paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
aea0940e-f59d-4b1b-b597-9b1bfd0ed6b7 | fisherposes-for-human-action-recognition | null | null | https://ieeexplore.ieee.org/abstract/document/8219411 | http://sharif.edu/~hoda/papers/action_sensors.pdf | Fisherposes for Human Action Recognition Using Kinect Sensor Data | This paper proposes a new method for view-invariant action recognition that utilizes the temporal position of skeletal joints obtained by Kinect sensor. In this method, the actions are represented as sequences of several pre-defined poses. After pre-processing, which includes skeleton alignment and scaling, the appropr... | ['Mozhgan Mokari', 'Hoda Mohammadzade', 'Benyamin Ghojogh'] | 2018-02-15 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.4337252e-01 -5.7138079e-01 -3.0029452e-01 -3.2504696e-01
-5.7406276e-01 -2.4055798e-01 4.9709624e-01 -2.0526898e-01
-7.7833056e-01 5.8633381e-01 2.8719768e-01 3.6750516e-01
-4.3451533e-01 -5.2915144e-01 -1.6636252e-01 -9.2055380e-01
-1.6286870e-02 5.7332337e-01 3.6730751e-01 -6.2252991e-02
3.9151093e-01... | [7.818530559539795, 0.3194855749607086] |
0cdbe157-82fa-4287-a037-20cb925c0abc | online-heavy-tailed-change-point-detection | 2306.09548 | null | https://arxiv.org/abs/2306.09548v2 | https://arxiv.org/pdf/2306.09548v2.pdf | Online Heavy-tailed Change-point detection | We study algorithms for online change-point detection (OCPD), where samples that are potentially heavy-tailed, are presented one at a time and a change in the underlying mean must be detected as early as possible. We present an algorithm based on clipped Stochastic Gradient Descent (SGD), that works even if we only ass... | ['Narayanaswamy', 'Balakrishnan', 'Abishek Sankararaman'] | 2023-06-15 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [-4.16176617e-02 -3.87512535e-01 -1.46241054e-01 1.15344115e-01
-1.11912858e+00 -8.83200288e-01 5.07593095e-01 3.88868004e-01
-4.25056338e-01 9.62520838e-01 -3.99270386e-01 -6.19855046e-01
-8.86566490e-02 -5.83254695e-01 -1.00183833e+00 -7.69318461e-01
-7.62220025e-01 4.80791003e-01 4.84831333e-01 2.29603946... | [7.110558032989502, 3.997058629989624] |
73a22e98-4940-44a9-ae79-88410ce9b087 | on-planetary-systems-as-ordered-sequences | 2105.09966 | null | https://arxiv.org/abs/2105.09966v1 | https://arxiv.org/pdf/2105.09966v1.pdf | On planetary systems as ordered sequences | A planetary system consists of a host star and one or more planets, arranged into a particular configuration. Here, we consider what information belongs to the configuration, or ordering, of 4286 Kepler planets in their 3277 planetary systems. First, we train a neural network model to predict the radius and period of a... | ['Michael Collins', 'David Kipping', 'Emily Sandford'] | 2021-05-20 | null | null | null | null | ['unsupervised-part-of-speech-tagging'] | ['natural-language-processing'] | [-3.62496555e-01 4.83452201e-01 -6.41364083e-02 -1.75809547e-01
2.67264336e-01 -8.25761974e-01 1.10603106e+00 -2.53788441e-01
-8.62168893e-02 6.59282029e-01 1.17170662e-02 -8.26378405e-01
-1.62125349e-01 -9.38727796e-01 -9.61304784e-01 -9.04608548e-01
-3.71275038e-01 1.00479639e+00 4.01155561e-01 -4.12362039... | [6.84966516494751, 3.299739360809326] |
c53c2791-ded0-4660-8028-bdab21c4c1b3 | revisit-out-of-vocabulary-problem-for-slot | 2302.13584 | null | https://arxiv.org/abs/2302.13584v1 | https://arxiv.org/pdf/2302.13584v1.pdf | Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations | In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV robust slot filling model based on multi-level data augmentations to solve the OOV problem fr... | ['Weiran Xu', 'Xinyue Cui', 'Keqing He', 'Zechen Wang', 'Xuefeng Li', 'LiWen Wang', 'Tingfeng Hui', 'Chen Zeng', 'Yuxiang Wu', 'Dayuan Fu', 'Guanting Dong', 'Daichi Guo'] | 2023-02-27 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 4.41952422e-02 8.82298708e-01 -5.71119130e-01 -1.95688039e-01
-4.92403418e-01 -4.45830822e-01 7.05210626e-01 3.00174266e-01
-4.56011415e-01 9.01500583e-01 4.59187061e-01 -5.65502882e-01
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2.37884477e-01 8.25792134e-01 5.16455233e-01 -8.64547610... | [12.55858325958252, 7.384539604187012] |
c7d2bed6-2f5a-49da-af77-51a470eb14c1 | extracting-drug-drug-interactions-from | null | null | https://www.sciencedirect.com/science/article/pii/S1532046415000441 | https://www.sciencedirect.com/science/article/pii/S1532046415000441 | Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach | Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug–drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for d... | ['W. John Wilbur', 'Lana Yeganova', 'Haibin Liu', 'Sun Kim'] | 2015-03-19 | null | null | null | journal-of-biomedical-informatics-2015-3 | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 1.55502006e-01 -2.61021823e-01 -6.47007465e-01 -2.49246091e-01
-8.47873390e-01 -6.74692154e-01 6.82544649e-01 1.08060956e+00
-2.56488919e-01 1.08057618e+00 -2.62273669e-01 -7.26067364e-01
-3.40265989e-01 -5.18397987e-01 -4.97323155e-01 -7.89857626e-01
-2.15142071e-01 6.97067499e-01 9.32785869e-02 1.99903026... | [8.342612266540527, 8.666462898254395] |
9e4ed0a4-b054-4c5a-a3bb-93fc2268256f | seeing-the-forest-and-the-trees-detection-and | 2005.02966 | null | https://arxiv.org/abs/2005.02966v1 | https://arxiv.org/pdf/2005.02966v1.pdf | Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes | Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references -- recognize singular events across multiple sentences ... | ['Benjamin J. Radford'] | 2020-05-06 | null | null | null | null | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 2.69009292e-01 5.15460260e-02 -3.99097502e-01 -6.24767244e-01
-1.63318372e+00 -1.03578222e+00 1.48949468e+00 8.74783874e-01
-8.81270170e-01 9.26115692e-01 1.02449059e+00 -3.12206954e-01
-5.69234014e-01 -6.43449485e-01 -3.82737041e-01 -1.94145203e-01
-5.13135493e-02 1.04452014e+00 4.59224805e-02 -4.00776684... | [9.129959106445312, 9.538833618164062] |
b143a603-e31d-4376-b46c-4481fc5c5ebb | variational-quantum-classifiers-for-natural | 2303.02469 | null | https://arxiv.org/abs/2303.02469v1 | https://arxiv.org/pdf/2303.02469v1.pdf | Variational Quantum Classifiers for Natural-Language Text | As part of the recent research effort on quantum natural language processing (QNLP), variational quantum sentence classifiers (VQSCs) have been implemented and supported in lambeq / DisCoPy, based on the DisCoCat model of sentence meaning. We discuss in some detail VQSCs, including category theory, DisCoCat for modelin... | ['Daniel T. Chang'] | 2023-03-04 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.47408736e-01 3.50540340e-01 2.67473534e-02 -5.16544044e-01
-5.38092732e-01 -9.55582857e-01 7.60386288e-01 5.68415999e-01
-3.24463040e-01 6.56253219e-01 3.15646410e-01 -5.61438799e-01
-4.85533237e-01 -9.44122612e-01 -5.34150004e-01 -6.41571999e-01
5.05138457e-01 3.84331584e-01 4.32108432e-01 -8.29724073... | [5.751733303070068, 5.047903060913086] |
