paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34cbf272-0d79-412e-a858-d13e1d7325ff | craspell-a-contextual-typo-robust-approach-to | null | null | https://aclanthology.org/2022.findings-acl.237 | https://aclanthology.org/2022.findings-acl.237.pdf | CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction | Recently, Bert-based models have dominated the research of Chinese spelling correction (CSC). These methods have two limitations: (1) they have poor performance on multi-typo texts. In such texts, the context of each typo contains at least one misspelled character, which brings noise information. Such noisy context lea... | ['Shengli Sun', 'TingHao Yu', 'Huihui Cai', 'Tao Yang', 'Tianchi Yue', 'Shengkang Song', 'Shulin Liu'] | null | null | null | null | findings-acl-2022-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.56540614e-01 -4.83336926e-01 -3.22485358e-01 -2.21011087e-01
-7.82743216e-01 -3.38233054e-01 2.74959683e-01 2.48659417e-01
-6.18790925e-01 9.79562104e-01 3.30177009e-01 -2.97627151e-01
5.45787513e-01 -6.51605546e-01 -5.05053878e-01 -7.19120502e-01
6.05373442e-01 1.35138288e-01 4.34853852e-01 -3.16208035... | [10.92446517944336, 10.811261177062988] |
02d26e63-afb2-4316-873b-1026f33bebec | protein-language-models-and-structure | 2211.16742 | null | https://arxiv.org/abs/2211.16742v1 | https://arxiv.org/pdf/2211.16742v1.pdf | Protein Language Models and Structure Prediction: Connection and Progression | The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks a... | ['Stan Z. Li', 'Yongjie Xu', 'Yufei Huang', 'Cheng Tan', 'Jiangbin Zheng', 'Jun Xia', 'Bozhen Hu'] | 2022-11-30 | null | null | null | null | ['protein-language-model', 'protein-folding'] | ['medical', 'natural-language-processing'] | [ 5.16022682e-01 -9.60289389e-02 -3.44020873e-01 -3.29043627e-01
-3.77521217e-01 -4.15931165e-01 1.08444333e-01 3.82537901e-01
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-1.45006612e-01 4.34910238e-01 4.73249778e-02 -2.92099774... | [4.687937259674072, 5.64888858795166] |
7d32ce9b-de71-4eab-b42e-34df199df918 | investigating-active-learning-sampling | null | null | https://aclanthology.org/2022.lrec-1.490 | https://aclanthology.org/2022.lrec-1.490.pdf | Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification | Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language. In this work, we aim to build classifiers on these datasets using Active Learning in order to reduce the labeling effort. We outline the challenges when deali... | ['Jasmina Bogojeska', 'Jonas Kuhn', 'Katsiaryna Mirylenka', 'Lukas Wertz'] | null | null | null | null | lrec-2022-6 | ['multi-label-text-classification', 'extreme-multi-label-classification', 'multi-label-text-classification'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 5.89065671e-01 2.87877202e-01 -4.46897805e-01 -7.83761322e-01
-1.36746728e+00 -8.41844499e-01 6.24751449e-01 5.50521076e-01
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2.91718900e-01 9.43727911e-01 1.19511485e-01 -5.84795885... | [9.590781211853027, 4.420337677001953] |
ca9741ab-1019-4244-a53e-1a08bf0637f9 | actc-active-threshold-calibration-for-cold | 2305.06395 | null | https://arxiv.org/abs/2305.06395v2 | https://arxiv.org/pdf/2305.06395v2.pdf | ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion | Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by calibrating the scoring model, typically by adjusting the prediction thresholds using manually annotat... | ['Benjamin Roth', 'Anastasiia Sedova'] | 2023-05-10 | null | null | null | null | ['knowledge-graph-completion'] | ['knowledge-base'] | [-4.50517163e-02 7.28863060e-01 -5.40245414e-01 -7.07046211e-01
-1.33614659e+00 -8.64180386e-01 2.07288146e-01 8.81270945e-01
-3.81869495e-01 7.72617698e-01 4.39132228e-02 1.14787305e-02
-6.40693977e-02 -8.03057373e-01 -7.92132080e-01 -2.60081798e-01
-3.00442666e-01 1.56096649e+00 4.96094763e-01 2.23309502... | [9.8359375, 6.62231969833374] |
526d06c8-87d7-4330-93ac-e0fe1280a114 | semantic-segmentation-enhanced-transformer | 2301.11022 | null | https://arxiv.org/abs/2301.11022v1 | https://arxiv.org/pdf/2301.11022v1.pdf | Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction | Saliency Prediction aims to predict the attention distribution of human eyes given an RGB image. Most of the recent state-of-the-art methods are based on deep image feature representations from traditional CNNs. However, the traditional convolution could not capture the global features of the image well due to its smal... | ['Shuo Zhang'] | 2023-01-26 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 2.66215175e-01 8.84601027e-02 2.65579879e-01 -3.26421529e-01
-1.39403805e-01 -1.53310508e-01 3.04266036e-01 -1.37037620e-01
-3.55011731e-01 5.18846095e-01 4.83318232e-02 -4.77656946e-02
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6.79528892e-01 -1.57868154e-02 7.21166670e-01 -8.74342024... | [9.88437271118164, -0.3949331045150757] |
35e8f3d0-74c9-4f43-843d-fdfd285ede75 | propagation-map-reconstruction-via | 2207.13473 | null | https://arxiv.org/abs/2207.13473v2 | https://arxiv.org/pdf/2207.13473v2.pdf | Propagation Map Reconstruction via Interpolation Assisted Matrix Completion | Constructing a propagation map from a set of scattered measurements finds important applications in many areas, such as localization, spectrum monitoring and management. Classical interpolation-type methods have poor performance in regions with very sparse measurements. Recent advance in matrix completion has the poten... | ['Junting Chen', 'Hao Sun'] | 2022-07-27 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.67818740e-01 -3.82871360e-01 2.91591614e-01 -2.02453658e-01
-1.12335098e+00 -3.05583298e-01 3.95975292e-01 2.69484341e-01
-2.22248644e-01 1.09531033e+00 1.99010774e-01 -2.37910658e-01
-6.95441604e-01 -8.01711857e-01 -5.48203588e-01 -9.26494122e-01
-5.05777359e-01 8.75494108e-02 -2.04546824e-02 -2.34501973... | [6.433151721954346, 1.3039906024932861] |
9a538159-e720-45cc-b947-72110db0f66b | mf-pam-accurate-pitch-estimation-through | 2306.09640 | null | https://arxiv.org/abs/2306.09640v1 | https://arxiv.org/pdf/2306.09640v1.pdf | MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion | We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments. Our model leverages the periodic characteristics of audio signals and involves two key steps: e... | ['Hong-Goo Kang', 'Soo-Whan Chung', 'Doyeon Kim', 'Woo-Jin Chung'] | 2023-06-16 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [-2.50959426e-01 -5.41179538e-01 2.84413427e-01 3.05079874e-02
-1.34207678e+00 -5.66882789e-01 -5.22977673e-03 -2.50883214e-02
-2.32632756e-01 1.75942898e-01 7.07971573e-01 3.34184766e-01
-2.17929319e-01 -4.65289682e-01 -5.15128136e-01 -6.55592561e-01
-7.99510717e-01 -2.73582339e-01 -1.61014512e-01 -1.55607119... | [15.586923599243164, 5.65280818939209] |
d8c2052c-8824-4107-9f72-4ed7f1f7da4b | synthetic-tumors-make-ai-segment-tumors | 2210.14845 | null | https://arxiv.org/abs/2210.14845v1 | https://arxiv.org/pdf/2210.14845v1.pdf | Synthetic Tumors Make AI Segment Tumors Better | We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segm... | ['Zongwei Zhou', 'Alan Yuille', 'Jie-Neng Chen', 'Shuwen Sun', 'Yixiong Chen', 'Junfei Xiao', 'Qixin Hu'] | 2022-10-26 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.09874892e-01 1.01604295e+00 8.59559327e-02 -1.42341316e-01
-8.45753074e-01 -2.38447100e-01 6.46963298e-01 1.14672408e-01
-2.82736029e-02 8.88868511e-01 2.29115188e-02 -4.19776917e-01
3.89629632e-01 -9.34546351e-01 -5.01931906e-01 -7.14173019e-01
-3.89410816e-02 1.06991696e+00 3.59882802e-01 1.86057631... | [14.625883102416992, -2.361525774002075] |
e89249e7-7748-4530-857a-baaa939ac0fc | multi-type-conversational-question-answer | 2210.12979 | null | https://arxiv.org/abs/2210.12979v1 | https://arxiv.org/pdf/2210.12979v1.pdf | Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions | Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity. In this paper, we introduce a novel method to synthesize data for CQA with various question types, including o... | ['Gary Geunbae Lee', 'Yunsu Kim', 'Seonjeong Hwang'] | 2022-10-24 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 7.52562359e-02 6.20296001e-01 6.59703374e-01 -5.58468223e-01
-1.31041372e+00 -9.77911830e-01 6.37578130e-01 8.44872184e-03
5.14058210e-03 8.94733250e-01 6.76823080e-01 -4.79488611e-01
1.46355838e-01 -8.74394298e-01 -3.35563123e-01 -4.21759225e-02
5.05217135e-01 8.33795249e-01 2.53434092e-01 -7.31833398... | [11.836332321166992, 8.050026893615723] |
6f4ce30b-db76-4d4f-b32f-20bd5f2cfcf3 | on-the-robustness-of-reading-comprehension-1 | null | null | https://openreview.net/forum?id=lXczoncSyt0 | https://openreview.net/pdf?id=lXczoncSyt0 | On the Robustness of Reading Comprehension Models to Entity Renaming | We study the robustness of machine reading comprehension (MRC) models to entity renaming---do models make more wrong predictions when answer entities have different names? Such failures imply that models overly rely on entity information to answer questions, and thus may generalize poorly when facts about the world cha... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['continual-pretraining'] | ['methodology'] | [ 2.77147353e-01 4.77552861e-01 -3.76453176e-02 -5.74835062e-01
-8.90886188e-01 -9.43532646e-01 5.52186906e-01 2.98917621e-01
-7.03823507e-01 9.64661896e-01 6.69350982e-01 -5.75637519e-01
-6.02038726e-02 -9.66734827e-01 -1.03607762e+00 6.39107898e-02
1.21344313e-01 6.51361763e-01 4.03853357e-01 -5.92129469... | [10.955958366394043, 8.145346641540527] |
0d5677c2-65f2-4ce1-9475-2dc679235873 | progressive-seed-generation-auto-encoder-for-1 | 2112.05213 | null | https://arxiv.org/abs/2112.05213v1 | https://arxiv.org/pdf/2112.05213v1.pdf | Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning | With the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning is essential for understanding and utilizing point clouds without an expensive annotation process. In this paper, we propose a novel framewo... | ['Junmo Kim', 'Haeil Lee', 'Doyeon Kim', 'Pyunghwan Ahn', 'JuYoung Yang'] | 2021-12-09 | progressive-seed-generation-auto-encoder-for | http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Progressive_Seed_Generation_Auto-Encoder_for_Unsupervised_Point_Cloud_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Progressive_Seed_Generation_Auto-Encoder_for_Unsupervised_Point_Cloud_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-reconstruction', '3d-point-cloud-linear-classification'] | ['computer-vision', 'computer-vision'] | [ 1.40255511e-01 4.72478904e-02 -9.63929072e-02 -5.48131943e-01
-8.35241854e-01 -3.47213596e-01 7.61226296e-01 5.73337600e-02
-1.83885783e-01 1.60330951e-01 2.54950151e-02 -7.08253756e-02
3.24616209e-02 -8.51522744e-01 -1.12185025e+00 -2.72707045e-01
-1.89465404e-01 7.35764682e-01 2.70146340e-01 4.29430492... | [8.210528373718262, -3.3955237865448] |
ef07be92-0ec0-49cc-9842-e0803ec5a247 | efficient-neural-architecture-search-for-end | 2011.05649 | null | https://arxiv.org/abs/2011.05649v1 | https://arxiv.org/pdf/2011.05649v1.pdf | Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients | Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacing expert-designed networks with learned, task-specific architectures. In contrast to early computational-demanding NAS methods, recent gradi... | ['Zhijian Ou', 'Keyu An', 'Huahuan Zheng'] | 2020-11-11 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.20091148e-01 1.79982074e-02 1.45602554e-01 -3.93027484e-01
-1.02855432e+00 -5.00028789e-01 3.18218499e-01 -4.71545935e-01
-3.35059524e-01 3.88464004e-01 2.22040251e-01 -6.99962735e-01
-2.53351361e-01 -3.48673224e-01 -8.08602810e-01 -3.48260581e-01
-1.92073390e-01 4.59415704e-01 -1.12838387e-01 -3.55519801... | [8.731154441833496, 3.3932509422302246] |
a271ed53-892b-4f8c-b6a0-935ecda3f69b | a-wrong-answer-or-a-wrong-question-an | 2010.06835 | null | https://arxiv.org/abs/2010.06835v2 | https://arxiv.org/pdf/2010.06835v2.pdf | A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering | The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answe... | ['Raviteja Anantha', 'Zhucheng Tu', 'Shayne Longpre', 'Svitlana Vakulenko'] | 2020-10-13 | null | https://aclanthology.org/2020.scai-1.2 | https://aclanthology.org/2020.scai-1.2.pdf | emnlp-scai-2020-11 | ['passage-ranking', 'question-rewriting', 'answer-selection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.28263015e-01 2.10278541e-01 2.06602842e-01 -4.29456532e-01
-1.10193288e+00 -1.00296366e+00 8.50603759e-01 6.36123776e-01
-4.90399271e-01 6.19936228e-01 5.29222488e-01 -7.61076033e-01
-4.88944590e-01 -6.92462862e-01 -7.14829683e-01 -2.93368459e-01
1.60264954e-01 5.50854385e-01 8.00176382e-01 -8.86382520... | [11.425881385803223, 8.117757797241211] |
e98ceff4-e291-4e96-bca1-4424d71ce2f0 | inductive-mutual-information-estimation-a | 2102.13182 | null | https://arxiv.org/abs/2102.13182v3 | https://arxiv.org/pdf/2102.13182v3.pdf | MIND: Inductive Mutual Information Estimation, A Convex Maximum-Entropy Copula Approach | We propose a novel estimator of the mutual information between two ordinal vectors $x$ and $y$. Our approach is inductive (as opposed to deductive) in that it depends on the data generating distribution solely through some nonparametric properties revealing associations in the data, and does not require having enough d... | ['Yves-Laurent Kom Samo'] | 2021-02-25 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 1.93090156e-01 2.83935845e-01 -3.36846918e-01 -9.84582528e-02
