paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3a73b715-c057-45c6-a3ff-6e98ea0864ab | on-the-use-of-semantically-aligned-speech | 2210.05291 | null | https://arxiv.org/abs/2210.05291v1 | https://arxiv.org/pdf/2210.05291v1.pdf | On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding | In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU). We employ the recently-introduced SAMU-XLSR model, which is designed to generate a single embedding that captures the semantics at the utterance level, semantically aligned across differen... | ['Yannick Estève', 'Themos Stafylakis', 'Mickaël Rouvier', 'Valentin Pelloin', 'Gaëlle Laperrière'] | 2022-10-11 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 2.03948379e-01 4.05937195e-01 1.71517655e-01 -7.97809482e-01
-1.23769367e+00 -4.63454217e-01 7.53153086e-01 3.04356180e-02
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1.47941336e-01 -3.82382005e-01 -5.88947713e-01 -4.70936522e-02
2.84958817e-02 3.88198465e-01 1.09572057e-02 -6.24645531... | [14.053627967834473, 6.999042987823486] |
c0a383ce-9e18-48e2-902f-6117ec464670 | lexical-normalization-of-user-generated | null | null | https://aclanthology.org/W19-3202 | https://aclanthology.org/W19-3202.pdf | Lexical Normalization of User-Generated Medical Text | In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature. The extraction of this knowledge is complicated by colloquial language use and misspellings. Yet, lexical normalization of such data has not been addressed properly... | ['Wessel Kraaij', 'Suzan Verberne', 'Anne Dirkson'] | 2019-08-01 | null | null | null | ws-2019-8 | ['mistake-detection', 'lexical-normalization'] | ['computer-vision', 'natural-language-processing'] | [ 4.71247643e-01 1.79426625e-01 -1.33959115e-01 -8.24051425e-02
-9.13574755e-01 -3.27899784e-01 2.46961519e-01 1.40375030e+00
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5.57109654e-01 4.26486462e-01 1.81610435e-01 -3.24255109... | [8.63771915435791, 8.842308044433594] |
ac4c5cc4-b78c-4cad-8a3f-b61fa7f6faaf | identifying-protein-protein-interactions-in | null | null | https://aclanthology.org/I17-2041 | https://aclanthology.org/I17-2041.pdf | Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory | In this paper, we propose a recurrent neural network model for identifying protein-protein interactions in biomedical literature. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that our approach significantly surpasses state-of-the-art methods with relative improvements of 10{\%} ... | ['Wen-Lian Hsu', 'Nai-Wen Chang', 'Yung-Chun Chang', 'Yu-Lun Hsieh'] | 2017-11-01 | identifying-protein-protein-interactions-in-1 | https://aclanthology.org/I17-2041 | https://aclanthology.org/I17-2041.pdf | ijcnlp-2017-11 | ['cross-corpus'] | ['computer-vision'] | [ 4.43171740e-01 2.18348298e-02 -1.72229409e-01 -5.31418204e-01
-7.56315291e-01 -2.03844845e-01 1.23899095e-01 3.74095291e-01
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-2.41581593e-02 5.72398841e-01 -3.50980759e-02 -1.97364479... | [4.775875568389893, 5.748755931854248] |
83ed9ccc-1ec8-42cf-98aa-f42029cc3165 | recent-advances-in-optimal-transport-for | 2306.16156 | null | https://arxiv.org/abs/2306.16156v1 | https://arxiv.org/pdf/2306.16156v1.pdf | Recent Advances in Optimal Transport for Machine Learning | Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning, such as generative modeling and transfer learni... | ['Antoine Souloumiac', 'Fred Ngolè Mboula', 'Eduardo Fernandes Montesuma'] | 2023-06-28 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 7.19665885e-02 -2.25734785e-01 -9.17534530e-01 -2.98777550e-01
-7.74743497e-01 -4.55956131e-01 1.04699230e+00 4.41218242e-02
-4.27029938e-01 1.24872911e+00 7.85360336e-02 -3.65082264e-01
-6.47098660e-01 -9.88816857e-01 -8.81254733e-01 -1.01680171e+00
-4.83504146e-01 6.62763953e-01 1.63999334e-01 5.63498493... | [6.831689834594727, 3.6767516136169434] |
219f8020-ffbb-4521-acc9-cd6f163997bd | do-response-selection-models-really-know-what | 2009.04703 | null | https://arxiv.org/abs/2009.04703v2 | https://arxiv.org/pdf/2009.04703v2.pdf | Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection | In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., BERT, RoBERTa, and ELECTRA) showed significant improvements in various natural language processing tasks. This and simil... | ['Dong-hun Lee', 'Taesun Whang', 'Dongsuk Oh', 'Kijong Han', 'Chanhee Lee', 'Saebyeok Lee', 'Dongyub Lee'] | 2020-09-10 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [-3.76399904e-02 9.71222147e-02 -4.50553149e-01 -7.60895193e-01
-7.30973184e-01 -5.95142007e-01 9.09180045e-01 3.24718714e-01
-3.48124892e-01 6.54198349e-01 2.77872950e-01 -4.69721079e-01
8.95800665e-02 -4.11132157e-01 -1.67921066e-01 -3.05812627e-01
3.32024544e-01 7.12527096e-01 5.10667324e-01 -8.40272784... | [12.611032485961914, 7.762442588806152] |
ea88ac44-629e-4ff1-b12a-b7089a7293c8 | best-arm-identification-in-stochastic-bandits | 2301.03785 | null | https://arxiv.org/abs/2301.03785v2 | https://arxiv.org/pdf/2301.03785v2.pdf | Best Arm Identification in Stochastic Bandits: Beyond $β-$optimality | This paper investigates a hitherto unaddressed aspect of best arm identification (BAI) in stochastic multi-armed bandits in the fixed-confidence setting. Two key metrics for assessing bandit algorithms are computational efficiency and performance optimality (e.g., in sample complexity). In stochastic BAI literature, th... | ['Ali Tajer', 'Arpan Mukherjee'] | 2023-01-10 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-4.60168608e-02 -2.31169954e-01 -9.52962101e-01 5.53385494e-03
-1.24170089e+00 -7.51163185e-01 1.52862981e-01 -1.91002600e-02
-2.81548768e-01 1.10293841e+00 -5.41282967e-02 -8.04774463e-01
-8.90162051e-01 -7.53369391e-01 -7.26220250e-01 -1.03574145e+00
-4.39459421e-02 5.39651453e-01 -2.15507776e-01 5.48656024... | [4.559055805206299, 3.2856345176696777] |
d2ec3956-ad97-4f06-b620-f7df18d6bf67 | amvnet-assertion-based-multi-view-fusion | 2012.04934 | null | https://arxiv.org/abs/2012.04934v1 | https://arxiv.org/pdf/2012.04934v1.pdf | AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation | In this paper, we present an Assertion-based Multi-View Fusion network (AMVNet) for LiDAR semantic segmentation which aggregates the semantic features of individual projection-based networks using late fusion. Given class scores from different projection-based networks, we perform assertion-guided point sampling on sco... | ['Zhuang Jie Chong', 'Dhananjai Sharma', 'Sergi Widjaja', 'Thi Ngoc Tho Nguyen', 'Venice Erin Liong'] | 2020-12-09 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 1.86577857e-01 3.80561918e-01 -4.29303586e-01 -7.56792009e-01
-8.89636755e-01 -4.73173380e-01 6.80891573e-01 1.44192092e-02
-1.50881916e-01 4.74087983e-01 -2.73253262e-01 -9.63473786e-03
-1.49882600e-01 -9.46947038e-01 -7.11864531e-01 -4.78934318e-01
3.29168320e-01 1.16366720e+00 1.27176750e+00 -2.71321118... | [8.212959289550781, -2.9059951305389404] |
1418f5ce-a964-4923-acc7-3156a32bde98 | a-simple-method-for-unsupervised-bilingual | 2305.14012 | null | https://arxiv.org/abs/2305.14012v1 | https://arxiv.org/pdf/2305.14012v1.pdf | A Simple Method for Unsupervised Bilingual Lexicon Induction for Data-Imbalanced, Closely Related Language Pairs | Existing approaches for unsupervised bilingual lexicon induction (BLI) often depend on good quality static or contextual embeddings trained on large monolingual corpora for both languages. In reality, however, unsupervised BLI is most likely to be useful for dialects and languages that do not have abundant amounts of m... | ['Rachel Bawden', 'Benoît Sagot', 'Josef van Genabith', 'Cristina España-Bonet', 'Niyati Bafna'] | 2023-05-23 | null | null | null | null | ['bilingual-lexicon-induction'] | ['natural-language-processing'] | [-4.65571672e-01 -2.04537705e-01 -5.52193761e-01 -3.84980738e-01
-1.05647230e+00 -9.00604546e-01 7.67115116e-01 1.75637245e-01
-9.87540424e-01 1.06004298e+00 4.39882636e-01 -8.10607910e-01
1.29285395e-01 -6.00746810e-01 -5.07221043e-01 -5.07752419e-01
-2.37821952e-01 1.15464807e+00 -1.67293977e-02 -7.12123513... | [10.835322380065918, 10.074652671813965] |
46d7b79b-6800-4fa9-9798-7516f00a7373 | clip3dstyler-language-guided-3d-arbitrary | 2305.15732 | null | https://arxiv.org/abs/2305.15732v2 | https://arxiv.org/pdf/2305.15732v2.pdf | CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer | In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler). We aim at stylizing any 3D scene with an arbitrary style from a text description, and synthesizing the novel stylized view, which is more flexible than the image-conditioned style transfer. Compared with the prev... | ['Mingming Gong', 'Chenkai Zhao', 'Tingbo Hou', 'Yang Zhao', 'Yanwu Xu', 'Ming Gao'] | 2023-05-25 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 1.87541068e-01 -2.94623435e-01 2.69822329e-01 -3.37748528e-01
-6.40741110e-01 -8.35434854e-01 7.20191419e-01 -5.00748038e-01
-1.01757504e-01 4.07075793e-01 3.03961430e-02 -3.76252793e-02
2.80957431e-01 -7.00458765e-01 -8.11080277e-01 -5.29733360e-01
7.92153716e-01 6.59382164e-01 2.53334820e-01 -2.10849538... | [9.25784683227539, -3.282867670059204] |
fa112a7e-de67-4fe5-a757-c6504519a563 | semantic-aware-dynamic-retrospective | 2305.08059 | null | https://arxiv.org/abs/2305.08059v1 | https://arxiv.org/pdf/2305.08059v1.pdf | Semantic-aware Dynamic Retrospective-Prospective Reasoning for Event-level Video Question Answering | Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to obtain the visual information needed to provide optimal answers. However, despite significant progress in model performance, few studies have focused on using the explicit semantic connections between the question and visual i... | ['Jennifer Foster', 'Yvette Graham', 'Tianbo Ji', 'Chenyang Lyu'] | 2023-05-14 | null | null | null | null | ['video-question-answering', 'semantic-role-labeling'] | ['computer-vision', 'natural-language-processing'] | [ 1.22614786e-01 5.60135171e-02 -1.41159758e-01 -6.16182804e-01
-5.78278303e-01 -5.46213925e-01 3.80941004e-01 2.01900437e-01
-2.88777739e-01 6.57403111e-01 6.70646846e-01 -4.78186637e-01
-9.05124694e-02 -6.46104217e-01 -6.43077493e-01 -3.74582291e-01
2.33429074e-01 2.86816090e-01 8.94393325e-01 -2.46296257... | [10.43026351928711, 1.1045786142349243] |
24c92771-da3f-4a2f-88f9-75f88b0310c6 | dsfer-net-a-deep-supervision-and-feature | 2304.01101 | null | https://arxiv.org/abs/2304.01101v1 | https://arxiv.org/pdf/2304.01101v1.pdf | Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks | Change detection, as an important application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. With the rapid growth in the quantity of high-resolution remote sensing data and the complexity of texture features, a number of quantitative deep learning-based me... | ['Pedram Ghamisi', 'Michael Kopp', 'Shizhen Chang'] | 2023-04-03 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [-4.42243144e-02 -4.93876129e-01 -5.86011000e-02 -7.44436741e-01
-7.03783810e-01 -3.04896981e-01 8.91194284e-01 -1.41345650e-01
-4.47295874e-01 2.85064310e-01 2.45524645e-01 -2.31270306e-02
-4.32825118e-01 -1.12641740e+00 -6.12070322e-01 -7.94190764e-01
-3.98873180e-01 7.81707242e-02 2.43817382e-02 -4.98088211... | [9.656076431274414, -1.2613346576690674] |
20aff5c0-5bda-4b13-9eb6-dee50ed40250 | fcdsn-dc-an-accurate-and-lightweight | 2209.06525 | null | https://arxiv.org/abs/2209.06525v1 | https://arxiv.org/pdf/2209.06525v1.pdf | FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion | We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training... | ['Friedrich Fraundorfer', 'Dominik Hirner'] | 2022-09-14 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 9.85026509e-02 1.10085629e-01 3.89737219e-01 -6.52501225e-01
-6.52006090e-01 -4.12802935e-01 3.50546688e-01 -2.00420916e-01
-7.72892356e-01 7.35213816e-01 1.04275189e-01 8.37229490e-02
1.05620779e-01 -8.70983720e-01 -1.10674775e+00 -6.80404365e-01
1.57723621e-01 5.44627547e-01 5.93353450e-01 -3.17238837... | [8.748614311218262, -2.4585893154144287] |
a5e2b5e7-aa28-4985-9e27-fbc19b44cc13 | graph-based-global-robot-localization | 2303.02076 | null | https://arxiv.org/abs/2303.02076v1 | https://arxiv.org/pdf/2303.02076v1.pdf | Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs | In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environme... | ['Holger Voos', 'Javier Civera', 'Jose Luis Sanchez-Lopez', 'Hriday Bavle', 'Jose Andres Millan-Romera', 'Muhammad Shaheer'] | 2023-03-03 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 8.20667371e-02 7.13983119e-01 2.25007713e-01 -3.74151617e-01
-7.34773457e-01 -6.18182421e-01 4.62784380e-01 3.60567272e-01
-8.82019401e-02 4.29780453e-01 -7.75294453e-02 1.70116469e-01
-6.16034381e-02 -1.09657443e+00 -9.04296219e-01 -3.33798319e-01
-1.90535948e-01 9.62761521e-01 7.98325658e-01 -2.95149833... | [7.32467794418335, -2.235128164291382] |
8fb967c4-77f7-4283-b868-daeb9c6c1e0a | multi-modal-music-information-retrieval | 2002.00251 | null | https://arxiv.org/abs/2002.00251v1 | https://arxiv.org/pdf/2002.00251v1.pdf | Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis | This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective. This thesis focuses on the information provided by the visual layer of music videos and how it can be harnessed to augment and improve tasks of the MIR research domain. The main h... | ['Alexander Schindler'] | 2020-02-01 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.07177922e-01 -4.20745760e-01 -5.92277013e-02 1.03511237e-01
-5.03090739e-01 -7.06980705e-01 7.85564482e-01 5.53319573e-01
-2.35698521e-01 2.35119104e-01 4.10132200e-01 2.59466082e-01
-4.87218201e-01 -5.96040964e-01 -2.85762221e-01 -6.79665208e-01
