paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e5ab51c6-504b-4d36-8927-6c261f50c81f | naturalproofs-mathematical-theorem-proving-in | 2104.01112 | null | https://arxiv.org/abs/2104.01112v2 | https://arxiv.org/pdf/2104.01112v2.pdf | NaturalProofs: Mathematical Theorem Proving in Natural Language | Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning. As a step in this direction, we develop NaturalProofs, a multi-domain corpus of mathematical stateme... | ['Kyunghyun Cho', 'Yejin Choi', 'Hannaneh Hajishirzi', 'Ronan Le Bras', 'Jiacheng Liu', 'Sean Welleck'] | 2021-03-24 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.51381391e-01 -6.25073761e-02 -2.99115062e-01 -2.87598252e-01
-1.13746762e+00 -1.12221766e+00 1.20007312e+00 6.97987974e-01
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-3.19870502e-01 -1.01810145e+00 -9.94315743e-01 -2.06136122e-01
-3.16600531e-01 3.56164098e-01 1.31792009e-01 -4.36157733... | [9.433427810668945, 7.320314407348633] |
2cd22d36-9290-4949-b580-cfc38aa9c606 | visualsparta-sparse-transformer-fragment | 2101.00265 | null | https://arxiv.org/abs/2101.00265v2 | https://arxiv.org/pdf/2101.00265v2.pdf | VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words | Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of bo... | ['Kyusong Lee', 'Tiancheng Zhao', 'Xiaopeng Lu'] | 2021-01-01 | null | https://aclanthology.org/2021.acl-long.389 | https://aclanthology.org/2021.acl-long.389.pdf | acl-2021-5 | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 0.21146666 -0.91497165 -0.27187505 -0.05693946 -1.5694447 -0.7036804
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0.35485992 1.3177412 0.511... | [10.778765678405762, 0.7568899989128113] |
59c08668-eac6-4617-8dc8-09a8bd9fe582 | estisr-adapting-efficient-scene-text-image | 2306.02443 | null | https://arxiv.org/abs/2306.02443v1 | https://arxiv.org/pdf/2306.02443v1.pdf | ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes | While scene text image super-resolution (STISR) has yielded remarkable improvements in accurately recognizing scene text, prior methodologies have placed excessive emphasis on optimizing performance, rather than paying due attention to efficiency - a crucial factor in ensuring deployment of the STISR-STR pipeline. In t... | ['Jie Shao', 'Yihan Xu', 'Xin Man', 'Minghao Fu'] | 2023-06-04 | null | null | null | null | ['image-super-resolution', 'super-resolution', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.12367988e-01 -2.83708096e-01 -4.76467423e-02 -4.16916132e-01
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3.87866765e-01 -3.29112150e-02 4.01023239e-01 -1.64724156... | [11.260126113891602, -1.9385666847229004] |
8a1a451a-4bfe-48b4-a56b-bf544a591d45 | guideformer-transformers-for-image-guided | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.pdf | GuideFormer: Transformers for Image Guided Depth Completion | Depth completion has been widely studied to predict a dense depth image from its sparse measurement and a single color image. However, most state-of-the-art methods rely on static convolutional neural networks (CNNs) which are not flexible enough for capturing the dynamic nature of input contexts. In this paper, we... | ['Youngjung Kim', 'Jinsung Ha', 'Kyeongha Rho'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['depth-completion'] | ['computer-vision'] | [ 2.51489013e-01 -3.80151421e-02 -2.11977080e-01 -5.77525198e-01
-7.67215431e-01 -2.57594019e-01 5.91977119e-01 -2.49708056e-01
-1.98042005e-01 4.00218219e-01 4.18113291e-01 -1.69013608e-02
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1.23647422e-01 1.81690902e-01 3.23584437e-01 -1.49266332... | [8.937849044799805, -2.4447083473205566] |
25bf4838-dc9a-4cf0-b12f-75ebabf978af | estimating-egocentric-3d-human-pose-in-the | 2201.07929 | null | https://arxiv.org/abs/2201.07929v1 | https://arxiv.org/pdf/2201.07929v1.pdf | Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision | Egocentric 3D human pose estimation with a single fisheye camera has drawn a significant amount of attention recently. However, existing methods struggle with pose estimation from in-the-wild images, because they can only be trained on synthetic data due to the unavailability of large-scale in-the-wild egocentric datas... | ['Christian Theobalt', 'Diogo Luvizon', 'Kripasindhu Sarkar', 'Weipeng Xu', 'Lingjie Liu', 'Jian Wang'] | 2022-01-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.pdf | cvpr-2022-1 | ['egocentric-pose-estimation'] | ['computer-vision'] | [-1.03399418e-01 1.78849518e-01 1.52540933e-02 -4.59379792e-01
-4.38356310e-01 -2.53029466e-01 3.40056837e-01 -7.05529153e-01
-5.22241950e-01 5.10696590e-01 3.30815583e-01 6.06671333e-01
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1.55118182e-01 6.44700468e-01 1.41123071e-01 -2.32862160... | [7.0363688468933105, -0.8949124813079834] |
f4eab759-b830-4f88-88e5-9ba44b341f90 | a-collaborative-approach-using-neural | 2205.10559 | null | https://arxiv.org/abs/2205.10559v1 | https://arxiv.org/pdf/2205.10559v1.pdf | A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning | In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Blu... | ['Elena Simona Lohan', 'Sven Casteleyn', 'Joaquín Torres-Sospedra', 'Pavel Pascacio'] | 2022-05-21 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.73644245e-01 -2.89931387e-01 -7.43342787e-02 -3.51080686e-01
-6.83564901e-01 -5.37854135e-01 1.97323129e-01 5.98490238e-01
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-5.75789392e-01 -8.79443526e-01 -5.91519237e-01 -9.37517166e-01
-1.38794137e-02 1.57439131e-02 1.31621018e-01 1.44530430... | [6.412262439727783, 0.9387201070785522] |
87f4a677-515a-412a-ac56-8cbec26611e3 | fpgahart-a-toolflow-for-throughput-oriented | 2305.19896 | null | https://arxiv.org/abs/2305.19896v1 | https://arxiv.org/pdf/2305.19896v1.pdf | fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs | Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high computational and memory requirements, especially when integrated into systems ... | ['Dimitrios Tzovaras', 'Christos-Savvas Bouganis', 'Petros Toupas'] | 2023-05-31 | null | null | null | null | ['video-retrieval', 'autonomous-vehicles', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 8.84099826e-02 -7.08683953e-02 -5.62097847e-01 -2.46648863e-01
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-4.00938541e-01 1.46249369e-01 -1.37876153e-01 -7.08076179e-01
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-2.36140966e-01 -5.82459942e-02 4.76313502e-01 1.33333579... | [8.307645797729492, 2.704787254333496] |
048861e6-b361-4fa8-818d-3f9787799825 | a-closer-look-at-the-training-dynamics-of | 2303.11098 | null | https://arxiv.org/abs/2303.11098v1 | https://arxiv.org/pdf/2303.11098v1.pdf | A closer look at the training dynamics of knowledge distillation | In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encod... | ['Krystian Mikolajczyk', 'Roy Miles'] | 2023-03-20 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.74014345e-01 2.03290135e-01 -2.73994356e-01 -2.13178307e-01
-3.53084028e-01 -5.35969973e-01 7.47348607e-01 6.76295236e-02
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-1.42729715e-01 3.37026954e-01 1.44370809e-01 -1.38227731... | [9.370302200317383, 3.296186923980713] |
1adad9f7-91a5-4202-9340-73eecdb8319d | chard-clinical-health-aware-reasoning-across | 2210.04191 | null | https://arxiv.org/abs/2210.04191v2 | https://arxiv.org/pdf/2210.04191v2.pdf | CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models | We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an... | ['Eduard Hovy', 'Anatole Gershman', 'Bogdan Sacaleanu', 'Vivek Khetan', 'Steven Y. Feng'] | 2022-10-09 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.02502626e-01 1.38600767e+00 -5.92063248e-01 -4.84949440e-01
-8.87177885e-01 -1.90321252e-01 7.25474775e-01 7.63513982e-01
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-3.91108185e-01 -5.97928405e-01 -2.78722435e-01 5.70327649e-03
-2.57298917e-01 9.04852450e-01 -4.20104861e-01 -8.63968432... | [9.127631187438965, 7.801990509033203] |
d16514e2-3410-401c-b5fe-cede025f2ec5 | proof-supplement-learning-sparse-causal | 1411.1557 | null | http://arxiv.org/abs/1411.1557v1 | http://arxiv.org/pdf/1411.1557v1.pdf | Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013) | This article contains detailed proofs and additional examples related to the
UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It
describes the FCI+ algorithm: a method for sound and complete causal model
discovery in the presence of latent confounders and/or selection bias, that has
worst case polyno... | ['Tom Heskes', 'Joris M. Mooij', 'Tom Claassen'] | 2014-11-06 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 4.37819511e-01 7.09031343e-01 -6.47313952e-01 -3.08643818e-01
-8.60518873e-01 -5.34090102e-01 1.98174402e-01 2.81367719e-01
-1.46367073e-01 1.39354897e+00 1.04799099e-01 -6.63429916e-01
-9.52885509e-01 -8.85950804e-01 -1.14499605e+00 -7.70600736e-01
-1.19234037e+00 8.22761297e-01 2.19467327e-01 4.06984448... | [7.661834716796875, 5.309223651885986] |
b2cc982c-4a6a-4482-873f-446620e730b7 | data-boost-text-data-augmentation-through-1 | 2012.02952 | null | https://arxiv.org/abs/2012.02952v1 | https://arxiv.org/pdf/2012.02952v1.pdf | Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation | Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on t... | ['Soroush Vosoughi', 'Lili Wang', 'Weicheng Ma', 'Chenyan Jia', 'Guangxuan Xu', 'Ruibo Liu'] | 2020-12-05 | data-boost-text-data-augmentation-through | https://aclanthology.org/2020.emnlp-main.726 | https://aclanthology.org/2020.emnlp-main.726.pdf | emnlp-2020-11 | ['text-augmentation'] | ['natural-language-processing'] | [ 3.63675714e-01 3.21550876e-01 -4.83643621e-01 -4.74227428e-01
-6.54690027e-01 -4.08022314e-01 9.72905993e-01 6.65815473e-01
-7.69292891e-01 1.23489547e+00 4.48538810e-01 -3.59446555e-01
2.29442015e-01 -7.26101220e-01 -5.88162124e-01 -3.59731555e-01
4.22246695e-01 6.40425861e-01 -5.00984967e-01 -5.37361920... | [10.740715026855469, 8.093915939331055] |
7931adde-d062-4eb9-bc0c-24e1746b3eb1 | unsupervised-manifold-alignment-with-joint | 2207.02968 | null | https://arxiv.org/abs/2207.02968v2 | https://arxiv.org/pdf/2207.02968v2.pdf | Unsupervised Manifold Alignment with Joint Multidimensional Scaling | We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (... | ['Karsten Borgwardt', 'Carlos Oliver', 'Bowen Fan', 'Dexiong Chen'] | 2022-07-06 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 1.07421927e-01 -6.39536008e-02 -8.58059004e-02 -5.00242114e-01
-8.18470061e-01 -7.81329811e-01 3.24817866e-01 4.95853931e-01
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-4.18417633e-01 -5.23342669e-01 -5.35473347e-01 -6.90784872e-01
-2.94102058e-02 7.94846833e-01 -2.27785856e-03 3.47484760... | [7.895127773284912, 4.093605041503906] |
31e067f8-662d-4571-852d-af5201726bf0 | sign-language-translation-in-a-healthcare | null | null | https://aclanthology.org/2021.triton-1.13 | https://aclanthology.org/2021.triton-1.13.pdf | Sign Language Translation in a Healthcare Setting | Communication between healthcare professionals and deaf patients is challenging, and the current COVID-19 pandemic makes this issue even more acute. Sign language interpreters can often not enter hospitals and face masks make lipreading impossible. To address this urgent problem, we developed a system which allows heal... | ['Anika Smeijers', 'Shani Mende-Gillings', 'Lyke Esselink', 'Floris Roelofsen'] | null | null | null | null | triton-2021-7 | ['lipreading', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 2.49416325e-02 1.70855612e-01 3.93585814e-03 -1.88096836e-01
-2.56295174e-01 -6.72508121e-01 5.32722354e-01 -1.35036513e-01
-8.67815256e-01 9.16597605e-01 5.08738220e-01 -6.87322855e-01
1.95388392e-01 -2.04600528e-01 -7.59689063e-02 -4.79185611e-01
2.74084777e-01 7.32337534e-01 1.37237519e-01 -1.78466797... | [9.091231346130371, -6.394140243530273] |
6a3b393e-2fb6-4ff3-b1fe-2d042050d9f3 | skill-based-differences-in-spatio-temporal | 1603.07738 | null | http://arxiv.org/abs/1603.07738v1 | http://arxiv.org/pdf/1603.07738v1.pdf | Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2 | Multiplayer Online Battle Arena (MOBA) games are among the most played
digital games in the world. In these games, teams of players fight against each
other in arena environments, and the gameplay is focused on tactical combat.
