in open surgery video data. Furthermore, combining multiple views such as a Top-view camera covering the full operative field and a... | ['Shlomi Laufer', 'Carla M Pugh', 'Adam Goldbraikh', 'Kristina Basiev'] | 2021-11-11 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-1.73859578e-02 -2.41288394e-01 -2.55533457e-01 2.24777222e-01
-8.38415146e-01 -7.52811074e-01 -2.88690231e-03 2.45499402e-01
-6.68683589e-01 1.46245345e-01 -1.43050015e-01 -1.42444327e-01
-6.25956878e-02 -4.63463694e-01 -6.68932617e-01 -6.63345098e-01
-5.37517853e-03 -6.48473203e-02 4.70421016e-01 7.35568479... | [14.036246299743652, -3.348606824874878] |
9de860eb-58f1-44d4-abb3-2bd18042aeb0 | calls-japanese-empathetic-dialogue-speech | 2305.13713 | null | https://arxiv.org/abs/2305.13713v1 | https://arxiv.org/pdf/2305.13713v1.pdf | CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center | We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue. The existing STUDIES corpus covers only empathetic dialogue between a teacher and student in a school. To extend the application range of empathetic dialogue speech synthesis (EDSS),... | ['Hiroshi Saruwatari', 'Kentaro Tachibana', 'Shinnosuke Takamichi', 'Eiji Iimori', 'Yuki Saito'] | 2023-05-23 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-4.73948717e-01 6.87366605e-01 1.79879472e-01 -5.88765979e-01
-8.15553188e-01 -4.61049676e-01 5.78686059e-01 -3.83951873e-01
-1.63080260e-01 8.13586831e-01 6.55694544e-01 -9.89853889e-02
2.68374771e-01 -4.47942734e-01 -4.04001474e-02 -6.37082338e-01
6.08476758e-01 8.59488249e-01 1.13801636e-01 -9.31759953... | [13.09902286529541, 7.687444686889648] |
fb8ac24e-9050-4e61-a432-0e6d2d939f7a | exact-recovery-for-the-non-uniform-hypergraph | 2304.13139 | null | https://arxiv.org/abs/2304.13139v1 | https://arxiv.org/pdf/2304.13139v1.pdf | Exact recovery for the non-uniform Hypergraph Stochastic Block Model | Consider the community detection problem in random hypergraphs under the non-uniform hypergraph stochastic block model (HSBM), where each hyperedge appears independently with some given probability depending only on the labels of its vertices. We establish, for the first time in the literature, a sharp threshold for ex... | ['Haixiao Wang', 'Ioana Dumitriu'] | 2023-04-25 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 4.71468896e-01 4.13977742e-01 -2.73026645e-01 3.95429015e-01
-6.13149226e-01 -8.00762236e-01 1.23859599e-01 3.87707114e-01
-2.26486221e-01 8.75810623e-01 -2.40681425e-01 -2.13791400e-01
-5.36142230e-01 -1.11350822e+00 -8.06255996e-01 -1.30162835e+00
-3.61611038e-01 1.06621313e+00 3.92482877e-01 1.54122859... | [6.810599327087402, 5.037517070770264] |
4ba4716d-29c9-4ddd-8264-af714ae0aafa | multi-source-morphosyntactic-tagging-for | null | null | https://aclanthology.org/W17-1210 | https://aclanthology.org/W17-1210.pdf | Multi-source morphosyntactic tagging for spoken Rusyn | This paper deals with the development of morphosyntactic taggers for spoken varieties of the Slavic minority language Rusyn. As neither annotated corpora nor parallel corpora are electronically available for Rusyn, we propose to combine existing resources from the etymologically close Slavic languages Russian, Ukrainia... | ['Yves Scherrer', 'Achim Rabus'] | 2017-04-01 | null | null | null | ws-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [-3.87747735e-01 2.20671475e-01 -5.35786152e-01 -2.16474652e-01
-7.87770748e-01 -9.59320247e-01 5.35950720e-01 2.27636456e-01
-7.24039853e-01 1.12868261e+00 1.40804097e-01 -6.58503830e-01
-9.35838223e-02 -6.26272976e-01 2.29162380e-01 -3.03508162e-01
2.46060312e-01 1.07757366e+00 4.11347479e-01 -5.07799625... | [10.367143630981445, 10.113484382629395] |
a433c11e-1959-4987-824c-028b84ab145b | quantifying-the-knowledge-in-a-dnn-to-explain | 2208.08741 | null | https://arxiv.org/abs/2208.08741v1 | https://arxiv.org/pdf/2208.08741v1.pdf | Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification | Compared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. This paper provides a new perspective to explain the success of knowledge distillation, i.e., quantifying knowledge points encoded in intermediate layers of a DNN for classification, based on the ... | ['Zhefan Rao', 'Yilan Chen', 'Xu Cheng', 'Quanshi Zhang'] | 2022-08-18 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-2.09098175e-01 1.07850201e-01 -1.46266118e-01 -2.63871193e-01
-8.50318447e-02 -6.01871371e-01 6.14930801e-02 7.79938176e-02
-4.92747337e-01 6.62132680e-01 -1.89388826e-01 -2.05848530e-01
-5.27415335e-01 -9.81747925e-01 -8.65394711e-01 -8.57524037e-01
2.36495182e-01 1.78055137e-01 4.06884819e-01 -7.24364538... | [9.528505325317383, 3.2108795642852783] |
6ed1a259-09d8-4d22-840e-6fb8e96b4c43 | few-shot-audio-visual-learning-of-environment | 2206.04006 | null | https://arxiv.org/abs/2206.04006v2 | https://arxiv.org/pdf/2206.04006v2.pdf | Few-Shot Audio-Visual Learning of Environment Acoustics | Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics. Whereas traditional methods to estimate RIRs assume dense geometry and/or sound measurements throughout the environment, w... | ['Kristen Grauman', 'Ziad Al-Halah', 'Changan Chen', 'Sagnik Majumder'] | 2022-06-08 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 3.49571407e-01 -1.64700404e-01 1.02245283e+00 -3.31593603e-01
-1.38222933e+00 -6.05718791e-01 4.94642943e-01 -1.13602266e-01
-4.31724302e-02 1.39636979e-01 6.14189565e-01 -1.21609271e-01
-4.71745022e-02 -5.60089767e-01 -9.25332844e-01 -3.75294685e-01
-3.85981858e-01 3.82646054e-01 1.04301900e-01 -4.04823184... | [15.032825469970703, 5.496488571166992] |
39b33544-11d1-49ac-9e2d-4d52f0468fe2 | jointly-visual-and-semantic-aware-graph | 2303.01046 | null | https://arxiv.org/abs/2303.01046v2 | https://arxiv.org/pdf/2303.01046v2.pdf | Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos | Temporal sentence localization in videos (TSLV) aims to retrieve the most interested segment in an untrimmed video according to a given sentence query. However, almost of existing TSLV approaches suffer from the same limitations: (1) They only focus on either frame-level or object-level visual representation learning a... | ['Pan Zhou', 'Daizong Liu'] | 2023-03-02 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-1.93751693e-01 -2.49950871e-01 -3.79452795e-01 -3.90348792e-01
-6.64977133e-01 -3.19465369e-01 4.22832221e-01 1.93567544e-01
-1.38955876e-01 1.98675275e-01 4.42897916e-01 -5.36152394e-03
-5.59673160e-02 -7.61863470e-01 -7.12783635e-01 -4.78089631e-01
2.07338840e-01 8.75101089e-02 8.77712369e-01 -1.14709064... | [10.130155563354492, 0.9351569414138794] |
f8348a40-d232-489f-8f86-61a9daca5359 | a-digital-score-of-tumour-associated-stroma | 2104.12862 | null | https://arxiv.org/abs/2104.12862v1 | https://arxiv.org/pdf/2104.12862v1.pdf | A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma | The infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim is to explore the prognostic significance of tumour-associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carc... | ['Nasir Rajpoot', 'Syed Ali Khurram', 'Hisham Mehanna', 'Max Robinson', 'Neil Sharma', 'Paul Nankivell', 'Jill Brooks', 'Nikolaos Batis', 'Asif Loya', 'Sajid Mushtaq', 'Arif Jamshed', 'Mariam Hassan', 'Shan E Ahmed Raza', 'Muhammad Shaban'] | 2021-04-16 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.30173241e-02 -9.29456502e-02 -5.51844537e-01 2.48552665e-01
-1.21938622e+00 -3.71011704e-01 4.16221708e-01 7.08364844e-01
-8.81544888e-01 6.42447948e-01 4.47106421e-01 -4.29766178e-01
-2.40631312e-01 -8.84572327e-01 1.52095512e-01 -1.52854574e+00
-7.10571604e-03 9.65211272e-01 1.06451772e-01 -1.37325019... | [15.149307250976562, -3.1091551780700684] |
06d7f318-27b5-4f9e-b8b6-778d70b0206a | onednn-graph-compiler-a-hybrid-approach-for | 2301.01333 | null | https://arxiv.org/abs/2301.01333v1 | https://arxiv.org/pdf/2301.01333v1.pdf | oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation | With the rapid development of deep learning models and hardware support for dense computing, the deep learning (DL) workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operati... | ['Dan Lavery', 'Eric Lin', 'Jason Ye', 'Baihui Jin', 'Xianhang Cheng', 'Longsheng Du', 'Yifei Zhang', 'Ciyong Chen', 'Yunfei Song', 'Jingze Cui', 'Yijie Mei', 'Zhennan Qin', 'Jianhui Li'] | 2023-01-03 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-4.44770694e-01 -1.71223626e-01 -9.40221623e-02 -5.05230725e-01
-2.60394812e-01 -4.16613042e-01 2.68565953e-01 3.25291216e-01
-5.35522997e-01 -1.25702292e-01 2.94265747e-01 -7.96550810e-01
2.35913917e-02 -1.12593877e+00 -7.29328215e-01 -5.74426711e-01
-4.79577005e-01 6.98766172e-01 1.85986787e-01 -4.21224624... | [8.296557426452637, 3.0453877449035645] |
2dc0d686-930b-46b3-81f8-4dfb0412b926 | sketch-bert-learning-sketch-bidirectional | 2005.09159 | null | https://arxiv.org/abs/2005.09159v1 | https://arxiv.org/pdf/2005.09159v1.pdf | Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt | Previous researches of sketches often considered sketches in pixel format and leveraged CNN based models in the sketch understanding. Fundamentally, a sketch is stored as a sequence of data points, a vector format representation, rather than the photo-realistic image of pixels. SketchRNN studied a generative neural rep... | ['xiangyang xue', 'Yu-Gang Jiang', 'Hangyu Lin', 'Yanwei Fu'] | 2020-05-19 | sketch-bert-learning-sketch-bidirectional-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Sketch-BERT_Learning_Sketch_Bidirectional_Encoder_Representation_From_Transformers_by_Self-Supervised_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Sketch-BERT_Learning_Sketch_Bidirectional_Encoder_Representation_From_Transformers_by_Self-Supervised_CVPR_2020_paper.pdf | cvpr-2020-6 | ['sketch-recognition'] | ['computer-vision'] | [ 1.79115593e-01 -8.98969769e-02 -1.08586185e-01 -4.39288795e-01
-3.19752455e-01 -4.79626417e-01 1.12150860e+00 -6.19069755e-01
1.30681574e-01 3.54499698e-01 2.67604560e-01 -3.59153718e-01
-1.06674306e-01 -1.28913665e+00 -9.55770910e-01 -5.60041368e-01
2.21003741e-01 3.08415622e-01 -4.18048650e-01 -1.40190154... | [11.773115158081055, 0.387342244386673] |
5eaf581c-c85f-4461-aff2-fc4c836a8f62 | generating-a-temporally-coherent-image | null | null | https://openreview.net/forum?id=hyQFVwTynuA | https://openreview.net/pdf?id=hyQFVwTynuA | Generating a Temporally Coherent Image Sequence for a Story by Multimodal Recurrent Transformers | Story visualization is a challenging text-to-image generation task for the difficulty of rendering visual details from abstract text descriptions.