47c52457-29b0-4bdd-a9ea-73913267adff | a-cad-system-for-colorectal-cancer-from-wsi-a | 2301.02608 | null | https://arxiv.org/abs/2301.02608v1 | https://arxiv.org/pdf/2301.02608v1.pdf | A CAD System for Colorectal Cancer from WSI: A Clinically Validated Interpretable ML-based Prototype | The integration of Artificial Intelligence (AI) and Digital Pathology has been increasing over the past years. Nowadays, applications of deep learning (DL) methods to diagnose cancer from whole-slide images (WSI) are, more than ever, a reality within different research groups. Nonetheless, the development of these syst... | ['Jaime S. Cardoso', 'Isabel M. Pinto', 'Inti Zlobec', 'Stefan Reinhard', 'Sofia Gonçalves', 'Liliana Ribeiro', 'João Monteiro', 'Ana Monteiro', 'João Fraga', 'Domingos Oliveira', 'Sara P. Oliveira', 'Diana Montezuma', 'Pedro C. Neto'] | 2023-01-06 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.23373383e-01 5.06947577e-01 -3.13040167e-02 -1.16058059e-01
-7.93413520e-01 -2.68089414e-01 5.59091032e-01 4.45285916e-01
-4.55437958e-01 6.36751175e-01 7.64672756e-02 -4.76663083e-01
-4.39299166e-01 -6.28349483e-01 -4.12172854e-01 -9.62586462e-01
-5.62566370e-02 6.05800211e-01 2.34003738e-01 1.12678967... | [15.05111026763916, -2.988070487976074] |
80359d0d-eda4-43e9-8fcf-8b80821e4478 | image-reconstruction-using-superpixel | 2305.09564 | null | https://arxiv.org/abs/2305.09564v1 | https://arxiv.org/pdf/2305.09564v1.pdf | Image Reconstruction using Superpixel Clustering and Tensor Completion | This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different ... | ['Andrzej Cichocki', 'Zaher Al Aghbari', 'Salman Ahmadi-Asl', 'Anh Huy Phan', 'Maame G. Asante-Mensah'] | 2023-05-16 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 2.77798057e-01 -2.59865612e-01 -4.68494385e-01 -2.18068168e-01
-1.02718961e+00 -8.64799768e-02 1.82637185e-01 -1.05144903e-01
-3.23715776e-01 2.64183640e-01 3.64725292e-01 1.55795276e-01
3.22938412e-02 -7.90822029e-01 -5.67900002e-01 -8.26773345e-01
-1.93235371e-02 4.43281680e-01 6.23056531e-01 1.36520863... | [10.607281684875488, -1.5488932132720947] |
e95ca604-5678-4ad0-91bb-cc1e8e7d37b5 | an-unsupervised-multiple-task-and-multiple-1 | null | null | https://aclanthology.org/2022.acl-long.14 | https://aclanthology.org/2022.acl-long.14.pdf | An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition | Cross-lingual named entity recognition task is one of the critical problems for evaluating the potential transfer learning techniques on low resource languages. Knowledge distillation using pre-trained multilingual language models between source and target languages have shown their superiority in transfer. However, ex... | ['Richong Zhang', 'Wenyi Qin', 'Junfan Chen', 'Xiaohui Guo', 'Chunming Hu', 'Zhuoran Li'] | null | null | null | null | acl-2022-5 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.13171004e-01 -7.83105493e-02 -3.08935434e-01 -4.61390346e-01
-1.00074041e+00 -7.50527680e-01 6.41531408e-01 1.07910387e-01
-9.63764489e-01 8.70959461e-01 5.78079410e-02 -4.46725935e-01
1.22643746e-01 -6.20831668e-01 -6.22967780e-01 -4.85716611e-01
4.41217422e-01 6.40640378e-01 2.90909618e-01 -2.53880829... | [9.980419158935547, 9.676006317138672] |
939d29e0-2450-4e5e-b2a6-a348dfed97c2 | aggregation-of-local-parametric-candidates | 1407.5759 | null | http://arxiv.org/abs/1407.5759v1 | http://arxiv.org/pdf/1407.5759v1.pdf | Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow | Handling all together large displacements, motion details and occlusions
remains an open issue for reliable computation of optical flow in a video
sequence. We propose a two-step aggregation paradigm to address this problem.
The idea is to supply local motion candidates at every pixel in a first step,
and then to combi... | ['Patrick Bouthemy', 'Denis Fortun', 'Charles Kervrann'] | 2014-07-22 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.87673029e-02 -4.71473157e-01 -1.17512189e-01 -1.13417907e-03
-7.35883355e-01 -5.20182550e-01 4.73679155e-01 1.46830574e-01
-5.41376472e-01 7.51027226e-01 2.33775869e-01 7.20319748e-02
4.63600382e-02 -4.75291580e-01 -5.78031361e-01 -7.55118787e-01
4.96835820e-03 2.55510956e-01 6.97932959e-01 6.60755439... | [8.793898582458496, -1.7970376014709473] |
8306b73a-18a8-4d41-966e-8790054a07ba | programmatically-interpretable-reinforcement | 1804.02477 | null | http://arxiv.org/abs/1804.02477v3 | http://arxiv.org/pdf/1804.02477v3.pdf | Programmatically Interpretable Reinforcement Learning | We present a reinforcement learning framework, called Programmatically
Interpretable Reinforcement Learning (PIRL), that is designed to generate
interpretable and verifiable agent policies. Unlike the popular Deep
Reinforcement Learning (DRL) paradigm, which represents policies by neural
networks, PIRL represents polic... | ['Swarat Chaudhuri', 'Vijayaraghavan Murali', 'Rishabh Singh', 'Pushmeet Kohli', 'Abhinav Verma'] | 2018-04-06 | programmatically-interpretable-reinforcement-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2203 | http://proceedings.mlr.press/v80/verma18a/verma18a.pdf | icml-2018-7 | ['carracing-v0'] | ['playing-games'] | [ 9.64636430e-02 7.13175237e-01 -5.90243280e-01 -4.02182281e-01
-6.97594583e-01 -7.97721922e-01 8.28162670e-01 2.99489610e-02
-3.88145387e-01 1.11621737e+00 -3.39974687e-02 -9.08916056e-01
-2.48332828e-01 -7.97063351e-01 -1.45649338e+00 -5.61210215e-01
-3.79210472e-01 8.30835581e-01 8.12743753e-02 -1.70982420... | [4.276098728179932, 1.7747491598129272] |
24d3735c-05f6-4111-aa25-211bd22396eb | end-to-end-interpretable-learning-of-non | 2007.01769 | null | https://arxiv.org/abs/2007.01769v2 | https://arxiv.org/pdf/2007.01769v2.pdf | End-to-end Interpretable Learning of Non-blind Image Deblurring | Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal... | ['Jean Ponce', 'Jian Sun', 'Thomas Eboli'] | 2020-07-03 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2749_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620307.pdf | eccv-2020-8 | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.75272197e-01 -1.88890636e-01 1.92072079e-01 -6.08847886e-02
-7.07003534e-01 -4.10766959e-01 5.74064493e-01 -3.14946830e-01
-5.19358993e-01 6.19627714e-01 4.10280228e-01 -1.69497490e-01
-3.07200879e-01 -1.46513626e-01 -7.69243896e-01 -9.67603683e-01
7.75252411e-04 2.08070129e-01 -1.65547758e-01 1.14570417... | [11.639678001403809, -2.6214439868927] |
e0ae5ee1-5083-4649-a1bd-b03744138042 | time-delay-multi-feature-correlation-analysis | 2305.09478 | null | https://arxiv.org/abs/2305.09478v2 | https://arxiv.org/pdf/2305.09478v2.pdf | Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals | Electroencephalography (EEG) signals are resultants of extremely complex brain activity. Some details of this hidden dynamics might be accessible through e.g. joint distributions $\rho_{\Delta t}$ of signals of pairs of electrodes shifted by various time delays (lag $\Delta t$). A standard approach is monitoring a sing... | ['Jarek Duda'] | 2023-04-24 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-9.45586413e-02 -9.99827832e-02 4.32626128e-01 -1.93615615e-01