Mastering MOBAs requires extensive practice, as is exemplified in the popular
MOBA Defence o... | ['Matthias Schubert', 'John Maguire', 'Derrek Chu', 'Iris Yuhui Wang', 'Diego Klabjan', 'Anders Drachen', 'Tobias Mahlmann', 'Matthew Yancey'] | 2016-03-24 | null | null | null | null | ['dota-2', 'time-series-clustering'] | ['playing-games', 'time-series'] | [-5.64711154e-01 -4.92663920e-01 4.78743643e-01 1.54536650e-01
-3.58575225e-01 -9.93627191e-01 2.78507143e-01 3.55881691e-01
-7.81720757e-01 6.30129099e-01 3.08096129e-02 -2.72969276e-01
-1.22485757e+00 -9.87568140e-01 -1.37950212e-01 -6.15438998e-01
-4.82801676e-01 7.89286792e-01 6.76552534e-01 -1.07083797... | [6.4727559089660645, 0.39598318934440613] |
a40f87c0-8e40-4463-be12-ecdbe73bd112 | a-probabilistic-autoencoder-for-causal | 2212.04235 | null | https://arxiv.org/abs/2212.04235v1 | https://arxiv.org/pdf/2212.04235v1.pdf | A probabilistic autoencoder for causal discovery | The paper addresses the problem of finding the causal direction between two associated variables. The proposed solution is to build an autoencoder of their joint distribution and to maximize its estimation capacity relative to both the marginal distributions. It is shown that the resulting two capacities cannot, in gen... | ['Matthias Feiler'] | 2022-12-08 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.29933634e-01 5.15112340e-01 -2.27001414e-01 -3.32080692e-01
-1.29641712e-01 -3.02247882e-01 9.08108473e-01 -1.58255380e-02
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-5.11657119e-01 -9.23239291e-01 -7.18799770e-01 -1.19862425e+00
-7.80513510e-02 7.41171360e-01 -1.70225948e-01 3.05404335... | [8.334309577941895, 5.598961353302002] |
be72f08c-2d6d-4f84-b1fa-c030c3ee0756 | conditional-goal-oriented-trajectory | 2210.15449 | null | https://arxiv.org/abs/2210.15449v1 | https://arxiv.org/pdf/2210.15449v1.pdf | Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation | This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: ... | ['Dongbin Zhao', 'Yifeng Pan', 'Shuai Lu', 'Qichao Zhang', 'Ding Li'] | 2022-10-19 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-4.56898883e-02 9.50589702e-02 -3.06467742e-01 -5.20078063e-01
-7.11335778e-01 1.41183182e-03 9.54749227e-01 -1.39936507e-01
-1.66347876e-01 7.45138347e-01 6.16888106e-01 -1.82368994e-01
-3.47798586e-01 -8.72403502e-01 -4.98085946e-01 -6.03999615e-01
-6.43024206e-01 5.61060190e-01 5.28370500e-01 -1.86337337... | [5.91002893447876, 0.8435134887695312] |
bdc0c73c-7bc3-408c-a8a3-12e1cb398169 | leveraging-monolingual-data-with-self | 2005.04816 | null | https://arxiv.org/abs/2005.04816v1 | https://arxiv.org/pdf/2005.04816v1.pdf | Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation | Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The second direction employs monolingual data with self-supervision to pr... | ['Yonghui Wu', 'Mia Chen', 'Ankur Bapna', 'Aditya Siddhant', 'Sneha Kudugunta', 'Orhan Firat', 'Naveen Arivazhagan', 'Yuan Cao'] | 2020-05-11 | leveraging-monolingual-data-with-self-1 | https://aclanthology.org/2020.acl-main.252 | https://aclanthology.org/2020.acl-main.252.pdf | acl-2020-6 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 7.17895338e-03 -1.77528828e-01 -7.01171696e-01 -3.31878275e-01
-1.25097930e+00 -8.73075843e-01 8.60207200e-01 -2.90696084e-01
-7.62627065e-01 1.07188332e+00 4.09716189e-01 -8.32300544e-01
4.83197957e-01 -4.40829515e-01 -1.05659425e+00 -6.67689368e-02
4.12995100e-01 8.52860808e-01 -4.26316082e-01 -8.34887624... | [11.568503379821777, 10.34704303741455] |
5269b841-2697-470f-bf3f-37fcf48a7e5e | parts-unsupervised-segmentation-with-slots | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zoran_PARTS_Unsupervised_Segmentation_With_Slots_Attention_and_Independence_Maximization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zoran_PARTS_Unsupervised_Segmentation_With_Slots_Attention_and_Independence_Maximization_ICCV_2021_paper.pdf | PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization | From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that ... | ['Danilo J. Rezende', 'Alexander Lerchner', 'Rishabh Kabra', 'Daniel Zoran'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['scene-segmentation'] | ['computer-vision'] | [ 2.93819904e-01 3.20601724e-02 -1.63238216e-02 -5.77370465e-01
-4.29681897e-01 -6.14546657e-01 8.90457988e-01 -1.72626451e-01
-1.21662892e-01 2.69712538e-01 2.89093971e-01 -6.53360263e-02
1.22123189e-01 -4.39318299e-01 -1.02177238e+00 -4.85771328e-01
-8.82339254e-02 6.04197621e-01 4.20705676e-01 -3.23568583... | [9.745216369628906, 0.1370559185743332] |
5899d7a7-b108-4e6b-9300-30f9201bd6aa | computer-aided-diagnosis-of-lung-nodule-using | 1708.05897 | null | http://arxiv.org/abs/1708.05897v2 | http://arxiv.org/pdf/1708.05897v2.pdf | Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization | We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 b... | ['Osamu Sugiyama', 'Mizuho Nishio', 'Tomohiro Kuroda', 'Mitsuo Nishizawa', 'Ryosuke Kojima', 'Masahiro Yakami', 'Kaori Togashi'] | 2017-08-19 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-2.36647248e-01 -1.87030673e-01 -6.43307388e-01 -4.12420154e-01
-7.84630775e-01 -1.92872345e-01 5.40351391e-01 4.13395762e-01
-3.16496968e-01 8.62282574e-01 4.73680235e-02 -9.99769270e-01
-5.99888980e-01 -7.84542620e-01 -1.16025582e-01 -8.37195218e-01
-1.96029350e-01 6.81377769e-01 7.07211018e-01 2.77032912... | [15.338608741760254, -2.371774435043335] |
bde499b4-6c11-41e3-9ea5-7e554b64babe | cldice-a-novel-topology-preserving-loss | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.pdf | clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation | Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirel... | ['Bjoern H. Menze', 'Ulrich Bauer', 'Josien P. W. Pluim', 'Andrey Zhylka', 'Alexander Unger', 'Ivan Ezhov', 'Anjany Sekuboyina', 'Johannes C. Paetzold', 'Suprosanna Shit'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['graph-similarity'] | ['graphs'] | [-2.43475810e-02 4.17650938e-01 -1.06088318e-01 -3.49847645e-01
1.57407179e-01 -7.84170270e-01 3.96951348e-01 6.44738972e-01
-3.25515300e-01 5.28199673e-01 -1.25708461e-01 -4.92049336e-01
-1.56339526e-01 -1.08746350e+00 -7.20328212e-01 -5.26272297e-01
-3.75013828e-01 4.08456534e-01 7.49957383e-01 -5.15879616... | [14.29952335357666, -2.6468253135681152] |
7fee4d6b-b83e-4f28-b196-5bdd0ba1558c | selection-strategies-for-commonsense | 2202.09163 | null | https://arxiv.org/abs/2202.09163v2 | https://arxiv.org/pdf/2202.09163v2.pdf | Selection Strategies for Commonsense Knowledge | Selection strategies are broadly used in first-order logic theorem proving to select those parts of a large knowledge base that are necessary to proof a theorem at hand. Usually, these selection strategies do not take the meaning of symbol names into account. In knowledge bases with commonsense knowledge, symbol names ... | ['Claudia Schon'] | 2022-02-18 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.78568631e-01 -1.90421734e-02 -5.13218701e-01 -2.64702857e-01
-5.78498095e-03 -6.53495312e-01 7.19916821e-01 5.18406212e-01
-4.68425602e-01 8.26002300e-01 2.80029923e-01 -7.22181201e-01
-5.64561367e-01 -1.35014725e+00 -5.26202559e-01 -4.15011942e-01
1.13358855e-01 2.90170074e-01 2.84095436e-01 -5.97673953... | [10.061448097229004, 8.56805419921875] |
c634aab2-9c8e-4673-b5dd-6640c3abc52c | symmetry-based-text-line-detection-in-natural | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Symmetry-Based_Text_Line_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Symmetry-Based_Text_Line_2015_CVPR_paper.pdf | Symmetry-Based Text Line Detection in Natural Scenes | Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes. Therefore, scene text detection and recognition have become active research topics in computer vision. In this work, we investigate the problem of scene text detection from ... | ['Wei Shen', 'Cong Yao', 'Zheng Zhang', 'Xiang Bai'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['line-detection'] | ['computer-vision'] | [ 5.73408306e-01 -6.53220117e-01 -1.23542473e-01 -1.16652735e-01
-2.47856215e-01 -4.56225932e-01 9.89112020e-01 3.28921616e-01
-5.61521590e-01 3.36575538e-01 9.82012972e-02 -2.75247216e-01
2.34260768e-01 -7.34405398e-01 -2.31589422e-01 -7.61720836e-01
4.10208851e-01 1.35041758e-01 6.26599729e-01 -1.67192705... | [11.923713684082031, 2.4290897846221924] |
d4fd58c1-6951-4162-b6d4-e78df300b0f8 | recognizing-textures-with-mobile-cameras-for | 1711.00558 | null | http://arxiv.org/abs/1711.00558v1 | http://arxiv.org/pdf/1711.00558v1.pdf | Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications | As smartphone rooted distractions become commonplace, the lack of compelling
safety measures has led to a rise in the number of injuries to distracted
walkers. Various solutions address this problem by sensing a pedestrian's
walking environment. Existing camera-based approaches have been largely limited
to obstacle det... | ['Marco Gruteser', 'Shubham Jain'] | 2017-11-01 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 2.04020366e-01 -4.15913522e-01 -7.02460557e-02 8.11462253e-02
-9.89020288e-01 -4.57836121e-01 4.91626054e-01 2.10991040e-01
-4.11053807e-01 5.49006343e-01 2.44515091e-01 -5.82653880e-01
4.12512302e-01 -1.05345368e+00 -4.25092876e-01 -5.78051567e-01
3.68312478e-01 -1.58547610e-01 5.81301928e-01 -3.48780632... | [7.859802722930908, -0.984255313873291] |
572c3dbd-d97c-4fb1-a5de-12b00f586c27 | inf-net-automatic-covid-19-lung-infection | 2004.14133 | null | https://arxiv.org/abs/2004.14133v4 | https://arxiv.org/pdf/2004.14133v4.pdf | Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images | Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infect... | ['Ge-Peng Ji', 'Deng-Ping Fan', 'Huazhu Fu', 'Jianbing Shen', 'Geng Chen', 'Tao Zhou', 'Yi Zhou', 'Ling Shao'] | 2020-04-22 | null | null | null | null | ['camouflage-segmentation'] | ['computer-vision'] | [ 3.98660660e-01 -6.22516684e-02 -3.39653134e-01 -2.76606828e-01
-7.86087394e-01 -2.80786008e-01 1.83314174e-01 -1.15012832e-01
-4.81400818e-01 5.84962308e-01 -1.80688454e-03 -3.75750810e-01
1.99106783e-01 -6.42896950e-01 -5.29001296e-01 -7.45755792e-01
1.22500844e-01 8.02579105e-01 4.64940518e-01 4.16588068... | [15.448090553283691, -1.8404510021209717] |
ac95a276-8419-469c-af02-ba372d85a34f | conservative-distributional-reinforcement | 2201.07286 | null | https://arxiv.org/abs/2201.07286v2 | https://arxiv.org/pdf/2201.07286v2.pdf | Conservative Distributional Reinforcement Learning with Safety Constraints | Safety exploration can be regarded as a constrained Markov decision problem where the expected long-term cost is constrained. Previous off-policy algorithms convert the constrained optimization problem into the corresponding unconstrained dual problem by introducing the Lagrangian relaxation technique. However, the cos... | ['Kai Lv', 'Shuo Wang', 'Sheng Han', 'Youfang Lin', 'Hengrui Zhang'] | 2022-01-18 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.50360495e-01 2.16193900e-01 -9.20728028e-01 -7.71493092e-02
-8.67434502e-01 -2.95471162e-01 2.64300585e-01 5.01966439e-02
-7.96045542e-01 1.39809573e+00 3.91646959e-02 -5.68904281e-01
-3.02818894e-01 -7.13614285e-01 -4.37979072e-01 -1.03826988e+00
-5.30173108e-02 2.14483857e-01 1.13933414e-01 -5.16473651... | [4.394171714782715, 2.2477989196777344] |
d9ea23fb-05c0-4bce-bd91-183db94a8263 | highly-efficient-binary-neural-networks-for | 2202.12375 | null | https://arxiv.org/abs/2202.12375v1 | https://arxiv.org/pdf/2202.12375v1.pdf | Highly-Efficient Binary Neural Networks for Visual Place Recognition | VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications. CNN-based techniques... | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini'] | 2022-02-24 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 2.11172611e-01 -6.98800012e-02 -2.23391354e-01 -4.53358255e-02
-4.68775630e-02 -4.36480343e-01 1.93730757e-01 2.48134509e-01
-9.65122879e-01 4.62161154e-01 -6.14196360e-01 -9.58435178e-01
-4.91886400e-02 -1.18308020e+00 -7.70197093e-01 -4.38844174e-01
2.33221263e-01 1.52951315e-01 3.03415835e-01 -5.27573884... | [8.098162651062012, -1.8869426250457764] |