Besides the difficulty of image generation, the generator also need to conform to the narrative of a multi-sentence story input.
While prior arts in this domain has focuse... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['story-visualization'] | ['computer-vision'] | [ 5.12616515e-01 2.49189705e-01 2.59059012e-01 -2.57422596e-01
-7.72150338e-01 -6.12635732e-01 1.13610983e+00 -2.73869604e-01
2.56328970e-01 7.69479752e-01 7.17723429e-01 -4.97758426e-02
2.04703644e-01 -5.10366321e-01 -6.63993359e-01 -4.22514468e-01
3.97220224e-01 3.90353084e-01 8.59589055e-02 -1.44216552... | [11.178227424621582, 0.6317145824432373] |
57115ef3-4f07-44bb-9f7b-826e2238e2d6 | nominal-metaphor-generation-with-multitask | 2206.05195 | null | https://arxiv.org/abs/2206.05195v3 | https://arxiv.org/pdf/2206.05195v3.pdf | Nominal Metaphor Generation with Multitask Learning | Metaphor generation is a challenging task which can impact many downstream tasks such as improving user satisfaction with dialogue systems and story generation. This paper tackles the problem of Chinese nominal metaphor generation by introducing a multitask metaphor generation framework with self-training and metaphor ... | ['Frank Geurin', 'Chenghua Lin', 'Yucheng Li'] | 2022-06-10 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 8.73006359e-02 4.17366356e-01 -2.64991764e-02 -1.90622747e-01
-3.22458982e-01 -7.53132105e-01 8.72259617e-01 7.96064213e-02
-3.99372190e-01 6.83974385e-01 6.45114899e-01 -3.43810856e-01
2.01893315e-01 -9.07733440e-01 -1.86356679e-01 -3.84940445e-01
5.16228497e-01 8.46844196e-01 -4.88135695e-01 -8.88521135... | [11.247831344604492, 9.077905654907227] |
648d9ccc-7caa-4132-a7cf-d08bbf66aca5 | non-convex-optimizations-for-machine-learning | 2306.16557 | null | https://arxiv.org/abs/2306.16557v1 | https://arxiv.org/pdf/2306.16557v1.pdf | Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning | Despite the recent development in machine learning, most learning systems are still under the concept of "black box", where the performance cannot be understood and derived. With the rise of safety and privacy concerns in public, designing an explainable learning system has become a new trend in machine learning. In ge... | ['Shuai Zhang'] | 2023-06-28 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 8.40975493e-02 3.39727193e-01 -2.41115674e-01 -6.79777026e-01
-7.52081692e-01 -4.35538769e-01 1.25454351e-01 -7.71397725e-03
-7.94738382e-02 1.03808308e+00 1.91167314e-02 -2.74233282e-01
-2.92369813e-01 -4.23781604e-01 -8.75035405e-01 -7.02909827e-01
5.50700948e-02 4.28942323e-01 -7.30194092e-01 1.06309280... | [7.27926778793335, 4.387484073638916] |
1059fa8e-8a26-4f60-9c3e-c3fe7a475ca9 | verifiably-safe-exploration-for-end-to-end | 2007.01223 | null | https://arxiv.org/abs/2007.01223v1 | https://arxiv.org/pdf/2007.01223v1.pdf | Verifiably Safe Exploration for End-to-End Reinforcement Learning | Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first approach toward enforcing formal safety constraints on end-to-end policies with visual inputs. Our approach draws on recent advances in object de... | ['Armando Solar-Lezama', 'Nghia Hoang', 'Nathan Fulton', 'Sara Magliacane', 'Subhro Das', 'Nathan Hunt'] | 2020-07-02 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.44318414e-02 5.28330564e-01 -3.60689193e-01 -9.06883851e-02
-7.30075538e-01 -9.61444795e-01 7.90229261e-01 3.67282592e-02
-5.23250222e-01 1.10923660e+00 -1.14583366e-01 -5.51681399e-01
-2.80367076e-01 -5.13842463e-01 -1.01814950e+00 -6.42111659e-01
-6.24666095e-01 2.60785341e-01 4.96066988e-01 -3.84094447... | [4.466373443603516, 2.09000301361084] |
8870134a-d112-41f0-a9ba-d843631c3c5c | 6d-vnet-end-to-end-6dof-vehicle-pose | null | null | http://openaccess.thecvf.com/content_CVPRW_2019/html/WAD/Wu_6D-VNet_End-To-End_6-DoF_Vehicle_Pose_Estimation_From_Monocular_RGB_Images_CVPRW_2019_paper.html | http://openaccess.thecvf.com/content_CVPRW_2019/papers/WAD/Wu_6D-VNet_End-To-End_6-DoF_Vehicle_Pose_Estimation_From_Monocular_RGB_Images_CVPRW_2019_paper.pdf | 6D-VNet: End-to-end 6DoF Vehicle Pose Estimation from Monocular RGB Images | We present a conceptually simple framework for 6DoF object pose estimation, especially for autonomous driving scenario. Our approach efficiently detects traffic partic- ipants in a monocular RGB image while simultaneously regressing their 3D translation and rotation vectors. The method, called 6D-VNet, extends Mask R-C... | ['Wenbin Zou and Xia Li', 'Di Wu', 'Zhaoyong Zhuang', 'Canqun Xiang'] | 2019-06-15 | null | null | null | the-ieee-conference-on-computer-vision-and-1 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-3.52922648e-01 1.30257010e-01 -2.18887553e-01 -5.34450948e-01
-5.29008746e-01 -3.37057799e-01 7.19322920e-01 -4.39014912e-01
-6.14774525e-01 3.65049452e-01 1.05612189e-01 -3.73847902e-01
1.16029061e-01 -5.31764925e-01 -1.18333721e+00 -7.53762662e-01
1.97447807e-01 5.80192804e-01 3.06155741e-01 -2.39898443... | [8.025219917297363, -2.116116762161255] |
c776702f-9ae5-42bf-89a2-a792153580ca | cross-x-learning-for-fine-grained-visual | 1909.04412 | null | https://arxiv.org/abs/1909.04412v1 | https://arxiv.org/pdf/1909.04412v1.pdf | Cross-X Learning for Fine-Grained Visual Categorization | Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation. Recent work tackles this problem in a weakly-supervised manner: object parts are first detected and the corresponding part-specific features are extracted for fine... | ['Ser-Nam Lim', 'Jian Yang', 'Xianjie Mo', 'Jun Li', 'Wei Luo', 'Larry S. Davis', 'Yuheng Lu', 'Xitong Yang'] | 2019-09-10 | cross-x-learning-for-fine-grained-visual-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Luo_Cross-X_Learning_for_Fine-Grained_Visual_Categorization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Cross-X_Learning_for_Fine-Grained_Visual_Categorization_ICCV_2019_paper.pdf | iccv-2019-10 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.51490927e-01 -2.71574736e-01 -3.80558997e-01 -6.19191468e-01
-9.78748620e-01 -6.72367871e-01 5.44501305e-01 1.28834739e-01
-3.28633815e-01 2.47830659e-01 8.06703120e-02 2.47557461e-01
-2.69997329e-01 -5.58698177e-01 -9.43525195e-01 -5.60466528e-01
1.19016871e-01 2.95316905e-01 6.46728754e-01 1.09152608... | [9.598058700561523, 1.9769309759140015] |
fd66338a-ac7e-43d9-b724-e6a9635f327d | multi-level-temporal-channel-speaker | 2305.07204 | null | https://arxiv.org/abs/2305.07204v1 | https://arxiv.org/pdf/2305.07204v1.pdf | Multi-level Temporal-channel Speaker Retrieval for Robust Zero-shot Voice Conversion | Zero-shot voice conversion (VC) converts source speech into the voice of any desired speaker using only one utterance of the speaker without requiring additional model updates. Typical methods use a speaker representation from a pre-trained speaker verification (SV) model or learn speaker representation during VC train... | ['Yuping Wang', 'Qiao Tian', 'Yuanzhe Chen', 'Lei Xie', 'Qiuqiang Kong', 'Liumeng Xue', 'Zhichao Wang'] | 2023-05-12 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speaker-verification'] | ['audio', 'speech', 'speech'] | [ 1.58770680e-01 -2.41318911e-01 -2.66346604e-01 -2.31336817e-01
-1.02527022e+00 -5.26966035e-01 6.01569831e-01 -3.19229901e-01
1.33198738e-01 2.36789137e-01 4.93020236e-01 -2.35517681e-01
6.75198361e-02 -5.07557213e-01 -3.41287673e-01 -6.87149584e-01
2.11188272e-01 1.14377499e-01 1.26714930e-02 -3.23844910... | [14.959050178527832, 6.511525630950928] |
9c88691f-d1bf-4ffd-9848-377518a17a95 | cross-language-learning-with-adversarial-1 | 1706.06749 | null | http://arxiv.org/abs/1706.06749v1 | http://arxiv.org/pdf/1706.06749v1.pdf | Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering | We address the problem of cross-language adaptation for question-question
similarity reranking in community question answering, with the objective to
port a system trained on one input language to another input language given
labeled training data for the first language and only unlabeled data for the
second language. ... | ['Lluís Màrquez', 'Preslav Nakov', 'Israa Jaradat', 'Shafiq Joty'] | 2017-06-21 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 2.77895570e-01 1.91533178e-01 2.18656778e-01 -4.36876327e-01
-1.33171487e+00 -8.60007286e-01 7.12461710e-01 2.68528789e-01
-7.39540815e-01 4.53711450e-01 3.75353187e-01 -5.26295424e-01
2.31227770e-01 -7.49990940e-01 -5.94134450e-01 -1.47838384e-01
1.54947877e-01 8.34268332e-01 5.00905395e-01 -5.04260898... | [11.326654434204102, 8.202881813049316] |
cc2c78a0-d8ca-4318-894f-c5548bf75951 | position-bias-mitigation-a-knowledge-aware | 2106.03518 | null | https://arxiv.org/abs/2106.03518v2 | https://arxiv.org/pdf/2106.03518v2.pdf | Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction | The Emotion Cause Extraction (ECE)} task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or ar... | ['Yulan He', 'Gabriele Pergola', 'Lin Gui', 'Hanqi Yan'] | 2021-06-07 | null | https://aclanthology.org/2021.acl-long.261 | https://aclanthology.org/2021.acl-long.261.pdf | acl-2021-5 | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 5.51586747e-01 6.20831609e-01 -1.25623912e-01 -5.57600975e-01
-7.44722366e-01 -9.27044988e-01 7.86182880e-01 3.42940956e-01
3.85094956e-02 7.26212740e-01 5.18897831e-01 -1.21294148e-01
5.68845756e-02 -9.91441905e-01 -8.05683315e-01 -4.01982814e-01
-5.71098253e-02 2.73319542e-01 -7.77291879e-02 -6.02769434... | [12.588302612304688, 6.197442054748535] |
b7a44fd7-6c05-4e58-a7be-8e04055c0abd | scribbleseg-scribble-based-interactive-image | 2303.11320 | null | https://arxiv.org/abs/2303.11320v1 | https://arxiv.org/pdf/2303.11320v1.pdf | ScribbleSeg: Scribble-based Interactive Image Segmentation | Interactive segmentation enables users to extract masks by providing simple annotations to indicate the target, such as boxes, clicks, or scribbles. Among these interaction formats, scribbles are the most flexible as they can be of arbitrary shapes and sizes. This enables scribbles to provide more indications of the ta... | ['Hengshuang Zhao', 'Ser-Nam Lim', 'Yau Shing Jonathan Cheung', 'Xi Chen'] | 2023-03-20 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 9.60147530e-02 -4.50040959e-02 -4.86619212e-02 -4.18361813e-01