-4.17985857e-01 -6.42325699e-01 5.99225402e-01 1.95786327e-01
-2.17328742e-01 1.20114017e+00 -1.13596104e-01 -2.62079448e-01
-9.49937761e-01 -5.80144346e-01 -5.40561318e-01 -1.26108587e+00
-1.04747033e+00 4.45449322e-01 3.40535827e-02 -1.94572359... | [12.905491828918457, 3.454606056213379] |
b00c638e-bd22-4a1c-86a0-7938a5727707 | alignment-uniformity-aware-representation | 2203.15381 | null | https://arxiv.org/abs/2203.15381v1 | https://arxiv.org/pdf/2203.15381v1.pdf | Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification | Most methods tackle zero-shot video classification by aligning visual-semantic representations within seen classes, which limits generalization to unseen classes. To enhance model generalizability, this paper presents an end-to-end framework that preserves alignment and uniformity properties for representations on both... | ['Mao Zheng', 'Kaili Zhao', 'Shi Pu'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Pu_Alignment-Uniformity_Aware_Representation_Learning_for_Zero-Shot_Video_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Pu_Alignment-Uniformity_Aware_Representation_Learning_for_Zero-Shot_Video_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 2.36678854e-01 -5.11787012e-02 -4.88778114e-01 -6.19153142e-01
-7.11039782e-01 -5.10429382e-01 6.49525106e-01 4.38755229e-02
-2.29117692e-01 5.23241460e-01 3.33550185e-01 1.65599078e-01
4.71156724e-02 -5.66677392e-01 -8.87065530e-01 -6.89178467e-01
9.99905169e-02 8.38893279e-02 1.64120361e-01 2.13468954... | [9.773507118225098, 2.3816916942596436] |
062862b6-1c22-4746-bae6-043cf186dcd9 | aggression-identification-and-multi-lingual | null | null | https://aclanthology.org/W18-4409 | https://aclanthology.org/W18-4409.pdf | Aggression Identification and Multi Lingual Word Embeddings | The system presented here took part in the 2018 Trolling, Aggression and Cyberbullying shared task (Forest and Trees team) and uses a Gated Recurrent Neural Network architecture (Cho et al., 2014) in an attempt to assess whether combining pre-trained English and Hindi fastText (Mikolov et al., 2018) word embeddings as ... | ['Efstathios Charitos', 'Thiago Galery', 'Ye Tian'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [ 1.97539292e-02 1.57009855e-01 -1.60983011e-01 -2.74424106e-01
-5.37899613e-01 -4.52396840e-01 7.35661268e-01 3.35105687e-01
-9.40978765e-01 4.98994410e-01 7.38047302e-01 -6.94902539e-01
-1.83011591e-02 -5.36852181e-01 -4.80894059e-01 -5.09053826e-01
-4.01442358e-03 3.79420161e-01 -1.04089968e-01 -3.65110308... | [9.096390724182129, 10.656740188598633] |
e102ed8d-7732-4343-8583-e8ff3f4ad5ab | spatio-temporal-crop-classification-on | 2103.10050 | null | https://arxiv.org/abs/2103.10050v1 | https://arxiv.org/pdf/2103.10050v1.pdf | Spatio-temporal Crop Classification On Volumetric Data | Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random For... | ['Abubakr Muhammad', 'Murtaza Taj', 'Salar Saeed', 'Muhammad Usman Qadeer'] | 2021-03-18 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 2.12325469e-01 -6.14433885e-01 -1.45061314e-01 -1.86772153e-01
-4.14044857e-01 -6.45949483e-01 6.94374084e-01 3.93516034e-01
-5.81116617e-01 9.94798958e-01 -4.19646323e-01 -4.84831452e-01
-3.35028470e-01 -1.50293219e+00 -7.03429878e-01 -7.72846282e-01
-4.57845777e-01 8.30170363e-02 2.77454287e-01 -2.80945927... | [9.386289596557617, -1.5526238679885864] |
4bd90a8c-866c-4fd7-9f85-3e0df4dad19b | optimal-visual-search-based-on-a-model-of | null | null | http://proceedings.neurips.cc/paper/2020/hash/691dcb1d65f31967a874d18383b9da75-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/691dcb1d65f31967a874d18383b9da75-Paper.pdf | Optimal visual search based on a model of target detectability in natural images | To analyse visual systems, the concept of an ideal observer promises an optimal response for a given task. Bayesian ideal observers can provide optimal responses under uncertainty, if they are given the true distributions as input. In visual search tasks, prior studies have used signal to noise ratio (SNR) or psychoph... | ['Lars Kulik', 'Andrew Turpin', 'Krista Ehinger', 'Shima Rashidi'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['foveation'] | ['computer-vision'] | [ 3.64450127e-01 -1.28110364e-01 2.37628203e-02 -3.31528127e-01
-3.24106365e-01 -5.18172860e-01 5.40811241e-01 5.17012775e-02
-1.01874626e+00 6.41049623e-01 -1.81593195e-01 -4.25828695e-01
-1.67067200e-01 -1.59479648e-01 -6.39981151e-01 -6.01806998e-01
2.84170121e-01 1.09599560e-01 7.64717996e-01 9.98362303... | [9.981282234191895, 2.0468661785125732] |
a2a724dd-a4e5-4c14-864d-5c826cdd8bf7 | from-pixels-to-sentiment-fine-tuning-cnns-for | 1604.03489 | null | http://arxiv.org/abs/1604.03489v2 | http://arxiv.org/pdf/1604.03489v2.pdf | From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction | Visual multimedia have become an inseparable part of our digital social
lives, and they often capture moments tied with deep affections. Automated
visual sentiment analysis tools can provide a means of extracting the rich
feelings and latent dispositions embedded in these media. In this work, we
explore how Convolution... | ['Xavier Giro-i-Nieto', 'Brendan Jou', 'Victor Campos'] | 2016-04-12 | null | null | null | null | ['visual-sentiment-prediction'] | ['computer-vision'] | [ 1.57526597e-01 9.23878625e-02 -1.54778391e-01 -4.09288138e-01
1.90177828e-01 -5.67116380e-01 8.58599365e-01 3.39859217e-01
-1.37696907e-01 3.56282234e-01 7.20495343e-01 -2.22129479e-01
4.27580714e-01 -6.38817549e-01 -5.45480251e-01 -3.86398584e-01
-2.13261038e-01 -1.63948044e-01 -3.76261562e-01 -6.81369901... | [11.04300308227539, 2.7011077404022217] |
5fddf519-5de8-4ddf-815a-6d9e7ab77a5f | ad-vo-scale-resilient-visual-odometry-using | 2001.02090 | null | https://arxiv.org/abs/2001.02090v1 | https://arxiv.org/pdf/2001.02090v1.pdf | AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map | Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity ... | ['Tae-young Chung', 'Sangwon Hwang', 'Kyungjae Lee', 'Woo Jin Kim', 'Sangyoun Lee', 'Joosung Lee', 'Junhyeop Lee'] | 2020-01-07 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-4.06288773e-01 -4.54036027e-01 -1.68286845e-01 -5.83669305e-01
1.20712809e-01 -2.97241330e-01 3.25453758e-01 -3.85194093e-01
-7.25508988e-01 1.04392302e+00 -6.07858561e-02 -8.15160871e-02
1.14383243e-01 -5.55426598e-01 -5.38948894e-01 -4.29478168e-01
1.13017477e-01 2.74043083e-01 7.07985640e-01 -4.06411886... | [7.699239253997803, -2.1064963340759277] |
eb659e9f-67bd-4491-af30-01cb737d179b | efficient-multivariate-sequence | 1409.8211 | null | http://arxiv.org/abs/1409.8211v2 | http://arxiv.org/pdf/1409.8211v2.pdf | Efficient multivariate sequence classification | Kernel-based approaches for sequence classification have been successfully
applied to a variety of domains, including the text categorization, image
classification, speech analysis, biological sequence analysis, time series and
music classification, where they show some of the most accurate results.