-1.05993831e+00 -7.26476073e-01 1.24156095e-01 -3.22200209e-01
-3.91975164e-01 1.12456572e+00 -2.87762225e-01 -3.58254075e-01
-4.45362866e-01 -1.12271917e+00 -9.87631381e-01 -9.01652753e-01
-4.20547485e-01 3.37571323e-01 -4.90390867e-01 2.18150258... | [7.211301326751709, 4.225233554840088] |
b84ade75-d69f-4b6e-99cc-76515175c0b8 | barfed-byzantine-attack-resistant-federated | 2111.04550 | null | https://arxiv.org/abs/2111.04550v2 | https://arxiv.org/pdf/2111.04550v2.pdf | ARFED: Attack-Resistant Federated averaging based on outlier elimination | In federated learning, each participant trains its local model with its own data and a global model is formed at a trusted server by aggregating model updates coming from these participants. Since the server has no effect and visibility on the training procedure of the participants to ensure privacy, the global model b... | ['Altan Kocyigit', 'Gorkem Polat', 'Ece Isik-Polat'] | 2021-11-08 | null | null | null | null | ['model-posioning'] | ['adversarial'] | [-4.57915753e-01 -2.82053620e-01 -1.49553902e-02 -3.01351666e-01
-4.52547401e-01 -9.94508624e-01 5.59158385e-01 4.57788140e-01
-4.69858825e-01 5.74313521e-01 -3.29418689e-01 -2.98397094e-01
-8.20576996e-02 -8.78969669e-01 -7.34200299e-01 -9.82430995e-01
-1.77772760e-01 6.49161279e-01 3.36738348e-01 1.18282884... | [5.749098777770996, 6.884314060211182] |
3f528bd6-9c68-4832-9f94-75e9c27091c1 | learning-to-separate-object-sounds-by | 1804.01665 | null | http://arxiv.org/abs/1804.01665v2 | http://arxiv.org/pdf/1804.01665v2.pdf | Learning to Separate Object Sounds by Watching Unlabeled Video | Perceiving a scene most fully requires all the senses. Yet modeling how
objects look and sound is challenging: most natural scenes and events contain
multiple objects, and the audio track mixes all the sound sources together. We
propose to learn audio-visual object models from unlabeled video, then exploit
the visual c... | ['Kristen Grauman', 'Ruohan Gao', 'Rogerio Feris'] | 2018-04-05 | learning-to-separate-object-sounds-by-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.pdf | eccv-2018-9 | ['audio-denoising', 'audio-source-separation'] | ['audio', 'audio'] | [ 3.54065746e-01 -5.36976635e-01 9.98838320e-02 -3.55372019e-02
-1.62061191e+00 -9.11688149e-01 4.58386727e-03 -1.44607261e-01
4.71826605e-02 3.25712591e-01 5.17834842e-01 4.48811382e-01
-1.54034168e-01 -1.00483536e-03 -9.28516865e-01 -9.42140400e-01
-2.30555937e-01 -6.35343865e-02 5.87390326e-02 2.14454725... | [14.847721099853516, 4.962626934051514] |
ce20841e-2fb9-42fd-b620-cd45152e825a | tttflow-unsupervised-test-time-training-with | 2210.11389 | null | https://arxiv.org/abs/2210.11389v1 | https://arxiv.org/pdf/2210.11389v1.pdf | TTTFlow: Unsupervised Test-Time Training with Normalizing Flow | A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time. Recent methods have proposed to address this problem with test-time training (TTT), where a two-branch model is trained to learn a main classification task and also a self-supervised task used to perf... | ['Christian Desrosiers', 'Ismail Ben Ayed', 'Milad Cheraghalikhani', 'Mehrdad Noori', 'Gustavo A. Vargas Hakim', 'David Osowiechi'] | 2022-10-20 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.0208811e-01 -1.5865959e-02 -4.0950471e-01 -7.3278284e-01
-3.9191499e-01 -5.2303374e-01 5.6479239e-01 -2.3236895e-01
-4.8162940e-01 6.8489403e-01 -4.1078755e-01 -4.7208610e-01
-1.6696896e-01 -8.2596338e-01 -8.1867319e-01 -6.6809690e-01
1.2657082e-02 5.7217747e-01 5.4839909e-01 2.1778971e-01
3.0043268e-01... | [9.817587852478027, 2.9376659393310547] |
f3f10883-20c0-422c-9dcb-e27ff0d75209 | paxion-patching-action-knowledge-in-video | 2305.10683 | null | https://arxiv.org/abs/2305.10683v3 | https://arxiv.org/pdf/2305.10683v3.pdf | Paxion: Patching Action Knowledge in Video-Language Foundation Models | Action knowledge involves the understanding of textual, visual, and temporal aspects of actions. We introduce the Action Dynamics Benchmark (ActionBench) containing two carefully designed probing tasks: Action Antonym and Video Reversal, which targets multimodal alignment capabilities and temporal understanding skills ... | ['Heng Ji', 'Mohit Bansal', 'Zineng Tang', 'Jaemin Cho', 'Genglin Liu', 'Sha Li', 'Ansel Blume', 'Zhenhailong Wang'] | 2023-05-18 | null | null | null | null | ['action-understanding', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.97716281e-01 -1.23976909e-01 -6.43217146e-01 -1.07393317e-01
-5.46250582e-01 -7.94906437e-01 9.49234307e-01 -2.89554209e-01
-4.41991508e-01 1.77590370e-01 5.26013672e-01 -3.12721372e-01
-1.74218029e-01 -1.57629192e-01 -9.22384918e-01 -4.33102459e-01
7.95443580e-02 3.29146236e-01 3.73785377e-01 -5.30734695... | [9.457538604736328, 0.853891909122467] |
b678d23d-7464-4df8-97b4-6000b48d80c1 | vision-language-transformers-a-survey | 2307.03254 | null | https://arxiv.org/abs/2307.03254v1 | https://arxiv.org/pdf/2307.03254v1.pdf | Vision Language Transformers: A Survey | Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \citet{vaswani2017attention} to vision language modeling. Transform... | ['Casey Kennington', 'Clayton Fields'] | 2023-07-06 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 3.96001995e-01 1.64943337e-01 -4.98404726e-02 -6.60944045e-01
-6.14749134e-01 -5.31345069e-01 1.07200468e+00 -3.51737201e-01
-5.19785166e-01 4.94847536e-01 2.11562335e-01 -5.58820248e-01
4.14236516e-01 -7.34288096e-01 -7.75006235e-01 -3.86368901e-01
4.84645665e-01 5.87065101e-01 3.22161376e-01 -1.49261564... | [10.676665306091309, 1.6563628911972046] |
5d05e279-4ca3-4043-88a7-7469b72161ae | on-multitask-loss-function-for-audio-event | 2009.05527 | null | https://arxiv.org/abs/2009.05527v1 | https://arxiv.org/pdf/2009.05527v1.pdf | On Multitask Loss Function for Audio Event Detection and Localization | Audio event localization and detection (SELD) have been commonly tackled using multitask models. Such a model usually consists of a multi-label event classification branch with sigmoid cross-entropy loss for event activity detection and a regression branch with mean squared error loss for direction-of-arrival estimatio... | ['Alfred Mertins', 'Philipp Koch', 'Ngoc Q. K. Duong', 'Huy Phan', 'Lam Pham', 'Ian McLoughlin'] | 2020-09-11 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.11347526e-01 4.27479148e-02 5.45533225e-02 -3.36091101e-01
-1.64362931e+00 -2.74399549e-01 4.16965514e-01 4.28091437e-01
-7.03085661e-01 6.81355476e-01 -1.33534297e-01 1.43973202e-01
-2.36667469e-02 -2.91908175e-01 -7.06865132e-01 -7.39253759e-01
-2.33935878e-01 3.46447043e-02 4.39539850e-01 3.04631561... | [15.207016944885254, 5.1565842628479] |
ebad0b72-fb1a-43bb-96a2-1bec3de8734e | fair-contrastive-learning-for-facial | 2203.16209 | null | https://arxiv.org/abs/2203.16209v1 | https://arxiv.org/pdf/2203.16209v1.pdf | Fair Contrastive Learning for Facial Attribute Classification | Learning visual representation of high quality is essential for image classification. Recently, a series of contrastive representation learning methods have achieved preeminent success. Particularly, SupCon outperformed the dominant methods based on cross-entropy loss in representation learning. However, we notice that... | ['Hyeran Byun', 'Dohyung Kim', 'Sunhee Hwang', 'Pilhyeon Lee', 'Jewook Lee', 'Sungho Park'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['facial-attribute-classification'] | ['computer-vision'] | [ 2.79730678e-01 1.06896318e-01 -4.74756479e-01 -5.83901644e-01
-6.74996316e-01 -1.73989236e-01 4.87559497e-01 3.36638510e-01
-3.70368600e-01 7.49189913e-01 3.26642722e-01 -5.10309115e-02
-3.10284585e-01 -5.78387976e-01 -4.10104871e-01 -8.18089306e-01
1.15378618e-01 -1.19959176e-01 -5.15733302e-01 8.66272748... | [9.332056045532227, 3.840271472930908] |
698c876e-f284-4126-914b-f3f8d3181884 | when-automatic-voice-disguise-meets-automatic | 2009.06863 | null | https://arxiv.org/abs/2009.06863v1 | https://arxiv.org/pdf/2009.06863v1.pdf | When Automatic Voice Disguise Meets Automatic Speaker Verification | The technique of transforming voices in order to hide the real identity of a speaker is called voice disguise, among which automatic voice disguise (AVD) by modifying the spectral and temporal characteristics of voices with miscellaneous algorithms are easily conducted with softwares accessible to the public. AVD has p... | ['Meng Sun', 'Jiakang Li', 'Xiongwei Zhang', 'Thomas Fang Zheng', 'Linlin Zheng'] | 2020-09-15 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 2.00482920e-01 1.05297461e-01 5.53561330e-01 1.28315702e-01
-7.01106369e-01 -9.28520620e-01 6.69090271e-01 -1.17804430e-01
-3.21970791e-01 5.88494539e-01 4.89424884e-01 -2.72322595e-01
-1.89056560e-01 -4.09158289e-01 -3.96741003e-01 -9.34723616e-01
2.29897238e-02 -2.03115553e-01 4.04795952e-04 -3.85282755... | [15.043525695800781, 5.9060540199279785] |
8dcf04ae-b4ec-4a20-9bc2-0dd01345b85e | cross-spectral-face-completion-for-nir-vis | 1902.03565 | null | http://arxiv.org/abs/1902.03565v1 | http://arxiv.org/pdf/1902.03565v1.pdf | Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition | Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the
process of matching NIR to VIS face images. Current heterogeneous methods try
to extend VIS face recognition methods to the NIR spectrum by synthesizing VIS
images from NIR images. However, due to self-occlusion and sensing gap, NIR
face image... | ['Tieniu Tan', 'Zhenan Sun', 'Jie Cao', 'Ran He', 'Lingxiao Song'] | 2019-02-10 | null | null | null | null | ['heterogeneous-face-recognition', 'facial-inpainting'] | ['computer-vision', 'computer-vision'] | [ 7.48698294e-01 3.15608270e-02 2.03608111e-01 -5.45919359e-01
-9.58138227e-01 -3.37878138e-01 3.30446094e-01 -1.03521073e+00
1.10398360e-01 7.13858664e-01 8.97584017e-03 2.01911315e-01
1.14146844e-01 -9.13219392e-01 -9.30254161e-01 -1.20612407e+00
6.36408806e-01 1.84667066e-01 -5.19847214e-01 -1.95936963... | [12.937686920166016, 0.13290131092071533] |
0ee8861e-d89e-4bca-923c-4c3c50d2c73d | a-provably-correct-and-robust-algorithm-for | 1906.06899 | null | https://arxiv.org/abs/1906.06899v4 | https://arxiv.org/pdf/1906.06899v4.pdf | A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization | In this paper, we propose a provably correct algorithm for convolutive nonnegative matrix factorization (CNMF) under separability assumptions. CNMF is a convolutive variant of nonnegative matrix factorization (NMF), which functions as an NMF with additional sequential structure. This model is useful in a number of appl... | ['Nicolas Gillis', 'Anthony Degleris'] | 2019-06-17 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.88011980e-01 -1.88404784e-01 4.81192907e-03 1.69569324e-03
-7.05188274e-01 -1.03301013e+00 1.18497364e-01 -7.38089979e-02
-2.67510772e-01 5.89783669e-01 6.79149851e-02 -7.92206824e-01
-5.72496891e-01 -2.81655908e-01 -6.16388142e-01 -7.32452691e-01
-4.10275757e-01 3.91423941e-01 -1.37265101e-01 -2.24128544... | [7.20707893371582, 4.536574840545654] |
dcefd4b7-b06c-4f7a-af0e-6b2f29a8c2c6 | time-aware-multiway-adaptive-fusion-network | 2302.12529 | null | https://arxiv.org/abs/2302.12529v2 | https://arxiv.org/pdf/2302.12529v2.pdf | Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering | Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be capable to learn \e... | ['Rui Jiang', 'Wei Wu', 'Sirui Wang', 'Fang Fang', 'Di Liang', 'Yonghao Liu'] | 2023-02-24 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [ 8.91861245e-02 3.51032168e-01 -1.09558497e-02 -5.43971419e-01
-8.55874240e-01 -6.53794527e-01 6.85158014e-01 4.14474398e-01
-5.29986024e-01 5.49188375e-01 3.72328579e-01 -5.17000139e-01
-5.26609778e-01 -9.34972584e-01 -7.28740513e-01 -4.89225954e-01
1.96290299e-01 4.69629854e-01 4.74115103e-01 -6.18442595... | [10.673678398132324, 8.017251014709473] |
218d77ff-bec6-4bdb-b5dc-5ecee3a38c3a | representation-learning-on-heterostructures | 2201.06972 | null | https://arxiv.org/abs/2201.06972v1 | https://arxiv.org/pdf/2201.06972v1.pdf | Representation Learning on Heterostructures via Heterogeneous Anonymous Walks | Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks while the related study on heterogeneous networks ... | ['Wenjun Wang', 'Danyang Shi', 'Mengyu Jia', 'Wang Zhang', 'Ting Pan', 'Pengfei Jiao', 'Xuan Guo'] | 2022-01-18 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.90437024e-02 6.15077280e-02 -1.64722234e-01 -1.14943601e-01
-2.81141222e-01 -5.16481578e-01 7.00093210e-01 1.45937562e-01
-3.07151824e-02 8.83487582e-01 2.46768668e-01 -4.78249580e-01
-3.36439461e-01 -1.34125662e+00 -4.17974412e-01 -1.13258934e+00
-1.09625928e-01 4.71593529e-01 6.15771532e-01 -3.07578683... | [7.174341201782227, 6.156317710876465] |
ac4b1dc8-d403-4c46-8b25-9ad03234157c | entsum-a-data-set-for-entity-centric | null | null | https://openreview.net/forum?id=1llL_tYlV54 | https://openreview.net/pdf?id=1llL_tYlV54 | EntSUM: A Data Set for Entity-Centric Extractive Summarization | Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.
