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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1ff5829b-f457-40c5-83c7-fd1fc31138ee | decorrelated-jet-substructure-tagging-using | 1703.03507 | null | http://arxiv.org/abs/1703.03507v1 | http://arxiv.org/pdf/1703.03507v1.pdf | Decorrelated Jet Substructure Tagging using Adversarial Neural Networks | We describe a strategy for constructing a neural network jet substructure
tagger which powerfully discriminates boosted decay signals while remaining
largely uncorrelated with the jet mass. This reduces the impact of systematic
uncertainties in background modeling while enhancing signal purity, resulting
in improved di... | ['Andreas Søgaard', 'Daniel Whiteson', 'Pierre Baldi', 'Peter Sadowski', 'Edward Goul', 'Chase Shimmin', 'Edison Weik'] | 2017-03-10 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [ 3.13054651e-01 1.42162636e-01 -1.32073194e-01 -4.01151776e-01
-1.07884741e+00 -9.24221337e-01 8.53089213e-01 -1.19936347e-01
-4.31810766e-01 7.34461367e-01 1.86669677e-01 -6.41520679e-01
1.10302359e-01 -8.68688703e-01 -6.54192686e-01 -9.97396588e-01
-7.87272826e-02 8.34380269e-01 5.38061142e-01 -1.30286336... | [15.688187599182129, 2.9239747524261475] |
8f83bf4f-68c1-4256-ad6b-cb755e773675 | meta-learning-transferable-parameterized | 2206.03597 | null | https://arxiv.org/abs/2206.03597v3 | https://arxiv.org/pdf/2206.03597v3.pdf | Meta-Learning Parameterized Skills | We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks. We propose to leverage off-policy Meta-RL combined with a trajectory-centric smoothness term to learn a set o... | ['George Konidaris', 'Michael Littman', 'Saket Tiwari', 'Shangqun Yu', 'Haotian Fu'] | 2022-06-07 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.18656226e-01 3.36824238e-01 -6.90790772e-01 -1.29197776e-01
-1.27204442e+00 -5.58135331e-01 7.26711512e-01 -3.11380327e-01
-5.10213554e-01 1.15214479e+00 3.12006980e-01 -4.23260689e-01
-5.19683659e-01 -4.62676346e-01 -9.53172624e-01 -6.80822194e-01
-6.64454699e-01 6.23101056e-01 3.64211649e-01 -2.49020383... | [4.186468124389648, 1.55370032787323] |
5f35282f-c0fa-422b-9209-2d1d5150fc78 | context-matters-recovering-human-semantic | 1910.06954 | null | https://arxiv.org/abs/1910.06954v3 | https://arxiv.org/pdf/1910.06954v3.pdf | Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora | Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts. However, efforts to date have shown a substa... | ['Cameron T. Ellis', 'Marius Cătălin Iordan', 'Tyler Giallanza', 'Nicole M. Beckage', 'Jonathan D. Cohen'] | 2019-10-15 | null | null | null | null | ['empirical-judgments'] | ['natural-language-processing'] | [ 2.33432800e-01 6.78422600e-02 -9.54270065e-02 -7.13067412e-01
-4.08067405e-01 -5.62946379e-01 9.11549211e-01 9.22575295e-01
-8.41173589e-01 2.00415537e-01 8.53892267e-01 -3.15487504e-01
-1.12602189e-01 -8.55403602e-01 -8.54506567e-02 2.87596192e-02
3.13851535e-01 7.91306078e-01 1.60115957e-01 -3.28724533... | [10.272279739379883, 8.737728118896484] |
a6e55528-c81b-4f9d-9b4e-ff25767966db | reading-text-in-the-wild-with-convolutional | 1412.1842 | null | http://arxiv.org/abs/1412.1842v1 | http://arxiv.org/pdf/1412.1842v1.pdf | Reading Text in the Wild with Convolutional Neural Networks | In this work we present an end-to-end system for text spotting -- localising
and recognising text in natural scene images -- and text based image retrieval.
This system is based on a region proposal mechanism for detection and deep
convolutional neural networks for recognition. Our pipeline uses a novel
combination of ... | ['Andrew Zisserman', 'Karen Simonyan', 'Andrea Vedaldi', 'Max Jaderberg'] | 2014-12-04 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 8.49046052e-01 -2.12288067e-01 2.31494054e-01 -3.65203410e-01
-1.11731267e+00 -6.07364357e-01 1.18823564e+00 2.89429873e-01
-8.30233514e-01 1.16613291e-01 4.70382906e-02 -3.41368139e-01
2.53316462e-01 -6.35894358e-01 -6.32946610e-01 -3.32826436e-01
4.57802981e-01 9.45177138e-01 6.07967257e-01 -3.30995053... | [11.811220169067383, 2.3506577014923096] |
52ff4c6b-f937-4006-afa5-f3c733587c16 | optimizing-pessimism-in-dynamic-treatment | 2210.14420 | null | https://arxiv.org/abs/2210.14420v2 | https://arxiv.org/pdf/2210.14420v2.pdf | Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach | In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting. When the coverage condition does not hold, which is common for offline data, the existing solutions would produce sub-optimal policies. The pessimism principle addresses this issue ... | ['Lexin Li', 'Chengchun Shi', 'Zhengling Qi', 'Yunzhe Zhou'] | 2022-10-26 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.03309706e-01 2.21322730e-01 -1.05132079e+00 -3.23793679e-01
-8.44322145e-01 -3.70969027e-01 2.51877218e-01 1.94186121e-01
-4.14793909e-01 9.37927246e-01 3.91089767e-02 -6.27307236e-01
-5.73982000e-01 -7.41875708e-01 -5.80357909e-01 -9.43527341e-01
4.47101295e-02 6.58849001e-01 2.24437311e-01 6.22159392... | [4.54005241394043, 3.1942732334136963] |
c812904e-28b4-4ed6-abdf-e7ebf02633d1 | facial-expression-retargeting-from-human-to | 2008.05110 | null | https://arxiv.org/abs/2008.05110v1 | https://arxiv.org/pdf/2008.05110v1.pdf | Facial Expression Retargeting from Human to Avatar Made Easy | Facial expression retargeting from humans to virtual characters is a useful technique in computer graphics and animation. Traditional methods use markers or blendshapes to construct a mapping between the human and avatar faces. However, these approaches require a tedious 3D modeling process, and the performance relies ... | ['Juyong Zhang', 'Keyu Chen', 'Jianmin Zheng'] | 2020-08-12 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [-1.59731403e-01 -2.04843655e-03 -4.23710793e-02 -5.88468552e-01
-4.37332094e-01 -5.53131998e-01 4.66776967e-01 -6.20922267e-01
-8.84529203e-02 3.44335169e-01 1.32496521e-01 2.82685280e-01
5.72241366e-01 -5.93190849e-01 -3.01025420e-01 -5.03578961e-01
4.38558519e-01 3.92804950e-01 -2.90517181e-01 -5.06737113... | [12.934860229492188, -0.38018229603767395] |
63cca976-f7e3-4740-a913-c4e12a42e691 | component-aware-anomaly-detection-framework | 2305.08509 | null | https://arxiv.org/abs/2305.08509v1 | https://arxiv.org/pdf/2305.08509v1.pdf | Component-aware anomaly detection framework for adjustable and logical industrial visual inspection | Industrial visual inspection aims at detecting surface defects in products during the manufacturing process. Although existing anomaly detection models have shown great performance on many public benchmarks, their limited adjustability and ability to detect logical anomalies hinder their broader use in real-world setti... | ['Zhuo Zhao', 'Liuyi Jin', 'Xiao Jin', 'Bingke Jiang', 'Xiao Du', 'Bing Li', 'Tongkun Liu'] | 2023-05-15 | null | null | null | null | ['unsupervised-semantic-segmentation', 'anomaly-classification'] | ['computer-vision', 'computer-vision'] | [ 1.33746892e-01 -7.31799081e-02 2.09738478e-01 -3.71199995e-01
-3.48376393e-01 -4.41742301e-01 2.51637727e-01 5.66383481e-01
5.29746175e-01 -2.62827277e-01 -7.67463803e-01 -4.30269927e-01
-2.56488919e-01 -8.97861779e-01 -4.58706349e-01 -5.91068268e-01
1.37049317e-01 3.59357893e-01 1.47152677e-01 -1.94514692... | [7.535694122314453, 1.9798685312271118] |
0d28c572-6c93-4705-92d4-f0f985efd20d | learning-robust-speech-representation-with-an | 2104.03204 | null | https://arxiv.org/abs/2104.03204v1 | https://arxiv.org/pdf/2104.03204v1.pdf | Learning robust speech representation with an articulatory-regularized variational autoencoder | It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representation could improve the performances of a deep generative model (here a variational autoencoder) trained to encode and decode acoustic speech f... | ['Thomas Hueber', 'Jean-Luc Schwartz', 'Laurent Girin', 'Marc-Antoine Georges'] | 2021-04-07 | null | null | null | null | ['speech-denoising'] | ['speech'] | [-4.79588434e-02 2.25295633e-01 2.14076057e-01 -1.32436201e-01
-4.21112210e-01 -6.44082487e-01 8.57171416e-01 -3.74066144e-01
-1.97565094e-01 4.73638535e-01 5.35941482e-01 -9.15137008e-02
-1.17988713e-01 -6.58207059e-01 -8.17334890e-01 -8.95170867e-01
3.04648191e-01 3.89470220e-01 -2.17836484e-01 -2.95022111... | [15.026107788085938, 6.338521957397461] |
e95baa59-e1c3-4a5e-8539-7e433c79fa5b | spoken-conversational-search-for-general | 1909.11980 | null | https://arxiv.org/abs/1909.11980v1 | https://arxiv.org/pdf/1909.11980v1.pdf | Spoken Conversational Search for General Knowledge | We present a spoken conversational question answering proof of concept that is able to answer questions about general knowledge from Wikidata. The dialogue component does not only orchestrate various components but also solve coreferences and ellipsis. | ['Frédéric Herledan', 'Géraldine Damnati', 'Olivier Le-Blouch', 'Martinho Dos-Santos', 'Lina M. Rojas-Barahona', 'Pascal Bellec', 'Benoit Besset', 'Johannes Heinecke', 'Jean Y. Lancien', 'Munshi Asadullah', 'Emmanuel Mory'] | 2019-09-26 | spoken-conversational-search-for-general-1 | https://aclanthology.org/W19-5914 | https://aclanthology.org/W19-5914.pdf | ws-2019-9 | ['conversational-search'] | ['natural-language-processing'] | [-2.84441054e-01 1.30680966e+00 3.98986489e-01 -2.98340559e-01
-5.77657461e-01 -8.10447216e-01 8.80274832e-01 5.00170767e-01
-2.22540170e-01 1.08338666e+00 5.69438279e-01 -6.16415262e-01
-7.24344611e-01 -8.10403466e-01 -2.20234748e-02 -2.91442615e-03
-3.34796086e-02 9.51573193e-01 8.34369063e-01 -1.19469380... | [12.441058158874512, 8.052445411682129] |
6f69d7b0-a232-489e-82f8-20e2905d79fc | scalable-learning-of-latent-language | 2305.20018 | null | https://arxiv.org/abs/2305.20018v1 | https://arxiv.org/pdf/2305.20018v1.pdf | Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency | We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as a form of self-learning where the semantic parser being trained is used to generate annotations for unlabeled text that are then used as new... | ['Alexander Gray', 'Salim Roukos', 'Pavan Kapanipathi', 'Subhajit Chaudhury', 'Tahira Naseem', 'Ramon Astudillo', 'Maxwell Crouse'] | 2023-05-31 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.29803872e-01 9.91238236e-01 -1.75687209e-01 -4.50369388e-01
-1.27671814e+00 -7.88684249e-01 8.94500613e-01 2.35830605e-01
-3.72926742e-01 7.31175959e-01 3.76998663e-01 -2.55230278e-01
5.43591678e-01 -6.82475448e-01 -1.20685136e+00 -5.98376751e-01
3.79154891e-01 8.27911854e-01 3.00809860e-01 8.50183442... | [10.576879501342773, 9.288956642150879] |
9ee07213-b15a-4c5a-99e8-a71909a9060f | learning-to-fix-build-errors-with-graph2diff | 1911.01205 | null | https://arxiv.org/abs/1911.01205v1 | https://arxiv.org/pdf/1911.01205v1.pdf | Learning to Fix Build Errors with Graph2Diff Neural Networks | Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning architecture, called Graph2Diff, for automatically localizing and fixing build errors. We represent source code, build config... | ['Pierre-Antoine Manzagol', 'Zimin Chen', 'Subhodeep Moitra', 'Daniel Tarlow', 'Charles Sutton', 'Edward Aftandilian', 'Andrew Rice'] | 2019-11-04 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-9.91768483e-03 3.93523425e-01 -3.04303449e-02 -4.17580187e-01
-6.81346953e-01 -6.52781785e-01 3.65460999e-02 6.15676582e-01
2.21655712e-01 2.31059358e-01 2.69770380e-02 -9.04888690e-01
1.85379669e-01 -8.03710759e-01 -1.09334099e+00 1.50242567e-01
-9.30727422e-02 3.87484320e-02 2.83502758e-01 -2.09001824... | [7.60707950592041, 7.763955593109131] |
45c8f5cb-17f8-47cb-93f3-10a38e04e6d8 | semi-supervised-monaural-singing-voice | 1812.06087 | null | https://arxiv.org/abs/1812.06087v3 | https://arxiv.org/pdf/1812.06087v3.pdf | Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures | We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music. Our solution employs a single mapping function g, which, applied to a mixed sample, recovers the underlying instrum... | ['Michael Michelashvili', 'Sagie Benaim', 'Lior Wolf'] | 2018-12-14 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 7.23967016e-01 2.81676441e-01 -4.95108888e-02 2.20972183e-03
-1.19264591e+00 -8.48797739e-01 5.62494099e-01 -8.00448477e-01
-1.13133363e-01 8.59726071e-01 4.97039407e-01 1.77867234e-01
-2.28030413e-01 -4.03262496e-01 -6.83209956e-01 -9.00363982e-01
-4.60762009e-02 7.20651686e-01 -2.89919019e-01 -1.27875179... | [15.550756454467773, 5.6529340744018555] |
1cacacad-2760-46eb-9c06-6083999462a4 | curriculum-learning-a-regularization-method | 2108.06084 | null | https://arxiv.org/abs/2108.06084v4 | https://arxiv.org/pdf/2108.06084v4.pdf | The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models | Recent works have demonstrated great success in pre-training large-scale autoregressive language models on massive GPUs. To reduce the wall-clock training time, a common practice is to increase the batch size and learning rate. However, such practice is often brittle and leads to a so-called stability-efficiency dilemm... | ['Yuxiong He', 'Minjia Zhang', 'Conglong Li'] | 2021-08-13 | curriculum-learning-a-regularization-method-1 | https://openreview.net/forum?id=rhDaUTtfsqs | https://openreview.net/pdf?id=rhDaUTtfsqs | null | ['lambada'] | ['natural-language-processing'] | [-5.18288603e-03 -5.07640064e-01 -7.29574412e-02 -8.18674937e-02