Typical kernel fu... | ['Pavel P. Kuksa'] | 2014-09-29 | null | null | null | null | ['music-classification'] | ['music'] | [ 8.00601900e-01 -7.99846411e-01 -1.22108437e-01 -2.55325884e-01
-6.47991121e-01 -7.81931937e-01 2.29766384e-01 5.47494829e-01
-6.45179451e-01 6.13723814e-01 -3.98561835e-01 -4.18129504e-01
-3.88240188e-01 -2.71168590e-01 -4.51790929e-01 -1.08311689e+00
-3.39948863e-01 1.57647327e-01 2.03910828e-01 -5.59175722... | [7.458225250244141, 3.9595859050750732] |
fa18f0d1-ee8d-4aac-9efe-3db8bbbbe2a9 | jointist-joint-learning-for-multi-instrument | 2206.10805 | null | https://arxiv.org/abs/2206.10805v2 | https://arxiv.org/pdf/2206.10805v2.pdf | Jointist: Joint Learning for Multi-instrument Transcription and Its Applications | In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outpu... | ['Dorien Herremans', 'Ju-Chiang Wang', 'Amy Hung', 'Minz Won', 'Bochen Li', 'Qiuqiang Kong', 'Keunwoo Choi', 'Kin Wai Cheuk'] | 2022-06-22 | null | null | null | null | ['chord-recognition', 'instrument-recognition'] | ['audio', 'audio'] | [ 3.62075478e-01 -1.95944414e-01 -6.93766475e-02 1.72504053e-01
-1.09742546e+00 -9.96344209e-01 3.08353513e-01 1.94644388e-02
8.40950198e-03 2.93992519e-01 1.98646769e-01 -1.75795350e-02
-4.53283012e-01 -3.28382373e-01 -2.11220786e-01 -6.25628054e-01
1.00454085e-01 2.95404077e-01 -9.76715237e-02 -5.08914649... | [15.785526275634766, 5.4031291007995605] |
a43b1ba0-31a8-445c-8800-361987e39b41 | domain-expert-platform-for-goal-oriented | null | null | https://aclanthology.org/2021.eacl-demos.35 | https://aclanthology.org/2021.eacl-demos.35.pdf | Domain Expert Platform for Goal-Oriented Dialog Collection | Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions... | ['Gunta Ne{\\v{s}}pore-B{\\=e}rzkalne', 'Normunds Gruzitis', 'Inguna Skadina', 'Arturs Znotins', 'Didzis Go{\\v{s}}ko'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-1.85966194e-01 7.22893238e-01 2.40609720e-01 -7.72027373e-01
-1.57713369e-01 -6.31722808e-01 9.23356056e-01 4.65191245e-01
-5.23377419e-01 9.31535602e-01 2.91942000e-01 -5.57407379e-01
5.51484637e-02 -9.07529235e-01 2.73352992e-02 -1.89778164e-01
1.16926983e-01 1.34547234e+00 2.39766166e-01 -1.00000906... | [12.906583786010742, 7.9586005210876465] |
972d8edc-1d23-4e1f-8386-962a9b964ce2 | persistent-homology-in-sparse-regression-and | 1409.0177 | null | http://arxiv.org/abs/1409.0177v2 | http://arxiv.org/pdf/1409.0177v2.pdf | Persistent Homology in Sparse Regression and Its Application to Brain Morphometry | Sparse systems are usually parameterized by a tuning parameter that
determines the sparsity of the system. How to choose the right tuning parameter
is a fundamental and difficult problem in learning the sparse system. In this
paper, by treating the the tuning parameter as an additional dimension,
persistent homological... | ['Moo. K. Chung', 'Jamie L. Hanson', 'Richard J. Davidson', 'Seth D. Pollak', 'Jieping Ye'] | 2014-08-31 | null | null | null | null | ['brain-morphometry'] | ['medical'] | [-5.62492125e-02 -2.51460969e-02 -3.04707140e-01 -4.08806980e-01
-5.81624135e-02 -3.54572117e-01 1.87270671e-01 4.04794455e-01
-1.64150581e-01 3.83225352e-01 3.33345324e-01 2.41945803e-01
-5.76027155e-01 -5.90828180e-01 -3.77155215e-01 -8.63826036e-01
-7.03106165e-01 3.67121339e-01 1.01995744e-01 -1.79006219... | [12.411349296569824, 3.3731727600097656] |
34441838-1dc7-43f6-89b1-2423a4cdd0e0 | unsupervised-rgb-to-thermal-domain-adaptation | 2210.04367 | null | https://arxiv.org/abs/2210.04367v1 | https://arxiv.org/pdf/2210.04367v1.pdf | Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network | This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network. Our method does not require any thermal annotations or co-registered RGB-thermal pairs, enabling robots to perform visual tasks at... | ['Soon-Jo Chung', 'Connor Lee', 'Lu Gan'] | 2022-10-09 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.80137974e-01 4.87614125e-02 -3.27338539e-02 -7.14271426e-01
-9.05780971e-01 -8.93348575e-01 3.77568454e-01 -2.98987001e-01
-7.10587919e-01 5.29719710e-01 -2.59229988e-01 -5.05445227e-02
2.18167737e-01 -7.85706818e-01 -7.77857721e-01 -9.82701659e-01
3.13890576e-01 7.13606000e-01 1.26792222e-01 -2.70861030... | [8.84432601928711, -1.8350147008895874] |
2725e026-9fa7-44e1-966e-e66222c9d1a7 | training-transformers-with-4-bit-integers | 2306.11987 | null | https://arxiv.org/abs/2306.11987v2 | https://arxiv.org/pdf/2306.11987v2.pdf | Training Transformers with 4-bit Integers | Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical formats which are not supported by contemporary hardware. In this work, we propose a training method for transformers with all matrix multiplicat... | ['Jun Zhu', 'Jianfei Chen', 'Changhao Li', 'Haocheng Xi'] | 2023-06-21 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 1.65653795e-01 -3.60657781e-01 -3.91683310e-01 -4.05651271e-01
-5.45757174e-01 -1.84781268e-01 1.62712522e-02 2.79458493e-01
-8.16513181e-01 3.40233237e-01 -1.92171618e-01 -8.90386760e-01
4.20389384e-01 -7.46150374e-01 -1.01569438e+00 -4.01619762e-01
-1.38409913e-01 -1.46737350e-02 1.99587032e-01 -1.17388532... | [8.53760814666748, 3.127624988555908] |
98ebee25-ad76-4cd2-b0ac-a66f9ec1506c | continuous-variable-neural-network-quantum | 2107.07105 | null | https://arxiv.org/abs/2107.07105v1 | https://arxiv.org/pdf/2107.07105v1.pdf | Continuous-variable neural-network quantum states and the quantum rotor model | We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization. A simple family of continuous-variable trial wavefunctons is introduced which naturally generalizes the restricted Boltzmann machine (RBM) wavefunction introduced for analyzi... | ['Giuseppe Carleo', 'Shravan Veerapaneni', 'Saibal De', 'James Stokes'] | 2021-07-15 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 3.41242105e-01 -1.67556837e-01 -1.05092771e-01 -2.36424655e-01
