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
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fee21738-9e1a-4016-85c8-6d77fe479f7a | came-context-aware-mixture-of-experts-for | 2208.07109 | null | https://arxiv.org/abs/2208.07109v3 | https://arxiv.org/pdf/2208.07109v3.pdf | Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation | Scene graph generation (SGG) has gained tremendous progress in recent years. However, its underlying long-tailed distribution of predicate classes is a challenging problem. For extremely unbalanced predicate distributions, existing approaches usually construct complicated context encoders to extract the intrinsic relev... | ['Yangsheng Xu', 'Tin Lun Lam', 'Yuhongze Zhou', 'Liguang Zhou'] | 2022-08-15 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.70853651e-01 2.56601900e-01 -2.37293467e-01 -4.67812747e-01
-5.17752588e-01 -6.05549812e-01 4.80983049e-01 4.25633192e-02
-8.73414204e-02 7.50044823e-01 2.40149096e-01 -3.19550991e-01
-1.07234277e-01 -8.77271771e-01 -8.82110894e-01 -6.25964820e-01
2.63015091e-01 5.94439685e-01 5.96126974e-01 -1.30207434... | [10.27757453918457, 1.7564754486083984] |
d3daaabf-5f96-476f-bca2-f681c17d9d89 | a-vision-transformer-based-approach-to | 2208.07070 | null | https://arxiv.org/abs/2208.07070v2 | https://arxiv.org/pdf/2208.07070v2.pdf | A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals | Rolling bearings are the most crucial components of rotating machinery. Identifying defective bearings in a timely manner may prevent the malfunction of an entire machinery system. The mechanical condition monitoring field has entered the big data phase as a result of the fast advancement of machine parts. When working... | ['Minoru Kuribayashi', 'Asad Malik', 'Aquib Iqbal', 'Aeyan Ashraf', 'Abid Hasan Zim'] | 2022-08-15 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-6.43457100e-02 -3.88032049e-01 2.81760454e-01 -1.05856238e-02
-2.18721420e-01 2.11395264e-01 1.51588976e-01 -1.91883430e-01
-1.73590660e-01 3.49022061e-01 -3.99475455e-01 -8.83711055e-02
-3.23969156e-01 -7.84280717e-01 -2.98844934e-01 -8.01027238e-01
8.13030303e-02 2.84853548e-01 2.22270042e-01 -5.87144732... | [6.970479488372803, 2.182816743850708] |
9229ed87-9bda-4581-b8c3-868e46e04910 | embedding-fourier-for-ultra-high-definition | 2302.11831 | null | https://arxiv.org/abs/2302.11831v1 | https://arxiv.org/pdf/2302.11831v1.pdf | Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement | Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-light image enhancement (LLIE), especially in dealing with the intricate issue of joint luminance enhancement and noise removal while remaini... | ['Chen Change Loy', 'Ruicheng Feng', 'Shangchen Zhou', 'Zhexin Liang', 'Man Zhou', 'Chun-Le Guo', 'Chongyi Li'] | 2023-02-23 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.90713096e-01 -4.36213195e-01 2.82332361e-01 -2.36138731e-01
-7.92358220e-01 -2.15121299e-01 2.89804846e-01 -2.79662758e-01
-5.20892084e-01 5.79300702e-01 1.95680171e-01 -1.39064655e-01
-1.11969993e-01 -8.67505789e-01 -3.91332239e-01 -1.14462066e+00
6.03102967e-02 -5.77632010e-01 3.11452329e-01 -2.43996412... | [10.79545783996582, -2.457303524017334] |
b0345a9f-3761-4d29-bf1d-a9d8b4a44160 | learning-localization-aware-target-confidence | 2204.14093 | null | https://arxiv.org/abs/2204.14093v1 | https://arxiv.org/pdf/2204.14093v1.pdf | Learning Localization-aware Target Confidence for Siamese Visual Tracking | Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by the classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high ta... | ['Zhekang Dong', 'Mingyu Gao', 'Yuxiang Yang', 'Zhiwei He', 'Han Wu', 'Jiahao Nie'] | 2022-04-29 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-5.54113835e-02 -3.15960765e-01 -3.37669641e-01 -3.76753002e-01
-6.94370985e-01 -3.72327387e-01 6.39279544e-01 2.35721067e-01
-5.39086163e-01 5.55444241e-01 -2.09122404e-01 1.23636827e-01
-3.39844339e-02 -4.65194792e-01 -6.49080038e-01 -8.68606389e-01
-1.12395234e-01 2.05077320e-01 7.16232061e-01 1.22644760... | [6.320675849914551, -2.1238369941711426] |
5cfa5407-cd79-4686-a314-cf64ccf2e29a | a-machine-learning-framework-for-authorship | 1912.10204 | null | https://arxiv.org/abs/1912.10204v1 | https://arxiv.org/pdf/1912.10204v1.pdf | A Machine Learning Framework for Authorship Identification From Texts | Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In or... | ['Carolyn Penstein Rose', 'Rahul Radhakrishnan Iyer'] | 2019-12-21 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 4.82099541e-02 -8.35619420e-02 -1.73044801e-01 -1.68601915e-01
-5.70554078e-01 -8.33910406e-01 9.26890790e-01 5.74283242e-01
-6.04121029e-01 7.88901091e-01 1.33248642e-01 -1.40521631e-01
-2.75081098e-01 -5.67842722e-01 -3.22859883e-01 -3.43366057e-01
6.68670356e-01 1.03093088e+00 -1.66791692e-01 -6.21945634... | [9.588364601135254, 10.576342582702637] |
757da7fb-27a4-406b-9dae-2feb5bc30fe3 | taln-at-semeval-2016-task-11-modelling | null | null | https://aclanthology.org/S16-1157 | https://aclanthology.org/S16-1157.pdf | TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features | null | ['Luis Espinosa-Anke', "Ahmed Abura{'}ed", 'Francesco Ronzano', 'Horacio Saggion'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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... | [-7.324227809906006, 3.6887588500976562] |
e2f18758-0c56-46c6-9681-bc4be31eb74a | deep-sequence-models-for-text-classification | 2207.08880 | null | https://arxiv.org/abs/2207.08880v1 | https://arxiv.org/pdf/2207.08880v1.pdf | Deep Sequence Models for Text Classification Tasks | The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed w... | ['Saminu Mohammad Aliyu', 'Musa Bello', 'Abdulkadir Abdullahi', 'Ahmad Muhammad Aminu', 'Abdulrasheed Mustapha', 'Shamsuddeen Hassan Muhammad', 'Sun Yiming', 'Saheed Salahudeen Abdullahi'] | 2022-07-18 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.42411837e-01 -1.22973263e-01 -3.63557965e-01 -5.51877379e-01
-1.72087729e-01 -6.02668464e-01 5.14156401e-01 3.34038824e-01
-5.38780987e-01 9.41945076e-01 3.63130212e-01 -5.75313985e-01
3.30280401e-02 -8.64883482e-01 -4.58720177e-01 -2.64465034e-01
-5.40510193e-03 5.73428094e-01 -3.78573686e-02 -6.52561009... | [10.549797058105469, 8.113161087036133] |
c01532a6-53c9-46cc-b2d7-1aded94c27ba | linguistically-routing-capsule-network-for | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cao_Linguistically_Routing_Capsule_Network_for_Out-of-Distribution_Visual_Question_Answering_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cao_Linguistically_Routing_Capsule_Network_for_Out-of-Distribution_Visual_Question_Answering_ICCV_2021_paper.pdf | Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering | Generalization on out-of-distribution (OOD) test data is an essential but underexplored topic in visual question answering. Current state-of-the-art VQA models often exploit the biased correlation between data and labels, which results in a large performance drop when the test and training data have different distr... | ['Liang Lin', 'Xiaodan Liang', 'Keze Wang', 'Wentao Wan', 'Qingxing Cao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['novel-concepts'] | ['reasoning'] | [-1.88645348e-01 4.45814997e-01 -5.72966179e-03 -5.22256970e-01
-6.35746837e-01 -9.46579635e-01 5.36739945e-01 3.18513334e-01
-2.48433843e-01 4.68294233e-01 4.11721319e-01 -1.14458814e-01
1.41240135e-01 -7.36097336e-01 -8.22834849e-01 -5.67727685e-01
-1.66432351e-01 6.89526379e-01 3.31733733e-01 -4.07763645... | [10.87995719909668, 1.8127361536026] |
ad30eb76-ba40-418e-8278-6fda8fae248e | spatial-temporal-multi-task-learning-for | 1811.06665 | null | http://arxiv.org/abs/1811.06665v1 | http://arxiv.org/pdf/1811.06665v1.pdf | Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction | Understanding and accurately predicting within-field spatial variability of
crop yield play a key role in site-specific management of crop inputs such as
irrigation water and fertilizer for optimized crop production. However, such a
task is challenged by the complex interaction between crop growth and
environmental and... | ['Wenxuan Guo', 'Hanxiang Du', 'Zhou Yang', 'Fang Jin', 'Zhe Lin', 'Jia Zhen', 'Long Nguyen'] | 2018-11-16 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-1.89806949e-02 -9.07089412e-01 -6.92121625e-01 -2.19673395e-01
-6.30487144e-01 -6.94690943e-01 -3.96814123e-02 6.52287841e-01
-1.72560975e-01 9.92131293e-01 1.10206842e-01 -6.70172751e-01
-4.39855218e-01 -1.17708504e+00 -7.31447339e-01 -8.78959715e-01
-3.51657957e-01 -2.29648679e-01 -1.15962796e-01 -3.50925803... | [9.37141227722168, -1.597701072692871] |
d7807c15-0aa2-4750-b83a-341ec82bdc00 | kb4rec-a-dataset-for-linking-knowledge-bases | 1807.11141 | null | https://arxiv.org/abs/1807.11141v7 | https://arxiv.org/pdf/1807.11141v7.pdf | KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems | To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize pr... | ['Ji-Rong Wen', 'Siqi Ouyang', 'Hongjian Dou', 'Wayne Xin Zhao', 'Jin Huang', 'Gaole He'] | 2018-07-30 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-7.56520152e-01 2.32995465e-01 -8.84942472e-01 -3.41890514e-01
-5.21965981e-01 -7.39907324e-01 3.46037596e-01 1.42345443e-01
-3.39971423e-01 1.11921549e+00 8.09051454e-01 -4.37676348e-02
-7.00527191e-01 -1.12109995e+00 -7.37712204e-01 -2.33599573e-01
-5.21660689e-03 5.25354683e-01 6.85249746e-01 -8.43590915... | [9.983725547790527, 5.82672119140625] |
bf826217-cb4d-400b-baab-f69641a825a4 | uncertainty-quantification-techniques-for | 2201.02067 | null | https://arxiv.org/abs/2201.02067v1 | https://arxiv.org/pdf/2201.02067v1.pdf | Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application | Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and Earth. These systems are tightly coupled and not well understood. This creates a need... | ['Piyush M. Mehta', 'Richard J. Licata'] | 2022-01-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.89811581e-01 -2.27850564e-02 -1.48385301e-01 -3.53414595e-01
-6.24783039e-01 -5.79433501e-01 9.13516462e-01 -6.95349425e-02
-2.38353267e-01 1.39392459e+00 -2.38138050e-01 -7.04714715e-01
-6.74957782e-02 -8.12687695e-01 -4.91227865e-01 -8.62616122e-01
-1.07231893e-01 8.58667910e-01 3.42951626e-01 -4.82863575... | [6.594473361968994, 3.123544931411743] |
0112a9f9-7bd8-482b-b908-614baeb2853c | an-attention-based-graph-neural-network-for | 1912.10832 | null | https://arxiv.org/abs/1912.10832v1 | https://arxiv.org/pdf/1912.10832v1.pdf | An Attention-based Graph Neural Network for Heterogeneous Structural Learning | In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector spac... | ['Yu-Cheng Lin', 'Zang Li', 'Hantao Guo', 'Xiaoqing Yang', 'Jieping Ye', 'Huiting Hong'] | 2019-12-19 | null | null | null | null | ['heterogeneous-node-classification'] | ['graphs'] | [-9.62723717e-02 4.06969935e-01 -3.79659027e-01 -8.65846723e-02
-2.95991451e-01 -3.80803555e-01 4.55921382e-01 1.74514428e-01
-7.18140081e-02 4.57417548e-01 5.81491947e-01 -1.73732236e-01
-4.11727369e-01 -1.27559853e+00 -8.05771768e-01 -4.96323735e-01
-4.65053469e-02 5.09204745e-01 1.48517892e-01 -3.14664930... | [7.392643928527832, 6.375277519226074] |
57b920cb-7936-4623-9697-96ffdea43599 | auto-fedavg-learnable-federated-averaging-for | 2104.10195 | null | https://arxiv.org/abs/2104.10195v1 | https://arxiv.org/pdf/2104.10195v1.pdf | Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation | Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on... | ['Holger Roth', 'Alan Yuille', 'Anna Ierardi', 'Gianpaolo Carrafiello', 'Elvira Stellato', 'Francesca Patella', 'Bradford Wood', 'Baris Turkbey', 'Evrim Turkbey', 'Stephanie Harmon', 'Peng An', 'Hitoshi Mori', 'Hirofumi Obinata', 'Daguang Xu', 'Andriy Myronenko', 'Wenqi Li', 'Dong Yang', 'Yingda Xia'] | 2021-04-20 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-5.28123342e-02 -1.75349340e-01 -3.66771221e-01 -6.15866840e-01
-9.49962378e-01 -6.43729091e-01 2.19486415e-01 6.75317869e-02
-5.44936359e-01 6.23875976e-01 1.49333388e-01 -4.63672251e-01
-2.63709515e-01 -4.97288257e-01 -6.97386861e-01 -1.03875566e+00
-2.02301636e-01 6.71759963e-01 -1.52113698e-02 5.79539061... | [6.099250316619873, 6.472746849060059] |
981e375b-5bae-4f52-8272-daaefeb9aed8 | orthoseg-a-deep-multimodal-convolutional | 1811.07859 | null | http://arxiv.org/abs/1811.07859v2 | http://arxiv.org/pdf/1811.07859v2.pdf | OrthoSeg: A Deep Multimodal Convolutional Neural Network for Semantic Segmentation of Orthoimagery | This paper addresses the task of semantic segmentation of orthoimagery using
multimodal data e.g. optical RGB, infrared and digital surface model. We
propose a deep convolutional neural network architecture termed OrthoSeg for
semantic segmentation using multimodal, orthorectified and coregistered data.