We introduce a human-annotated data set EntSUM for cont... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.25250497e-01 8.31466496e-01 -5.61563849e-01 -4.16319340e-01
-1.31500590e+00 -9.13918734e-01 7.36494124e-01 7.97821939e-01
-1.82744250e-01 1.06817114e+00 1.32242846e+00 1.29872724e-01
-5.03944978e-02 -5.49223721e-01 -3.37160826e-01 -6.10562451e-02
1.15979932e-01 8.01773787e-01 4.67528962e-02 -3.49249154... | [12.478752136230469, 9.411611557006836] |
aabbc17d-7868-4d7e-ad6f-9997e05e7ebc | neuralodf-learning-omnidirectional-distance | 2206.05837 | null | https://arxiv.org/abs/2206.05837v3 | https://arxiv.org/pdf/2206.05837v3.pdf | NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation | In visual computing, 3D geometry is represented in many different forms including meshes, point clouds, voxel grids, level sets, and depth images. Each representation is suited for different tasks thus making the transformation of one representation into another (forward map) an important and common problem. We propose... | ['Srinath Sridhar', 'Rao Fu', 'Shivam Duggal', 'Cheng-You Lu', 'Trevor Houchens'] | 2022-06-12 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.20850772e-01 2.72412986e-01 1.42115414e-01 -4.03350234e-01
-3.52402747e-01 -6.89346969e-01 6.67225718e-01 1.48966968e-01
2.15604782e-01 3.92705262e-01 -5.54057434e-02 -2.91675985e-01
-9.24250484e-02 -1.51133144e+00 -1.22373748e+00 -2.58456230e-01
-2.78830767e-01 8.76360655e-01 2.66494840e-01 -1.47715867... | [8.675738334655762, -3.650305986404419] |
24bb83f0-434c-4771-b08f-fc96052fa893 | are-nlp-models-really-able-to-solve-simple | 2103.07191 | null | https://arxiv.org/abs/2103.07191v2 | https://arxiv.org/pdf/2103.07191v2.pdf | Are NLP Models really able to Solve Simple Math Word Problems? | The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often consi... | ['Navin Goyal', 'Satwik Bhattamishra', 'Arkil Patel'] | 2021-03-12 | null | https://aclanthology.org/2021.naacl-main.168 | https://aclanthology.org/2021.naacl-main.168.pdf | naacl-2021-4 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-1.40567645e-01 4.03843701e-01 -2.57016033e-01 -2.25654200e-01
-1.19117856e+00 -1.03075898e+00 1.44551396e-01 4.66548741e-01
-3.25144649e-01 9.00077581e-01 1.56344771e-01 -7.34664261e-01
-3.45529795e-01 -1.27640545e+00 -1.05980134e+00 -2.15068027e-01
1.62909821e-01 8.62528622e-01 1.44314513e-01 -5.07558525... | [9.685223579406738, 7.3841776847839355] |
987f43f2-fea8-46c2-ada0-4eda074ecd94 | pathologist-level-classification-of | 1901.11489 | null | http://arxiv.org/abs/1901.11489v1 | http://arxiv.org/pdf/1901.11489v1.pdf | Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks | Classification of histologic patterns in lung adenocarcinoma is critical for
determining tumor grade and treatment for patients. However, this task is often
challenging due to the heterogeneous nature of lung adenocarcinoma and the
subjective criteria for evaluation. In this study, we propose a deep learning
model that... | ['Yevgeniy A. Linnik', 'Saeed Hassanpour', 'Louis J. Vaickus', 'Jason W. Wei', 'Naofumi Tomita', 'Laura J. Tafe'] | 2019-01-31 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-5.05335517e-02 9.24249366e-02 -4.95711893e-01 -6.40239492e-02
-1.26920390e+00 -1.02911413e+00 1.31201446e-01 5.11433601e-01
-5.79808950e-01 5.16408980e-01 7.27283955e-02 -8.53366613e-01
1.49566501e-01 -7.98689604e-01 -3.16515654e-01 -9.63033557e-01
2.70503014e-01 6.05601907e-01 2.96353728e-01 4.20234561... | [15.178449630737305, -2.974123001098633] |
fb4d7b56-8d5b-4fd9-ba94-dc37c7ad51c5 | integrating-local-material-recognition-with | 1604.01345 | null | http://arxiv.org/abs/1604.01345v4 | http://arxiv.org/pdf/1604.01345v4.pdf | Integrating Local Material Recognition with Large-Scale Perceptual Attribute Discovery | Material attributes have been shown to provide a discriminative intermediate
representation for recognizing materials, especially for the challenging task
of recognition from local material appearance (i.e., regardless of object and
scene context). In the past, however, material attributes have been recognized
separate... | ['Ko Nishino', 'Gabriel Schwartz'] | 2016-04-05 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 8.44298959e-01 8.31356198e-02 4.59435135e-02 -6.92004621e-01
-2.77584702e-01 -7.02969491e-01 8.79854441e-01 3.89818877e-01
-2.98439264e-01 3.71657133e-01 -6.53165728e-02 7.53784403e-02
-3.71933341e-01 -9.43720996e-01 -1.14830494e+00 -9.74324524e-01
1.43460918e-03 3.07516307e-01 2.18247101e-01 -8.74111895... | [10.190380096435547, -0.016658896580338478] |
b29868d2-0bd4-4743-bf91-97c59b172c82 | galaxy-morphology-prediction-using-capsule | 1809.08377 | null | http://arxiv.org/abs/1809.08377v1 | http://arxiv.org/pdf/1809.08377v1.pdf | Galaxy morphology prediction using capsule networks | Understanding morphological types of galaxies is a key parameter for studying
their formation and evolution. Neural networks that have been used previously
for galaxy morphology classification have some disadvantages, such as not being
invariant under rotation. In this work, we studied the performance of Capsule
Networ... | ['Yadi Zhou', 'Razvan Bunescu', 'Ryan Chornock', 'Reza Katebi'] | 2018-09-22 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-4.01824355e-01 -6.30404986e-03 4.75767493e-01 -3.47883880e-01
-1.12373218e-01 -8.91119957e-01 7.07069099e-01 4.42904048e-02
-4.54360425e-01 4.04755741e-01 1.43103808e-01 -5.09689629e-01
-5.00008821e-01 -1.02875960e+00 -3.55750740e-01 -8.76548588e-01
-6.23388477e-02 8.22046578e-01 7.58443117e-01 8.53828490... | [7.8902788162231445, 2.9677891731262207] |
cd795aaf-f144-4163-9474-7f2fff3e7526 | plot-writing-from-pre-trained-language-models-1 | 2206.03021 | null | https://arxiv.org/abs/2206.03021v1 | https://arxiv.org/pdf/2206.03021v1.pdf | Plot Writing From Pre-Trained Language Models | Pre-trained language models (PLMs) fail to generate long-form narrative text because they do not consider global structure. As a result, the generated texts are often incohesive, repetitive, or lack content. Recent work in story generation reintroduced explicit content planning in the form of prompts, keywords, or sema... | ['Dittaya Wanvarie', 'Vishakha Kadam', 'Yiping Jin'] | 2022-06-07 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 3.31702411e-01 4.47203517e-01 -1.91842452e-01 -2.12892294e-01
-9.92875993e-01 -8.17719281e-01 1.24157441e+00 2.00043097e-01
5.11649773e-02 9.76254523e-01 1.10745454e+00 -1.80001166e-02
1.30412042e-01 -9.99192595e-01 -6.92499876e-01 -2.05064178e-01
4.58252549e-01 6.13113046e-01 3.09505612e-01 -3.21508110... | [11.683140754699707, 8.848231315612793] |
6fdbb6a2-7bac-4bcb-9baf-2be84a25e7f2 | thermal-to-visible-synthesis-of-face-images | 1803.07599 | null | http://arxiv.org/abs/1803.07599v1 | http://arxiv.org/pdf/1803.07599v1.pdf | Thermal to Visible Synthesis of Face Images using Multiple Regions | Synthesis of visible spectrum faces from thermal facial imagery is a
promising approach for heterogeneous face recognition; enabling existing face
recognition software trained on visible imagery to be leveraged, and allowing
human analysts to verify cross-spectrum matches more effectively. We propose a
new synthesis me... | ['Nathaniel J. Short', 'Benjamin S. Riggan', 'Shuowen Hu'] | 2018-03-20 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 5.83874345e-01 -8.57035890e-02 -8.20305869e-02 -4.18414980e-01
-9.33214664e-01 -6.65832996e-01 5.63331664e-01 -5.35749853e-01
1.53165519e-01 1.98641837e-01 1.78302839e-01 4.90818396e-02
2.52181023e-01 -7.55916059e-01 -6.24157906e-01 -1.03245556e+00
2.49897555e-01 -9.88322049e-02 -3.89226139e-01 -8.63036513... | [12.993993759155273, 0.3300498127937317] |
bc75e268-55d5-460d-b1e9-d521042f3f7c | template-matching-with-white-balance | 2208.02035 | null | https://arxiv.org/abs/2208.02035v1 | https://arxiv.org/pdf/2208.02035v1.pdf | Template matching with white balance adjustment under multiple illuminants | In this paper, we propose a novel template matching method with a white balancing adjustment, called N-white balancing, which was proposed for multi-illuminant scenes. To reduce the influence of lighting effects, N-white balancing is applied to images for multi-illumination color constancy, and then a template matching... | ['Hitoshi Kiya', 'Yuma Kinoshita', 'Teruaki Akazawa'] | 2022-08-03 | null | null | null | null | ['template-matching', 'color-constancy'] | ['computer-vision', 'computer-vision'] | [ 6.28416002e-01 -8.96128953e-01 5.71405776e-02 -2.11387858e-01
1.43291980e-01 -1.56026423e-01 2.81623751e-01 -6.57335877e-01
-3.95579487e-01 5.25136411e-01 1.08580813e-02 -1.34882346e-01
5.31764030e-02 -5.96998930e-01 -1.91753313e-01 -8.15087259e-01
8.22644532e-01 -4.25908804e-01 3.52850556e-01 -1.02741949... | [10.634193420410156, -2.577362060546875] |
cd98a465-a6bb-4c2b-a70d-c492d2e8e7cf | language-informed-transfer-learning-for | 2301.05318 | null | https://arxiv.org/abs/2301.05318v1 | https://arxiv.org/pdf/2301.05318v1.pdf | Language-Informed Transfer Learning for Embodied Household Activities | For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users. Fine-tuning neural networks on a variety of downstream tasks has been successful in many vision and language doma... | ['Gaurav Sukhatme', 'Govind Thattai', 'Qiaozi Gao', 'Yuqian Jiang'] | 2023-01-12 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 3.51949781e-02 4.41771783e-02 -2.23874021e-03 -4.48925555e-01
-4.66148287e-01 -4.83213454e-01 7.72368789e-01 -4.18904386e-02
-6.01481259e-01 9.45817411e-01 5.72494566e-01 4.38557044e-02
-5.59608489e-02 -5.49445987e-01 -7.64257669e-01 -6.79403603e-01
-4.10421520e-01 6.69769764e-01 1.63338915e-01 -4.32727486... | [4.3838090896606445, 1.0798568725585938] |
0a9fd608-c97f-40a8-a9d3-02d90167ae73 | formal-covariate-benchmarking-to-bound | 2306.10562 | null | https://arxiv.org/abs/2306.10562v1 | https://arxiv.org/pdf/2306.10562v1.pdf | Formal Covariate Benchmarking to Bound Omitted Variable Bias | Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after residualizing the unobserved confoun... | ['Deepankar Basu'] | 2023-06-18 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 3.07286590e-01 3.61565560e-01 -1.03188801e+00 -6.70638442e-01
-7.00752437e-01 -5.09086490e-01 4.26901132e-01 3.25543940e-01
-5.13078153e-01 9.95145619e-01 1.07575417e+00 -8.95836115e-01
-5.94331503e-01 -4.61267322e-01 -5.72623372e-01 -5.73605478e-01
-2.58825868e-02 1.02772087e-01 -5.19732714e-01 3.87163490... | [7.99672794342041, 5.342092990875244] |
32d435b7-21e5-4a03-99b3-48eb76045819 | a-methodology-based-on-trace-based-clustering | null | null | https://www.sciencedirect.com/science/article/pii/S0950705121007310 | https://reader.elsevier.com/reader/sd/pii/S0950705121007310?token=AC1F6F1CB4E9FDFF110EA7B6F114EFA66366F1BB4D4640BE93020F90873D828C42D9DFC5AB1C530870F3AFC8EA0F4394&originRegion=eu-west-1&originCreation=20220120171012 | A methodology based on Trace-based clustering for patient phenotyping | Background:
The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for decision making. One of the key aspects is precision medicine, which focuses on finding phenotypes of pa... | ['Bernardo Canovas-Segura', 'Manuel Campos', 'Jose M. Juarez', 'Antonio Lopez-Martinez-Carrasco'] | 2021-11-28 | null | null | null | knowledge-based-systems-2021-11 | ['patient-phenotyping', 'data-mining', 'data-mining'] | ['medical', 'methodology', 'natural-language-processing'] | [ 3.21032673e-01 -3.38941664e-01 1.02987178e-01 -1.40672892e-01
-2.26012111e-01 -7.53013849e-01 2.51633406e-01 1.01350915e+00
-4.46288615e-01 6.06759846e-01 -2.75995165e-01 -6.04130566e-01
-8.85464549e-01 -6.15009010e-01 -1.09716147e-01 -1.05497718e+00
-3.46447557e-01 1.06040859e+00 1.93999588e-01 2.85024732... | [7.6144490242004395, 4.591550827026367] |