-7.54239976e-01 -5.15734613e-01 2.62605548e-01 8.01365077e-02
-7.27785945e-01 4.81622219e-01 -3.68567854e-01 -8.56633186e-01
2.64672816e-01 -6.05765700e-01 -7.85827577e-01 -7.14848220e-01
-8.61776844e-02 3.94402504e-01 4.71780598e-01 -2.48806849... | [8.638585090637207, 3.4745960235595703] |
21b95924-652f-4b44-8427-fe58ef649e41 | unsupervised-feature-learning-by-cross-level | 2008.03813 | null | https://arxiv.org/abs/2008.03813v5 | https://arxiv.org/pdf/2008.03813v5.pdf | Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination | Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets. However, natural data could be highly correlated and long-tail distributed. Natural between-instance similarity conflicts with the pre... | ['Xudong Wang', 'Ziwei Liu', 'Stella X. Yu'] | 2020-08-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Unsupervised_Feature_Learning_by_Cross-Level_Instance-Group_Discrimination_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-image-classification'] | ['computer-vision'] | [ 2.44712397e-01 2.24999581e-02 -6.58495784e-01 -6.42514765e-01
-8.16241324e-01 -5.83943129e-01 8.82330060e-01 1.31222591e-01
-2.77163655e-01 7.75408506e-01 1.92650720e-01 9.86729562e-02
-4.83619362e-01 -6.17599308e-01 -5.60375690e-01 -8.20075095e-01
-1.30127341e-01 6.09236419e-01 1.35768265e-01 -1.52727395... | [9.572707176208496, 3.0152173042297363] |
5db2d13b-b48a-42b6-9863-323dcd156d75 | paraamr-a-large-scale-syntactically-diverse | 2305.16585 | null | https://arxiv.org/abs/2305.16585v1 | https://arxiv.org/pdf/2305.16585v1.pdf | ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation | Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually... | ['Aram Galstyan', 'Kai-Wei Chang', 'Anoop Kumar', 'I-Hung Hsu', 'Varun Iyer', 'Kuan-Hao Huang'] | 2023-05-26 | null | null | null | null | ['paraphrase-generation', 'sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity', 'semantic-similarity', 'paraphrase-generation'] | ['computer-code', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.57105225e-01 6.23348281e-02 -3.23554635e-01 -4.60416079e-01
-1.16597641e+00 -6.66485012e-01 6.06040418e-01 4.81288791e-01
-1.01184301e-01 8.73455763e-01 8.21786046e-01 -2.17648104e-01
1.86852917e-01 -7.96132445e-01 -7.95139074e-01 -1.72927126e-01
6.47368968e-01 5.79884887e-01 -8.40917155e-02 -6.87307954... | [11.63687515258789, 9.250875473022461] |
4f2b654d-5d68-4743-863b-21e67c27492c | open-set-semantic-segmentation-for-point | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Open-Set_Semantic_Segmentation_for_Point_Clouds_via_Adversarial_Prototype_Framework_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Open-Set_Semantic_Segmentation_for_Point_Clouds_via_Adversarial_Prototype_Framework_CVPR_2023_paper.pdf | Open-Set Semantic Segmentation for Point Clouds via Adversarial Prototype Framework | Recently, point cloud semantic segmentation has attracted much attention in computer vision. Most of the existing works in literature assume that the training and testing point clouds have the same object classes, but they are generally invalid in many real-world scenarios for identifying the 3D objects whose class... | ['Qiulei Dong', 'Jianan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-semantic-segmentation'] | ['computer-vision'] | [ 1.25961989e-01 8.82293284e-02 -4.21952493e-02 -4.42328215e-01
-6.84863925e-01 -7.04640388e-01 5.73852777e-01 -1.74179435e-01
-1.37189806e-01 2.45705500e-01 -6.60387397e-01 -5.03043123e-02
3.71695049e-02 -1.04821169e+00 -9.45075810e-01 -8.75542760e-01
2.15724885e-01 9.36785221e-01 5.68824649e-01 -9.19998682... | [8.02165412902832, -3.268444776535034] |
027363d7-baea-4146-9ae3-349a66525841 | unifying-architectures-tasks-and-modalities | 2202.03052 | null | https://arxiv.org/abs/2202.03052v2 | https://arxiv.org/pdf/2202.03052v2.pdf | OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework | In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, a Task-Agnostic and Modality-Agnostic framework that supports Task Comprehensiveness. OFA unifies a diverse set of cross-modal and unimodal tasks, including image... | ['Hongxia Yang', 'Jingren Zhou', 'Chang Zhou', 'Jianxin Ma', 'Zhikang Li', 'Shuai Bai', 'Junyang Lin', 'Rui Men', 'An Yang', 'Peng Wang'] | 2022-02-07 | null | null | null | null | ['self-supervised-image-classification', 'object-categorization', 'visual-entailment'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 4.23612535e-01 -1.17586657e-01 -1.25291035e-01 -4.97214764e-01
-1.10159707e+00 -9.21062231e-01 1.02433753e+00 -1.65344536e-01
-6.59820855e-01 4.83597040e-01 7.16859549e-02 -5.87997735e-01
2.60744184e-01 -2.89827913e-01 -9.05878544e-01 -4.06398624e-01
5.38170993e-01 5.09673178e-01 3.12872529e-02 -3.48776042... | [10.847809791564941, 1.588831901550293] |
7c030892-61b0-49e1-867d-4700d8fe3c95 | the-chime-7-dasr-challenge-distant-meeting | 2306.13734 | null | https://arxiv.org/abs/2306.13734v1 | https://arxiv.org/pdf/2306.13734v1.pdf | The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios | The CHiME challenges have played a significant role in the development and evaluation of robust speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR (DASR) task, within the 7th CHiME challenge. This task comprises joint ASR and diarization in far-field settings with multiple, and possibly heterogeneou... | ['Sanjeev Khudanpur', 'Stefano Squartini', 'Zhong-Qiu Wang', 'Yoshiki Masuyama', 'Paola Garcia', 'Xuankai Chang', 'Desh Raj', 'Shinji Watanabe', 'Matthew Wiesner', 'Samuele Cornell'] | 2023-06-23 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 0.20283559 -0.2901863 0.21535327 -0.45481366 -1.8407807 -0.88660604
0.55902404 -0.28754058 -0.00931375 0.28748435 0.67651534 -0.43465373
-0.1735164 0.2303351 -0.52890986 -0.35400018 -0.33774838 0.23698422
-0.05085959 -0.49953628 -0.10109179 0.72496283 -1.3642713 0.5444038
0.27442208 0.8960828 0.1... | [14.814629554748535, 6.078979015350342] |
072c0a74-a971-459c-9b62-cfef5963f8fa | discovars-a-new-data-analysis-perspective | 2304.03983 | null | https://arxiv.org/abs/2304.03983v1 | https://arxiv.org/pdf/2304.03983v1.pdf | DiscoVars: A New Data Analysis Perspective -- Application in Variable Selection for Clustering | We present a new data analysis perspective to determine variable importance regardless of the underlying learning task. Traditionally, variable selection is considered an important step in supervised learning for both classification and regression problems. The variable selection also becomes critical when costs associ... | ['Ayhan Demiriz'] | 2023-04-08 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.17228970e-01 -4.40692119e-02 -2.18968213e-01 -5.28410971e-01
-2.57106096e-01 -4.33469176e-01 2.53733844e-01 7.56796658e-01
-5.07197618e-01 1.11432648e+00 -2.43467361e-01 -5.48978031e-01
-6.46267295e-01 -1.36017954e+00 -5.03895730e-02 -7.77603090e-01
-4.85409379e-01 7.48323202e-01 -8.15735385e-02 -3.63668911... | [7.813797473907471, 4.679835319519043] |
ce4e4982-ae93-4a2a-b434-0bc30d031e26 | solution-of-physics-based-bayesian-inverse | 2107.02926 | null | https://arxiv.org/abs/2107.02926v2 | https://arxiv.org/pdf/2107.02926v2.pdf | Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors | Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach ... | ['Assad A Oberai', 'Deep Ray', 'Dhruv V Patel'] | 2021-07-06 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 4.92115617e-01 -2.28205863e-02 4.68629628e-01 -1.84779823e-01
-6.25496864e-01 -5.30945778e-01 6.23934865e-01 -6.38116062e-01
-1.05462924e-01 1.18104148e+00 1.60945743e-01 -1.55356422e-01
-6.96398973e-01 -8.45772505e-01 -8.55923057e-01 -9.56105649e-01
3.35367978e-01 6.40832126e-01 -1.48913950e-01 -4.10184637... | [6.763023376464844, 3.5579512119293213] |
3ebd4d79-f559-4494-bc79-c2620c1ae68c | low-resource-named-entity-recognition-based | 2109.07118 | null | https://arxiv.org/abs/2109.07118v3 | https://arxiv.org/pdf/2109.07118v3.pdf | Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger | This paper presents a simple and effective approach in low-resource named entity recognition (NER) based on multi-hop dependency trigger. Dependency trigger refer to salient nodes relative to a entity in the dependency graph of a context sentence. Our main observation is that there often exists trigger which play an im... | ['Jiangxu Wu'] | 2021-09-15 | null | https://aclanthology.org/2022.ccl-1.85 | https://aclanthology.org/2022.ccl-1.85.pdf | ccl-2022-10 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.13023840e-01 3.35396707e-01 1.32421613e-01 -6.14819527e-01
-6.00875437e-01 -9.13812876e-01 5.76558888e-01 5.61863184e-01
-9.16005433e-01 9.83680248e-01 8.03825200e-01 -1.40357107e-01
-1.30718844e-02 -6.52014911e-01 -1.68268710e-01 -1.23188533e-01
-1.74513429e-01 3.98284018e-01 6.11014247e-01 -5.40195584... | [9.725305557250977, 9.576695442199707] |
87e4bf19-ae1b-4a5f-aecb-24338ec9dec8 | qpic-query-based-pairwise-human-object | 2103.05399 | null | https://arxiv.org/abs/2103.05399v1 | https://arxiv.org/pdf/2103.05399v1.pdf | QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information | We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection. HOIs are so diverse in spatial distribution in an image that existing CNN-based methods face the following three major drawbacks; they cannot leverage image-wide features due to CNN's locality, they rely on a manually defin... | ['Tomoaki Yoshinaga', 'Hiroki Ohashi', 'Masato Tamura'] | 2021-03-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Tamura_QPIC_Query-Based_Pairwise_Human-Object_Interaction_Detection_With_Image-Wide_Contextual_Information_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Tamura_QPIC_Query-Based_Pairwise_Human-Object_Interaction_Detection_With_Image-Wide_Contextual_Information_CVPR_2021_paper.pdf | cvpr-2021-1 | ['human-object-interaction-concept-discovery'] | ['computer-vision'] | [-2.92732954e-01 -2.21197903e-01 -1.12912543e-01 -1.17010951e-01
-9.52901125e-01 -3.17905486e-01 5.31119525e-01 1.40824825e-01
-5.78389764e-01 4.72042471e-01 3.46248478e-01 -9.82496366e-02
-2.21674234e-01 -7.97176063e-01 -5.61256230e-01 -7.98309803e-01
-2.27643088e-01 2.18648046e-01 4.50150579e-01 -1.56249568... | [9.512704849243164, 1.2649269104003906] |
2bd2e6be-8fa7-4952-b87e-1baa271b5d48 | automated-hypothesis-generation-via | null | null | https://openreview.net/forum?id=PnraKzlFvp | https://openreview.net/pdf?id=PnraKzlFvp | Automated hypothesis generation via Evolutionary Abduction | Abduction is a powerful form of causal inference employed in many artificial intelligence tasks, such as medical diagnosis, criminology, root cause analysis, intent recognition. Given an effect, the abductive reasoning allows advancing a plausible set of explanatory hypotheses for its causes. This paper presents a new ... | ['Roberto Pietrantuono'] | 2021-09-29 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 5.00317276e-01 7.55903184e-01 -4.87668604e-01 -4.35179353e-01
-2.55773753e-01 -2.73222089e-01 8.35241258e-01 4.17758524e-01
2.55511720e-02 1.11596000e+00 4.13860947e-01 -7.37396538e-01
-9.82521772e-01 -1.01926017e+00 -7.42438078e-01 -3.28246713e-01
-4.71439928e-01 9.45732832e-01 4.59211320e-02 -2.03464627... | [8.684447288513184, 5.654546737670898] |
749a94b6-8a17-46a7-a67c-724070dffead | explore-image-deblurring-via-encoded-blur | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.pdf | Explore Image Deblurring via Encoded Blur Kernel Space | This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an uns... | ['Minh Hoai', 'Quynh Phung', 'Anh Tuan Tran', 'Phong Tran'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['blind-image-deblurring'] | ['computer-vision'] | [ 7.39692226e-02 -2.86075622e-01 3.71195972e-01 -2.99697608e-01
-2.99966305e-01 -5.41982830e-01 2.98093081e-01 -7.02444971e-01
-2.78403997e-01 7.86980569e-01 3.92666847e-01 -1.17447570e-01
-2.60021597e-01 -3.27342808e-01 -7.92081594e-01 -8.22061598e-01
3.30785573e-01 -1.98799506e-01 -5.80122285e-02 2.15430647... | [11.584650993347168, -2.7253878116607666] |
1bc1162c-592c-4b2a-b9ca-b50358e4baef | large-language-models-are-not-abstract | 2305.19555 | null | https://arxiv.org/abs/2305.19555v1 | https://arxiv.org/pdf/2305.19555v1.pdf | Large Language Models Are Not Abstract Reasoners | Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However, the mechanisms responsible for this success remain unknown, and it is unclear whether LLMs can achieve human-like cognitive capabilities or... | ['Gillian Dobbie', 'Michael Witbrock', 'Qiming Bao', 'Gaël Gendron'] | 2023-05-31 | null | null | null | null | ['reading-comprehension', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 1.56320244e-01 5.00153482e-01 9.04324204e-02 -3.17334265e-01
-4.51291114e-01 -5.73571980e-01 1.05318892e+00 4.60424304e-01
-6.57755136e-01 5.93501389e-01 3.81811321e-01 -7.45930076e-01
-4.60217565e-01 -7.01394439e-01 -4.92893815e-01 -2.53442526e-01
8.12137797e-02 8.80057454e-01 4.52714801e-01 -3.75797570... | [9.588494300842285, 7.3509840965271] |