-6.78375363e-01 -9.59041417e-01 5.21424353e-01 -2.32924800e-02
-2.42742509e-01 4.76336181e-01 -2.76677996e-01 -5.48589587e-01
1.71168000e-01 -6.29155099e-01 -6.03054404e-01 -5.36670268e-01
1.61388367e-01 6.89989686e-01 8.94389331e-01 -2.51356781... | [9.466835021972656, -0.09707864373922348] |
277e241f-90d7-4a77-ad02-ebba764a20f5 | dynamic-spatiotemporal-graph-convolutional | 2109.08357 | null | https://arxiv.org/abs/2109.08357v1 | https://arxiv.org/pdf/2109.08357v1.pdf | Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns | Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation, there still exist two limitations to be addressed: first, existing approaches fail to capture the complex spatiotemporal dependencies in t... | ['Lijun Sun', 'Zhan Zhao', 'Yuebing Liang'] | 2021-09-17 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-1.47945330e-01 -4.82645035e-01 -6.33255422e-01 -5.33562422e-01
-1.04915507e-01 -9.62573977e-05 2.96268314e-01 -4.37186748e-01
1.12056553e-01 8.08146060e-01 5.78285456e-01 -8.50834191e-01
-5.01231611e-01 -9.29208755e-01 -9.69783425e-01 -3.75929683e-01
-5.64173013e-02 5.06492138e-01 2.10262924e-01 -6.49257183... | [6.512206554412842, 2.0647900104522705] |
ae76514d-f7a9-475d-a1ea-f6950bd360f8 | glass-segmentation-with-rgb-thermal-image | 2204.05453 | null | https://arxiv.org/abs/2204.05453v4 | https://arxiv.org/pdf/2204.05453v4.pdf | Glass Segmentation with RGB-Thermal Image Pairs | This paper proposes a new glass segmentation method utilizing paired RGB and thermal images. Due to the large difference between the transmission property of visible light and that of the thermal energy through the glass where most glass is transparent to the visible light but opaque to thermal energy, glass regions of... | ['Yee-Hong Yang', 'Yiming Qian', 'Jian Wang', 'Dong Huo'] | 2022-04-12 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 2.46830449e-01 1.34234846e-01 1.33829147e-01 -5.34823477e-01
-7.00290859e-01 -5.51395535e-01 7.58216381e-02 -4.11834776e-01
-2.03435421e-01 1.28334031e-01 2.31351843e-03 -1.09989077e-01
2.81355351e-01 -9.54699934e-01 -6.41707182e-01 -1.16592169e+00
6.16276562e-01 -1.08378232e-01 2.52002388e-01 -2.01159455... | [9.436257362365723, -1.214186191558838] |
421fef95-ffc6-4a37-ad07-d131039d3a9e | stg2seq-spatial-temporal-graph-to-sequence | 1905.10069 | null | https://arxiv.org/abs/1905.10069v1 | https://arxiv.org/pdf/1905.10069v1.pdf | STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting | Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman... | ['Salil S. Kanhere', 'Quan Z. Sheng', 'Lina Yao', 'Xianzhi Wang', 'Lei Bai'] | 2019-05-24 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [-4.28786069e-01 -9.51754972e-02 -4.95319009e-01 -7.95129299e-01
-6.56540871e-01 -2.13656113e-01 6.32132649e-01 -8.24953914e-02
7.73200616e-02 6.80356503e-01 7.28662193e-01 -7.32167304e-01
-1.24945842e-01 -1.08752310e+00 -7.13100731e-01 -1.74980789e-01
-4.26541984e-01 7.55013287e-01 4.63083029e-01 -7.04048514... | [6.531729698181152, 2.136101245880127] |
0801b56b-0cef-41bf-a177-82d9d10eca59 | haa4d-few-shot-human-atomic-action | 2202.07308 | null | https://arxiv.org/abs/2202.07308v1 | https://arxiv.org/pdf/2202.07308v1.pdf | HAA4D: Few-Shot Human Atomic Action Recognition via 3D Spatio-Temporal Skeletal Alignment | Human actions involve complex pose variations and their 2D projections can be highly ambiguous. Thus 3D spatio-temporal or 4D (i.e., 3D+T) human skeletons, which are photometric and viewpoint invariant, are an excellent alternative to 2D+T skeletons/pixels to improve action recognition accuracy. This paper proposes a n... | ['Yu-Wing Tai', 'Chi-Keung Tang', 'Abhishek Gupta', 'Mu-Ruei Tseng'] | 2022-02-15 | null | null | null | null | ['atomic-action-recognition'] | ['computer-vision'] | [ 2.44425878e-01 9.49211568e-02 -4.73028600e-01 -1.20215580e-01
-4.74179149e-01 -2.69300938e-01 5.43526232e-01 -3.43935907e-01
-3.95607024e-01 4.30934668e-01 2.77117580e-01 4.12990570e-01
2.04578951e-01 -5.47198713e-01 -6.51590884e-01 -7.38332152e-01
7.01778978e-02 5.51212728e-01 6.10145450e-01 -3.06129046... | [7.8349761962890625, 0.3679516017436981] |
2818762d-ec50-4fd3-8fa1-a3a36b37875e | multinet-real-time-joint-semantic-reasoning | 1612.07695 | null | http://arxiv.org/abs/1612.07695v2 | http://arxiv.org/pdf/1612.07695v2.pdf | MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving | While most approaches to semantic reasoning have focused on improving
performance, in this paper we argue that computational times are very important
in order to enable real time applications such as autonomous driving. Towards
this goal, we present an approach to joint classification, detection and
semantic segmentati... | ['Raquel Urtasun', 'Michael Weber', 'Marvin Teichmann', 'Marius Zoellner', 'Roberto Cipolla'] | 2016-12-22 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 2.81558812e-01 4.15667921e-01 -1.07398391e-01 -5.76102316e-01
-9.60460305e-01 -5.35771132e-01 7.00045764e-01 -7.11387619e-02
-7.01231897e-01 5.13904274e-01 -1.71859369e-01 -6.61141217e-01
1.14346422e-01 -6.76088631e-01 -7.75050938e-01 -1.77265391e-01
1.13897629e-01 8.57086539e-01 9.19817686e-01 -2.87798166... | [9.405842781066895, 0.12609069049358368] |
a9c24209-fcb4-4bc6-ae53-7cf4d39829d1 | multi-view-3d-reconstruction-with-transformer | 2103.12957 | null | https://arxiv.org/abs/2103.12957v1 | https://arxiv.org/pdf/2103.12957v1.pdf | Multi-view 3D Reconstruction with Transformer | Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these methods - multi-view feature extraction and fusion, are usually investigated separately, and the object relations in different views are rarel... | ['Rabab Ward', 'Z. Jane Wang', 'Septimiu Salcudean', 'Tianyang Shi', 'Zhengxia Zou', 'Xun Chen', 'Xinrui Cui', 'Dan Wang'] | 2021-03-24 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [-1.56255871e-01 -1.49737805e-01 1.77240763e-02 -5.20772040e-01
-9.05216098e-01 -4.09934551e-01 5.81711888e-01 -4.61653531e-01
2.82089770e-01 2.81495005e-01 5.54636240e-01 -2.42520019e-01
-7.24119991e-02 -8.49389315e-01 -9.57727671e-01 -4.49048698e-01
2.80778110e-01 6.32923603e-01 2.99499750e-01 -3.65507305... | [8.273987770080566, -3.551143169403076] |
0e7b6ed9-f112-4c3e-9007-f898751dbd3a | pseudocell-hard-negative-mining-as-pseudo | 2307.03211 | null | https://arxiv.org/abs/2307.03211v1 | https://arxiv.org/pdf/2307.03211v1.pdf | PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection | Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optim... | ['Theerawit Wilaiprasitporn', 'Chanitra Thuwajit', 'Sumeth Yuenyong', 'Narit Hnoohom', 'Napat Angkathunyakul', 'Ananya Pongpaibul', 'Komgrid Charngkaew', 'Phoomraphee Luenam', 'Kanyakorn Veerakanjana', 'Supasan Sripodok', 'Paisarn Boonsakan', 'Peti Thuwajit', 'Phattarapong Sawangjai', 'Thapanun Sudhawiyangkul', 'Piyali... | 2023-07-06 | null | null | null | null | ['whole-slide-images', 'object-detection', 'cell-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.77789440e-02 1.69634536e-01 -1.98102817e-01 -1.41220316e-01
-1.24668550e+00 -6.35039568e-01 -3.27449664e-02 6.22781694e-01
-3.95330131e-01 6.52428627e-01 -2.39318192e-01 -3.75049919e-01
1.00325033e-01 -9.43375289e-01 -1.84196427e-01 -1.26064074e+00
5.85751295e-01 8.68849456e-01 3.21623087e-01 7.63739049... | [15.125106811523438, -3.119310140609741] |
a88c27eb-9c13-45eb-93d3-7e92185866d7 | align-mlm-word-embedding-alignment-is-crucial | 2211.08547 | null | https://arxiv.org/abs/2211.08547v1 | https://arxiv.org/pdf/2211.08547v1.pdf | ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-training | Multilingual pre-trained models exhibit zero-shot cross-lingual transfer, where a model fine-tuned on a source language achieves surprisingly good performance on a target language. While studies have attempted to understand transfer, they focus only on MLM, and the large number of differences between natural languages ... | ['Karthik Narasimhan', 'Ameet Deshpande', 'Henry Tang'] | 2022-11-15 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-3.54966475e-03 -2.95682345e-02 -4.31867480e-01 -4.09323037e-01
-9.68883932e-01 -9.14492846e-01 1.00420666e+00 3.52684200e-01
-9.09526646e-01 6.25912249e-01 5.35773933e-01 -7.14109182e-01
7.28461817e-02 -5.45600235e-01 -8.75670135e-01 -3.50224674e-01
6.09937087e-02 3.97058189e-01 -3.64107601e-02 -3.90523314... | [10.912108421325684, 9.998140335083008] |
bafb69a2-0a1e-401e-a769-49dbc8d80b76 | an-improved-analysis-of-variance-reduced-1 | 2211.07937 | null | https://arxiv.org/abs/2211.07937v2 | https://arxiv.org/pdf/2211.07937v2.pdf | An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods | In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG (NPG) methods, and their variance-reduced variants, under general smooth policy parametrizations. More specifically, with the Fisher information matrix of the policy being positive definite: i) we show that a state-of-the-art vari... | ['Wotao Yin', 'Tamer Başar', 'Kaiqing Zhang', 'Yanli Liu'] | 2022-11-15 | an-improved-analysis-of-variance-reduced | http://proceedings.neurips.cc/paper/2020/hash/56577889b3c1cd083b6d7b32d32f99d5-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/56577889b3c1cd083b6d7b32d32f99d5-Paper.pdf | neurips-2020-12 | ['policy-gradient-methods'] | ['methodology'] | [-1.72936529e-01 8.50892067e-02 -4.12708580e-01 3.09209585e-01
-8.48626912e-01 -7.75382936e-01 6.09588623e-01 -8.05774480e-02
-3.94466430e-01 1.09593523e+00 1.33139074e-01 -8.16029251e-01
-5.41141868e-01 -4.51138824e-01 -8.08570027e-01 -8.63327503e-01
-3.14882100e-01 2.89139092e-01 3.19621116e-01 -3.80411714... | [4.2951860427856445, 2.61540150642395] |