We also propose... | ['Kumar Shreshtha', 'Pankaj Bodani', 'Shashikant Sharma'] | 2018-11-19 | null | null | null | null | ['2d-semantic-segmentation', 'semantic-segmentation-of-orthoimagery'] | ['computer-vision', 'medical'] | [ 5.31142652e-01 2.11437464e-01 2.13052958e-01 -5.60232401e-01
-8.06266010e-01 -4.38864708e-01 2.69622862e-01 -2.11302340e-01
-5.08751750e-01 4.00364637e-01 -1.47504032e-01 -2.12012514e-01
-2.12770939e-01 -1.06227803e+00 -8.41926873e-01 -6.64997816e-01
-2.36198723e-01 4.22863126e-01 2.25245045e-03 -1.83085993... | [9.17192268371582, -1.396296739578247] |
fa618ba9-cef9-4d09-add8-1591b2de2771 | analysis-of-generalized-bregman-surrogate | 2112.09191 | null | https://arxiv.org/abs/2112.09191v1 | https://arxiv.org/pdf/2112.09191v1.pdf | Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning | Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex. This paper focuses on a broad Bregman-surrogate algorithm framework including the local linear approximation, mirror descent, iterative thresholding, DC programming and many others as particular instan... | ['Jiuwu Jin', 'Zhifeng Wang', 'Yiyuan She'] | 2021-12-16 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-2.00159237e-01 1.04030676e-01 -3.66443157e-01 -2.30010048e-01
-1.36244142e+00 -3.71236265e-01 1.84134796e-01 -7.90845901e-02
-2.38371655e-01 1.24914718e+00 6.17012568e-02 1.72295630e-01
-5.17123997e-01 -3.36633891e-01 -9.25702631e-01 -1.12901962e+00
-2.70782053e-01 5.82089067e-01 -2.63740450e-01 7.09363222... | [6.887960910797119, 4.350683689117432] |
5047ecd0-d2d0-4a87-864c-9137430a4321 | towards-explainable-conversational | 2305.18363 | null | https://arxiv.org/abs/2305.18363v1 | https://arxiv.org/pdf/2305.18363v1.pdf | Towards Explainable Conversational Recommender Systems | Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which ... | ['Zhaochun Ren', 'Zhumin Chen', 'Pengjie Ren', 'Weiwei Sun', 'Shuo Zhang', 'Shuyu Guo'] | 2023-05-27 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [-9.20070261e-02 7.72476315e-01 -1.37906477e-01 -6.51878297e-01
-7.97500491e-01 -7.07731187e-01 6.61281168e-01 -4.66312468e-01
2.09418297e-01 7.48728871e-01 8.72575998e-01 -6.06792867e-01
-2.25416765e-01 -4.74374026e-01 -3.21074933e-01 -2.46984400e-02
3.75070214e-01 6.71681345e-01 -1.15411855e-01 -7.09821105... | [12.281087875366211, 7.482175350189209] |
376a6313-0a46-4e06-b860-b632ace8df6e | convolutional-capsule-network-for | 1804.08376 | null | http://arxiv.org/abs/1804.08376v1 | http://arxiv.org/pdf/1804.08376v1.pdf | Convolutional capsule network for classification of breast cancer histology images | Automatization of the diagnosis of any kind of disease is of great importance
and it's gaining speed as more and more deep learning solutions are applied to
different problems. One of such computer aided systems could be a decision
support too able to accurately differentiate between different types of breast
cancer hi... | ['Tomas Iesmantas', 'Robertas Alzbutas'] | 2018-04-23 | null | null | null | null | ['classification-of-breast-cancer-histology'] | ['medical'] | [-1.80555522e-01 3.22241366e-01 -9.91124138e-02 -2.89900601e-01
-3.98235619e-01 -3.56008857e-01 1.98169783e-01 5.00850856e-01
-5.80130816e-01 6.06697321e-01 -3.09888780e-01 -6.00326300e-01
-1.50053516e-01 -7.69908667e-01 -6.20317124e-02 -7.74832904e-01
-3.90381932e-01 8.54533672e-01 2.56378621e-01 -1.20077424... | [15.236570358276367, -2.8773794174194336] |
172dc7c9-eab6-4c93-8ef1-d46869c8c4bd | semi-supervised-sequence-modeling-with-cross | 1809.08370 | null | http://arxiv.org/abs/1809.08370v1 | http://arxiv.org/pdf/1809.08370v1.pdf | Semi-Supervised Sequence Modeling with Cross-View Training | Unsupervised representation learning algorithms such as word2vec and ELMo
improve the accuracy of many supervised NLP models, mainly because they can
take advantage of large amounts of unlabeled text. However, the supervised
models only learn from task-specific labeled data during the main training
phase. We therefore ... | ['Minh-Thang Luong', 'Quoc V. Le', 'Kevin Clark', 'Christopher D. Manning'] | 2018-09-22 | semi-supervised-sequence-modeling-with-cross-1 | https://aclanthology.org/D18-1217 | https://aclanthology.org/D18-1217.pdf | emnlp-2018-10 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 2.57260650e-01 6.43005610e-01 -5.91023147e-01 -6.41346514e-01
-9.66282725e-01 -7.64213085e-01 5.37296176e-01 2.96142343e-02
-3.67043108e-01 7.71138728e-01 3.90660107e-01 -4.56818402e-01
6.41373038e-01 -6.73634291e-01 -9.88979995e-01 -5.22828579e-01
3.95656824e-01 8.92691195e-01 4.49765213e-02 -8.62613022... | [10.685961723327637, 8.512051582336426] |
a0f6bc63-86da-4a12-95d4-d4faf70bfc14 | heterogeneous-hand-guise-classification-based | 2101.06715 | null | https://arxiv.org/abs/2101.06715v1 | https://arxiv.org/pdf/2101.06715v1.pdf | Heterogeneous Hand Guise Classification Based on Surface Electromyographic Signals Using Multichannel Convolutional Neural Network | Electromyography (EMG) is a way of measuring the bioelectric activities that take place inside the muscles. EMG is usually performed to detect abnormalities within the nerves or muscles of a target area. The recent developments in the field of Machine Learning allow us to use EMG signals to teach machines the complex p... | ['Abdullah-Al Nahid', 'Abu Shamim Mohammad Arif', 'Niloy Sikder'] | 2021-01-17 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 6.17149591e-01 -1.61861718e-01 -3.55080009e-01 -5.04305400e-02
-1.60546929e-01 -2.12471783e-01 2.96814173e-01 -5.70585966e-01
-4.73515779e-01 6.52690828e-01 -8.26940611e-02 7.28614777e-02
-2.90021718e-01 -5.53142488e-01 -6.27907455e-01 -8.31289053e-01
-2.53134251e-01 1.86977476e-01 7.47980177e-02 -2.15153649... | [6.868078708648682, 0.2210196703672409] |
f4857cb9-2d3d-4889-a12b-ec0b4f6c4504 | multithreshold-entropy-linear-classifier | 1408.1054 | null | http://arxiv.org/abs/1408.1054v1 | http://arxiv.org/pdf/1408.1054v1.pdf | Multithreshold Entropy Linear Classifier | Linear classifiers separate the data with a hyperplane. In this paper we
focus on the novel method of construction of multithreshold linear classifier,
which separates the data with multiple parallel hyperplanes. Proposed model is
based on the information theory concepts -- namely Renyi's quadratic entropy
and Cauchy-S... | ['Wojciech Marian Czarnecki', 'Jacek Tabor'] | 2014-08-04 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 1.81338027e-01 3.64215642e-01 -2.66895473e-01 -3.88027817e-01
-5.38375914e-01 -6.04544878e-01 4.20919716e-01 7.06114888e-01
-4.08480227e-01 1.18332863e+00 3.89285162e-02 -3.35423708e-01
-5.76342940e-01 -5.22113979e-01 -4.14053261e-01 -1.01542914e+00
-3.51399094e-01 5.49883723e-01 3.81171525e-01 -2.93114781... | [5.185458660125732, 5.456269264221191] |
98505ba2-51ef-4139-a791-f6afa6b11710 | multi-task-learning-with-multi-view-attention | 1812.02354 | null | http://arxiv.org/abs/1812.02354v1 | http://arxiv.org/pdf/1812.02354v1.pdf | Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering | Answer selection and knowledge base question answering (KBQA) are two
important tasks of question answering (QA) systems. Existing methods solve
these two tasks separately, which requires large number of repetitive work and
neglects the rich correlation information between tasks. In this paper, we
tackle answer selecti... | ['Yang Deng', 'Yaliang Li', 'Nan Du', 'Yuexiang Xie', 'Kai Lei', 'Wei Fan', 'Min Yang', 'Ying Shen'] | 2018-12-06 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 0.0100119 -0.16805562 -0.07197787 -0.37403318 -1.1596065 -0.39147225
0.2058873 0.15029456 -0.4114011 0.78458047 0.5126274 -0.11081531
-0.36180627 -0.85676223 -0.36544695 -0.5159474 0.54823226 0.52224195
0.6417084 -0.7299377 0.2817153 -0.18846504 -1.4306601 0.7556991
1.236446 1.0309926 0.4... | [10.974316596984863, 8.005582809448242] |
4ba788d9-e998-47d1-9f09-d11ff1b17e31 | diverse-retrieval-augmented-in-context | 2307.01453 | null | https://arxiv.org/abs/2307.01453v1 | https://arxiv.org/pdf/2307.01453v1.pdf | Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking | There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods ... | ['Jeffrey Flanigan', 'Brendan King'] | 2023-07-04 | null | null | null | null | ['few-shot-learning', 'retrieval', 'dialogue-state-tracking'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 2.14285299e-01 2.51870334e-01 -3.08248162e-01 -5.74579000e-01
-1.26963365e+00 -5.36803067e-01 9.25374210e-01 2.21304476e-01
-6.74419820e-01 7.39082158e-01 7.34039426e-01 -1.51993707e-01
2.94577867e-01 -3.40592682e-01 -1.85649753e-01 -1.47380397e-01
2.50297654e-02 9.17996228e-01 5.64618468e-01 -7.40413308... | [12.758464813232422, 7.939003944396973] |
5150f054-3b92-40a2-aba4-df6fef09e22c | fast-vehicle-detection-in-aerial-imagery | 1709.08666 | null | http://arxiv.org/abs/1709.08666v1 | http://arxiv.org/pdf/1709.08666v1.pdf | Fast Vehicle Detection in Aerial Imagery | In recent years, several real-time or near real-time object detectors have
been developed. However these object detectors are typically designed for
first-person view images where the subject is large in the image and do not
directly apply well to detecting vehicles in aerial imagery. Though some
detectors have been de... | ['Jennifer Carlet', 'Bernard Abayowa'] | 2017-09-25 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [-1.24155045e-01 -5.08380353e-01 2.59870328e-02 -7.64701590e-02
-1.76938400e-01 -6.52139783e-01 5.01553714e-01 -1.52606294e-01
-6.04023814e-01 2.85435081e-01 -3.48848879e-01 -2.46688277e-01
5.95772453e-02 -7.72975445e-01 -1.99282959e-01 -5.04161894e-01
-2.13016897e-01 1.60744917e-02 1.01589453e+00 -3.91775250... | [8.625153541564941, -0.8613033294677734] |
c277edd3-1cd5-4f6e-8f3f-2a9848639034 | finetuning-from-offline-reinforcement | 2303.17396 | null | https://arxiv.org/abs/2303.17396v1 | https://arxiv.org/pdf/2303.17396v1.pdf | Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions | Offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment. Online finetuning of such offline models can further improve performance. But how should we ideally finetune agents obtained from offline RL training? While offline RL algo... | ['Marc Peter Deisenroth', 'Edward Grefenstette', 'Jackie Kay', 'Yicheng Luo'] | 2023-03-30 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-9.73611102e-02 1.69197634e-01 -3.25658441e-01 2.43273173e-02
-8.52371573e-01 -9.87230361e-01 4.90448564e-01 2.36896470e-01
-9.74367201e-01 1.20465279e+00 -1.32424161e-01 -6.55302703e-01
-2.00924069e-01 -5.62623799e-01 -9.18522179e-01 -8.03782165e-01
-2.31890514e-01 6.67324662e-01 1.80481762e-01 -1.39010385... | [4.0948076248168945, 2.2215936183929443] |
095e9b38-d784-45f5-b249-c225862b9a0a | mapp-a-scalable-multi-agent-path-planning | 1401.3905 | null | http://arxiv.org/abs/1401.3905v1 | http://arxiv.org/pdf/1401.3905v1.pdf | MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees | Multi-agent path planning is a challenging problem with numerous real-life
applications. Running a centralized search such as A* in the combined state
space of all units is complete and cost-optimal, but scales poorly, as the
state space size is exponential in the number of mobile units. Traditional
decentralized appro... | ['Ko-Hsin Cindy Wang', 'Adi Botea'] | 2014-01-16 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-5.44407777e-02 5.00757337e-01 -1.76686347e-01 1.44083843e-01
-1.05456579e+00 -1.12317610e+00 1.20331421e-01 3.72551620e-01
-4.80869442e-01 1.27719700e+00 -8.39435607e-02 -7.91767299e-01
-7.18278050e-01 -1.33302236e+00 -8.02342653e-01 -6.79046869e-01
-6.92434788e-01 1.31270313e+00 7.13173449e-01 -5.34098029... | [4.951192855834961, 1.8236145973205566] |
8b7de313-241c-4bf2-8d9e-c93b737de879 | distant-supervision-and-noisy-label-learning | 2003.08370 | null | https://arxiv.org/abs/2003.08370v2 | https://arxiv.org/pdf/2003.08370v2.pdf | Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá | The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries. Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. Additionally, to allevia... | ['David Ifeoluwa Adelani', 'Esther van den Berg', 'Michael A. Hedderich', 'Dietrich Klakow', 'Dawei Zhu'] | 2020-03-18 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.81413293e-01 9.76029187e-02 -2.02215254e-01 -5.20371914e-01