4c7c984d-4186-4c41-87e1-1de300c5f8c3 | feature-transformation-ensemble-model-with | 2005.08463 | null | https://arxiv.org/abs/2005.08463v3 | https://arxiv.org/pdf/2005.08463v3.pdf | Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification | In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we proposes to construct an ensemble prediction model by performing diverse feature transformations after a feature extraction network. On each br... | ['Zhenpeng Li', 'Jieping Ye', 'Zhen Zhao', 'Yuhong Guo', 'Jianan Jiang', 'Bingyu Liu'] | 2020-05-18 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 6.38686121e-01 -6.74272180e-02 -1.87997103e-01 -6.37788296e-01
-6.22134387e-01 -3.24445754e-01 4.59449261e-01 -1.97252199e-01
-1.75127357e-01 8.16987216e-01 7.31933638e-02 7.21562132e-02
-2.77451456e-01 -7.12484121e-01 -3.84789467e-01 -5.39862514e-01
2.42066860e-01 1.88980162e-01 7.22684935e-02 -4.26596880... | [10.028399467468262, 3.042167901992798] |
6514b025-8447-42e2-aeb5-932cf2195ef9 | neural-distance-embeddings-for-biological | 2109.09740 | null | https://arxiv.org/abs/2109.09740v2 | https://arxiv.org/pdf/2109.09740v2.pdf | Neural Distance Embeddings for Biological Sequences | The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research. However, popular machine learning approaches, based on continuous Euclidean spaces, have struggled with the discrete combinatorial formulati... | ['Pietro Liò', 'Jure Leskovec', 'Petar Veličković', 'Michal Pándy', 'Rex Ying', 'Gabriele Corso'] | 2021-09-20 | null | http://proceedings.neurips.cc/paper/2021/hash/9a1de01f893e0d2551ecbb7ce4dc963e-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/9a1de01f893e0d2551ecbb7ce4dc963e-Paper.pdf | neurips-2021-12 | ['multiple-sequence-alignment'] | ['medical'] | [ 3.86848956e-01 -2.19391491e-02 3.41150939e-01 -3.96237791e-01
-5.87288618e-01 -5.18291175e-01 5.91703176e-01 8.42598379e-01
-6.91957474e-01 6.06959105e-01 1.02717742e-01 -6.05336623e-04
-5.28642237e-01 -5.23868740e-01 -5.30191362e-01 -1.07768917e+00
-4.07211453e-01 5.37502766e-01 1.26493469e-01 -2.56656379... | [4.936313152313232, 5.612502098083496] |
5509f9ed-4a2d-45bc-8605-f4f90f594948 | memoreader-large-scale-reading-comprehension | null | null | https://aclanthology.org/D18-1237 | https://aclanthology.org/D18-1237.pdf | MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller | Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text. Existing approaches made a significant progress comparable to human-level performance, but they are still limited in understanding, up to a few paragraphs, failing to properly comprehend lengthy docume... | ['Sathish Reddy Indurthi', 'Seohyun Back', 'Jihie Kim', 'Jaegul Choo', 'Seunghak Yu'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['triviaqa'] | ['miscellaneous'] | [ 2.04301447e-01 -2.17777919e-02 2.20914662e-01 -3.91593248e-01
-7.43450701e-01 -2.42010728e-01 4.73288536e-01 2.04674244e-01
-4.87882406e-01 7.18049824e-01 4.43460375e-01 -5.11235833e-01
-1.56207262e-02 -7.86852598e-01 -8.78904521e-01 -5.09212315e-01
2.10304722e-01 4.75736111e-01 2.73341537e-01 -4.74958628... | [11.214556694030762, 8.242598533630371] |
0f8dc25d-6f0d-4e77-917f-d599dd485163 | interactive-segmentation-as-gaussian-process | 2302.14578 | null | https://arxiv.org/abs/2302.14578v1 | https://arxiv.org/pdf/2302.14578v1.pdf | Interactive Segmentation as Gaussian Process Classification | Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and prop... | ['Yefeng Zheng', 'Deyu Meng', 'Yawen Huang', 'Yuexiang Li', 'Qian Zhao', 'Hong Wang', 'Minghao Zhou'] | 2023-02-28 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 1.73959032e-01 9.98584032e-02 -3.14590782e-01 -2.99072504e-01
-1.01385033e+00 -3.69694471e-01 4.58177269e-01 -1.64378315e-01
-3.68724048e-01 6.43378198e-01 -3.53514940e-01 -1.94624513e-01
-8.83724019e-02 -6.89620256e-01 -8.01410198e-01 -9.60227787e-01
5.51587164e-01 3.43163520e-01 6.02081895e-01 4.23084825... | [9.459694862365723, 0.12130722403526306] |
aa277e7b-dd76-4f87-925e-d9d01554f8c9 | few-shot-class-incremental-learning-by | 2203.17030 | null | https://arxiv.org/abs/2203.17030v2 | https://arxiv.org/pdf/2203.17030v2.pdf | Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks | New classes arise frequently in our ever-changing world, e.g., emerging topics in social media and new types of products in e-commerce. A model should recognize new classes and meanwhile maintain discriminability over old classes. Under severe circumstances, only limited novel instances are available to incrementally u... | ['De-Chuan Zhan', 'ShiLiang Pu', 'Di Xie', 'Liang Ma', 'Han-Jia Ye', 'Da-Wei Zhou'] | 2022-03-31 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.22497612e-01 -5.40576987e-02 -3.98561209e-01 -5.70527911e-01
-4.08069104e-01 -4.81112629e-01 5.28288603e-01 -8.74702036e-02
-3.68985802e-01 7.11511314e-01 -2.43726641e-01 1.13076732e-01
6.24932051e-02 -1.00202858e+00 -9.03415263e-01 -5.04280686e-01
1.22709863e-01 3.71032000e-01 5.81205130e-01 -3.43809545... | [9.804945945739746, 3.444457769393921] |
5bfa02b6-46d6-4125-b019-80a391899d5e | detecting-post-stroke-aphasia-using-eeg-based | 2303.07739 | null | https://arxiv.org/abs/2303.07739v1 | https://arxiv.org/pdf/2303.07739v1.pdf | Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech | [Objective]. After a stroke, one-third of patients suffer from aphasia, a language disorder that impairs communication ability. The standard behavioral tests used to diagnose aphasia are time-consuming and have low ecological validity. Neural tracking of the speech envelope is a promising tool for investigating brain r... | ['Maaike Vandermosten', 'Tom Francart', 'Jonas Vanthornhout', 'Ramtin Mehraram', 'Jill Kries', 'Pieter De Clercq'] | 2023-03-14 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 1.09115012e-01 -4.13322270e-01 -9.57019851e-02 1.32097438e-01
-6.61559999e-01 -5.68981886e-01 3.54354084e-01 3.78618568e-01
-7.29626298e-01 7.60214567e-01 6.35057867e-01 -3.61225545e-01
-4.37434018e-01 -6.28591001e-01 -6.10796809e-02 -5.31006396e-01
-5.43086886e-01 5.13230935e-02 1.95254236e-01 -9.32092965... | [13.19955825805664, 3.370156764984131] |
ea472f9f-4228-49c7-9e0e-a213402eb416 | deep-learning-for-asynchronous-massive-access | 2305.07278 | null | https://arxiv.org/abs/2305.07278v1 | https://arxiv.org/pdf/2305.07278v1.pdf | Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity | Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devic... | ['Petar Popovski', 'Bo Ai', 'Wei Chen', 'Yanna Bai'] | 2023-05-12 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 8.76097798e-01 1.29900843e-01 -7.70858467e-01 1.87967256e-01
-6.50998354e-01 -1.66094095e-01 3.22761953e-01 -7.57314079e-03
-2.71827966e-01 7.93946981e-01 3.03387612e-01 -4.65885878e-01
-2.02292521e-02 -4.20627892e-01 -3.56785089e-01 -8.17617536e-01
-5.71969330e-01 1.48096889e-01 1.64006057e-03 2.60165155... | [6.257346153259277, 1.40834379196167] |
28c9bfee-6356-457f-bcb3-632521fbd111 | passive-indoor-localization-with-wifi | 2111.14281 | null | https://arxiv.org/abs/2111.14281v1 | https://arxiv.org/pdf/2111.14281v1.pdf | Passive Indoor Localization with WiFi Fingerprints | This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow contr... | ['Kishore Reddy Tarimala', 'Robert Westendorp', 'Tao Lu', 'Xiaodai Dong', 'Ahmed Elmoogy', 'Kai Ren', 'Brosnan Yuen', 'Minh Tu Hoang'] | 2021-11-29 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.44321102e-01 -2.21018478e-01 -5.75448096e-01 -3.31140995e-01
-8.35273266e-01 -8.10795128e-01 6.76656365e-02 -1.10376015e-01
-3.79232526e-01 1.07596195e+00 -2.91727215e-01 -6.60331130e-01
-1.74014315e-01 -9.50259209e-01 -7.25728154e-01 -6.51490867e-01
-3.84340554e-01 -4.98742498e-02 2.27966249e-01 6.20076239... | [6.394775867462158, 0.9306458234786987] |
33bd8e91-d771-4516-af85-08dc2bd19d32 | attribute-based-representations-for-accurate | 2212.00789 | null | https://arxiv.org/abs/2212.00789v1 | https://arxiv.org/pdf/2212.00789v1.pdf | Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection | Video anomaly detection (VAD) is a challenging computer vision task with many practical applications. As anomalies are inherently ambiguous, it is essential for users to understand the reasoning behind a system's decision in order to determine if the rationale is sound. In this paper, we propose a simple but highly eff... | ['Yedid Hoshen', 'Tal Reiss'] | 2022-12-01 | null | null | null | null | ['video-anomaly-detection', 'abnormal-event-detection-in-video', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology'] | [-9.39983949e-02 -1.84884429e-01 1.54985234e-01 -4.79360610e-01
-5.90224385e-01 -4.02269840e-01 5.13345182e-01 3.27268630e-01
-1.22684017e-01 3.80899191e-01 -1.30292073e-01 -6.45773768e-01
1.05322160e-01 -5.62016666e-01 -6.38074577e-01 -3.62118274e-01
-2.41965160e-01 3.15543622e-01 2.59123921e-01 -2.08368897... | [7.802177429199219, 1.8951516151428223] |
cba88ff1-92c4-4fb2-93e4-d617cbe5efac | mpg-a-multi-ingredient-pizza-image-generator | 2012.02821 | null | https://arxiv.org/abs/2012.02821v2 | https://arxiv.org/pdf/2012.02821v2.pdf | MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs | Multilabel conditional image generation is a challenging problem in computer vision. In this work we propose Multi-ingredient Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizing multilabel images. We design MPG based on a state-of-the-art GAN structure called StyleGAN2, in wh... | ['Vladimir Pavlovic', 'Ricardo Guerrero', 'Guoyao Hao', 'Fangda Han'] | 2020-12-04 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 6.06050134e-01 4.35087174e-01 -4.04790640e-02 -3.03693831e-01
-1.01683319e+00 -8.50139856e-01 9.38542008e-01 -5.60574651e-01
1.55029103e-01 7.97375917e-01 1.59139946e-01 -1.09825015e-01
2.46187523e-01 -9.27545726e-01 -1.13987672e+00 -9.18464780e-01
4.30661142e-01 5.42821884e-01 -4.06932294e-01 -1.50159001... | [11.570938110351562, -0.3113924562931061] |
482450d4-00b9-400d-b07f-e9c2ca7b8771 | condition-invariant-semantic-segmentation | 2305.17349 | null | https://arxiv.org/abs/2305.17349v1 | https://arxiv.org/pdf/2305.17349v1.pdf | Condition-Invariant Semantic Segmentation | Adaptation of semantic segmentation networks to different visual conditions from those for which ground-truth annotations are available at training is vital for robust perception in autonomous cars and robots. However, previous work has shown that most feature-level adaptation methods, which employ adversarial training... | ['Luc van Gool', 'Fisher Yu', 'David Bruggemann', 'Christos Sakaridis'] | 2023-05-27 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 0.31835076 0.23397242 0.03405418 -0.6897573 -0.78668517 -0.9214415
0.6456972 -0.40937957 -0.62783813 0.68172514 -0.0939568 -0.33089235
0.49431476 -0.625046 -1.2575952 -0.5738063 0.46761551 0.47144982
0.3430166 -0.446908 -0.19166185 0.50987196 -1.4461296 0.0628941
0.8987546 0.9216936 0.09... | [9.79237174987793, 1.2982429265975952] |
35695ec7-f000-4342-81ee-f95286d76231 | collabkg-a-learnable-human-machine | 2307.00769 | null | https://arxiv.org/abs/2307.00769v1 | https://arxiv.org/pdf/2307.00769v1.pdf | CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction | In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, not supporting automatic updates. In this work, we present... | ['Wenjuan Han', 'Jinan Xu', 'Xingyu Cui', 'Ning Cheng', 'Yufeng Chen', 'Xiang Wei'] | 2023-07-03 | null | null | null | null | ['graph-construction', 'knowledge-graphs', 'event-extraction', 'named-entity-recognition-ner', 'cg'] | ['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-6.77199423e-01 4.57449675e-01 -4.02517110e-01 -2.18998894e-01
-5.80132067e-01 -3.90674978e-01 3.92699659e-01 3.95044327e-01
-4.10294741e-01 9.91230667e-01 1.18347138e-01 -2.65812486e-01
-3.79647434e-01 -8.67129028e-01 -5.24049878e-01 -4.58738565e-01
-1.32450283e-01 5.92191637e-01 5.18172979e-01 -3.37310694... | [9.354348182678223, 8.819525718688965] |
b6d40f5a-81ea-43fb-a96f-2f23a9ccb1d0 | how-robust-are-character-based-word | 1704.04441 | null | http://arxiv.org/abs/1704.04441v1 | http://arxiv.org/pdf/1704.04441v1.pdf | How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse? | This paper investigates the robustness of NLP against perturbed word forms.