750cdc60-e108-4e77-89cc-586474f373e1 | quick-tune-quickly-learning-which-pretrained | 2306.03828 | null | https://arxiv.org/abs/2306.03828v3 | https://arxiv.org/pdf/2306.03828v3.pdf | Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How | With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dataset. In this paper, we propose a methodology that jointly searches for the optimal pretrained model and the hyperparameters for finetuning ... | ['Josif Grabocka', 'Frank Hutter', 'Arlind Kadra', 'Fabio Ferreira', 'Sebastian Pineda Arango'] | 2023-06-06 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 3.22138518e-01 -2.09742531e-01 -3.05095345e-01 -4.30957526e-01
-1.34841812e+00 -6.64325356e-01 1.66088909e-01 -9.31996405e-02
-6.65002167e-01 6.58629119e-01 -9.27845109e-03 6.32665411e-04
-5.42172074e-01 -4.70063001e-01 -7.89065003e-01 -9.27970111e-01
2.38358006e-01 8.83265793e-01 1.08793363e-01 1.75038353... | [9.050699234008789, 3.298794746398926] |
bb36fd84-6429-4e9e-9478-d8c43a45901a | explaining-face-presentation-attack-detection | 2111.04862 | null | https://arxiv.org/abs/2111.04862v1 | https://arxiv.org/pdf/2111.04862v1.pdf | Explaining Face Presentation Attack Detection Using Natural Language | A large number of deep neural network based techniques have been developed to address the challenging problem of face presentation attack detection (PAD). Whereas such techniques' focus has been on improving PAD performance in terms of classification accuracy and robustness against unseen attacks and environmental cond... | ['Wael Abd-Almageed', 'Jonathan May', 'Leonidas Spinoulas', 'Mohamed E. Hussein', 'Hengameh Mirzaalian'] | 2021-11-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 4.88497913e-01 4.34336573e-01 -2.73773214e-03 -5.38581610e-01
-9.23796237e-01 -2.04352364e-01 6.63185596e-01 1.46659285e-01
-7.56355887e-03 6.00685060e-01 3.41288269e-01 -1.80728242e-01
5.35969697e-02 -6.19832218e-01 -8.15985441e-01 -3.13567191e-01
-1.43095404e-01 2.57073045e-01 -2.76305795e-01 -1.51877433... | [12.91537094116211, 1.066261887550354] |
879e4d11-bb8b-43da-b0ec-c20e1f3191e8 | direct-output-connection-for-a-high-rank | 1808.10143 | null | http://arxiv.org/abs/1808.10143v2 | http://arxiv.org/pdf/1808.10143v2.pdf | Direct Output Connection for a High-Rank Language Model | This paper proposes a state-of-the-art recurrent neural network (RNN)
language model that combines probability distributions computed not only from a
final RNN layer but also from middle layers. Our proposed method raises the
expressive power of a language model based on the matrix factorization
interpretation of langu... | ['Jun Suzuki', 'Sho Takase', 'Masaaki Nagata'] | 2018-08-30 | direct-output-connection-for-a-high-rank-1 | https://aclanthology.org/D18-1489 | https://aclanthology.org/D18-1489.pdf | emnlp-2018-10 | ['headline-generation'] | ['natural-language-processing'] | [-1.80124238e-01 1.56491637e-01 -5.89245617e-01 -1.17172375e-01
-1.07227528e+00 -5.12391984e-01 6.88117683e-01 -3.53497356e-01
-5.56905329e-01 1.05138397e+00 5.87582350e-01 -9.79057252e-01
3.94036144e-01 -5.96014082e-01 -8.52598369e-01 -4.38003868e-01
4.26196307e-01 6.10311329e-01 -2.30852097e-01 -3.25202554... | [11.662351608276367, 9.304156303405762] |
6b656e19-39f9-4151-b4b8-0da8699c657a | leveraging-bert-for-extractive-text | 1906.04165 | null | https://arxiv.org/abs/1906.04165v1 | https://arxiv.org/pdf/1906.04165v1.pdf | Leveraging BERT for Extractive Text Summarization on Lectures | In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to ... | ['Derek Miller'] | 2019-06-07 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [-2.97371864e-01 1.77259609e-01 -6.16083555e-02 -4.99337971e-01
-1.24330854e+00 -6.53391719e-01 3.03124398e-01 8.80589545e-01
-9.32305530e-02 5.35300553e-01 8.79050910e-01 -2.20669419e-01
-3.10122129e-02 -5.63522220e-01 -3.92172873e-01 -5.98912418e-01
2.47910574e-01 4.43535715e-01 6.98852837e-02 -2.63243347... | [12.493980407714844, 9.486716270446777] |
ab523ed7-29e0-4e3e-b3ff-efef7366c759 | semi-supervised-object-detection-via-multi | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Semi-Supervised_Object_Detection_via_Multi-Instance_Alignment_With_Global_Class_Prototypes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Semi-Supervised_Object_Detection_via_Multi-Instance_Alignment_With_Global_Class_Prototypes_CVPR_2022_paper.pdf | Semi-Supervised Object Detection via Multi-Instance Alignment With Global Class Prototypes | Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD methods individually learn from labeled data and unlabeled data, without considering the relation between them. To make full use of labeled dat... | ['Zhenguo Li', 'Peng Yuan', 'Aoxue Li'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-3.36650610e-02 1.30840778e-01 -4.98685449e-01 -7.01158822e-01
-8.10854673e-01 -4.28615332e-01 5.36199987e-01 9.28864554e-02
-3.91198516e-01 5.56407392e-01 -4.18773681e-01 6.21778220e-02
3.59412991e-02 -5.65412343e-01 -7.14624763e-01 -7.41668522e-01
3.13822895e-01 6.66694462e-01 5.98826110e-01 4.23769027... | [9.257606506347656, 1.3531970977783203] |
b91140fb-5b00-4254-8d65-c3a68a4729b7 | featfsda-towards-few-shot-domain-adaptation | 2305.08420 | null | https://arxiv.org/abs/2305.08420v1 | https://arxiv.org/pdf/2305.08420v1.pdf | FeatFSDA: Towards Few-shot Domain Adaptation for Video-based Activity Recognition | Domain adaptation is essential for activity recognition, as common spatiotemporal architectures risk overfitting due to increased parameters arising from the temporal dimension. Unsupervised domain adaptation methods have been extensively studied, yet, they require large-scale unlabeled data from the target domain. In ... | ['Alina Roitberg', 'Rainer Stiefelhagen', 'M. Saquib Sarfraz', 'Kailun Yang', 'Jiaming Zhang', 'David Schneider', 'Di Wen', 'Kunyu Peng'] | 2023-05-15 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 3.26095730e-01 -2.71474391e-01 -7.14676499e-01 -2.46276230e-01
-7.54773557e-01 -4.23815399e-01 5.18848300e-01 -3.11035454e-01
-4.23497200e-01 8.68486106e-01 4.43002611e-01 1.45887643e-01
-8.64812434e-02 -5.44175267e-01 -7.57123172e-01 -7.07154751e-01
-1.16073310e-01 4.09128666e-01 3.34218532e-01 1.66116640... | [8.694497108459473, 0.8528569340705872] |
0d2798ec-cecc-4d4f-8d90-b2934ee1e8f1 | modeling-multi-action-policy-for-task | 1908.11546 | null | https://arxiv.org/abs/1908.11546v1 | https://arxiv.org/pdf/1908.11546v1.pdf | Modeling Multi-Action Policy for Task-Oriented Dialogues | Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of in... | ['Piero Molino', 'Bing Liu', 'Hu Xu', 'Lei Shu'] | 2019-08-30 | modeling-multi-action-policy-for-task-1 | https://aclanthology.org/D19-1130 | https://aclanthology.org/D19-1130.pdf | ijcnlp-2019-11 | ['dialogue-management'] | ['natural-language-processing'] | [-2.15517238e-01 5.26680827e-01 -2.62022406e-01 -4.04688656e-01
-4.36131835e-01 -6.57143772e-01 8.26965511e-01 -1.05033360e-01
-3.16109866e-01 1.17243731e+00 5.31936049e-01 -4.86305207e-01
3.33677322e-01 -4.95004714e-01 2.38810509e-01 -3.29771847e-01
3.63730669e-01 6.58300102e-01 3.22447628e-01 -8.83787990... | [12.85023021697998, 7.975614070892334] |
94683713-2456-4ab4-83dc-1cf7ce255f55 | a-theory-of-unsupervised-translation | 2211.11081 | null | https://arxiv.org/abs/2211.11081v1 | https://arxiv.org/pdf/2211.11081v1.pdf | A Theory of Unsupervised Translation Motivated by Understanding Animal Communication | Recent years have seen breakthroughs in neural language models that capture nuances of language, culture, and knowledge. Neural networks are capable of translating between languages -- in some cases even between two languages where there is little or no access to parallel translations, in what is known as Unsupervised ... | ['Orr Paradise', 'Adam Tauman Kalai', 'David F. Gruber', 'Shafi Goldwasser'] | 2022-11-20 | null | null | null | null | ['unsupervised-machine-translation', 'culture'] | ['natural-language-processing', 'speech'] | [ 4.34470683e-01 3.16627562e-01 -2.92720199e-01 -2.81463683e-01
-7.54459620e-01 -8.46577466e-01 9.15970564e-01 3.39136839e-01
-8.24071050e-01 8.31077337e-01 3.07875276e-01 -6.48241043e-01
1.09019853e-01 -8.49331737e-01 -1.00108922e+00 -4.80309069e-01
4.89104316e-02 7.69140184e-01 -4.63441946e-02 -3.70133460... | [11.085549354553223, 9.936565399169922] |
d9f62205-0585-47ea-9b5e-e76d6735e282 | selective-inference-for-sparse-high-order | null | null | https://icml.cc/Conferences/2017/Schedule?showEvent=801 | http://proceedings.mlr.press/v70/suzumura17a/suzumura17a.pdf | Selective Inference for Sparse High-Order Interaction Models |
Finding statistically significant high-order interactions in predictive modeling is important but challenging task because the possible number of high-order interactions is extremely large (e.g., $> 10^{17}$). In this paper we study feature selection and statistical inference for sparse high-order interaction mode... | ['Kazuya Nakagawa', 'Ichiro Takeuchi', 'Yuta Umezu', 'Shinya Suzumura', 'Koji Tsuda'] | 2017-08-01 | null | null | null | icml-2017-8 | ['drug-response-prediction'] | ['medical'] | [ 6.68890595e-01 -1.63666204e-01 -6.92830503e-01 -4.84931707e-01
-6.05058610e-01 -1.22493699e-01 2.84008622e-01 3.85995507e-01
-1.21652313e-01 1.35682702e+00 1.00103393e-02 -4.77344930e-01
-6.08690798e-01 -6.37781680e-01 -9.63821530e-01 -6.69598937e-01
-7.06623137e-01 9.10596132e-01 -1.50670081e-01 2.33091861... | [7.658432483673096, 4.959460258483887] |
f6386680-9220-4c70-9f0c-1e5203faf478 | a-neural-divide-and-conquer-reasoning | 2305.02265 | null | https://arxiv.org/abs/2305.02265v2 | https://arxiv.org/pdf/2305.02265v2.pdf | A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text | Pretrained Vision-Language Models (VLMs) have achieved remarkable performance in image retrieval from text. However, their performance drops drastically when confronted with linguistically complex texts that they struggle to comprehend. Inspired by the Divide-and-Conquer algorithm and dual-process theory, in this paper... | ['Yuxin Ding', 'Min Zhang', 'Lin Ma', 'Baotian Hu', 'Yunxin Li'] | 2023-05-03 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.29553765e-01 1.45159587e-01 4.83526774e-02 -3.71624112e-01