-5.94091535e-01 -4.28243816e-01 8.55168939e-01 -6.18506610e-01
-6.97594285e-01 1.26164913e+00 -2.71144986e-01 -6.62811220e-01
-2.80968994e-01 -1.03542399e+00 -3.01080644e-01 -1.32331312e+00
-1.94695488e-01 8.58721614e-01 -3.55806172e-01 -6.55027688... | [5.505845069885254, 4.998385429382324] |
cf540f0a-724e-4303-a276-01f3e75ac591 | text-data-augmentation-towards-better | 2007.02033 | null | https://arxiv.org/abs/2007.02033v2 | https://arxiv.org/pdf/2007.02033v2.pdf | Text Data Augmentation: Towards better detection of spear-phishing emails | Text data augmentation, i.e., the creation of new textual data from an existing text, is challenging. Indeed, augmentation transformations should take into account language complexity while being relevant to the target Natural Language Processing (NLP) task (e.g., Machine Translation, Text Classification). Initially mo... | ['Sébastien Goutal', 'Mehdi Regina', 'Maxime Meyer'] | 2020-07-04 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 7.24293590e-01 4.13080841e-01 -4.17004600e-02 -2.56461114e-01
-9.74682450e-01 -6.32786751e-01 1.08991146e+00 8.13793063e-01
-7.46322989e-01 7.24458039e-01 3.11992079e-01 -6.93883777e-01
1.36654109e-01 -7.34286308e-01 -6.12795651e-01 -1.05270460e-01
7.16336608e-01 9.51254249e-01 -1.03026733e-01 -5.12123227... | [10.678742408752441, 8.597139358520508] |
bf354900-fc49-4926-aa32-24658a87b49a | s2f2-self-supervised-high-fidelity-face | 2203.07732 | null | https://arxiv.org/abs/2203.07732v2 | https://arxiv.org/pdf/2203.07732v2.pdf | S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image | We present a novel face reconstruction method capable of reconstructing detailed face geometry, spatially varying face reflectance from a single monocular image. We build our work upon the recent advances of DNN-based auto-encoders with differentiable ray tracing image formation, trained in self-supervised manner. Whil... | ['Louis Chevallier', 'Philippe-Henri Gosselin', 'Cedric Thebault', 'Junghyun Ahn', 'Abdallah Dib'] | 2022-03-15 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.83186167e-01 1.81229785e-01 4.10898864e-01 -4.88765627e-01
-7.17767715e-01 -1.83479235e-01 6.29667878e-01 -6.98726237e-01
3.19234990e-02 7.14266658e-01 1.80514514e-01 -6.29966904e-04
2.84267068e-02 -1.08753109e+00 -1.07744479e+00 -4.29744422e-01
3.60512793e-01 6.22762978e-01 -2.85731584e-01 -2.17654482... | [12.872145652770996, -0.2965932786464691] |
c670d4fd-7397-46a1-b7b4-69750abd6803 | darker-than-black-box-face-reconstruction | 2106.14290 | null | https://arxiv.org/abs/2106.14290v2 | https://arxiv.org/pdf/2106.14290v2.pdf | Darker than Black-Box: Face Reconstruction from Similarity Queries | Several methods for inversion of face recognition models were recently presented, attempting to reconstruct a face from deep templates. Although some of these approaches work in a black-box setup using only face embeddings, usually, on the end-user side, only similarity scores are provided. Therefore, these algorithms ... | ['Aleksandr Petiushko', 'Igor Udovichenko', 'Klim Kireev', 'Anton Razzhigaev'] | 2021-06-27 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 1.28825858e-01 2.00570002e-01 9.29627195e-02 -5.72455287e-01
-4.96401787e-01 -4.63299602e-01 5.69332242e-01 -5.48556328e-01
-2.07315490e-01 3.65450829e-01 -2.28562340e-01 -2.53812134e-01
-6.01040423e-02 -7.38325298e-01 -7.90673018e-01 -6.52004838e-01
3.74492288e-01 6.30556524e-01 -4.41944189e-02 2.43140236... | [13.125481605529785, 0.30603325366973877] |
84f1d3b1-29a7-404c-8381-c1fe943f9798 | fastventricle-cardiac-segmentation-with-enet | 1704.04296 | null | http://arxiv.org/abs/1704.04296v1 | http://arxiv.org/pdf/1704.04296v1.pdf | FastVentricle: Cardiac Segmentation with ENet | Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac
structure and function. One disadvantage of CMR is that post-processing of
exams is tedious. Without automation, precise assessment of cardiac function
via CMR typically requires an annotator to spend tens of minutes per case
manually contourin... | ['Jesse Lieman-Sifry', 'Sean Sall', 'Matthieu Le', 'Felix Lau', 'Daniel Golden'] | 2017-04-13 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [-4.46944647e-02 1.24236003e-01 1.54198959e-01 -4.66884464e-01
-4.81522381e-01 -7.27214932e-01 -2.35906944e-01 3.44981968e-01
-5.85684776e-01 5.81548154e-01 -2.91374147e-01 -7.55600572e-01
3.31902087e-01 -6.15657926e-01 -2.66756505e-01 -1.83443740e-01
-8.92661735e-02 8.01557183e-01 4.15773362e-01 2.07477465... | [14.258268356323242, -2.5152790546417236] |
0f851c87-73a5-4611-a046-268e72e4b365 | talking-head-generation-with-probabilistic | 2212.04248 | null | https://arxiv.org/abs/2212.04248v1 | https://arxiv.org/pdf/2212.04248v1.pdf | Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors | In this paper, we introduce a simple and novel framework for one-shot audio-driven talking head generation. Unlike prior works that require additional driving sources for controlled synthesis in a deterministic manner, we instead probabilistically sample all the holistic lip-irrelevant facial motions (i.e. pose, expres... | ['Baoyuan Wang', 'Finn Wong', 'Duomin Wang', 'Deyu Zhou', 'Zixin Yin', 'Zhentao Yu'] | 2022-12-07 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 3.32728736e-02 1.11291394e-01 -1.59461275e-01 -3.04086745e-01
-1.25472963e+00 -4.29746002e-01 6.28435731e-01 -6.02598965e-01