-6.57055005e-02 1.68588027e-01 -7.58777261e-02 -3.90859991... | [15.739297866821289, 5.152281761169434] |
788e242e-f07c-4afd-8f0f-43b53060c936 | 3dv-3d-dynamic-voxel-for-action-recognition | 2005.05501 | null | https://arxiv.org/abs/2005.05501v1 | https://arxiv.org/pdf/2005.05501v1.pdf | 3DV: 3D Dynamic Voxel for Action Recognition in Depth Video | To facilitate depth-based 3D action recognition, 3D dynamic voxel (3DV) is proposed as a novel 3D motion representation. With 3D space voxelization, the key idea of 3DV is to encode 3D motion information within depth video into a regular voxel set (i.e., 3DV) compactly, via temporal rank pooling. Each available 3DV vox... | ['Joey Tianyi Zhou', 'Zhiguo Cao', 'Wenxiang Jiang', 'Yancheng Wang', 'Fu Xiong', 'Yang Xiao', 'Junsong Yuan'] | 2020-05-12 | 3dv-3d-dynamic-voxel-for-action-recognition-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_3DV_3D_Dynamic_Voxel_for_Action_Recognition_in_Depth_Video_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_3DV_3D_Dynamic_Voxel_for_Action_Recognition_in_Depth_Video_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [-1.14219241e-01 -3.65021139e-01 -6.89408839e-01 -2.98780918e-01
-4.64600325e-01 -2.03631490e-01 4.40640092e-01 -5.19715965e-01
-2.14578435e-01 2.25516930e-01 5.69351017e-01 1.08642787e-01
3.61440815e-02 -6.41727328e-01 -6.67159915e-01 -8.68719399e-01
-4.02620018e-01 6.01020493e-02 4.26036566e-01 1.23181522... | [8.26118278503418, 0.23761463165283203] |
d7e15527-f233-40a8-b3f8-b5749e0cd3e6 | reformulating-the-sir-model-in-terms-of-the | 2110.00364 | null | https://arxiv.org/abs/2110.00364v1 | https://arxiv.org/pdf/2110.00364v1.pdf | Reformulating the SIR model in terms of the number of COVID-19 detected cases: well-posedness of the observational model | Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data is typically akin of a boundary value type problem: we observe some of ... | ['Anotida Madzvamuse', 'Duc-Lam Duong', 'James Van Yperen', 'Hayley Wragg', 'Eduard Campillo-Funollet'] | 2021-10-01 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 2.30446503e-01 -2.71301568e-01 -8.71708393e-02 1.64403781e-01
5.86425737e-02 -4.68404382e-01 2.17859089e-01 3.34711522e-01
-6.16830111e-01 1.00432193e+00 -4.52795625e-01 -2.71953017e-01
-6.41778290e-01 -6.53389633e-01 -3.99470389e-01 -1.19255745e+00
-3.26273173e-01 6.75863504e-01 4.79456261e-02 -1.18411653... | [5.966383457183838, 4.322572231292725] |
7ca197a3-e486-4b9d-aa42-8244c438bcca | adaptive-unsupervised-self-training-for | null | null | https://aclanthology.org/2022.coling-1.632 | https://aclanthology.org/2022.coling-1.632.pdf | Adaptive Unsupervised Self-training for Disfluency Detection | Supervised methods have achieved remarkable results in disfluency detection. However, in real-world scenarios, human-annotated data is difficult to obtain. Recent works try to handle disfluency detection with unsupervised self-training, which can exploit existing large-scale unlabeled data efficiently. However, their s... | ['Wanxiang Che', 'Shaolei Wang', 'YiXuan Wang', 'Zhongyuan Wang'] | null | null | null | null | coling-2022-10 | ['selection-bias'] | ['natural-language-processing'] | [-3.41637507e-02 -9.65587571e-02 -5.54294467e-01 -5.20617127e-01
-6.91256583e-01 -5.36175191e-01 3.11128907e-02 2.26537481e-01
-4.40366179e-01 8.65067601e-01 1.96974218e-01 -2.17866778e-01
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2.76396573e-01 6.05608582e-01 2.58455634e-01 -1.57204568... | [9.65064811706543, 4.1355977058410645] |
52b9c429-c9f5-4f75-ae71-97c2b8cefe8f | multiview-learning-of-weighted-majority-vote | 1805.10212 | null | http://arxiv.org/abs/1805.10212v1 | http://arxiv.org/pdf/1805.10212v1.pdf | Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization | We tackle the issue of classifier combinations when observations have
multiple views. Our method jointly learns view-specific weighted majority vote
classifiers (i.e. for each view) over a set of base voters, and a second
weighted majority vote classifier over the set of these view-specific weighted
majority vote class... | ['Massih-Reza Amini', 'Emilie Morvant', 'Anil Goyal'] | 2018-05-25 | null | null | null | null | ['multiview-learning', 'multilingual-text-classification'] | ['computer-vision', 'miscellaneous'] | [ 6.16347566e-02 3.34871083e-01 -7.50601649e-01 -8.44177425e-01
-1.26461899e+00 -7.02222049e-01 9.47867692e-01 5.34327440e-02
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5.43629110e-01 5.58918953e-01 3.26455981e-02 1.64840221... | [8.55849838256836, 4.461905002593994] |
115e88ec-c037-46ed-b6ea-41388ae546b2 | enhancing-space-time-video-super-resolution | 2207.08960 | null | https://arxiv.org/abs/2207.08960v3 | https://arxiv.org/pdf/2207.08960v3.pdf | Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction | The target of space-time video super-resolution (STVSR) is to increase both the frame rate (also referred to as the temporal resolution) and the spatial resolution of a given video. Recent approaches solve STVSR using end-to-end deep neural networks. A popular solution is to first increase the frame rate of the video; ... | ['Miaojing Shi', 'Zijie Yue'] | 2022-07-18 | null | null | null | null | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [-8.90900865e-02 -6.70317173e-01 -1.84504122e-01 -2.82635421e-01
-5.24572313e-01 -2.06361845e-01 3.10142308e-01 -1.64591178e-01
-7.24466562e-01 7.68438041e-01 1.69639826e-01 1.82001084e-01
-1.02170408e-01 -8.72499526e-01 -6.28496170e-01 -6.17409110e-01
-1.86746389e-01 -4.50927109e-01 7.56333530e-01 -2.07941622... | [11.055828094482422, -1.8473185300827026] |
2f40dec3-20bc-4a2c-ae01-43988e471f44 | c-3po-cyclic-three-phase-optimization-for | 1909.11303 | null | https://arxiv.org/abs/1909.11303v3 | https://arxiv.org/pdf/1909.11303v3.pdf | C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning | Motion retargeting between heterogeneous polymorphs with different sizes and kinematic configurations requires a comprehensive knowledge of (inverse) kinematics. Moreover, it is non-trivial to provide a kinematic independent general solution. In this study, we developed a cyclic three-phase optimization method based on... | ['Joo-Haeng Lee', 'Taewoo Kim'] | 2019-09-25 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-1.83449700e-01 2.47189283e-01 -3.31463993e-01 4.34890479e-01
-4.17235285e-01 -3.43764007e-01 4.44308430e-01 -6.83397502e-02
-7.37472594e-01 8.69856834e-01 1.94796864e-02 -1.13413341e-01
-3.97620261e-01 -6.25225008e-01 -8.44548285e-01 -9.71967876e-01
-1.22408949e-01 5.49694955e-01 2.68144250e-01 -4.81090963... | [4.831786155700684, 1.070684790611267] |
7467bd2b-17c4-430f-8f56-8a57405c63ac | copr-towards-accurate-visual-localization | 2304.07426 | null | https://arxiv.org/abs/2304.07426v1 | https://arxiv.org/pdf/2304.07426v1.pdf | CoPR: Towards Accurate Visual Localization With Continuous Place-descriptor Regression | Visual Place Recognition (VPR) is an image-based localization method that estimates the camera location of a query image by retrieving the most similar reference image from a map of geo-tagged reference images. In this work, we look into two fundamental bottlenecks for its localization accuracy: reference map sparsenes... | ['Julian Francisco Pieter Kooij', 'Liangliang Nan', 'Mubariz Zaffar'] | 2023-04-14 | null | null | null | null | ['image-based-localization', 'visual-localization', 'visual-place-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.20832331e-01 -2.57261038e-01 -4.29056019e-01 -4.14212584e-01
-1.38157308e+00 -7.31079221e-01 8.91939282e-01 1.87996998e-01
-5.67328811e-01 5.40991843e-01 3.21163386e-01 1.28886640e-01
-1.11992732e-01 -6.19618773e-01 -1.11088681e+00 -3.26756895e-01
-2.30104979e-02 1.50628820e-01 4.19189423e-01 -1.48131475... | [7.659852504730225, -1.9807161092758179] |
c02562e0-ea61-4e85-878d-5b2d3683d5e6 | regula-sub-rosa-latent-backdoor-attacks-on | 1905.10447 | null | https://arxiv.org/abs/1905.10447v1 | https://arxiv.org/pdf/1905.10447v1.pdf | Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks | Recent work has proposed the concept of backdoor attacks on deep neural networks (DNNs), where misbehaviors are hidden inside "normal" models, only to be triggered by very specific inputs. In practice, however, these attacks are difficult to perform and highly constrained by sharing of models through transfer learning.... | ['Hai-Tao Zheng', 'Yuanshun Yao', 'Ben Y. Zhao', 'Huiying Li'] | 2019-05-24 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.98916808e-01 3.26443106e-01 -2.96945989e-01 -2.06090599e-01
-3.40505809e-01 -1.30188465e+00 6.53558195e-01 -5.51078916e-01
-3.01845461e-01 7.13037193e-01 -6.15884066e-01 -1.01475370e+00
1.28248632e-02 -8.07480514e-01 -1.18394375e+00 -8.70477140e-01
-2.34404817e-01 4.03757431e-02 3.98194373e-01 -8.03164318... | [5.750176429748535, 7.655880928039551] |
bb3ce437-3b9e-4f54-8d44-c99f54607756 | model-free-motion-planning-of-autonomous | 2305.00561 | null | https://arxiv.org/abs/2305.00561v1 | https://arxiv.org/pdf/2305.00561v1.pdf | Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments | Motion planning of autonomous agents in partially known environments with incomplete information is a challenging problem, particularly for complex tasks. This paper proposes a model-free reinforcement learning approach to address this problem. We formulate motion planning as a probabilistic-labeled partially observabl... | ['Shaoping Xiao', 'Zhen Kan', 'Mingyu Cai', 'Junchao Li'] | 2023-04-30 | null | null | null | null | ['q-learning', 'motion-planning'] | ['methodology', 'robots'] | [ 1.59165598e-02 1.91388682e-01 -3.43099207e-01 -7.59073421e-02
-7.30235040e-01 -4.09228295e-01 5.40660501e-01 -3.09948862e-01
-5.02939880e-01 9.54191148e-01 -1.29579008e-01 -7.24950671e-01
-3.41422051e-01 -8.44942331e-01 -6.72195256e-01 -7.92158246e-01
-4.90269989e-01 6.11122966e-01 3.22402596e-01 -8.61194357... | [4.282928943634033, 2.123770236968994] |
80633492-d9cb-491a-9591-f7516f8f5d22 | robust-deep-learning-based-protein-sequence | null | null | https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1 | https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1.full.pdf | Robust deep learning based protein sequence design using ProteinMPNN | While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in ... | ['D. Baker', 'N. P. King', 'A. K. Bera', 'B. Sankaran', 'A. Kang', 'H. Nguyen', 'B. Koepnick', 'F. Chan', 'D. Tischer', 'S. Pellock', 'T. F. Huddy', 'P. J. Y. Leung', 'N. Bethel', 'R. J. de Haas', 'A. Courbet', 'B. I. M. Wicky', 'L. F. Milles', 'R. J. Ragotte', 'H. Bai', 'N. Bennett', 'I. Anishchenko', 'J. Dauparas'] | 2022-06-04 | null | null | null | biorxiv-2022-6 | ['protein-design', 'protein-function-prediction'] | ['medical', 'medical'] | [ 1.41878039e-01 2.69381180e-02 -7.96702281e-02 -2.04674274e-01
-5.71855009e-01 -7.49430418e-01 -8.16885568e-03 1.48095489e-02
-4.24486399e-01 1.47132742e+00 1.32609069e-01 -7.82063723e-01
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-3.95635962e-02 8.50254297e-01 -1.39023125e-01 -3.17972332... | [4.729691505432129, 5.5946550369262695] |
584b26ef-0227-41f9-8b8f-bcf7a60ac405 | latent-variable-nested-set-transformers | 2104.00563 | null | https://arxiv.org/abs/2104.00563v3 | https://arxiv.org/pdf/2104.00563v3.pdf | Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction | Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transforme... | ["Jim Aldon D'Souza", 'Christopher Pal', 'Felix Heide', 'Samira Ebrahimi Kahou', 'Martin Weiss', 'Felipe Codevilla', 'Florian Golemo', 'Roger Girgis'] | 2021-02-19 | latent-variable-sequential-set-transformers | https://openreview.net/forum?id=Dup_dDqkZC5 | https://openreview.net/pdf?id=Dup_dDqkZC5 | iclr-2022-4 | ['trajectory-modeling'] | ['time-series'] | [-2.02208802e-01 4.17401016e-01 -1.47124052e-01 -2.52900422e-01
-7.63714373e-01 -5.69886923e-01 1.11378801e+00 -1.54156595e-01
-3.75497758e-01 6.55146658e-01 4.76219863e-01 -2.99701542e-01
-7.39805326e-02 -5.87180078e-01 -1.14067447e+00 -6.92134738e-01
-4.00152296e-01 1.02309442e+00 3.57535660e-01 -3.69494557... | [5.843475818634033, 0.783833920955658] |
2a25aaa0-5465-4514-b7f0-b66017923b53 | loss-of-plasticity-in-continual-deep | 2303.07507 | null | https://arxiv.org/abs/2303.07507v1 | https://arxiv.org/pdf/2303.07507v1.pdf | Loss of Plasticity in Continual Deep Reinforcement Learning | The ability to learn continually is essential in a complex and changing world. In this paper, we characterize the behavior of canonical value-based deep reinforcement learning (RL) approaches under varying degrees of non-stationarity. In particular, we demonstrate that deep RL agents lose their ability to learn good po... | ['Marlos C. Machado', 'Adam White', 'Joseph Modayil', 'Rosie Zhao', 'Zaheer Abbas'] | 2023-03-13 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-1.72886997e-01 -1.52800933e-01 2.52517253e-01 9.69369859e-02
-2.18401596e-01 -7.19595015e-01 7.20046818e-01 -1.91526249e-01
-1.04761398e+00 1.19019902e+00 2.28943750e-01 -2.14665771e-01
-3.26897591e-01 -4.73158777e-01 -9.33822215e-01 -9.33290184e-01
-7.34742105e-01 2.02970952e-01 2.82620847e-01 -7.35111833... | [3.994443655014038, 1.9861928224563599] |
1354b5dc-305c-4088-86f8-32ec54893b61 | busybot-learning-to-interact-reason-and-plan | 2207.08192 | null | https://arxiv.org/abs/2207.08192v2 | https://arxiv.org/pdf/2207.08192v2.pdf | BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environment | We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual feedback for robot interactions. Based on this environment, we introduce a learning framework, BusyBot, which allows an agent to jointly acqu... | ['Shuran Song', 'Zhenjia Xu', 'Zeyi Liu'] | 2022-07-17 | null | null | null | null | ['scene-graph-generation', 'robot-manipulation', 'robot-task-planning'] | ['computer-vision', 'robots', 'robots'] | [-1.84533611e-01 4.82503086e-01 -2.56202459e-01 -1.19415864e-01