c158ebc1-a551-45fe-a2e0-3fa58fdcffa0 | episodic-memory-reader-learning-what-to | 1903.06164 | null | https://arxiv.org/abs/1903.06164v3 | https://arxiv.org/pdf/1903.06164v3.pdf | Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data | We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability. To tackle this problem, we propose a novel end-to... | ['Hyunwoo Jung', 'Sung Ju Hwang', 'Moonsu Han', 'Minki Kang'] | 2019-03-14 | episodic-memory-reader-learning-what-to-1 | https://aclanthology.org/P19-1434 | https://aclanthology.org/P19-1434.pdf | acl-2019-7 | ['triviaqa'] | ['miscellaneous'] | [ 4.68030572e-01 4.62499976e-01 2.46970162e-01 -4.52450901e-01
-1.29597890e+00 -5.20310104e-01 4.11422700e-01 2.39430413e-01
-5.84426582e-01 5.99694908e-01 5.46659231e-01 -5.92644334e-01
-1.74069509e-03 -9.54887211e-01 -1.37376094e+00 -4.32954222e-01
2.20790938e-01 9.90108907e-01 4.33642924e-01 -1.70733884... | [11.213239669799805, 7.901501655578613] |
d7ef86a8-3ba6-4d79-8da5-bdbdd6088daf | sketch-guided-scenery-image-outpainting | 2006.09788 | null | https://arxiv.org/abs/2006.09788v2 | https://arxiv.org/pdf/2006.09788v2.pdf | Sketch-Guided Scenery Image Outpainting | The outpainting results produced by existing approaches are often too random to meet users' requirement. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network ... | ['Yunchao Wei', 'Xueming Qian', 'Yi Yang', 'Yaxiong Wang', 'Li Zhu'] | 2020-06-17 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 4.08354729e-01 1.42451197e-01 4.75491174e-02 -4.07101065e-01
-5.60967803e-01 -4.60761487e-01 7.20515847e-01 -4.01113212e-01
1.35126829e-01 7.34970927e-01 2.61325121e-01 8.61979946e-02
3.53254706e-01 -8.86166930e-01 -1.04514802e+00 -4.63374019e-01
5.32353759e-01 2.91696817e-01 1.28483132e-01 -3.12659949... | [11.43587875366211, -0.6970183253288269] |
67a70d65-7e6e-47a3-8be0-49d9e47069af | event-based-temporally-dense-optical-flow | 2210.01244 | null | https://arxiv.org/abs/2210.01244v1 | https://arxiv.org/pdf/2210.01244v1.pdf | Event-based Temporally Dense Optical Flow Estimation with Sequential Neural Networks | Prior works on event-based optical flow estimation have investigated several gradient-based learning methods to train neural networks for predicting optical flow. However, they do not utilize the fast data rate of event data streams and rely on a spatio-temporal representation constructed from a collection of events ov... | ['Kaushik Roy', 'Chamika Mihiranga Liyanagedera', 'Wachirawit Ponghiran'] | 2022-10-03 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 2.05759481e-01 -5.38501918e-01 6.51259497e-02 -8.51487592e-02
-2.30972528e-01 -4.09358531e-01 4.56620574e-01 -4.22960445e-02
-8.44368875e-01 1.12833405e+00 1.12420909e-01 -6.82253912e-02
2.12105904e-02 -8.91814232e-01 -8.45979869e-01 -5.34468293e-01
-5.46050310e-01 9.49782785e-03 5.93152463e-01 4.43664223... | [8.704345703125, -1.320690393447876] |
c4fafaf8-fbbc-4ac0-b3f7-8245272d2109 | single-camera-3d-head-fitting-for-mixed | 2109.02740 | null | https://arxiv.org/abs/2109.02740v2 | https://arxiv.org/pdf/2109.02740v2.pdf | Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications | We address the problem of estimating the shape of a person's head, defined as the geometry of the complete head surface, from a video taken with a single moving camera, and determining the alignment of the fitted 3D head for all video frames, irrespective of the person's pose. 3D head reconstructions commonly tend to f... | ['Elena Bernardis', 'Philippos Mordohai', 'Kostas Daniilidis', 'Aylar Bayramova', 'Tejas Mane'] | 2021-09-06 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [-1.23602979e-01 3.42903405e-01 3.52707773e-01 -4.78853434e-01
-8.22521567e-01 -4.96738493e-01 2.73335248e-01 -3.92330289e-01
-2.51086265e-01 3.07559907e-01 4.57741439e-01 5.85856974e-01
3.11400324e-01 -3.40936899e-01 -6.43946350e-01 -5.65788269e-01
3.27568442e-01 9.23033357e-01 5.86547405e-02 2.14347437... | [13.277661323547363, 0.014215996488928795] |
667dac45-bba4-42f0-b5e3-f03b38944cc2 | mist-multi-modal-iterative-spatial-temporal | 2212.09522 | null | https://arxiv.org/abs/2212.09522v1 | https://arxiv.org/pdf/2212.09522v1.pdf | MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering | To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must. Existing multi-modal VQA models achieve promising performance on images or short video clips, especially with the recent success of large-... | ['Mike Zheng Shou', 'Yi Yang', 'Linchao Zhu', 'Lei Ji', 'Luowei Zhou', 'Difei Gao'] | 2022-12-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 1.05371876e-02 -4.45258796e-01 -7.82459304e-02 -4.31356132e-01
-1.04473126e+00 -4.79469448e-01 3.11951905e-01 4.42026816e-02
-4.54062104e-01 4.68897223e-01 3.65004957e-01 -1.44243017e-01
2.91979369e-02 -6.57051206e-01 -7.66472995e-01 -3.97072792e-01
2.56776094e-01 4.70567912e-01 6.64197564e-01 -4.26412791... | [10.377554893493652, 1.0255918502807617] |
f42a2d14-f667-4b29-8a79-a2955daa6011 | addressing-the-selection-bias-in-voice | 2301.00646 | null | https://arxiv.org/abs/2301.00646v1 | https://arxiv.org/pdf/2301.00646v1.pdf | Addressing the Selection Bias in Voice Assistance: Training Voice Assistance Model in Python with Equal Data Selection | In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focuse... | ['Tauheed Khan Mohd', 'Estephanos Jebessa', 'Cameran Frank', 'Srijal Shrestha', 'Kashav Piya'] | 2022-12-20 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-3.12076628e-01 1.94238037e-01 -3.42959046e-01 -5.96835554e-01
-1.22996256e-01 -6.40354931e-01 4.93270487e-01 -2.45968416e-01
-4.93368685e-01 2.24739939e-01 7.14201510e-01 -6.99036181e-01
3.74712765e-01 -8.36751401e-01 3.90916504e-02 -4.95186225e-02
5.02908230e-01 8.09706151e-01 -4.01521891e-01 -5.27760148... | [14.18553638458252, 6.664111614227295] |
1391d123-bffd-4c6f-99d7-37c05b520521 | brain-diffuser-natural-scene-reconstruction | 2303.05334 | null | https://arxiv.org/abs/2303.05334v2 | https://arxiv.org/pdf/2303.05334v2.pdf | Natural scene reconstruction from fMRI signals using generative latent diffusion | In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, desc... | ['Rufin VanRullen', 'Furkan Ozcelik'] | 2023-03-09 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 4.32557940e-01 8.71150196e-02 5.53981841e-01 -3.39764059e-01
-3.52229327e-01 -5.14438272e-01 1.03575754e+00 -1.33100422e-02
-4.37030226e-01 6.77779555e-01 3.05727839e-01 1.75531860e-02
-6.20906055e-02 -8.72306347e-01 -9.87495005e-01 -9.37571704e-01
7.61651620e-02 4.52343881e-01 1.79260716e-01 -8.03175271... | [10.720532417297363, 2.498286008834839] |
cc5b1db4-6113-4743-8f2b-467b8f1b0872 | acpl-anti-curriculum-pseudo-labelling-forsemi | 2111.12918 | null | https://arxiv.org/abs/2111.12918v3 | https://arxiv.org/pdf/2111.12918v3.pdf | ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification | Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence).... | ['Gustavo Carneiro', 'Vasileios Belagiannis', 'Yuyuan Liu', 'Yuanhong Chen', 'Yu Tian', 'Fengbei Liu'] | 2021-11-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 6.55118287e-01 1.54380187e-01 -8.94376338e-01 -4.72119331e-01
-1.35415113e+00 -2.21614882e-01 2.68824577e-01 4.90071952e-01
-2.83542335e-01 8.37006032e-01 -1.18563294e-01 -3.30486715e-01
-4.82711583e-01 -5.46304822e-01 -5.16475618e-01 -1.02072787e+00
3.69359404e-01 8.86128426e-01 1.58572823e-01 3.39685470... | [15.008624076843262, -2.419468402862549] |
fe4abce4-b4c9-486f-9575-f51a34f1378c | at-which-level-should-we-extract-an-empirical | 2004.02664 | null | https://arxiv.org/abs/2004.02664v2 | https://arxiv.org/pdf/2004.02664v2.pdf | At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization | Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy is... | ['Furu Wei', 'Qingyu Zhou', 'Ming Zhou'] | 2020-04-06 | null | https://aclanthology.org/2020.coling-main.492 | https://aclanthology.org/2020.coling-main.492.pdf | coling-2020-8 | ['constituency-parsing', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.28348887e-01 6.09284818e-01 -3.66026342e-01 -2.63731658e-01
-1.09604871e+00 -7.09627926e-01 4.95884657e-01 5.84439695e-01
-6.48750722e-01 1.05853117e+00 1.18998599e+00 -2.66313404e-01
3.08927000e-01 -5.99693716e-01 -3.99450123e-01 -2.92483151e-01
-8.98049846e-02 2.80296151e-02 1.23033151e-02 -3.12613964... | [12.527207374572754, 9.495282173156738] |
716daabc-1aed-4a6d-9b7b-50cf44c4b57a | cgi-stereo-accurate-and-real-time-stereo | 2301.02789 | null | https://arxiv.org/abs/2301.02789v2 | https://arxiv.org/pdf/2301.02789v2.pdf | CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction | In this paper, we propose CGI-Stereo, a novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization ability. The core of our CGI-Stereo is a Context and Geometry Fusion (CGF) block which adaptively fuses context and geometry information for more... | ['Xin Yang', 'Huan Zhou', 'Gangwei Xu'] | 2023-01-07 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-6.65234774e-02 -4.08703655e-01 -6.80444390e-02 -5.46922505e-01
-5.90103805e-01 -2.07849309e-01 5.77452183e-01 -1.95985716e-02
-4.82565135e-01 4.46297169e-01 3.66093099e-01 -1.16322607e-01
-4.75905210e-01 -9.71849740e-01 -8.39072108e-01 -5.88249803e-01
1.04605183e-01 3.47211033e-01 3.12760174e-01 -3.47855359... | [8.850839614868164, -2.213054656982422] |
4e004a82-b606-4b7d-bbeb-3a862b390f7b | semi-supervised-haptic-material-recognition | 1707.02796 | null | http://arxiv.org/abs/1707.02796v2 | http://arxiv.org/pdf/1707.02796v2.pdf | Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks | Material recognition enables robots to incorporate knowledge of material
properties into their interactions with everyday objects. For example, material
recognition opens up opportunities for clearer communication with a robot, such
as "bring me the metal coffee mug", and recognizing plastic versus metal is
crucial whe... | ['Sonia Chernova', 'Zackory Erickson', 'Charles C. Kemp'] | 2017-07-10 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 5.59614301e-01 4.94356453e-01 2.60007381e-03 -4.18487132e-01
-7.49296129e-01 -6.08830452e-01 7.26167187e-02 -6.08137175e-02
-1.35795459e-01 8.15044999e-01 -2.09373757e-01 5.50022833e-02
1.27971604e-01 -9.48131025e-01 -1.54296458e+00 -6.71732247e-01
-1.18492462e-01 7.58245409e-01 -1.64893925e-01 -3.17428201... | [5.786373615264893, -0.7535569071769714] |
b034cb9d-c69e-4fcf-b5b8-d631e379dbc2 | mitigating-frequency-bias-in-next-basket | 2211.09072 | null | https://arxiv.org/abs/2211.09072v1 | https://arxiv.org/pdf/2211.09072v1.pdf | Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders | Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user's interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that... | ['Kannan Achan', 'Philip Yu', 'Stephen Guo', 'Kaushiki Nag', 'Luyi Ma', 'Zheng Liu', 'Xiaohan Li'] | 2022-11-16 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [-3.31637174e-01 -1.18570961e-01 -1.01530576e+00 -5.54135144e-01
1.77052617e-01 -1.97076678e-01 2.16264993e-01 -3.11899781e-01
-1.87800363e-01 4.93282706e-01 9.49983895e-01 -3.32045972e-01
-3.88661355e-01 -1.03570163e+00 -7.83968270e-01 -4.13826972e-01
-2.62263089e-01 1.74736008e-01 -1.05902441e-01 -2.98633665... | [9.911152839660645, 5.59434175491333] |
e5874343-5baa-456b-9c92-31cbd6f558fd | assessing-hidden-risks-of-llms-an-empirical | 2305.10235 | null | https://arxiv.org/abs/2305.10235v3 | https://arxiv.org/pdf/2305.10235v3.pdf | Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility | The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-sourced models, and plugins. However, with their widespread deployment, there is a general lack of research that thoroughly discusses and ana... | ['Junbo Zhao', 'Haobo Wang', 'Gang Chen', 'Jie Fu', 'Sai Wu', 'Yifan Yanggong', 'Xuetao Ma', 'Yipeng chen', 'Tianyi Li', 'Mingfeng Ou', 'Wentao Ye'] | 2023-05-15 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.49493068e-01 -1.87495071e-02 5.42379282e-02 -8.40462521e-02
-1.09924853e+00 -1.09663785e+00 6.81211948e-01 1.99023902e-01
-4.08075362e-01 4.61165339e-01 -1.35912478e-01 -8.59674335e-01
-1.96096718e-01 -8.10916245e-01 -8.41526330e-01 -2.20854998e-01