1.64979056e-01 5.75270712e-01 8.54361296e-01 5.12807846e-01
1.67006910e-01 -2.77991235e-01 -6.94809496e-01 -9.59570587e-01
3.21515352e-01 -1.09019363e-02 2.25457307e-02 -2.79726028... | [13.224620819091797, -0.41813868284225464] |
f80d13bd-c872-42d8-bb96-a702b41f4a50 | face-image-quality-assessment-a-literature | 2009.01103 | null | https://arxiv.org/abs/2009.01103v3 | https://arxiv.org/pdf/2009.01103v3.pdf | Face Image Quality Assessment: A Literature Survey | The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey pro... | ['Torsten Schlett', 'Julian Fierrez', 'Olaf Henniger', 'Javier Galbally', 'Christoph Busch', 'Christian Rathgeb'] | 2020-09-02 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 4.12968069e-01 -2.05793485e-01 2.10767929e-02 -7.26381302e-01
-6.39233351e-01 -4.19852257e-01 4.36545044e-01 -1.30217686e-01
-2.07909152e-01 4.27539021e-01 9.11687464e-02 -3.59911025e-02
-6.55321717e-01 -8.77084970e-01 -1.73141479e-01 -1.05905187e+00
8.74240696e-02 3.74149024e-01 -7.36462891e-01 -3.80639806... | [13.107956886291504, 0.8547645211219788] |
6d67f1b1-dbd6-4759-99e4-b2262764a053 | multi-scale-spatial-temporal-interaction | 2306.10239 | null | https://arxiv.org/abs/2306.10239v2 | https://arxiv.org/pdf/2306.10239v2.pdf | Multi-scale Spatial-temporal Interaction Network for Video Anomaly Detection | Video Anomaly Detection (VAD) is an essential yet challenging task in signal processing. Since certain anomalies cannot be detected by isolated analysis of either temporal or spatial information, the interaction between these two types of data is considered crucial for VAD. However, current dual-stream architectures ei... | ['Zile Wang', 'Zhengliang Guo', 'Liang Song', 'Zhangxun Li', 'Zhiyuan Ning'] | 2023-06-17 | null | null | null | null | ['video-anomaly-detection', 'anomaly-detection'] | ['computer-vision', 'methodology'] | [ 2.29033642e-02 -5.21823049e-01 1.66672945e-01 -2.53381670e-01
-3.02672416e-01 -1.73003495e-01 5.31221211e-01 2.05974802e-01
-3.55179369e-01 3.14125389e-01 5.80203719e-02 -2.45635271e-01
-9.93578807e-02 -6.42440677e-01 -4.52631474e-01 -7.08559632e-01
-3.32288057e-01 -2.70347744e-01 5.60782611e-01 -1.63233206... | [7.898193359375, 1.5592492818832397] |
5d604986-51d1-43ac-a35c-4fbed1d9a38e | integrating-semantic-scenario-and-word | null | null | https://aclanthology.org/2021.emnlp-main.196 | https://aclanthology.org/2021.emnlp-main.196.pdf | Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization | Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we pro... | ['Hu Zhang', 'XiaoLi Li', 'Ru Li', 'Shaoru Guo', 'Yong Guan'] | null | null | null | null | emnlp-2021-11 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 3.31946820e-01 2.93039858e-01 -3.93456310e-01 -1.89045757e-01
-4.93985653e-01 -3.03957433e-01 5.35721779e-01 6.69014692e-01
-1.81367263e-01 1.02480352e+00 1.27294374e+00 8.59081522e-02
-1.93575144e-01 -9.49909329e-01 -4.46714848e-01 -2.96469003e-01
3.57727140e-01 2.70033479e-01 4.12183046e-01 -5.49592972... | [12.609848022460938, 9.556604385375977] |
1b803022-58ab-48c9-8e5a-ceb63fbe6d8c | on-feature-diversity-in-energy-based-models-1 | 2306.01489 | null | https://arxiv.org/abs/2306.01489v1 | https://arxiv.org/pdf/2306.01489v1.pdf | On Feature Diversity in Energy-based Models | Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-model(s) that learn a combination of the different features to generate an energy mapping for each input configuration. In this paper, we foc... | ['Moncef Gabbouj', 'Alexandros Iosifidis', 'Jenni Raitoharju', 'Firas Laakom'] | 2023-06-02 | on-feature-diversity-in-energy-based-models | https://openreview.net/forum?id=ks3Q08yy66r | https://openreview.net/pdf?id=ks3Q08yy66r | iclr-workshop-ebm-2021-5 | ['generalization-bounds'] | ['methodology'] | [ 1.77506760e-01 -9.97998640e-02 -9.33450162e-02 -2.84396857e-01
-8.49103749e-01 -5.08168519e-01 5.70511222e-01 2.91753411e-02
-1.70941412e-01 7.02927232e-01 -1.00590838e-02 -3.35352011e-02
-3.68412256e-01 -8.14464569e-01 -1.08234310e+00 -1.11980486e+00
1.10072151e-01 2.44890228e-01 1.17691174e-01 -6.03496134... | [7.29017448425293, 3.8291916847229004] |
4ddb6fb0-799a-4cf5-ad1f-89358010f6bb | gazeformer-scalable-effective-and-fast | 2303.15274 | null | https://arxiv.org/abs/2303.15274v3 | https://arxiv.org/pdf/2303.15274v3.pdf | Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention | Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate in their spatial and temporal gaze predictions. Recent scanpath prediction models focus on goal-directed attention (search). Such models are... | ['Minh Hoai', 'Gregory Zelinsky', 'Dimitris Samaras', 'Seoyoung Ahn', 'Zhibo Yang', 'Sounak Mondal'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mondal_Gazeformer_Scalable_Effective_and_Fast_Prediction_of_Goal-Directed_Human_Attention_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mondal_Gazeformer_Scalable_Effective_and_Fast_Prediction_of_Goal-Directed_Human_Attention_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scanpath-prediction', 'eye-tracking'] | ['computer-vision', 'computer-vision'] | [ 5.26250482e-01 1.62197500e-01 -5.31680703e-01 -3.30390334e-01
-5.37713170e-01 -4.48959544e-02 4.51977164e-01 -2.14120790e-01
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-9.60596055e-02 -2.20288724e-01 -5.53622782e-01 -3.00174713e-01
2.32117891e-01 2.83116758e-01 5.22182703e-01 -4.52070445... | [14.002553939819336, 0.05459184944629669] |