-7.77170897e-01 -7.41556346e-01 7.74908006e-01 4.34718192e-01
-1.17787445e+00 7.00636089e-01 4.87366468e-01 -4.63555098e-01
2.16476038e-01 -5.30551791e-01 -2.02802867e-01 -4.64543521e-01
-6.45306110e-02 4.16513056e-01 2.03353260e-02 -1.30472764... | [10.206592559814453, 9.631115913391113] |
9caeba9c-0475-48e1-9dfc-de138b56ea0c | incorporating-explicit-knowledge-in-pre | 2204.11673 | null | https://arxiv.org/abs/2204.11673v1 | https://arxiv.org/pdf/2204.11673v1.pdf | Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking | Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural language understanding. However, existing PLM based re-rankers may easily suffer from vocabulary mismatch a... | ['Dawei Yin', 'Shuzi Niu', 'Zhicong Cheng', 'Shuaiqiang Wang', 'Suqi Cheng', 'Yiding Liu', 'Qian Dong'] | 2022-04-25 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-3.83397862e-02 7.34140426e-02 -5.32065868e-01 -4.22439277e-02
-9.13842320e-01 -7.91643918e-01 6.49027765e-01 2.02513099e-01
-4.10589784e-01 8.64191294e-01 7.56902456e-01 -1.96849480e-01
-4.31294411e-01 -1.07177353e+00 -7.37790942e-01 -1.61212549e-01
1.69314131e-01 5.20220637e-01 3.76295328e-01 -3.73951018... | [10.986830711364746, 7.958011627197266] |
8df60866-2782-419e-930c-119d87cd9627 | a-comprehensive-review-of-data-driven-co | 2301.05339 | null | https://arxiv.org/abs/2301.05339v4 | https://arxiv.org/pdf/2301.05339v4.pdf | A Comprehensive Review of Data-Driven Co-Speech Gesture Generation | Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social... | ['Michael Neff', 'Gustav Eje Henter', 'Chaitanya Ahuja', 'Taras Kucherenko', 'Simbarashe Nyatsanga'] | 2023-01-13 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 1.71506509e-01 4.05592695e-02 3.68480035e-03 -9.96422097e-02
-7.08313704e-01 -6.16976976e-01 1.14585292e+00 -7.98090994e-01
-2.05263481e-01 3.88808399e-01 9.56094444e-01 -2.66519096e-02
-8.46913271e-03 -5.45362711e-01 -3.93430114e-01 -9.04412210e-01
-2.03360289e-01 4.70165253e-01 -7.35898390e-02 -4.64973629... | [5.613471031188965, -0.09579368680715561] |
8e539bc7-a73a-447e-b630-ab738eb160bd | reverse-survival-model-rsm-a-pipeline-for | 2210.15674 | null | https://arxiv.org/abs/2210.15674v1 | https://arxiv.org/pdf/2210.15674v1.pdf | Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models | The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in surv... | ['Nick Sajadi', 'Mansour Abolghasemian', 'Mohammad Alavinia', 'Mohammad Shafiee', 'Amir Sameizadeh', 'Navid Ziaei', 'Ebrahim Pourjafari', 'Reza Saadati Fard', 'Mohammad R. Rezaei'] | 2022-10-27 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.93566903e-01 1.59196332e-01 -1.90843135e-01 -7.30353892e-01
-2.71467566e-01 -1.04096636e-01 -2.81919912e-02 6.88919604e-01
-3.69092703e-01 8.88655066e-01 4.11844701e-01 -7.26166368e-01
-4.89555240e-01 -7.72957802e-01 -8.33071619e-02 -7.51798570e-01
-8.60970765e-02 7.99340904e-01 -3.54723841e-01 9.03667212... | [8.03765869140625, 6.025014400482178] |
36c3f2f0-c553-4ebb-b93b-23e349bc6e6a | table-detection-in-the-wild-a-novel-diverse-1 | 2209.09207 | null | https://arxiv.org/abs/2209.09207v1 | https://arxiv.org/pdf/2209.09207v1.pdf | Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method | Recent deep learning approaches in table detection achieved outstanding performance and proved to be effective in identifying document layouts. Currently, available table detection benchmarks have many limitations, including the lack of samples diversity, simple table structure, the lack of training cases, and samples ... | ['Sanjay G', 'Siddhant Swaroop Dash', 'Nikhil Fande', 'Shashank Shekhar', 'Mrinal Haloi'] | 2022-08-31 | table-detection-in-the-wild-a-novel-diverse | https://arxiv.org/abs/2209.09207 | https://arxiv.org/pdf/2209.09207 | null | ['table-detection'] | ['miscellaneous'] | [-2.80174166e-02 -4.08563793e-01 -2.79782206e-01 -3.36582482e-01
-9.30935204e-01 -9.10414815e-01 3.16177607e-01 6.66119993e-01
1.20951138e-01 5.32997847e-01 4.29104924e-01 -2.43135676e-01
-1.04503088e-01 -1.14137876e+00 -7.87173867e-01 -2.25663483e-01
-2.04124257e-01 6.20314717e-01 -3.80960032e-02 -4.34235841... | [11.691835403442383, 3.0111327171325684] |
171c5933-f19e-44b4-a6db-36f43219425b | heterogeneous-federated-knowledge-graph | 2302.02069 | null | https://arxiv.org/abs/2302.02069v2 | https://arxiv.org/pdf/2302.02069v2.pdf | Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning | Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, fede... | ['Wei Hu', 'Guangyao Li', 'Xiangrong Zhu'] | 2023-02-04 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-4.30260867e-01 2.02634886e-01 -4.14485812e-01 -7.28549138e-02
-5.13706625e-01 -5.17521024e-01 3.61985862e-01 -7.89206550e-02
-2.53885537e-01 9.69806790e-01 2.83213586e-01 1.33137301e-01
-5.69801867e-01 -7.97385514e-01 -1.01446128e+00 -1.09557688e+00
-3.65049727e-02 4.84215647e-01 9.48850159e-03 7.73816407... | [5.8477654457092285, 6.339504718780518] |
7b4bd3ae-a41c-4f99-a254-43e06f208a97 | improving-opinion-spam-detection-by | 2012.13905 | null | https://arxiv.org/abs/2012.13905v1 | https://arxiv.org/pdf/2012.13905v1.pdf | Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution | Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opi... | ['Marinella Petrocchi', 'Gianluca Lax', 'Francesco Buccafurri', 'Michela Fazzolari'] | 2020-12-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.34884208e-01 4.44816090e-02 -2.27478772e-01 -4.68110502e-01
-1.93698764e-01 -2.55730033e-01 8.00914884e-01 6.52756453e-01
-5.34677684e-01 8.20270956e-01 -1.07990332e-01 -3.33144635e-01
-1.14776090e-01 -9.51184154e-01 -3.04329962e-01 -5.24766028e-01
3.56730260e-02 2.55305231e-01 3.65044862e-01 -3.57889056... | [7.866215705871582, 10.050219535827637] |
f9fcdb26-4a82-49db-8c01-6882cb5efd3d | blind-image-deconvolution-using-pretrained | 1908.07404 | null | https://arxiv.org/abs/1908.07404v1 | https://arxiv.org/pdf/1908.07404v1.pdf | Blind Image Deconvolution using Pretrained Generative Priors | This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks. We employ two separate deep generative models - one trained to produce sharp images while the other trained to generate blur kernels from lower dimensional paramete... | ['Muhammad Asim', 'Fahad Shamshad', 'Ali Ahmed'] | 2019-08-20 | null | null | null | null | ['blind-image-deblurring', 'image-deconvolution'] | ['computer-vision', 'computer-vision'] | [ 1.92614466e-01 -1.76542103e-01 5.17749429e-01 -1.29141912e-01
-5.42552888e-01 -4.96906817e-01 6.36221945e-01 -1.22812414e+00
-1.67996302e-01 9.45610583e-01 6.78980291e-01 -1.45536616e-01
-4.18319367e-02 -3.71154517e-01 -7.29872763e-01 -1.02217257e+00
3.79583985e-01 2.46996760e-01 -2.52393574e-01 2.67926097... | [11.639214515686035, -2.7278928756713867] |
52085377-75c6-4804-b7d7-ae96de5b6092 | learning-to-drop-points-for-lidar-scan | 2102.11952 | null | https://arxiv.org/abs/2102.11952v2 | https://arxiv.org/pdf/2102.11952v2.pdf | Learning to Drop Points for LiDAR Scan Synthesis | 3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditions, and so on. Generative modeling of LiDAR data as scene priors is one of the promising solutions t... | ['Ryo Kurazume', 'Kazuto Nakashima'] | 2021-02-23 | null | null | null | null | ['sensor-modeling', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 3.34299862e-01 2.32788950e-01 1.83682665e-01 -4.31233883e-01
-7.68959403e-01 -3.11228424e-01 5.20658255e-01 -5.53121746e-01
-3.27411205e-01 1.03535497e+00 -6.73445091e-02 -2.86844205e-02
-1.59848258e-02 -1.20697665e+00 -1.12759244e+00 -8.68967831e-01
5.27863264e-01 6.72649860e-01 -4.44169343e-02 6.67854920... | [8.666633605957031, -3.4974606037139893] |
f4292095-97fa-46b5-88b3-de9a879ccf69 | surgicalgpt-end-to-end-language-vision-gpt | 2304.09974 | null | https://arxiv.org/abs/2304.09974v1 | https://arxiv.org/pdf/2304.09974v1.pdf | SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery | Advances in GPT-based large language models (LLMs) are revolutionizing natural language processing, exponentially increasing its use across various domains. Incorporating uni-directional attention, these autoregressive LLMs can generate long and coherent paragraphs. However, for visual question answering (VQA) tasks th... | ['Hongliang Ren', 'Gokul Kannan', 'Mobarakol Islam', 'Lalithkumar Seenivasan'] | 2023-04-19 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 2.89195210e-01 6.35232866e-01 -1.12410270e-01 -1.40217364e-01
-1.10502613e+00 -7.55191684e-01 6.84961140e-01 2.19805866e-01
-4.98829186e-01 3.36228870e-02 5.76845169e-01 -7.49797463e-01
2.96820372e-01 -5.95903456e-01 -9.37800229e-01 -4.85372931e-01
4.25504744e-01 3.73359203e-01 -2.50981152e-01 -4.31982726... | [10.934577941894531, 1.6200332641601562] |
5bfb0e46-79bf-443a-b75b-30f37b53c1f4 | a-visuospatial-dataset-for-naturalistic-verb | 2010.15225 | null | https://arxiv.org/abs/2010.15225v1 | https://arxiv.org/pdf/2010.15225v1.pdf | A Visuospatial Dataset for Naturalistic Verb Learning | We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access: That is, naturalistic, spontaneous speech paired with richly grounded ... | ['Ellie Pavlick', 'Dylan Ebert'] | 2020-10-28 | null | https://aclanthology.org/2020.starsem-1.16 | https://aclanthology.org/2020.starsem-1.16.pdf | joint-conference-on-lexical-and-computational | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.82041109e-01 3.93789530e-01 -2.13723592e-02 -6.66157126e-01
-6.61365211e-01 -6.14575326e-01 7.12091088e-01 7.69113362e-01
-7.65928566e-01 3.41673493e-01 9.14245844e-01 -3.56758744e-01
-7.65274391e-02 -1.18305326e+00 -9.31933999e-01 -1.82925060e-01
-2.50717998e-01 5.02793789e-01 1.39813974e-01 -4.06650513... | [10.092988014221191, 8.544432640075684] |
a24ded9e-1dea-472b-ad52-594d17f289f5 | view-gcn-view-based-graph-convolutional | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.pdf | View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis | View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view features to be a global shape descriptor. In this work, we propose a novel view-based Graph Convolutiona... | [' Jian Sun', ' Ruixuan Yu', 'Xin Wei'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-3.64501595e-01 -3.91266942e-01 -8.37660655e-02 -5.94042599e-01
-5.87935925e-01 -8.04020762e-01 6.66910708e-01 -9.27796289e-02
5.18020093e-01 -3.71631145e-01 2.44165435e-01 -1.96485773e-01
-1.81065291e-01 -1.17205787e+00 -4.58288133e-01 -5.63167512e-01
-6.56854808e-02 7.66728818e-01 1.16385795e-01 -1.02454215... | [8.152425765991211, -3.8412628173828125] |
0db00088-57d7-4c30-a4e9-eb4d2a927149 | language-agnostic-twitter-bot-detection | null | null | https://aclanthology.org/R19-1065 | https://aclanthology.org/R19-1065.pdf | Language-Agnostic Twitter-Bot Detection | In this paper we address the problem of detecting Twitter bots. We analyze a dataset of 8385 Twitter accounts and their tweets consisting of both humans and different kinds of bots. We use this data to train machine learning classifiers that distinguish between real and bot accounts. We identify features that are easy ... | ['J{\\"u}rgen Knauth'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.16489270e-01 -1.82924286e-01 -2.14198768e-01 2.24871691e-02
-2.11598665e-01 -6.54257655e-01 9.67324853e-01 4.98986542e-01
-7.88292825e-01 7.03302503e-01 -7.16725066e-02 -4.97708261e-01
2.32804596e-01 -9.04662132e-01 9.54425260e-02 -4.59285110e-01
-9.43785757e-02 6.67091131e-01 6.91625595e-01 -5.40271521... | [8.068071365356445, 10.124698638916016] |
06f30453-c25e-4274-8d83-56deae85d12b | using-mise-en-scene-visual-features-based-on | 1704.06109 | null | http://arxiv.org/abs/1704.06109v1 | http://arxiv.org/pdf/1704.06109v1.pdf | Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation | Item features play an important role in movie recommender systems, where
recommendations can be generated by using explicit or implicit preferences of
users on traditional features (attributes) such as tag, genre, and cast.