93c7d141-1b56-45be-9050-763d59f5f364 | pose-recognition-in-the-wild-animal-pose | 2111.08259 | null | https://arxiv.org/abs/2111.08259v1 | https://arxiv.org/pdf/2111.08259v1.pdf | Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning | Animal pose estimation has recently come into the limelight due to its application in biology, zoology, and aquaculture. Deep learning methods have effectively been applied to human pose estimation. However, the major bottleneck to the application of these methods to animal pose estimation is the unavailability of suff... | ['Sk Shahnawaz', 'Samayan Bhattacharya'] | 2021-11-16 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.52749091e-01 -8.87111854e-03 1.42034382e-01 -2.54249305e-01
-2.94789106e-01 -5.70088208e-01 4.68892545e-01 2.96684861e-01
-9.72735584e-01 5.95043659e-01 -2.67513245e-01 -2.14993209e-02
-6.08018413e-02 -6.47710621e-01 -7.84764171e-01 -7.58918524e-01
-2.94188678e-01 6.79760635e-01 3.77918392e-01 -1.17655033... | [7.6866888999938965, -0.9599111080169678] |
fd5c0912-e678-4986-992f-46af9def4bcf | mt-cgcnn-integrating-crystal-graph | 1811.05660 | null | http://arxiv.org/abs/1811.05660v1 | http://arxiv.org/pdf/1811.05660v1.pdf | MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction | Developing accurate, transferable and computationally inexpensive machine
learning models can rapidly accelerate the discovery and development of new
materials. Some of the major challenges involved in developing such models are,
(i) limited availability of materials data as compared to other fields, (ii)
lack of unive... | ['Abhishek Kumar', 'Naganand Yadati', 'Soumya Sanyal', 'Partha Talukdar', 'Janakiraman Balachandran', 'Suchismita Sanyal', 'Padmini Rajagopalan'] | 2018-11-14 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 1.85524598e-01 -3.50562394e-01 -2.29653135e-01 -3.96396220e-02
-9.97858465e-01 -2.22702846e-01 4.32485878e-01 2.61675835e-01
-1.07266620e-01 1.02134132e+00 -1.58794448e-01 -3.01443368e-01
-1.95022240e-01 -1.07084203e+00 -9.32153821e-01 -1.00216079e+00
1.15113623e-01 5.45814335e-01 3.05718064e-01 -2.64887899... | [5.242095470428467, 5.449158191680908] |
fe5d0f5b-4be2-4815-9fab-9225f5b21dc6 | moss-monocular-shape-sensing-for-continuum | 2303.00891 | null | https://arxiv.org/abs/2303.00891v2 | https://arxiv.org/pdf/2303.00891v2.pdf | MoSS: Monocular Shape Sensing for Continuum Robots | Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge. Embedded shape sensing has high hardware complexity and ... | ['Jessica Burgner-Kahrs', 'David B. Lindell', 'Puspita Triana Dewi', 'Chaojun Chen', 'Enxu Li', 'Chengnan Shentu'] | 2023-03-02 | null | null | null | null | ['camera-calibration', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 1.27889559e-01 -2.28148606e-02 2.08170973e-02 8.32161680e-03
-7.91569591e-01 -6.50519490e-01 -2.11549029e-01 7.36574605e-02
-3.76847476e-01 3.31096470e-01 -6.07865095e-01 -3.05164278e-01
1.72129467e-01 -4.10395503e-01 -8.35884333e-01 -4.71517146e-01
1.60468608e-01 6.28661513e-01 3.36079568e-01 -9.20908973... | [6.217565536499023, -1.0098737478256226] |
36f76b5d-6056-467b-a3a8-035b582c3a03 | revealing-weaknesses-of-vietnamese-language | 2303.13355 | null | https://arxiv.org/abs/2303.13355v1 | https://arxiv.org/pdf/2303.13355v1.pdf | Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension | Although the curse of multilinguality significantly restricts the language abilities of multilingual models in monolingual settings, researchers now still have to rely on multilingual models to develop state-of-the-art systems in Vietnamese Machine Reading Comprehension. This difficulty in researching is because of the... | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Phong Nguyen-Thuan Do', 'Son Quoc Tran'] | 2023-03-16 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.59393579e-01 3.82983536e-01 -7.54342005e-02 -3.39856327e-01
-1.21522868e+00 -8.84301782e-01 4.46130127e-01 5.55087626e-01
-7.03394532e-01 7.78697908e-01 5.32322943e-01 -9.92875457e-01
8.19856599e-02 -7.67941236e-01 -7.95162082e-01 -5.35711162e-02
5.67799926e-01 5.92864990e-01 6.40082583e-02 -8.51615846... | [11.309456825256348, 8.394969940185547] |
1957db81-6171-4b07-99a0-e18692f788e5 | context-guided-triple-matching-for-multiple-1 | null | null | https://openreview.net/forum?id=3fBNtKp72iV | https://openreview.net/pdf?id=3fBNtKp72iV | Context-guided Triple Matching for Multiple Choice Question Answering | The task of multiple choice question answering (MCQA) refers to identifying a suitable answer from multiple candidates, by estimating the matching score among the \emph{triple} of the passage, question and answer. Despite the general research interest in this regard, existing methods decouple the process into several p... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.2370108e-01 -8.4471449e-02 1.4359394e-01 -4.4738337e-01
-1.4792269e+00 -6.0248142e-01 4.5229784e-01 3.7264374e-01
-6.3789165e-01 4.9942115e-01 2.5037694e-01 -1.8953741e-01
-3.2708156e-01 -4.5352721e-01 -4.9439967e-01 -6.8612784e-01
7.1021557e-01 4.0329641e-01 5.5966985e-01 -2.9485735e-01
6.3773990e-01... | [11.395598411560059, 8.036478042602539] |
74b5b1b1-6070-42b4-9ded-52455784d20d | utterance-weighted-multi-dilation-temporal | 2205.08455 | null | https://arxiv.org/abs/2205.08455v3 | https://arxiv.org/pdf/2205.08455v3.pdf | Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation | Speech dereverberation is an important stage in many speech technology applications. Recent work in this area has been dominated by deep neural network models. Temporal convolutional networks (TCNs) are deep learning models that have been proposed for sequence modelling in the task of dereverberating speech. In this wo... | ['Thomas Hain', 'Stefan Goetze', 'William Ravenscroft'] | 2022-05-17 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.97946638e-01 -7.89498836e-02 1.65372714e-01 -2.84439981e-01
-2.92255819e-01 -2.70447075e-01 6.23737037e-01 -5.04965484e-01
-6.09423041e-01 1.59949720e-01 6.23350799e-01 -8.29307258e-01
-4.71131951e-02 -2.44180456e-01 -4.76457000e-01 -9.41173613e-01
-1.18040852e-01 -2.39093989e-01 5.39148808e-01 -2.46454760... | [14.79050350189209, 5.968505382537842] |
da13f8a0-1ff0-4698-a188-d2eb9020409e | fastaudio-a-learnable-audio-front-end-for | 2109.02774 | null | https://arxiv.org/abs/2109.02774v1 | https://arxiv.org/pdf/2109.02774v1.pdf | FastAudio: A Learnable Audio Front-End for Spoof Speech Detection | Voice assistants, such as smart speakers, have exploded in popularity. It is currently estimated that the smart speaker adoption rate has exceeded 35% in the US adult population. Manufacturers have integrated speaker identification technology, which attempts to determine the identity of the person speaking, to provide ... | ['Douglas C. Schmidt', 'Maria Powell', 'Jules White', 'Zhongwei Teng', 'Quchen Fu'] | 2021-09-06 | null | null | null | null | ['voice-anti-spoofing', 'speaker-identification'] | ['audio', 'speech'] | [ 2.62213141e-01 1.16899811e-01 -4.20826860e-02 -5.56725442e-01
-8.75509381e-01 -7.30688393e-01 2.01226249e-01 -3.26284170e-02
-4.44379777e-01 3.86143565e-01 2.98880816e-01 -4.78821874e-01
4.02233079e-02 -4.88700271e-01 -4.83664811e-01 -6.88564360e-01
-3.10472981e-03 4.62026238e-01 4.93441485e-02 -6.62726834... | [14.168641090393066, 5.954559803009033] |
81b3fcb2-2368-4d8a-a05a-2bcd0993c79f | a-simple-episodic-linear-probe-improves | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liang_A_Simple_Episodic_Linear_Probe_Improves_Visual_Recognition_in_the_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liang_A_Simple_Episodic_Linear_Probe_Improves_Visual_Recognition_in_the_CVPR_2022_paper.pdf | A Simple Episodic Linear Probe Improves Visual Recognition in the Wild | Understanding network generalization and feature discrimination is an open research problem in visual recognition. Many studies have been conducted to assess the quality of feature representations. One of the simple strategies is to utilize a linear probing classifier to quantitatively evaluate the class accuracy u... | ['Yi Yang', 'Xiaohan Wang', 'Linchao Zhu', 'Yuanzhi Liang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.69890746e-01 -3.93617004e-01 -5.87636709e-01 -6.54090881e-01
-4.33470577e-01 -5.11183679e-01 5.69864154e-01 -1.07025154e-01
-4.11741227e-01 5.07805228e-01 -1.48895472e-01 -1.68367639e-01
-3.97543758e-01 -6.90040827e-01 -5.98838508e-01 -9.79226589e-01
-2.43818119e-01 4.83949147e-02 2.68791348e-01 2.56545544... | [9.616520881652832, 2.661231756210327] |
4f98e676-c905-474b-95b5-9889faa839a1 | analyzing-the-generalizability-of-deep | 2303.12936 | null | https://arxiv.org/abs/2303.12936v1 | https://arxiv.org/pdf/2303.12936v1.pdf | Analyzing the Generalizability of Deep Contextualized Language Representations For Text Classification | This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification and sentiment analysis of product reviews. A "cross-context" setting is enabled using test sets that are distinct from the training data... | ['Berfu Buyukoz'] | 2023-03-22 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-6.06617443e-02 -7.13081509e-02 -5.95586240e-01 -7.48238862e-01
-7.21101165e-01 -6.73192918e-01 8.08131158e-01 3.32600683e-01
-6.21464312e-01 7.51331806e-01 1.81384027e-01 -7.14909613e-01
4.77047339e-02 -8.12707543e-01 -6.78946733e-01 -3.81133288e-01
1.06122367e-01 5.74636340e-01 -1.50819704e-01 -6.48058236... | [11.071942329406738, 7.205007553100586] |
d76dd62d-0ec5-46b3-8e74-188ffd715277 | self-supervised-robustifying-guidance-for | 2112.14382 | null | https://arxiv.org/abs/2112.14382v3 | https://arxiv.org/pdf/2112.14382v3.pdf | Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction | Despite the recent developments in 3D Face Reconstruction from occluded and noisy face images, the performance is still unsatisfactory. Moreover, most existing methods rely on additional dependencies, posing numerous constraints over the training procedure. Therefore, we propose a Self-Supervised RObustifying GUidancE ... | ['Yong-Sheng Chen', 'K. S. Venkatesh', 'Kevin Jou', 'Hung-Jen Chen', 'Hsien-Kai Kuo', 'Yi-Min Tsai', 'Min-Hung Chen', 'Hitika Tiwari'] | 2021-12-29 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-6.81896433e-02 8.69126245e-03 3.89405131e-01 -7.35297978e-01
-9.76449072e-01 -3.83212388e-01 5.11162460e-01 -5.29698014e-01
1.04729859e-02 4.58629519e-01 -2.94748023e-02 9.61004049e-02
-1.82293862e-01 -4.27613944e-01 -7.62907386e-01 -9.66257036e-01
-8.15729871e-02 1.97096676e-01 -4.80750322e-01 -3.27378437... | [12.993574142456055, 0.27214401960372925] |
5508f1db-6d75-4c2a-89f8-17f983a3a66d | memory-based-dual-gaussian-processes-for | 2306.03566 | null | https://arxiv.org/abs/2306.03566v1 | https://arxiv.org/pdf/2306.03566v1.pdf | Memory-Based Dual Gaussian Processes for Sequential Learning | Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters, and inducing points, making accurate learning challenging. Here, we p... | ['Mohammad Emtiyaz Khan', 'Arno Solin', 'S. T. John', 'Prakhar Verma', 'Paul E. Chang'] | 2023-06-06 | null | null | null | null | ['active-learning', 'gaussian-processes', 'bayesian-optimization', 'active-learning'] | ['methodology', 'methodology', 'methodology', 'natural-language-processing'] | [-6.11113459e-02 -6.89922720e-02 -2.13574007e-01 -2.83896029e-01
-1.20370829e+00 -4.28224891e-01 7.16360092e-01 5.81480801e-01
-5.93831956e-01 1.27122939e+00 1.95631515e-02 -3.55623662e-02
-3.38073581e-01 -7.84996569e-01 -1.03628147e+00 -8.61252487e-01
-7.64622912e-02 7.99432874e-01 2.54288584e-01 3.31988752... | [6.958805561065674, 3.8535611629486084] |
90d8f48f-3fac-46b9-9873-45a3106d6de3 | extracting-clinical-concepts-from-user | 1912.06262 | null | https://arxiv.org/abs/1912.06262v2 | https://arxiv.org/pdf/1912.06262v2.pdf | Extracting clinical concepts from user queries | Clinical concept extraction often begins with clinical Named Entity Recognition (NER). Often trained on annotated clinical notes, clinical NER models tend to struggle with tagging clinical entities in user queries because of the structural differences between clinical notes and user queries. User queries, unlike clinic... | ['Yue Zhao', 'John Handley'] | 2019-12-12 | null | null | null | null | ['clinical-concept-extraction'] | ['medical'] | [-3.12785767e-02 4.91573870e-01 -1.02651842e-01 -3.34989130e-01
-1.41031611e+00 -6.48983061e-01 6.49492443e-02 9.96942163e-01
-7.79969573e-01 8.41779709e-01 6.31632805e-01 -5.85554183e-01
-7.38773821e-03 -5.56822479e-01 6.30457848e-02 -8.51047859e-02
-1.68409362e-01 1.04513669e+00 -4.78366315e-02 2.23256983... | [8.45869255065918, 8.753642082214355] |
7ac30b45-d210-4be2-8c33-44f8e53e17b6 | borex-bayesian-optimization-based-refinement | 2210.17130 | null | https://arxiv.org/abs/2210.17130v1 | https://arxiv.org/pdf/2210.17130v1.pdf | BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models | Explaining a classification result produced by an image- and video-classification model is one of the important but challenging issues in computer vision. Many methods have been proposed for producing heat-map--based explanations for this purpose, including ones based on the white-box approach that uses the internal in... | ['Kohei Suenaga', 'Masaki Waga', 'Kotaro Uchida', 'Atsushi Kikuchi'] | 2022-10-31 | null | null | null | null | ['video-classification', 'gpr', 'gpr'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.57809481e-01 4.42316562e-01 1.16066495e-02 -5.00344813e-01
-3.52656603e-01 -3.32313664e-02 7.05271363e-01 2.18419135e-01