While neural approaches can achieve (almost) human-like accuracy for certain
tasks and conditions, they often are sensitive to small changes in the input
such as non-canonical input (e.g., typos). Yet both stability and robustness
are desired p... | ['Günter Neumann', 'Josef van Genabith', 'Georg Heigold'] | 2017-04-14 | how-robust-are-character-based-word-1 | https://aclanthology.org/W18-1807 | https://aclanthology.org/W18-1807.pdf | ws-2018-3 | ['morphological-tagging'] | ['natural-language-processing'] | [ 4.31189597e-01 -1.70188114e-01 4.77772439e-03 5.21485284e-02
-6.64501667e-01 -1.17143059e+00 5.80295980e-01 3.76106352e-01
-8.06636810e-01 9.79596138e-01 1.05846748e-01 -7.28800535e-01
2.06553832e-01 -7.98043370e-01 -8.99404228e-01 -6.97683275e-01
2.06378940e-02 3.82895768e-01 3.28096628e-01 -2.53575951... | [6.143894672393799, 8.160381317138672] |
bfa0a607-4781-461b-8a7d-d47cef740478 | delete-retrieve-generate-a-simple-approach-to | 1804.06437 | null | http://arxiv.org/abs/1804.06437v1 | http://arxiv.org/pdf/1804.06437v1.pdf | Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer | We consider the task of text attribute transfer: transforming a sentence to
alter a specific attribute (e.g., sentiment) while preserving its
attribute-independent content (e.g., changing "screen is just the right size"
to "screen is too small"). Our training data includes only sentences labeled
with their attribute (e... | ['Percy Liang', 'Robin Jia', 'Juncen Li', 'He He'] | 2018-04-17 | delete-retrieve-generate-a-simple-approach-to-1 | https://aclanthology.org/N18-1169 | https://aclanthology.org/N18-1169.pdf | naacl-2018-6 | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 7.94094265e-01 2.98929870e-01 1.73353270e-01 -9.84964848e-01
-9.64604855e-01 -1.30490184e+00 5.28650165e-01 3.02282363e-01
-5.11724353e-01 1.02250636e+00 2.92254567e-01 -2.85003096e-01
6.20283842e-01 -1.01340795e+00 -9.44027007e-01 -5.41012943e-01
6.57579124e-01 6.13572419e-01 -3.71327341e-01 -5.11223197... | [11.598651885986328, 9.570221900939941] |
98335196-92bf-4b5a-ac42-5ef2cc6a5728 | unmasking-the-abnormal-events-in-video | 1705.08182 | null | http://arxiv.org/abs/1705.08182v3 | http://arxiv.org/pdf/1705.08182v3.pdf | Unmasking the abnormal events in video | We propose a novel framework for abnormal event detection in video that
requires no training sequences. Our framework is based on unmasking, a
technique previously used for authorship verification in text documents, which
we adapt to our task. We iteratively train a binary classifier to distinguish
between two consecut... | ['Marius Popescu', 'Radu Tudor Ionescu', 'Sorina Smeureanu', 'Bogdan Alexe'] | 2017-05-23 | unmasking-the-abnormal-events-in-video-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Ionescu_Unmasking_the_Abnormal_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Ionescu_Unmasking_the_Abnormal_ICCV_2017_paper.pdf | iccv-2017-10 | ['abnormal-event-detection-in-video', 'abnormal-event-detection-in-video', 'authorship-verification'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 5.35827875e-01 -1.65997326e-01 -1.52669877e-01 -1.51257530e-01
-2.40573511e-01 -4.49285001e-01 8.63741934e-01 4.41810876e-01
-6.13227248e-01 4.21121240e-01 -2.53359139e-01 -2.23620281e-01
2.70126402e-01 -4.10321176e-01 -4.11485553e-01 -4.77900147e-01
-3.52375418e-01 2.26754382e-01 4.90820438e-01 1.52322456... | [7.964761734008789, 1.5574697256088257] |
0102f958-906e-458f-b43b-11c11e3e81b6 | a-review-of-the-trends-and-challenges-in | 2301.08826 | null | https://arxiv.org/abs/2301.08826v1 | https://arxiv.org/pdf/2301.08826v1.pdf | A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis | Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview o... | ['Linda Galligan', 'Petrea Redmond', 'Jacquie Mcdonald', 'Christopher Dann', 'Yan Li', 'Xiaohui Tao', 'Thanveer Shaik'] | 2023-01-20 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.04444110e-01 3.83718222e-01 -2.78704375e-01 -4.10713434e-01
-1.02166243e-01 -6.40756249e-01 4.64446545e-01 1.02095056e+00
-1.91272289e-01 6.75711572e-01 6.05071783e-01 -3.99458736e-01
-4.11402792e-01 -7.10003674e-01 -1.83480233e-01 -4.12779778e-01
5.03727198e-01 4.44970936e-01 -1.46480769e-01 -8.90853167... | [11.200889587402344, 7.029209136962891] |
5241fdda-2be9-488c-96bf-3c2a7b2f6d00 | multi3nlu-a-multilingual-multi-intent-multi | 2212.10455 | null | https://arxiv.org/abs/2212.10455v2 | https://arxiv.org/pdf/2212.10455v2.pdf | MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue | Task-oriented dialogue (TOD) systems have been widely deployed in many industries as they deliver more efficient customer support. These systems are typically constructed for a single domain or language and do not generalise well beyond this. To support work on Natural Language Understanding (NLU) in TOD across multipl... | ['Alexandra Birch', 'Anna Korhonen', 'Ivan Vulić', 'Liane Guillou', 'Evgeniia Razumovskaia', 'Nikita Moghe'] | 2022-12-20 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [-3.63877207e-01 2.02599347e-01 -3.54778528e-01 -4.15380895e-01
-1.02588463e+00 -9.91322935e-01 9.14625525e-01 1.03281282e-01
-5.55718362e-01 9.99008000e-01 6.46416068e-01 -4.95468199e-01
2.71691620e-01 -3.84918749e-01 -2.11801216e-01 4.71053421e-02
1.45843521e-01 1.41271758e+00 1.27039820e-01 -1.02043855... | [12.428485870361328, 8.375839233398438] |
a99a8ace-9310-46de-b019-ad3779fa9731 | dekgci-a-double-sided-recommendation-model | 2306.13837 | null | https://arxiv.org/abs/2306.13837v1 | https://arxiv.org/pdf/2306.13837v1.pdf | DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph | Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in u... | ['Ruirui Shang', 'Mao Chen', 'Zeyu Zeng', 'Yajing Yang'] | 2023-06-24 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-2.01468393e-01 -1.19756117e-01 -6.11913383e-01 -3.29400867e-01
-5.63243404e-02 -4.15633440e-01 3.13205510e-01 1.57202575e-02
-7.93312863e-03 3.89968604e-01 6.34265482e-01 -6.02644160e-02
-4.93558854e-01 -8.07263136e-01 -3.87727141e-01 -3.95259291e-01
-1.22707024e-01 1.41594484e-01 1.09219618e-01 -2.70095646... | [10.209549903869629, 5.622807025909424] |
74c29d9b-0a10-4fe3-842a-dafe799d0cec | gaussian-process-probes-gpp-for-uncertainty | 2305.18213 | null | https://arxiv.org/abs/2305.18213v1 | https://arxiv.org/pdf/2305.18213v1.pdf | Gaussian Process Probes (GPP) for Uncertainty-Aware Probing | Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data. We introduce Gaussian process probes (GPP), a unified and simple framework for probing and measuring uncertainty about concepts represented... | ['Been Kim', 'Thomas L. Griffiths', 'Jason Baldridge', 'Alexander Ku', 'Zi Wang'] | 2023-05-29 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 3.66595574e-02 3.66684914e-01 -1.00481123e-01 -3.60214919e-01
-9.51249242e-01 -9.73239243e-01 9.66550052e-01 3.40885967e-01
-3.05760533e-01 5.93746603e-01 -8.78228247e-02 -3.64983261e-01
-1.37504935e-01 -9.43893254e-01 -8.66312861e-01 -8.24894309e-01
-9.52904206e-03 1.04401064e+00 5.91962457e-01 3.20225060... | [7.599348545074463, 3.913699150085449] |
ca0122fe-6413-49bc-85d5-b1ee3cf4ba39 | detect-camouflaged-spam-content-via | 1908.11561 | null | https://arxiv.org/abs/1908.11561v1 | https://arxiv.org/pdf/1908.11561v1.pdf | Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation | The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-en... | ['Guoxiu He', 'Zhuoren Jiang', 'Zhe Gao', 'Yangyang Kang', 'Xiaozhong Liu', 'Qiong Zhang', 'Luo Si', 'Changlong Sun'] | 2019-08-30 | detect-camouflaged-spam-content-via-1 | https://aclanthology.org/D19-1640 | https://aclanthology.org/D19-1640.pdf | ijcnlp-2019-11 | ['spam-detection'] | ['natural-language-processing'] | [-1.24157168e-01 -6.52753770e-01 -1.62634730e-01 -2.89068580e-01
-4.16070998e-01 -2.87464380e-01 8.77619326e-01 -8.91439393e-02
-2.69487858e-01 1.77672148e-01 5.05396426e-01 -5.72884917e-01
2.71855921e-01 -4.77252305e-01 -6.21010289e-02 -7.96139181e-01
2.17322826e-01 1.36901259e-01 5.66363692e-01 -3.67653638... | [7.900217533111572, 9.943011283874512] |
e8900320-e8a6-484a-bf12-98df187b957b | a-human-machine-collaborative-framework-for | null | null | https://aclanthology.org/2021.acl-long.436 | https://aclanthology.org/2021.acl-long.436.pdf | A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues | Conversational dialogue systems (CDSs) are hard to evaluate due to the complexity of natural language. Automatic evaluation of dialogues often shows insufficient correlation with human judgements. Human evaluation is reliable but labor-intensive. We introduce a human-machine collaborative framework, HMCEval, that can g... | ['Maarten de Rijke', 'Pengjie Ren', 'Yangjun Zhang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.62143677e-01 8.31633031e-01 2.54846483e-01 -8.22957158e-01
-8.24968338e-01 -6.01127326e-01 7.46398211e-01 3.63012433e-01
-6.48688078e-01 9.53800678e-01 1.45548331e-02 -2.54683226e-01
1.38229430e-01 -5.60216725e-01 1.64917693e-01 -2.63671398e-01
2.86044508e-01 1.24313426e+00 1.95677146e-01 -2.57230610... | [12.899422645568848, 8.051072120666504] |
4233c31f-9caf-4589-9d8b-ef488c85ee98 | cs60075-team2-at-semeval-2021-task-1-lexical | 2106.02340 | null | https://arxiv.org/abs/2106.02340v1 | https://arxiv.org/pdf/2106.02340v1.pdf | cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora | This paper describes the performance of the team cs60075_team2 at SemEval 2021 Task 1 - Lexical Complexity Prediction. The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from... | ['Sai Mahesh Pokala', 'Tanurima Halder', 'Sayantan Adak', 'Abhilash Nandy'] | 2021-06-04 | null | null | null | null | ['lexical-complexity-prediction', 'lexical-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-5.11990726e-01 1.27697095e-01 6.02844357e-02 -1.79222897e-01
-1.00724721e+00 -7.34092474e-01 6.82658672e-01 4.01186913e-01
-9.01309192e-01 6.95752025e-01 3.01774025e-01 -4.85267580e-01
-3.32935527e-02 -6.04277670e-01 -4.83266145e-01 7.63665959e-02
-1.22643918e-01 6.52941704e-01 2.96507895e-01 -4.66666788... | [10.58790397644043, 10.34936809539795] |
2e1d9926-0a66-4531-8d69-e55db9346dd2 | sdf-stylegan-implicit-sdf-based-stylegan-for | 2206.12055 | null | https://arxiv.org/abs/2206.12055v1 | https://arxiv.org/pdf/2206.12055v1.pdf | SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation | We present a StyleGAN2-based deep learning approach for 3D shape generation, called SDF-StyleGAN, with the aim of reducing visual and geometric dissimilarity between generated shapes and a shape collection. We extend StyleGAN2 to 3D generation and utilize the implicit signed distance function (SDF) as the 3D shape repr... | ['Xin Tong', 'Peng-Shuai Wang', 'Yang Liu', 'Xin-Yang Zheng'] | 2022-06-24 | null | null | null | null | ['3d-shape-generation', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 1.33305058e-01 5.94452135e-02 3.80394578e-01 -3.64538819e-01
-7.16745734e-01 -1.00542092e+00 7.33417988e-01 -5.87654352e-01
3.24830472e-01 5.05000055e-01 2.31648758e-01 -2.48502851e-01
3.07879210e-01 -1.25154841e+00 -7.23569274e-01 -5.30138671e-01
2.50453770e-01 4.32346940e-01 -3.81438464e-01 -2.32553467... | [9.061468124389648, -3.550464630126953] |
c0b98e6a-fbb9-416f-b61a-a8fe2c37f1bb | e-fcnn-for-tiny-facial-expression-recognition | null | null | https://doi.org/10.1007/s10489-020-01855-5 | https://sci-hub.st//https://link.springer.com/article/10.1007/s10489-020-01855-5 | E-FCNN for tiny facial expression recognition | As a hot issue in recent years, facial expression recognition(FER) has been widely applied in many fields, but it still
faces great challenges in tiny facial expression recognition. Currently, most of the FER networks only consider images of
ideal sizes. Their recognition accuracy would significantly decrease as the ... | ['Qiyu Cheng', 'Jie Shao'] | 2020-08-20 | null | null | null | applied-intelligence-2020-8 | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.41723675e-01 -4.25107539e-01 -8.27072114e-02 -5.66677868e-01
-4.11785722e-01 2.77695358e-01 1.58332825e-01 -8.85535777e-01
-1.29962415e-01 7.54180133e-01 2.03049302e-01 3.34930867e-01
2.13368684e-01 -8.63749146e-01 -5.84585845e-01 -9.31014836e-01
1.63435817e-01 -5.17055750e-01 9.90464911e-02 -5.52105904... | [13.590967178344727, 1.613089919090271] |