Typically, movie features are human-generated, either editorially (e.g., genre
and cast) or by l... | ['Paolo Cremonesi', 'Massimo Quadrana', 'Yashar Deldjoo', 'Mehdi Elahi'] | 2017-04-20 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.17062861e-01 -4.57540900e-01 -6.21163175e-02 -5.24306655e-01
-3.46327186e-01 -1.00016010e+00 5.89521348e-01 4.64063942e-01
-2.98654169e-01 4.33277220e-01 4.37121630e-01 9.20432955e-02
-1.72029108e-01 -8.17893147e-01 -6.70542300e-01 -4.40070927e-01
-6.91145435e-02 3.76086570e-02 1.69504300e-01 -5.04426420... | [10.1624755859375, 5.5232954025268555] |
919d6477-e724-4351-bf41-7a16f8a9a3d5 | robustness-testing-of-ai-systems-a-case-study | 2108.06159 | null | https://arxiv.org/abs/2108.06159v1 | https://arxiv.org/pdf/2108.06159v1.pdf | Robustness testing of AI systems: A case study for traffic sign recognition | In the last years, AI systems, in particular neural networks, have seen a tremendous increase in performance, and they are now used in a broad range of applications. Unlike classical symbolic AI systems, neural networks are trained using large data sets and their inner structure containing possibly billions of paramete... | ['Arndt von Twickel', 'Petar Tsankov', 'Matthias Neu', 'Pavol Bielik', 'Christian Berghoff'] | 2021-08-13 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 6.40331566e-01 2.24075139e-01 1.06314577e-01 -4.80703890e-01
1.54562980e-01 -6.29983008e-01 8.39105487e-01 1.75350189e-01
-5.11775553e-01 6.64661944e-01 -6.47499979e-01 -6.55844569e-01
-2.23016992e-01 -8.99342954e-01 -7.67339706e-01 -7.87367284e-01
-2.90344268e-01 3.53505582e-01 6.61606491e-01 -2.96849132... | [5.546503067016602, 7.6186065673828125] |
19e25b63-141b-44e0-827b-b22b0c0315e7 | recognize-anything-a-strong-image-tagging | 2306.03514 | null | https://arxiv.org/abs/2306.03514v3 | https://arxiv.org/pdf/2306.03514v3.pdf | Recognize Anything: A Strong Image Tagging Model | We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale i... | ['Lei Zhang', 'Yandong Guo', 'Shilong Liu', 'Yaqian Li', 'Tong Luo', 'Yuzhuo Qin', 'Yanchun Xie', 'Zhaochuan Luo', 'Zhaoyang Li', 'Jinyu Ma', 'Xinyu Huang', 'Youcai Zhang'] | 2023-06-06 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.08806598e-01 2.14443162e-01 -2.69121170e-01 -4.82743502e-01
-1.11383510e+00 -6.45661592e-01 5.37712038e-01 3.91973518e-02
-5.74745953e-01 2.07149670e-01 1.30942047e-01 -1.35522023e-01
4.64928657e-01 -3.69636983e-01 -6.67295635e-01 -5.03626466e-01
2.88895905e-01 5.07515132e-01 4.87939924e-01 2.38243733... | [10.033058166503906, 1.6083707809448242] |
53ed3b03-1f17-4209-aa0e-c29b2b4d440b | babe-enhancing-fairness-via-estimation-of | 2307.02891 | null | https://arxiv.org/abs/2307.02891v1 | https://arxiv.org/pdf/2307.02891v1.pdf | BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables | We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead to bad accuracy and do not really achieve fairness in situations where there is a correlation between the sensitive attribute S and the leg... | ['Catuscia Palamidessi', 'Daniele Gorla', 'Ruta Binkyte'] | 2023-07-06 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 2.90205687e-01 2.88931906e-01 -3.47584456e-01 -8.84577334e-01
-4.73341078e-01 -3.75882238e-01 5.63578546e-01 4.49248731e-01
-6.25124216e-01 1.12463272e+00 5.65999076e-02 -2.98251033e-01
-2.70988703e-01 -1.01594639e+00 -2.71730691e-01 -7.36906826e-01
2.95840174e-01 4.55469400e-01 -1.08193411e-02 2.16227993... | [8.724580764770508, 5.256608963012695] |
31ce3021-cacb-4e0e-815b-e023d1d37382 | decision-making-under-uncertainty-a-game-of | 2012.07509 | null | https://arxiv.org/abs/2012.07509v1 | https://arxiv.org/pdf/2012.07509v1.pdf | Decision Making under Uncertainty: A Game of Two Selves | In this paper we characterize the niveloidal preferences that satisfy the Weak Order, Monotonicity, Archimedean, and Weak C-Independence Axioms from the point of view of an intra-personal, leader-follower game. We also show that the leader's strategy space can serve as an ambiguity aversion index. | ['Jianming Xia'] | 2020-12-14 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-3.06708246e-01 2.64241189e-01 -3.42971683e-01 -6.03058875e-01
2.10508332e-01 -1.28475642e+00 3.44849676e-01 -1.08542420e-01
-1.04191363e+00 9.29382563e-01 1.85117692e-01 -5.87871313e-01
-8.99270475e-01 -7.69460917e-01 1.10212259e-01 -6.09778285e-01
-3.23936284e-01 6.54212058e-01 1.82433248e-01 -5.84095716... | [4.38200044631958, 3.089066982269287] |
2f19b3f5-dc8b-4fa1-b9ac-d748c97d498a | deep-discriminative-spatial-and-temporal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.pdf | Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring | How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for bett... | ['Jinhui Tang', 'Jianjun Ge', 'Jiangxin Dong', 'Boming Xu', 'Jinshan Pan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 1.90778807e-01 -8.87055039e-01 -2.97792137e-01 -1.12706810e-01
-5.81274748e-01 -4.20009881e-01 3.43655616e-01 -3.48507673e-01
-3.13352138e-01 5.57383299e-01 4.99599963e-01 -1.47879630e-01
-2.84043163e-01 -4.61294204e-01 -5.65526724e-01 -9.94764209e-01
-2.36066595e-01 -4.02799487e-01 3.83754224e-01 -5.77356070... | [11.142617225646973, -1.977879524230957] |
916d3819-38ca-40d4-b1bf-aa39d3544143 | simple-open-vocabulary-object-detection-with | 2205.06230 | null | https://arxiv.org/abs/2205.06230v2 | https://arxiv.org/pdf/2205.06230v2.pdf | Simple Open-Vocabulary Object Detection with Vision Transformers | Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, w... | ['Neil Houlsby', 'Thomas Kipf', 'Xiaohua Zhai', 'Xiao Wang', 'Zhuoran Shen', 'Mostafa Dehghani', 'Anurag Arnab', 'Aravindh Mahendran', 'Alexey Dosovitskiy', 'Dirk Weissenborn', 'Maxim Neumann', 'Austin Stone', 'Alexey Gritsenko', 'Matthias Minderer'] | 2022-05-12 | null | null | null | null | ['one-shot-object-detection', 'open-vocabulary-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.32157099e-01 -2.46536776e-01 -5.26104979e-02 -5.12906253e-01
-9.99025047e-01 -4.98930901e-01 7.83293307e-01 3.26135792e-02
-7.90102899e-01 1.24493912e-01 6.09391369e-02 -2.60357380e-01
2.36021727e-01 -2.73787975e-01 -7.44137764e-01 -5.10185421e-01
3.45649183e-01 5.00535369e-01 6.15616858e-01 2.95619480... | [9.63412094116211, 1.5336800813674927] |
48f5ec90-f9f4-4049-b2a0-49f9d84e19b4 | novel-class-discovery-an-introduction-and-key | 2302.12028 | null | https://arxiv.org/abs/2302.12028v1 | https://arxiv.org/pdf/2302.12028v1.pdf | Novel Class Discovery: an Introduction and Key Concepts | Novel Class Discovery (NCD) is a growing field where we are given during training a labeled set of known classes and an unlabeled set of different classes that must be discovered. In recent years, many methods have been proposed to address this problem, and the field has begun to mature. In this paper, we provide a com... | ['Sandrine Vaton', 'Joachim Flocon-Cholet', 'Alexandre Reiffers-Masson', 'Stéphane Gosselin', 'Vincent Lemaire', 'Colin Troisemaine'] | 2023-02-22 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.87038255e-01 7.07052350e-02 -7.08441079e-01 -5.80457926e-01
-5.67001283e-01 -8.32218587e-01 7.99991548e-01 1.41489446e-01
-2.51648009e-01 1.00339174e+00 -3.79876941e-01 -2.49414772e-01
-1.53086782e-01 -6.71361208e-01 -2.69999355e-01 -7.16550350e-01
-1.05853632e-01 7.50602603e-01 3.02940905e-01 1.01067670... | [9.6454496383667, 3.0387330055236816] |
f7a31a05-93ee-4769-88d9-80c6caa5bedf | impact-of-different-desired-velocity-profiles | 2306.01971 | null | https://arxiv.org/abs/2306.01971v1 | https://arxiv.org/pdf/2306.01971v1.pdf | Impact of Different Desired Velocity Profiles and Controller Gains on Convoy Driveability of Cooperative Adaptive Cruise Control Operated Platoons | As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (... | ['Levent Guvenc', 'Santhosh Tamilarasan'] | 2023-06-03 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-3.15703630e-01 3.40101659e-01 -2.76879430e-01 -5.16836233e-02
1.47522911e-01 -4.70314354e-01 8.20962787e-01 1.73629209e-01
-5.55846512e-01 7.96615005e-01 -4.52416271e-01 -6.85803413e-01
-7.19275773e-01 -5.87539315e-01 -4.41185027e-01 -1.01123750e+00
-1.79480508e-01 5.07655203e-01 5.23606002e-01 -5.25261402... | [5.503403663635254, 1.8036282062530518] |
c224d428-4639-40e1-8c9a-ae2f7d85eec9 | unet-2022-exploring-dynamics-in-non | 2210.15566 | null | https://arxiv.org/abs/2210.15566v1 | https://arxiv.org/pdf/2210.15566v1.pdf | UNet-2022: Exploring Dynamics in Non-isomorphic Architecture | Recent medical image segmentation models are mostly hybrid, which integrate self-attention and convolution layers into the non-isomorphic architecture. However, one potential drawback of these approaches is that they failed to provide an intuitive explanation of why this hybrid combination manner is beneficial, making ... | ['Yizhou Yu', 'Liansheng Wang', 'Hong-Yu Zhou', 'Jiansen Guo'] | 2022-10-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.20401970e-02 4.03039426e-01 -1.05794132e-01 -2.69020766e-01
-4.10782307e-01 -4.19385463e-01 4.76445943e-01 3.44556600e-01
-5.29948831e-01 4.62679952e-01 -7.79580995e-02 -5.06530046e-01
3.11800409e-02 -6.16467416e-01 -6.06785119e-01 -6.06223583e-01
1.43482253e-01 5.06767213e-01 3.88575554e-01 -4.51192036... | [14.595770835876465, -2.498765468597412] |
dfa9d303-1221-4e22-a240-e2ba0a46683e | vae-based-text-style-transfer-with-pivot | 2112.03154 | null | https://arxiv.org/abs/2112.03154v1 | https://arxiv.org/pdf/2112.03154v1.pdf | VAE based Text Style Transfer with Pivot Words Enhancement Learning | Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content. Due to the scarcity of high-quality parallel training data, unsupervised learning has become a trending direction for TST tasks. In this paper, we propose a novel VAE based Text Styl... | ['Chenlei Guo', 'Chengyuan Ma', 'Zhongkai Sun', 'Sixing Lu', 'Haoran Xu'] | 2021-12-06 | null | https://aclanthology.org/2021.icon-main.20 | https://aclanthology.org/2021.icon-main.20.pdf | icon-2021-12 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.59145498e-01 -2.66457051e-01 6.47359993e-03 -5.62629640e-01
-3.91202956e-01 -6.00873530e-01 7.65797496e-01 -2.24488959e-01
-4.47925866e-01 6.51134074e-01 4.20475572e-01 -1.95644736e-01
8.70568454e-02 -6.64665341e-01 -4.83445436e-01 -6.47840798e-01
7.05221951e-01 5.76546431e-01 9.09410417e-02 -6.83839738... | [11.687421798706055, 9.579690933227539] |
d7dfad0f-711f-4755-b49f-f84524a9a080 | improving-and-diagnosing-knowledge-based | 2112.06888 | null | https://arxiv.org/abs/2112.06888v1 | https://arxiv.org/pdf/2112.06888v1.pdf | Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection | Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. Recent single modality text work has shown knowledge injection into pre-trained language models, specifically entity enhanced knowledge graph embeddi... | ['Joydeep Ghosh', 'Yasumasa Onoe', 'Diego Garcia-Olano'] | 2021-12-13 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 1.69688724e-02 5.22902429e-01 -9.51644480e-02 -2.58384109e-01
-1.10499096e+00 -8.23385954e-01 8.66209209e-01 4.41449642e-01
-6.79521322e-01 6.54581368e-01 6.04579628e-01 -5.18211365e-01
-1.29079195e-02 -5.58674276e-01 -1.05217803e+00 -2.70754516e-01
4.48133618e-01 6.74274027e-01 4.75309163e-01 -2.81216145... | [10.842841148376465, 1.7876826524734497] |