7.68948123e-02 3.82623792e-01 -3.39701353e-03 -3.65076929e-01
-1.24515086e-01 -5.06492555e-01 -8.64582777e-01 -8.06597412e-01
3.58014941e-01 3.67971003e-01 3.50093186e-01 -1.17357532... | [9.967728614807129, 2.18422794342041] |
f29bffa6-0407-4c00-8096-27a301e2f212 | synthesis-of-contrast-enhanced-breast-mri | 2307.00895 | null | https://arxiv.org/abs/2307.00895v1 | https://arxiv.org/pdf/2307.00895v1.pdf | Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism | Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue, and has become an indispensable technique in the detection and evaluation of... | ['Ritse Mann', 'Tao Tan', 'Regina Beets-Tan', 'Jonas Teuwen', 'Chunyao Lu', 'Yuan Gao', 'Xin Wang', "Anna D'Angelo", 'Luyi Han', 'Tianyu Zhang'] | 2023-07-03 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.38330299e-01 -1.30660817e-01 -2.26638332e-01 -1.15051523e-01
-7.14620411e-01 -2.32353047e-01 5.55708230e-01 2.75933772e-01
-5.05527854e-01 4.90512371e-01 2.54483610e-01 -3.79242569e-01
-3.01320881e-01 -8.05167258e-01 -6.93862289e-02 -1.03042853e+00
-2.41325140e-01 4.06751454e-01 1.84767872e-01 -4.33733761... | [14.405495643615723, -2.4221982955932617] |
21078ba9-b9f1-4088-aca2-71c9ced730da | tsp-temporally-sensitive-pretraining-of-video | 2011.11479 | null | https://arxiv.org/abs/2011.11479v3 | https://arxiv.org/pdf/2011.11479v3.pdf | TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks | Due to the large memory footprint of untrimmed videos, current state-of-the-art video localization methods operate atop precomputed video clip features. These features are extracted from video encoders typically trained for trimmed action classification tasks, making such features not necessarily suitable for temporal ... | ['Bernard Ghanem', 'Silvio Giancola', 'Humam Alwassel'] | 2020-11-23 | null | null | null | null | ['temporal-action-proposal-generation', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 5.57332158e-01 -5.31342268e-01 -6.90922797e-01 -4.08793241e-01
-1.09867489e+00 -4.78993863e-01 5.10928869e-01 -1.92697287e-01
-5.17915666e-01 6.60021126e-01 5.05782723e-01 1.42464027e-01
1.77629381e-01 -2.45944530e-01 -9.78383124e-01 -5.99885643e-01
-6.20865881e-01 -6.89124968e-03 6.56521440e-01 2.63000935... | [8.513266563415527, 0.6222942471504211] |
1672c094-565a-4b29-a443-8baa60841e32 | norma-neighborhood-sensitive-maps-for | null | null | https://aclanthology.org/D18-1047 | https://aclanthology.org/D18-1047.pdf | NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings | Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages. However, accuracy drops substantially when translating between distant languages. Given that languages exhibit differences in vocabulary, grammar, written for... | ['Ndapa Nakashole'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['multilingual-word-embeddings'] | ['methodology'] | [-5.35039186e-01 -1.97428495e-01 -5.52405953e-01 -2.67862737e-01
-9.75419581e-01 -9.82130110e-01 8.04382503e-01 2.34331220e-01
-4.97372627e-01 6.16696000e-01 8.01209331e-01 -6.01527393e-01
1.70231298e-01 -7.71984041e-01 -6.46872461e-01 -1.40822038e-01
1.91088513e-01 5.89781642e-01 -1.33596748e-01 -6.17332637... | [11.006823539733887, 9.988432884216309] |
cd95533e-756c-4c8f-8ff1-854f69692f22 | end-to-end-spoken-language-understanding-3 | 2207.08179 | null | https://arxiv.org/abs/2207.08179v1 | https://arxiv.org/pdf/2207.08179v1.pdf | End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting | Spoken Language Understanding (SLU) is a core task in most human-machine interaction systems. With the emergence of smart homes, smart phones and smart speakers, SLU has become a key technology for the industry. In a classical SLU approach, an Automatic Speech Recognition (ASR) module transcribes the speech signal into... | ['Michel Vacher', 'François Portet', 'Thierry Desot'] | 2022-07-17 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.67849854e-01 3.60206753e-01 2.56303728e-01 -5.80007851e-01
-6.17708385e-01 -3.93450826e-01 5.04505157e-01 -7.58384690e-02
-2.97935754e-01 2.71213651e-01 5.44682264e-01 -4.23553884e-01
2.76819348e-01 -5.17872930e-01 -3.11129659e-01 -2.67540574e-01
2.80731115e-02 3.68242621e-01 -7.00135827e-02 -4.45527554... | [14.095683097839355, 6.873390197753906] |
de8288b5-11bd-4630-9c85-faa8f47cc134 | metric-learning-vs-classification-for | 2008.03729 | null | https://arxiv.org/abs/2008.03729v2 | https://arxiv.org/pdf/2008.03729v2.pdf | Metric Learning vs Classification for Disentangled Music Representation Learning | Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two central methods for representation learning include deep metric learning and classification, both having the same goal of learning a representati... | ['Nicholas J. Bryan', 'Justin Salamon', 'Juhan Nam', 'Zeyu Jin', 'Jongpil Lee'] | 2020-08-09 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [ 2.88770735e-01 -3.53596777e-01 -4.62963611e-01 -3.62665087e-01
-1.12486708e+00 -8.85742724e-01 5.44288039e-01 2.39206716e-01
-2.01523438e-01 3.52348506e-01 4.97601599e-01 2.11200207e-01
-7.85580873e-01 -5.91482759e-01 8.65942892e-03 -6.79939032e-01
-2.28346452e-01 5.58572888e-01 -3.52593511e-01 -3.18683863... | [15.74463176727295, 5.138858318328857] |
6619ed07-8961-4792-9259-ca6c70245efe | tri-axial-self-attention-for-concurrent | 1812.02817 | null | http://arxiv.org/abs/1812.02817v1 | http://arxiv.org/pdf/1812.02817v1.pdf | Tri-axial Self-Attention for Concurrent Activity Recognition | We present a system for concurrent activity recognition. To extract features
associated with different activities, we propose a feature-to-activity
attention that maps the extracted global features to sub-features associated
with individual activities. To model the temporal associations of individual
activities, we pro... | ['Kaixiang Huang', 'Xinyu Li', 'Ivan Marsic', 'Yehan Wang', 'Yanyi Zhang', 'Shuhong Chen'] | 2018-12-06 | null | null | null | null | ['concurrent-activity-recognition', 'activity-prediction', 'activity-prediction'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 5.16273022e-01 -1.17237419e-01 -3.14006895e-01 -4.73326743e-01
-4.92427438e-01 -3.83784771e-01 7.77259052e-01 2.98066437e-01
-2.88057089e-01 5.07003903e-01 4.21715975e-01 -6.44959435e-02
-3.46444845e-01 -6.57942057e-01 -6.51332080e-01 -5.09141743e-01
-8.00354481e-01 2.81606734e-01 4.19372648e-01 1.53469056... | [8.278986930847168, 0.6972343921661377] |
8b6becde-f8f6-4b1d-90d2-57a288817f55 | empowering-relational-network-by-self | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/410_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460069.pdf | Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition | This paper presents a novel relational network for group activity recognition. The core of our network is to augment the conditional random fields (CRF), amenable to learning inter-dependency of correlated observations, with the newly devised temporal and spatial self-attention to learn the temporal evolution and spati... | ['Wen Hsien Fang', 'Yie Tarng Chen', 'Rizard Renanda Adhi Pramono'] | null | null | null | null | eccv-2020-8 | ['group-activity-recognition'] | ['computer-vision'] | [-7.96631072e-03 -1.36536717e-01 -1.80614606e-01 -4.93145198e-01
-1.66985840e-01 -2.30642408e-01 8.55559051e-01 1.06687360e-01
-3.01459253e-01 5.68729579e-01 4.73650336e-01 1.01411752e-01
-5.77481687e-01 -7.23048270e-01 -8.32508206e-01 -8.99119556e-01
-5.12863219e-01 3.07866275e-01 3.51740569e-01 -6.79030791... | [8.399045944213867, 0.7013359069824219] |
c4be4a34-8c1e-4a23-af11-ea9cabde8c07 | a-critical-analysis-of-image-based-camera | 2201.05816 | null | https://arxiv.org/abs/2201.05816v1 | https://arxiv.org/pdf/2201.05816v1.pdf | A Critical Analysis of Image-based Camera Pose Estimation Techniques | Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR). In this survey, we first introduce specific application areas and the evaluation metrics for camera localization pose accord... | ['Pengfei Xu', 'Stefan Poslad', 'Jian Ren', 'Jun Zhang', 'Bin Xu', 'Youchen Wang', 'Meng Xu'] | 2022-01-15 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-7.06744269e-02 -1.17970422e-01 -4.13813412e-01 -4.03173178e-01
-9.26450908e-01 -7.94569016e-01 6.08423293e-01 -1.24738842e-01
-5.83650231e-01 3.73290658e-01 1.95387322e-02 -8.08590874e-02
-2.16493279e-01 -7.66895562e-02 -6.99345350e-01 -5.20530045e-01
2.67980583e-02 4.79569703e-01 1.58536926e-01 1.47247612... | [7.565536022186279, -2.1579835414886475] |
d1609fb7-e952-4e50-afec-80b0ce7329c3 | representation-power-of-graph-convolutions | 2210.09809 | null | https://arxiv.org/abs/2210.09809v2 | https://arxiv.org/pdf/2210.09809v2.pdf | Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel | The fundamental principle of Graph Neural Networks (GNNs) is to exploit the structural information of the data by aggregating the neighboring nodes using a `graph convolution' in conjunction with a suitable choice for the network architecture, such as depth and activation functions. Therefore, understanding the influen... | ['Debarghya Ghoshdastidar', 'Pascal Esser', 'Mahalakshmi Sabanayagam'] | 2022-10-18 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 5.11225313e-02 2.85481513e-01 -1.14138857e-01 -8.29976276e-02
2.89317310e-01 -6.23444021e-01 6.08838022e-01 3.54377091e-01
-5.12925327e-01 4.76372242e-01 -3.92314158e-02 -5.02472937e-01
-4.90657032e-01 -1.05200982e+00 -8.20727170e-01 -1.13425994e+00
-3.76459688e-01 2.25303739e-01 3.18077922e-01 -3.27072978... | [6.823948860168457, 6.033977031707764] |
aaaf68bf-bbfc-4212-b5ac-625bd8f67ccf | 3d-human-pose-estimation-under-limited | 1908.05293 | null | https://arxiv.org/abs/1908.05293v3 | https://arxiv.org/pdf/1908.05293v3.pdf | Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation | The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire. To reduce this annotation dependency, we propose Multiview-Consistent Semi Supervised Learning (... | ['Nitesh B. Gundavarapu', 'Rahul Mitra', 'Arjun Jain', 'Abhishek Sharma'] | 2019-08-14 | multiview-consistent-semi-supervised-learning | http://openaccess.thecvf.com/content_CVPR_2020/html/Mitra_Multiview-Consistent_Semi-Supervised_Learning_for_3D_Human_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mitra_Multiview-Consistent_Semi-Supervised_Learning_for_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['pose-retrieval'] | ['computer-vision'] | [-1.27921402e-01 -1.88069552e-01 -3.27701807e-01 -4.77711827e-01
-8.83773386e-01 -6.08116269e-01 3.80087316e-01 -4.13630605e-01
-6.70979857e-01 5.24047971e-01 4.90747273e-01 7.59645641e-01
1.73666865e-01 -3.91686447e-02 -8.06193769e-01 -2.51796961e-01
-1.36041820e-01 8.49689960e-01 1.15395218e-01 -2.85046011... | [7.028799533843994, -0.8625867962837219] |
1e019abc-ec62-4834-88da-82d9ac5b1ab7 | cross-attention-guided-dense-network-for | 2109.11393 | null | https://arxiv.org/abs/2109.11393v2 | https://arxiv.org/pdf/2109.11393v2.pdf | Cross Attention-guided Dense Network for Images Fusion | In recent years, various applications in computer vision have achieved substantial progress based on deep learning, which has been widely used for image fusion and shown to achieve adequate performance. However, suffering from limited ability in modeling the spatial correspondence of different source images, it still r... | ['Jiangyu Wang', 'Yulian Li', 'Zaiyu Pan', 'Jun Wang', 'Zhengwen Shen'] | 2021-09-23 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 1.54419914e-01 -3.34826678e-01 3.19953039e-02 -3.38265091e-01
-1.03581250e+00 8.24977681e-02 5.02808988e-01 -1.61116961e-02
-3.28823030e-01 4.54026818e-01 3.89138281e-01 1.60377860e-01
-3.04742426e-01 -7.26215661e-01 -6.22754097e-01 -9.64090288e-01
5.18740058e-01 -9.58245061e-03 2.61377960e-01 -3.06928456... | [10.550753593444824, -1.83033287525177] |
77ff703c-cff0-4108-a004-6a2a61425c6c | t-ner-an-all-round-python-library-for-1 | 2209.12616 | null | https://arxiv.org/abs/2209.12616v1 | https://arxiv.org/pdf/2209.12616v1.pdf | T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition | Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the stu... | ['Jose Camacho-Collados', 'Asahi Ushio'] | 2022-09-09 | t-ner-an-all-round-python-library-for | https://aclanthology.org/2021.eacl-demos.7 | https://aclanthology.org/2021.eacl-demos.7.pdf | eacl-2021-2 | ['face-model'] | ['computer-vision'] | [-3.51087362e-01 1.09058106e-02 -1.40580878e-01 -5.17882645e-01
-1.36006749e+00 -1.26168442e+00 3.66353571e-01 3.13820802e-02
-7.29510248e-01 7.56904662e-01 4.53208864e-01 -4.90182549e-01
1.76808108e-02 -4.77384597e-01 -5.30881584e-01 -1.84069723e-02
2.35609010e-01 5.68575442e-01 1.42197143e-02 -2.58956611... | [10.063929557800293, 9.659943580627441] |
692f717b-5631-4405-8292-6878e05358c3 | srlgrn-semantic-role-labeling-graph-reasoning | 2010.03604 | null | https://arxiv.org/abs/2010.03604v2 | https://arxiv.org/pdf/2010.03604v2.pdf | SRLGRN: Semantic Role Labeling Graph Reasoning Network | This work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly. The proposed graph is a heterogeneous ... | ['Parisa Kordjamshidi', 'Chen Zheng'] | 2020-10-07 | null | https://aclanthology.org/2020.emnlp-main.714 | https://aclanthology.org/2020.emnlp-main.714.pdf | emnlp-2020-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.32581180e-02 9.11207318e-01 -2.91974485e-01 -5.06945312e-01