-1.35180786e-01 2.96087861e-01 3.78509730e-01 -3.73157203... | [6.169615745544434, 7.761898517608643] |
c54f7bb3-4943-4ab3-9fe5-652994e74374 | multi-channel-attention-selection-gan-with | 1904.06807 | null | http://arxiv.org/abs/1904.06807v2 | http://arxiv.org/pdf/1904.06807v2.pdf | Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation | Cross-view image translation is challenging because it involves images with
drastically different views and severe deformation. In this paper, we propose a
novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that
makes it possible to generate images of natural scenes in arbitrary viewpoints,
based ... | ['Yan Yan', 'Hao Tang', 'Yanzhi Wang', 'Nicu Sebe', 'Jason J. Corso', 'Dan Xu'] | 2019-04-15 | multi-channel-attention-selection-gan-with-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Tang_Multi-Channel_Attention_Selection_GAN_With_Cascaded_Semantic_Guidance_for_Cross-View_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Multi-Channel_Attention_Selection_GAN_With_Cascaded_Semantic_Guidance_for_Cross-View_CVPR_2019_paper.pdf | cvpr-2019-6 | ['cross-view-image-to-image-translation', 'bird-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.15011317e-01 2.34188080e-01 2.09385633e-01 -4.06528860e-01
-7.22517788e-01 -3.50175172e-01 6.61166251e-01 -5.42399645e-01
-1.20487370e-01 7.87774205e-01 1.40500382e-01 1.68616727e-01
2.39399582e-01 -8.81251812e-01 -9.42280650e-01 -7.52331495e-01
7.35776961e-01 4.85233337e-01 1.56768322e-01 -3.55870038... | [11.500542640686035, -0.6671644449234009] |
1bafc0a1-0439-4ffa-b583-0aad9a4638db | federated-domain-generalization-a-survey | 2306.01334 | null | https://arxiv.org/abs/2306.01334v1 | https://arxiv.org/pdf/2306.01334v1.pdf | Federated Domain Generalization: A Survey | Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge no... | ['Schahram Dustdar', 'Min Huang', 'Ilir Murturi', 'Praveen Kumar Donta', 'Rongfei Zeng', 'Xingwei Wang', 'Ying Li'] | 2023-06-02 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 2.52127767e-01 -2.68620133e-01 -5.45267642e-01 -7.39161789e-01
-4.54008609e-01 -8.87617052e-01 3.94735456e-01 3.36363286e-01
-1.42868504e-01 1.12095237e+00 -2.50883371e-01 -1.47116438e-01
-3.42762828e-01 -8.92167985e-01 -4.64625537e-01 -7.01070786e-01
-4.51279022e-02 6.55586123e-01 -2.06992462e-01 -1.20301545... | [10.372408866882324, 3.197762966156006] |
874db3ac-d9d5-4a78-b6cf-3c54ee2dadc2 | soft-landing-strategy-for-alleviating-the | 2211.06023 | null | https://arxiv.org/abs/2211.06023v1 | https://arxiv.org/pdf/2211.06023v1.pdf | Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization Tasks | Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy problem. While existing TAL methods mitigate this issue either by retraining the encoder with a pr... | ['Seon Joo Kim', 'Minsu Cho', 'Joungbin An', 'Hanjung Kim', 'Hyolim Kang'] | 2022-11-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 6.41686141e-01 -8.92579556e-03 -1.95490181e-01 -5.22028685e-01
-9.82140362e-01 -3.50882024e-01 5.84606647e-01 -1.92087665e-01
-6.48557305e-01 4.86688465e-01 1.97149292e-01 -1.74327776e-01
1.04196638e-01 -2.79470742e-01 -8.48978341e-01 -7.37193644e-01
4.76327632e-03 3.87254246e-02 7.39304602e-01 -1.83206141... | [8.53880500793457, 0.6180397868156433] |
a339d511-92f1-4f19-90d1-6f0b3bbd6724 | on-reinforcement-learning-for-the-game-of | 2212.11087 | null | https://arxiv.org/abs/2212.11087v2 | https://arxiv.org/pdf/2212.11087v2.pdf | On Reinforcement Learning for the Game of 2048 | 2048 is a single-player stochastic puzzle game. This intriguing and addictive game has been popular worldwide and has attracted researchers to develop game-playing programs. Due to its simplicity and complexity, 2048 has become an interesting and challenging platform for evaluating the effectiveness of machine learning... | ['Hung Guei'] | 2022-12-21 | null | null | null | null | ['2048'] | ['playing-games'] | [-3.89671147e-01 -2.04922870e-01 -1.89423770e-01 2.55195022e-01
-6.89566314e-01 -3.78958195e-01 -2.05354951e-03 7.53612816e-02
-4.09727395e-01 9.39564884e-01 -4.30732191e-01 -6.25284672e-01
-4.64170158e-01 -1.24251270e+00 -4.82550055e-01 -7.13332593e-01
-3.43919665e-01 3.95781517e-01 2.92422235e-01 -6.02178037... | [3.4980831146240234, 1.5367058515548706] |
38de504a-e6b8-41dc-abf3-3f515902ace6 | diagnostic-questions-the-neurips-2020 | 2007.12061 | null | https://arxiv.org/abs/2007.12061v3 | https://arxiv.org/pdf/2007.12061v3.pdf | Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge | Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about th... | ['José Miguel Hernández-Lobato', 'Evgeny Saveliev', 'Pashmina Cameron', 'Simon Woodhead', 'Richard E. Turner', 'Zichao Wang', 'Yordan Zaykov', 'Simon Peyton Jones', 'Cheng Zhang', 'Richard G. Baraniuk', 'Craig Barton', 'Angus Lamb'] | 2020-07-23 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-2.88253397e-01 -5.13822958e-02 -3.26807708e-01 -2.56571263e-01
-7.46089756e-01 -1.05224931e+00 3.25564712e-01 9.21939075e-01
-2.27435634e-01 3.02389383e-01 4.81882691e-01 -9.05095756e-01
-5.61731040e-01 -1.20982015e+00 -6.62877262e-01 4.28998843e-03
2.50176311e-01 3.30710918e-01 6.33737743e-01 -5.99930882... | [10.106058120727539, 7.34358024597168] |
3a8371ce-2f0c-4e88-b0fe-e80268ad8858 | transition-forests-learning-discriminative | 1607.02737 | null | http://arxiv.org/abs/1607.02737v3 | http://arxiv.org/pdf/1607.02737v3.pdf | Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection | A human action can be seen as transitions between one's body poses over time,
where the transition depicts a temporal relation between two poses. Recognizing
actions thus involves learning a classifier sensitive to these pose transitions
as well as to static poses. In this paper, we introduce a novel method called
tran... | ['Tae-Kyun Kim', 'Guillermo Garcia-Hernando'] | 2016-07-10 | transition-forests-learning-discriminative-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Garcia-Hernando_Transition_Forests_Learning_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Garcia-Hernando_Transition_Forests_Learning_CVPR_2017_paper.pdf | cvpr-2017-7 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 8.08172107e-01 2.52762586e-01 -5.38806736e-01 -3.82325441e-01
-5.23987114e-01 -2.61161089e-01 5.72904408e-01 2.29059830e-01
-3.61747950e-01 6.27336264e-01 1.65930569e-01 1.02756999e-01
-2.59339754e-02 -7.01242030e-01 -5.16998410e-01 -8.69870484e-01
-3.46694291e-01 7.36094415e-01 9.99531806e-01 1.02097325... | [8.221026420593262, 0.46107083559036255] |
1169e10e-8e3f-424a-943b-cf6dbe7b8d44 | learning-collision-free-and-torque-limited | 2103.03793 | null | https://arxiv.org/abs/2103.03793v3 | https://arxiv.org/pdf/2103.03793v3.pdf | Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors | This paper presents an approach for learning online generation of collision-free and torque-limited robot trajectories. In order to generate future motions, a neural network is periodically invoked. Based on the current kinematic state of the robot and the network output, a trajectory for the current time interval can ... | ['Torsten Kröger', 'Jonas C. Kiemel'] | 2021-03-05 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 8.96878764e-02 4.99591947e-01 -3.66095304e-01 1.62725121e-01
-2.10956261e-01 -4.62453514e-01 3.08021218e-01 9.12023932e-02
-8.28840792e-01 1.05988014e+00 -5.69008827e-01 -4.39505577e-01
-4.10459161e-01 -1.00521958e+00 -8.73897314e-01 -8.75188231e-01
-3.85154992e-01 6.78844392e-01 3.03971380e-01 -3.17038596... | [4.7477569580078125, 1.529340147972107] |
58906c85-914c-4673-a957-71d1b5783cda | clinicalbert-modeling-clinical-notes-and | 1904.05342 | null | https://arxiv.org/abs/1904.05342v3 | https://arxiv.org/pdf/1904.05342v3.pdf | ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission | Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirection... | ['Jaan Altosaar', 'Kexin Huang', 'Rajesh Ranganath'] | 2019-04-10 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [-2.68175930e-01 3.04884106e-01 -5.60815156e-01 -5.73011041e-01
-8.01533878e-01 -7.06452429e-01 1.79825753e-01 1.17048180e+00
1.40967160e-01 9.01726723e-01 1.39478230e+00 -4.67011660e-01
-6.32040739e-01 -7.45655775e-01 -5.49412705e-02 -1.91222265e-01
-7.49935091e-01 1.16464448e+00 -6.91588521e-01 1.44488022... | [7.96398401260376, 6.489977836608887] |
b6436857-b94e-4de1-bdf4-68bf805348af | recipe-for-a-general-powerful-scalable-graph | 2205.12454 | null | https://arxiv.org/abs/2205.12454v4 | https://arxiv.org/pdf/2205.12454v4.pdf | Recipe for a General, Powerful, Scalable Graph Transformer | We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack ... | ['Dominique Beaini', 'Guy Wolf', 'Anh Tuan Luu', 'Vijay Prakash Dwivedi', 'Mikhail Galkin', 'Ladislav Rampášek'] | 2022-05-25 | null | null | null | null | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [ 8.58426839e-02 3.74511093e-01 -2.54078776e-01 -9.79899168e-02
-4.59733188e-01 -6.15537941e-01 4.91054386e-01 4.13177639e-01
-1.71160743e-01 5.71768403e-01 -6.74786195e-02 -7.19932377e-01
-4.54102606e-01 -1.39088118e+00 -9.62058842e-01 -6.85832381e-01
-6.25707984e-01 6.27773285e-01 4.85007137e-01 -4.95663166... | [6.965756416320801, 6.21083927154541] |
75aa7bc6-0921-428e-a023-9a750f2ffe32 | shortcomings-of-question-answering-based | 2210.06748 | null | https://arxiv.org/abs/2210.06748v2 | https://arxiv.org/pdf/2210.06748v2.pdf | Shortcomings of Question Answering Based Factuality Frameworks for Error Localization | Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (QA)-based factuality metrics. These have been shown to work well at predicting summary-level factual... | ['Greg Durrett', 'Tanya Goyal', 'Ryo Kamoi'] | 2022-10-13 | null | null | null | null | ['abstractive-text-summarization', 'question-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.85681337e-01 5.66615939e-01 -2.50322241e-02 -1.47127226e-01
-1.60810149e+00 -8.02741766e-01 8.61961722e-01 7.26604044e-01
-1.48376495e-01 1.03404403e+00 8.44923794e-01 -4.67343241e-01
-2.72743553e-01 -5.83161354e-01 -7.64780164e-01 -5.07288612e-02
3.86177450e-01 5.68121552e-01 3.57297868e-01 -3.99741828... | [12.149264335632324, 9.297818183898926] |
23668df6-3157-411a-b836-4015bb1a2020 | decentralized-stochastic-multi-player-multi | 2212.06279 | null | https://arxiv.org/abs/2212.06279v1 | https://arxiv.org/pdf/2212.06279v1.pdf | Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits | Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by applications to cognitive radio systems. Most research for this problem focuses exclusively on the settings that players have \textit{full access} to all arms and receive no reward when pulling the same arm. Hence all play... | ['Jian Li', 'Guojun Xiong'] | 2022-12-12 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 9.86989290e-02 3.31881762e-01 -7.66803682e-01 1.79165095e-01
-8.22792828e-01 -8.48082304e-01 -1.21180743e-01 -8.80533010e-02
-8.46621692e-01 1.18212748e+00 -3.46728593e-01 -7.29241610e-01
-8.50347698e-01 -1.12141418e+00 -5.26954770e-01 -1.08736050e+00
-1.27709642e-01 7.78495312e-01 2.08147056e-02 -1.86237484... | [4.52764368057251, 3.2689225673675537] |
d6933c70-3336-4c0b-8c1f-d91b837d15fe | text-annotation-graphs-annotating-complex | 1711.00529 | null | http://arxiv.org/abs/1711.00529v2 | http://arxiv.org/pdf/1711.00529v2.pdf | Text Annotation Graphs: Annotating Complex Natural Language Phenomena | This paper introduces a new web-based software tool for annotating text, Text
Annotation Graphs, or TAG. It provides functionality for representing complex
relationships between words and word phrases that are not available in other
software tools, including the ability to define and visualize relationships
between the... | ['Marco A. Valenzuela-Escárcega', 'Gus Hahn-Powell', 'Mihai Surdeanu', 'Angus G. Forbes', 'Kristine Lee'] | 2017-11-01 | text-annotation-graphs-annotating-complex-2 | https://aclanthology.org/L18-1169 | https://aclanthology.org/L18-1169.pdf | lrec-2018-5 | ['text-annotation'] | ['natural-language-processing'] | [ 2.73476601e-01 4.95970905e-01 -1.55415446e-01 -4.13297921e-01