b1ce8202-afc3-442b-91a8-b4e6ccf15d5c | token-level-sequence-labeling-for-spoken | 2210.15734 | null | https://arxiv.org/abs/2210.15734v1 | https://arxiv.org/pdf/2210.15734v1.pdf | Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models | End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation. However, these systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulati... | ['Shinji Watanabe', 'Alan W Black', 'Florian Metze', 'Brian Yan', 'Siddharth Dalmia', 'Siddhant Arora'] | 2022-10-27 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 4.45572257e-01 4.14988220e-01 -1.93792969e-01 -8.18058252e-01
-1.10656869e+00 -7.91924119e-01 5.43297350e-01 1.44410580e-01
-7.10570872e-01 6.09038591e-01 7.09536731e-01 -5.86176157e-01
7.99175799e-01 -4.99273688e-01 -8.27473700e-01 -1.06249593e-01
2.87846811e-02 6.34670973e-01 2.45585535e-02 -1.47271097... | [14.045759201049805, 7.005550384521484] |
4f114dff-146d-4441-8b81-065c89d4fb93 | addressing-limitations-of-encoder-decoder | null | null | https://aclanthology.org/2022.coling-1.137 | https://aclanthology.org/2022.coling-1.137.pdf | Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL | Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases. For the popular Spider dataset, despite models achieving 70% accuracy on its development or test sets, the same models show a huge decline below 20% accuracy for unseen databases. Th... | ['Vadim Sheinin', 'Elahe Khorashani', 'Hangu Yeo', 'Ngoc Phuoc An Vo', 'Irene Manotas', 'Octavian Popescu'] | null | null | null | null | coling-2022-10 | ['text-to-sql'] | ['computer-code'] | [ 1.35370083e-02 3.63856703e-01 -1.09292246e-01 -8.77980173e-01
-8.48158360e-01 -4.09327000e-01 6.51594698e-01 4.19740468e-01
-2.36428931e-01 8.30113292e-01 1.08756535e-01 -5.74064493e-01
1.44104570e-01 -1.06423509e+00 -1.28139508e+00 2.30659336e-01
2.00571403e-01 1.06959426e+00 5.65633774e-01 -6.84784591... | [9.842573165893555, 7.837931156158447] |
0490d6d7-4bd4-46f6-b7ba-5e4df7255017 | learned-cone-beam-ct-reconstruction-using | 2201.07562 | null | https://arxiv.org/abs/2201.07562v1 | https://arxiv.org/pdf/2201.07562v1.pdf | Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations | Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data. This way, they achieve high reconstruction performance while ensuring consistency with the measured data. In computed tomography, extending such approa... | ['Andreas Maier', 'Lina Felsner', 'Lukas Folle', 'Mingxuan Gu', 'Fabian Wagner', 'Mareike Thies'] | 2022-01-19 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 2.71426290e-01 1.65600330e-01 4.41871047e-01 -3.04997265e-01
-7.17080712e-01 -2.07207754e-01 2.02278852e-01 1.08192731e-02
-8.18829000e-01 7.49819160e-01 -2.14267120e-01 -6.16627812e-01
-4.31222916e-01 -8.01296413e-01 -5.55767536e-01 -7.52739131e-01
9.21919346e-02 8.09815884e-01 -3.25639546e-02 7.62002766... | [13.358211517333984, -2.5856244564056396] |
a85de8c3-74a1-4aaf-938c-3653d18f997d | towards-robust-named-entity-recognition-for | 1906.07592 | null | https://arxiv.org/abs/1906.07592v1 | https://arxiv.org/pdf/1906.07592v1.pdf | Towards Robust Named Entity Recognition for Historic German | Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference. We apply pre-trained language models to low-resource named entity recognition for Historic German. We show on ... | ['Johannes Baiter', 'Stefan Schweter'] | 2019-06-18 | towards-robust-named-entity-recognition-for-1 | https://aclanthology.org/W19-4312 | https://aclanthology.org/W19-4312.pdf | ws-2019-8 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.53428152e-01 3.99865545e-02 -2.40631923e-01 -5.26439667e-01
-1.03755581e+00 -6.86802208e-01 6.79317832e-01 3.45919371e-01
-1.03953075e+00 9.52765942e-01 4.91962105e-01 -6.20380461e-01
4.09347534e-01 -8.58488560e-01 -8.16359162e-01 8.00758079e-02
-1.17026888e-01 6.74631059e-01 -9.03532803e-02 -1.63172796... | [9.944001197814941, 9.782585144042969] |
1df7efe3-7a21-4c3d-9a12-fe30630e60b5 | pretrained-ensemble-learning-for-fine-grained | null | null | https://aclanthology.org/D19-5020 | https://aclanthology.org/D19-5020.pdf | Pretrained Ensemble Learning for Fine-Grained Propaganda Detection | In this paper, we describe our team{'}s effort on the fine-grained propaganda detection on sentence level classification (SLC) task of NLP4IF 2019 workshop co-located with the EMNLP-IJCNLP 2019 conference. Our top performing system results come from applying ensemble average on three pretrained models to make their pre... | ['Mahmoud Al-Ayyoub', 'Ibraheem Tuffaha', 'Ali Fadel'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 1.59645677e-01 3.01110029e-01 -1.25237688e-01 -8.48186463e-02
-1.02827299e+00 -6.09054565e-01 1.09602427e+00 2.44622186e-01
-5.08003533e-01 9.32545602e-01 7.98581123e-01 -5.05793810e-01
8.72404501e-03 -5.43942094e-01 -8.57217014e-01 -1.39791206e-01
1.54352754e-01 2.00942546e-01 5.23337582e-03 -4.87332523... | [8.484968185424805, 10.676398277282715] |
0c2315bf-e275-431a-adcd-3da5c6320eb6 | high-similarity-pass-attention-for-single | 2305.15768 | null | https://arxiv.org/abs/2305.15768v1 | https://arxiv.org/pdf/2305.15768v1.pdf | High-Similarity-Pass Attention for Single Image Super-Resolution | Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually used the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomeno... | ['C. L. Philip Chen', 'Wenzhong Guo', 'Guang-Yong Chen', 'Min Gan', 'Jian-Nan Su'] | 2023-05-25 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 2.38832533e-01 -9.55321640e-02 3.23440693e-02 -4.03273374e-01
-8.90532017e-01 -7.11769015e-02 4.81431723e-01 -3.04200083e-01
-1.12144351e-01 6.28956079e-01 5.27022660e-01 1.52441874e-01
-3.92060041e-01 -7.52986133e-01 -9.31672215e-01 -8.63013089e-01
1.92298144e-02 2.03674033e-01 3.59406441e-01 -3.93325269... | [10.997241973876953, -1.869980812072754] |
91966bb7-62d9-4dac-bb45-18b67b08755f | therbligs-in-action-video-understanding | 2304.03631 | null | https://arxiv.org/abs/2304.03631v1 | https://arxiv.org/pdf/2304.03631v1.pdf | Therbligs in Action: Video Understanding through Motion Primitives | In this paper we introduce a rule-based, compositional, and hierarchical modeling of action using Therbligs as our atoms. Introducing these atoms provides us with a consistent, expressive, contact-centered representation of action. Over the atoms we introduce a differentiable method of rule-based reasoning to regulariz... | ['Yiannis Aloimonos', 'Cornelia Fermuller', 'Michael Maynord', 'Eadom Dessalene'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-anticipation', 'action-recognition-in-videos', 'video-understanding', 'action-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.44883925e-01 7.19960511e-01 -6.20997906e-01 -4.99602079e-01
-5.86566687e-01 -2.64372885e-01 8.04583371e-01 1.96942929e-02
-7.48021081e-02 6.30218267e-01 6.71274424e-01 1.99317992e-01
-1.33877471e-01 -6.20379150e-01 -8.55450690e-01 -3.53994220e-01
-8.90492946e-02 2.15127245e-01 3.02486092e-01 -2.97942996... | [8.23095989227295, 0.5774156451225281] |
b9799594-9d57-43bd-9671-15a1724604f9 | leveraging-frequency-analysis-for-deep-fake | 2003.08685 | null | https://arxiv.org/abs/2003.08685v3 | https://arxiv.org/pdf/2003.08685v3.pdf | Leveraging Frequency Analysis for Deep Fake Image Recognition | Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements have been largely made possible by Generative Adversarial Networks (GANs). While deep fake images have been thoroughly investigated in the ima... | ['Lea Schönherr', 'Joel Frank', 'Dorothea Kolossa', 'Thorsten Holz', 'Asja Fischer', 'Thorsten Eisenhofer'] | 2020-03-19 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1539-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1539-Paper.pdf | icml-2020-1 | ['image-forensics'] | ['computer-vision'] | [ 4.94448811e-01 2.42560118e-01 3.75404239e-01 3.07065509e-02
-5.04822493e-01 -6.86053276e-01 6.89075708e-01 -1.74964011e-01
-1.00764848e-01 8.07967901e-01 7.93730766e-02 -1.52589470e-01
1.09171465e-01 -9.53308821e-01 -8.91426146e-01 -7.64072895e-01
1.10171489e-01 1.30797327e-01 -4.50097118e-03 -4.47072476... | [12.433215141296387, 1.035606861114502] |
474052a4-9894-466e-9978-f1c746fb3228 | graph-convolutional-network-for-swahili-news | 2103.09325 | null | https://arxiv.org/abs/2103.09325v1 | https://arxiv.org/pdf/2103.09325v1.pdf | Graph Convolutional Network for Swahili News Classification | This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification. In particular, we focus our experimentation on the sparsely-labelled semi-supervised context which i... | ['Tyler Martin', 'Alexandros Kastanos'] | 2021-03-16 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 4.29025114e-01 2.98113078e-01 -3.62321645e-01 -6.55189991e-01
-3.47181141e-01 -3.90336782e-01 1.13011205e+00 6.49453580e-01
-8.09260845e-01 4.65817094e-01 9.88662660e-01 -9.50155795e-01
-1.36757120e-01 -1.00911355e+00 -3.45395267e-01 -3.52041811e-01
-5.11345804e-01 7.42749929e-01 -6.54841959e-02 -4.49040711... | [10.546930313110352, 8.629924774169922] |
4a34c29c-a466-4333-b343-8be2251d712f | doubly-aligned-incomplete-multi-view | 1903.02785 | null | http://arxiv.org/abs/1903.02785v1 | http://arxiv.org/pdf/1903.02785v1.pdf | Doubly Aligned Incomplete Multi-view Clustering | Nowadays, multi-view clustering has attracted more and more attention. To
date, almost all the previous studies assume that views are complete. However,
in reality, it is often the case that each view may contain some missing
instances. Such incompleteness makes it impossible to directly use traditional
multi-view clus... | ['Songcan Chen', 'Menglei Hu'] | 2019-03-07 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [ 7.64843524e-02 -2.04654098e-01 -1.97248191e-01 -1.97175920e-01
-4.22563344e-01 -5.22098243e-01 2.81994462e-01 -5.46371564e-02
-1.23018980e-01 5.08862674e-01 1.26799300e-01 3.79364428e-05
-4.99850929e-01 -5.12620211e-01 -3.40294302e-01 -9.48110878e-01
3.91671330e-01 5.55652857e-01 -2.06387788e-01 3.53256315... | [8.220624923706055, 4.645137310028076] |
37e24c35-0c83-413a-a72c-52b2d7997abc | semiblind-hyperspectral-unmixing-in-the | 1507.01661 | null | http://arxiv.org/abs/1507.01661v1 | http://arxiv.org/pdf/1507.01661v1.pdf | Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches | The dictionary-aided sparse regression (SR) approach has recently emerged as
a promising alternative to hyperspectral unmixing (HU) in remote sensing. By
using an available spectral library as a dictionary, the SR approach identifies
the underlying materials in a given hyperspectral image by selecting a small
subset of... | ['Tsung-Han Chan', 'José Bioucas-Dias', 'Wing-Kin Ma', 'Xiao Fu'] | 2015-07-07 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.91629970e-01 -4.35238272e-01 -9.10524130e-02 -8.62119123e-02
-7.28501439e-01 -3.44222069e-01 1.43466681e-01 -2.50459492e-01
-5.25907911e-02 6.48020446e-01 1.74307302e-01 8.45913193e-04
-4.23060149e-01 -8.03432941e-01 -3.66374046e-01 -1.30469692e+00
3.80701363e-01 2.47148022e-01 -4.15311486e-01 -2.53028601... | [10.177962303161621, -2.042933464050293] |
33856af2-5505-4029-91ce-b9b924247b82 | explainable-deep-few-shot-anomaly-detection | 2108.00462 | null | https://arxiv.org/abs/2108.00462v1 | https://arxiv.org/pdf/2108.00462v1.pdf | Explainable Deep Few-shot Anomaly Detection with Deviation Networks | Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples). One notorious issue with these approaches is that they are weak in discriminating anomalies from normal samples due to the lack of the knowledge about the anoma... | ['Anton Van Den Hengel', 'Chunhua Shen', 'Choubo Ding', 'Guansong Pang'] | 2021-08-01 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 4.00616199e-01 -3.95641923e-02 -5.88887520e-02 -5.21100342e-01
-9.11345065e-01 -2.02805877e-01 6.18559718e-01 2.61252254e-01
-5.29581718e-02 1.08136043e-01 -6.20888397e-02 -4.87229750e-02
-7.91634247e-02 -5.72800636e-01 -6.19984329e-01 -8.13775539e-01
-2.85220414e-01 6.38681948e-01 2.97182295e-02 -1.15049124... | [7.63838529586792, 2.331756591796875] |