-6.50191903e-02 -5.50038636e-01 5.67765415e-01 2.39506718e-02
-9.33115631e-02 7.95403957e-01 1.34733692e-01 6.00519814e-02
-4.24258024e-01 -7.35484838e-01 -8.94685447e-01 -5.73683321e-01
-4.61021185e-01 7.72869945e-01 3.76779974e-01 -2.28431582... | [4.542417049407959, 0.8327354788780212] |
95a0134f-0f63-4fa4-903b-cb510e835494 | molecular-property-prediction-by-semantic | 2303.06902 | null | https://arxiv.org/abs/2303.06902v1 | https://arxiv.org/pdf/2303.06902v1.pdf | Molecular Property Prediction by Semantic-invariant Contrastive Learning | Contrastive learning have been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, exiting methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency probl... | ['Shuigeng Zhou', 'Jihong Guan', 'Ailin Xie', 'Ziqiao Zhang'] | 2023-03-13 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.20595157e-01 -2.44640023e-01 -5.80723584e-01 -3.61879647e-01
-7.04027712e-01 -4.62414324e-01 6.64476752e-01 3.35140198e-01
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-1.11007884e-01 -7.42153764e-01 -7.90234804e-01 -9.70367849e-01
9.90032181e-02 2.53451705e-01 1.77317962e-01 -1.82296082... | [5.144710063934326, 5.894207000732422] |
4b94d7f2-e02f-426c-b990-1ea8e57326b9 | impact-of-ecg-dataset-diversity-on | null | null | https://doi.org/10.1109/ACCESS.2019.2927726 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8758818 | Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex | Detection of QRS complexes in electrocardiogram (ECG) signal is crucial for automated cardiac diagnosis. Automated QRS detection has been a research topic for over three decades and several of the traditional QRS detection methods show acceptable detection accuracy, however, the applicability of these methods beyond th... | ['Chandan Karmakar', 'John Yearwood', 'Ahsan Habib'] | 2019-07-10 | null | null | null | ieee-access-2019-7 | ['qrs-complex-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 1.64730474e-01 -2.98392028e-01 2.07425326e-01 -4.31939602e-01
-8.90094280e-01 -5.67373574e-01 -1.00410208e-01 4.27455693e-01
-5.28808892e-01 8.78866613e-01 -4.80865419e-01 -3.25667262e-01
-4.28274781e-01 -3.42053682e-01 -1.49621665e-01 -5.86892068e-01
-4.53359365e-01 7.63296932e-02 6.80492222e-02 -5.60030676... | [14.324152946472168, 3.298428773880005] |
92bf76ca-d9af-4a2e-adc6-0dab1fb7873f | multi-normal-estimation-via-pair-consistency | null | null | https://ieeexplore.ieee.org/document/8340177 | https://ieeexplore.ieee.org/document/8340177 | Multi-Normal Estimation via Pair Consistency Voting | The normals of feature points, i.e., the intersection points of multiple smooth surfaces, are ambiguous and undefined. This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals. This definition facilitates several succeedi... | ['Ligang Liu', 'Bo Li', 'He Chen', 'Xiuping Liu', 'Junjie Cao', 'Jie Zhang'] | 2019-04-01 | null | null | null | null | ['surface-normals-estimation-from-point-clouds'] | ['computer-vision'] | [-5.52339070e-02 -1.77863911e-01 -3.44608761e-02 -3.42199087e-01
-7.92427838e-01 -8.12902749e-02 4.95400012e-01 -6.58361092e-02
-3.30638111e-01 2.50837028e-01 -2.46548817e-01 2.26150542e-01
-2.59882808e-01 -9.51325893e-01 -6.73924327e-01 -7.78102100e-01
1.20472714e-01 7.40693748e-01 5.02509773e-01 -2.84463257... | [7.911673545837402, -3.0505049228668213] |
8ded5ff5-2889-4073-a06f-4d23197a00bf | supertagging-with-ccg-primitives | null | null | https://aclanthology.org/2020.repl4nlp-1.23 | https://aclanthology.org/2020.repl4nlp-1.23.pdf | Supertagging with CCG primitives | In CCG and other highly lexicalized grammars, supertagging a sentence{'}s words with their lexical categories is a critical step for efficient parsing. Because of the high degree of lexicalization in these grammars, the lexical categories can be very complex. Existing approaches to supervised CCG supertagging treat the... | ['Gerald Penn', 'Aditya Bhargava'] | 2020-07-01 | null | null | null | ws-2020-7 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 3.20716172e-01 7.98101306e-01 -2.17981219e-01 -5.93420565e-01
-8.24556530e-01 -1.03323090e+00 6.47830069e-01 2.60227889e-01
-4.04747635e-01 5.51544189e-01 3.89948845e-01 -9.89286304e-01
5.66303790e-01 -1.03341949e+00 -6.08329237e-01 -4.47321355e-01
6.17284998e-02 6.76883817e-01 4.85206455e-01 -3.04084897... | [10.451216697692871, 9.576507568359375] |
08123195-2cf3-418c-a2cb-f24e124a5ca4 | setrank-a-setwise-bayesian-approach-for | 2002.09841 | null | https://arxiv.org/abs/2002.09841v1 | https://arxiv.org/pdf/2002.09841v1.pdf | SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback | The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts hav... | ['Chuan Qin', 'HengShu Zhu', 'Hui Xiong', 'Chen Zhu', 'Chao Wang'] | 2020-02-23 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [ 4.79080677e-02 -2.62299478e-01 -2.86784410e-01 -7.66048849e-01
-6.68516099e-01 -5.47498643e-01 2.34320715e-01 -1.36867100e-02
-2.31477499e-01 7.27119446e-01 3.06139827e-01 -2.64887720e-01
-8.28674436e-01 -7.23417282e-01 -5.79479635e-01 -6.40408993e-01
-3.13902646e-01 4.88259614e-01 -6.45018592e-02 -2.35368729... | [9.935080528259277, 5.591276168823242] |
b5de4649-dbe0-4fc9-99b7-e6880c4c4b65 | pain-detection-in-masked-faces-during | 2211.06694 | null | https://arxiv.org/abs/2211.06694v1 | https://arxiv.org/pdf/2211.06694v1.pdf | Pain Detection in Masked Faces during Procedural Sedation | Pain monitoring is essential to the quality of care for patients undergoing a medical procedure with sedation. An automated mechanism for detecting pain could improve sedation dose titration. Previous studies on facial pain detection have shown the viability of computer vision methods in detecting pain in unoccluded fa... | ['B. Taati', 'A. Conway', 'S. Mafeld', 'Y. Zarghami'] | 2022-11-12 | null | null | null | null | ['medical-procedure'] | ['medical'] | [ 1.18450791e-01 2.77413756e-01 -2.84851462e-01 -3.64667684e-01
-9.34400678e-01 -2.95084059e-01 -1.14826702e-01 2.07539842e-01
-7.81066895e-01 3.03664804e-01 2.17707351e-01 -1.46964699e-01
7.53745735e-02 -1.48809135e-01 -3.49455237e-01 -6.63066864e-01
-5.18097043e-01 -5.82788587e-02 -7.03577220e-01 2.37928554... | [13.612739562988281, 2.124228000640869] |
cf8eb524-5376-4a99-926b-38251b5e42d6 | occluded-person-re-identification-via | 2212.04712 | null | https://arxiv.org/abs/2212.04712v1 | https://arxiv.org/pdf/2212.04712v1.pdf | Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning | Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects, especially in crowded scenes. In addition to the processes performed during holistic person Re-ID, occluded person Re-ID involves the removal of obstacles and ... | ['Sangyoun Lee', 'Suhwan Cho', 'Heansung Lee', 'MyeongAh Cho', 'Minjung Kim'] | 2022-12-09 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-3.17885987e-02 -2.77709574e-01 8.36399645e-02 -4.08552319e-01
-3.39327127e-01 -8.84104967e-02 3.63890052e-01 -9.97500271e-02
-5.63796699e-01 7.00393200e-01 3.87315869e-01 4.13732886e-01
1.69680998e-01 -5.68614960e-01 -5.07560492e-01 -5.53520858e-01
3.23123366e-01 4.13134485e-01 2.91895419e-01 9.97715816... | [14.677999496459961, 0.8942734599113464] |
6f46f64a-178e-4bea-aaa5-17086fc597f7 | a-shared-representation-for-photorealistic | 2112.05134 | null | https://arxiv.org/abs/2112.05134v1 | https://arxiv.org/pdf/2112.05134v1.pdf | A Shared Representation for Photorealistic Driving Simulators | A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidelity images. The main challenge is synthesizing photorealistic im... | ['Alexandre Alahi', 'Taylor Mordan', 'Siyuan Li', 'Saeed Saadatnejad'] | 2021-12-09 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 5.30985177e-01 5.29599726e-01 1.64471731e-01 -3.59870970e-01
-7.86942363e-01 -6.61842704e-01 9.50862348e-01 -5.64566016e-01
-1.18589848e-01 7.34489977e-01 -3.01806051e-02 -1.79629907e-01
3.81375223e-01 -1.08381987e+00 -1.31397724e+00 -5.93260527e-01
2.78061897e-01 6.32044256e-01 1.13126859e-01 -5.17902017... | [11.409423828125, -0.29389166831970215] |
2d0703ba-5397-410b-acf0-224bd1439231 | dynamic-joint-variational-graph-autoencoders | 1910.01963 | null | https://arxiv.org/abs/1910.01963v1 | https://arxiv.org/pdf/1910.01963v1.pdf | Dynamic Joint Variational Graph Autoencoders | Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for stati... | ['Shima Khoshraftar', 'Sedigheh Mahdavi', 'Aijun An'] | 2019-10-04 | null | null | null | null | ['learning-network-representations'] | ['methodology'] | [-6.22347355e-01 3.74374725e-03 -1.23152629e-01 -4.08203080e-02
4.12388742e-01 -4.73626852e-01 4.99840140e-01 1.04073450e-01
5.48871830e-02 3.86091650e-01 2.06599936e-01 -3.19686502e-01
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-6.85552537e-01 5.62054396e-01 1.90139994e-01 -1.31145731... | [7.207624912261963, 6.1372389793396] |
162c49f7-7e3a-470f-a168-79e21398fe2b | a-new-probabilistic-distance-metric-with | 2306.07309 | null | https://arxiv.org/abs/2306.07309v1 | https://arxiv.org/pdf/2306.07309v1.pdf | A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction | This paper presents a new distance metric to compare two continuous probability density functions. The main advantage of this metric is that, unlike other statistical measurements, it can provide an analytic, closed-form expression for a mixture of Gaussian distributions while satisfying all metric properties. These ch... | ['Konstantinos N. Plataniotis', 'Yuri A. Lawryshyn', 'Ahmad Sajedi'] | 2023-06-12 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-3.86614740e-01 -4.75631267e-01 5.05605433e-03 -1.92334116e-01
-7.92843401e-01 -6.00319840e-02 4.86035079e-01 3.53912972e-02
-2.92943358e-01 6.72605097e-01 -1.98164493e-01 -2.29500145e-01
-3.89701933e-01 -5.57316363e-01 -9.38385725e-02 -9.86622274e-01
-3.03644836e-01 6.86475873e-01 3.39184254e-01 1.03386566... | [7.322110652923584, 4.2317705154418945] |
c82e950b-6092-497b-b8a8-a2da496eadc1 | minimizing-bias-in-massive-multi-arm | null | null | https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01383-x | https://bmcmedresmethodol.biomedcentral.com/counter/pdf/10.1186/s12874-021-01383-x.pdf?pdf=button%20sticky | Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision | Observational studies are increasingly being used to provide supplementary evidence in addition to Randomized Control Trials (RCTs) because they provide a scale and diversity of participants and outcomes that would be infeasible in an RCT. Additionally, they more closely reflect the settings in which the studied interv... | ['Beau Norgeot', 'Will Stedden', 'Chinmay Belthangady'] | 2021-09-20 | null | null | null | bmc-medical-research-methodology-2021-9 | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.50288153e-01 -1.12501070e-01 -1.14593744e+00 -3.29412699e-01
-6.04024768e-01 -6.18972003e-01 6.40444458e-01 6.67525411e-01
-5.11299372e-01 8.05644333e-01 7.10272670e-01 -1.00128281e+00
-4.88171518e-01 -7.39677191e-01 -4.89649534e-01 -3.37569922e-01
-1.23803869e-01 5.76776981e-01 -1.49419785e-01 3.50980282... | [8.000696182250977, 5.493827819824219] |
2c6b24a3-d3f4-4f67-bdb5-145b9f2f8e62 | shrec-2022-protein-ligand-binding-site | 2206.06035 | null | https://arxiv.org/abs/2206.06035v4 | https://arxiv.org/pdf/2206.06035v4.pdf | SHREC 2022: Protein-ligand binding site recognition | This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. T... | ['Petros Daras', 'Stelios Mylonas', 'Apostolos Axenopoulos', 'Daisuke Kihara', 'Charles Christoffer', 'Xiao Wang', 'Yuanyuan Zhang', 'Yi Fang', 'Boulbaba Ben Amor', 'Hao Huang', 'Walter Rocchia', 'Silvia Biasotti', 'Ulderico Fugacci', 'Andrea Raffo', 'Luca Gagliardi'] | 2022-06-13 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.87400144e-01 4.87215482e-02 -4.83322024e-01 -3.84928226e-01
-9.86662924e-01 -5.51451862e-01 2.80264407e-01 3.46799582e-01
-4.82107371e-01 1.30341220e+00 -1.06643014e-01 -5.48061311e-01
-1.81376010e-01 -2.58545041e-01 -6.60902619e-01 -1.17234886e+00
1.54986950e-02 9.44587350e-01 4.71373260e-01 -1.76755235... | [4.806020259857178, 5.480987548828125] |
5263bae8-0418-4925-961b-7b7854ffe5fc | global-context-aware-progressive-aggregation | 2003.00651 | null | https://arxiv.org/abs/2003.00651v1 | https://arxiv.org/pdf/2003.00651v1.pdf | Global Context-Aware Progressive Aggregation Network for Salient Object Detection | Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple level feature integration yet ignored the gap between different features. Besides, th... | ['Qianqian Xu', 'Zuyao Chen', 'Runmin Cong', 'Qingming Huang'] | 2020-03-02 | null | null | null | null | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 1.41613230e-01 -2.32898951e-01 3.56099494e-02 -4.19154495e-01
-3.41782302e-01 1.77180484e-01 4.16426420e-01 3.51739854e-01
-5.03770709e-01 4.27141517e-01 4.44361269e-01 2.16636017e-01
-2.42390633e-01 -9.37759578e-01 -5.59441745e-01 -7.80849516e-01
8.86519626e-02 -5.22463739e-01 9.58670497e-01 -4.26331043... | [9.739144325256348, -0.4727803170681] |
5d863a25-470c-40ba-84a7-6b63a7beda65 | rethinking-image-based-table-recognition | 2303.07641 | null | https://arxiv.org/abs/2303.07641v1 | https://arxiv.org/pdf/2303.07641v1.pdf | Rethinking Image-based Table Recognition Using Weakly Supervised Methods | Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images. Detailed table image annotation, e.g., cell or text bounding box annotation, however, is costly and often subjective. In this paper, we propose a weakly supervised model named WSTabNet for table r... | ['Hideaki Takeda', 'Phuc Nguyen', 'Atsuhiro Takasu', 'Nam Tuan Ly'] | 2023-03-14 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 2.37285167e-01 -8.27713162e-02 -3.86151612e-01 -5.00256419e-01