-6.49268687e-01 -9.61102188e-01 4.84239519e-01 1.07375562e+00
-1.40091255e-01 5.50782442e-01 6.70294762e-01 -6.10554755e-01
-1.67010292e-01 -7.04829931e-01 1.56715773e-02 -2.45655179e-01
1.10912714e-02 3.68996799e-01 3.90865743e-01 -6.15965463... | [8.919607162475586, 8.754169464111328] |
4fc2a287-54b9-4482-a897-91e9e3ad336d | repbin-constraint-based-graph-representation | 2112.11696 | null | https://arxiv.org/abs/2112.11696v1 | https://arxiv.org/pdf/2112.11696v1.pdf | RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning | Mixed communities of organisms are found in many environments (from the human gut to marine ecosystems) and can have profound impact on human health and the environment. Metagenomics studies the genomic material of such communities through high-throughput sequencing that yields DNA subsequences for subsequent analysis.... | ['Yu Lin', 'Vaibhav Rajan', 'Yujia Zhang', 'Vijini Mallawaarachchi', 'Hansheng Xue'] | 2021-12-22 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 5.43752015e-01 -2.91616231e-01 -5.41695841e-02 1.75846536e-02
1.26824275e-01 -8.90734494e-01 4.18931305e-01 8.09395730e-01
5.05499355e-02 7.27512240e-01 2.31959745e-01 -4.23265696e-01
-5.88216066e-01 -1.02327156e+00 -7.51738369e-01 -1.19594085e+00
-6.35151148e-01 7.76788116e-01 3.21668051e-02 2.82697976... | [6.761887550354004, 5.407739162445068] |
60a351ad-bb39-492b-b3e2-5b76d9223857 | evaluate-confidence-instead-of-perplexity-for | 2208.11007 | null | https://arxiv.org/abs/2208.11007v1 | https://arxiv.org/pdf/2208.11007v1.pdf | Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning | Commonsense reasoning is an appealing topic in natural language processing (NLP) as it plays a fundamental role in supporting the human-like actions of NLP systems. With large-scale language models as the backbone, unsupervised pre-training on numerous corpora shows the potential to capture commonsense knowledge. Curre... | ['Hai Zhao', 'Zuchao Li', 'Letian Peng'] | 2022-08-23 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 5.26977301e-01 3.45141292e-01 -6.36419952e-02 -3.14178020e-01
-6.92406416e-01 -4.79918659e-01 9.98753369e-01 6.37191474e-01
-6.59068763e-01 7.38866568e-01 7.47022450e-01 -6.09051287e-01
-2.83822775e-01 -1.15511930e+00 -5.05632401e-01 -2.79514313e-01
5.17080307e-01 4.76874471e-01 5.14567912e-01 -8.74160230... | [10.074423789978027, 8.036636352539062] |
89e7271f-50a0-4fc3-8a43-2e09c09a978f | a-unified-model-and-dimension-for-interactive | 2306.06184 | null | https://arxiv.org/abs/2306.06184v1 | https://arxiv.org/pdf/2306.06184v1.pdf | A Unified Model and Dimension for Interactive Estimation | We study an abstract framework for interactive learning called interactive estimation in which the goal is to estimate a target from its "similarity'' to points queried by the learner. We introduce a combinatorial measure called dissimilarity dimension which largely captures learnability in our model. We present a simp... | ['Robert Schapire', 'Aldo Pacchiano', 'Miroslav Dudik', 'Nataly Brukhim'] | 2023-06-09 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 4.69199270e-02 4.29955721e-01 -9.24566627e-01 -4.51895088e-01
-1.74610937e+00 -1.19723403e+00 3.90020490e-01 2.60299116e-01
-4.61901426e-01 1.00682855e+00 -1.63353290e-02 -2.48107567e-01
-8.13977420e-01 -4.82698768e-01 -1.15865731e+00 -7.30645716e-01
-3.97620380e-01 6.49679720e-01 1.11491732e-01 3.29784065... | [4.657975196838379, 3.3001856803894043] |
d780f657-3d1d-46f1-ac20-656d224a2bb6 | answer-ranking-in-community-question | 2212.01218 | null | https://arxiv.org/abs/2212.01218v1 | https://arxiv.org/pdf/2212.01218v1.pdf | Answer ranking in Community Question Answering: a deep learning approach | Community Question Answering is the field of computational linguistics that deals with problems derived from the questions and answers posted to websites such as Quora or Stack Overflow. Among some of these problems we find the issue of ranking the multiple answers posted in reply to each question by how informative th... | ['Lucas Valentin'] | 2022-10-16 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-1.74232602e-01 1.29880711e-01 2.44189441e-01 -4.65949416e-01
-1.01054657e+00 -6.59581125e-01 5.16491354e-01 6.59062803e-01
-5.57517588e-01 4.10133034e-01 8.83322418e-01 -7.29826331e-01
-4.10628140e-01 -9.77939785e-01 -4.66907382e-01 -1.65306240e-01
5.45030609e-02 6.08065546e-01 4.26706225e-01 -5.19516110... | [11.43991470336914, 8.12093734741211] |
ac86d165-c341-4c41-8270-3a2bd759d40f | prompt-and-trait-relation-aware-cross-prompt | 2305.16826 | null | https://arxiv.org/abs/2305.16826v1 | https://arxiv.org/pdf/2305.16826v1.pdf | Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring | Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. However, such settings conflict with real-education situations; pre-graded essays for ... | ['Gary Geunbae Lee', 'Yunsu Kim', 'Heejin Do'] | 2023-05-26 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-4.48715920e-03 -3.29574496e-01 -3.93319517e-01 -5.87757528e-01
-1.06875551e+00 -7.56834745e-01 3.71582896e-01 3.22880566e-01
-6.95153251e-02 5.36935687e-01 4.54980761e-01 1.80937499e-01
-3.57503176e-01 -5.46757698e-01 -9.72855017e-02 -3.16471249e-01
7.36788273e-01 3.33953738e-01 -6.84967265e-02 -1.38413399... | [11.318387031555176, 9.29783821105957] |
6b45bab0-0465-4b00-831d-eabd405af930 | vidas-video-depth-aware-saliency-network | 2305.11729 | null | https://arxiv.org/abs/2305.11729v1 | https://arxiv.org/pdf/2305.11729v1.pdf | ViDaS Video Depth-aware Saliency Network | We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network to address the problem of attention modeling ``in-the-wild", via saliency prediction in videos. Contrary to existing visual saliency approaches using only RGB frames as input, our network employs also depth as an additional modali... | ['Petros Maragos', 'Petros Koutras', 'Antigoni Tsiami', 'Ioanna Diamanti'] | 2023-05-19 | null | null | null | null | ['saliency-prediction', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 4.33173448e-01 8.35841820e-02 -2.14135885e-01 -1.54705018e-01
-5.50708354e-01 -2.01736420e-01 4.91540372e-01 7.75198489e-02
-4.04264331e-01 5.48397422e-01 2.76081890e-01 1.06143720e-01
3.11434537e-01 -3.59523207e-01 -9.24948931e-01 -6.16210282e-01
2.81183776e-02 -5.54527231e-02 9.67457116e-01 -3.31079096... | [9.682500839233398, -0.33550482988357544] |
0aa6af9c-5fdc-41b8-8977-11edb99088d8 | ball-trajectory-inference-from-multi-agent | 2306.08206 | null | https://arxiv.org/abs/2306.08206v1 | https://arxiv.org/pdf/2306.08206v1.pdf | Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM | As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball... | ['Sang-Ki Ko', 'Jinsung Yoon', 'Chang Jo Kim', 'Han-Jun Choi', 'Hyunsung Kim'] | 2023-06-14 | null | null | null | null | ['sports-analytics', 'imputation', 'imputation', 'imputation'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'time-series'] | [-1.89942315e-01 -1.52378917e-01 -1.84044018e-01 -9.93862078e-02
-7.61971414e-01 -5.81495643e-01 2.24825561e-01 1.36982530e-01
-4.67987984e-01 6.46139383e-01 2.66914338e-01 9.94398445e-02
-7.09819138e-01 -8.90514076e-01 -9.53835607e-01 -4.55563277e-01
-3.94914687e-01 9.44489896e-01 5.52837372e-01 -5.25008857... | [7.008459568023682, -0.02646215446293354] |
7f10f894-90b4-44b9-9453-7fd08d5c7119 | micro-net-a-unified-model-for-segmentation-of | 1804.08145 | null | http://arxiv.org/abs/1804.08145v2 | http://arxiv.org/pdf/1804.08145v2.pdf | Micro-Net: A unified model for segmentation of various objects in microscopy images | Object segmentation and structure localization are important steps in
automated image analysis pipelines for microscopy images. We present a
convolution neural network (CNN) based deep learning architecture for
segmentation of objects in microscopy images. The proposed network can be used
to segment cells, nuclei and g... | ['Simon Graham', 'Michael Khan', 'Nasir M. Rajpoot', 'Muhammad Shaban', 'David Epstein', 'Stella Pelengaris', 'Shan E Ahmed Raza', 'Linda Cheung'] | 2018-04-22 | null | null | null | null | ['multi-tissue-nucleus-segmentation'] | ['medical'] | [ 4.08917755e-01 -9.70464125e-02 3.37167263e-01 -6.63477361e-01
-5.26457310e-01 -6.43976212e-01 2.24788964e-01 2.61779577e-01
-1.19647217e+00 5.21405458e-01 -6.08027101e-01 -1.20625585e-01
2.70994306e-01 -5.34817517e-01 -6.39206111e-01 -1.03328526e+00
1.16811410e-01 3.70171636e-01 6.49633050e-01 2.77139783... | [14.592484474182129, -3.1331982612609863] |
3c4684f1-a853-41ec-b5c0-3171d3d9ddd0 | mbt-a-memory-based-part-of-speech-tagger | cmp-lg/9607012 | null | https://arxiv.org/abs/cmp-lg/9607012v1 | https://arxiv.org/pdf/cmp-lg/9607012v1.pdf | MBT: A Memory-Based Part of Speech Tagger-Generator | We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when ... | ['Steven Gillis', 'Peter Berck', 'Jakub Zavrel', 'Walter Daelemans'] | 1996-07-11 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 2.22702906e-01 2.96306044e-01 -2.66260117e-01 -3.63103837e-01
-1.18282640e+00 -6.73510313e-01 6.36743307e-01 6.91892087e-01
-5.19505382e-01 1.07324409e+00 1.11001268e-01 -6.48533463e-01
-5.05105734e-01 -8.27047050e-01 -2.80940115e-01 -6.43754661e-01
-2.27310076e-01 1.02481687e+00 7.28697002e-01 -1.65942177... | [10.220121383666992, 9.872031211853027] |
9199e6c2-eef8-41bf-903f-bc238056d27c | dsi-updating-transformer-memory-with-new | 2212.09744 | null | https://arxiv.org/abs/2212.09744v1 | https://arxiv.org/pdf/2212.09744v1.pdf | DSI++: Updating Transformer Memory with New Documents | Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive becau... | ['Donald Metzler', 'Emma Strubell', 'Marc Najork', 'Jinfeng Rao', 'Vinh Q. Tran', 'Mostafa Dehghani', 'Yi Tay', 'Jai Gupta', 'Sanket Vaibhav Mehta'] | 2022-12-19 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 0.538778 -0.03259462 -0.12807411 -0.22552375 -1.286987 -0.81930363
0.6507446 0.2128698 -0.9233722 0.64680976 0.04342129 -0.30046952
-0.34060523 -0.633627 -1.1354699 -0.41051477 -0.085579 1.0498921
0.5556187 -0.35135955 0.47523004 0.32165718 -1.7447184 0.19345267
0.8253717 0.77185124 0.4... | [11.376113891601562, 7.64998197555542] |
13a9388f-3264-4cb6-bba2-2f43752b2a54 | advanced-medical-image-representation-for | 2305.15411 | null | https://arxiv.org/abs/2305.15411v1 | https://arxiv.org/pdf/2305.15411v1.pdf | Advanced Medical Image Representation for Efficient Processing and Transfer in Multisite Clouds | An important topic in medical research is the process of improving the images obtained from medical devices. As a consequence, there is also a need to improve medical image resolution and analysis. Another issue in this field is the large amount of stored medical data [16]. Human brain databases at medical institutes, ... | ['Ciprian-Octavian Truică', 'Elena-Simona Apostol'] | 2023-04-29 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 1.83977619e-01 -2.12864250e-01 1.13024302e-01 -5.49329758e-01
-8.37200582e-02 4.34673904e-03 -1.36982556e-02 9.23761368e-01
-8.99180710e-01 4.63574767e-01 9.53397807e-03 -1.07083425e-01
-3.63011032e-01 -1.42676163e+00 -3.68792325e-01 -6.99217856e-01
1.23825550e-01 6.14739776e-01 3.36312592e-01 1.78596154... | [14.236849784851074, -1.6791952848434448] |
bd503126-1a51-4421-a55d-1d7268120f64 | generalizing-unmasking-for-short-texts | null | null | https://aclanthology.org/N19-1068 | https://aclanthology.org/N19-1068.pdf | Generalizing Unmasking for Short Texts | Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allo... | ['Benno Stein', 'Matthias Hagen', 'Martin Potthast', 'Janek Bevendorff'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['authorship-verification'] | ['natural-language-processing'] | [ 4.19055253e-01 1.32328272e-01 -1.95362940e-01 -1.43319845e-01
-8.49706531e-01 -9.89055276e-01 7.64736593e-01 4.52165246e-01
-5.71544170e-01 8.68218303e-01 -3.69929612e-01 -5.34326315e-01
-1.95888743e-01 -6.36643052e-01 -3.74533236e-01 -4.90515471e-01