0e101dfe-0a42-4b0b-a434-8359680c35ea | acoustic-echo-cancellation-with-the-dual | 2010.14337 | null | https://arxiv.org/abs/2010.14337v1 | https://arxiv.org/pdf/2010.14337v1.pdf | Acoustic echo cancellation with the dual-signal transformation LSTM network | This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transformation and a learned feature representation in a stacked network approach, which enables robust information processing in the time-frequency and... | ['Bernd T. Meyer', 'Nils L. Westhausen'] | 2020-10-27 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 4.25390244e-01 -3.16713721e-01 8.72363150e-01 -3.25131327e-01
-1.52634764e+00 -4.93565977e-01 3.72508854e-01 -2.83669084e-01
-5.53673744e-01 3.18067104e-01 5.53559005e-01 -3.18093866e-01
2.42486224e-03 -2.45415762e-01 -6.65151894e-01 -7.65385628e-01
-5.51711023e-01 -2.83827871e-01 -7.91294128e-02 -4.62005019... | [15.00440788269043, 5.947412967681885] |
17745c1a-c3b6-4725-9bb3-e6a42497a1af | an-f-ratio-based-method-for-estimating-the | 2306.05892 | null | https://arxiv.org/abs/2306.05892v1 | https://arxiv.org/pdf/2306.05892v1.pdf | An F-ratio-Based Method for Estimating the Number of Active Sources in MEG | Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeli... | ['Dimitrios Pantazis', 'Amir Adler', 'John C. Mosher', 'Amita Giri'] | 2023-06-09 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-2.76836362e-02 -4.07106459e-01 3.52772981e-01 -2.81529784e-01
-7.35464036e-01 -4.67600495e-01 3.48960310e-01 3.91464919e-01
-5.20549893e-01 6.79605365e-01 2.19541013e-01 -2.69622952e-01
-3.40168089e-01 -5.72550774e-01 -5.97812057e-01 -7.96098053e-01
-5.61773479e-01 2.42276698e-01 4.07807946e-01 1.89695746... | [12.95888900756836, 3.3711729049682617] |
40634443-ec46-4d9a-8020-4d75e96aed3e | mo-dehb-evolutionary-based-hyperband-for | 2305.04502 | null | https://arxiv.org/abs/2305.04502v2 | https://arxiv.org/pdf/2305.04502v2.pdf | MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization | Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that must be considered. Optimizing these objectives simultaneously on a complex and diverse search space r... | ['Janek Thomas', 'Philipp Muller', 'Frank Hutter', 'Ayushi Sharma', 'Noor Awad'] | 2023-05-08 | null | null | null | null | ['hyperparameter-optimization', 'architecture-search'] | ['methodology', 'methodology'] | [-1.98965415e-01 -7.62068331e-01 -4.81027216e-01 -3.54087979e-01
-8.41905355e-01 -3.69468480e-01 -1.34784907e-01 -1.09815896e-01
-5.17862201e-01 1.08390760e+00 -3.16563964e-01 -1.70547843e-01
-7.08409131e-01 -3.91830623e-01 -5.92975080e-01 -8.83914948e-01
1.24994025e-01 8.89571369e-01 -1.16010241e-01 -3.23533386... | [6.7087297439575195, 3.918152332305908] |
c886d75f-5f25-4859-9da9-042565f05360 | scalable-decipherment-for-machine-translation | null | null | https://aclanthology.org/P13-1036 | https://aclanthology.org/P13-1036.pdf | Scalable Decipherment for Machine Translation via Hash Sampling | null | ['Sujith Ravi'] | 2013-08-01 | null | null | null | acl-2013-8 | ['decipherment'] | ['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... | [-7.283787250518799, 3.7306830883026123] |
6790b4ec-a89e-4882-85ba-50e230d46077 | leveraging-passage-retrieval-with-generative | 2007.01282 | null | https://arxiv.org/abs/2007.01282v2 | https://arxiv.org/pdf/2007.01282v2.pdf | Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering | Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from ret... | ['Edouard Grave', 'Gautier Izacard'] | 2020-07-02 | null | https://aclanthology.org/2021.eacl-main.74 | https://aclanthology.org/2021.eacl-main.74.pdf | eacl-2021-2 | ['triviaqa'] | ['miscellaneous'] | [-2.86462903e-01 1.77654505e-01 1.45593345e-01 -1.03394344e-01
-1.79338503e+00 -8.96923184e-01 7.58402348e-01 2.03022093e-01
-4.16236609e-01 1.09281862e+00 4.27789599e-01 -3.69911730e-01
-1.78516909e-01 -8.80521357e-01 -8.77430916e-01 -3.55259776e-01
9.18296203e-02 1.06892455e+00 5.40186524e-01 -6.42503917... | [11.33189868927002, 7.952084064483643] |
5b9eaf18-3c51-431c-ba72-c250e22997a5 | language-and-dialect-discrimination-using | null | null | https://aclanthology.org/W16-4825 | https://aclanthology.org/W16-4825.pdf | Language and Dialect Discrimination Using Compression-Inspired Language Models | The DSL 2016 shared task continued previous evaluations from 2014 and 2015 that facilitated the study of automated language and dialect identification. This paper describes results for this year{'}s shared task and from several related experiments conducted at the Johns Hopkins University Human Language Technology Cent... | ['Paul McNamee'] | 2016-12-01 | null | null | null | ws-2016-12 | ['spam-detection', 'text-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.79370418e-01 -2.49318048e-01 4.44021486e-02 -3.70627552e-01
-9.35056865e-01 -7.23276734e-01 1.01794279e+00 5.30129254e-01
-7.65325487e-01 5.51776528e-01 4.97103781e-01 -8.07351053e-01
-2.19022349e-01 -5.08964658e-01 -2.59617299e-01 -1.97534293e-01
1.25159472e-02 8.96999776e-01 -2.55324505e-02 -3.14312786... | [10.302373886108398, 10.552934646606445] |
33976af8-76ab-4fa9-9972-faa1ad5928ae | nipd-a-federated-learning-person-detection | 2306.15932 | null | https://arxiv.org/abs/2306.15932v1 | https://arxiv.org/pdf/2306.15932v1.pdf | NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data | Federated learning (FL), a privacy-preserving distributed machine learning, has been rapidly applied in wireless communication networks. FL enables Internet of Things (IoT) clients to obtain well-trained models while preventing privacy leakage. Person detection can be deployed on edge devices with limited computing pow... | ['Zhiguo Wang', 'Guangqiang Yin', 'Xinhui Ji', 'Jie Fu', 'Dongsheng Chen', 'Zhihua Dong', 'Zhen Ding', 'Kangning Yin'] | 2023-06-28 | null | null | null | null | ['person-identification', 'human-detection'] | ['computer-vision', 'computer-vision'] | [-6.33002371e-02 -4.10602748e-01 -1.25334516e-01 -3.00162464e-01
-1.87079564e-01 -5.08458793e-01 1.89990401e-01 -3.67111862e-01
-3.49396735e-01 7.00318873e-01 1.26818076e-01 -2.73627490e-02
1.08584752e-02 -8.03361475e-01 -4.07099485e-01 -9.19197023e-01
7.29590207e-02 2.63641160e-02 5.48847169e-02 2.96822280... | [5.897302150726318, 6.1941399574279785] |
fe659097-2f84-41a5-a026-547be4bfef47 | ghostfacenets-lightweight-face-recognition | null | null | https://ieeexplore.ieee.org/document/10098610 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10098610 | GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations | The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules re... | ['Naoufel Werghi', 'Yahya Zweiri', 'Abdulhadi Shoufan', 'Sajid Javed', 'Oussama Abdul Hay', 'Mohamad Alansari'] | 2023-04-10 | null | null | null | ieee-access-2023-4 | ['face-recognition', 'face-identification', 'face-verification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.09019421e-01 -1.84703141e-01 -3.94097269e-02 -6.70482337e-01
-5.00520170e-01 -1.90667227e-01 5.52644372e-01 -4.68473643e-01
-4.56531733e-01 3.87568831e-01 -1.24327719e-01 -3.91863614e-01
7.17807887e-03 -7.21406460e-01 -6.25409067e-01 -4.75055486e-01
-1.03464290e-01 1.85522661e-01 -1.96956962e-01 -2.88477600... | [13.311089515686035, 0.7946535348892212] |
59ff8b1b-c5ab-482c-9b91-edb47527041f | dipiq-blind-image-quality-assessment-by | 1904.06505 | null | http://arxiv.org/abs/1904.06505v1 | http://arxiv.org/pdf/1904.06505v1.pdf | dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs | Objective assessment of image quality is fundamentally important in many
image processing tasks. In this work, we focus on learning blind image quality
assessment (BIQA) models which predict the quality of a digital image with no
access to its original pristine-quality counterpart as reference. One of the
biggest chall... | ['DaCheng Tao', 'Tongliang Liu', 'Kede Ma', 'Zhou Wang', 'Wentao Liu'] | 2019-04-13 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 2.28125393e-01 -3.16176832e-01 7.64337368e-03 -4.38675255e-01
-1.57088089e+00 -6.32453144e-01 3.32030237e-01 1.27810568e-01
-2.87520915e-01 8.70806754e-01 1.85162410e-01 -1.02706261e-01
-4.59862381e-01 -5.93787372e-01 -7.99425006e-01 -7.71308959e-01
-1.07035398e-01 5.16759694e-01 -7.34485984e-02 -1.76978588... | [11.886041641235352, -1.8371281623840332] |
677bb60c-fb04-44c0-b85f-c95cfca95770 | similarity-based-unsupervised-spelling | null | null | https://medinform.jmir.org/2021/2/e25530/ | https://medinform.jmir.org/2021/2/e25530/PDF | Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports | Background:
Existing bacterial culture test results for infectious diseases are written in unrefined text, resulting in many problems, including typographical errors and stop words. Effective spelling correction processes are needed to ensure the accuracy and reliability of data for the study of infectious diseases, i... | ['Jang Wook Sohn', 'Hyung Joon Joo', 'Jong-Ho Kim', 'Se Ha Lee', 'Minji Kang', 'Sung Won Han', 'Taehyeong Kim'] | 2021-02-22 | null | null | null | jmir-medical-informatics-2021-2 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.13559943e-01 -5.42145848e-01 -1.04798853e-01 1.51658515e-02
-3.23149621e-01 -3.93819064e-01 -3.23166922e-02 1.18270993e+00
-9.83416617e-01 7.11257815e-01 3.88372868e-01 -4.59610641e-01
-4.34335500e-01 -8.47286522e-01 -3.60369742e-01 -6.00550294e-01
1.39782533e-01 3.98205131e-01 -2.46418156e-02 -2.24099532... | [8.482513427734375, 8.657266616821289] |
e41c0e3e-e783-43ac-89be-aade04aaf486 | foldingnet-point-cloud-auto-encoder-via-deep | 1712.07262 | null | http://arxiv.org/abs/1712.07262v2 | http://arxiv.org/pdf/1712.07262v2.pdf | FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation | Recent deep networks that directly handle points in a point set, e.g.,
PointNet, have been state-of-the-art for supervised learning tasks on point
clouds such as classification and segmentation. In this work, a novel
end-to-end deep auto-encoder is proposed to address unsupervised learning
challenges on point clouds. O... | ['Yaoqing Yang', 'Chen Feng', 'Dong Tian', 'Yiru Shen'] | 2017-12-19 | foldingnet-point-cloud-auto-encoder-via-deep-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-point-cloud-linear-classification', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision'] | [ 2.77392380e-02 5.10871708e-01 6.26580864e-02 -4.45564777e-01
-7.15372205e-01 -3.34294945e-01 3.02820176e-01 1.21945418e-01
-2.20792085e-01 2.15711728e-01 -4.70537841e-01 -2.71005720e-01
3.01137328e-01 -9.10912573e-01 -1.43537199e+00 -3.52305919e-01
1.61992967e-01 8.58265758e-01 2.91290492e-01 -1.04933940... | [8.193890571594238, -3.509267568588257] |
b23b8be1-a071-4713-872f-de2a9891fe8c | conceptbeam-concept-driven-target-speech | 2207.11964 | null | https://arxiv.org/abs/2207.11964v1 | https://arxiv.org/pdf/2207.11964v1.pdf | ConceptBeam: Concept Driven Target Speech Extraction | We propose a novel framework for target speech extraction based on semantic information, called ConceptBeam. Target speech extraction means extracting the speech of a target speaker in a mixture. Typical approaches have been exploiting properties of audio signals, such as harmonic structure and direction of arrival. In... | ['Kunio Kashino', 'Noboru Harada', 'Akisato Kimura', 'Daisuke Niizumi', 'Daiki Takeuchi', 'Shoko Araki', 'Tsubasa Ochiai', 'Marc Delcroix', 'Yasunori Ohishi'] | 2022-07-25 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 4.82394129e-01 1.64906010e-01 1.11498661e-01 -4.87220496e-01
-1.27254641e+00 -8.52386653e-01 9.48076010e-01 1.83589205e-01
-3.52309376e-01 3.94207239e-01 5.42330742e-01 4.11480665e-02
-1.22035727e-01 -5.55313468e-01 -6.87708616e-01 -9.79562223e-01
2.50662237e-01 4.85068917e-01 -7.80427456e-02 -7.33127221... | [15.082518577575684, 5.170448303222656] |
2fbf1721-090b-40de-af62-58d9afce6125 | expediting-large-scale-vision-transformer-for | 2210.01035 | null | https://arxiv.org/abs/2210.01035v1 | https://arxiv.org/pdf/2210.01035v1.pdf | Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning | Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens. Many advanced approaches have been developed to reduce the total number of tokens in large-scale vision transformers, especially for image cl... | ['Han Hu', 'Chao Zhang', 'Zheng Zhang', 'Ding Jia', 'WeiHong Lin', 'Xiao Luo', 'Henghui Ding', 'Yuhui Yuan', 'Weicong Liang'] | 2022-10-03 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.68893802e-01 -9.28008705e-02 1.59892011e-02 -2.67819881e-01
-5.84026396e-01 -3.70138772e-02 5.12522995e-01 2.98273385e-01
-5.43263912e-01 2.09433645e-01 -6.70386851e-02 1.25152767e-02