177cb64d-5335-48af-bf0e-3062a2e68f1a | an-energy-based-prior-for-generative-saliency | 2204.08803 | null | https://arxiv.org/abs/2204.08803v3 | https://arxiv.org/pdf/2204.08803v3.pdf | An Energy-Based Prior for Generative Saliency | We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the latent space of a saliency generator network that generates the saliency map based on a continuous latent variables and an observed image. Bot... | ['Ping Li', 'Nick Barnes', 'Jianwen Xie', 'Jing Zhang'] | 2022-04-19 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 2.34468430e-01 3.26658070e-01 -3.70196626e-02 -2.45376498e-01
-5.74444175e-01 -1.82976797e-01 6.69425011e-01 -4.54854339e-01
-1.40082657e-01 7.91086078e-01 1.97807714e-01 5.56067452e-02
1.56182274e-02 -9.88631368e-01 -1.07105649e+00 -9.56804395e-01
3.96440774e-01 2.51418293e-01 4.70547587e-01 1.29244417... | [10.124529838562012, -0.3191272020339966] |
195e6607-624b-46dd-8e1e-5f1514693e2f | ncis-deep-color-gradient-maps-regression-and | 2306.15784 | null | https://arxiv.org/abs/2306.15784v1 | https://arxiv.org/pdf/2306.15784v1.pdf | NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images | Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells... | ['Enrico Grisan', 'Livio Corain', 'Livio Finos', 'Jean-Marie Graïc', 'Antonella Peruffo', 'Valentina Vadori'] | 2023-06-27 | null | null | null | null | ['instance-segmentation'] | ['computer-vision'] | [ 2.54097313e-01 2.96675950e-01 2.59836137e-01 -3.72282058e-01
-1.94086805e-01 -3.63526851e-01 5.99269509e-01 4.99195904e-01
-1.09136057e+00 7.77960002e-01 -3.87601286e-01 -2.00688735e-01
3.95035148e-01 -7.09637702e-01 -6.32580101e-01 -1.01169729e+00
-1.23926930e-01 5.60529709e-01 4.51567560e-01 8.50197151... | [14.485764503479004, -3.1207244396209717] |
1d9179b9-ee26-4f46-968b-acebfee2e04e | topological-data-analysis-for-arrhythmia | 1906.05795 | null | https://arxiv.org/abs/1906.05795v1 | https://arxiv.org/pdf/1906.05795v1.pdf | Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks | This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that tu... | ['Meryll Dindin', 'Frederic Chazal', 'Yuhei Umeda'] | 2019-06-13 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [-1.98907722e-02 1.35614052e-01 4.78811413e-01 -7.31017888e-02
-3.24823916e-01 -4.51121211e-01 1.98800027e-01 3.84962231e-01
-2.28263870e-01 7.68088043e-01 -1.59157768e-01 -3.19598675e-01
-4.89961892e-01 -6.43040240e-01 -3.91119987e-01 -6.32477224e-01
-7.24896193e-01 4.78840977e-01 7.12011456e-02 -3.25028956... | [14.318808555603027, 3.289825677871704] |
b692bfaf-3a5e-4a99-96a8-d59b93932211 | a-mathematical-framework-for-learning | 2212.11481 | null | https://arxiv.org/abs/2212.11481v2 | https://arxiv.org/pdf/2212.11481v2.pdf | A Mathematical Framework for Learning Probability Distributions | The modeling of probability distributions, specifically generative modeling and density estimation, has become an immensely popular subject in recent years by virtue of its outstanding performance on sophisticated data such as images and texts. Nevertheless, a theoretical understanding of its success is still incomplet... | ['Hongkang Yang'] | 2022-12-22 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.56452417e-01 1.07158177e-01 -1.93568617e-01 -1.29255086e-01
-3.52764010e-01 -3.87203157e-01 4.88306940e-01 -3.33427871e-03
-2.38607854e-01 1.03498828e+00 -2.54388988e-01 -3.14022601e-01
-2.77031511e-01 -1.00386548e+00 -7.50780106e-01 -1.19755149e+00
-5.20060062e-02 3.82878959e-01 -6.94053993e-03 -1.68425232... | [7.447727680206299, 3.913508415222168] |
4a9f5ad8-34f3-44ad-b3d1-ead98d89e346 | robust-model-based-3d-head-pose-estimation | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Meyer_Robust_Model-Based_3D_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Meyer_Robust_Model-Based_3D_ICCV_2015_paper.pdf | Robust Model-Based 3D Head Pose Estimation | We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes... | ['Dikpal Reddy', 'Jan Kautz', 'Iuri Frosio', 'Gregory P. Meyer', 'Shalini Gupta'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['head-pose-estimation'] | ['computer-vision'] | [ 5.70427924e-02 3.67715836e-01 1.49978563e-01 -4.85386848e-01
-8.34226668e-01 -4.88368034e-01 3.13169032e-01 2.23854825e-01
-6.99160397e-01 5.22502244e-01 -1.56095967e-01 2.82636553e-01
9.09865797e-02 -5.04221678e-01 -5.98060906e-01 -6.62587881e-01
5.35797328e-04 1.05700982e+00 2.55179316e-01 -8.24473277... | [13.576201438903809, 0.162910595536232] |
8f8953f2-a429-41a6-845e-3ee4567ce864 | exploring-looping-effects-in-rnn-based | null | null | https://aclanthology.org/2020.alta-1.15 | https://aclanthology.org/2020.alta-1.15.pdf | Exploring Looping Effects in RNN-based Architectures | The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems. More specifically, we conduct a study on neural models with recurrent units by explicitly altering their decoder internal state. We use a task of... | ['Ekaterina Vylomova', 'Saliha Muradoglu', 'Andrei Shcherbakov'] | null | null | null | null | alta-2020-12 | ['morphological-inflection'] | ['natural-language-processing'] | [ 8.67929101e-01 5.69257617e-01 -2.35319734e-02 2.06463598e-02
-1.71423435e-01 -6.95476413e-01 9.76719439e-01 -4.79121739e-03
-3.42663229e-01 8.14710498e-01 7.17858076e-01 -7.97995090e-01
6.43372536e-01 -8.17313731e-01 -1.14432204e+00 -4.70302999e-01
1.65391237e-01 2.26927370e-01 -3.87733728e-02 -3.80922496... | [11.630517959594727, 9.199417114257812] |
a9df3c25-97da-4498-aeda-76fa356b474e | zscribbleseg-zen-and-the-art-of-scribble | 2301.04882 | null | https://arxiv.org/abs/2301.04882v1 | https://arxiv.org/pdf/2301.04882v1.pdf | ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation | Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images. To alleviate this problem, we propose to utilize solely scribble annotations for weakly supervised segmentation. Existing solutions mainly leverage selective losses computed solely on anno... | ['Xiahai Zhuang', 'Ke Zhang'] | 2023-01-12 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 4.24134791e-01 3.96199226e-01 -1.44196823e-01 -4.66631085e-01
-1.06011844e+00 -4.47969288e-01 1.19747452e-01 -1.72679070e-02
-3.09941798e-01 8.92372727e-01 -5.80613315e-02 2.02197712e-02
2.70394310e-02 -6.13468945e-01 -8.77761543e-01 -1.01634288e+00
4.29824650e-01 4.37178403e-01 4.08745050e-01 7.44736120... | [14.593476295471191, -2.04736065864563] |
ba446b5e-09d8-418d-a907-4e28529894d3 | revisiting-the-shape-bias-of-deep-learning | 2206.06466 | null | https://arxiv.org/abs/2206.06466v1 | https://arxiv.org/pdf/2206.06466v1.pdf | Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification | It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily ex... | ['Sheraz Ahmed', 'Andreas Dengel', 'Shoaib Ahmed Siddiqui', 'Christoph Peter Balada', 'Fabian Schmeisser', 'Adriano Lucieri'] | 2022-06-13 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.16994441e-01 4.11667824e-01 -1.19424373e-01 -4.02183682e-01
-8.07910711e-02 -6.90059960e-01 9.06810880e-01 2.63131231e-01
-3.00304890e-01 1.76088259e-01 3.95543247e-01 -4.27799016e-01
-3.99808377e-01 -7.11065054e-01 -3.32258105e-01 -1.19343746e+00
2.08559990e-01 4.78763245e-02 -1.98533207e-01 -1.73695520... | [9.969280242919922, 2.40162992477417] |
db110fd8-55c0-4dff-b5fa-36fb6df7f400 | broad-coverage-semantic-parsing-as | 1909.02607 | null | https://arxiv.org/abs/1909.02607v2 | https://arxiv.org/pdf/1909.02607v2.pdf | Broad-Coverage Semantic Parsing as Transduction | We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without ... | ['Kevin Duh', 'Xutai Ma', 'Benjamin Van Durme', 'Sheng Zhang'] | 2019-09-05 | broad-coverage-semantic-parsing-as-1 | https://aclanthology.org/D19-1392 | https://aclanthology.org/D19-1392.pdf | ijcnlp-2019-11 | ['ucca-parsing'] | ['natural-language-processing'] | [ 8.06436181e-01 5.97752035e-01 -2.40029350e-01 -7.08581924e-01
-1.28913629e+00 -6.87672019e-01 3.36588532e-01 2.38135591e-01
-1.52514800e-01 4.20649022e-01 4.33410317e-01 -6.84745014e-01
4.55063313e-01 -9.02481854e-01 -1.06381106e+00 -9.09352601e-02
3.85922998e-01 6.41346097e-01 7.89258629e-02 -4.10006255... | [10.481311798095703, 9.373234748840332] |
438a1012-a257-498c-97d1-43d7d45af134 | chateval-a-tool-for-chatbot-evaluation | null | null | https://aclanthology.org/N19-4011 | https://aclanthology.org/N19-4011.pdf | ChatEval: A Tool for Chatbot Evaluation | Open-domain dialog systems (i.e. chatbots) are difficult to evaluate. The current best practice for analyzing and comparing these dialog systems is the use of human judgments. However, the lack of standardization in evaluation procedures, and the fact that model parameters and code are rarely published hinder systemati... | ['Chris Callison-Burch', 'Arun Kirubarajan', 'Jo{\\~a}o Sedoc', 'Daphne Ippolito', 'Jai Thirani', 'Lyle Ungar'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['open-domain-dialog'] | ['natural-language-processing'] | [-5.74530661e-01 -2.69565061e-02 -1.13877237e-01 -7.15831339e-01
-8.91429782e-01 -1.18537664e+00 7.19147563e-01 2.47522956e-03
-6.06241584e-01 7.38321006e-01 4.82523650e-01 -5.00991344e-01
2.90830523e-01 -2.99986720e-01 4.82052825e-02 -4.92686704e-02
4.51826453e-01 8.54697943e-01 2.55186349e-01 -5.68073750... | [12.767059326171875, 8.01355266571045] |
23ce8435-ad2c-491b-98e5-41291932a58b | real-world-anomaly-detection-in-surveillance | 1801.04264 | null | http://arxiv.org/abs/1801.04264v3 | http://arxiv.org/pdf/1801.04264v3.pdf | Real-world Anomaly Detection in Surveillance Videos | Surveillance videos are able to capture a variety of realistic anomalies. In
this paper, we propose to learn anomalies by exploiting both normal and
anomalous videos. To avoid annotating the anomalous segments or clips in
training videos, which is very time consuming, we propose to learn anomaly
through the deep multip... | ['Chen Chen', 'Waqas Sultani', 'Mubarak Shah'] | 2018-01-12 | real-world-anomaly-detection-in-surveillance-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Sultani_Real-World_Anomaly_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Sultani_Real-World_Anomaly_Detection_CVPR_2018_paper.pdf | cvpr-2018-6 | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'semi-supervised-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.72030622e-01 -3.52597177e-01 -1.40128523e-01 -3.73167008e-01
-7.42073715e-01 -5.32115281e-01 5.75536549e-01 2.10535843e-02
-1.97790548e-01 2.76762187e-01 2.13711500e-01 -1.11389264e-01
2.12814763e-01 -3.42273384e-01 -1.15563738e+00 -7.71723747e-01
-7.01892912e-01 1.59109190e-01 2.66862959e-01 3.27705056... | [7.860646724700928, 1.561623454093933] |
5bbaa1e0-4d38-47c9-9a08-ae9c9fb28b46 | proceedings-eighteenth-conference-on | 2106.10886 | null | https://arxiv.org/abs/2106.10886v1 | https://arxiv.org/pdf/2106.10886v1.pdf | Proceedings Eighteenth Conference on Theoretical Aspects of Rationality and Knowledge | The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a biannual conference that aims to bring together researchers from a wide variety of fields, including computer science, artificial intelligence, game theory, decision theory, philosophy, logic, linguistics, and cognitive science. Its goal is to ... | ['Andrés Perea', 'Joseph Halpern'] | 2021-06-21 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-2.64327854e-01 7.01045871e-01 -1.52836412e-01 -6.15829937e-02
-1.99789673e-01 -7.37533808e-01 5.67102671e-01 3.72743905e-01
-3.33609730e-01 1.05138123e+00 1.15637712e-01 -5.11967123e-01
-6.99925423e-01 -1.00501275e+00 -7.16281980e-02 -2.51581281e-01
-7.72069097e-02 5.36609888e-01 4.10858631e-01 -3.99765015... | [8.768035888671875, 6.67987585067749] |