-1.44826615e+00 -7.78814256e-01 2.67339438e-01 3.18248600e-01
-1.66367620e-01 9.40740049e-01 3.23287636e-01 -6.00134917e-02
4.75268811e-01 -9.17281926e-01 -1.31838775e+00 -5.24468362e-01
2.04550683e-01 8.37396145e-01 1.72814861e-01 -7.10353628... | [11.70032024383545, 3.046144485473633] |
e1b6a50c-1183-4918-b6be-771abb618249 | inter-gps-interpretable-geometry-problem | 2105.04165 | null | https://arxiv.org/abs/2105.04165v3 | https://arxiv.org/pdf/2105.04165v3.pdf | Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning | Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale ... | ['Song-Chun Zhu', 'Xiaodan Liang', 'Siyuan Huang', 'Liang Qiu', 'Shibiao Jiang', 'Ran Gong', 'Pan Lu'] | 2021-05-10 | null | https://aclanthology.org/2021.acl-long.528 | https://aclanthology.org/2021.acl-long.528.pdf | acl-2021-5 | ['scene-parsing', 'mathematical-question-answering', 'mathematical-reasoning', 'arithmetic-reasoning'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [-1.94400996e-01 4.82167006e-01 -1.62447602e-01 -4.39569920e-01
-8.14079165e-01 -8.66469443e-01 1.95228934e-01 1.02294579e-01
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-3.88289303e-01 -1.22140098e+00 -1.18511748e+00 -2.28410270e-02
-1.07263923e-01 5.17559350e-01 1.54634610e-01 1.27745524... | [9.378515243530273, 7.431276798248291] |
df02f351-d91e-4dc7-9d01-8caaa78c5aab | training-strategies-for-neural-multilingual | null | null | https://aclanthology.org/2021.sigmorphon-1.26 | https://aclanthology.org/2021.sigmorphon-1.26.pdf | Training Strategies for Neural Multilingual Morphological Inflection | This paper presents the submission of team GUCLASP to SIGMORPHON 2021 Shared Task on Generalization in Morphological Inflection Generation. We develop a multilingual model for Morphological Inflection and primarily focus on improving the model by using various training strategies to improve accuracy and generalization ... | ['Jean-Philippe Bernardy', 'Adam Ek'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [-1.94198802e-01 2.74519138e-02 -2.28342965e-01 -6.06963694e-01
-8.42595816e-01 -9.96919632e-01 4.82246667e-01 4.41863835e-01
-8.38428080e-01 6.52647853e-01 5.87033093e-01 -6.29060686e-01
1.11945346e-01 -5.82756579e-01 -4.91533905e-01 7.24214921e-03
-7.06969276e-02 5.77483296e-01 -1.74923331e-01 -6.18677378... | [10.731457710266113, 9.735614776611328] |
c101e9d7-1985-4728-890d-d4b66478d087 | augmenting-small-data-to-classify | null | null | https://aclanthology.org/2020.lrec-1.74 | https://aclanthology.org/2020.lrec-1.74.pdf | Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization | Our goal is to develop an intelligent assistant to support users explore data via visualizations. We have collected a new corpus of conversations, CHICAGO-CRIME-VIS, geared towards supporting data visualization exploration, and we have annotated it for a variety of features, including contextualized dialogue acts. In t... | ['Abhinav Kumar', 'Jillian Aurisano', 'Andrew Johnson', 'Barbara Di Eugenio'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.22478463e-01 6.80703700e-01 1.44589208e-02 -5.77745199e-01
-3.22952569e-01 -5.24475932e-01 1.14512217e+00 3.96900952e-01
-7.61096597e-01 8.48892093e-01 9.77568388e-01 -7.33005345e-01
1.58398837e-01 -6.08552694e-01 5.03760669e-03 -4.00391847e-01
-1.43093139e-01 8.39741051e-01 -2.36374885e-01 -4.75238681... | [12.726888656616211, 7.763911247253418] |
91367a14-84dc-458d-b57c-6cdb034f10c7 | deep-neural-networks-in-video-human-action | 2305.15692 | null | https://arxiv.org/abs/2305.15692v1 | https://arxiv.org/pdf/2305.15692v1.pdf | Deep Neural Networks in Video Human Action Recognition: A Review | Currently, video behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks... | ['Yifan Zheng', 'Zhi Liu', 'Yang Yang', 'Zihan Wang'] | 2023-05-25 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.06795478e-01 -4.89025772e-01 -2.52785593e-01 -3.90196472e-01
6.51010722e-02 -1.84008479e-01 4.15677965e-01 -5.82653940e-01
-6.74183011e-01 6.11733317e-01 2.59610593e-01 -5.73015399e-02
-1.23016804e-01 -8.18089902e-01 -5.35695255e-01 -6.75042331e-01
-2.64807284e-01 -3.47478032e-01 3.37252825e-01 -9.45850685... | [7.954170227050781, 0.5038995742797852] |
593540e9-8f0c-499c-b279-c5ddbb3a8d10 | modeling-sense-structure-in-word-usage-graphs | null | null | https://aclanthology.org/2021.starsem-1.23 | https://aclanthology.org/2021.starsem-1.23.pdf | Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model | We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences. By providing a probabilistic model of g... | ['Sabine Schulte im Walde', 'Jonas Kuhn', 'Enrique Castaneda', 'Dominik Schlechtweg'] | 2021-08-01 | null | null | null | joint-conference-on-lexical-and-computational-1 | ['stochastic-block-model'] | ['graphs'] | [ 1.43744692e-01 2.39163920e-01 -3.99904788e-01 -3.34709585e-01
4.45429049e-02 -6.67346895e-01 1.06491756e+00 5.76730311e-01
-8.35787237e-01 6.12708211e-01 6.06097043e-01 -4.95689541e-01
-4.44487303e-01 -9.82283175e-01 -5.60513176e-02 -4.55874026e-01
1.04219861e-01 3.14720303e-01 3.20463657e-01 -4.17833388... | [10.267486572265625, 8.883526802062988] |
1f8a99c5-01e6-4fa3-9d25-a12f9cac7fbd | deceptive-ai-ecosystems-the-case-of-chatgpt | 2306.13671 | null | https://arxiv.org/abs/2306.13671v1 | https://arxiv.org/pdf/2306.13671v1.pdf | Deceptive AI Ecosystems: The Case of ChatGPT | ChatGPT, an AI chatbot, has gained popularity for its capability in generating human-like responses. However, this feature carries several risks, most notably due to its deceptive behaviour such as offering users misleading or fabricated information that could further cause ethical issues. To better understand the impa... | ['Stefan Sarkadi', 'Yifan Xu', 'Xiao Zhan'] | 2023-06-18 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-1.33274108e-01 5.41161478e-01 3.31420511e-01 -2.21689437e-02
-3.24074119e-01 -7.56692290e-01 6.42213225e-01 5.71902767e-02
-1.95528701e-01 7.52374291e-01 3.89945418e-01 -3.94809783e-01
1.92891985e-01 -4.21971142e-01 1.06082000e-01 -5.54107964e-01
3.98875207e-01 1.86853081e-01 6.19150437e-02 -5.26974082... | [10.424187660217285, 7.3156938552856445] |
d396014f-495a-4e95-bd20-c639cbf76f11 | fhdr-hdr-image-reconstruction-from-a-single | 1912.11463 | null | https://arxiv.org/abs/1912.11463v1 | https://arxiv.org/pdf/1912.11463v1.pdf | FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network | High dynamic range (HDR) image generation from a single exposure low dynamic range (LDR) image has been made possible due to the recent advances in Deep Learning. Various feed-forward Convolutional Neural Networks (CNNs) have been proposed for learning LDR to HDR representations. To better utilize the power of CNNs, we... | ['Shanmuganathan Raman', 'Mukul Khanna', 'Zeeshan Khan'] | 2019-12-24 | null | null | null | null | ['single-image-based-hdr-reconstruction'] | ['computer-vision'] | [ 1.89344108e-01 -1.16754189e-01 -4.76094857e-02 -5.27208388e-01
-4.66338903e-01 9.73727368e-03 5.10428190e-01 -5.37930071e-01
-1.52937755e-01 5.93934178e-01 7.06506371e-01 -5.79850487e-02
4.91055474e-02 -9.49207902e-01 -8.23325574e-01 -5.57413459e-01
1.52079895e-01 -9.64593813e-02 1.37419298e-01 -3.72563839... | [10.88201904296875, -2.202371835708618] |
7f43a08e-279c-4003-a6ba-60e4d8637c21 | moet-interpretable-and-verifiable-1 | null | null | https://openreview.net/forum?id=BJlxdCVKDB | https://openreview.net/pdf?id=BJlxdCVKDB | MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees | Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, such as defeating the best human player in the game of Go. However, decisions made by the DRL agent are not explainable, hindering its applicability in safety-critical settings. Viper, a recently proposed technique, constru... | ['Sarfraz Khurshid', 'Rishabh Singh', 'Mladen Nikolic', 'Kaiyuan Wang', 'Andrija Petrovic', 'Marko Vasic'] | 2019-09-25 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.50718451e-01 6.87588453e-01 -4.17188674e-01 -3.11719161e-02
-2.61519700e-01 -9.70557153e-01 3.02939504e-01 1.01994105e-01
-4.86393899e-01 1.07395756e+00 -1.39953882e-01 -7.31435537e-01
-2.36983314e-01 -8.23724866e-01 -9.92278814e-01 -3.97319078e-01
-1.77765399e-01 8.16883981e-01 4.79655623e-01 -3.37162852... | [4.261503219604492, 1.7179113626480103] |
cdb1a1de-18e7-4aad-a61a-e2d3693c70bd | toward-cross-theory-discourse-relation | null | null | https://aclanthology.org/W19-2702 | https://aclanthology.org/W19-2702.pdf | Toward Cross-theory Discourse Relation Annotation | In this exploratory study, we attempt to automatically induce PDTB-style relations from RST trees. We work with a German corpus of news commentary articles, annotated for RST trees and explicit PDTB-style relations and we focus on inducing the implicit relations in an automated way. Preliminary results look promising a... | ['Olha Zolotarenko', 'Peter Bourgonje'] | 2019-06-01 | null | null | null | ws-2019-6 | ['implicit-relations'] | ['natural-language-processing'] | [ 1.43344849e-01 1.16636777e+00 -5.31276762e-01 -3.78996432e-01
-7.21055567e-01 -7.28195310e-01 1.11530399e+00 6.00067735e-01
-2.72601128e-01 1.35285437e+00 6.56415343e-01 -8.05754662e-01
-2.89791465e-01 -7.60806680e-01 -3.27835113e-01 -1.32396653e-01
-1.50495172e-01 1.10163343e+00 6.91353142e-01 -5.97579420... | [10.702935218811035, 9.336030960083008] |
b32dcfe6-e15f-4579-945c-8a4c121839c6 | cross-layer-attention-network-for-fine | 2210.08784 | null | https://arxiv.org/abs/2210.08784v1 | https://arxiv.org/pdf/2210.08784v1.pdf | Cross-layer Attention Network for Fine-grained Visual Categorization | Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based works, our work does not explicitly define or localize the part regions of interest; instead, we leverage the complementary properties of diffe... | ['Huazhong Yang', 'Yu Wang', 'Ranran Huang'] | 2022-10-17 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-2.19534934e-01 -2.80249506e-01 3.50235193e-03 -4.00256634e-01
-5.54951787e-01 -4.94045287e-01 7.36853004e-01 4.81643975e-02
-3.54391992e-01 3.03601533e-01 3.54997247e-01 3.04841530e-02
-2.95590222e-01 -8.42335820e-01 -7.18829513e-01 -5.68353117e-01
-5.52958213e-02 -7.65879825e-02 4.29065406e-01 -1.05599210... | [9.669601440429688, 2.0472490787506104] |
c3e72cb1-5b8d-4094-b725-eae8cc20455b | enhancing-representation-learning-on-high | 2306.15661 | null | https://arxiv.org/abs/2306.15661v1 | https://arxiv.org/pdf/2306.15661v1.pdf | Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs | Variational Autoencoders and their many variants have displayed impressive ability to perform dimensionality reduction, often achieving state-of-the-art performance. Many current methods however, struggle to learn good representations in High Dimensional, Low Sample Size (HDLSS) tasks, which is an inherently challengin... | ['Nikola Simidjievski', 'Mateja Jamnik', 'Andrei Margeloiu', 'Navindu Leelarathna'] | 2023-06-27 | null | null | null | null | ['dimensionality-reduction', 'disentanglement'] | ['methodology', 'methodology'] | [ 1.03972785e-01 2.18775511e-01 -2.10456386e-01 -3.64776671e-01
-1.03646374e+00 -4.59766120e-01 9.44591343e-01 -2.84811378e-01
-3.84319961e-01 8.95602047e-01 4.10808563e-01 2.80398056e-02
-4.36519057e-01 -5.63277900e-01 -7.31793940e-01 -8.72898459e-01
-4.88920510e-02 4.99325186e-01 -3.56814802e-01 5.62397577... | [7.870304107666016, 3.922283172607422] |
c3fb6f60-379e-4186-b05f-a7ba15395511 | neuroimaging-feature-extraction-using-a | 2207.10794 | null | https://arxiv.org/abs/2207.10794v1 | https://arxiv.org/pdf/2207.10794v1.pdf | Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics | A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of... | ['Farouk S. Nathoo', 'Mirza Faisal Beg', 'Leno Rocha', 'Jiguo Cao', 'Michelle F. Miranda', 'Erin Gibson', 'Sidi Wu', 'Cédric Beaulac'] | 2022-07-08 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 5.4059005e-01 1.8938307e-01 -1.1918078e-01 -9.7822195e-01
-6.2547511e-01 2.6513807e-02 2.5326023e-01 2.3145869e-01
-4.6038389e-01 7.1128774e-01 4.9482775e-01 -1.7170437e-01
-7.9573244e-01 -5.2898997e-01 -3.0127102e-01 -3.0472389e-01
-5.4811686e-01 3.9508301e-01 -2.8196126e-01 4.2334509e-01
2.2862612e-01... | [6.681713581085205, 5.518716812133789] |
1e415517-0899-46f5-9946-5741e58a36f5 | voice-and-accompaniment-separation-in-music | 2003.08954 | null | https://arxiv.org/abs/2003.08954v1 | https://arxiv.org/pdf/2003.08954v1.pdf | Voice and accompaniment separation in music using self-attention convolutional neural network | Music source separation has been a popular topic in signal processing for decades, not only because of its technical difficulty, but also due to its importance to many commercial applications, such as automatic karoake and remixing. In this work, we propose a novel self-attention network to separate voice and accompani... | ['Yuzhou Liu', 'Balaji Thoshkahna', 'Trausti Kristjansson', 'Ali Milani'] | 2020-03-19 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.47811095e-02 -4.42010999e-01 -1.74431447e-02 6.13718899e-03
-5.90646684e-01 -4.32478249e-01 3.94925568e-03 -4.04504985e-01
-2.32682273e-01 4.04273421e-01 5.12033284e-01 1.04969330e-01
-1.13929711e-01 -4.34675753e-01 -6.50712907e-01 -6.84941113e-01
7.42099360e-02 -1.60078034e-01 -2.09548287e-02 -1.89805686... | [15.550687789916992, 5.500222206115723] |
f50aa5e4-62ec-4da2-8bd4-cef0cdc55536 | universal-planning-networks | 1804.00645 | null | http://arxiv.org/abs/1804.00645v2 | http://arxiv.org/pdf/1804.00645v2.pdf | Universal Planning Networks | A key challenge in complex visuomotor control is learning abstract
representations that are effective for specifying goals, planning, and
generalization. To this end, we introduce universal planning networks (UPN).