3.89448330e-02 -1.04577684e+00 -6.84001088e-01 -8.83262038e-01
3.24858934e-01 6.34166300e-01 9.23078418e-01 2.82189280... | [9.586217880249023, 0.2651039659976959] |
2c85eda5-9df9-426e-91f0-7b85117150a2 | on-the-power-of-refined-skat-selection | 2104.02997 | null | https://arxiv.org/abs/2104.02997v1 | https://arxiv.org/pdf/2104.02997v1.pdf | On the Power of Refined Skat Selection | Skat is a fascinating combinatorial card game, show-casing many of the intrinsic challenges for modern AI systems such as cooperative and adversarial behaviors (among the players), randomness (in the deal), and partial knowledge (due to hidden cards). Given the larger number of tricks and higher degree of uncertainty, ... | ['Stefan Edelkamp'] | 2021-04-07 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.86568135e-01 1.97969690e-01 6.54442832e-02 1.09649785e-01
-5.47484338e-01 -8.93002510e-01 2.75224268e-01 -2.24692762e-01
-5.98900259e-01 9.28910255e-01 -2.68665731e-01 -2.23966137e-01
-6.42898560e-01 -1.03623474e+00 -5.12261450e-01 -8.57967496e-01
-1.13718629e-01 1.04054618e+00 8.64536822e-01 -9.20671046... | [3.4895999431610107, 1.5087355375289917] |
5af341a7-4ed2-427a-b05e-d93ba51556e4 | color-image-denoising-by-chromatic-edges | 1304.5587 | null | http://arxiv.org/abs/1304.5587v2 | http://arxiv.org/pdf/1304.5587v2.pdf | Color image denoising by chromatic edges based vector valued diffusion | In this letter we propose to denoise digital color images via an improved
geometric diffusion scheme. By introducing edges detected from all three color
channels into the diffusion the proposed scheme avoids color smearing
artifacts. Vector valued diffusion is used to control the smoothing and the
geometry of color ima... | ['Juan C. Moreno', 'V. B. Surya Prasath', 'K. Palaniappan'] | 2013-04-20 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 5.84909543e-02 -5.84499180e-01 4.81827855e-01 -1.11875460e-02
-2.06732631e-01 -4.78705376e-01 4.08782989e-01 8.29698518e-03
-7.85156727e-01 6.04169250e-01 5.75709641e-02 4.88142446e-02
-7.85678104e-02 -8.49134445e-01 -4.17804951e-03 -1.02944446e+00
-1.78963274e-01 -3.38686079e-01 6.97138190e-01 -2.27219671... | [11.1241455078125, -2.5553011894226074] |
516af153-1946-4294-a180-aa4674a26650 | a-generic-framework-for-privacy-preserving | 1811.04017 | null | http://arxiv.org/abs/1811.04017v2 | http://arxiv.org/pdf/1811.04017v2.pdf | A generic framework for privacy preserving deep learning | We detail a new framework for privacy preserving deep learning and discuss
its assets. The framework puts a premium on ownership and secure processing of
data and introduces a valuable representation based on chains of commands and
tensors. This abstraction allows one to implement complex privacy preserving
constructs ... | ['Jonathan Passerat-Palmbach', 'Bobby Wagner', 'Theo Ryffel', 'Jason Mancuso', 'Morten Dahl', 'Daniel Rueckert', 'Andrew Trask'] | 2018-11-09 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-7.19287619e-02 2.48457417e-01 6.42144540e-03 -1.08652771e+00
-9.80119944e-01 -9.05333877e-01 5.42769611e-01 4.55241054e-01
-6.87533915e-01 6.95939004e-01 3.58851850e-01 -5.90122581e-01
-2.80444026e-02 -8.54435802e-01 -5.87133884e-01 -7.93208241e-01
-6.42275751e-01 6.17296733e-02 1.00289658e-02 -6.58376217... | [5.8924760818481445, 6.7900848388671875] |
caf94cc0-eece-4c63-bb2f-f44f51935b95 | local-primordial-non-gaussianity-from-the | 2307.01753 | null | https://arxiv.org/abs/2307.01753v1 | https://arxiv.org/pdf/2307.01753v1.pdf | Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies | We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range 0.2< z < 1.35. We ... | ['Hu Zou', 'Zhimin Zhou', 'Christophe Yèche', 'Benjamin Alan Weaver', 'Gregory Tarlé', 'Michael Schubnell', 'Eusebio Sanchez', 'Graziano Rossi', 'Claire Poppett', 'Will Percival', 'Nathalie Palanque-Delabrouille', 'Jundan Nie', 'Jeffrey A. Newman', 'Adam Myers', 'Eva-Maria Mueller', 'Ramon Miquel', 'Aaron Meisner', 'Ma... | 2023-07-04 | null | null | null | null | ['clustering'] | ['methodology'] | [ 2.44531352e-02 -4.96990122e-02 3.00504804e-01 -3.60582590e-01
-4.23564374e-01 -6.21134162e-01 6.95244849e-01 -5.52203357e-01
-6.24314249e-01 7.47662485e-01 -4.64999378e-02 -7.28083551e-01
-3.65351081e-01 -7.98369706e-01 -4.61748987e-01 -1.31032920e+00
2.59807575e-02 5.69258034e-01 4.53236431e-01 3.65498662... | [7.255756378173828, 3.3255221843719482] |
310add11-a82d-4136-bbbc-541f2220421d | 190411126 | 1904.11126 | null | http://arxiv.org/abs/1904.11126v1 | http://arxiv.org/pdf/1904.11126v1.pdf | Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks | In the last few years, Deep Learning (DL) has been showing superior
performance in different modalities of biomedical image analysis. Several DL
architectures have been proposed for classification, segmentation, and
detection tasks in medical imaging and computational pathology. In this paper,
we propose a new DL archi... | ['Theus Aspiras', 'Tarek M. Taha', 'Md Zahangir Alom', 'Vijayan K. Asari'] | 2019-04-25 | null | null | null | null | ['skin-cancer-segmentation', 'skin-cancer-classification'] | ['medical', 'medical'] | [ 6.20124221e-01 2.42476583e-01 -3.90903533e-01 -1.94696978e-01
-8.30714822e-01 -1.56944007e-01 2.87990570e-01 1.04582377e-01
-4.78723288e-01 5.33507943e-01 -8.59337226e-02 -2.48214155e-01
-4.90342407e-03 -6.42907679e-01 -2.58913219e-01 -9.41610038e-01
1.75047383e-01 -6.79792762e-02 4.24444109e-01 6.06231615... | [15.6450834274292, -2.9633471965789795] |
82409106-0ab0-4bcf-a560-e7f639c79ac1 | variation-based-cause-effect-identification | 2211.12016 | null | https://arxiv.org/abs/2211.12016v1 | https://arxiv.org/pdf/2211.12016v1.pdf | Variation-based Cause Effect Identification | Mining genuine mechanisms underlying the complex data generation process in real-world systems is a fundamental step in promoting interpretability of, and thus trust in, data-driven models. Therefore, we propose a variation-based cause effect identification (VCEI) framework for causal discovery in bivariate systems fro... | ['Bin Yang', 'Karim Said Barsim', 'Mohamed Amine ben Salem'] | 2022-11-22 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.68384957e-01 3.74816239e-01 -4.43732381e-01 -2.44729966e-02
-3.10456961e-01 -5.80548108e-01 9.37280834e-01 3.79339546e-01
1.38727492e-02 9.83212769e-01 3.79484445e-01 -5.40294468e-01
-8.79183292e-01 -7.84833193e-01 -1.14317489e+00 -9.87497330e-01
-3.87202054e-01 2.12451786e-01 -1.16080165e-01 -3.64574157... | [7.814264297485352, 5.316257476806641] |
b4f6841e-88a4-4449-a3be-eaf383c5ad86 | deep-learning-of-semi-competing-risk-data-via | 2212.12028 | null | https://arxiv.org/abs/2212.12028v1 | https://arxiv.org/pdf/2212.12028v1.pdf | Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm | Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's disease course involves non-terminal (e.g., disease progression) and terminal (e.g., d... | ['Yi Li', 'Stephen Salerno'] | 2022-12-22 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.77219534e-01 -3.16919506e-01 -6.42864048e-01 -3.11497450e-01
-1.38378692e+00 -1.17227854e-02 5.81670105e-01 6.88334286e-01
-5.62177181e-01 7.76766479e-01 5.60360491e-01 -8.02117944e-01
-3.94066244e-01 -8.46281707e-01 -3.87225270e-01 -7.49732196e-01
-5.27782142e-01 8.74737144e-01 -1.26194283e-01 2.61324376... | [7.88730001449585, 5.6276068687438965] |
670cd8f3-5453-4c33-9615-3d5d75a50c6c | on-time-series-representations-for-multi | null | null | https://link.springer.com/article/10.1007/s00521-020-04916-5 | https://rdcu.be/b3Vh2 | On time series representations for multi-label NILM | Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more essential for privacy concerns and economic reasons. However, current NILM... | ['Christoforos Nalmpantis', 'Dimitris Vrakas'] | 2020-05-02 | null | null | null | neural-computing-and-applications-2020-5 | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-2.08865166e-01 -1.67652562e-01 -4.18753177e-01 -6.14065051e-01
-5.58435142e-01 -7.25948811e-01 5.13579011e-01 1.91428140e-01
-1.86769322e-01 4.37433153e-01 1.61104634e-01 -3.21595609e-01
2.85860132e-02 -7.92525709e-01 -2.70914614e-01 -7.47498989e-01
3.56280327e-01 3.57429266e-01 -5.96943736e-01 3.63182127... | [16.031904220581055, 7.563180923461914] |
21e32289-51b8-44ac-b0cc-349c18a7b8b8 | an-integrated-transfer-learning-and-multitask | 1812.09073 | null | http://arxiv.org/abs/1812.09073v1 | http://arxiv.org/pdf/1812.09073v1.pdf | An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction | Background: Pharmacokinetic evaluation is one of the key processes in drug
discovery and development. However, current absorption, distribution,
metabolism, excretion prediction models still have limited accuracy. Aim: This
study aims to construct an integrated transfer learning and multitask learning
approach for deve... | ['Zhuyifan Ye', 'Dongsheng Cao', 'Yilong Yang', 'Defang Ouyang', 'Xiaoshan Li'] | 2018-12-21 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [-1.73235282e-01 -5.99185705e-01 -3.27642351e-01 -3.11619252e-01
-4.92808610e-01 -3.82579565e-01 7.01761767e-02 6.03210926e-01
-5.76351225e-01 1.09952402e+00 -4.57366228e-01 -4.19196725e-01
-6.38739347e-01 -5.07526398e-01 -3.35271508e-01 -8.47051799e-01
-5.12251556e-01 6.77402139e-01 -1.05552420e-01 1.73667297... | [5.114924430847168, 5.739138603210449] |
e86addb7-b326-4731-9cdf-efe91ac51240 | deepmapping2-self-supervised-large-scale | 2212.06331 | null | https://arxiv.org/abs/2212.06331v2 | https://arxiv.org/pdf/2212.06331v2.pdf | DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization | LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cann... | ['Chen Feng', 'Li Ding', 'Yiming Li', 'Xinhao Liu', 'Chao Chen'] | 2022-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-registration'] | ['computer-vision'] | [-2.81690687e-01 -5.89873530e-02 -1.29783183e-01 -6.60833061e-01
-8.81121039e-01 -6.39507055e-01 7.59891987e-01 -1.26822427e-01
-5.81494868e-01 7.88077593e-01 -1.27500117e-01 -1.07719742e-01
-7.85212368e-02 -7.98023880e-01 -1.20165515e+00 -1.52516022e-01
-2.34039262e-01 1.00163007e+00 5.55135667e-01 -2.73486465... | [7.591259479522705, -2.2764580249786377] |
a2bb0ef4-9f46-43fe-87f1-b587fcc6b758 | disco-distilling-phrasal-counterfactuals-with | 2212.10534 | null | https://arxiv.org/abs/2212.10534v3 | https://arxiv.org/pdf/2212.10534v3.pdf | DISCO: Distilling Counterfactuals with Large Language Models | Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsourced, such data is typically limited in scale and diversity; when... | ['Kyle Richardson', 'Ashish Sabharwal', 'Antoine Bosselut', 'Qiyue Gao', 'Zeming Chen'] | 2022-12-20 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 2.35834286e-01 5.88743329e-01 -4.02465075e-01 -3.93814266e-01
-1.10700011e+00 -8.18145156e-01 1.23555648e+00 1.30502939e-01
-3.47469240e-01 1.51956344e+00 1.05122745e+00 -6.14057899e-01
1.04647540e-01 -7.65307605e-01 -1.12774169e+00 -9.47970971e-02
-5.52754514e-02 5.60006380e-01 -4.74317431e-01 -2.47772440... | [10.068821907043457, 8.065438270568848] |
01606442-89c0-4dea-ad14-aff5e58ef4a8 | a-human-visual-system-inspired-no-reference | null | null | https://www.mdpi.com/1424-8220/22/18/6775 | https://www.mdpi.com/1424-8220/22/18/6775 | A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors | Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired... | ['Domonkos Varga'] | 2022-09-07 | null | null | null | sensors-2022-9 | ['blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.42110556e-01 -6.49014831e-01 1.06819451e-01 -2.42232114e-01
-6.92407429e-01 -1.53711095e-01 4.49475795e-01 2.53067940e-01
-4.51333374e-01 5.80766261e-01 1.31629586e-01 1.67055070e-01
-4.90078151e-01 -7.35274017e-01 -2.11902350e-01 -7.87647486e-01
-2.00941227e-02 -4.43353713e-01 4.08552825e-01 -2.29274929... | [11.756346702575684, -1.9289743900299072] |
551802ab-07ef-4f22-ac70-34f72ac7de6e | end-to-end-facial-deep-learning-feature | 2002.03627 | null | https://arxiv.org/abs/2002.03627v1 | https://arxiv.org/pdf/2002.03627v1.pdf | End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement | In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency. In particular, the extracted features are compactly coded in an end-to-end man... | ['Shiqi Wang', 'Wenhan Yang', 'Shurun Wang'] | 2020-02-10 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 3.59279931e-01 -1.43195257e-01 -2.84379333e-01 -6.20990694e-01