-8.26663911e-01 -7.74096668e-01 2.86243230e-01 8.81115377e-01
6.79417849e-02 7.95831382e-01 6.43046260e-01 -5.33216238e-01
-5.77363968e-01 -1.11674154e+00 -9.64213252e-01 -6.32507205e-02
-1.27860963e-01 9.20648813e-01 9.93075430e-01 -6.55338407... | [10.686893463134766, 7.906129360198975] |
255fc505-0707-4f78-b917-808403bdfa47 | few-shot-human-motion-prediction-for | 2212.11771 | null | https://arxiv.org/abs/2212.11771v2 | https://arxiv.org/pdf/2212.11771v2.pdf | Few-shot human motion prediction for heterogeneous sensors | Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning, where we strive to forecast motion sequences for previously unseen actions based on only a few examples. Despite this, almost all related ap... | ['Lars Schmidt-Thieme', 'Lukas Brinkmeyer', 'Rafael Rego Drumond'] | 2022-12-22 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 3.04123461e-01 1.97416708e-01 -3.96042347e-01 8.02513584e-02
-5.58050215e-01 -2.99530178e-02 6.93291485e-01 2.24611517e-02
-1.48527801e-01 3.78040701e-01 5.59124887e-01 -3.33732069e-02
9.11756456e-02 -7.43838966e-01 -8.22012544e-01 -5.37137568e-01
-3.09907943e-01 2.84152508e-01 9.10864592e-01 -3.66051108... | [7.529101371765137, 0.024860821664333344] |
34403cd6-2ada-46e3-97bb-190ab65737ff | luminance-attentive-networks-for-hdr-image | 2109.06688 | null | https://arxiv.org/abs/2109.06688v1 | https://arxiv.org/pdf/2109.06688v1.pdf | Luminance Attentive Networks for HDR Image and Panorama Reconstruction | It is very challenging to reconstruct a high dynamic range (HDR) from a low dynamic range (LDR) image as an ill-posed problem. This paper proposes a luminance attentive network named LANet for HDR reconstruction from a single LDR image. Our method is based on two fundamental observations: (1) HDR images stored in relat... | ['Chunxia Xiao', 'Qin Zou', 'Bo Dong', 'Chengjiang Long', 'Wentao Liu', 'Hanning Yu'] | 2021-09-14 | null | null | null | null | ['hdr-reconstruction', 'tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.19934398e-01 -3.11364055e-01 -2.02165898e-02 -1.41684696e-01
-2.99096912e-01 -2.22017527e-01 3.40702504e-01 -6.50684297e-01
-2.44051903e-01 8.02626669e-01 1.67578965e-01 -2.58805603e-01
1.18488364e-01 -1.14674556e+00 -7.56040931e-01 -6.02806151e-01
3.11450213e-01 -2.64962137e-01 2.47679651e-01 -5.87655365... | [10.897172927856445, -2.230468988418579] |
1ce02ac1-1e75-4686-810e-30ad1439e33d | dadagger-disagreement-augmented-dataset | 2301.01348 | null | https://arxiv.org/abs/2301.01348v1 | https://arxiv.org/pdf/2301.01348v1.pdf | DADAgger: Disagreement-Augmented Dataset Aggregation | DAgger is an imitation algorithm that aggregates its original datasets by querying the expert on all samples encountered during training. In order to reduce the number of samples queried, we propose a modification to DAgger, known as DADAgger, which only queries the expert for state-action pairs that are out of distrib... | ['Samarendra Chandan Bindu Dash', 'Karim Hamadeh', 'Akash Haridas'] | 2023-01-03 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-1.98906228e-01 4.39448118e-01 -4.82405007e-01 -4.20552760e-01
-1.30968463e+00 -8.74430835e-01 4.91123110e-01 -1.60073414e-01
-6.43943906e-01 1.05909801e+00 -1.35417342e-01 -3.91394049e-01
-8.07364136e-02 -5.50163329e-01 -1.25561607e+00 -4.96257216e-01
-8.14512521e-02 1.13894129e+00 4.13574159e-01 3.33087772... | [4.098577499389648, 2.1370291709899902] |
61766279-5f07-475d-a9e1-ffcec1822d95 | proseqo-projection-sequence-networks-for-on | null | null | https://aclanthology.org/D19-1402 | https://aclanthology.org/D19-1402.pdf | ProSeqo: Projection Sequence Networks for On-Device Text Classification | We propose a novel on-device sequence model for text classification using recurrent projections. Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings. This results in fast and compact neural networks that can perform on-device inference for complex short a... | ['Zornitsa Kozareva', 'Sujith Ravi'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['product-categorization'] | ['miscellaneous'] | [ 1.49660736e-01 6.59757331e-02 -6.02199912e-01 -6.71827853e-01
-4.92002547e-01 -5.10021746e-01 6.81858480e-01 4.63178307e-01
-5.71199417e-01 2.62631088e-01 5.27873039e-01 -8.08748960e-01
2.14407235e-01 -5.08822203e-01 -4.38570708e-01 -4.34903428e-02
5.33082187e-01 6.54026031e-01 4.09475043e-02 -1.80343971... | [10.776817321777344, 7.841664791107178] |
65c50443-02b5-4bdb-a100-b589afc4057c | dynasp25-dynamic-programming-on-tree | 1706.09370 | null | http://arxiv.org/abs/1706.09370v1 | http://arxiv.org/pdf/1706.09370v1.pdf | DynASP2.5: Dynamic Programming on Tree Decompositions in Action | A vibrant theoretical research area are efficient exact parameterized
algorithms. Very recent solving competitions such as the PACE challenge show
that there is also increasing practical interest in the parameterized
algorithms community. An important research question is whether dedicated
parameterized exact algorithm... | ['Johannes K. Fichte', 'Stefan Woltran', 'Markus Hecher', 'Michael Morak'] | 2017-06-28 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [-2.81592906e-02 7.96238720e-01 -3.42233390e-01 -3.36825222e-01
-4.83709544e-01 -7.44073272e-01 -2.34555732e-02 3.63230556e-01
4.19382453e-02 9.54427898e-01 -1.80499434e-01 -5.16762257e-01
-7.34462142e-01 -1.33469188e+00 -1.07040405e+00 -1.80019438e-01
-2.75389373e-01 1.38878107e+00 8.12607765e-01 -4.52892363... | [8.532633781433105, 6.632782936096191] |
c2693c27-59f9-4e28-8086-9a1153ef2f72 | contrastive-audio-language-learning-for-music | 2208.12208 | null | https://arxiv.org/abs/2208.12208v1 | https://arxiv.org/pdf/2208.12208v1.pdf | Contrastive Audio-Language Learning for Music | As one of the most intuitive interfaces known to humans, natural language has the potential to mediate many tasks that involve human-computer interaction, especially in application-focused fields like Music Information Retrieval. In this work, we explore cross-modal learning in an attempt to bridge audio and language i... | ['György Fazekas', 'Elio Quinton', 'Emmanouil Benetos', 'Ilaria Manco'] | 2022-08-25 | null | null | null | null | ['genre-classification', 'music-information-retrieval'] | ['computer-vision', 'music'] | [ 2.74715543e-01 -3.28186862e-02 -1.67146355e-01 -2.36510932e-01
-1.51961672e+00 -6.86874866e-01 6.61110818e-01 3.74105334e-01
-3.58306885e-01 2.36475408e-01 5.91368139e-01 5.40073104e-02
-2.78618515e-01 -5.04953384e-01 -6.12275541e-01 -4.11372542e-01
-7.67257437e-02 5.73440731e-01 3.33927423e-02 -3.41828614... | [15.506377220153809, 5.0801920890808105] |
f786fdd9-bea2-4de3-bc26-981e1af54978 | budget-sensitive-reannotation-of-noisy | 2112.13320 | null | https://arxiv.org/abs/2112.13320v1 | https://arxiv.org/pdf/2112.13320v1.pdf | Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy | Large crowd-sourced datasets are often noisy and relation classification (RC) datasets are no exception. Reannotating the entire dataset is one probable solution however it is not always viable due to time and budget constraints. This paper addresses the problem of efficient reannotation of a large noisy dataset for th... | ['Amit Awekar', 'Ashish Anand', 'Akshay Parekh'] | 2021-12-26 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 1.80915907e-01 5.25672495e-01 -5.16981408e-02 -2.81749994e-01
-7.00441897e-01 -4.68777210e-01 1.84892461e-01 2.60386109e-01
-4.92021531e-01 9.92804825e-01 3.82766277e-01 -3.17640722e-01
-3.47704411e-01 -8.26502025e-01 -4.42924172e-01 -6.12807572e-01
3.25040311e-01 5.57359219e-01 1.53150201e-01 -3.41939896... | [9.442434310913086, 8.590822219848633] |
8c98189c-0874-442d-920a-1831f6e53091 | multi-modality-deep-network-for-jpeg | 2305.02760 | null | https://arxiv.org/abs/2305.02760v1 | https://arxiv.org/pdf/2305.02760v1.pdf | Multi-Modality Deep Network for JPEG Artifacts Reduction | In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for extreme low-bitrate image compression artifacts reduction. The main challenge is that the highly compressed image loses too much informatio... | ['Liquan Shen', 'Bo Yan', 'Chenxi Ma', 'Qing Lin', 'Weimin Tan', 'Xuhao Jiang'] | 2023-05-04 | null | null | null | null | ['image-deblocking'] | ['computer-vision'] | [ 5.58439791e-01 -6.29886448e-01 -2.04873130e-01 -1.39413416e-01
-7.61802614e-01 9.29525029e-03 1.64084390e-01 1.82708632e-02
-3.64021468e-03 5.36925077e-01 6.56706512e-01 -3.88737693e-02
4.72680479e-02 -6.31506979e-01 -5.75527787e-01 -6.16521120e-01
2.89381623e-01 -3.10839951e-01 -2.92310156e-02 -2.62277037... | [11.287613868713379, -1.8955105543136597] |
07629e08-0f49-4562-b0aa-da20a0731a3c | generative-diffusion-models-on-graphs-methods | 2302.02591 | null | https://arxiv.org/abs/2302.02591v2 | https://arxiv.org/pdf/2302.02591v2.pdf | Generative Diffusion Models on Graphs: Methods and Applications | Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation. Graph generation is a crucial computational task on graphs with numerous real-world applications. It aims to learn the distribut... | ['Chengyi Liu', 'Qing Li', 'Jiliang Tang', 'Hui Liu', 'Hang Li', 'Jiatong Li', 'Yunqing Liu', 'Wenqi Fan'] | 2023-02-06 | null | null | null | null | ['video-generation', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.50546175e-01 3.50595713e-01 1.55196831e-01 -6.10571839e-02
-6.18545234e-01 -4.71419692e-01 7.96795547e-01 -1.94367602e-01
1.74496949e-01 9.64599848e-01 3.79926354e-01 -1.63212582e-01
-5.50912868e-04 -1.10062861e+00 -5.45061171e-01 -1.08036220e+00
6.37660921e-02 6.74026012e-01 4.22984622e-02 -5.07734157... | [11.261378288269043, -0.09948194772005081] |
65d7d7e2-7b40-4af1-8d4a-a7555c132643 | empirical-study-on-airline-delay-analysis-and | 2002.10254 | null | https://arxiv.org/abs/2002.10254v1 | https://arxiv.org/pdf/2002.10254v1.pdf | Empirical Study on Airline Delay Analysis and Prediction | The Big Data analytics are a logical analysis of very large scale datasets. The data analysis enhances an organization and improve the decision making process. In this article, we present Airline Delay Analysis and Prediction to analyze airline datasets with the combination of weather dataset. In this research work, we... | ['Ripon Patgiri', 'Sajid Hussain', 'Aditya Nongmeikapam'] | 2020-02-17 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-3.65129769e-01 -6.49764657e-01 -2.64153630e-01 -8.39598835e-01
5.35093620e-02 -4.62209165e-01 3.96262780e-02 5.02318501e-01
-4.23403710e-01 8.81534815e-01 1.68229550e-01 -5.56511402e-01
-7.52610981e-01 -1.28515434e+00 -5.56675978e-02 -5.22455990e-01
-1.23086996e-01 5.01347721e-01 1.29177049e-02 -5.04577607... | [8.408199310302734, 4.763503551483154] |
c1c39fc2-4f21-4038-ab35-d5eb6a2dce08 | a-transformer-based-approach-to-video-frame | 2303.09293 | null | https://arxiv.org/abs/2303.09293v2 | https://arxiv.org/pdf/2303.09293v2.pdf | A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild | In recent years, transformer architecture has been a dominating paradigm in many applications, including affective computing. In this report, we propose our transformer-based model to handle Emotion Classification Task in the 5th Affective Behavior Analysis In-the-wild Competition. By leveraging the attentive model and... | ['Hyung-Jeong Yang', 'Sudarshan Pant', 'Ngoc-Huynh Ho', 'Dang-Khanh Nguyen'] | 2023-03-16 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-2.20734850e-01 -6.66272268e-02 2.55438667e-02 -5.50192416e-01
-3.83581132e-01 -3.43251556e-01 2.04380080e-01 3.45678926e-02
-5.40730357e-01 5.88789761e-01 1.26489952e-01 4.60176200e-01
3.77016097e-01 -3.89027715e-01 -1.60411701e-01 -4.22129691e-01
-2.06217721e-01 4.51187938e-01 -1.69584140e-01 -5.63535750... | [13.568730354309082, 2.3003458976745605] |
1cdc0d5b-4a35-4cbc-8d07-87fbf6e1a2cb | the-2021-image-similarity-dataset-and | 2106.09672 | null | https://arxiv.org/abs/2106.09672v4 | https://arxiv.org/pdf/2106.09672v4.pdf | The 2021 Image Similarity Dataset and Challenge | This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1~million. The benchmark features a variety of... | ['Cristian Canton Ferrer', 'Ondřej Chum', 'Ismail Elezi', 'Laura Leal-Taixé', 'Maxim Maximov', 'Tomas Jenicek', 'Filip Radenovic', 'Lowik Chanussot', 'Zoë Papakipos', 'Ed Pizzi', 'Giorgos Tolias', 'Matthijs Douze'] | 2021-06-17 | null | null | null | null | ['image-similarity-detection'] | ['computer-vision'] | [ 6.29857719e-01 -1.01838671e-01 -3.91847491e-02 -2.64169097e-01
-6.68687880e-01 -6.68167949e-01 9.07691061e-01 1.71267703e-01
-8.65981698e-01 3.76405269e-01 1.94656625e-01 -1.86542064e-01
1.15234844e-01 -3.12173873e-01 -1.09704745e+00 -4.66397285e-01
-9.51689258e-02 2.95443892e-01 3.96937877e-01 -4.41634178... | [10.649794578552246, 0.8357535004615784] |
3053d10e-3093-4bd4-b7f9-9058f39d5f1d | an-inexact-newton-krylov-algorithm-for | 1408.6299 | null | http://arxiv.org/abs/1408.6299v3 | http://arxiv.org/pdf/1408.6299v3.pdf | An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration | We propose numerical algorithms for solving large deformation diffeomorphic
image registration problems. We formulate the nonrigid image registration
problem as a problem of optimal control. This leads to an infinite-dimensional
partial differential equation (PDE) constrained optimization problem.