abb4de10-b259-43e2-84a0-87c9ebbe3787 | greyc-fintoc-2022-handling-document-layout | null | null | https://aclanthology.org/2022.fnp-1.15 | https://aclanthology.org/2022.fnp-1.15.pdf | GREYC@FinTOC-2022: Handling Document Layout and Structure in Native PDF Bundle of Documents | n this paper, we present our contribution to the FinTOC-2022 Shared Task “Financial Document Structure Extraction”. We participated in the three tracks dedicated to English, French and Spanish document processing. Our main contribution consists in considering financial prospectus as a bundle of documents, i.e., a set o... | ['Nadine Lucas', 'Emmanuel Giguet'] | null | null | null | null | fnp-lrec-2022-6 | ['boundary-detection'] | ['computer-vision'] | [ 1.39701605e-01 -5.45915589e-02 1.64380416e-01 -7.84271359e-02
-8.33899081e-01 -1.12015462e+00 9.86950457e-01 5.36106408e-01
-1.52882308e-01 3.41997862e-01 3.24277371e-01 -2.40909562e-01
-3.46013039e-01 -6.72199130e-01 -5.22760928e-01 -4.51106906e-01
-1.52949160e-02 4.22782421e-01 1.72738880e-01 1.54303014... | [11.739574432373047, 2.7524518966674805] |
a2b8e1ea-770f-445e-84db-f4e4e6f7709a | ai-imu-dead-reckoning | 1904.06064 | null | http://arxiv.org/abs/1904.06064v1 | http://arxiv.org/pdf/1904.06064v1.pdf | AI-IMU Dead-Reckoning | In this paper we propose a novel accurate method for dead-reckoning of
wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the
context of intelligent vehicles, robust and accurate dead-reckoning based on
the IMU may prove useful to correlate feeds from imaging sensors, to safely
navigate through obstr... | ['Silvère Bonnabel', 'Axel Barrau', 'Martin Brossard'] | 2019-04-12 | null | null | null | null | ['dead-reckoning-prediction'] | ['miscellaneous'] | [-4.57597733e-01 -2.14718580e-01 -1.53343707e-01 -2.94331700e-01
-4.89547789e-01 -5.85269392e-01 7.95397520e-01 -2.21991897e-01
-8.28520417e-01 7.06853390e-01 -1.48562208e-01 -6.02215767e-01
2.63213348e-02 -7.52257228e-01 -9.98938203e-01 -5.49770415e-01
2.15097398e-01 6.58512414e-01 3.70161712e-01 -3.56857091... | [7.474061965942383, -1.9905478954315186] |
b0238946-3636-452a-92e3-10cb392e51d6 | multi-label-transformer-for-action-unit | 2203.12531 | null | https://arxiv.org/abs/2203.12531v3 | https://arxiv.org/pdf/2203.12531v3.pdf | Multi-label Transformer for Action Unit Detection | Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements. It is key to unlock unbiased computational face representations and has therefore aroused great interest in the past few years. One of the main obstacles toward building efficient deep learning ba... | ['Kevin Bailly', 'Arnaud Dapogny', 'Edouard Yvinec', 'Gauthier Tallec'] | 2022-03-23 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 3.52411121e-01 4.31614071e-01 -1.78496197e-01 -5.55869877e-01
-7.80071378e-01 -3.57453257e-01 5.04242599e-01 -2.81308860e-01
-4.44531441e-01 3.83269489e-01 3.24146718e-01 3.48453820e-01
3.59380245e-01 -2.96485245e-01 -5.06303310e-01 -6.77546263e-01
9.40157287e-03 2.28508174e-01 -2.60568172e-01 -3.31808001... | [13.563921928405762, 1.8258509635925293] |
8dc9770b-6c73-401c-ad8e-502a3d0c9c31 | effect-of-temporal-resolution-on-the | 2302.10761 | null | https://arxiv.org/abs/2302.10761v2 | https://arxiv.org/pdf/2302.10761v2.pdf | Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing | Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification. ... | ['Makoto Naruse', 'Ryoichi Horisaki', 'Takatomo Mihana', 'André Röhm', 'Kohei Tsuchiyama'] | 2023-01-27 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.12154552e-01 -3.84351403e-01 2.54940148e-02 2.60007262e-01
2.96094753e-02 -5.58133006e-01 9.18051958e-01 1.97093382e-01
-3.07281047e-01 8.96572948e-01 -1.20615549e-01 -2.90226758e-01
5.01450486e-02 -1.07216918e+00 -4.94833350e-01 -1.08145320e+00
-5.49980879e-01 2.76003554e-02 3.09334368e-01 -3.38322282... | [6.618561744689941, 3.430763006210327] |
2bc90bd7-d019-4788-bc24-d132025ab701 | spectral-inference-networks-unifying-spectral | 1806.02215 | null | https://arxiv.org/abs/1806.02215v3 | https://arxiv.org/pdf/1806.02215v3.pdf | Spectral Inference Networks: Unifying Deep and Spectral Learning | We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, the... | ['Stig Petersen', 'David G. T. Barrett', 'Kimberly L. Stachenfeld', 'David Pfau', 'Ashish Agarwal'] | 2018-06-06 | spectral-inference-networks-unifying-deep-and | https://openreview.net/forum?id=SJzqpj09YQ | https://openreview.net/pdf?id=SJzqpj09YQ | iclr-2019-5 | ['variational-monte-carlo'] | ['miscellaneous'] | [ 5.22057533e-01 -1.30357184e-02 -3.56800258e-01 -2.64740586e-01
-7.76607275e-01 -6.60008252e-01 4.96455044e-01 -3.04313123e-01
-2.04006419e-01 6.79632008e-01 3.87146115e-01 -3.19649279e-01
-5.93239486e-01 -6.72373652e-01 -8.29882264e-01 -8.96300316e-01
-5.03941119e-01 6.44578099e-01 -1.18925326e-01 9.17611122... | [5.79521369934082, 4.789361000061035] |
cf0b31e1-fe9c-472b-96c3-f97149009f9e | point-convolutional-neural-networks-by | 1803.10091 | null | http://arxiv.org/abs/1803.10091v1 | http://arxiv.org/pdf/1803.10091v1.pdf | Point Convolutional Neural Networks by Extension Operators | This paper presents Point Convolutional Neural Networks (PCNN): a novel
framework for applying convolutional neural networks to point clouds. The
framework consists of two operators: extension and restriction, mapping point
cloud functions to volumetric functions and vise-versa. A point cloud
convolution is defined by ... | ['Haggai Maron', 'Yaron Lipman', 'Matan Atzmon'] | 2018-03-27 | null | null | null | null | ['classify-3d-point-clouds', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.64218205e-02 -2.53671765e-01 2.15260964e-03 -2.27344319e-01
-2.61030793e-01 -8.87896180e-01 8.67480874e-01 1.00537181e-01
-2.09208623e-01 1.83771998e-01 -3.96011144e-01 -4.08343762e-01
1.39384915e-03 -1.24388325e+00 -1.25783193e+00 -4.13311690e-01
-2.43371576e-01 8.06949079e-01 2.53409594e-01 -2.22560152... | [7.885570526123047, -3.750418186187744] |
6bff9f6f-eca1-4541-a97d-c0f6f1dd58fe | style-transfer-counterfactual-explanations-an | null | null | https://www.sciencedirect.com/science/article/pii/S0933365722002093 | https://www.sciencedirect.com/science/article/pii/S0933365722002093/pdfft?md5=f109e9c0643e156c487a0c39375a17db&pid=1-s2.0-S0933365722002093-main.pdf | Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients | In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to developing interpretable machine learning model... | ['Panagiotis Papapetrou', 'Vasiliki Kougia', 'Isak Samsten', 'Zhendong Wang'] | 2023-01-01 | null | null | null | artificial-intelligence-in-medicine-2023-1 | ['style-transfer', 'interpretable-machine-learning', 'counterfactual-explanation', 'text-style-transfoer'] | ['computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [ 6.52115643e-01 9.62344050e-01 -2.65819311e-01 -6.20317638e-01
-6.12951398e-01 -1.41265571e-01 4.95046943e-01 3.54558647e-01
-1.04066417e-01 1.26675582e+00 5.02826035e-01 -8.98046851e-01
-2.11968169e-01 -6.05063975e-01 -6.74404860e-01 -1.67655632e-01
-2.39120468e-01 4.86095041e-01 -4.42850083e-01 9.07944068... | [8.580146789550781, 5.671929836273193] |
4c7f4891-92a9-439d-b73c-2a5cefc16a47 | directed-acyclic-graph-network-for | 2105.12907 | null | https://arxiv.org/abs/2105.12907v2 | https://arxiv.org/pdf/2105.12907v2.pdf | Directed Acyclic Graph Network for Conversational Emotion Recognition | The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network,... | ['Xiaojun Quan', 'Yunyi Yang', 'Siyue Wu', 'Weizhou Shen'] | 2021-05-27 | null | https://aclanthology.org/2021.acl-long.123 | https://aclanthology.org/2021.acl-long.123.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 7.74975345e-02 1.88127026e-01 1.44668326e-01 -7.10734367e-01
-8.63678288e-03 -4.42513004e-02 6.43614411e-01 3.34164016e-02
-1.30317092e-01 3.78120303e-01 1.03226542e+00 -3.55512828e-01
2.16614325e-02 -6.41270995e-01 -1.44141734e-01 -5.06581366e-01
-6.73782051e-01 7.27577806e-02 -1.70593411e-01 -6.09527051... | [12.955738067626953, 6.239774703979492] |
2ceec28f-465a-4b75-9a81-30ff1f9a4032 | banmo-building-animatable-3d-neural-models | 2112.12761 | null | https://arxiv.org/abs/2112.12761v3 | https://arxiv.org/pdf/2112.12761v3.pdf | BANMo: Building Animatable 3D Neural Models from Many Casual Videos | Prior work for articulated 3D shape reconstruction often relies on specialized sensors (e.g., synchronized multi-camera systems), or pre-built 3D deformable models (e.g., SMAL or SMPL). Such methods are not able to scale to diverse sets of objects in the wild. We present BANMo, a method that requires neither a speciali... | ['Hanbyul Joo', 'Andrea Vedaldi', 'Deva Ramanan', 'Natalia Neverova', 'Minh Vo', 'Gengshan Yang'] | 2021-12-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_BANMo_Building_Animatable_3D_Neural_Models_From_Many_Casual_Videos_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_BANMo_Building_Animatable_3D_Neural_Models_From_Many_Casual_Videos_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-shape-reconstruction-from-videos'] | ['computer-vision'] | [ 1.47795096e-01 -1.27892911e-01 9.30259451e-02 -7.13395402e-02
-5.34686446e-01 -8.52678299e-01 6.60596192e-01 -4.79521453e-01
1.73969064e-02 3.45565617e-01 2.65801817e-01 3.13898206e-01
6.87176958e-02 -7.19494939e-01 -1.00857913e+00 -5.31047702e-01
1.52082630e-02 5.10483682e-01 2.44199574e-01 -2.81776160... | [8.78839111328125, -2.8213307857513428] |
192c552f-9990-4e61-8caa-09c4653620da | scaling-adversarial-training-to-large | 2210.09852 | null | https://arxiv.org/abs/2210.09852v1 | https://arxiv.org/pdf/2210.09852v1.pdf | Scaling Adversarial Training to Large Perturbation Bounds | The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending attacks constrained within low magnitude Lp norm bounds, real-world adversaries are not limited by such constraints. In this work, we aim to a... | ['R. Venkatesh Babu', 'Gaurang Sriramanan', 'Samyak Jain', 'Sravanti Addepalli'] | 2022-10-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.93107474e-01 3.45589548e-01 2.82563984e-01 -2.46584579e-01
-9.33935583e-01 -1.45491612e+00 4.49757665e-01 -2.50010341e-01
-3.71288449e-01 6.30089223e-01 -1.36974692e-01 -8.14868450e-01
-2.49521151e-01 -7.31368899e-01 -1.27666509e+00 -9.14301693e-01
-4.24591064e-01 4.06460129e-02 2.44482696e-01 -3.58615816... | [5.587157249450684, 7.890392780303955] |
95f2b290-5cc3-44dd-b717-4149063c7d81 | a-neural-probabilistic-structured-prediction | null | null | https://aclanthology.org/P15-1117 | https://aclanthology.org/P15-1117.pdf | A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing | null | ['Shu-Jian Huang', 'Jia-Jun Chen', 'Yue Zhang', 'Hao Zhou'] | 2015-07-01 | a-neural-probabilistic-structured-prediction-1 | https://aclanthology.org/P15-1117 | https://aclanthology.org/P15-1117.pdf | ijcnlp-2015-7 | ['transition-based-dependency-parsing'] | ['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... | [-7.299790859222412, 3.819453716278076] |
be9b00fb-7750-482b-9db1-ff96f4338a04 | a-joint-learning-approach-for-semi-supervised | 2204.03208 | null | https://arxiv.org/abs/2204.03208v1 | https://arxiv.org/pdf/2204.03208v1.pdf | A Joint Learning Approach for Semi-supervised Neural Topic Modeling | Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to t... | ['Finale Doshi-Velez', 'Abhishek Sharma', 'Neehal Tumma', 'Rajat Mittal', 'Jeffrey Chiu'] | 2022-04-07 | null | https://aclanthology.org/2022.spnlp-1.5 | https://aclanthology.org/2022.spnlp-1.5.pdf | spnlp-acl-2022-5 | ['topic-models'] | ['natural-language-processing'] | [-8.76257792e-02 5.03655910e-01 -5.18235803e-01 -4.97142017e-01
-1.08411610e+00 -3.31084371e-01 1.31699610e+00 -3.38951237e-02