UPNs embed differentiable planning within a goal-directed policy. This planning
computation unrolls a for... | ['Pieter Abbeel', 'Sergey Levine', 'Allan Jabri', 'Chelsea Finn', 'Aravind Srinivas'] | 2018-04-02 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 2.70481139e-01 4.64754254e-01 -3.71327460e-01 -2.24060670e-01
-7.54835546e-01 -6.56189978e-01 8.74805093e-01 -1.22263536e-01
-3.21966916e-01 7.85710871e-01 5.37891269e-01 -2.16205679e-02
-2.80472338e-01 -7.36724198e-01 -1.00887883e+00 -6.44179761e-01
-4.11765307e-01 6.59996867e-01 -1.48888618e-01 -3.84863853... | [4.491209030151367, 0.9587392807006836] |
f51af56e-0f83-4448-a0c4-ab0275988bae | gradient-descent-type-methods-background-and | 2212.09413 | null | https://arxiv.org/abs/2212.09413v1 | https://arxiv.org/pdf/2212.09413v1.pdf | Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis | In this book chapter, we briefly describe the main components that constitute the gradient descent method and its accelerated and stochastic variants. We aim at explaining these components from a mathematical point of view, including theoretical and practical aspects, but at an elementary level. We will focus on basic ... | ['Marten van Dijk', 'Quoc Tran-Dinh'] | 2022-12-19 | null | null | null | null | ['type'] | ['speech'] | [-4.09685597e-02 -1.76001996e-01 9.47972089e-02 -2.26073757e-01
-5.07227302e-01 -4.66458201e-01 4.95133013e-01 -3.49569947e-01
-5.00617325e-01 1.10913062e+00 -1.49660736e-01 -5.52812517e-01
-3.65594089e-01 -3.82559180e-01 -1.59971595e-01 -1.06668532e+00
-3.49148393e-01 1.19503051e-01 9.37899388e-03 -7.61070669... | [6.917212963104248, 4.270504474639893] |
8f9e7c5f-da5b-4a9b-a37d-fd740dc41fdb | interpretable-multimodal-sentiment-analysis | 2305.06162 | null | https://arxiv.org/abs/2305.06162v3 | https://arxiv.org/pdf/2305.06162v3.pdf | Interpretable multimodal sentiment analysis based on textual modality descriptions by using large-scale language models | Multimodal sentiment analysis is an important area for understanding the user's internal states. Deep learning methods were effective, but the problem of poor interpretability has gradually gained attention. Previous works have attempted to use attention weights or vector distributions to provide interpretability. Howe... | ['Shogo Okada', 'Sixia Li'] | 2023-05-07 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 1.96708009e-01 3.94647807e-01 -1.67999014e-01 -9.60197270e-01
-8.03069949e-01 -3.80308002e-01 5.34930408e-01 1.40654594e-01
-1.67601258e-01 5.94072282e-01 6.43926263e-01 1.37987584e-01
1.28556356e-01 -1.80438429e-01 -3.61206979e-01 -5.38568854e-01
4.25723463e-01 1.45585135e-01 -5.97787499e-01 -3.30201179... | [13.215063095092773, 5.227729797363281] |
59fac8e4-0784-427d-b6a4-ea080a18e6a5 | cinematic-l1-video-stabilization-with-a-log | 2011.08144 | null | https://arxiv.org/abs/2011.08144v2 | https://arxiv.org/pdf/2011.08144v2.pdf | Cinematic-L1 Video Stabilization with a Log-Homography Model | We present a method for stabilizing handheld video that simulates the camera motions cinematographers achieve with equipment like tripods, dollies, and Steadicams. We formulate a constrained convex optimization problem minimizing the $\ell_1$-norm of the first three derivatives of the stabilized motion. Our approach ex... | ['Rudolph van der Merwe', 'Joseph Triscari', 'Jason Klivington', 'Arwen Bradley'] | 2020-11-16 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.86511323e-01 2.02099085e-01 -1.89654574e-01 1.71866938e-02
-6.77851379e-01 -8.18048775e-01 1.46379516e-01 -2.74257720e-01
-3.15206587e-01 5.93380630e-01 2.03779295e-01 -1.70158654e-01
-1.41170416e-02 -2.91056514e-01 -9.54019248e-01 -4.26518023e-01
-2.45373115e-01 -1.09709300e-01 4.30961639e-01 -1.29714027... | [10.590492248535156, -1.376934289932251] |
84146c69-146a-4e1f-9cc4-cc65b54c6caa | historical-and-modern-features-for-buddha | 1909.12921 | null | https://arxiv.org/abs/1909.12921v2 | https://arxiv.org/pdf/1909.12921v2.pdf | Historical and Modern Features for Buddha Statue Classification | While Buddhism has spread along the Silk Roads, many pieces of art have been displaced. Only a few experts may identify these works, subjectively to their experience. The construction of Buddha statues was taught through the definition of canon rules, but the applications of those rules greatly varies across time and s... | ['Matheus Oliveira Franca', 'Yutaka Fujioka', 'Jueren Wang', 'Ayaka Uesaka', 'Van Le', 'Hajime Nagahara', 'Yuta Nakashima', 'Noa Garcia', 'Jacob Chan', 'Benjamin Renoust'] | 2019-09-17 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [-2.63396114e-01 3.79600376e-02 -2.32909173e-01 -1.37540400e-01
-3.52303952e-01 -1.04516232e+00 1.24868822e+00 1.26446605e-01
-1.38789132e-01 2.87847251e-01 5.11566341e-01 -2.41063144e-02
-2.92568296e-01 -1.23518944e+00 -5.30059159e-01 -5.52214265e-01
3.83549273e-01 1.17599297e+00 -8.37194994e-02 -4.75061536... | [11.33073902130127, 0.23037120699882507] |
1376a609-1d93-493e-a421-5329a2a627e4 | unsupervised-entity-alignment-for-temporal | 2302.00796 | null | https://arxiv.org/abs/2302.00796v2 | https://arxiv.org/pdf/2302.00796v2.pdf | Unsupervised Entity Alignment for Temporal Knowledge Graphs | Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have... | ['Yunjun Gao', 'Lu Chen', 'Tianyi Li', 'Junyang Wu', 'Xiaoze Liu'] | 2023-02-01 | null | null | null | null | ['graph-matching', 'entity-alignment', 'data-integration', 'entity-alignment'] | ['graphs', 'knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-5.51246963e-02 1.20223589e-01 -4.52552170e-01 -1.54670939e-01
-5.35103202e-01 -4.08033907e-01 5.09869814e-01 6.83207512e-01
-5.75151861e-01 3.63377035e-01 1.00002475e-01 -1.87098771e-01
-9.88941714e-02 -1.09963644e+00 -7.81124890e-01 -4.10911053e-01
-3.19895834e-01 2.67107189e-01 4.87401366e-01 -1.75623029... | [8.664586067199707, 7.923033714294434] |
61e31f59-962a-4706-a22e-2ae14a8f5624 | blip-2-bootstrapping-language-image-pre | 2301.12597 | null | https://arxiv.org/abs/2301.12597v3 | https://arxiv.org/pdf/2301.12597v3.pdf | BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models | The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large ... | ['Steven Hoi', 'Silvio Savarese', 'Dongxu Li', 'Junnan Li'] | 2023-01-30 | null | null | null | null | ['generative-visual-question-answering', 'open-vocabulary-attribute-detection', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'reasoning'] | [ 2.25240722e-01 2.72028059e-01 -5.05129918e-02 -4.55396265e-01
-1.45089018e+00 -4.67413425e-01 8.92097294e-01 -4.64128554e-01
-5.45865119e-01 5.54447353e-01 2.46159866e-01 -5.42322099e-01
9.09609437e-01 -5.32495975e-01 -1.12952399e+00 -3.67271423e-01
6.24310851e-01 7.00616658e-01 1.91583723e-01 -1.85218364... | [10.874812126159668, 1.5114136934280396] |
95a49a24-7f8f-4e44-8af2-139e39789bdd | lipo-lcd-combining-lines-and-points-for | 2009.09897 | null | https://arxiv.org/abs/2009.09897v1 | https://arxiv.org/pdf/2009.09897v1.pdf | LiPo-LCD: Combining Lines and Points for Appearance-based Loop Closure Detection | Visual SLAM approaches typically depend on loop closure detection to correct the inconsistencies that may arise during the map and camera trajectory calculations, typically making use of point features for detecting and closing the existing loops. In low-textured scenarios, however, it is difficult to find enough point... | ['Emilio Garcia-Fidalgo', 'Joan P. Company-Corcoles', 'Alberto Ortiz'] | 2020-09-03 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.43841609e-01 -2.98549324e-01 2.35438108e-01 -2.07958013e-01
-5.56465149e-01 -5.97664952e-01 7.77446330e-01 9.46000457e-01
-5.42337060e-01 5.41520655e-01 -2.80185461e-01 -2.44697571e-01
-2.11749434e-01 -7.96107471e-01 -6.93823516e-01 -4.39718664e-01
-1.48664698e-01 5.57434201e-01 7.59822905e-01 -3.43600363... | [7.386581897735596, -2.1255929470062256] |
13eeabb5-9a53-48cd-968d-7377d97c97ef | broadening-the-perspective-for-sustainable-ai | 2306.13686 | null | https://arxiv.org/abs/2306.13686v1 | https://arxiv.org/pdf/2306.13686v1.pdf | Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems | The increased use of AI systems is associated with multi-faceted societal, environmental, and economic consequences. These include non-transparent decision-making processes, discrimination, increasing inequalities, rising energy consumption and greenhouse gas emissions in AI model development and application, and an in... | ['Ulrich Petschow', 'Marcus Voss', 'Philipp Reinhard', 'Andreas Meyer', 'Josephin Wagner', 'Friederike Rohde'] | 2023-06-22 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 4.23165739e-01 2.94380724e-01 -6.21834159e-01 1.10724472e-01
-2.22324520e-01 -5.88224649e-01 8.47875834e-01 1.78339958e-01
-1.91798478e-01 6.42649651e-01 5.32013476e-01 -3.41676503e-01
-5.72797894e-01 -6.28438711e-01 -1.99316427e-01 -7.05167115e-01
2.14468747e-01 9.71907899e-02 -3.77501905e-01 -2.92154253... | [9.014663696289062, 6.351232528686523] |
750b14b8-3539-420b-9ca8-eb1438a2cf7d | neural-symbolic-inference-for-robust | 2301.11459 | null | https://arxiv.org/abs/2301.11459v1 | https://arxiv.org/pdf/2301.11459v1.pdf | Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification | Pre-trained seq2seq models excel at graph semantic parsing with rich annotated data, but generalize worse to out-of-distribution (OOD) and long-tail examples. In comparison, symbolic parsers under-perform on population-level metrics, but exhibit unique strength in OOD and tail generalization. In this work, we study com... | ['Jingbo Shang', 'Jeremiah Liu', 'Zi Lin'] | 2023-01-26 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.95348966e-01 8.91946435e-01 -2.32672498e-01 -5.57314515e-01
-1.06103396e+00 -8.27658415e-01 5.00286460e-01 3.01703393e-01
5.89697026e-02 7.55142808e-01 3.40073615e-01 -4.87946302e-01
-2.51314163e-01 -9.70114052e-01 -1.03334975e+00 -2.18249202e-01
-3.17273468e-01 8.71282756e-01 1.45663112e-01 -1.41218886... | [10.489459991455078, 9.166807174682617] |
2fd7fae0-d99f-4913-a681-ea59ae6975eb | a-non-anatomical-graph-structure-for-isolated | 2207.07619 | null | https://arxiv.org/abs/2207.07619v1 | https://arxiv.org/pdf/2207.07619v1.pdf | A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences | Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handc... | ['Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo'] | 2022-07-15 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition', 'boundary-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.09622547e-01 -2.68025368e-01 -2.54415035e-01 -1.60134748e-01
-4.29036826e-01 -5.39693795e-02 4.27497298e-01 -7.85275578e-01
-7.41255283e-01 3.41323555e-01 4.33909357e-01 9.16876346e-02
-4.44306917e-02 -3.01383317e-01 -3.01854104e-01 -9.90155280e-01
-2.66918749e-01 2.59635895e-01 4.78336900e-01 3.32691334... | [6.765812873840332, -0.2401949167251587] |
cb071fc9-d05b-4728-8b10-f7b087e851b5 | residual-feature-pyramid-network-for | 2306.17200 | null | https://arxiv.org/abs/2306.17200v1 | https://arxiv.org/pdf/2306.17200v1.pdf | Residual Feature Pyramid Network for Enhancement of Vascular Patterns | The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the re... | ['Sebastien Marcel', 'Ketan Kotwal'] | 2023-06-29 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 4.75778282e-01 -1.72343060e-01 2.60604382e-01 -1.29228726e-01
-1.36698157e-01 -1.05755103e+00 6.17354453e-01 -1.12384800e-02
-1.95559472e-01 4.77108300e-01 2.02317953e-01 1.14188597e-01
5.37206940e-02 -9.56319153e-01 -1.74782917e-01 -4.47147250e-01