4.08854336e-01 7.93514490e-01 3.91252249e-01 1.67118162... | [9.57089614868164, 10.605074882507324] |
4a8b92d5-d8f4-4127-860c-5b7169b457a7 | learning-phase-mask-for-privacy-preserving | null | null | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/7139_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670497.pdf | Learning Phase Mask for Privacy-Preserving Passive Depth Estimation | With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others. However, severe concerns regarding the privacy implications o... | ['Francesco Pittaluga', 'Manmohan Chandraker', 'Ashok Veeraraghavan', 'Xiang Yu', 'Yi-Hsuan Tsai', 'Giovanni Milione', 'Zaid Tasneem'] | 2022-11-13 | null | null | null | european-conference-on-computer-vision-eccv-1 | ['depth-estimation', 'autonomous-navigation', 'face-identification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.51036537e-01 3.56995881e-01 6.09612800e-02 -4.75339264e-01
-6.07074916e-01 -1.12278008e+00 4.43771213e-01 -3.05568188e-01
-5.22763193e-01 4.10525501e-01 1.60930425e-01 -3.08206737e-01
4.48846593e-02 -2.32637882e-01 -4.86476809e-01 -5.04592478e-01
2.67329901e-01 -2.65530437e-01 -6.09518476e-02 2.66704082... | [12.730330467224121, 0.7480543851852417] |
6238a491-02c3-4ecc-aca6-ff13d1ee6db8 | feature-disentanglement-learning-with | 2212.09498 | null | https://arxiv.org/abs/2212.09498v1 | https://arxiv.org/pdf/2212.09498v1.pdf | Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification | In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames. Existing methods tend to focus only on how to use temporal information, which often leads to networks being fooled by similar appearances and same backgrounds. In this paper, we propose... | ['Sangyoun Lee', 'MyeongAh Cho', 'Minjung Kim'] | 2022-12-16 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 7.73376971e-02 -4.55632448e-01 4.87189293e-02 -3.35308015e-01
-1.76718026e-01 -4.03870791e-01 7.42900968e-01 -1.23024307e-01
-5.69329679e-01 5.50656617e-01 1.85949624e-01 4.21254039e-01
-1.86879169e-02 -5.30933559e-01 -2.23134488e-01 -7.00080633e-01
-6.06520399e-02 1.44206006e-02 2.79395670e-01 -1.62238255... | [14.673478126525879, 0.9669617414474487] |
50e71e4c-8016-450b-8139-3bb5ac2434b3 | hierarchical-attention-networks-for-document | null | null | https://aclanthology.org/N16-1174 | https://aclanthology.org/N16-1174.pdf | Hierarchical Attention Networks for Document Classification | null | ['Xiaodong He', 'Chris Dyer', 'Zichao Yang', 'Alex Smola', 'Eduard Hovy', 'Diyi Yang'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['citation-intent-classification'] | ['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... | [-7.290099620819092, 3.6119353771209717] |
6755d77a-54fa-4ccd-b49f-1da90df85bad | submodular-maximization-through-barrier | 2002.03523 | null | https://arxiv.org/abs/2002.03523v1 | https://arxiv.org/pdf/2002.03523v1.pdf | Submodular Maximization Through Barrier Functions | In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but also provides the state of the art guarantee. More precisely, for maximizing a m... | ['Ehsan Kazemi', 'Ashwinkumar Badanidiyuru', 'Amin Karbasi', 'Jan Vondrak'] | 2020-02-10 | null | http://proceedings.neurips.cc/paper/2020/hash/061412e4a03c02f9902576ec55ebbe77-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/061412e4a03c02f9902576ec55ebbe77-Paper.pdf | neurips-2020-12 | ['movie-recommendation'] | ['miscellaneous'] | [ 6.50871992e-02 4.52418268e-01 -5.67278266e-01 -2.02585056e-01
-8.65095377e-01 -7.08457470e-01 -3.55759799e-01 5.17227232e-01
-5.90871453e-01 9.86273408e-01 -3.49335335e-02 -1.36132315e-01
-6.46836281e-01 -7.45335937e-01 -9.18071926e-01 -6.08776629e-01
-3.57281595e-01 5.05693853e-01 -2.08528921e-01 -3.59526038... | [6.558967590332031, 4.8821940422058105] |
31cfb9de-33a3-4cbd-9498-acb51bd21b46 | improving-clinical-document-understanding-on | 2012.04005 | null | https://arxiv.org/abs/2012.04005v1 | https://arxiv.org/pdf/2012.04005v1.pdf | Improving Clinical Document Understanding on COVID-19 Research with Spark NLP | Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We present a clinical text mining system that improves on previous efforts in three ways. First, it can recognize over 100 different entity types in... | ['David Talby', 'Veysel Kocaman'] | 2020-12-07 | null | null | null | null | ['clinical-concept-extraction', 'clinical-assertion-status-detection'] | ['medical', 'natural-language-processing'] | [-3.27903062e-01 -1.20360866e-01 -2.65630215e-01 -1.86159045e-01
-6.32710099e-01 -6.57049716e-01 3.42740715e-01 1.29291308e+00
-6.62053227e-01 7.42339373e-01 5.59883058e-01 -7.20420778e-01
-1.05735347e-01 -7.25987792e-01 -2.69015223e-01 -3.50164235e-01
-6.00588143e-01 6.58052981e-01 -1.99784741e-01 1.42308518... | [8.465924263000488, 8.716089248657227] |
1023d7a2-a11f-4142-a41f-8fe0304c6286 | qa4qg-using-question-answering-to-constrain | 2202.06538 | null | https://arxiv.org/abs/2202.06538v1 | https://arxiv.org/pdf/2202.06538v1.pdf | QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation | Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG has focused on exploring graph-based networks to equip the traditional Sequence-to-sequence framework with reasoning ability. However, these ... | ['Pascale Fung', 'Peng Xu', 'Dan Su'] | 2022-02-14 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 5.26950508e-03 8.01098943e-01 3.45148683e-01 -8.68739784e-02
-1.09975982e+00 -7.52057076e-01 7.65815377e-01 2.28647679e-01
-2.15145305e-01 9.12868202e-01 5.25040388e-01 -8.07736337e-01
-2.71571666e-01 -1.18245280e+00 -6.06321454e-01 2.01864481e-01
2.82668173e-01 7.94678867e-01 6.83928728e-01 -9.75665212... | [11.27752685546875, 8.148984909057617] |
420ecd13-fb9c-4148-9e7c-231f82de9738 | query-translation-for-cross-language | null | null | https://aclanthology.org/W16-3716 | https://aclanthology.org/W16-3716.pdf | Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters | In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve. We propose a technique towards solving this problem with the help of multilingual word clusters obtained from multilingual word embeddings. We use word embeddings of th... | ['Paheli Bhattacharya', 'Sudeshna Sarkar', 'Pawan Goyal'] | 2016-12-01 | null | null | null | ws-2016-12 | ['multilingual-word-embeddings'] | ['methodology'] | [-3.90306592e-01 -6.15245581e-01 -2.75747567e-01 1.96626380e-01
-1.23180842e+00 -9.54738915e-01 8.91727448e-01 5.93875945e-01
-8.77429366e-01 5.52703440e-01 5.46379387e-01 -6.74898505e-01
-1.03976063e-01 -5.94361007e-01 -3.56736869e-01 -4.01540309e-01
2.68718898e-01 9.16106522e-01 1.57049611e-01 -4.84419644... | [11.15380573272705, 9.943927764892578] |
efe57c52-0e7c-434a-8b4b-4c06cda1f7fb | diffalign-few-shot-learning-using-diffusion | 2212.05404 | null | https://arxiv.org/abs/2212.05404v1 | https://arxiv.org/pdf/2212.05404v1.pdf | DiffAlign : Few-shot learning using diffusion based synthesis and alignment | We address the problem of few-shot classification where the goal is to learn a classifier from a limited set of samples. While data-driven learning is shown to be effective in various applications, learning from less data still remains challenging. To address this challenge, existing approaches consider various data au... | ['Rama Chellappa', 'Anirban Roy', 'Ketul Shah', 'Anshul Shah', 'Aniket Roy'] | 2022-12-11 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 4.25403118e-01 1.39790103e-02 -2.24359542e-01 -4.85651314e-01
-1.08868563e+00 -2.32639953e-01 9.43894744e-01 -2.36863568e-01
-3.93907517e-01 9.12738442e-01 -1.64686665e-02 1.63963526e-01
1.68585494e-01 -7.67391860e-01 -8.70835543e-01 -6.12214565e-01
5.06328642e-01 7.12576568e-01 2.77814716e-01 -2.41728246... | [10.012258529663086, 2.968355894088745] |
cb16274e-cf59-44a9-9a12-8b33b32e046b | neural-event-extraction-from-movies | null | null | https://aclanthology.org/W18-1507 | https://aclanthology.org/W18-1507.pdf | Neural Event Extraction from Movies Description | We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of {``}who{''}, {``}did what{''} {``}to whom{''}, {``}where{''}, and {``}when{''}. We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic pars... | ["Dejan Jovanovi{\\'c}", 'Alex Tozzo', 'Mohamed Amer'] | 2018-06-01 | null | null | null | ws-2018-6 | ['visual-storytelling', 'story-completion'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.54549950e-01 4.59105283e-01 2.91089684e-01 -4.92812097e-01
-9.72453296e-01 -7.81600416e-01 6.84504628e-01 6.54378653e-01
-3.17191720e-01 9.27967727e-01 6.90036535e-01 -3.80154341e-01
-1.39139056e-01 -9.10941601e-01 -7.82351077e-01 -1.85973197e-01
-1.61704659e-01 6.10272229e-01 4.89316076e-01 -4.47474808... | [11.058633804321289, 8.939070701599121] |
7da48562-98f0-4796-b9df-16f47251cb92 | gauge-equivariant-neural-networks-for-2-1d-u | 2211.03198 | null | https://arxiv.org/abs/2211.03198v1 | https://arxiv.org/pdf/2211.03198v1.pdf | Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation | Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science. In quantum simulations of lattice gauge theory, an important step is to construct a wave function that obeys gauge symmetry. In this paper, we have developed gauge equivar... | ['Bryan K. Clark', 'James Stokes', 'Shunyue Yuan', 'Di Luo'] | 2022-11-06 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 2.11316556e-01 -2.71277338e-01 1.64019182e-01 -1.99920878e-01
-6.05237126e-01 -2.70074129e-01 6.45878375e-01 -2.86454111e-01
-6.21057332e-01 1.16926467e+00 5.93849532e-02 -4.52755213e-01
-3.16402256e-01 -1.44185877e+00 -4.53056037e-01 -1.26739216e+00
-1.35284543e-01 1.00546873e+00 -1.54193372e-01 -7.02664018... | [5.384171485900879, 5.126083850860596] |
ba570014-e143-4038-a6dd-fc3c925ea853 | unsupervised-semantic-segmentation-of-3d | 2304.08965 | null | https://arxiv.org/abs/2304.08965v2 | https://arxiv.org/pdf/2304.08965v2.pdf | Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering | Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this paper, we take the first attempt for fully unsupervised semantic segmentation of point clouds, whic... | ['Hongbin Xu', 'Zisheng Chen'] | 2023-04-18 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 1.84805438e-01 2.49458373e-01 1.33938223e-01 -3.79642546e-01
-8.95624697e-01 -5.72464943e-01 7.08574355e-01 3.74856651e-01
-2.05438063e-01 2.52600163e-01 -3.51074427e-01 -5.43539152e-02
-6.56835437e-02 -7.47672975e-01 -6.87805474e-01 -5.37729025e-01
1.14211924e-01 1.14424717e+00 7.58072674e-01 3.59652787... | [8.0349760055542, -3.1140568256378174] |
dc35d9c3-826d-4934-9148-da3e9ed980ee | dr3-value-based-deep-reinforcement-learning-1 | 2112.04716 | null | https://arxiv.org/abs/2112.04716v1 | https://arxiv.org/pdf/2112.04716v1.pdf | DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization | Despite overparameterization, deep networks trained via supervised learning are easy to optimize and exhibit excellent generalization. One hypothesis to explain this is that overparameterized deep networks enjoy the benefits of implicit regularization induced by stochastic gradient descent, which favors parsimonious so... | ['Sergey Levine', 'George Tucker', 'Aaron Courville', 'Tengyu Ma', 'Rishabh Agarwal', 'Aviral Kumar'] | 2021-12-09 | dr3-value-based-deep-reinforcement-learning | https://openreview.net/forum?id=POvMvLi91f | https://openreview.net/pdf?id=POvMvLi91f | iclr-2022-4 | ['d4rl'] | ['robots'] | [-1.78940073e-02 5.57033777e-01 -3.05695713e-01 -2.12720394e-01
-3.84599656e-01 -7.52902329e-01 3.73441011e-01 -3.66666585e-01
-6.03066683e-01 1.10312808e+00 -5.31681534e-03 -3.25152278e-01
-4.43595052e-01 -5.65139115e-01 -8.85260999e-01 -9.12695348e-01
-1.98106825e-01 2.16930509e-01 -5.19343801e-02 -4.34030682... | [4.212368965148926, 2.1013922691345215] |