8c9e44e6-102a-4f9e-a5fb-e1d9120d6113 | rfnet-4d-joint-object-reconstruction-and-flow | 2203.16482 | null | https://arxiv.org/abs/2203.16482v2 | https://arxiv.org/pdf/2203.16482v2.pdf | RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds | Object reconstruction from 3D point clouds has achieved impressive progress in the computer vision and computer graphics research field. However, reconstruction from time-varying point clouds (a.k.a. 4D point clouds) is generally overlooked. In this paper, we propose a new network architecture, namely RFNet-4D, that jo... | ['Duc Thanh Nguyen', 'Sai-Kit Yeung', 'Quang-Hieu Pham', 'Binh-Son Hua', 'Tuan-Anh Vu'] | 2022-03-30 | null | null | null | null | ['3d-human-reconstruction', 'object-reconstruction', 'human-dynamics'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.08290446e-01 -6.53239310e-01 -3.33703786e-01 -2.87164748e-01
-2.12572172e-01 -5.51590919e-01 5.21816015e-01 -2.14702874e-01
-8.28641653e-02 4.45727468e-01 1.33418947e-01 -2.48571947e-01
-6.31656870e-02 -8.50412130e-01 -8.90089035e-01 -4.45339173e-01
-3.17567945e-01 3.54613811e-01 4.06428874e-01 2.77333111... | [8.533296585083008, -2.0291359424591064] |
e69a9e39-4694-478e-9a22-65141a075ae4 | the-cl-scisumm-shared-task-2018-results-and | 1909.00764 | null | https://arxiv.org/abs/1909.00764v1 | https://arxiv.org/pdf/1909.00764v1.pdf | The CL-SciSumm Shared Task 2018: Results and Key Insights | This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers ... | ['Min-Yen Kan', 'Muthu Kumar Chandrasekaran', 'Kokil Jaidka', 'Dragomir Radev', 'Michihiro Yasunaga'] | 2019-09-02 | null | null | null | null | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 3.99168901e-04 2.02699080e-01 -4.45437193e-01 5.77699021e-02
-1.56306219e+00 -7.74955630e-01 7.64459968e-01 8.60758424e-01
-5.44787288e-01 1.05245721e+00 9.78327751e-01 -2.16116041e-01
-1.84569925e-01 -1.66347995e-01 -4.89150465e-01 -2.38131866e-01
1.49210170e-02 4.66001272e-01 -3.45519260e-02 -7.60371611... | [12.386625289916992, 9.572855949401855] |
1c7da8e7-c98b-45b8-a2ba-676a3d0059a6 | zero-shot-visual-slot-filling-as-question | 2011.12340 | null | https://arxiv.org/abs/2011.12340v2 | https://arxiv.org/pdf/2011.12340v2.pdf | Zero-Shot Visual Slot Filling as Question Answering | This paper presents a new approach to slot filling by reformulating the slot filling task as Question Answering, and replacing slot tags with rich natural language questions that capture the semantics of visual information and lexical text often displayed on device screens. These questions are paired with the user's ut... | ['Simon Heck', 'Larry Heck'] | 2020-11-24 | null | null | null | null | ['zero-shot-slot-filling'] | ['natural-language-processing'] | [ 4.24803138e-01 6.94930077e-01 -3.49155664e-01 -4.84359145e-01
-1.30692232e+00 -4.29050803e-01 4.72842276e-01 3.02568704e-01
-3.52525949e-01 4.88529295e-01 4.62378770e-01 -7.37563014e-01
2.63907284e-01 -3.81233364e-01 -5.05622447e-01 8.80965143e-02
4.82194930e-01 1.04287446e+00 7.76225448e-01 -4.27475065... | [12.539170265197754, 7.314788818359375] |
1fa181bb-06c2-4f9b-918e-700f44b64799 | multimodal-generation-of-novel-action | 2208.01910 | null | https://arxiv.org/abs/2208.01910v1 | https://arxiv.org/pdf/2208.01910v1.pdf | Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living | Domain shifts, such as appearance changes, are a key challenge in real-world applications of activity recognition models, which range from assistive robotics and smart homes to driver observation in intelligent vehicles. For example, while simulations are an excellent way of economical data collection, a Synthetic-to-R... | ['Rainer Stiefelhagen', 'Alina Roitberg', 'David Schneider', 'Zdravko Marinov'] | 2022-08-03 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 3.10405225e-01 2.14209333e-01 -2.50933319e-01 -4.09806281e-01
-5.83195090e-01 -5.77975869e-01 8.51965487e-01 -3.54716897e-01
-3.95887077e-01 8.95048797e-01 2.20629022e-01 2.57876694e-01
8.46837163e-02 -5.58449030e-01 -9.73958433e-01 -8.36037397e-01
-8.05396140e-02 5.67588389e-01 1.89906999e-01 -2.22918093... | [8.078276634216309, 0.5704615116119385] |
daaa86a0-6a23-4217-afdf-d2f1247980df | teaching-language-models-to-support-answers | 2203.11147 | null | https://arxiv.org/abs/2203.11147v1 | https://arxiv.org/pdf/2203.11147v1.pdf | Teaching language models to support answers with verified quotes | Recent large language models often answer factual questions correctly. But users can't trust any given claim a model makes without fact-checking, because language models can hallucinate convincing nonsense. In this work we use reinforcement learning from human preferences (RLHP) to train "open-book" QA models that gene... | ['Nat McAleese', 'Geoffrey Irving', 'Lucy Campbell-Gillingham', 'Susannah Young', 'Mia Glaese', 'Martin Chadwick', 'Francis Song', 'John Aslanides', 'Vladimir Mikulik', 'Maja Trebacz', 'Jacob Menick'] | 2022-03-21 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [-3.55076820e-01 8.66671860e-01 -2.50722706e-01 -1.89159989e-01
-1.56700528e+00 -1.05243158e+00 7.13065147e-01 3.24140728e-01
-5.12517452e-01 1.25685108e+00 5.13780951e-01 -9.30873990e-01
-8.15543905e-02 -9.48642373e-01 -1.02513957e+00 -1.19625621e-01
6.32872641e-01 7.58723497e-01 1.85705721e-01 -5.46137035... | [10.699180603027344, 7.865109443664551] |
b3fb821b-a183-477f-9cac-38e73bd250ca | detection-and-resolution-of-rumours-in-social | 1704.00656 | null | http://arxiv.org/abs/1704.00656v3 | http://arxiv.org/pdf/1704.00656v3.pdf | Detection and Resolution of Rumours in Social Media: A Survey | Despite the increasing use of social media platforms for information and news
gathering, its unmoderated nature often leads to the emergence and spread of
rumours, i.e. pieces of information that are unverified at the time of posting.