-6.20088398e-01 -4.51003909e-01 1.07521212e+00 -1.40012987e-02
-3.52236181e-01 2.28020921e-01 1.40424743e-01 -5.29645026e-01
-1.74490064e-01 -8.23256075e-01 -7.57703424e-01 -4.24125969e-01
5.97616315e-01 7.07430780e-01 1.46133989e-01 -1.94931656... | [10.730905532836914, 1.7294589281082153] |
abe00e8c-04bf-4c2d-8edd-ffac05b7218f | fra-rir-fast-random-approximation-of-the | 2208.04101 | null | https://arxiv.org/abs/2208.04101v1 | https://arxiv.org/pdf/2208.04101v1.pdf | FRA-RIR: Fast Random Approximation of the Image-source Method | The training of modern speech processing systems often requires a large amount of simulated room impulse response (RIR) data in order to allow the systems to generalize well in real-world, reverberant environments. However, simulating realistic RIR data typically requires accurate physical modeling, and the acceleratio... | ['Jianwei Yu', 'Yi Luo'] | 2022-08-08 | null | null | null | null | ['room-impulse-response', 'speech-denoising'] | ['audio', 'speech'] | [-2.16453131e-02 -6.10933602e-01 1.05368745e+00 -3.37942392e-01
-1.16132045e+00 -3.90557677e-01 3.46692026e-01 -3.03227305e-01
-3.12564403e-01 2.92850107e-01 2.29941070e-01 -7.41910517e-01
3.51578027e-01 -8.12262416e-01 -7.58265078e-01 -6.46285295e-01
6.42556027e-02 1.63603753e-01 1.42108887e-01 -4.27359134... | [15.191695213317871, 5.7816243171691895] |
22148177-5cf3-415b-bb5e-07c3eeae9fb5 | human-pose-estimation-on-privacy-preserving | 2007.08340 | null | https://arxiv.org/abs/2007.08340v2 | https://arxiv.org/pdf/2007.08340v2.pdf | Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images | Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR). The 24/7 use of images coming from cameras mounted on the OR ceiling can however raise concerns for privacy, even in the case of depth images captured by RGB-D sensors. Being able to solely ... | ['Nicolas Padoy', 'Vinkle Srivastav', 'Afshin Gangi'] | 2020-07-16 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 6.20218337e-01 3.86341900e-01 1.26600757e-01 -5.96656740e-01
-7.97223032e-01 -2.94702291e-01 1.48030251e-01 -2.12664783e-01
-9.55696642e-01 5.85732639e-01 5.18596828e-01 4.00051445e-01
-3.91382724e-03 -6.27100408e-01 -5.71149886e-01 -2.22286761e-01
-1.27687141e-01 3.32641095e-01 2.74812698e-01 -2.78262705... | [7.018146991729736, -0.8923249840736389] |
cc583f04-772a-440e-b0fc-d5cdc44f626d | conversational-machine-reading-comprehension | 2105.01542 | null | https://arxiv.org/abs/2105.01542v6 | https://arxiv.org/pdf/2105.01542v6.pdf | Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts | Machine reading comprehension (MRC) is a sub-field in natural language processing that aims to assist computers understand unstructured texts and then answer questions related to them. In practice, the conversation is an essential way to communicate and transfer information. To help machines understand conversation tex... | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Khiem Vinh Tran', 'Loi Duc Nguyen', 'Mao Nguyen Bui', 'Son T. Luu'] | 2021-05-04 | null | null | null | null | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 4.21377867e-01 7.44244695e-01 -1.19454851e-02 -3.88153315e-01
-1.17025054e+00 -6.70752347e-01 5.84693313e-01 6.75312519e-01
-4.30266529e-01 9.04178143e-01 1.00209689e+00 -8.15791607e-01
2.86915690e-01 -6.89480066e-01 -4.76388574e-01 -3.57827246e-01
1.81472883e-01 8.88976574e-01 1.07170999e-01 -7.81164467... | [11.756671905517578, 8.083256721496582] |
152f1f9c-0c18-4e2e-83cb-913c1eaba9b3 | selecting-computations-theory-and | 1408.2048 | null | http://arxiv.org/abs/1408.2048v1 | http://arxiv.org/pdf/1408.2048v1.pdf | Selecting Computations: Theory and Applications | Sequential decision problems are often approximately solvable by simulating
possible future action sequences. Metalevel decision procedures have been
developed for selecting which action sequences to simulate, based on estimating
the expected improvement in decision quality that would result from any
particular simulat... | ['David Tolpin', 'Solomon Eyal Shimony', 'Stuart Russell', 'Nicholas Hay'] | 2014-08-09 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 3.24327022e-01 2.92057216e-01 -7.76862264e-01 -2.77248532e-01
-1.02482128e+00 -5.34426212e-01 6.21656597e-01 -1.36218682e-01
-7.48993218e-01 1.35744822e+00 4.03248578e-01 -9.03262079e-01
-8.18514645e-01 -8.33557367e-01 -5.18626511e-01 -6.84535623e-01
9.61064454e-03 9.00863111e-01 1.83543116e-01 1.62391260... | [4.486444473266602, 3.1057615280151367] |
f80af314-7499-4b16-9f33-2df0ef5efdeb | a-study-of-face-obfuscation-in-imagenet | 2103.06191 | null | https://arxiv.org/abs/2103.06191v3 | https://arxiv.org/pdf/2103.06191v3.pdf | A Study of Face Obfuscation in ImageNet | Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmar... | ['Olga Russakovsky', 'Jia Deng', 'Li Fei-Fei', 'Jacqueline Yau', 'Kaiyu Yang'] | 2021-03-10 | a-study-of-face-obfuscation-in-imagenet-1 | https://openreview.net/forum?id=KVYq2Ea90PC | https://openreview.net/pdf?id=KVYq2Ea90PC | null | ['scene-recognition'] | ['computer-vision'] | [ 2.82132179e-01 1.08757108e-01 -1.64211974e-01 -5.07982433e-01
-4.57229435e-01 -8.88562739e-01 7.46934950e-01 -2.81525254e-01
-4.27813143e-01 6.31011844e-01 7.33328685e-02 -4.37097818e-01
3.35592955e-01 -4.28017706e-01 -1.00184464e+00 -7.00302660e-01
-2.82139570e-01 -7.91101623e-03 -5.58338761e-01 3.51942450... | [12.780167579650879, 0.8319613337516785] |
7693e987-06e4-46ce-9707-22a80e466b34 | bootstrapping-deep-music-separation-from | 1910.11133 | null | https://arxiv.org/abs/1910.11133v1 | https://arxiv.org/pdf/1910.11133v1.pdf | Bootstrapping deep music separation from primitive auditory grouping principles | Separating an audio scene such as a cocktail party into constituent, meaningful components is a core task in computer audition. Deep networks are the state-of-the-art approach. They are trained on synthetic mixtures of audio made from isolated sound source recordings so that ground truth for the separation is known. Ho... | ['Prem Seetharaman', 'Bryan Pardo', 'Jonathan Le Roux', 'Gordon Wichern'] | 2019-10-23 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 4.12997007e-01 -5.77348433e-02 1.59077615e-01 -1.37877494e-01
-1.36355853e+00 -1.05276787e+00 1.60819367e-01 -3.18369046e-02
-2.31147319e-01 4.24897373e-01 2.84320831e-01 3.33432965e-02
2.04982623e-01 -2.04369888e-01 -8.42845559e-01 -5.49893677e-01
1.22564554e-01 2.35214591e-01 -6.21381216e-02 6.18483461... | [15.437000274658203, 5.545867919921875] |
db524a0d-9866-4223-83ed-d214563b5f11 | melting-pot-2-0 | 2211.13746 | null | https://arxiv.org/abs/2211.13746v5 | https://arxiv.org/pdf/2211.13746v5.pdf | Melting Pot 2.0 | Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-age... | ['Joel Z. Leibo', 'Dean Mobbs', 'Igor Mordatch', 'Julia Haas', 'Sukhdeep Singh', 'Michael B. Johanson', 'DJ Strouse', 'Ramona Comanescu', 'Kavya Kopparapu', 'Udari Madhushani', 'Raphael Köster', 'Peter Sunehag', 'Yiran Mao', 'Jayd Matyas', 'Edgar A. Duéñez-Guzmán', 'Alexander Sasha Vezhnevets', 'John P. Agapiou'] | 2022-11-24 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-6.06327057e-02 4.89883035e-01 -1.65360302e-01 8.29722285e-02
-1.48991647e-03 -7.14851260e-01 9.26179588e-01 1.13925554e-01
-7.10507691e-01 1.17494857e+00 -2.28856364e-03 -3.26194167e-01
-7.33521402e-01 -8.13838005e-01 -2.29611218e-01 -9.15470064e-01
-4.12487507e-01 1.01432085e+00 -7.41743483e-03 -7.12719858... | [3.882479667663574, 2.2166242599487305] |
280375d0-4ee7-42f5-bb5d-8c473473e117 | improving-offline-rl-by-blending-heuristics | 2306.00321 | null | https://arxiv.org/abs/2306.00321v1 | https://arxiv.org/pdf/2306.00321v1.pdf | Improving Offline RL by Blending Heuristics | We propose Heuristic Blending (HUBL), a simple performance-improving technique for a broad class of offline RL algorithms based on value bootstrapping. HUBL modifies Bellman operators used in these algorithms, partially replacing the bootstrapped values with Monte-Carlo returns as heuristics. For trajectories with high... | ['Ching-An Cheng', 'Andrey Kolobov', 'Aldo Pacchiano', 'Sinong Geng'] | 2023-06-01 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [-6.23047769e-01 1.77129149e-01 -8.85135531e-01 -4.30435874e-02
-1.09796858e+00 -1.26828015e+00 5.05195498e-01 1.60784140e-01
-6.21840835e-01 1.31920099e+00 2.75411546e-01 -8.44857872e-01
-2.71225780e-01 -5.70930600e-01 -8.32445383e-01 -6.10808194e-01
-5.74775457e-01 9.18142855e-01 3.95702571e-01 -1.48208559... | [3.9904587268829346, 2.282942056655884] |
88cbf7ae-d32e-48ba-b47f-b7280b0c235e | beliefppg-uncertainty-aware-heart-rate | 2306.07730 | null | https://arxiv.org/abs/2306.07730v2 | https://arxiv.org/pdf/2306.07730v2.pdf | BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation | We present a novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG). We consider the evolution of the heart rate in the context of a discrete-time stochastic process that we represent as a hidden Markov model. ... | ['Christian Holz', 'Berken Utku Demirel', 'Paul Streli', 'Valentin Bieri'] | 2023-06-13 | null | null | null | null | ['photoplethysmography-ppg-heart-rate', 'heart-rate-estimation', 'time-series-anomaly-detection', 'time-series'] | ['medical', 'medical', 'time-series', 'time-series'] | [ 2.69354671e-01 2.02237099e-01 -2.84656227e-01 -7.06576943e-01
-9.66473401e-01 -2.39121690e-01 3.69546652e-01 -5.28007559e-03
-2.44106218e-01 8.63523662e-01 2.14237005e-01 1.07511878e-01
7.57474378e-02 -3.70166451e-01 -4.29409057e-01 -8.42754841e-01
-3.32484633e-01 1.47980839e-01 -5.26659824e-02 5.49970329... | [13.928325653076172, 2.8725457191467285] |
fea84ef2-1388-4eed-9059-1b8ee0fbc291 | diffusion-models-already-have-a-semantic | 2210.10960 | null | https://arxiv.org/abs/2210.10960v2 | https://arxiv.org/pdf/2210.10960v2.pdf | Diffusion Models already have a Semantic Latent Space | Diffusion models achieve outstanding generative performance in various domains. Despite their great success, they lack semantic latent space which is essential for controlling the generative process. To address the problem, we propose asymmetric reverse process (Asyrp) which discovers the semantic latent space in froze... | ['Youngjung Uh', 'Jaeseok Jeong', 'Mingi Kwon'] | 2022-10-20 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [-2.59065658e-01 4.34423238e-02 7.79288486e-02 -3.14535856e-01