The PDE constraint ... | ['George Biros', 'Andreas Mang'] | 2014-08-27 | null | null | null | null | ['constrained-diffeomorphic-image-registration'] | ['computer-vision'] | [ 3.22522298e-02 9.58906114e-02 2.78723061e-01 -3.83714177e-02
-6.13237321e-01 -1.99487045e-01 2.53611743e-01 7.09644184e-02
-7.16375470e-01 8.51788819e-01 -1.44187072e-02 1.66063830e-01
-3.49301428e-01 -6.61565602e-01 -5.82510293e-01 -9.93588805e-01
-1.39631450e-01 4.34967041e-01 -6.39733335e-04 -3.38405460... | [6.513278961181641, 3.3870885372161865] |
50821bff-f7df-4b76-9ac9-de706a1c2699 | hybrid-sequence-to-sequence-model-for-video | 2010.05069 | null | https://arxiv.org/abs/2010.05069v2 | https://arxiv.org/pdf/2010.05069v2.pdf | Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching | One-shot Video Object Segmentation~(VOS) is the task of pixel-wise tracking an object of interest within a video sequence, where the segmentation mask of the first frame is given at inference time. In recent years, Recurrent Neural Networks~(RNNs) have been widely used for VOS tasks, but they often suffer from limitati... | ['Andreas Dengel', 'Joern Hees', 'Federico Raue', 'Stanislav Frolov', 'Fatemeh Azimi'] | 2020-10-10 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 3.51156414e-01 -2.63760298e-01 -2.48689160e-01 -3.25608075e-01
-6.78036273e-01 -2.89704651e-01 4.64031696e-02 -4.79158223e-01
-6.00587964e-01 3.51831764e-01 -1.38597786e-01 -1.29403815e-01
5.55042446e-01 -3.76192719e-01 -9.30879414e-01 -7.02943027e-01
2.25429624e-01 2.68725544e-01 1.09527552e+00 -1.93258852... | [9.205306053161621, -0.08392012119293213] |
6f0343db-c4b2-498a-ad70-5c08605f53cc | compositional-processing-emerges-in-neural | 2105.08961 | null | https://arxiv.org/abs/2105.08961v1 | https://arxiv.org/pdf/2105.08961v1.pdf | Compositional Processing Emerges in Neural Networks Solving Math Problems | A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., grammatical rules) implicit in their sensory observations (e.g., auditory speech), and use this knowledge to guide the composition of simpler... | ['Jianfeng Gao', 'Paul Smolensky', 'Nebojsa Jojic', 'Eric Rosen', 'Hamid Palangi', 'Roland Fernandez', 'Jacob Russin'] | 2021-05-19 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 4.91037190e-01 4.04834837e-01 1.60863355e-01 -5.39579272e-01
2.60313898e-01 -1.01555645e+00 8.40530574e-01 6.19228482e-01
-3.03496808e-01 4.36632484e-01 5.00787497e-01 -8.09245825e-01
-1.73901886e-01 -1.25377238e+00 -8.32226455e-01 -4.44370389e-01
-1.50109187e-01 3.93829674e-01 -2.16362439e-02 -6.01975620... | [9.463369369506836, 7.3119306564331055] |
4494babc-bf86-4f0a-ae0a-3a512d4d0de3 | designing-bert-for-convolutional-networks | 2301.03580 | null | https://arxiv.org/abs/2301.03580v2 | https://arxiv.org/pdf/2301.03580v2.pdf | Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling | We identify and overcome two key obstacles in extending the success of BERT-style pre-training, or the masked image modeling, to convolutional networks (convnets): (i) convolution operation cannot handle irregular, random-masked input images; (ii) the single-scale nature of BERT pre-training is inconsistent with convne... | ['Zehuan Yuan', 'LiWei Wang', 'Chen Lin', 'Qishuai Diao', 'Yi Jiang', 'Keyu Tian'] | 2023-01-09 | null | null | null | null | ['self-supervised-image-classification', '2d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 5.40522456e-01 5.47299683e-01 1.45413317e-02 -2.89255321e-01
-8.61036062e-01 -6.84585989e-01 7.92255461e-01 -4.93570328e-01
-3.37083608e-01 5.11717021e-01 7.88835213e-02 -6.40891790e-01
6.87835217e-02 -8.57185721e-01 -1.25314510e+00 -6.29744351e-01
-6.09074272e-02 4.29050952e-01 5.37313521e-01 -1.29380867... | [9.768131256103516, 0.29606303572654724] |
5f2fd55b-7474-466b-9860-5fa6d2b4d8a8 | reddit-a-gold-mine-for-personality-prediction | null | null | https://aclanthology.org/W18-1112 | https://aclanthology.org/W18-1112.pdf | Reddit: A Gold Mine for Personality Prediction | Automated personality prediction from social media is gaining increasing attention in natural language processing and social sciences communities. However, due to high labeling costs and privacy issues, the few publicly available datasets are of limited size and low topic diversity. We address this problem by introduci... | ['Jan {\\v{S}}najder', "Matej Gjurkovi{\\'c}"] | 2018-06-01 | null | null | null | ws-2018-6 | ['type-prediction'] | ['computer-code'] | [-2.42290720e-01 4.39186692e-01 -2.07303748e-01 -5.48689723e-01
-4.61564004e-01 -4.27362233e-01 5.23701906e-01 7.02233553e-01
-4.68660951e-01 1.00129735e+00 3.39519769e-01 3.33150417e-01
-3.79881442e-01 -5.85886061e-01 5.11765806e-03 -3.96268249e-01
-2.14278847e-01 6.98570788e-01 -5.34781720e-04 2.54857000... | [9.442756652832031, 10.279058456420898] |
06ce4e9a-f86f-4bcf-b4e3-95935f065141 | a-framework-for-real-time-object-detection | 2303.09190 | null | https://arxiv.org/abs/2303.09190v2 | https://arxiv.org/pdf/2303.09190v2.pdf | Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy | The Transformer-based method has demonstrated remarkable performance for image super-resolution in comparison to the method based on the convolutional neural networks (CNNs). However, using the self-attention mechanism like SwinIR (Image Restoration Using Swin Transformer) to extract feature information from images nee... | ['Chun-Tse Chien', 'Wei-Han Chen', 'Yu-Shian Lin', 'Jen-Shiun Chiang', 'Chih-Chia Chen', 'Rui-Yang Ju'] | 2023-03-16 | null | null | null | null | ['image-super-resolution', 'real-time-object-detection', 'image-cropping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.95369184e-01 -2.86074698e-01 -3.42976525e-02 3.82858291e-02
-4.33389664e-01 1.80557221e-02 2.09392205e-01 -6.05565429e-01
-4.11208928e-01 7.33820260e-01 1.27456769e-01 -2.11972877e-01
-1.41438410e-01 -9.72985744e-01 -8.47313285e-01 -6.94454432e-01
1.15223631e-01 -3.01991582e-01 5.73172390e-01 -5.39214611... | [11.048661231994629, -1.954351544380188] |
60bd3bff-fabb-4755-97bc-65ebdd7a299d | mina-multilevel-knowledge-guided-attention | 1905.11333 | null | https://arxiv.org/abs/1905.11333v3 | https://arxiv.org/pdf/1905.11333v3.pdf | MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals | Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attenti... | ['Hongyan Li', 'Cao Xiao', 'Tengfei Ma', 'Shenda Hong', 'Jimeng Sun'] | 2019-05-27 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.40444905e-01 4.20420527e-01 -1.44103676e-01 -6.11226499e-01
-8.48473251e-01 -2.99894720e-01 -2.37201110e-01 4.69572812e-01
1.09419711e-01 7.75588453e-01 3.12510282e-01 -6.35547876e-01
-5.34995317e-01 -4.77685630e-01 -5.94770074e-01 -3.44783068e-01
-4.79570717e-01 3.87885153e-01 -6.27488256e-01 1.17832147... | [14.339581489562988, 3.267864465713501] |
3a394b25-ee9b-4504-b108-540b8325a6c5 | tafim-targeted-adversarial-attacks-against | 2112.09151 | null | https://arxiv.org/abs/2112.09151v2 | https://arxiv.org/pdf/2112.09151v2.pdf | TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations | Face manipulation methods can be misused to affect an individual's privacy or to spread disinformation. To this end, we introduce a novel data-driven approach that produces image-specific perturbations which are embedded in the original images. The key idea is that these protected images prevent face manipulation by ca... | ['Matthias Niessner', 'Lev Markhasin', 'Shivangi Aneja'] | 2021-12-16 | null | null | null | null | ['detecting-image-manipulation'] | ['computer-vision'] | [ 8.46931875e-01 1.79470584e-01 1.23161800e-01 -6.89767599e-02
-6.02951586e-01 -1.04016745e+00 5.47821283e-01 -1.64386630e-01
-2.42448032e-01 3.47386986e-01 -1.06239552e-02 -3.28257561e-01
2.35125482e-01 -7.81122565e-01 -1.03668082e+00 -5.94735086e-01
1.15214035e-01 -1.36350974e-01 -8.07077810e-02 -6.36324510... | [12.707466125488281, 0.9327552914619446] |
a06a73bf-79bd-4691-a9f7-61e9c6eafe37 | video-reflection-removal-through-spatio | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Nandoriya_Video_Reflection_Removal_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Nandoriya_Video_Reflection_Removal_ICCV_2017_paper.pdf | Video Reflection Removal Through Spatio-Temporal Optimization | Reflections can obstruct content during video capture and hence their removal is desirable. Current removal techniques are designed for still images, extracting only one reflection (foreground) and one background layer from the input. When extended to videos, unpleasant artifacts such as temporal flickering and incompl... | ['Mohamed Hefeeda', 'Ajay Nandoriya', 'Wojciech Matusik', 'Mohamed Elgharib', 'Changil Kim'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['reflection-removal'] | ['computer-vision'] | [ 7.29397058e-01 -2.79551029e-01 2.72250414e-01 4.19419780e-02
-6.49702907e-01 -6.56722665e-01 3.99401665e-01 -4.62412655e-01
-3.61489207e-01 5.66280186e-01 4.13024306e-01 -4.68209460e-02
7.60214627e-02 -6.21780008e-02 -7.16708302e-01 -7.47374058e-01
-5.37074283e-02 -3.51704031e-01 5.51896811e-01 1.97068498... | [10.681740760803223, -1.7394400835037231] |
17e48713-662a-4660-8b32-88cba93c52d7 | toward-risk-based-optimistic-exploration-for | 2303.01768 | null | https://arxiv.org/abs/2303.01768v1 | https://arxiv.org/pdf/2303.01768v1.pdf | Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning | The multi-agent setting is intricate and unpredictable since the behaviors of multiple agents influence one another. To address this environmental uncertainty, distributional reinforcement learning algorithms that incorporate uncertainty via distributional output have been integrated with multi-agent reinforcement lear... | ['Se-Young Yun', 'Minchan Jeong', 'Joonkee Kim', 'Jihwan Oh'] | 2023-03-03 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.50258142e-01 3.87633473e-01 -2.67695099e-01 -1.67184114e-01
-8.85115921e-01 -4.55893070e-01 4.62579250e-01 5.87182701e-01
-7.12622166e-01 1.31511176e+00 1.54492050e-01 -1.94294125e-01
-5.84716678e-01 -1.19781148e+00 -5.93299925e-01 -1.00794029e+00
-3.66052359e-01 7.28376389e-01 8.93245339e-02 -4.00496840... | [4.187962055206299, 2.4530372619628906] |
a8eb3310-c1f3-41df-96cd-d627ea64d48c | iiitsurat-lt-edi-acl2022-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.13 | https://aclanthology.org/2022.ltedi-1.13.pdf | IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning | This paper addresses the issue of Hope Speech detection using machine learning techniques. Designing a robust model that helps in predicting the target class with higher accuracy is a challenging task in machine learning, especially when the distribution of the class labels is highly imbalanced. This study uses and com... | ['Bharathi Raja Chakravarthi', 'Abhinav Kumar', 'Snehaan Bhawal', 'Pradeep Roy'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.43180859e-01 5.71812829e-03 -8.10936511e-01 -3.79366785e-01
-1.31685877e+00 5.90144545e-02 3.51650625e-01 5.74383676e-01
-2.23057643e-01 6.37389243e-01 8.75698090e-01 -2.64244735e-01
3.05424854e-02 -6.04341507e-01 -1.77842200e-01 -4.26224023e-01
4.35023963e-01 6.84285760e-01 -2.16388166e-01 -3.37954223... | [9.033310890197754, 10.684954643249512] |
116a7999-a32c-454a-9fa2-0f784c95975d | launcher-attitude-control-based-on | 2307.00372 | null | https://arxiv.org/abs/2307.00372v1 | https://arxiv.org/pdf/2307.00372v1.pdf | Launcher Attitude Control based on Incremental Nonlinear Dynamic Inversion: A Feasibility Study Towards Fast and Robust Design Approaches | The so-called ``New Space era'' has seen a disruptive change in the business models and manufacturing technologies of launch vehicle companies. However, limited consideration has been given to the benefits that innovation in control theory can bring; not only in terms of increasing the limits of performance but also re... | ['Samir Bennani', 'Paul Acquatella', 'Pedro Simplício'] | 2023-07-01 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 8.99742693e-02 1.00128897e-01 -1.96571976e-01 2.45861128e-01
3.10400967e-03 -9.44563329e-01 8.30168903e-01 3.48988809e-02
-2.31578767e-01 6.88158572e-01 -6.82570562e-02 -8.47656846e-01
-6.80020213e-01 -4.51828361e-01 -6.47486448e-01 -6.85005903e-01
-2.30893478e-01 2.72973865e-01 1.40361354e-01 -8.67034853... | [5.380246639251709, 2.283017635345459] |
e22be01f-5af1-40dd-b658-607b6db5a027 | cross-sectional-stock-price-prediction-using | 2002.06975 | null | https://arxiv.org/abs/2002.06975v1 | https://arxiv.org/pdf/2002.06975v1.pdf | Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management | Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a "factor" in the field of finance. Many empirical studies in finance have i... | ['Kei Nakagawa', 'Masaya Abe'] | 2020-02-17 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-1.01674783e+00 -4.47635829e-01 -3.81547600e-01 -3.06048185e-01