6.79598376e-02 1.24757975e-01 6.06293499e-01 -3.33047770... | [13.047359466552734, 1.0282206535339355] |
97504218-fc78-4d78-8083-0c8b155aeda6 | bridging-the-gap-providing-post-hoc-symbolic | 2002.01080 | null | https://arxiv.org/abs/2002.01080v4 | https://arxiv.org/pdf/2002.01080v4.pdf | Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations | As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the vocabulary mismatch betwe... | ['Utkarsh Soni', 'Subbarao Kambhampati', 'Sarath Sreedharan', 'Mudit Verma', 'Siddharth Srivastava'] | 2020-02-04 | bridging-the-gap-providing-post-hoc-symbolic-1 | https://openreview.net/forum?id=TETmEkko7e5 | https://openreview.net/pdf?id=TETmEkko7e5 | iclr-2022-4 | ['montezumas-revenge'] | ['playing-games'] | [ 2.06826448e-01 9.54859138e-01 -1.87878788e-01 -6.40352666e-01
-4.74008322e-01 -7.39133418e-01 9.58058953e-01 2.03928441e-01
-1.56144410e-01 7.05628872e-01 4.48878795e-01 -8.66170049e-01
-4.49250489e-02 -4.09788370e-01 -8.58531147e-02 1.39473751e-01
7.03317747e-02 1.18136764e+00 2.96611458e-01 -6.86413407... | [9.292256355285645, 6.813237190246582] |
eaead7db-0f1a-423e-833c-067ae9537bb0 | privacy-preserved-neural-graph-similarity | 2210.11730 | null | https://arxiv.org/abs/2210.11730v1 | https://arxiv.org/pdf/2210.11730v1.pdf | Privacy-Preserved Neural Graph Similarity Learning | To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehen... | ['Ji-Rong Wen', 'Yaliang Li', 'Wayne Xin Zhao', 'Yupeng Hou'] | 2022-10-21 | null | null | null | null | ['graph-similarity', 'graph-matching'] | ['graphs', 'graphs'] | [ 3.43114763e-01 1.75644308e-01 -3.76901209e-01 -3.48873198e-01
-5.42922616e-01 -7.72898197e-01 3.40797752e-01 5.02238214e-01
7.81173483e-02 4.52341348e-01 2.71223076e-02 -4.99452025e-01
-2.94895947e-01 -1.07002044e+00 -8.62067580e-01 -7.60437787e-01
-2.48637125e-01 -3.14122945e-01 -1.08267114e-01 -1.00715291... | [6.01462459564209, 7.023069858551025] |
de6b8b77-ea98-4c3f-970d-0b69d4ba4440 | differentiable-rendering-with-perturbed | 2110.09107 | null | https://arxiv.org/abs/2110.09107v1 | https://arxiv.org/pdf/2110.09107v1.pdf | Differentiable Rendering with Perturbed Optimizers | Reasoning about 3D scenes from their 2D image projections is one of the core problems in computer vision. Solutions to this inverse and ill-posed problem typically involve a search for models that best explain observed image data. Notably, images depend both on the properties of observed scenes and on the process of im... | ['Justin Carpentier', 'Cordelia Schmid', 'Ivan Laptev', 'Quentin Le Lidec'] | 2021-10-18 | null | http://proceedings.neurips.cc/paper/2021/hash/ab233b682ec355648e7891e66c54191b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ab233b682ec355648e7891e66c54191b-Paper.pdf | neurips-2021-12 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 5.11282325e-01 1.71051115e-01 2.03925982e-01 -4.25934762e-01
-6.93277180e-01 -4.78945464e-01 6.81884527e-01 -1.64149061e-01
-1.30398124e-01 2.56411523e-01 1.05771147e-01 -2.22075850e-01
-2.85537243e-01 -5.34240842e-01 -9.48238492e-01 -5.92224240e-01
1.22683987e-01 5.50904274e-01 -2.29344890e-02 -4.01926458... | [9.179031372070312, -3.1324303150177] |
2e74ee78-d450-48b5-a3de-66546b0d140c | on-anomaly-interpretation-via-shapley-values | 2004.04464 | null | https://arxiv.org/abs/2004.04464v3 | https://arxiv.org/pdf/2004.04464v3.pdf | A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores | In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for interpreting the results of anomaly detection. We particularly investigate the use of the Shapley value for attributing anomaly scores of s... | ['Yoshinobu Kawahara', 'Naoya Takeishi'] | 2020-04-09 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.42625141e-01 2.26127446e-01 7.42348284e-02 -7.40333974e-01
-4.69063342e-01 -3.97160530e-01 7.53659308e-01 5.49043655e-01
-2.81532764e-01 4.86720145e-01 -1.23044677e-01 -2.34794244e-01
-6.24301076e-01 -7.71561086e-01 -1.45521343e-01 -6.55957758e-01
-3.84065598e-01 4.46226299e-01 1.46287233e-01 -1.08221941... | [7.623112678527832, 2.5726993083953857] |
555493b3-523b-4e9a-bc34-2d0642df5d3e | spotnet-learned-iterations-for-cell-detection | 1810.06132 | null | http://arxiv.org/abs/1810.06132v1 | http://arxiv.org/pdf/1810.06132v1.pdf | SpotNet - Learned iterations for cell detection in image-based immunoassays | Accurate cell detection and counting in the image-based ELISpot and
FluoroSpot immunoassays is a challenging task. Methodology recently proposed by
our group matches human accuracy by leveraging knowledge of the underlying
physical process of these assays and using state-of-the-art iterative
techniques to solve an inve... | ['Joakim Jaldén', 'Vidit Saxena', 'Pol del Aguila Pla'] | 2018-10-15 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.45110831e-01 -1.67821273e-01 1.95696339e-01 -1.11453116e-01
-4.47752267e-01 -7.43141651e-01 3.14371824e-01 5.88852167e-01
-6.00761533e-01 7.87401557e-01 -5.46099305e-01 -3.42985928e-01
7.20481351e-02 -6.50346220e-01 -8.24701667e-01 -6.39251292e-01
-3.32076758e-01 9.10711944e-01 1.63893104e-01 1.07203983... | [14.344950675964355, -3.1908881664276123] |
c4af7dbd-275a-4daa-ab48-bb28babf621e | generalized-organ-segmentation-by-imitating | 2103.16344 | null | https://arxiv.org/abs/2103.16344v1 | https://arxiv.org/pdf/2103.16344v1.pdf | Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation | Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned fr... | ['Yefeng Zheng', 'Kai Ma', 'Yizhou Yu', 'Chixiang Lu', 'Dong Wei', 'Shilei Cao', 'Hualuo Liu', 'Hong-Yu Zhou'] | 2021-03-30 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 1.78681925e-01 5.96407235e-01 -6.60272315e-02 -3.63320410e-01
-6.23800755e-01 -2.95879692e-01 2.21606359e-01 3.53841007e-01
-3.65545869e-01 5.69335222e-01 -1.15888901e-01 2.94121820e-02
-1.34582162e-01 -7.75882959e-01 -6.11284375e-01 -6.58552229e-01
-6.63399771e-02 7.03913331e-01 3.81912917e-01 -2.02152923... | [14.657119750976562, -2.2725820541381836] |
2490e3e1-7e32-4bd4-8784-040042254587 | a-neural-corpus-indexer-for-document | 2206.02743 | null | https://arxiv.org/abs/2206.02743v3 | https://arxiv.org/pdf/2206.02743v3.pdf | A Neural Corpus Indexer for Document Retrieval | Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In this paper, we aim to show that an end-to-end deep neural network unifying training and indexing stages can significantly improve the recall... | ['Mao Yang', 'Qi Zhang', 'Weiwei Deng', 'Hao Allen Sun', 'Xing Xie', 'Zheng Liu', 'Guoshuai Zhao', 'Chengmin Chi', 'Yuqing Xia', 'Qi Chen', 'Hao Sun', 'Shibin Wu', 'Ziming Miao', 'Haonan Wang', 'Yingyan Hou', 'Yujing Wang'] | 2022-06-06 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 2.85837233e-01 -4.21940356e-01 -5.18079698e-01 -2.56462932e-01
-1.47460902e+00 -6.88206851e-01 8.05568755e-01 1.03909485e-01
-7.21510351e-01 4.31395262e-01 3.51470381e-01 -1.83844209e-01
-2.21326619e-01 -5.02945542e-01 -8.15895140e-01 -3.70087892e-01
1.07354037e-01 8.36680353e-01 1.10767506e-01 -4.88394469... | [11.446245193481445, 7.63766622543335] |
b28561d8-306f-49be-97c1-9f7a47e025e5 | isia-food-500-a-dataset-for-large-scale-food | 2008.05655 | null | https://arxiv.org/abs/2008.05655v1 | https://arxiv.org/pdf/2008.05655v1.pdf | ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network | Food recognition has received more and more attention in the multimedia community for its various real-world applications, such as diet management and self-service restaurants. A large-scale ontology of food images is urgently needed for developing advanced large-scale food recognition algorithms, as well as for provid... | ['Zhiling Wang', 'Xiaolin Wei', 'Weiqing Min', 'Zhengdong Luo', 'Xiaoming Wei', 'Linhu Liu', 'Shuqiang Jiang'] | 2020-08-13 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 2.50720561e-01 -4.83788252e-01 -4.41367269e-01 -4.82686192e-01
-7.11793244e-01 -5.25811851e-01 1.45621434e-01 8.14255118e-01
-2.84568459e-01 1.08884990e-01 5.32411814e-01 5.20767346e-02
4.24723588e-02 -1.31273985e+00 -1.07998383e+00 -9.88268614e-01
-1.72921091e-01 -1.85144365e-01 1.18104391e-01 -1.62760586... | [11.540565490722656, 4.374851703643799] |
2d258325-0ece-4c3e-a2eb-5b7bb0d14d03 | unsupervised-foreground-background | 2104.00483 | null | https://arxiv.org/abs/2104.00483v2 | https://arxiv.org/pdf/2104.00483v2.pdf | Learning Foreground-Background Segmentation from Improved Layered GANs | Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to automatically synthesize paired photo-realistic images and segmentation masks for the use of training a f... | ['Xiangyang Ji', 'Wing Yin Cheung', 'Qiran Zou', 'Hakan Bilen', 'Yu Yang'] | 2021-04-01 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 7.37775624e-01 4.77584958e-01 6.56556115e-02 -2.72097468e-01
-1.14331102e+00 -5.66477776e-01 4.52279866e-01 -7.07721293e-01
-1.14705518e-01 8.83140206e-01 -2.91511208e-01 -1.84210911e-01
5.10659039e-01 -1.05824709e+00 -1.00279570e+00 -1.02283204e+00
5.08697331e-01 5.48425078e-01 4.36683036e-02 2.15688005... | [11.31605052947998, -0.40196460485458374] |
d7ccdf0f-23a7-4b03-a879-0660e268d54f | exploring-open-vocabulary-semantic | 2306.00450 | null | https://arxiv.org/abs/2306.00450v1 | https://arxiv.org/pdf/2306.00450v1.pdf | Exploring Open-Vocabulary Semantic Segmentation without Human Labels | Semantic segmentation is a crucial task in computer vision that involves segmenting images into semantically meaningful regions at the pixel level. However, existing approaches often rely on expensive human annotations as supervision for model training, limiting their scalability to large, unlabeled datasets. To addres... | ['Sean Chang Culatana', 'Mohamed Elhoseiny', 'Fanyi Xiao', 'Chenchen Zhu', 'Zhicheng Yan', 'Bernard Ghanem', 'Guocheng Qian', 'Deyao Zhu', 'Jun Chen'] | 2023-06-01 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 4.47108150e-01 2.96169281e-01 -3.41523767e-01 -3.69907618e-01
-7.64223456e-01 -7.02731252e-01 4.29159433e-01 2.68507004e-01
-5.81989884e-01 2.17784122e-01 -1.20042801e-01 -8.86180177e-02
5.22253692e-01 -7.97731638e-01 -8.69278729e-01 -5.64003706e-01
4.60724205e-01 3.97670001e-01 8.22858930e-01 -9.71649066... | [9.617168426513672, 0.8470037579536438] |
b7dd1141-decd-4550-804f-3fc865944c9f | safeml-safety-monitoring-of-machine-learning | 2005.13166 | null | https://arxiv.org/abs/2005.13166v1 | https://arxiv.org/pdf/2005.13166v1.pdf | SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure | Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box syst... | ['Declan Whiting', 'Koorosh Aslansefat', 'Yiannis Papadopoulos', 'Ramin Tavakoli Kolagari', 'Ioannis Sorokos'] | 2020-05-27 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.73437127e-02 -6.85131177e-02 -4.04929668e-02 -3.31701010e-01
-3.39943975e-01 -9.26651001e-01 6.30815983e-01 6.31722510e-01
-2.69304752e-01 6.26083791e-01 -4.36866283e-01 -1.19799840e+00
-7.47280598e-01 -6.89679980e-01 -5.77010393e-01 -4.96527135e-01
-3.38166356e-01 6.91592917e-02 3.52465123e-01 -1.20007493... | [5.358952045440674, 7.227952480316162] |
88d173ee-6be9-4901-8032-b8f789ec1c7c | free-as-in-free-word-order-an-energy-based | 1809.01446 | null | http://arxiv.org/abs/1809.01446v2 | http://arxiv.org/pdf/1809.01446v2.pdf | Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit | The configurational information in sentences of a free word order language
such as Sanskrit is of limited use. Thus, the context of the entire sentence
will be desirable even for basic processing tasks such as word segmentation. We
propose a structured prediction framework that jointly solves the word