-4.02645141e-01 -6.19044006e-01 1.02092505e+00 -5.21981657e-01
-1.02556013e-01 6.03899717e-01 4.00153279e-01 -7.93739036e-03
-2.15842187e-01 -9.61296916e-01 -5.47579110e-01 -8.36594105e-01
2.98467815e-01 6.42449081e-01 5.50207086e-02 -1.43179342... | [11.348504066467285, -0.24705089628696442] |
127e8757-32f5-4541-88cb-23e4393e8bfd | learning-the-enigma-with-recurrent-neural | 1708.07576 | null | http://arxiv.org/abs/1708.07576v2 | http://arxiv.org/pdf/1708.07576v2.pdf | Learning the Enigma with Recurrent Neural Networks | Recurrent neural networks (RNNs) represent the state of the art in
translation, image captioning, and speech recognition. They are also capable of
learning algorithmic tasks such as long addition, copying, and sorting from a
set of training examples. We demonstrate that RNNs can learn decryption
algorithms -- the mappi... | ['Sam Greydanus'] | 2017-08-24 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 5.24798691e-01 2.64804401e-02 5.08557633e-02 1.10804223e-01
-6.42010033e-01 -7.98962474e-01 9.64773238e-01 -1.58980519e-01
-7.78524458e-01 5.49307287e-01 2.97217853e-02 -1.23908913e+00
2.65046448e-01 -8.05988908e-01 -1.13566577e+00 -1.12826574e+00
-2.83660740e-01 4.95621920e-01 -6.08329415e-01 -5.93387127... | [10.500907897949219, 7.154221057891846] |
159e28d8-6e5b-4dfc-b9b2-ecc3f82fc5ee | topicspam-a-topic-model-based-approach-for | null | null | https://aclanthology.org/P13-2039 | https://aclanthology.org/P13-2039.pdf | TopicSpam: a Topic-Model based approach for spam detection | null | ['Jiwei Li', 'Sujian Li', 'Claire Cardie'] | 2013-08-01 | null | null | null | acl-2013-8 | ['spam-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... | [-7.192071437835693, 3.594228982925415] |
2b4476b2-c199-4eb9-a403-27e9759cd13e | poster-a-pyramid-cross-fusion-transformer | 2204.04083 | null | https://arxiv.org/abs/2204.04083v1 | https://arxiv.org/pdf/2204.04083v1.pdf | POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition | Facial Expression Recognition (FER) has received increasing interest in the computer vision community. As a challenging task, there are three key issues especially prevalent in FER: inter-class similarity, intra-class discrepancy, and scale sensitivity. Existing methods typically address some of these issues, but do no... | ['Chen Chen', 'Matias Mendieta', 'Ce Zheng'] | 2022-04-08 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.06001966e-01 -4.51689154e-01 5.76740019e-02 -5.31361341e-01
-5.06430149e-01 -3.39426771e-02 2.20735982e-01 -2.85110831e-01
-1.83491334e-01 5.58502257e-01 8.98430571e-02 3.18545312e-01
2.63422582e-04 -5.56082547e-01 -2.46372357e-01 -8.56794477e-01
1.78946599e-01 -4.18662667e-01 2.31895372e-01 -5.55279851... | [13.586246490478516, 1.5280145406723022] |
7882f20f-ce4e-4e81-b906-af5a16eea60f | a-flow-based-latent-state-generative-model-of | null | null | http://proceedings.neurips.cc/paper/2021/hash/84a529a92de322be42dd3365afd54f91-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/84a529a92de322be42dd3365afd54f91-Paper.pdf | A flow-based latent state generative model of neural population responses to natural images | We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture th... | ['Fabian Sinz', 'Andreas Tolias', 'Zhuokun Ding', 'Zhiwei Ding', 'Taliah Muhammad', 'Akshay Jagadish', 'Konstantin-Klemens Lurz', 'Edgar Walker', 'Mohammad Bashiri'] | 2021-12-01 | null | https://openreview.net/forum?id=1yeYYtLqq7K | https://openreview.net/pdf?id=1yeYYtLqq7K | neurips-2021-12 | ['pupil-dilation'] | ['computer-vision'] | [ 2.17559382e-01 -6.76945686e-01 6.04464300e-02 -2.62504488e-01
-7.42101014e-01 -5.88797629e-01 8.34965706e-01 -1.24446698e-01
-5.57432115e-01 6.70900047e-01 2.83935845e-01 -7.44268298e-02
-1.06302693e-01 -3.49780291e-01 -9.53174651e-01 -1.07532775e+00
-1.93316907e-01 1.79992646e-01 1.87035292e-01 4.18833233... | [9.386775016784668, 2.6345813274383545] |
05c90258-b1d0-48af-b3f4-57101478505a | low-rank-covariance-completion-for-graph | 2209.08273 | null | https://arxiv.org/abs/2209.08273v1 | https://arxiv.org/pdf/2209.08273v1.pdf | Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity | As a tool for estimating networks in high dimensions, graphical models are commonly applied to calcium imaging data to estimate functional neuronal connectivity, i.e. relationships between the activities of neurons. However, in many calcium imaging data sets, the full population of neurons is not recorded simultaneousl... | ['Genevera I. Allen', 'Lili Zheng', 'Andersen Chang'] | 2022-09-17 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 4.75681484e-01 1.29423544e-01 7.57285878e-02 -4.67364304e-02
-5.58898449e-01 -5.07427037e-01 2.28718400e-01 -9.21253711e-02
-5.23402929e-01 9.02292073e-01 7.69457966e-02 -1.31345645e-01
-6.01200283e-01 -1.88063055e-01 -8.94411683e-01 -1.05765402e+00
-4.09990788e-01 4.51464683e-01 -2.59668261e-01 3.43537956... | [7.080098628997803, 4.90025520324707] |
d9c47fe9-e293-4e95-98b7-c8591d2ff0dd | planet-photo-geolocation-with-convolutional | 1602.05314 | null | http://arxiv.org/abs/1602.05314v1 | http://arxiv.org/pdf/1602.05314v1.pdf | PlaNet - Photo Geolocation with Convolutional Neural Networks | Is it possible to build a system to determine the location where a photo was
taken using just its pixels? In general, the problem seems exceptionally
difficult: it is trivial to construct situations where no location can be
inferred. Yet images often contain informative cues such as landmarks, weather
patterns, vegetat... | ['Ilya Kostrikov', 'Tobias Weyand', 'James Philbin'] | 2016-02-17 | null | null | null | null | ['photo-geolocation-estimation'] | ['computer-vision'] | [-5.53558543e-02 -5.72027788e-02 -1.01765320e-01 -3.88579369e-01
-1.01539159e+00 -8.27799559e-01 8.72031093e-01 2.40751624e-01
-6.65714800e-01 5.55492759e-01 1.19365796e-01 2.32915580e-02
1.80581674e-01 -1.06980360e+00 -1.02336848e+00 -4.65506822e-01
-8.92499164e-02 4.10868406e-01 7.91942328e-02 -1.07125519... | [7.683464050292969, -1.84306800365448] |
5d11aeb3-0c45-4276-8291-dbbb8d4530fa | analyzing-the-impact-of-varied-window-hyper | 2209.05804 | null | https://arxiv.org/abs/2209.05804v1 | https://arxiv.org/pdf/2209.05804v1.pdf | Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification | The use of deep neural networks in electromyogram (EMG) based prostheses control provides a promising alternative to the hand-crafted features by automatically learning muscle activation patterns from the EMG signals. Meanwhile, the use of raw EMG signals as input to convolution neural networks (CNN) offers a simple, f... | ['Guanglin Li', 'Olumide Olayinka Obe', 'Mojisola Grace Asogbon', 'Oluwarotimi Williams Samuel', 'Frank Kulwa'] | 2022-09-13 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 2.90322691e-01 -1.15496784e-01 -3.03196251e-01 1.90363020e-01
-1.64765656e-01 1.26426339e-01 2.64033347e-01 -5.65818191e-01
-9.69474733e-01 7.10862339e-01 7.12755620e-02 -1.20016150e-01
-4.23360229e-01 -6.53605759e-01 -4.47552145e-01 -8.94928575e-01
-2.86998332e-01 -2.85273641e-01 8.17453638e-02 -1.64144129... | [6.859349250793457, 0.21105888485908508] |
33e72783-43ce-4e0c-a6b3-87423fcb0bb5 | audio-dequantization-using-co-sparse-non | 2010.16386 | null | https://arxiv.org/abs/2010.16386v2 | https://arxiv.org/pdf/2010.16386v2.pdf | Audio Dequantization Using (Co)Sparse (Non)Convex Methods | The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. In the experim... | ['Ondřej Mokrý', 'Pavel Rajmic', 'Pavel Záviška'] | 2020-10-30 | null | null | null | null | ['audio-dequantization'] | ['audio'] | [ 2.67743379e-01 2.39277706e-02 -1.45498320e-01 -1.18349232e-02
-1.16692901e+00 -2.50747353e-01 1.87579423e-01 -1.04650140e-01
-2.37889513e-01 7.92108238e-01 5.64807415e-01 2.18601048e-01
-2.85482556e-01 -4.99698035e-02 -1.85822889e-01 -7.22135723e-01
-4.53910261e-01 -1.36453867e-01 1.47430748e-01 -2.12285474... | [15.445869445800781, 5.622194766998291] |
f8a284fa-22df-408e-b686-141770949c7f | diffusion-based-speech-enhancement-with-joint | 2305.10734 | null | https://arxiv.org/abs/2305.10734v1 | https://arxiv.org/pdf/2305.10734v1.pdf | Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders | Diffusion-based speech enhancement (SE) has been investigated recently, but its decoding is very time-consuming. One solution is to initialize the decoding process with the enhanced feature estimated by a predictive SE system. However, this two-stage method ignores the complementarity between predictive and diffusion S... | ['Yuki Mitsufuji', 'Tatsuya Kawahara', 'Shusuke Takahashi', 'Zhi Zhong', 'Yuichiro Koyama', 'Takashi Shibuya', 'Masato Hirano', 'Kazuki Shimada', 'Hao Shi'] | 2023-05-18 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.36316636e-01 -1.25190735e-01 2.53643602e-01 -1.73605680e-01
-8.03174615e-01 -1.06395394e-01 5.71701825e-01 -2.09759489e-01
-5.45314968e-01 4.50325221e-01 4.55516040e-01 -9.61660668e-02
1.75549492e-01 -7.22665489e-01 -1.05304360e-01 -1.05304718e+00
3.79002750e-01 -5.89961559e-02 6.18123412e-01 -2.50880271... | [14.982165336608887, 5.985536098480225] |
e85bea85-c7ae-471f-86ae-65f001ea5daa | unsupervised-action-localization-crop-in | 2111.07426 | null | https://arxiv.org/abs/2111.07426v2 | https://arxiv.org/pdf/2111.07426v2.pdf | Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets | Untrimmed videos on social media or those captured by robots and surveillance cameras are of varied aspect ratios. However, 3D CNNs usually require as input a square-shaped video, whose spatial dimension is smaller than the original. Random- or center-cropping may leave out the video's subject altogether. To address th... | ['Partha Pratim Mohanta', 'Swarnabja Bhaumik', 'Prithwish Jana'] | 2021-11-14 | null | null | null | null | ['video-to-video-synthesis'] | ['computer-vision'] | [ 2.39065588e-01 -1.60891742e-01 -1.98250085e-01 1.75768867e-01