fb768012-c5bd-4d0f-aca8-1a4490082765 | why-the-rich-get-richer-on-the-balancedness | 2201.12697 | null | https://arxiv.org/abs/2201.12697v2 | https://arxiv.org/pdf/2201.12697v2.pdf | Why the Rich Get Richer? On the Balancedness of Random Partition Models | Random partition models are widely used in Bayesian methods for various clustering tasks, such as mixture models, topic models, and community detection problems. While the number of clusters induced by random partition models has been studied extensively, another important model property regarding the balancedness of p... | ['Huiyan Sang', 'Changwoo J. Lee'] | 2022-01-30 | null | null | null | null | ['topic-models', 'entity-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-3.43434513e-02 3.04833561e-01 -4.81894344e-01 -3.07330728e-01
-2.07811043e-01 -5.57206929e-01 7.76593864e-01 2.23438129e-01
-1.80091243e-02 7.79331744e-01 1.49082303e-01 -2.01530904e-01
-7.91655123e-01 -1.11165738e+00 -3.01387280e-01 -8.59741628e-01
-5.29602394e-02 1.08212423e+00 4.16884571e-01 1.25622556... | [7.004859924316406, 5.2276291847229] |
01410ca4-be79-4484-be51-4cb0e7cb3371 | multi-scale-distributed-representation-for | 1811.12069 | null | http://arxiv.org/abs/1811.12069v1 | http://arxiv.org/pdf/1811.12069v1.pdf | Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging | Recently machine learning algorithms based on deep layered artificial neural
networks (DNNs) have been applied to a wide variety of high energy physics
problems such as jet tagging or event classification. We explore a simple but
effective preprocessing step which transforms each real-valued observational
quantity or i... | ['Jason Lee', 'Inkyu Park', 'Sangnam Park'] | 2018-11-29 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-7.05698580e-02 -2.22757366e-02 -3.74279320e-01 -7.68064439e-01
-5.51589251e-01 -5.82899094e-01 7.34276175e-01 6.24156058e-01
-6.22291207e-01 8.45823884e-01 -7.62595050e-03 -6.68410003e-01
-2.66857147e-01 -1.20178974e+00 -7.63626158e-01 -7.41467416e-01
-1.81976229e-01 9.83224154e-01 4.40480351e-01 -1.39978006... | [15.70044231414795, 2.918612241744995] |
34fa3222-9cb8-4159-8825-106e556722af | non-contact-photoplethysmogram-and | 1902.05194 | null | http://arxiv.org/abs/1902.05194v1 | http://arxiv.org/pdf/1902.05194v1.pdf | Non-contact photoplethysmogram and instantaneous heart rate estimation from infrared face video | Extracting the instantaneous heart rate (iHR) from face videos has been well
studied in recent years. It is well known that changes in skin color due to
blood flow can be captured using conventional cameras. One of the main
limitations of methods that rely on this principle is the need of an
illumination source. Moreov... | ['Hau-Tieng Wu', 'Natalia Martinez', 'Martin Bertran', 'Guillermo Sapiro'] | 2019-02-14 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 4.10802662e-01 -9.56116915e-02 -6.05005585e-02 -2.23690376e-01
-1.13269828e-01 -4.42743748e-01 5.55794276e-02 -1.60383761e-01
-5.27481496e-01 7.27157295e-01 -1.24495640e-01 1.54175773e-01
1.98404595e-01 -6.24470413e-01 -2.00869352e-01 -8.16721499e-01
-2.56886426e-02 -2.76993126e-01 -9.12315696e-02 2.08518282... | [13.869243621826172, 2.7272183895111084] |
2672f267-8fe4-4adb-b8b3-97e2727c8d21 | scatterformer-locally-invariant-scattering | 2304.14919 | null | https://arxiv.org/abs/2304.14919v1 | https://arxiv.org/pdf/2304.14919v1.pdf | ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges | Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-gra... | ['Yuguo Yu', 'Tian Luo', 'Yi Wang', 'Jun Li', 'Ruizhe Zheng'] | 2023-04-26 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 1.81874558e-01 -4.05940443e-01 5.21460593e-01 -2.34012887e-01
-1.06535423e+00 -5.59589028e-01 2.16179326e-01 6.40287250e-02
1.01097003e-01 7.03568161e-01 4.90245342e-01 1.16692953e-01
-7.41067350e-01 -1.54945478e-01 -3.11678201e-01 -1.09899366e+00
-6.84586883e-01 1.68319680e-02 -1.57748982e-01 1.02011129... | [13.192484855651855, 3.4984853267669678] |
4af59bc1-e255-4e6b-96f4-a5084df534c6 | multi-instrument-music-synthesis-with | 2206.05408 | null | https://arxiv.org/abs/2206.05408v3 | https://arxiv.org/pdf/2206.05408v3.pdf | Multi-instrument Music Synthesis with Spectrogram Diffusion | An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific models that offer detailed control of only specific instruments, or raw wavef... | ['Jesse Engel', 'Ethan Manilow', 'Josh Gardner', 'Neil Zeghidour', 'Adam Roberts', 'Ian Simon', 'Curtis Hawthorne'] | 2022-06-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.82198215e-01 1.39220744e-01 2.81380385e-01 1.00228479e-02
-1.01480055e+00 -1.20703590e+00 6.98974967e-01 -5.56640565e-01
1.80518776e-01 6.80178404e-01 4.12245542e-01 -5.79594038e-02
-2.57514089e-01 -8.24585795e-01 -6.88625515e-01 -6.45696044e-01
2.46357080e-02 5.82679391e-01 -7.84887187e-03 -3.74071985... | [15.670188903808594, 5.86723518371582] |
af045ad4-5947-4e15-a169-18fcc8328eba | learning-algebraic-representation-for | null | null | https://openreview.net/forum?id=jQSBcVURlpW | https://openreview.net/pdf?id=jQSBcVURlpW | Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning | Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate (Fodor et al., 1988; Fodor &... | ['Song-Chun Zhu', 'Ying Nian Wu', 'Yixin Zhu', 'Baoxiong Jia', 'Sirui Xie', 'Chi Zhang'] | 2021-01-01 | null | null | null | null | ['abstract-algebra', 'systematic-generalization'] | ['reasoning', 'reasoning'] | [ 2.49330640e-01 5.09314060e-01 1.05881512e-01 -1.99178919e-01
1.08641014e-01 -4.62181002e-01 7.13427663e-01 2.94075906e-01
-2.51130939e-01 2.21179649e-01 7.32695386e-02 -6.55941546e-01
-7.78278112e-01 -7.75479972e-01 -4.89023268e-01 -4.38944042e-01
-4.67793532e-02 7.49996781e-01 1.22736193e-01 -6.49620593... | [10.6093111038208, 2.2774100303649902] |
aeb2f239-6389-4080-9024-2382772364ce | on-adversarial-examples-and-stealth-attacks | 2004.04479 | null | https://arxiv.org/abs/2004.04479v1 | https://arxiv.org/pdf/2004.04479v1.pdf | On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems | In this work we present a formal theoretical framework for assessing and analyzing two classes of malevolent action towards generic Artificial Intelligence (AI) systems. Our results apply to general multi-class classifiers that map from an input space into a decision space, including artificial neural networks used in ... | ['Ivan Y. Tyukin', 'Desmond J. Higham', 'Alexander N. Gorban'] | 2020-04-09 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 5.11153102e-01 4.15572643e-01 3.29445332e-01 1.55579671e-01
-8.69740173e-02 -1.01867366e+00 8.63035798e-01 2.50285268e-01
-4.12421674e-01 7.54324555e-01 -5.02439022e-01 -5.81116498e-01
-3.23858231e-01 -1.03727472e+00 -9.65227485e-01 -1.28844106e+00
-8.21128339e-02 5.47540247e-01 1.55973896e-01 -3.92166972... | [5.717202186584473, 7.652614593505859] |
9b0d4614-88df-468e-a568-678878f59dc8 | introducing-mantis-a-novel-multi-domain | 1912.04639 | null | https://arxiv.org/abs/1912.04639v1 | https://arxiv.org/pdf/1912.04639v1.pdf | Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset | Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs. Previous conceptual work described properties and actions a good agent should exhibit. Unlike them, we present a novel conceptual model defined in terms of conv... | ['Claudia Hauff', 'Gustavo Penha', 'Alexandru Balan'] | 2019-12-10 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.16526075e-01 5.28529942e-01 -5.73987961e-01 -4.22965050e-01
-9.14532244e-01 -7.06842005e-01 1.48675001e+00 1.28359511e-01
-2.33126059e-01 4.62343663e-01 1.07733285e+00 -4.78873193e-01
-4.70669001e-01 -3.06408167e-01 2.56199509e-01 -1.87672690e-01
7.54007772e-02 9.63372767e-01 -2.38455664e-02 -7.84720600... | [12.329024314880371, 7.827620506286621] |
c1cbdbab-8b6d-4fc9-949d-c45e354ee6d4 | gating-revisited-deep-multi-layer-rnns-that-1 | 1911.11033 | null | https://arxiv.org/abs/1911.11033v4 | https://arxiv.org/pdf/1911.11033v4.pdf | Gating Revisited: Deep Multi-layer RNNs That Can Be Trained | We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients. Stacking recurrent units into deep architectures suffers from two major limitations: (i) many recurrent cells ... | ["Stefano D'Aronco", 'Mehmet Ozgur Turkoglu', 'Konrad Schindler', 'Jan Dirk Wegner'] | 2019-11-25 | null | null | null | null | ['sequential-image-classification', 'music-modeling'] | ['computer-vision', 'music'] | [ 3.09491992e-01 -1.15020372e-01 1.60558715e-01 -6.94294348e-02
-1.82886526e-01 -4.61441338e-01 4.58623111e-01 -2.62214113e-02
-5.83949089e-01 7.28361309e-01 4.48902160e-01 -6.17757738e-01
4.59130853e-01 -6.45076215e-01 -8.79282713e-01 -7.37617254e-01
-1.48305506e-01 -9.84840095e-02 4.95842844e-01 -5.18175483... | [10.813361167907715, 6.38623046875] |
b5ed4760-a4ed-402c-961a-8e8d28b30333 | mass-segmentation-in-automated-3-d-breast | 2109.08330 | null | https://arxiv.org/abs/2109.08330v2 | https://arxiv.org/pdf/2109.08330v2.pdf | Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net | Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection. While ABUS has better performance in dense breasts, reading ABUS images is exhausting and time-consuming. So, a computer-aided detection system ... | ['Mohsen Soryani', 'Tao Tan', 'Ehsan Kozegar', 'Hamed Fayyaz'] | 2021-09-17 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.90268114e-01 3.20397049e-01 -3.02604347e-01 -3.89020264e-01
-4.65463310e-01 -5.83650582e-02 1.59756511e-01 4.63069171e-01
-5.24209559e-01 3.88660580e-01 -2.55754054e-01 -7.83470750e-01
1.59509510e-01 -9.00577188e-01 -4.23668593e-01 -7.49587774e-01
-1.76707119e-01 4.89995658e-01 5.57326436e-01 -5.25680324... | [15.189729690551758, -2.4330055713653564] |
2ec0b3e0-f1c1-4001-b8c8-c3e825393a45 | invariant-representation-driven-neural | 2201.07199 | null | https://arxiv.org/abs/2201.07199v5 | https://arxiv.org/pdf/2201.07199v5.pdf | Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging | We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a \emph{well-calibrated} and \emph{powerful enough fea... | ['Aaron Courville', 'Taoli Cheng'] | 2022-01-18 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [ 1.70104563e-01 -2.62035336e-02 -2.59821832e-01 -5.08982241e-01
-9.08131838e-01 -7.35958099e-01 8.82494569e-01 3.59955043e-01
-6.64062023e-01 4.45697933e-01 1.24370465e-02 -4.30964261e-01
-2.79972464e-01 -6.27538323e-01 -4.20379311e-01 -9.56135094e-01
5.56863435e-02 8.90023410e-01 5.23686588e-01 -2.81536460... | [15.689815521240234, 2.9239306449890137] |
5c6e2f0a-ab9d-43dc-892b-9b9ab5a053ed | an-efficient-provably-exact-algorithm-for-the | 2306.12344 | null | https://arxiv.org/abs/2306.12344v1 | https://arxiv.org/pdf/2306.12344v1.pdf | An efficient, provably exact algorithm for the 0-1 loss linear classification problem | Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly se... | ['Max A. Little', 'Xi He'] | 2023-06-21 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 1.70583069e-01 2.64079094e-01 -3.55890483e-01 -3.74262601e-01
-1.17260838e+00 -6.04463458e-01 -1.63359806e-01 4.01036143e-01
-3.56246382e-01 1.01197755e+00 -3.98708940e-01 -4.67358947e-01
-5.97153604e-01 -7.27132201e-01 -4.08545345e-01 -9.94094431e-01
-4.36025351e-01 1.09768653e+00 2.09397018e-01 2.77980324... | [7.598536014556885, 4.257548809051514] |
eda768f7-c97b-42a7-93c3-40ba37f41688 | padgan-a-generative-adversarial-network-for | 2002.11304 | null | https://arxiv.org/abs/2002.11304v5 | https://arxiv.org/pdf/2002.11304v5.pdf | PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs | Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: 1) generated designs lack diversity and do not cover all areas of the design space, 2) it is difficult to explicitly im... | ['Wei Chen', 'Faez Ahmed'] | 2020-02-26 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 7.37422053e-03 2.68084388e-02 -8.86199549e-02 1.37935698e-01
-5.32170475e-01 -7.62148082e-01 2.92994857e-01 -5.02565920e-01
4.35568511e-01 1.09734201e+00 1.76264390e-01 -2.66342342e-01
-3.61732900e-01 -1.18880594e+00 -8.09070289e-01 -6.56548500e-01
3.57694536e-01 5.30011714e-01 -3.64934295e-01 -4.14718896... | [5.810977935791016, 3.2923712730407715] |
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