2.12712616e-01 4.63442236e-01 8.38255405e-01 -2.85568476e-01
-3.17968011e-01 -9.38367248e-01 -5.65712154e-01 -5.05535066e-01
1.22164533e-01 1.02453530e+00 -2.15022802e-01 1.04322314... | [10.397970199584961, 6.933833599090576] |
f14c5865-608f-4477-bcff-129557eeaa57 | nuclei-panoptic-segmentation-and-composition | 2202.11804 | null | https://arxiv.org/abs/2202.11804v1 | https://arxiv.org/pdf/2202.11804v1.pdf | Nuclei panoptic segmentation and composition regression with multi-task deep neural networks | Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology. The Colon Nuclei Identification and Counting (CoNIC) Challenge is held to... | ['Satoshi Kasai', 'Satoshi Kondo'] | 2022-02-23 | null | null | null | null | ['explainable-models', 'nuclear-segmentation'] | ['computer-vision', 'medical'] | [ 5.71663857e-01 1.92103729e-01 -1.53489441e-01 -2.87544131e-01
-1.09088778e+00 -4.97209758e-01 4.94552672e-01 8.01559865e-01
-8.04868579e-01 8.41819167e-01 3.78491908e-01 -4.36652571e-01
4.01729383e-02 -5.35712123e-01 -4.20325935e-01 -1.24799693e+00
4.96641584e-02 8.51582766e-01 -2.80656628e-02 1.45476490... | [15.08060073852539, -3.1666016578674316] |
1fc657d7-3eba-4d51-9812-5f2d68b6ecc5 | elevater-a-benchmark-and-toolkit-for | 2204.08790 | null | https://arxiv.org/abs/2204.08790v6 | https://arxiv.org/pdf/2204.08790v6.pdf | ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models | Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of ... | ['Yong Jae Lee', 'Zicheng Liu', 'Jianfeng Gao', 'Houdong Hu', 'Ping Jin', 'Jianwei Yang', 'Jyoti Aneja', 'Pengchuan Zhang', 'Liunian Harold Li', 'Haotian Liu', 'Chunyuan Li'] | 2022-04-19 | null | null | null | null | ['zero-shot-object-detection', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.86376078e-02 -3.46262813e-01 -3.67864341e-01 -4.10578698e-01
-1.09080076e+00 -6.61088347e-01 9.45131302e-01 -2.46759802e-01
-5.06867468e-01 2.49833018e-01 1.33004427e-01 -4.68729854e-01
5.09424746e-01 -3.36472541e-01 -7.92364180e-01 -5.87747097e-01
1.02060854e-01 3.62118125e-01 3.24619651e-01 -9.50194430... | [10.172637939453125, 1.8176606893539429] |
4a524198-4e7c-497e-8c57-fb0fb90d6261 | online-self-attentive-gated-rnns-for-real | 2106.13493 | null | https://arxiv.org/abs/2106.13493v2 | https://arxiv.org/pdf/2106.13493v2.pdf | Online Self-Attentive Gated RNNs for Real-Time Speaker Separation | Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produce high-quality separations, they were mainly applied under offline settings, in which the model has access to the full input signal while s... | ['Anurag Kumar', 'Buye Xu', 'Zhenyu Tang', 'Yossi Adi', 'Ori Kabeli'] | 2021-06-25 | null | null | null | null | ['speaker-separation'] | ['speech'] | [-2.09678020e-02 -4.71762478e-01 3.31809103e-01 3.28662172e-02
-9.32829201e-01 -7.27887034e-01 4.32760626e-01 1.45111997e-02
-3.13769847e-01 6.33354247e-01 6.00225210e-01 -5.23217559e-01
-2.80383259e-01 -2.42875829e-01 -7.01720119e-01 -6.30883932e-01
-3.33158553e-01 -1.28524289e-01 9.75800529e-02 2.49804910... | [15.183355331420898, 5.72520112991333] |
49b97b51-fff3-4ff2-8fa5-be7274783eff | continuously-controllable-facial-expression | 2209.08289 | null | https://arxiv.org/abs/2209.08289v1 | https://arxiv.org/pdf/2209.08289v1.pdf | Continuously Controllable Facial Expression Editing in Talking Face Videos | Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expression... | ['Yong-Jin Liu', 'Yaoyuan Wang', 'Ziyang Zhang', 'Yanan sun', 'Tian Lv', 'Yu-Hui Wen', 'Zhiyao Sun'] | 2022-09-17 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 2.92896271e-01 -2.95528471e-02 -1.74279511e-02 -4.51963484e-01
-3.94677222e-01 -2.44853839e-01 4.23835665e-01 -7.87255526e-01
-5.19700870e-02 4.84692067e-01 2.21144497e-01 5.59756577e-01
3.14223409e-01 -4.32003796e-01 -7.93812335e-01 -9.79520679e-01
2.94277072e-01 -1.86647505e-01 -2.34121025e-01 -3.77397150... | [13.141609191894531, -0.3777103126049042] |
c3623a2b-fc8a-44a3-9e20-380ab807585b | pico-contrastive-label-disambiguation-for | 2201.08984 | null | https://arxiv.org/abs/2201.08984v3 | https://arxiv.org/pdf/2201.08984v3.pdf | PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning | Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we br... | ['Junbo Zhao', 'Gang Chen', 'Gang Niu', 'Lei Feng', 'Yixuan Li', 'Ruixuan Xiao', 'Haobo Wang'] | 2022-01-22 | null | null | null | null | ['partial-label-learning', 'pico'] | ['methodology', 'natural-language-processing'] | [ 6.88520133e-01 2.46523917e-01 -4.30157006e-01 -4.01702613e-01
-1.51374567e+00 -6.10813618e-01 5.43572426e-01 4.48687613e-01
-4.01183307e-01 9.33706462e-01 -1.36427134e-01 2.39450082e-01
-2.72320509e-01 -3.82502258e-01 -4.76414502e-01 -1.07233012e+00
3.09592098e-01 7.27072358e-01 -1.41175151e-01 5.94062135... | [9.456924438476562, 3.981279134750366] |
a7bb459f-9cde-4871-b549-8d3f51325570 | digital-accessibility-and-information-mining | null | null | https://aclanthology.org/2022.wildre-1.8 | https://aclanthology.org/2022.wildre-1.8.pdf | Digital Accessibility and Information Mining of Dharmaśāstric Knowledge Traditions | The heritage of Dharmaśāstra (DS) carries extensive cultural history and encapsulates the treatises of Ancient Indian Social Institutions (SI). DS is reckoned as an epitome of the primitive Indian knowledge tradition as it incorporates a variety of genres for sciences and arts such as family law and legislation, civili... | ['Subhash Chandra', 'Arooshi Nigam'] | null | null | null | null | wildre-lrec-2022-6 | ['culture'] | ['speech'] | [-1.51207000e-01 -7.85740931e-03 -3.54031414e-01 3.39044094e-01
4.04894464e-02 -8.60241950e-01 9.99768078e-01 4.58001286e-01
-6.03183448e-01 8.92852962e-01 6.07852995e-01 -6.61907613e-01
-3.92753571e-01 -8.13642800e-01 4.88571152e-02 -5.67075193e-01
1.42736599e-01 3.46796453e-01 9.13579836e-02 -6.46025419... | [9.103849411010742, 6.332812786102295] |
85a58d4f-086b-4c43-9886-042acdb810fc | low-resource-neural-headline-generation | 1707.09769 | null | http://arxiv.org/abs/1707.09769v1 | http://arxiv.org/pdf/1707.09769v1.pdf | Low-Resource Neural Headline Generation | Recent neural headline generation models have shown great results, but are
generally trained on very large datasets. We focus our efforts on improving
headline quality on smaller datasets by the means of pretraining. We propose
new methods that enable pre-training all the parameters of the model and
utilize all availab... | ['Tanel Alumäe', 'Ottokar Tilk'] | 2017-07-31 | low-resource-neural-headline-generation-1 | https://aclanthology.org/W17-4503 | https://aclanthology.org/W17-4503.pdf | ws-2017-9 | ['headline-generation'] | ['natural-language-processing'] | [-1.10136740e-01 4.55595315e-01 -4.03457999e-01 -4.92684841e-01
-1.29270387e+00 -3.87249261e-01 8.56651485e-01 1.22337230e-01
-8.11611176e-01 1.18267834e+00 6.69507384e-01 -4.35027957e-01
3.11654806e-01 -6.78287268e-01 -6.42033935e-01 -1.84782997e-01
9.19091851e-02 7.42851436e-01 1.70151889e-01 -4.51408178... | [11.816008567810059, 9.035338401794434] |
73e3c6d1-179e-48bd-949e-6562b9b03a7c | self-supervised-velocity-estimation-for | 2207.03146 | null | https://arxiv.org/abs/2207.03146v1 | https://arxiv.org/pdf/2207.03146v1.pdf | Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks | This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms of generating its own training signal for the velocities. Labels are only required for single-frame, oriented bounding boxes (OBBs). Labels ... | ['Holger Blume', 'André Treptow', 'Claudius Gläser', 'Florian Faion', 'Daniel Köhler', 'Sascha Braun', 'Michael Ulrich', 'Daniel Niederlöhner'] | 2022-07-07 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 2.70999998e-01 -1.16967096e-03 -2.13893652e-01 -6.95006430e-01
-4.39512134e-01 -4.36872542e-01 8.74981165e-01 -2.52547301e-02
-6.61825180e-01 7.48893917e-01 -7.30870306e-01 -4.96396035e-01
-2.91474730e-01 -9.90556180e-01 -6.45167589e-01 -9.36626375e-01
-4.13801134e-01 6.73649967e-01 7.56398857e-01 -1.31281717... | [7.950766086578369, -1.3927439451217651] |
aca3e950-3a7c-449d-a89a-3f0e4aa31851 | towards-multi-instrument-drum-transcription | 1806.06676 | null | http://arxiv.org/abs/1806.06676v2 | http://arxiv.org/pdf/1806.06676v2.pdf | Towards multi-instrument drum transcription | Automatic drum transcription, a subtask of the more general automatic music
transcription, deals with extracting drum instrument note onsets from an audio
source. Recently, progress in transcription performance has been made using
non-negative matrix factorization as well as deep learning methods. However,
these works ... | ['Peter Knees', 'Richard Vogl', 'Gerhard Widmer'] | 2018-06-18 | null | null | null | null | ['drum-transcription', 'music-transcription'] | ['music', 'music'] | [ 7.37660304e-02 -4.11801249e-01 1.09749883e-01 2.65180022e-01
-1.12564182e+00 -8.83601844e-01 -1.89829823e-02 -3.70925665e-01
-2.98321657e-02 6.23933375e-01 4.33787316e-01 -9.06796902e-02
-8.38726833e-02 -4.60925013e-01 -4.25036550e-01 -8.03996682e-01
-5.65190753e-03 3.29089284e-01 -3.08303267e-01 -3.94163042... | [15.902271270751953, 5.279184818267822] |
29142f01-c538-433b-9331-ce6069d5ec06 | tell-me-what-you-read-automatic-expertise | null | null | https://aclanthology.org/2021.ranlp-main.177 | https://aclanthology.org/2021.ranlp-main.177.pdf | Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains | This paper investigates the effectiveness of automatic annotator assignment for text annotation in expert domains. In the task of creating high-quality annotated corpora, expert domains often cover multiple sub-domains (e.g. organic and inorganic chemistry in the chemistry domain) either explicitly or implicitly. There... | ['Patrycja Swieczkowska', 'Rafal Rzepka', 'Kazutaka Shimada', 'Yasutaka Kumano', 'Hiroaki Yoshida', 'Kimi Kaneko', 'Tomoya Iwakura', 'Hiyori Yoshikawa'] | null | null | https://aclanthology.org/2021.ranlp-1.177 | https://aclanthology.org/2021.ranlp-1.177.pdf | ranlp-2021-9 | ['text-annotation'] | ['natural-language-processing'] | [-5.06711937e-02 6.57165825e-01 -7.76378065e-02 -5.24519503e-01
-1.05583596e+00 -1.16059816e+00 3.27392429e-01 8.28577638e-01
-5.81348419e-01 1.25697780e+00 1.86954319e-01 9.84144136e-02
-1.15165412e-02 -4.56403702e-01 -4.67050761e-01 -4.39086825e-01
7.06854165e-01 1.12287152e+00 4.93074179e-01 7.96419382... | [9.69964599609375, 4.836886882781982] |
fe6dc155-d291-4bb8-a2cc-2bc5fd647f3e | analysis-of-social-media-data-using | 2004.11838 | null | https://arxiv.org/abs/2004.11838v1 | https://arxiv.org/pdf/2004.11838v1.pdf | Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response | Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image co... | ['Muhammad Imran', 'Ferda Ofli', 'Firoj Alam'] | 2020-04-14 | null | null | null | null | ['small-data', 'multimodal-text-and-image-classification'] | ['computer-vision', 'methodology'] | [ 1.63617190e-02 -1.49188787e-01 -3.89938653e-02 -2.73379147e-01
-9.85021770e-01 -2.39066169e-01 6.87706828e-01 5.24748802e-01
-6.60192311e-01 7.80063450e-01 7.68436611e-01 -6.16380423e-02
1.54619440e-01 -1.12509203e+00 -5.12095869e-01 -6.96159005e-01
-1.57269239e-02 8.61097593e-03 -6.39936477e-02 -3.48587364... | [10.93497371673584, 1.6127463579177856] |
6dc0b37a-5d6e-4cbf-bad4-c4bfc31064c5 | mira-a-computational-neuro-based-cognitive | 1902.09291 | null | http://arxiv.org/abs/1902.09291v2 | http://arxiv.org/pdf/1902.09291v2.pdf | MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems | The human mind is still an unknown process of neuroscience in many aspects.