-3.33960861e-01 -7.73003340e-01 3.95442277e-01 -1.70766458e-01
-5.00433028e-01 6.44804657e-01 -6.04372099e-03 4.84886914e-02
2.38939703e-01 -5.59439719e-01 -1.02601898e+00 -9.44815755e-01
-2.00397044e-01 -1.53940603e-01 6.35462999e-01 2.25300729... | [9.17924976348877, -0.06889337301254272] |
2aa7c44b-4d7f-4310-ae34-6ebc24912fd9 | egnet-edge-guidance-network-for-salient | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_EGNet_Edge_Guidance_Network_for_Salient_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhao_EGNet_Edge_Guidance_Network_for_Salient_Object_Detection_ICCV_2019_paper.pdf | EGNet: Edge Guidance Network for Salient Object Detection | Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, most existing FCNs-based methods still suffer from coarse object boundaries. In this paper, to solve this problem, we focus on the complementarity between salient edge information and salient object inf... | [' Ming-Ming Cheng', ' Jufeng Yang', ' Yang Cao', ' Deng-Ping Fan', ' Jiang-Jiang Liu', 'Jia-Xing Zhao'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['co-saliency-detection', 'camouflaged-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.19896986e-01 -1.93124279e-01 -2.34059945e-01 -1.56893119e-01
-5.20383060e-01 -5.73986806e-02 3.67998421e-01 3.76203716e-01
-3.14855248e-01 5.43922961e-01 4.44801927e-01 2.47834131e-01
-1.98657691e-01 -6.68379605e-01 -5.59433818e-01 -8.16126406e-01
1.31090572e-02 -3.93057466e-01 8.16645622e-01 -2.28358656... | [9.725911140441895, -0.4665317237377167] |
012f73a8-4eac-48ed-b1b5-59491924ab5d | investigating-the-successes-and-failures-of | 1905.01758 | null | https://arxiv.org/abs/1905.01758v1 | https://arxiv.org/pdf/1905.01758v1.pdf | Investigating the Successes and Failures of BERT for Passage Re-Ranking | The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the reasons behind such substantial improvements. To this aim, we focus on the MS MARC... | ['W. Bruce Croft', 'Hamed Zamani', 'Harshith Padigela'] | 2019-05-05 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-3.30401987e-01 -2.34075755e-01 -1.78745583e-01 -3.45392913e-01
-1.33860862e+00 -6.68025851e-01 8.49491417e-01 3.97765309e-01
-5.79869568e-01 8.34922016e-01 9.59764957e-01 -3.74725968e-01
-5.67478180e-01 -4.77005929e-01 -6.40547872e-01 -1.59430683e-01
-2.77169317e-01 5.51840305e-01 1.92601681e-01 -6.43005073... | [11.525142669677734, 7.656160354614258] |
dcc80d70-fd4b-4267-86a1-70b87d24cdbc | embarrassingly-simple-unsupervised-aspect | 2004.13580 | null | https://arxiv.org/abs/2004.13580v1 | https://arxiv.org/pdf/2004.13580v1.pdf | Embarrassingly Simple Unsupervised Aspect Extraction | We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages. We introduce Contrastive Attention (CAt), a novel single-head attention mechanism base... | ['Stéphan Tulkens', 'Andreas van Cranenburgh'] | 2020-04-28 | embarrassingly-simple-unsupervised-aspect-1 | https://aclanthology.org/2020.acl-main.290 | https://aclanthology.org/2020.acl-main.290.pdf | acl-2020-6 | ['aspect-category-detection'] | ['natural-language-processing'] | [-1.00140549e-01 1.66426510e-01 -2.22434580e-01 -5.77361107e-01
-5.99818230e-01 -7.05662370e-01 8.24849248e-01 3.84174615e-01
-5.78180134e-01 4.23909038e-01 2.56932914e-01 -7.18874216e-01
2.63958901e-01 -6.48085296e-01 -3.17447096e-01 -4.84992355e-01
2.04569608e-01 3.80395323e-01 2.27847621e-02 -3.15978914... | [11.405454635620117, 6.753469944000244] |
66e2d1c2-b753-4bc6-94e0-ac7b4bcec950 | road-damage-detection-using-deep-neural | 1801.09454 | null | http://arxiv.org/abs/1801.09454v2 | http://arxiv.org/pdf/1801.09454v2.pdf | Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone | Research on damage detection of road surfaces using image processing
techniques has been actively conducted, achieving considerably high detection
accuracies. Many studies only focus on the detection of the presence or absence
of damage. However, in a real-world scenario, when the road managers from a
governing body ne... | ['Hiroya Maeda', 'Hiroshi Omata', 'Yoshihide Sekimoto', 'Takehiro Kashiyama', 'Toshikazu Seto'] | 2018-01-29 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [ 3.04449886e-01 -3.09321493e-01 2.15723678e-01 1.36785451e-02
-7.87782907e-01 -2.03539699e-01 2.02996776e-01 1.15023933e-01
-3.20076346e-01 5.47248483e-01 1.29827019e-02 -3.99363130e-01
1.36512771e-01 -1.54896843e+00 -7.62829781e-01 -7.52567112e-01
9.01194513e-02 4.36303206e-02 5.96833825e-01 -1.84025213... | [7.447514533996582, 1.1313998699188232] |
bff5c260-4db7-492b-935c-eecd47db72f7 | meta-mask-correction-for-nuclei-segmentation | 2111.12498 | null | https://arxiv.org/abs/2111.12498v1 | https://arxiv.org/pdf/2111.12498v1.pdf | Meta Mask Correction for Nuclei Segmentation in Histopathological Image | Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solutio... | ['Chen Li', 'Chunbao Wang', 'Tieliang Gong', 'Zeyu Gao', 'Chang Jia', 'Jiangbo Shi'] | 2021-11-24 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 5.32675207e-01 3.12495708e-01 -1.13797061e-01 -5.58605790e-01
-1.43066430e+00 -2.40431771e-01 1.73054680e-01 3.42410326e-01
-7.12610424e-01 6.90760016e-01 -9.97323543e-02 -1.51264323e-02
1.88122675e-01 -5.10238349e-01 -5.37717104e-01 -1.15676570e+00
5.73951960e-01 6.21759593e-01 5.30837893e-01 1.44264072... | [14.780974388122559, -2.612905263900757] |
17b02a34-2da6-417d-85a4-42c8803b1fd4 | auxiliary-learning-as-a-step-towards | 2212.00061 | null | https://arxiv.org/abs/2212.00061v1 | https://arxiv.org/pdf/2212.00061v1.pdf | Auxiliary Learning as a step towards Artificial General Intelligence | Auxiliary Learning is a machine learning approach in which the model acknowledges the existence of objects that do not come under any of its learned categories.The name Auxiliary learning was chosen due to the introduction of an auxiliary class. The paper focuses on increasing the generality of existing narrow purpose ... | ['Christeen T. Jose'] | 2022-11-30 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.75831521e-01 5.08128166e-01 -5.34673393e-01 -6.01478100e-01
-2.43222728e-01 -4.05151367e-01 8.38135898e-01 -2.49193814e-02
-4.60890889e-01 1.13376248e+00 -6.75178468e-02 -4.67129648e-01
-2.66369343e-01 -4.95217592e-01 -5.87131262e-01 -9.91681278e-01
-1.06014416e-01 4.73844260e-01 2.07994282e-01 -1.81376174... | [9.793664932250977, 2.912135601043701] |
f8500adf-4138-4273-8a0f-40d7a6c531ca | integrating-user-history-into-heterogeneous | null | null | https://aclanthology.org/2020.coling-main.372 | https://aclanthology.org/2020.coling-main.372.pdf | Integrating User History into Heterogeneous Graph for Dialogue Act Recognition | Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation. However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language ... | ['Ying Shen', 'Haitao Zheng', 'Ziran Li', 'Dong Wang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 8.52960870e-02 6.49529099e-02 1.62615523e-01 -8.49247694e-01
-1.75066128e-01 -4.11346287e-01 9.06857789e-01 1.91725671e-01
-6.16262078e-01 6.08799458e-01 8.66418958e-01 -1.42242059e-01
9.03624445e-02 -6.55924976e-01 8.92640725e-02 -5.31591475e-01
9.66506749e-02 4.35170412e-01 2.32955292e-01 -5.03254175... | [12.6980619430542, 7.727793216705322] |
56af1c04-9543-4706-8aee-f4b3f1c01bb4 | understanding-diffusion-models-a-unified | 2208.11970 | null | https://arxiv.org/abs/2208.11970v1 | https://arxiv.org/pdf/2208.11970v1.pdf | Understanding Diffusion Models: A Unified Perspective | Diffusion models have shown incredible capabilities as generative models; indeed, they power the current state-of-the-art models on text-conditioned image generation such as Imagen and DALL-E 2. In this work we review, demystify, and unify the understanding of diffusion models across both variational and score-based pe... | ['Calvin Luo'] | 2022-08-25 | null | null | null | null | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [ 3.76439877e-02 3.32491934e-01 6.79526180e-02 -1.47864595e-01
-9.14553702e-01 -6.05127394e-01 1.00234354e+00 -4.96887475e-01
-1.69166043e-01 6.05015218e-01 5.22273898e-01 -2.87801236e-01
-3.73365462e-01 -9.43288803e-01 -7.35839844e-01 -1.24693024e+00
1.19959213e-01 6.82409108e-01 -1.65712595e-01 -7.64819235... | [11.346065521240234, -0.11371827870607376] |
7e8e517b-004c-4174-a2fa-004282eb1072 | neural-network-models-for-paraphrase | 1806.04330 | null | http://arxiv.org/abs/1806.04330v2 | http://arxiv.org/pdf/1806.04330v2.pdf | Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering | In this paper, we analyze several neural network designs (and their
variations) for sentence pair modeling and compare their performance
extensively across eight datasets, including paraphrase identification,
semantic textual similarity, natural language inference, and question answering
tasks. Although most of these m... | ['Wuwei Lan', 'Wei Xu'] | 2018-06-12 | neural-network-models-for-paraphrase-1 | https://aclanthology.org/C18-1328 | https://aclanthology.org/C18-1328.pdf | coling-2018-8 | ['sentence-pair-modeling'] | ['natural-language-processing'] | [ 2.60293454e-01 -2.50324588e-02 -3.86610061e-01 -5.34086585e-01
-9.30138290e-01 -4.80255216e-01 7.16291368e-01 5.72168231e-01
-5.35536468e-01 7.83012033e-01 6.44537151e-01 -6.34830892e-01
-3.05168808e-01 -5.05652905e-01 -7.37191260e-01 -9.85392630e-02
1.02318965e-01 5.86921573e-01 8.31118226e-02 -4.10774410... | [11.191493034362793, 8.615317344665527] |
263e9f3c-db83-499d-b13d-afe052a9405d | in-distribution-interpretability-for | 2007.00758 | null | https://arxiv.org/abs/2007.00758v2 | https://arxiv.org/pdf/2007.00758v2.pdf | In-Distribution Interpretability for Challenging Modalities | It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the mode of operation of such models has advanced rapidly in the past few years. Recent work introduced an intuitive framework which utilizes gen... | ['Cosmas Heiß', 'Ron Levie', 'Joan Bruna', 'Gitta Kutyniok', 'Cinjon Resnick'] | 2020-07-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.68857083e-01 4.63568151e-01 1.74907491e-01 -5.21386325e-01