-9.93956551e-02 -4.49011087e-01 3.97522330e-01 -1.82875484e-01
-2.46325925e-01 8.48403990e-01 2.15821251e-01 -5.30466378e-01
-1.04695901e-01 -1.39691627e+00 -5.97343862e-01 -5.77969193e-01
-1.20727159e-01 1.95326120e-01 -2.09143367e-02 -4.96334612... | [4.435600280761719, 4.251047611236572] |
295bde75-249e-425c-84b5-0d35f92d9ef5 | bone-texture-analysis-for-prediction-of | 1902.04880 | null | http://arxiv.org/abs/1902.04880v1 | http://arxiv.org/pdf/1902.04880v1.pdf | Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study | Our aim was to assess the ability of radiography-based bone texture
parameters in proximal femur and acetabulum to predict incident radiographic
hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK
(Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone
texture using fracta... | ['Willem Evert van Spil', 'Saeed Arbabi', 'Willem Paul Gielis', 'Rintje Agricola', 'Harrie Weinans', 'Vahid Arbabi', 'Jukka Hirvasniemi'] | 2019-02-13 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-4.07263815e-01 1.69347033e-01 -7.24344432e-01 1.54862836e-01
-7.19170690e-01 1.00832008e-01 9.49444026e-02 4.44261283e-01
-4.25485402e-01 4.76999819e-01 6.06229961e-01 -3.42598557e-01
-7.27676868e-01 -1.22128069e+00 -6.71548486e-01 -9.08978656e-02
-1.08823550e+00 7.61624634e-01 6.05651200e-01 -1.70530334... | [14.522894859313965, -1.7803139686584473] |
5c9c2ffc-9849-45fa-9106-21862d6d6c55 | fast-algorithms-for-directed-graph | 2306.09128 | null | https://arxiv.org/abs/2306.09128v1 | https://arxiv.org/pdf/2306.09128v1.pdf | Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues | We consider a new semidefinite programming relaxation for directed edge expansion, which is obtained by adding triangle inequalities to the reweighted eigenvalue formulation. Applying the matrix multiplicative weight update method to this relaxation, we derive almost linear-time algorithms to achieve $O(\sqrt{\log{n}})... | ['Robert Wang', 'Kam Chuen Tung', 'Lap Chi Lau'] | 2023-06-15 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 1.80251658e-01 5.02943873e-01 -5.38448155e-01 -4.74138670e-02
-6.73723161e-01 -9.13318872e-01 -3.25885028e-01 2.62883544e-01
-2.68407613e-01 6.56879008e-01 -1.10016622e-01 -6.89072847e-01
-7.56450176e-01 -1.15631199e+00 -5.14394522e-01 -7.30555534e-01
-5.64809859e-01 1.01367104e+00 1.20105118e-01 -3.91586602... | [6.947055816650391, 5.126405715942383] |
69b7d7b2-26f2-4b88-9fed-33afc9f38409 | fine-tuning-distributional-semantic-models | null | null | https://aclanthology.org/2021.vardial-1.7 | https://aclanthology.org/2021.vardial-1.7.pdf | Fine-tuning Distributional Semantic Models for Closely-Related Languages | In this paper we compare the performance of three models: SGNS (skip-gram negative sampling) and augmented versions of SVD (singular value decomposition) and PPMI (Positive Pointwise Mutual Information) on a word similarity task. We particularly focus on the role of hyperparameter tuning for Hindi based on recommendati... | ['Ashwini Vaidya', 'Divyanshu Aggarwal', 'Kushagra Bhatia'] | null | null | null | null | eacl-vardial-2021-4 | ['word-similarity'] | ['natural-language-processing'] | [-1.92108542e-01 -2.60275543e-01 -3.42831731e-01 -3.71704370e-01
-1.03393745e+00 -8.72006476e-01 1.00984120e+00 2.81477302e-01
-9.38925326e-01 7.16158330e-01 9.43347156e-01 -6.76778078e-01
-4.34550792e-01 -5.11094928e-01 7.98674300e-02 -5.38804591e-01
-2.18849152e-01 6.61949456e-01 2.18892366e-01 -7.53665090... | [10.844442367553711, 9.97326374053955] |
6328020f-b6e3-4f29-b310-dafa96840e01 | hccl-at-semeval-2017-task-2-combining | null | null | https://aclanthology.org/S17-2033 | https://aclanthology.org/S17-2033.pdf | HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity | In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only mon... | ['Yonghong Yan', 'Xuemin Zhao', 'Junqing He', 'Long Wu'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['multilingual-word-embeddings', 'stock-price-prediction'] | ['methodology', 'time-series'] | [-5.08241713e-01 5.85970245e-02 -5.25233686e-01 -1.00866437e-01
-1.13380456e+00 -7.82998085e-01 8.52485061e-01 3.65220577e-01
-1.10613930e+00 8.46663535e-01 5.55701613e-01 -8.06808174e-01
2.16426849e-01 -3.17008615e-01 -5.13515174e-01 3.14951539e-02
2.89333999e-01 8.16241443e-01 -1.11143075e-01 -8.21807265... | [10.99714183807373, 9.870359420776367] |
8ce5b4e8-f257-4163-8d3a-37fbe12180f2 | intelligent-approaches-to-interact-with | 1303.02292 | null | http://arxiv.org/abs/1303.2292v1 | http://arxiv.org/pdf/1303.2292v1.pdf | Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey | Hand gestures recognition (HGR) is one of the main areas of research for the
engineers, scientists and bioinformatics. HGR is the natural way of Human
Machine interaction and today many researchers in the academia and industry are
working on different application to make interactions more easy, natural and
convenient w... | ['Ankit Chaudhary', 'Sonia Raheja', 'Karen Das', 'J. L. Raheja'] | 2013-03-10 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.88076288e-01 -5.11717677e-01 -5.50705306e-02 -3.22002053e-01
3.88571262e-01 -6.96428299e-01 3.87847573e-01 -3.97202849e-01
-5.03964067e-01 6.65931046e-01 -8.58890191e-02 -3.11423391e-01
-4.73623216e-01 -7.05786705e-01 1.31241366e-01 -7.23078012e-01
4.80438411e-01 7.26208866e-01 3.84748846e-01 -2.15811566... | [6.4852986335754395, -0.29907140135765076] |
70e13169-efde-4b7d-951b-2dcc285fd737 | occlumix-towards-de-occlusion-virtual-try-on | 2301.00965 | null | https://arxiv.org/abs/2301.00965v1 | https://arxiv.org/pdf/2301.00965v1.pdf | OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup | Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect ... | ['Liang Lin', 'Tianshui Chen', 'Hao Li', 'Yukai Shi', 'Junyang Chen', 'Zhijing Yang'] | 2023-01-03 | null | null | null | null | ['virtual-try-on', 'semantic-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 5.56030989e-01 3.11980426e-01 9.11262780e-02 -1.83812350e-01
-3.80560219e-01 -2.28169248e-01 2.17343673e-01 -4.90860969e-01
1.52171731e-01 2.91330397e-01 1.42468542e-01 4.24638279e-02
2.26696149e-01 -8.79421473e-01 -9.19781327e-01 -6.11866117e-01
5.87394655e-01 2.10818172e-01 3.67353499e-01 -3.32406253... | [11.888586044311523, -0.8728317022323608] |
eba722f2-a54f-44cc-8d3e-5af99d7a9146 | yourtts-towards-zero-shot-multi-speaker-tts | 2112.02418 | null | https://arxiv.org/abs/2112.02418v4 | https://arxiv.org/pdf/2112.02418v4.pdf | YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone | YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results compara... | ['Moacir Antonelli Ponti', 'Eren Gölge', 'Arnaldo Candido Junior', 'Christopher Shulby', 'Julian Weber', 'Edresson Casanova'] | 2021-12-04 | null | null | null | null | ['zero-shot-multi-speaker-tts'] | ['audio'] | [-1.51077479e-01 -5.20948768e-02 -8.36601928e-02 -4.04079407e-01
-1.72994256e+00 -5.43780982e-01 6.24128580e-01 -2.87844002e-01
-3.45403820e-01 3.15736264e-01 3.48150164e-01 -5.79828799e-01
2.32499033e-01 -1.36296570e-01 -3.47910404e-01 -3.82445961e-01
2.67579108e-01 8.10894787e-01 3.17616731e-01 -6.57681644... | [14.778468132019043, 6.736785888671875] |
07e8e7a4-b8c7-49e4-92cc-f107f2a735b2 | datsing-data-augmented-time-series | null | null | https://dl.acm.org/doi/abs/10.1145/3340531.3412155 | https://dl.acm.org/doi/pdf/10.1145/3340531.3412155 | DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation | Due to the high temporal uncertainty and low signal-to-noise ratio, transfer learning for univariate time series forecasting remains a challenging task. In addition, data scarcity, which is commonly encountered in business forecasting, further limits the application of conventional transfer learning protocols. In this ... | ['Chengcheng Bai', 'Mingjian Tang', 'Hailin Hu'] | 2020-10-19 | null | null | null | acm-international-conference-on-information-4 | ['univariate-time-series-forecasting'] | ['time-series'] | [ 2.33718663e-01 -3.21701467e-01 -1.48377508e-01 -5.16577065e-01
-9.20555949e-01 -7.80395031e-01 6.98697031e-01 -3.37486863e-01
1.99344143e-01 7.67079592e-01 1.62973076e-01 -6.75325334e-01
-2.25142568e-01 -6.91753566e-01 -9.32901502e-01 -7.99442410e-01
-3.18827182e-01 2.04282314e-01 -2.78916746e-01 -2.07992330... | [7.174508094787598, 2.994499444961548] |
78371278-4c9e-4012-b1d8-f7fa0b9911f4 | demystifying-the-base-and-novel-performances | 2206.10596 | null | https://arxiv.org/abs/2206.10596v1 | https://arxiv.org/pdf/2206.10596v1.pdf | Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning | Few-shot class-incremental learning (FSCIL) has addressed challenging real-world scenarios where unseen novel classes continually arrive with few samples. In these scenarios, it is required to develop a model that recognizes the novel classes without forgetting prior knowledge. In other words, FSCIL aims to maintain th... | ['Se-Young Yun', 'Jaehoon Oh'] | 2022-06-18 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.70520270e-01 -1.61616772e-01 -1.08837530e-01 -2.49717966e-01
-5.06706953e-01 -4.44698602e-01 7.40498960e-01 2.08988205e-01
-3.54889572e-01 7.71273971e-01 -3.68134558e-01 1.72627121e-02
-1.98315069e-01 -6.54477000e-01 -5.24117231e-01 -7.69614100e-01
1.05835177e-01 3.84129345e-01 8.43729258e-01 -1.36157885... | [9.92531967163086, 3.413419008255005] |
8acdc545-7174-491d-8b90-892313a1805d | towards-mapping-thesauri-onto-plwordnet | null | null | https://aclanthology.org/2018.gwc-1.6 | https://aclanthology.org/2018.gwc-1.6.pdf | Towards Mapping Thesauri onto plWordNet | plWordNet, the wordnet of Polish, has become a very comprehensive description of the Polish lexical system. This paper presents a plan of its semi-automated integration with thesauri, terminological databases and ontologies, as a further necessary step in its development. This will improve linking of plWordNet into Lin... | ['Maciej Piasecki', 'Marek Maziarz'] | null | null | null | null | gwc-2018-1 | ['keyword-extraction'] | ['natural-language-processing'] | [-3.85317445e-01 4.16161746e-01 -6.25294447e-01 1.61182284e-01
-5.60974240e-01 -7.14190900e-01 8.53326142e-01 7.58181512e-01
-8.90891790e-01 1.27111363e+00 7.74204135e-01 -1.19229637e-01
-6.92100883e-01 -1.15284145e+00 2.80816823e-01 -2.44326234e-01
4.52044040e-01 9.67365146e-01 8.01225305e-01 -7.62995124... | [9.523280143737793, 8.734704971313477] |
5296521e-15db-449b-898e-6b0af899543a | deep-learning-from-parametrically-generated | 2302.05283 | null | https://arxiv.org/abs/2302.05283v1 | https://arxiv.org/pdf/2302.05283v1.pdf | Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition | We study the use of parametric building information modeling (BIM) to automatically generate training data for artificial neural networks (ANNs) to recognize building objects in photos. Teaching artificial intelligence (AI) machines to detect building objects in images is the foundation toward AI-assisted semantic 3D r... | ['Wei Yan', 'Mohammad Alawadhi'] | 2023-01-03 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 6.33422136e-01 3.84468913e-01 6.07637584e-01 -5.96496820e-01
-8.22649479e-01 -5.04803121e-01 1.78928018e-01 -1.28132654e-02
-1.16531342e-01 3.50303739e-01 -1.73928648e-01 -5.02321422e-01
5.95590286e-02 -1.43574321e+00 -9.78007793e-01 -3.00287426e-01
1.12748612e-02 1.08335841e+00 2.44082332e-01 -3.35531294... | [9.65963077545166, 0.9172974824905396] |
0ab370d5-fc80-42cf-acfe-79ccef968bbe | automated-medical-coding-on-mimic-iii-and | 2304.10909 | null | https://arxiv.org/abs/2304.10909v1 | https://arxiv.org/pdf/2304.10909v1.pdf | Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study | Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze stat... | ['Lars Maaløe', 'Tuukka Ruotsalo', 'Maria Maistro', 'Lasse Borgholt', 'Jakob D. Havtorn', 'Alexander Junge', 'Joakim Edin'] | 2023-04-21 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 4.50270802e-01 4.21184391e-01 -6.18085682e-01 -5.52067816e-01
-1.35052466e+00 -6.20715618e-01 3.38891745e-01 7.00879514e-01
-3.33275884e-01 7.48071492e-01 4.02861804e-01 -7.52165616e-01
-3.63738030e-01 -1.34183750e-01 -3.29978734e-01 -2.55712420e-01
-1.07067443e-01 9.54642177e-01 -1.08016059e-01 4.01495725... | [8.012947082519531, 6.806243419647217] |
2e317930-59e6-413f-9cba-1081cb808364 | a-generic-ensemble-based-deep-convolutional | 2004.07995 | null | https://arxiv.org/abs/2004.07995v1 | https://arxiv.org/pdf/2004.07995v1.pdf | A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation | Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extreme... | ['Ruizhe Li', 'Dorothee Auer', 'Christian Wagner', 'Xin Chen'] | 2020-04-16 | null | null | null | null | ['semi-supervised-medical-image-segmentation', 'skin-lesion-segmentation', 'organ-detection'] | ['computer-vision', 'medical', 'medical'] | [ 6.65538371e-01 4.12306279e-01 -4.94482934e-01 -5.63530385e-01