segmentation and ... | ['Sasi Prasanth Bandaru', 'Pavankumar Satuluri', 'Gaurav Sahu', 'Vishnu Dutt Sharma', 'Amrith Krishna', 'Pawan Goyal', 'Bishal Santra'] | 2018-09-05 | free-as-in-free-word-order-an-energy-based-1 | https://aclanthology.org/D18-1276 | https://aclanthology.org/D18-1276.pdf | emnlp-2018-10 | ['morphological-tagging'] | ['natural-language-processing'] | [ 2.28529006e-01 3.56272578e-01 -1.28154337e-01 -4.88171101e-01
-8.66468906e-01 -8.37246776e-01 3.10925394e-01 5.48847258e-01
-7.88863361e-01 7.15023518e-01 1.27834484e-01 -8.59122753e-01
2.61199921e-01 -7.86742091e-01 -4.40047383e-01 -5.64563453e-01
1.79852515e-01 6.27665520e-01 5.39474368e-01 -1.23634420... | [10.342808723449707, 9.77509880065918] |
b27901a6-28f0-4674-ae1e-b0a2bdd6d62d | hle-upc-at-semeval-2021-task-5-multi-depth | 2104.00639 | null | https://arxiv.org/abs/2104.00639v3 | https://arxiv.org/pdf/2104.00639v3.pdf | HLE-UPC at SemEval-2021 Task 5: Multi-Depth DistilBERT for Toxic Spans Detection | This paper presents our submission to SemEval-2021 Task 5: Toxic Spans Detection. The purpose of this task is to detect the spans that make a text toxic, which is a complex labour for several reasons. Firstly, because of the intrinsic subjectivity of toxicity, and secondly, due to toxicity not always coming from single... | ['Albert Rial-Farràs', 'Rafel Palliser-Sans'] | 2021-04-01 | null | https://aclanthology.org/2021.semeval-1.131 | https://aclanthology.org/2021.semeval-1.131.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-1.38894007e-01 -2.80760556e-01 1.85510274e-02 1.64988369e-01
-6.90543592e-01 -9.45999861e-01 7.93891907e-01 8.06905866e-01
-6.56797171e-01 7.91223288e-01 8.87539923e-01 -7.38620162e-02
-3.89029458e-02 -6.51443720e-01 -6.03742361e-01 -6.76069260e-01
-2.54894495e-01 2.27270201e-01 9.87558588e-02 -2.92917162... | [8.914163589477539, 10.635584831237793] |
2e3484af-1c0e-4e49-8b37-6ea4ef860d4c | layernas-neural-architecture-search-in | 2304.11517 | null | https://arxiv.org/abs/2304.11517v1 | https://arxiv.org/pdf/2304.11517v1.pdf | LayerNAS: Neural Architecture Search in Polynomial Complexity | Neural Architecture Search (NAS) has become a popular method for discovering effective model architectures, especially for target hardware. As such, NAS methods that find optimal architectures under constraints are essential. In our paper, we propose LayerNAS to address the challenge of multi-objective NAS by transform... | ['Erik Vee', 'Da-Cheng Juan', 'Fotis Iliopoulos', 'Xin Wang', 'Yun Long', 'Keshav Kumar', 'Daiyi Peng', 'Jingyue Shen', 'Dana Alon', 'Yicheng Fan'] | 2023-04-23 | null | null | null | null | ['combinatorial-optimization', 'architecture-search'] | ['methodology', 'methodology'] | [-8.25872645e-02 -3.52196664e-01 -4.58640367e-01 -2.52975464e-01
-9.44898784e-01 -6.89159393e-01 -8.58063623e-02 -2.19713360e-01
-7.40090489e-01 6.06228232e-01 -3.53019476e-01 -6.42502010e-01
-3.50759059e-01 -5.49164593e-01 -7.06869602e-01 -4.06941593e-01
-2.76835352e-01 5.73266089e-01 3.32419366e-01 1.65896281... | [8.516645431518555, 3.1861915588378906] |
a03bfbe7-5ff4-47f8-97ed-f1819647dbd4 | video-reconstruction-by-spatio-temporal | 2010.10052 | null | https://arxiv.org/abs/2010.10052v2 | https://arxiv.org/pdf/2010.10052v2.pdf | Video Reconstruction by Spatio-Temporal Fusion of Blurred-Coded Image Pair | Learning-based methods have enabled the recovery of a video sequence from a single motion-blurred image or a single coded exposure image. Recovering video from a single motion-blurred image is a very ill-posed problem and the recovered video usually has many artifacts. In addition to this, the direction of motion is lo... | ['S Anupama', 'Kaushik Mitra', 'Abhishek Pal', 'Prasan Shedligeri'] | 2020-10-20 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 6.18685305e-01 -3.27609718e-01 1.71569467e-01 -1.13551132e-01
-7.01140285e-01 -3.48485887e-01 1.96573645e-01 -7.55547643e-01
-4.67441708e-01 8.47329259e-01 2.39804834e-01 1.41953245e-01
-2.08072752e-01 -4.37800586e-01 -7.91119277e-01 -9.14560616e-01
1.05521463e-01 -1.85630381e-01 2.27407485e-01 1.11531511... | [11.302306175231934, -2.4624574184417725] |
fceab5a2-7627-4626-a049-37e719cdddfd | an-efficient-algorithm-for-mining-frequent | 1604.01166 | null | http://arxiv.org/abs/1604.01166v1 | http://arxiv.org/pdf/1604.01166v1.pdf | An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming | The main advantage of Constraint Programming (CP) approaches for sequential
pattern mining (SPM) is their modularity, which includes the ability to add new
constraints (regular expressions, length restrictions, etc). The current best
CP approach for SPM uses a global constraint (module) that computes the
projected data... | ['John O. R. Aoga', 'Tias Guns', 'Pierre Schaus'] | 2016-04-05 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.44427013e-01 1.16084307e-01 -5.18389583e-01 1.14464760e-03
-1.67091250e-01 -5.43534160e-01 1.13031462e-01 4.82135385e-01
-4.67554212e-01 8.05432022e-01 -1.29901990e-01 -5.54804444e-01
-4.77824539e-01 -1.12970781e+00 -4.33901906e-01 -4.85018432e-01
-4.89146292e-01 9.15639162e-01 8.38591397e-01 -2.01798752... | [8.317964553833008, 6.31557559967041] |
3cbe5855-e755-4d68-8553-9115caf35906 | optimized-projection-for-sparse | 1502.00115 | null | http://arxiv.org/abs/1502.00115v1 | http://arxiv.org/pdf/1502.00115v1.pdf | Optimized Projection for Sparse Representation Based Classification | Dimensionality reduction (DR) methods have been commonly used as a principled
way to understand the high-dimensional data such as facial images. In this
paper, we propose a new supervised DR method called Optimized Projection for
Sparse Representation based Classification (OP-SRC), which is based on the
recent face rec... | ['De-Shuang Huang', 'Can-Yi Lu'] | 2015-01-31 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.28040203e-01 -1.62664443e-01 -3.02633286e-01 -5.33373296e-01
-3.50104243e-01 2.18772724e-01 2.96728939e-01 -7.81744897e-01
1.20946109e-01 3.78535509e-01 6.40935659e-01 3.22199672e-01
-4.70549643e-01 -6.04630589e-01 4.31193039e-03 -1.03845453e+00
3.78193706e-01 9.30180326e-02 -2.03558281e-01 -1.35747895... | [12.478577613830566, 0.41213634610176086] |
bef91b72-7820-459e-9ffc-6fbe67e275d9 | linkbert-pretraining-language-models-with | 2203.15827 | null | https://arxiv.org/abs/2203.15827v1 | https://arxiv.org/pdf/2203.15827v1.pdf | LinkBERT: Pretraining Language Models with Document Links | Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. In this work, we propose LinkBERT, an LM pretraining method that leverages lin... | ['Percy Liang', 'Jure Leskovec', 'Michihiro Yasunaga'] | 2022-03-29 | null | https://aclanthology.org/2022.acl-long.551 | https://aclanthology.org/2022.acl-long.551.pdf | acl-2022-5 | ['medical-relation-extraction', 'triviaqa', 'pico'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [-1.57010972e-01 5.72884619e-01 -7.40471363e-01 -2.20216274e-01
-1.17364371e+00 -7.06573009e-01 6.52817905e-01 5.45340717e-01
-6.05761409e-01 1.34807241e+00 4.53664631e-01 -4.44337308e-01
-1.44423708e-01 -6.12775862e-01 -1.17734039e+00 -4.01185393e-01
-1.65703401e-01 1.09175384e+00 3.18033546e-01 -1.46314576... | [8.802821159362793, 8.567546844482422] |
a6572bb9-40f1-4de7-a840-a939e81e9fb0 | h2-golden-retriever-methodology-and-tool-for | 2211.08614 | null | https://arxiv.org/abs/2211.08614v1 | https://arxiv.org/pdf/2211.08614v1.pdf | H2-Golden-Retriever: Methodology and Tool for an Evidence-Based Hydrogen Research Grantsmanship | Hydrogen is poised to play a major role in decarbonizing the economy. The need to discover, develop, and understand low-cost, high-performance, durable materials that can help maximize the cost of electrolysis as well as the need for an intelligent tool to make evidence-based Hydrogen research funding decisions relativ... | ['Gregory Renard', 'Rozhin Yasaei', 'Loveneesh Rana', 'Lorien Pratt', 'Joseph Wiggins', 'Olusola Olabanjo', 'Paul Seurin'] | 2022-11-16 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-5.26254952e-01 4.33336079e-01 -2.88945526e-01 1.85960189e-01
-3.95261586e-01 -5.43580234e-01 8.57679784e-01 6.90828562e-01
-3.03449154e-01 7.93086410e-01 4.90719259e-01 -5.95121980e-01
-7.60712802e-01 -1.29276133e+00 -2.38949761e-01 -5.62881112e-01
3.49112577e-03 8.94399464e-01 1.55854687e-01 -1.48540974... | [9.497227668762207, 8.207371711730957] |
fe598a82-1bc9-4517-9007-3dad3e7687e5 | tonet-tone-octave-network-for-singing-melody | 2202.00951 | null | https://arxiv.org/abs/2202.00951v1 | https://arxiv.org/pdf/2202.00951v1.pdf | TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music | Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the sung frequencies. However, this design does not lead to human-level performance in the perception of melody information for both tone (pitch-... | ['Shlomo Dubnov', 'Taylor Berg-Kirkpatrick', 'Wei Li', 'Cheng-i Wang', 'Shuai Yu', 'Ke Chen'] | 2022-02-02 | null | null | null | null | ['melody-extraction', 'music-information-retrieval'] | ['music', 'music'] | [ 2.31139049e-01 -5.44739902e-01 -2.01404884e-01 -2.49041408e-01
-1.08154213e+00 -6.23513162e-01 2.10250378e-01 2.28412207e-02
-9.65305120e-02 3.35760415e-01 5.88299930e-01 2.37809986e-01
-3.75343621e-01 -6.31625116e-01 -3.13498497e-01 -3.79828990e-01
2.84499768e-03 -2.84659058e-01 1.99350387e-01 -5.57495356... | [15.742176055908203, 5.442770957946777] |
3d35234c-5c2b-4bc1-8366-81b867bafa03 | channel-spatial-based-few-shot-bird-sounds | 2306.10499 | null | https://arxiv.org/abs/2306.10499v2 | https://arxiv.org/pdf/2306.10499v2.pdf | Channel-Spatial-Based Few-Shot Bird Sound Event Detection | In this paper, we propose a model for bird sound event detection that focuses on a small number of training samples within the everyday long-tail distribution. As a result, we investigate bird sound detection using the few-shot learning paradigm. By integrating channel and spatial attention mechanisms, improved feature... | ['Chenlei Jin', 'Xin Pan', 'Yajie Yang', 'Haitao Fu', 'Yuxuan Feng', 'Lingwen Liu'] | 2023-06-18 | null | null | null | null | ['sound-event-detection', 'sound-classification', 'few-shot-learning'] | ['audio', 'audio', 'methodology'] | [-1.60474643e-01 -6.24008894e-01 -3.59048918e-02 -2.34130114e-01
-8.01841319e-01 -3.06509018e-01 3.68602455e-01 3.37503999e-02
-4.90373194e-01 2.87436724e-01 2.46383533e-01 -5.37035242e-02
-3.04676834e-02 -8.58339369e-01 -5.72677195e-01 -5.47694087e-01
-4.19576317e-01 -5.94591856e-01 6.84919178e-01 1.81715470... | [15.159820556640625, 5.2250165939331055] |
093bf689-a80f-4fe8-b18d-48b6a26d5de1 | rule-of-thumb-deep-derotation-for-improved | 1507.05726 | null | http://arxiv.org/abs/1507.05726v1 | http://arxiv.org/pdf/1507.05726v1.pdf | Rule Of Thumb: Deep derotation for improved fingertip detection | We investigate a novel global orientation regression approach for articulated
objects using a deep convolutional neural network. This is integrated with an
in-plane image derotation scheme, DeROT, to tackle the problem of per-frame
fingertip detection in depth images. The method reduces the complexity of
learning in th... | ['Aaron Wetzler', 'Ron Slossberg', 'Ron Kimmel'] | 2015-07-21 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [ 2.79645231e-02 1.43423185e-01 1.23277634e-01 -1.82128206e-01
-7.02123702e-01 -8.08626294e-01 4.33562130e-01 -5.54957509e-01
-7.89728999e-01 3.78176779e-01 -1.59719978e-02 2.56079137e-01
-7.16262236e-02 -1.26730904e-01 -6.43219292e-01 -6.33243978e-01
2.21207246e-01 1.18223703e+00 5.40784359e-01 1.38940178... | [6.5501179695129395, -0.8015610575675964] |