1.54507622e-01 -4.20186341e-01 1.15634894e+00 -3.22105885e-01
1.52105480e-01 6.12335145e-01 3.59337896e-01 -6.71640575e-01
-4.97262955e-01 -7.65919864e-01 -4.56489325e-01 -4.60728943e-01
-1.33764222e-01 1.57554194e-01 -9.45866033e-02 -3.31073761... | [8.9204683303833, 5.612634181976318] |
376b75e5-0184-4299-a4de-747e8505d3d9 | extractive-adversarial-networks-high-recall | 1809.01499 | null | http://arxiv.org/abs/1809.01499v2 | http://arxiv.org/pdf/1809.01499v2.pdf | Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts | We introduce an adversarial method for producing high-recall explanations of
neural text classifier decisions. Building on an existing architecture for
extractive explanations via hard attention, we add an adversarial layer which
scans the residual of the attention for remaining predictive signal. Motivated
by the impo... | ['Samuel Carton', 'Paul Resnick', 'Qiaozhu Mei'] | 2018-09-01 | extractive-adversarial-networks-high-recall-1 | https://aclanthology.org/D18-1386 | https://aclanthology.org/D18-1386.pdf | emnlp-2018-10 | ['hard-attention'] | ['methodology'] | [ 5.41728914e-01 1.04296362e+00 -2.30043709e-01 -7.67230511e-01
-8.69107008e-01 -7.94879377e-01 9.80508626e-01 6.25593215e-02
-2.63625294e-01 5.35592079e-01 5.45756638e-01 -7.26258099e-01
3.41496795e-01 -5.52923441e-01 -1.07659185e+00 -9.22605991e-02
-6.94167465e-02 3.40909302e-01 1.09937809e-01 -2.48385698... | [6.003941059112549, 8.079684257507324] |
0d138be7-7f88-4619-9684-52dc75fc39cf | detecting-beats-in-the-photoplethysmogram | null | null | https://doi.org/10.1088/1361-6579/ac826d | https://iopscience.iop.org/article/10.1088/1361-6579/ac826d/pdf | Detecting beats in the photoplethysmogram: benchmarking open-source algorithms | The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework ... | ['Joachim A Behar and Panayiotis A Kyriacou', 'Callum Pettit', 'Jonathan Mant', 'Karthik Budidha', 'Philip J Aston', 'Elisa Mejía-Mejía', 'Kevin Kotzen', 'Peter H Charlton'] | 2022-07-19 | null | null | null | physiological-measurement-2022-7 | ['photoplethysmography-ppg-heart-rate', 'photoplethysmography-ppg-beat-detection'] | ['medical', 'medical'] | [ 1.92454934e-01 8.72481018e-02 -6.22911192e-02 1.92010682e-02
-2.11818859e-01 -6.05826497e-01 -1.27360329e-01 5.24433255e-01
-3.92014116e-01 5.81682742e-01 2.66353339e-01 -5.53528488e-01
-2.40340263e-01 -3.89612645e-01 -7.64724314e-02 -5.84983766e-01
-3.38293105e-01 1.47615716e-01 9.22071040e-02 3.97991568... | [14.040369033813477, 3.033963441848755] |
0e07620f-e935-4ef6-af92-413b2a82208a | tweester-at-semeval-2016-task-4-sentiment | null | null | https://aclanthology.org/S16-1023 | https://aclanthology.org/S16-1023.pdf | Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation | null | ['ros', 'Haris Papageorgiou', 'Mal', 'Fenia Christopoulou', 'Elisavet Palogiannidi', 'Filippos Kokkinos', 'Alex Potamianos', 'Shrikanth Narayanan', 'Nikolaos rakis', 'Elias Iosif', 'Athanasia Kolovou'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['twitter-sentiment-analysis'] | ['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... | [-7.291708946228027, 3.7703092098236084] |
63ba5046-337d-41ba-9e61-16f666aa5c77 | recent-applications-of-machine-learning | 2306.04566 | null | https://arxiv.org/abs/2306.04566v1 | https://arxiv.org/pdf/2306.04566v1.pdf | Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review | The integration of remote sensing and machine learning in agriculture is transforming the industry by providing insights and predictions through data analysis. This combination leads to improved yield prediction and water management, resulting in increased efficiency, better yields, and more sustainable agricultural pr... | ['Abdelghani Chehbouni', 'Ayoub Kechchour', 'Terence Epule Epule', 'Fatima Zahra Bassine'] | 2023-06-07 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 1.21782668e-01 -3.43270212e-01 -7.68414259e-01 -7.58410990e-03
1.31540254e-01 -6.82853162e-01 1.99992750e-02 7.86024153e-01
1.99047819e-01 7.01866627e-01 -2.35490471e-01 -9.09105957e-01
-2.68230289e-01 -1.69352686e+00 -2.48612776e-01 -9.13995564e-01
-1.43325448e-01 -1.97339416e-01 -2.35854119e-01 -5.45648277... | [9.341012001037598, -1.5977263450622559] |
41e9b8aa-9bc4-4e83-8566-393551e89c30 | fine-grained-visual-recognition-with-batch | 1910.12423 | null | https://arxiv.org/abs/1910.12423v3 | https://arxiv.org/pdf/1910.12423v3.pdf | ACE: Adaptive Confusion Energy for Natural World Data Distribution | With the development of deep learning, standard classification problems have achieved good results. However, conventional classification problems are often too idealistic. Most data in the natural world usually have imbalanced distribution and fine-grained characteristics. Recently, many state-of-the-art approaches ten... | ['Wan-Cyuan Fan', 'Tyng-Luh Liu', 'Ming-Sui Lee', 'Cheng-Yao Hong', 'Yen-Chi Hsu', 'Davi Geiger'] | 2019-10-28 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-5.42097151e-01 -7.82785177e-01 -8.53121206e-02 -4.74353820e-01
-5.93527079e-01 -1.82979062e-01 4.54261988e-01 2.92588174e-02
-3.52968574e-01 9.24862385e-01 1.14521710e-02 -1.39815032e-01
-5.17984331e-01 -7.44165957e-01 -5.28413892e-01 -9.24162805e-01
1.21235510e-03 6.59685433e-01 1.68528154e-01 -1.92754865... | [9.124969482421875, 3.8758511543273926] |
d02d9690-f228-461d-a11f-32ddead5e2e9 | improved-deep-spectral-convolution-network | 1808.01104 | null | https://arxiv.org/abs/1808.01104v4 | https://arxiv.org/pdf/1808.01104v4.pdf | Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty | In this study, we propose a novel framework for hyperspectral unmixing by using an improved deep spectral convolution network (DSCN++) combined with endmember uncertainty. DSCN++ is used to compute high-level representations which are further modeled with Multinomial Mixture Model to estimate abundance maps. In the rec... | ['Savas Ozkan', 'Gozde Bozdagi Akar'] | 2018-08-03 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.49879616e-01 -4.11346555e-01 2.63407350e-01 -1.31730869e-01
-6.61087394e-01 -4.71752584e-01 6.55969024e-01 -2.23735943e-01
-1.94961995e-01 9.45388854e-01 9.89459381e-02 -1.38475567e-01
-1.20866254e-01 -9.20175493e-01 -8.90167058e-01 -1.02614808e+00
8.01539496e-02 1.96219534e-01 -5.05602002e-01 1.21888414... | [10.013647079467773, -1.9585403203964233] |
73aafe54-c115-4b12-b2d0-8c0cb45ef7f5 | ap-10k-a-benchmark-for-animal-pose-estimation | 2108.12617 | null | https://arxiv.org/abs/2108.12617v2 | https://arxiv.org/pdf/2108.12617v2.pdf | AP-10K: A Benchmark for Animal Pose Estimation in the Wild | Accurate animal pose estimation is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. Previous works only focus on specific animals while ignoring the diversity of animal species, limiting the generalization ability. In this ... | ['DaCheng Tao', 'Ziyu Guan', 'Wei Zhao', 'Jing Zhang', 'Yufei Xu', 'Hang Yu'] | 2021-08-28 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [-2.15392470e-01 -3.17031056e-01 -4.51459199e-01 -5.48346162e-01
-5.49265087e-01 -6.90529346e-01 1.61127582e-01 1.69877350e-01
-7.49457359e-01 7.87558794e-01 8.33631083e-02 2.92180687e-01
-4.59422916e-02 -5.64141452e-01 -1.07872558e+00 -4.17488307e-01
-7.12384939e-01 5.14105856e-01 3.26486528e-01 -5.39084002... | [7.620604991912842, -0.9199326038360596] |
17f33e21-6dc0-47a0-93c5-35d798e1d5e5 | continuous-conditional-video-synthesis-by | 2210.05810 | null | https://arxiv.org/abs/2210.05810v2 | https://arxiv.org/pdf/2210.05810v2.pdf | A unified model for continuous conditional video prediction | Different conditional video prediction tasks, like video future frame prediction and video frame interpolation, are normally solved by task-related models even though they share many common underlying characteristics. Furthermore, almost all conditional video prediction models can only achieve discrete prediction. In t... | ['Guillaume-Alexandre Bilodeau', 'Xi Ye'] | 2022-10-11 | null | null | null | null | ['video-prediction', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 2.77694881e-01 -9.50298011e-02 -4.28644061e-01 -5.15520215e-01
-8.07003796e-01 -1.70461014e-01 6.47749364e-01 -3.84859622e-01
-5.95905930e-02 7.84242690e-01 3.61306578e-01 -3.12079281e-01
3.78116846e-01 -7.29153335e-01 -1.17637062e+00 -5.63832402e-01
2.73838416e-02 -3.37208770e-02 4.81152087e-01 3.55852872... | [10.486515045166016, -0.7490968704223633] |
8a4a76bd-7556-474b-a0f0-e879fbdad470 | multi-person-articulated-tracking-with | 1903.09214 | null | http://arxiv.org/abs/1903.09214v1 | http://arxiv.org/pdf/1903.09214v1.pdf | Multi-person Articulated Tracking with Spatial and Temporal Embeddings | We propose a unified framework for multi-person pose estimation and tracking.
Our framework consists of two main components,~\ie~SpatialNet and TemporalNet.
The SpatialNet accomplishes body part detection and part-level data association
in a single frame, while the TemporalNet groups human instances in consecutive
fram... | ['Sheng Jin', 'Wentao Liu', 'Chen Qian', 'Wanli Ouyang'] | 2019-03-21 | multi-person-articulated-tracking-with-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Jin_Multi-Person_Articulated_Tracking_With_Spatial_and_Temporal_Embeddings_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Jin_Multi-Person_Articulated_Tracking_With_Spatial_and_Temporal_Embeddings_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-person-pose-estimation-and-tracking'] | ['computer-vision'] | [-3.40216100e-01 -5.21193147e-02 -3.64986718e-01 -2.31780231e-01
-6.32770956e-01 -3.75910759e-01 3.85191441e-01 -1.80961639e-01
-5.06179929e-01 5.84240735e-01 2.95453131e-01 4.36739206e-01
-4.08123024e-02 -4.82521147e-01 -7.41806507e-01 -4.03581083e-01
-5.84664702e-01 5.93430281e-01 5.18066227e-01 -2.11733533... | [6.9854044914245605, -0.9724116325378418] |
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