-1.16719103e+00 -2.26557270e-01 2.88128257e-01 5.39757848e-01
-6.65175319e-01 6.37239635e-01 -2.44940650e-02 -1.24963202e-01
3.91563088e-01 -7.00358272e-01 -5.98622441e-01 -6.59480453e-01
2.45437846e-01 7.92304099e-01 5.24666488e-01 2.27740437... | [14.738351821899414, -2.3668978214263916] |
5bbf144d-0c1a-4c40-b195-1ec6b9a6ed49 | near-optimal-representation-learning-for | 1810.01257 | null | http://arxiv.org/abs/1810.01257v2 | http://arxiv.org/pdf/1810.01257v2.pdf | Near-Optimal Representation Learning for Hierarchical Reinforcement Learning | We study the problem of representation learning in goal-conditioned
hierarchical reinforcement learning. In such hierarchical structures, a
higher-level controller solves tasks by iteratively communicating goals which a
lower-level policy is trained to reach. Accordingly, the choice of
representation -- the mapping of ... | ['Sergey Levine', 'Shixiang Gu', 'Ofir Nachum', 'Honglak Lee'] | 2018-10-02 | near-optimal-representation-learning-for-1 | https://openreview.net/forum?id=H1emus0qF7 | https://openreview.net/pdf?id=H1emus0qF7 | iclr-2019-5 | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 3.21413010e-01 5.21829545e-01 -5.67172825e-01 -3.59056257e-02
-9.43260550e-01 -5.86968839e-01 6.55892968e-01 1.80413350e-01
-1.99914977e-01 1.01902187e+00 4.28579926e-01 -2.28654951e-01
-3.27752173e-01 -6.67192638e-01 -7.46628225e-01 -8.31453979e-01
-4.09365416e-01 4.07677531e-01 -1.60436392e-01 -4.03257400... | [4.167900085449219, 1.696533441543579] |
77cde550-a652-4b2d-a2cc-fba71f9858bf | a-study-on-passage-re-ranking-in-embedding | 1804.08057 | null | http://arxiv.org/abs/1804.08057v4 | http://arxiv.org/pdf/1804.08057v4.pdf | A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search | State of the art approaches for (embedding based) unsupervised semantic
search exploits either compositional similarity (of a query and a passage) or
pair-wise word (or term) similarity (from the query and the passage). By
design, word based approaches do not incorporate similarity in the larger
context (query/passage)... | ['Md. Faisal Mahbub Chowdhury', 'Alfio M. Gliozzo', 'Vijil Chenthamarakshan', 'Rishav Chakravarti'] | 2018-04-22 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 1.07807077e-01 -2.84208596e-01 -5.26644826e-01 -1.47457972e-01
-8.87823284e-01 -5.60304761e-01 1.09442282e+00 9.72525358e-01
-8.76891553e-01 4.70199943e-01 7.72676706e-01 -1.09710380e-01
-6.29962444e-01 -8.30173254e-01 -4.81563471e-02 -4.78106827e-01
8.00390169e-02 5.34666479e-01 6.57108963e-01 -5.33522904... | [10.661491394042969, 8.77890396118164] |
460344a1-2014-4b17-8f4f-f48ab9c4930a | sparsity-and-robustness-in-face-recognition | 1111.01014 | null | http://arxiv.org/abs/1111.1014v1 | http://arxiv.org/pdf/1111.1014v1.pdf | Sparsity and Robustness in Face Recognition | This report concerns the use of techniques for sparse signal representation
and sparse error correction for automatic face recognition. Much of the recent
interest in these techniques comes from the paper "Robust Face Recognition via
Sparse Representation" by Wright et al. (2009), which showed how, under certain
techni... | ['John Wright', 'Arvind Ganesh', 'Zihan Zhou', 'Yi Ma', 'Allen Yang'] | 2011-11-03 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 7.02101827e-01 -8.38494487e-03 1.98328774e-02 -3.85164350e-01
-4.93390739e-01 -7.07086846e-02 4.99604613e-01 -6.30859315e-01
1.56013653e-01 7.08295226e-01 2.81408966e-01 1.15211261e-02
-1.90915555e-01 -4.13727045e-01 -5.14587224e-01 -8.06531668e-01
-1.13055490e-01 -1.05842851e-01 -6.85223937e-01 -1.56118229... | [12.543692588806152, 0.3831537067890167] |
4c55a379-421a-4cd1-8733-d2f669315b85 | audio-visual-grouping-network-for-sound | 2303.17056 | null | https://arxiv.org/abs/2303.17056v1 | https://arxiv.org/pdf/2303.17056v1.pdf | Audio-Visual Grouping Network for Sound Localization from Mixtures | Sound source localization is a typical and challenging task that predicts the location of sound sources in a video. Previous single-source methods mainly used the audio-visual association as clues to localize sounding objects in each image. Due to the mixed property of multiple sound sources in the original space, ther... | ['Yapeng Tian', 'Shentong Mo'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mo_Audio-Visual_Grouping_Network_for_Sound_Localization_From_Mixtures_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mo_Audio-Visual_Grouping_Network_for_Sound_Localization_From_Mixtures_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-localization'] | ['computer-vision'] | [-7.27756321e-03 -4.99116570e-01 -2.82083452e-01 -2.22525466e-02
-1.29403114e+00 -8.19343805e-01 3.32630038e-01 3.40752192e-02
2.65612453e-01 2.24967137e-01 4.99464959e-01 1.99250698e-01
-2.11596191e-01 -4.90553170e-01 -7.95862615e-01 -7.08377540e-01
-2.34010056e-01 -1.13840237e-01 5.55534720e-01 2.13856310... | [14.799866676330566, 4.883406162261963] |
51f58ebd-0078-46fa-86da-0ad6897f78c2 | happy-people-image-synthesis-as-black-box | 2306.06684 | null | https://arxiv.org/abs/2306.06684v1 | https://arxiv.org/pdf/2306.06684v1.pdf | Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models | In recent years, optimization in the learned latent space of deep generative models has been successfully applied to black-box optimization problems such as drug design, image generation or neural architecture search. Existing models thereby leverage the ability of neural models to learn the data distribution from a li... | ['Margret Keuper', 'Claudia Schillings', 'Jan Christian Schwedhelm', 'Steffen Jung'] | 2023-06-11 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.75388205e-01 4.87976789e-01 -7.89462030e-02 -4.33943927e-01
-6.11226201e-01 -4.96423930e-01 8.59599352e-01 -1.74468264e-01
-1.34478718e-01 9.43415165e-01 7.04120100e-02 -7.51590803e-02
-1.71836659e-01 -8.50393116e-01 -8.51766527e-01 -8.98459077e-01
1.58040836e-01 7.91960895e-01 -4.39699501e-01 -1.85485989... | [11.644442558288574, -0.16647914052009583] |
cffa4099-4df7-4685-8a68-872a728f314f | one-agent-to-rule-them-all-towards-multi-1 | 2203.07665 | null | https://arxiv.org/abs/2203.07665v1 | https://arxiv.org/pdf/2203.07665v1.pdf | One Agent To Rule Them All: Towards Multi-agent Conversational AI | The increasing volume of commercially available conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. Though prior work has explored supporting a multitude of domains within the design of a single agent, the interaction exper... | ['Jason Mars', 'Lingjia Tang', 'Yiping Kang', 'Walter Lasecki', 'Kevin Leach', 'Walter Talamonti', 'Karthik Krishnamurthy', 'Joseph Joshua Peper', 'Christopher Clarke'] | 2022-03-15 | null | https://aclanthology.org/2022.findings-acl.257 | https://aclanthology.org/2022.findings-acl.257.pdf | findings-acl-2022-5 | ['conversational-response-selection', 'multi-agent-integration'] | ['natural-language-processing', 'natural-language-processing'] | [-7.66981766e-02 1.95461512e-01 6.00335337e-02 -4.60931480e-01
-1.17781425e+00 -8.43246281e-01 1.02710676e+00 5.11151031e-02
-4.43838418e-01 6.30424142e-01 5.90348899e-01 -2.41488442e-01
5.92231564e-02 -4.30077046e-01 -3.88573498e-01 -3.44216377e-02
7.08926618e-02 9.94195342e-01 1.19081661e-01 -8.04032087... | [12.544126510620117, 8.013285636901855] |
bd31d3c0-0ec4-4a23-8aec-6bbd0f8059ce | bayesian-optimisation-against-climate-change | 2306.04343 | null | https://arxiv.org/abs/2306.04343v1 | https://arxiv.org/pdf/2306.04343v1.pdf | Bayesian Optimisation Against Climate Change: Applications and Benchmarks | Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisation problems within climate change for which simulator models are una... | ['Nigel H. Goddard', 'Christopher G. Lucas', 'Sigrid Passano Hellan'] | 2023-06-07 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.84740418e-01 -2.07614481e-01 7.72851706e-02 -1.83456719e-01
-6.21295869e-01 -7.24721253e-01 6.07807815e-01 8.74943137e-02
-1.61941037e-01 9.14170623e-01 -1.77796587e-01 -1.09515774e+00
-8.16423595e-01 -8.22216988e-01 -6.13763332e-01 -1.17564750e+00
-3.68086517e-01 4.42533195e-01 -3.49622332e-02 -2.35676169... | [6.116626262664795, 3.6142234802246094] |
6f94f00c-c371-4816-8794-c7a463a29024 | boosting-graph-structure-learning-with-dummy | 2206.08561 | null | https://arxiv.org/abs/2206.08561v1 | https://arxiv.org/pdf/2206.08561v1.pdf | Boosting Graph Structure Learning with Dummy Nodes | With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure learning. However, most of those strategies are designed based on practical experience rather than theoretical analysis. In this paper, we u... | ['Xin Jiang', 'Yangqiu Song', 'Jiayang Cheng', 'Xin Liu'] | 2022-06-17 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-1.15840491e-02 5.64881861e-01 -5.42828381e-01 -2.73101658e-01
-1.82009399e-01 -6.34428859e-01 3.64588350e-01 3.77504379e-01
-1.33522958e-01 3.11695069e-01 1.78510740e-01 -4.66089249e-01
-3.31232920e-02 -1.15425611e+00 -8.47636521e-01 -6.06554806e-01
-7.86303222e-01 6.87988773e-02 1.32653564e-01 -1.13681190... | [6.9976277351379395, 6.224408149719238] |
2731f493-5cec-4d2c-bc18-273b18f4bfee | scene-labeling-using-beam-search-under-mutex | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Roy_Scene_Labeling_Using_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Roy_Scene_Labeling_Using_2014_CVPR_paper.pdf | Scene Labeling Using Beam Search Under Mutex Constraints | This paper addresses the problem of assigning object class labels to image pixels. Following recent holistic formulations, we cast scene labeling as inference of a conditional random field (CRF) grounded onto superpixels. The CRF inference is specified as quadratic program (QP) with mutual exclusion (mutex) constraints... | ['Anirban Roy', 'Sinisa Todorovic'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['scene-labeling'] | ['computer-vision'] | [ 8.04953814e-01 2.97425926e-01 -4.73113686e-01 -7.84056783e-01
-7.53365517e-01 -5.00265241e-01 3.56297344e-01 4.81307432e-02
-3.59566450e-01 9.42017019e-01 -1.33525729e-01 -1.42561853e-01
-9.49721038e-02 -9.00708318e-01 -9.65219855e-01 -7.77112484e-01
2.69145608e-01 6.59018278e-01 3.46462697e-01 4.16673541... | [9.531890869140625, 0.482128769159317] |
584fd857-49ab-4378-9757-7baf289295e4 | primitive-generation-and-semantic-related-1 | 2306.11087 | null | https://arxiv.org/abs/2306.11087v1 | https://arxiv.org/pdf/2306.11087v1.pdf | Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation | We study universal zero-shot segmentation in this work to achieve panoptic, instance, and semantic segmentation for novel categories without any training samples. Such zero-shot segmentation ability relies on inter-class relationships in semantic space to transfer the visual knowledge learned from seen categories to un... | ['Wei Jiang', 'Henghui Ding', 'Shuting He'] | 2023-06-19 | primitive-generation-and-semantic-related | http://openaccess.thecvf.com//content/CVPR2023/html/He_Primitive_Generation_and_Semantic-Related_Alignment_for_Universal_Zero-Shot_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_Primitive_Generation_and_Semantic-Related_Alignment_for_Universal_Zero-Shot_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation', 'zero-shot-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.28381979e-01 1.92150339e-01 -2.77853638e-01 -4.53794688e-01
-5.00185847e-01 -7.68233240e-01 5.82568228e-01 7.01945499e-02
-2.49203920e-01 5.17451644e-01 1.18808128e-01 1.65668763e-02
-9.56593305e-02 -1.15346301e+00 -7.18355894e-01 -7.62733340e-01
2.66061068e-01 3.30121547e-01 4.93133128e-01 -2.92376459... | [9.834799766540527, 1.7229254245758057] |
4102fd9d-aa5f-410c-9ee3-53870f2cf37c | learning-parametric-distributions-for-image | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Li_Learning_Parametric_Distributions_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Li_Learning_Parametric_Distributions_ICCV_2015_paper.pdf | Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding | Existing approaches toward Image super-resolution (SR) is often either data-driven (e.g., based on internet-scale matching and web image retrieval) or model-based (e.g., formulated as an Maximizing a Posterior estimation problem). The former is conceptually simple yet heuristic; while the latter is constrained by the f... | ['Xuemei Xie', 'Yongbo Li', 'Guangming Shi', 'Weisheng Dong'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['patch-matching'] | ['computer-vision'] | [ 5.20473123e-01 5.56046441e-02 -3.18049163e-01 -2.29950041e-01
-1.21076190e+00 -1.85915083e-01 5.55976212e-01 -2.56839991e-01
2.74537709e-02 6.90855742e-01 3.01809311e-01 1.60473436e-01
-3.81174982e-01 -7.01725960e-01 -4.52174455e-01 -9.11099315e-01
3.49137604e-01 -4.74433750e-02 2.77381510e-01 -2.35980406... | [11.069877624511719, -2.0517947673797607] |
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