-7.22324193e-01 -3.87734845e-02 2.19346017e-01 6.82039261e-02
-3.00324529e-01 1.98944420e-01 4.33925658e-01 3.97214219e-02
-5.01296639e-01 -8.32790613e-01 -4.79272902e-01 -8.14794421e-01
-7.84483459e-03 -3.72809350e-01 -4.13900703e-01 1.72542557... | [11.289397239685059, -1.5856057405471802] |
0db36baa-9834-41f8-8b31-9a7b27726932 | multi-axis-attentive-prediction-for-sparse | 2110.01794 | null | https://arxiv.org/abs/2110.01794v1 | https://arxiv.org/pdf/2110.01794v1.pdf | Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction | Spatiotemporal prediction of event data is a challenging task with a long history of research. While recent work in spatiotemporal prediction has leveraged deep sequential models that substantially improve over classical approaches, these models are prone to overfitting when the observation is extremely sparse, as in t... | ['Scott Sanner', 'Ga Wu', 'Yi Sui'] | 2021-10-05 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 2.22800747e-01 -1.15203723e-01 -3.85812521e-01 -4.70517188e-01
-4.95481044e-01 -1.95064902e-01 9.58467662e-01 4.33362544e-01
-3.08336258e-01 4.65331197e-01 1.06346774e+00 3.07771657e-03
-4.30079609e-01 -7.35347390e-01 -6.28478348e-01 -5.06224751e-01
-6.57524705e-01 2.23102421e-01 2.56520182e-01 -1.34037331... | [6.852080345153809, 1.9522889852523804] |
1d3d55a2-53f5-48d4-b853-2ff540ebbabf | rf-based-low-snr-classification-of-uavs-using | 2009.05519 | null | https://arxiv.org/abs/2009.05519v2 | https://arxiv.org/pdf/2009.05519v2.pdf | RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks | This paper investigates the problem of classification of unmanned aerial vehicles (UAVs) from radio frequency (RF) fingerprints at the low signal-to-noise ratio (SNR) regime. We use convolutional neural networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone cont... | ['Ismail Guvenc', 'Fatih Erden', 'Ender Ozturk'] | 2020-09-11 | null | null | null | null | ['drone-controller'] | ['robots'] | [ 4.79366690e-01 -3.12859207e-01 1.17819741e-01 4.65565138e-02
-5.39322913e-01 -7.90827990e-01 3.31293583e-01 -3.74209613e-01
-2.18251094e-01 6.29133701e-01 -1.55350164e-01 -4.92280871e-01
-5.41285932e-01 -1.00842512e+00 -5.83506882e-01 -9.21618283e-01
-6.28692210e-01 -5.39932489e-01 -1.14316605e-01 -2.86292076... | [15.238204002380371, 5.4719414710998535] |
2c039fa4-7d0c-438b-852a-7030ce4bc686 | coder-coupled-diversity-sensitive-momentum | 2208.09843 | null | https://arxiv.org/abs/2208.09843v1 | https://arxiv.org/pdf/2208.09843v1.pdf | CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval | Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities. Contrastive learning has been adopted by most prior arts. Except for limited amount of negative image-text pairs, the capability of constrastive learning is restricted by manually weighting negative pairs as well as unawareness of exte... | ['Jingdong Wang', 'Errui Ding', 'Zhong Ji', 'Yunlong Yu', 'Fu Li', 'Min Yang', 'Boyang xia', 'Wenhao Wu', 'Dongliang He', 'Haoran Wang'] | 2022-08-21 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 1.18351623e-01 -4.06185150e-01 -6.03803277e-01 -2.70708621e-01
-9.93315876e-01 -6.16192818e-01 7.76999950e-01 -8.55529383e-02
-4.68805552e-01 2.02800736e-01 1.49855554e-01 7.37774894e-02
-2.14916393e-01 -4.50128496e-01 -5.37644506e-01 -8.65019083e-01
2.67230630e-01 4.09302831e-01 6.93419576e-03 -9.35181379... | [10.84744644165039, 1.3085691928863525] |
93779c54-0f2f-4974-a6a8-48008b5c6395 | self-supervised-regional-and-temporal | 2107.14399 | null | https://arxiv.org/abs/2107.14399v1 | https://arxiv.org/pdf/2107.14399v1.pdf | Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition | Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs,... | ['ShiLiang Pu', 'Chunmao Wang', 'Qiang Li', 'Jingjing Wang', 'Jingwei Yan'] | 2021-07-30 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 1.48682103e-01 8.94178376e-02 -5.24310708e-01 -2.67059028e-01
-4.84595090e-01 -1.01735711e-01 4.09280866e-01 -6.48186564e-01
-1.21294931e-01 7.77502656e-01 4.61987525e-01 4.11501139e-01
-4.28688340e-02 -3.46119583e-01 -4.20282871e-01 -1.12350786e+00
1.87954724e-01 -2.84903467e-01 -6.34897174e-03 -2.54415452... | [13.617138862609863, 1.5806026458740234] |
d050db35-f1bd-40bf-9f2b-85db8fb6b1c6 | deep-siamese-networks-with-bayesian-non | 1811.07386 | null | http://arxiv.org/abs/1811.07386v1 | http://arxiv.org/pdf/1811.07386v1.pdf | Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking | We present a novel algorithm utilizing a deep Siamese neural network as a
general object similarity function in combination with a Bayesian optimization
(BO) framework to encode spatio-temporal information for efficient object
tracking in video. In particular, we treat the video tracking problem as a
dynamic (i.e. temp... | ['Anthony D. Rhodes', 'Manan Goel'] | 2018-11-18 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.65189117e-01 -5.82358241e-01 -1.28710136e-01 2.10424066e-02
-4.20785725e-01 -5.35139680e-01 6.18741989e-01 -1.98817238e-01
-8.25066686e-01 4.22730982e-01 5.14198048e-03 2.75422633e-01
-3.43461514e-01 -4.52949584e-01 -8.69204044e-01 -8.10673177e-01
-1.06064245e-01 5.38537204e-01 5.38453758e-01 3.77707988... | [6.338212966918945, -2.0738842487335205] |
de077cb9-fa5b-489e-94f2-019b958cb899 | fauno-the-italian-large-language-model-that | 2306.14457 | null | https://arxiv.org/abs/2306.14457v1 | https://arxiv.org/pdf/2306.14457v1.pdf | Fauno: The Italian Large Language Model that will leave you senza parole! | This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets... | ['Fabrizio Silvestri', 'Emanuele Rodolà', 'Andrea Santilli', 'Giovanni Trappolini', 'Andrea Bacciu'] | 2023-06-26 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-5.05731344e-01 2.67492592e-01 -1.45893827e-01 -1.43666655e-01
-8.75789523e-01 -8.56087387e-01 7.88130820e-01 -1.08287007e-01
-3.00632745e-01 8.15196455e-01 4.58173364e-01 -5.70681393e-01
2.34495997e-01 -6.74905360e-01 -2.84307599e-01 -3.22674245e-01
-6.99086785e-02 1.02747750e+00 2.33814850e-01 -5.59148908... | [12.078875541687012, 8.16612720489502] |
5a46d8f7-7160-450d-8159-934a8f77c0b6 | c2f2neus-cascade-cost-frustum-fusion-for-high | 2306.10003 | null | https://arxiv.org/abs/2306.10003v1 | https://arxiv.org/pdf/2306.10003v1.pdf | C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction | There is an emerging effort to combine the two popular technical paths, i.e., the multi-view stereo (MVS) and neural implicit surface (NIS), in scene reconstruction from sparse views. In this paper, we introduce a novel integration scheme that combines the multi-view stereo with neural signed distance function represen... | ['Wei Yang', 'Junle Wang', 'Zhaojie Zeng', 'Wenkai Liu', 'Yuesong Wang', 'Tao Guan', 'Luoyuan Xu'] | 2023-06-16 | null | null | null | null | ['depth-estimation'] | ['computer-vision'] | [ 2.49677330e-01 -1.74316809e-01 3.84226829e-01 -3.24477613e-01
-9.35258329e-01 -5.12210071e-01 4.52884316e-01 -3.19109321e-01
1.72047302e-01 5.29148400e-01 2.11021736e-01 2.49231577e-01
-5.73346317e-02 -1.03258383e+00 -8.09452295e-01 -4.97189224e-01
6.28451824e-01 1.76269501e-01 5.63369453e-01 -2.56029934... | [9.057347297668457, -2.869535207748413] |
520fc136-144f-4ee2-8200-40340397b90e | an-influence-based-approach-for-root-cause | 2105.03092 | null | https://arxiv.org/abs/2105.03092v1 | https://arxiv.org/pdf/2105.03092v1.pdf | An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks | Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast... | ['Junjian Ye', 'Xi Zhang', 'Min Zhou', 'Marcus Kalander', 'Keli Zhang'] | 2021-05-07 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 1.48069933e-01 5.91105483e-02 2.97549665e-02 -1.40706256e-01
-1.72189236e-01 -1.75183564e-01 3.76404375e-01 7.64933944e-01
2.00215369e-01 7.21512616e-01 1.25675872e-01 -5.59989333e-01
-9.75086033e-01 -1.09172368e+00 -2.51454294e-01 -5.57748616e-01
-7.02528834e-01 5.81031382e-01 2.98541874e-01 1.15226969... | [7.3744964599609375, 2.9248249530792236] |
eeb6cebe-16d9-41aa-af35-a7029f95d4e5 | channel-attention-is-all-you-need-for-video | null | null | https://ojs.aaai.org/index.php/AAAI/article/download/6693/6547 | https://ojs.aaai.org/index.php/AAAI/article/download/6693/6547 | Channel Attention Is All You Need for Video Frame Interpolation | Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effecti... | ['Kyoung Mu Lee', 'Ning Xu', 'Bohyung Han', 'Heewon Kim', 'Myungsub Choi'] | 2020-04-03 | null | null | null | aaai-conference-on-artificial-intelligence-7 | ['video-frame-interpolation'] | ['computer-vision'] | [ 1.91689998e-01 -4.37723011e-01 -1.61623687e-01 -1.64689049e-01
-2.38466278e-01 -2.18855470e-01 4.66768771e-01 -4.41872537e-01
-5.17546713e-01 8.62680078e-01 1.64157823e-01 -2.21289828e-01
3.31554174e-01 -7.26265013e-01 -9.01054621e-01 -6.20588303e-01
7.82241449e-02 -2.41687134e-01 3.42718303e-01 -6.93272874... | [10.694588661193848, -1.4331175088882446] |
0846b5c3-35cf-4526-b18f-4bf065b65045 | learning-policies-with-zero-or-bounded | 2106.02684 | null | https://arxiv.org/abs/2106.02684v3 | https://arxiv.org/pdf/2106.02684v3.pdf | Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs | We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it is possible to achieve a reward regret of $\tilde{\mathcal{O}}(\sqrt{K})$ while allowing an $\tilde{\mathcal{O}}(\sqrt{K})$ constraint viol... | ['Chao Tian', 'P. R. Kumar', 'Dileep Kalathil', 'Ruida Zhou', 'Tao Liu'] | 2021-06-04 | null | http://proceedings.neurips.cc/paper/2021/hash/8ec2ba5e96ec1c050bc631abda80f269-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8ec2ba5e96ec1c050bc631abda80f269-Paper.pdf | neurips-2021-12 | ['safe-exploration'] | ['robots'] | [ 1.50601044e-01 7.13812888e-01 -2.19116330e-01 -8.62233564e-02
-8.95209670e-01 -8.52482498e-01 1.97581336e-01 1.75618127e-01
-9.49953616e-01 1.38417006e+00 -4.16452259e-01 -6.73024297e-01
-6.71357453e-01 -8.80523980e-01 -7.51684546e-01 -1.00242162e+00
-4.33552444e-01 5.54534256e-01 3.77212167e-02 -1.24541193... | [4.390854358673096, 2.843553066253662] |
d7516c21-656d-437f-a74d-b4ee5a966b26 | learning-from-synthetic-humans | 1701.01370 | null | http://arxiv.org/abs/1701.01370v3 | http://arxiv.org/pdf/1701.01370v3.pdf | Learning from Synthetic Humans | Estimating human pose, shape, and motion from images and videos are
fundamental challenges with many applications. Recent advances in 2D human pose
estimation use large amounts of manually-labeled training data for learning
convolutional neural networks (CNNs). Such data is time consuming to acquire
and difficult to ex... | ['Naureen Mahmood', 'Gül Varol', 'Xavier Martin', 'Ivan Laptev', 'Michael J. Black', 'Cordelia Schmid', 'Javier Romero'] | 2017-01-05 | learning-from-synthetic-humans-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Varol_Learning_From_Synthetic_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Varol_Learning_From_Synthetic_CVPR_2017_paper.pdf | cvpr-2017-7 | ['human-part-segmentation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.88045219e-01 7.65698031e-02 2.95685321e-01 -4.49842244e-01
-6.35204554e-01 -6.96166635e-01 3.59340936e-01 -2.78748721e-01
-8.28296244e-01 8.70807648e-01 7.15016872e-02 4.47384894e-01
6.35916412e-01 -6.82525218e-01 -7.52258122e-01 -2.84976184e-01
-1.10424147e-03 1.07629788e+00 2.21053898e-01 -1.22630410... | [7.079774379730225, -0.8574716448783875] |
6af98c5a-9348-4662-a24d-89842e0d835a | how-multipurpose-are-language-models | null | null | https://openreview.net/forum?id=d7KBjmI3GmQ | https://openreview.net/pdf?id=d7KBjmI3GmQ | How Multipurpose Are Language Models? | We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent mode... | ['Jacob Steinhardt', 'Dawn Song', 'Mantas Mazeika', 'Andy Zou', 'Steven Basart', 'Collin Burns', 'Dan Hendrycks'] | 2021-01-01 | null | null | null | iclr-2021-1 | ['elementary-mathematics'] | ['reasoning'] | [-2.62778848e-01 3.62349570e-01 -5.96900940e-01 -1.99164540e-01
-1.02410614e+00 -7.42681384e-01 4.23004389e-01 2.98379749e-01
-4.01255786e-01 1.04800975e+00 -4.26276959e-02 -1.03276491e+00
-5.92621624e-01 -6.80464149e-01 -6.38676107e-01 -1.58598423e-01
5.11431754e-01 5.72007835e-01 -5.26159480e-02 -1.72908664... | [9.808096885681152, 7.556607246398926] |
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