Nevertheless, for decades the scientific community has proposed computational
models that try to simulate their parts, specific applications, or their
behavior in different situations. The most complete model in this line is
undoubtedly the LID... | ['Guilherme A. Wachs-Lopes', 'Amanda M. Lima', 'Paulo S. Rodrigues', 'Lucas A. Silva', 'Felipe S. Vargas', 'Mariana B. Santos'] | 2019-02-25 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.66603887e-01 1.86214432e-01 1.97357252e-01 -4.71830219e-02
7.41428852e-01 -5.17352164e-01 1.15717840e+00 3.20683479e-01
-7.80035317e-01 5.43163776e-01 -3.86813134e-02 5.48154563e-02
-7.44037926e-01 -7.39082277e-01 -4.04417813e-01 -5.93577981e-01
-2.67853271e-02 8.08948159e-01 3.22186172e-01 -5.97403467... | [5.654978275299072, 4.04446268081665] |
7acc4b83-3f7b-4382-9fa1-a1f290de9266 | zero-shot-adversarial-quantization | 2103.15263 | null | https://arxiv.org/abs/2103.15263v2 | https://arxiv.org/pdf/2103.15263v2.pdf | Zero-shot Adversarial Quantization | Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the high performance of full-precision models, most existing quantization methods focus on fine-tuning quantized model by assuming training datase... | ['Jun Wang', 'Wei zhang', 'Yuang Liu'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.06030035e-01 -1.72937363e-02 -4.48730379e-01 -3.96261692e-01
-1.00883496e+00 -2.72547334e-01 6.93178236e-01 -1.40267298e-01
-4.74372029e-01 8.44281197e-01 1.02557436e-01 -3.45441252e-01
1.18080571e-01 -9.72053826e-01 -9.78684962e-01 -8.67571473e-01
4.91644382e-01 2.28005260e-01 -1.41190831e-02 -2.47841746... | [8.762450218200684, 2.9547019004821777] |
dfdb0ab0-03ee-4708-8b47-4ea898bc50d8 | all4one-symbiotic-neighbour-contrastive | 2303.09417 | null | https://arxiv.org/abs/2303.09417v1 | https://arxiv.org/pdf/2303.09417v1.pdf | All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction | Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities. However, their computational efficiency decreases when more than one neighbour is used. In this paper, we propose a novel contrastive SSL approach, which ... | ['Petia Radeva', 'Bhalaji Nagarajan', 'Ignacio Sarasúa', 'Imanol G. Estepa'] | 2023-03-16 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.03547972e-01 1.14196494e-01 -3.06808412e-01 -2.76169568e-01
-7.45982528e-01 -4.52412486e-01 1.08912015e+00 4.91036534e-01
-7.12257266e-01 6.91184819e-01 2.62598544e-01 6.68085888e-02
-6.06205225e-01 -7.15696871e-01 -5.83794296e-01 -8.36265624e-01
-1.00340605e-01 3.86399597e-01 4.02117610e-01 -3.38685900... | [9.514603614807129, 2.9111342430114746] |
34e36ae2-f0a8-4e69-86e4-5566842e9f05 | rolling-horizon-based-temporal-decomposition | 2303.03475 | null | https://arxiv.org/abs/2303.03475v1 | https://arxiv.org/pdf/2303.03475v1.pdf | Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows | The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-o... | ['Samitha Samaranayake', 'Abhishek Dubey', 'Aron Laszka', 'Philip Pugliese', 'Michael Wilbur', 'Danushka Edirimanna', 'Youngseo Kim'] | 2023-03-06 | null | null | null | null | ['combinatorial-optimization', 'problem-decomposition'] | ['methodology', 'miscellaneous'] | [-1.02481544e-01 -5.42183034e-02 -4.16477323e-01 -2.40956899e-02
-9.65193331e-01 -9.49780941e-01 1.88268408e-01 2.65567482e-01
-1.62641495e-01 8.25323045e-01 -6.95063919e-02 -7.54501343e-01
-7.32684374e-01 -9.18302000e-01 -7.27388561e-01 -6.09068811e-01
-3.72784495e-01 7.37122238e-01 5.37121773e-01 -5.79598367... | [5.11208438873291, 2.676086902618408] |
bd1c705e-6cab-467b-8c70-c8110c6b69c4 | 3dvg-transformer-relation-modeling-for-visual | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.pdf | 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds | Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world. By formulating this task as a grounding-by-detection problem, lots of recent works focus on how to exploit more powerful detectors and comprehensive language features,... | ['Dong Xu', 'Lu Sheng', 'Daigang Cai', 'Lichen Zhao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['object-proposal-generation'] | ['computer-vision'] | [-4.48515862e-02 4.16940544e-03 1.07787669e-01 -3.78953099e-01
-8.44104052e-01 -5.25780320e-01 9.95367050e-01 3.14742655e-01
5.39609082e-02 -3.31609845e-02 -3.36802825e-02 -3.82467926e-01
-1.45603523e-01 -8.74535024e-01 -7.03437984e-01 -4.87963647e-01
1.15434872e-02 7.59365439e-01 6.02917373e-01 -4.94930387... | [7.9277215003967285, -2.893906831741333] |
05445c0d-cbd2-422e-9fbd-6ff600508287 | knowledge-base-completion-baseline-strikes | 2005.00804 | null | https://arxiv.org/abs/2005.00804v3 | https://arxiv.org/pdf/2005.00804v3.pdf | Knowledge Base Completion: Baseline strikes back (Again) | Knowledge Base Completion (KBC) has been a very active area lately. Several recent KBCpapers propose architectural changes, new training methods, or even new formulations. KBC systems are usually evaluated on standard benchmark datasets: FB15k, FB15k-237, WN18, WN18RR, and Yago3-10. Most existing methods train with a s... | ['Sushant Rathi', 'Prachi Jain', 'Mausam', 'Soumen Chakrabarti'] | 2020-05-02 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion', 'knowledge-base-population'] | ['graphs', 'knowledge-base', 'natural-language-processing'] | [-8.20567980e-02 -1.73425172e-02 -5.38488150e-01 -3.53076935e-01
-8.33348036e-01 -6.16634667e-01 7.26921141e-01 1.14798978e-01
-9.03026164e-01 1.51488972e+00 4.44875620e-02 -2.64768630e-01
-2.09722579e-01 -7.98240721e-01 -7.03032970e-01 -7.00599492e-01
-1.33228600e-01 7.51264691e-01 5.70452690e-01 -5.65340161... | [9.320541381835938, 8.356607437133789] |
8c8d8d39-2ecb-45dd-8572-0348aee27c83 | commonality-in-recommender-systems-evaluating | 2302.11360 | null | https://arxiv.org/abs/2302.11360v2 | https://arxiv.org/pdf/2302.11360v2.pdf | Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship | Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimize for personalized user experience, they can overlook impacts on cultural experience in the aggregate. After demonstrating that existing met... | ['Georgina Born', 'Fernando Diaz', 'Gustavo Ferreira', 'Andres Ferraro'] | 2023-02-22 | null | null | null | null | ['culture'] | ['speech'] | [-2.48317644e-01 -8.50730389e-02 -4.62186784e-01 -1.12890840e-01
-4.77763325e-01 -8.92364264e-01 7.95228124e-01 3.36921841e-01
-4.02715981e-01 1.83798268e-01 1.19981623e+00 -1.37336954e-01
-2.84171939e-01 -6.19923472e-01 -1.12050816e-01 -4.09628808e-01
2.60449916e-01 -3.15016657e-01 -5.75994372e-01 -7.36895263... | [9.766962051391602, 5.768564224243164] |
6f785ccd-d56b-4449-9dac-a9f5f37efbc3 | actions-generation-from-captions | 1902.11109 | null | http://arxiv.org/abs/1902.11109v1 | http://arxiv.org/pdf/1902.11109v1.pdf | Actions Generation from Captions | Sequence transduction models have been widely explored in many natural
language processing tasks. However, the target sequence usually consists of
discrete tokens which represent word indices in a given vocabulary. We barely
see the case where target sequence is composed of continuous vectors, where
each vector is an e... | ['Yida Xu', 'Xuan Liang'] | 2019-02-14 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 8.04416656e-01 4.39627916e-02 3.04750681e-01 -2.37997979e-01
-9.05245245e-01 -5.39546967e-01 1.15299976e+00 -4.90809739e-01
-1.04896724e-01 8.59948814e-01 4.27357227e-01 8.11568946e-02
4.41596746e-01 -8.64643872e-01 -8.89763832e-01 -8.25891852e-01
9.01566222e-02 4.20566171e-01 -1.01867199e-01 -5.31249881... | [15.238080024719238, 4.862293720245361] |
36e0fe95-bd9a-4ab8-9dcd-2c9ec6d1e7a9 | opinion-aspect-extraction-in-dutch-childrens | 1910.10502 | null | https://arxiv.org/abs/1910.10502v1 | https://arxiv.org/pdf/1910.10502v1.pdf | Opinion aspect extraction in Dutch childrens diary entries | Aspect extraction can be used in dialogue systems to understand the topic of opinionated text. Expressing an empathetic reaction to an opinion can strengthen the bond between a human and, for example, a robot. The aim of this study is three-fold: 1. create a new annotated dataset for both aspect extraction and opinion ... | ['Maaike H. T. de Boer', 'Hella Haanstra'] | 2019-10-21 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [-9.03246850e-02 9.51419532e-01 -2.74123866e-02 -8.28817666e-01
-7.09611356e-01 -5.76310277e-01 6.45432591e-01 4.55240071e-01
-6.45388305e-01 7.72679090e-01 6.60065234e-01 -1.77782163e-01
5.20208061e-01 -7.16301024e-01 -4.07872766e-01 -5.35933256e-01
4.38275009e-01 6.62983000e-01 -2.78327048e-01 -7.57732689... | [11.42932415008545, 6.78416633605957] |
16aa8e9d-2d17-49f7-b2e6-5606571adece | learning-to-rank-with-partitioned-preference | 2006.05067 | null | https://arxiv.org/abs/2006.05067v3 | https://arxiv.org/pdf/2006.05067v3.pdf | Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model | We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. Given $N$ items with $M$ partitions, calculating the likelihood of data wit... | ['Xinyang Yi', 'Lichan Hong', 'Weijing Tang', 'Zhe Zhao', 'Jiaqi Ma', 'Qiaozhu Mei', 'Ed H. Chi'] | 2020-06-09 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.68016230e-03 -4.26985294e-01 -4.95563269e-01 -6.09784782e-01
-1.43304265e+00 -6.52887821e-01 -8.29040185e-02 4.09406900e-01
-7.09291518e-01 8.21200669e-01 -9.62175876e-02 -3.27291071e-01
-6.27657950e-01 -5.70120990e-01 -5.91265142e-01 -8.70936990e-01
-5.31969547e-01 1.08708048e+00 -9.63226333e-03 9.17207748... | [9.213356018066406, 4.362555503845215] |
2eae98a3-ad69-4970-be91-e419a2453c43 | bcnet-a-deep-convolutional-neural-network-for | 2107.05037 | null | https://arxiv.org/abs/2107.05037v1 | https://arxiv.org/pdf/2107.05037v1.pdf | BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading | Breast cancer has become one of the most prevalent cancers by which people all over the world are affected and is posed serious threats to human beings, in a particular woman. In order to provide effective treatment or prevention of this cancer, disease diagnosis in the early stages would be of high importance. There h... | ['Hamidreza Bolhasani', 'Atefeh Safayari', 'Pouya Hallaj Zavareh'] | 2021-07-11 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.36311883e-01 8.27799141e-02 1.09865487e-01 -2.83993036e-01
-3.90785873e-01 1.49312645e-01 5.84428906e-01 3.76317710e-01
-8.45839024e-01 7.71001756e-01 -2.22044826e-01 -2.67664790e-01
-3.88767898e-01 -1.13336706e+00 -2.43186772e-01 -8.01064491e-01
6.65702373e-02 3.30548078e-01 4.20516878e-02 -2.20084921... | [15.151902198791504, -2.714329957962036] |
51868dda-cc6a-4ba9-9398-b32ddeff5991 | learn-to-adapt-for-generalized-zero-shot-text-1 | null | null | https://aclanthology.org/2022.acl-long.39 | https://aclanthology.org/2022.acl-long.39.pdf | Learn to Adapt for Generalized Zero-Shot Text Classification | Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationar... | ['Yongbin Liu', 'Ziwei Bai', 'Xiaojie Wang', 'Caixia Yuan', 'Yiwen Zhang'] | null | null | null | null | acl-2022-5 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.65357882e-01 -9.56637412e-02 -4.01287079e-01 -6.09833181e-01
-7.71662772e-01 -4.33594674e-01 6.64384902e-01 1.65936202e-01
-2.21888676e-01 7.86371350e-01 -8.74577463e-02 8.26164857e-02
-7.23663047e-02 -1.03036654e+00 -3.80263835e-01 -7.56953061e-01
4.09344494e-01 9.72526014e-01 3.60739976e-01 -2.93463260... | [10.147451400756836, 3.4589571952819824] |
d06a4da3-13f8-41ea-aa90-391fe1b0da2e | spatio-temporal-self-supervised | 2109.00179 | null | https://arxiv.org/abs/2109.00179v1 | https://arxiv.org/pdf/2109.00179v1.pdf | Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds | To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc. In this paper, we tackle this challenge by introducing a spat... | ['Yixin Zhu', 'Song-Chun Zhu', 'Yichen Xie', 'Siyuan Huang'] | 2021-09-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_Spatio-Temporal_Self-Supervised_Representation_Learning_for_3D_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_Spatio-Temporal_Self-Supervised_Representation_Learning_for_3D_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-retrieval', '3d-point-cloud-linear-classification', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.68255681e-01 -1.21674508e-01 -2.17987731e-01 -6.70747280e-01
-2.82198876e-01 -6.83451712e-01 5.66003680e-01 3.14655840e-01
-1.36101887e-01 1.26412228e-01 6.35035336e-02 -1.10215373e-01
-2.85193473e-02 -7.13872313e-01 -1.07523310e+00 -4.54576582e-01
-1.19873904e-01 2.84412086e-01 4.10250217e-01 1.72033235... | [8.169071197509766, -3.1904618740081787] |
4314b2de-840a-4820-b7b7-ee8d676231bd | towards-human-evaluation-of-mutual | null | null | https://aclanthology.org/2022.humeval-1.10 | https://aclanthology.org/2022.humeval-1.10.pdf | Towards Human Evaluation of Mutual Understanding in Human-Computer Spontaneous Conversation: An Empirical Study of Word Sense Disambiguation for Naturalistic Social Dialogs in American English | Current evaluation practices for social dialog systems, dedicated to human-computer spontaneous conversation, exclusively focus on the quality of system-generated surface text, but not human-verifiable aspects of mutual understanding between the systems and their interlocutors. This work proposes Word Sense Disambiguat... | ['Alex Lưu'] | null | null | null | null | humeval-acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.61301047e-01 4.41864818e-01 2.04183370e-01 -1.93073094e-01
-2.74149090e-01 -7.71378100e-01 1.08699870e+00 4.43033397e-01
-8.22027326e-01 6.85636103e-01 7.21825063e-01 -4.08362001e-01
-6.16741329e-02 -4.74120498e-01 4.40357029e-01 -1.86420783e-01
1.20849520e-01 8.51616502e-01 2.36420035e-01 -1.06609488... | [12.866583824157715, 8.037714004516602] |
d371ae50-0807-4830-84a1-e10c0a72a4fe | event-event-relation-extraction-using-1 | null | null | https://aclanthology.org/2022.acl-short.26 | https://aclanthology.org/2022.acl-short.26.pdf | Event-Event Relation Extraction using Probabilistic Box Embedding | To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a ph... | ['Andrew McCallum', 'Dongxu Zhang', 'Dhruvesh Patel', 'Tianyi Yang', 'Jay-Yoon Lee', 'EunJeong Hwang'] | null | null | null | null | acl-2022-5 | ['event-relation-extraction'] | ['natural-language-processing'] | [ 3.11525822e-01 4.68133241e-01 -4.91511911e-01 -4.29422349e-01
-2.99317479e-01 -7.07956553e-01 9.63839769e-01 6.94627523e-01
-8.39787647e-02 1.01345396e+00 4.23843861e-01 -5.14240324e-01
-3.11651081e-01 -1.10850894e+00 -6.36640489e-01 -6.50986508e-02
-3.43262926e-02 4.35123354e-01 5.66576421e-01 -9.28400531... | [9.107316970825195, 9.17096996307373] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.