3a235c80-bcdd-4f41-9d4e-068ce65555d6 | a-survey-on-knowledge-graph-based-methods-for | 2210.08119 | null | https://arxiv.org/abs/2210.08119v1 | https://arxiv.org/pdf/2210.08119v1.pdf | A Survey on Knowledge Graph-based Methods for Automated Driving | Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especia... | ['Lavdim Halilaj', 'Cory Henson', 'Sebastian Monka', 'Juergen Luettin'] | 2022-09-30 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-7.91987926e-02 3.75699759e-01 -4.82905656e-01 -4.83597100e-01
7.58307502e-02 -2.52057612e-01 4.90762949e-01 5.95534682e-01
-3.22275847e-01 4.39537883e-01 8.92019719e-02 -4.93806690e-01
-5.11161327e-01 -1.25599277e+00 -6.17478609e-01 -3.43067914e-01
-2.90787876e-01 6.33394957e-01 5.32354414e-01 -5.91343641... | [7.9755401611328125, -2.1430611610412598] |
74544db0-8a74-436e-8162-015ded4a92e5 | acoustic-scene-classification-using-audio | 2003.09164 | null | http://arxiv.org/abs/2003.09164v2 | http://arxiv.org/pdf/2003.09164v2.pdf | Acoustic Scene Classification using Audio Tagging | Acoustic scene classification systems using deep neural networks classify
given recordings into pre-defined classes. In this study, we propose a novel
scheme for acoustic scene classification which adopts an audio tagging system
inspired by the human perception mechanism. When humans identify an acoustic
scene, the exi... | [] | 2020-04-19 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 5.66377461e-01 -2.38321811e-01 7.95655191e-01 -5.52092671e-01
-8.90508592e-01 -4.74925280e-01 5.04775882e-01 2.56109685e-01
-8.01170230e-01 -1.46364584e-03 2.45261103e-01 1.57858580e-01
1.12677336e-01 -6.00413918e-01 -3.60243410e-01 -9.37062562e-01
1.14944823e-01 -1.18736714e-01 3.37184727e-01 1.60724178... | [15.12771224975586, 5.112823009490967] |
42b85084-cd23-4f3d-99d7-4540f4a35c3e | bayesian-networks-for-named-entity-prediction | 2302.13253 | null | https://arxiv.org/abs/2302.13253v1 | https://arxiv.org/pdf/2302.13253v1.pdf | Bayesian Networks for Named Entity Prediction in Programming Community Question Answering | Within this study, we propose a new approach for natural language processing using Bayesian networks to predict and analyze the context and how this approach can be applied to the Community Question Answering domain. We discuss how Bayesian networks can detect semantic relationships and dependencies between entities, a... | ['Sergey Kovalchuk', 'Alexey Gorbatovski'] | 2023-02-26 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 2.31003109e-03 5.56873024e-01 4.90834750e-02 -7.33401597e-01
-2.24308163e-01 -5.72062850e-01 5.96615911e-01 8.26239109e-01
-1.95915118e-01 5.58243275e-01 5.92961729e-01 -5.30989110e-01
-9.73268747e-01 -1.05502236e+00 -3.06656599e-01 -1.73231643e-02
-4.61135864e-01 5.93766451e-01 8.91052306e-01 3.69207077... | [9.312521934509277, 8.537528038024902] |
1f683495-da9c-4fc8-ab74-d54c6bce9ec7 | a-scalable-second-order-method-for-ill | 2106.02119 | null | https://arxiv.org/abs/2106.02119v1 | https://arxiv.org/pdf/2106.02119v1.pdf | A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples | We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate. It combines the favorable data-efficiency ... | ['Claudio Mayrink Verdun', 'Christian Kümmerle'] | 2021-06-03 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 2.10270748e-01 2.49566764e-01 -2.02393383e-02 -1.29203618e-01
-1.45709789e+00 -4.41243649e-01 4.14975554e-01 -1.59827083e-01
-4.53873247e-01 7.94920325e-01 2.07189143e-01 -2.79445410e-01
-4.81769472e-01 -2.10119560e-01 -8.17934752e-01 -6.81137025e-01
-3.47560912e-01 7.86172867e-01 -3.00708022e-02 -4.06065613... | [6.994635581970215, 4.597374439239502] |
2dfa78e6-3e46-48c8-8fca-4ff9721b456b | align-smatch-a-novel-evaluation-method-for | null | null | https://aclanthology.org/2022.lrec-1.638 | https://aclanthology.org/2022.lrec-1.638.pdf | Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation | Abstract Meaning Representation is a sentence-level meaning representation, which abstracts the meaning of sentences into a rooted acyclic directed graph. With the continuous expansion of Chinese AMR corpus, more and more scholars have developed parsing systems to automatically parse sentences into Chinese AMR. However... | ['Weiguang Qu', 'Junsheng Zhou', 'Minxuan Feng', 'Kairui Huo', 'Zhixing Xu', 'Bin Li', 'Liming Xiao'] | null | null | null | null | lrec-2022-6 | ['concept-alignment', 'amr-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 4.18567151e-01 3.58851969e-01 -4.36671339e-02 -6.86095893e-01
-6.01365328e-01 -5.56836307e-01 3.73814732e-01 5.23251414e-01
-2.11577788e-01 5.72096765e-01 6.23741329e-01 -6.23323619e-01
8.57715160e-02 -1.11058295e+00 -1.09613568e-01 -1.59416795e-01
3.37942809e-01 3.83151323e-01 1.83559045e-01 -4.53333259... | [10.408651351928711, 9.391386985778809] |
4ce4839d-cdb7-488a-8f52-0ce2af33afcd | a-deep-learning-search-for-technosignatures | 2301.12670 | null | https://arxiv.org/abs/2301.12670v1 | https://arxiv.org/pdf/2301.12670v1.pdf | A deep-learning search for technosignatures of 820 nearby stars | The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their "technosignatures". One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a genera... | ['S. Pete Worden', 'Sofia Z. Sheikh', 'Danny C. Price', 'Imke de Pater', 'David MacMahon', 'Matt Lebofsky', 'Howard Isaacson', 'John Hoang', 'Vishal Gajjar', 'Jamie Drew', 'Daniel Czech', 'Bryan Brzycki', 'Andrew P. V. Siemion', 'Steve Croft', 'Leandro Rizk', 'Cherry Ng', 'Peter Xiangyuan Ma'] | 2023-01-30 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [-1.78152062e-02 -9.43867937e-02 5.43555617e-02 -1.28291070e-01
-7.91188419e-01 -5.95596194e-01 1.04360259e+00 -7.49773562e-01
-4.83150899e-01 6.92710400e-01 1.98395297e-01 -3.86662424e-01
-5.87407291e-01 -4.53613907e-01 -4.50793803e-01 -8.92325580e-01
4.49652448e-02 9.36072469e-01 -2.49512017e-01 -3.75636667... | [7.568843841552734, 3.119579553604126] |
b686dc2c-081b-457e-955e-e69571352732 | a-theory-of-unsupervised-speech-recognition | 2306.07926 | null | https://arxiv.org/abs/2306.07926v1 | https://arxiv.org/pdf/2306.07926v1.pdf | A Theory of Unsupervised Speech Recognition | Unsupervised speech recognition (ASR-U) is the problem of learning automatic speech recognition (ASR) systems from unpaired speech-only and text-only corpora. While various algorithms exist to solve this problem, a theoretical framework is missing from studying their properties and addressing such issues as sensitivity... | ['Chang D. Yoo', 'Mark Hasegawa-Johnson', 'Liming Wang'] | 2023-06-09 | null | null | null | null | ['automatic-speech-recognition', 'unsupervised-speech-recognition'] | ['speech', 'speech'] | [ 3.02361608e-01 2.68620312e-01 -2.34046564e-01 -3.33879948e-01
-1.05130076e+00 -4.88193870e-01 4.73346323e-01 -1.34750441e-01
-1.21182710e-01 5.56858301e-01 -6.85165375e-02 -1.03238189e+00
-1.77320868e-01 -3.67074609e-01 -7.69486845e-01 -7.68534184e-01
-3.15626860e-01 3.33287656e-01 2.51570672e-01 -3.05728137... | [14.418644905090332, 6.684770584106445] |
81eaf081-ac86-44b7-9e86-eccbe4e210ea | accounting-ngrams-and-multi-word-terms-can | null | null | https://aclanthology.org/W16-1806 | https://aclanthology.org/W16-1806.pdf | Accounting ngrams and multi-word terms can improve topic models | null | ['Natalia Loukachevitch', 'Michael Nokel'] | 2016-08-01 | null | null | null | ws-2016-8 | ['text-clustering'] | ['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... | [-7.325442314147949, 3.6668591499328613] |
3248dca3-c8c7-4c12-9ac1-a3b197d0fb1b | adversarial-occlusion-aware-face-detection | 1709.05188 | null | http://arxiv.org/abs/1709.05188v6 | http://arxiv.org/pdf/1709.05188v6.pdf | Adversarial Occlusion-aware Face Detection | Occluded face detection is a challenging detection task due to the large
appearance variations incurred by various real-world occlusions. This paper
introduces an Adversarial Occlusion-aware Face Detector (AOFD) by
simultaneously detecting occluded faces and segmenting occluded areas.
Specifically, we employ an adversa... | ['Yujia Chen', 'Ran He', 'Lingxiao Song'] | 2017-09-15 | null | null | null | null | ['occluded-face-detection'] | ['computer-vision'] | [ 1.19860232e-01 2.52913624e-01 2.84068137e-02 -2.44960323e-01
-4.82161820e-01 -5.42185783e-01 2.96283096e-01 -5.59593499e-01
-1.96284316e-02 5.09325802e-01 -3.11558574e-01 -7.72814592e-03
5.35914540e-01 -7.67217100e-01 -6.88086808e-01 -9.12515879e-01
-1.20170906e-01 3.50229055e-01 8.72682333e-02 1.18634522... | [13.352252006530762, 0.5669070482254028] |
bb863397-0485-41f8-98e7-9b4061268715 | examining-performance-of-sketch-to-image | 1811.00249 | null | http://arxiv.org/abs/1811.00249v1 | http://arxiv.org/pdf/1811.00249v1.pdf | Examining Performance of Sketch-to-Image Translation Models with Multiclass Automatically Generated Paired Training Data | Image translation is a computer vision task that involves translating one
representation of the scene into another. Various approaches have been proposed
and achieved highly desirable results. Nevertheless, its accomplishment
requires abundant paired training data which are expensive to acquire.
Therefore, models for t... | ['Dichao Hu'] | 2018-11-01 | null | null | null | null | ['sketch-to-image-translation'] | ['computer-vision'] | [ 5.24432242e-01 3.24958146e-01 -2.23945886e-01 -5.82522154e-01
-1.02368951e+00 -6.98131144e-01 8.25626910e-01 -7.30973840e-01
-5.58155142e-02 7.19314277e-01 -5.02032638e-02 -1.64250508e-01
6.95674837e-01 -8.19598854e-01 -1.20784497e+00 -3.33094925e-01
6.33942246e-01 5.53204298e-01 -7.08965287e-02 -2.52181023... | [11.900199890136719, -0.09539579600095749] |
786e4524-00d4-48c3-b80e-a2aff3b12caa | theme-transformer-symbolic-music-generation | 2111.04093 | null | https://arxiv.org/abs/2111.04093v2 | https://arxiv.org/pdf/2111.04093v2.pdf | Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer | Attention-based Transformer models have been increasingly employed for automatic music generation. To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a priming sequence and ask a Transformer decoder to generate a continuation. ... | ['Yi-Hsuan Yang', 'Meinard Müller', 'Frank Zalkow', 'Shih-Lun Wu', 'Yi-Jen Shih'] | 2021-11-07 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 7.85852969e-01 -4.61672479e-03 3.11339617e-01 7.10958689e-02
-1.01943696e+00 -8.02958786e-01 8.10609877e-01 -4.20114063e-02
-3.02447490e-02 5.71812749e-01 4.78643566e-01 -4.23860438e-02
7.42210075e-02 -7.61952400e-01 -8.12061667e-01 -7.57334888e-01
3.04202110e-01 7.44039297e-01 -2.21937373e-01 -4.76258606... | [15.92122745513916, 5.617412567138672] |
68e20659-7d92-465b-9689-feec464bf1a4 | group-attention-single-shot-detector-ga-ssd | 1812.07166 | null | https://arxiv.org/abs/1812.07166v2 | https://arxiv.org/pdf/1812.07166v2.pdf | Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images | Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group... | ['Bjoern H. Menze', 'Wei-Shi Zheng', 'Jiechao Ma', 'Sen Liang', 'Rongguo Zhang', 'Xiang Li', 'Hongwei Li'] | 2018-12-18 | null | null | null | null | ['finding-pulmonary-nodules-in-large-scale-ct'] | ['medical'] | [ 2.17654392e-01 2.92728513e-01 -2.11317122e-01 -1.46758005e-01
-8.79208326e-01 -9.23167318e-02 2.43923664e-01 -1.25694156e-01
-4.88816112e-01 1.84177950e-01 3.75892967e-01 -5.01827657e-01
-2.36097917e-01 -3.99823546e-01 -4.82389331e-01 -7.85090625e-01
-8.14585611e-02 6.02142274e-01 9.53275323e-01 4.63573903... | [15.387787818908691, -2.1294288635253906] |
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