At the same time, the openness of social media platforms provides opportunities
to st... | ['Rob Procter', 'Kalina Bontcheva', 'Ahmet Aker', 'Maria Liakata', 'Arkaitz Zubiaga'] | 2017-04-03 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-3.81750047e-01 2.73704976e-01 -4.21281725e-01 -9.16401222e-02
-1.77525338e-02 -3.84951591e-01 9.66716468e-01 8.50464880e-01
-7.70411789e-02 7.45480359e-01 6.96309268e-01 -3.98147613e-01
7.40347728e-02 -8.11828136e-01 -1.43974498e-01 -1.29994899e-01
-4.74915773e-01 2.89699644e-01 2.17366502e-01 -7.58340240... | [8.241066932678223, 10.117903709411621] |
5e499a49-68ed-4d96-acc2-70d734e0c74f | visual-context-aware-convolution-filters-for | 1906.09986 | null | https://arxiv.org/abs/1906.09986v1 | https://arxiv.org/pdf/1906.09986v1.pdf | Visual Context-aware Convolution Filters for Transformation-invariant Neural Network | We propose a novel visual context-aware filter generation module which incorporates contextual information present in images into Convolutional Neural Networks (CNNs). In contrast to traditional CNNs, we do not employ the same set of learned convolution filters for all input image instances. Our proposed input-conditio... | ['Suraj Tripathi', 'Abhay Kumar', 'Chirag Singh'] | 2019-06-15 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 2.18769684e-01 -2.14227378e-01 1.74052149e-01 -5.01778662e-01
4.85879593e-02 -6.31737590e-01 6.50358379e-01 -1.48708925e-01
-8.60927641e-01 5.90702772e-01 6.71997219e-02 -3.28728169e-01
-1.71989053e-01 -8.64204526e-01 -9.80653107e-01 -4.70465511e-01
1.86922833e-01 -3.29535544e-01 3.88939500e-01 -2.04865828... | [9.047469139099121, 2.168177366256714] |
f727ae2c-2de2-4234-8291-cb332501f250 | visual-reference-resolution-using-attention | 1709.07992 | null | http://arxiv.org/abs/1709.07992v3 | http://arxiv.org/pdf/1709.07992v3.pdf | Visual Reference Resolution using Attention Memory for Visual Dialog | Visual dialog is a task of answering a series of inter-dependent questions
given an input image, and often requires to resolve visual references among the
questions. This problem is different from visual question answering (VQA),
which relies on spatial attention (a.k.a. visual grounding) estimated from an
image and qu... | ['Andreas Lehrmann', 'Paul Hongsuck Seo', 'Bohyung Han', 'Leonid Sigal'] | 2017-09-23 | visual-reference-resolution-using-attention-1 | http://papers.nips.cc/paper/6962-visual-reference-resolution-using-attention-memory-for-visual-dialog | http://papers.nips.cc/paper/6962-visual-reference-resolution-using-attention-memory-for-visual-dialog.pdf | neurips-2017-12 | ['parameter-prediction'] | ['miscellaneous'] | [ 7.51608796e-03 1.46266803e-01 2.19849870e-01 -2.51436472e-01
-1.03418648e+00 -6.44629717e-01 5.98401010e-01 2.42999136e-01
-5.70533097e-01 5.15820265e-01 3.48873287e-01 -4.00531352e-01
1.29115075e-01 -4.44716424e-01 -8.61256421e-01 -5.41789532e-01
5.72639763e-01 5.84510446e-01 6.52669549e-01 -3.35105866... | [10.804866790771484, 1.6226246356964111] |
7a292b3c-85c1-4e57-9ed5-7306d8dc2dc1 | stock-price-forecast-with-deep-learning | 2103.14081 | null | https://arxiv.org/abs/2103.14081v1 | https://arxiv.org/pdf/2103.14081v1.pdf | Stock price forecast with deep learning | In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different... | ['Ikhlaas Gurrib', 'Linda Smail', 'Firuz Kamalov'] | 2021-03-21 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-1.08055842e+00 -1.19594619e-01 -1.50680467e-01 -2.99023658e-01
-3.83591592e-01 -5.29024720e-01 5.30969381e-01 -1.98199958e-01
-4.07783478e-01 1.04692471e+00 1.35172531e-01 -6.40742958e-01
-3.08990479e-01 -8.31615746e-01 -3.17975372e-01 -4.96791363e-01
-6.34747982e-01 3.57532561e-01 1.64567865e-02 -5.04018903... | [4.45042610168457, 4.232295989990234] |
6e179221-cc97-42ba-9cee-6816318dc6e1 | multi-resolution-autoregressive-graph-to | 1907.11223 | null | https://arxiv.org/abs/1907.11223v2 | https://arxiv.org/pdf/1907.11223v2.pdf | Hierarchical Graph-to-Graph Translation for Molecules | The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize ... | ['Wengong Jin', 'Regina Barzilay', 'Tommi Jaakkola'] | 2019-06-11 | null | https://openreview.net/forum?id=rJeeKTNKDB | https://openreview.net/pdf?id=rJeeKTNKDB | null | ['graph-to-graph-translation'] | ['graphs'] | [ 6.75404966e-01 3.20915610e-01 -6.31526291e-01 -2.22334251e-01
-9.00022507e-01 -6.77220047e-01 3.62160623e-01 8.75645459e-01
-3.66226315e-01 1.17365718e+00 3.68315995e-01 -7.51993656e-01
2.62366295e-01 -5.92544854e-01 -1.32973361e+00 -5.33506155e-01
-1.22849397e-01 6.42952681e-01 -1.54670373e-01 -2.73002207... | [4.770712375640869, 5.928679466247559] |
bcc58847-db67-41f9-8d2e-a73fbd169569 | the-news-delivery-channel-recommendation | 2306.10022 | null | https://arxiv.org/abs/2306.10022v1 | https://arxiv.org/pdf/2306.10022v1.pdf | The News Delivery Channel Recommendation Based on Granular Neural Network | With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. The... | ['Wong-Hing Lam', 'Jiaxuan Liu', 'Rui Li', 'Lin Wu'] | 2023-05-30 | null | null | null | null | ['collaborative-filtering', 'machine-translation'] | ['miscellaneous', 'natural-language-processing'] | [-4.14751738e-01 -5.16982675e-01 -5.31111538e-01 -4.13458735e-01
-3.46375965e-02 -1.36994943e-01 6.27544701e-01 3.47426564e-01
-2.14860648e-01 4.29672331e-01 6.53422356e-01 -2.42098927e-01
-3.67916644e-01 -1.25593781e+00 -5.11293769e-01 -5.02100468e-01
2.28080571e-01 3.95485371e-01 2.13657349e-01 -4.16195214... | [10.139934539794922, 5.623075485229492] |
5694d657-aed4-479f-94ba-87adb7ba437a | clexis2-a-new-corpus-for-complex-word | null | null | https://aclanthology.org/2021.ranlp-main.121 | https://aclanthology.org/2021.ranlp-main.121.pdf | CLexIS2: A New Corpus for Complex Word Identification Research in Computing Studies | Reading is a complex process not only because of the words or sections that are difficult for the reader to understand. Complex word identification (CWI) is the task of detecting in the content of documents the words that are difficult or complex to understand by the people of a certain group. Annotated corpora for Eng... | ['Arturo Montejo-Ráez', 'Jenny A. Ortiz Zambrano'] | null | null | https://aclanthology.org/2021.ranlp-1.121 | https://aclanthology.org/2021.ranlp-1.121.pdf | ranlp-2021-9 | ['lexical-simplification', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.60575265e-01 2.09015206e-01 1.58453077e-01 -2.10728616e-01
-4.34201866e-01 -5.35541952e-01 6.16764247e-01 1.00049305e+00
-6.75591350e-01 6.57632589e-01 4.94712383e-01 -4.69501585e-01
-3.40377808e-01 -5.98511815e-01 -3.36935997e-01 -3.87222260e-01
5.28990746e-01 5.93022108e-01 3.34726982e-02 -4.35549736... | [10.738299369812012, 10.345609664916992] |
0adf03f2-159c-4484-a5df-c21a48e6b12a | learning-adaptive-receptive-fields-for-deep | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Wei_Learning_Adaptive_Receptive_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wei_Learning_Adaptive_Receptive_CVPR_2017_paper.pdf | Learning Adaptive Receptive Fields for Deep Image Parsing Network | In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically. Unlike previous works which have stressed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels, our approach uses two affine transformation layers i... | ['Zhen Wei', 'Jinqiao Wang', 'Si Liu', 'Yao Sun', 'Hanjiang Lai'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['face-parsing'] | ['computer-vision'] | [ 4.63952482e-01 5.61291397e-01 5.69486292e-03 -8.56853306e-01
-1.39893666e-01 -7.00480342e-01 4.14854556e-01 -4.10812050e-01
-7.26582229e-01 5.05558610e-01 6.01637848e-02 -1.21666059e-01
-3.07927523e-02 -8.66584599e-01 -8.20879579e-01 -6.71538234e-01
1.23482376e-01 -1.69075966e-01 2.81378120e-01 -3.46949786... | [13.429525375366211, 0.7268819212913513] |
a0897295-2f8f-4c4e-9699-60ec457960f0 | visually-grounded-few-shot-word-learning-in | 2306.11371 | null | https://arxiv.org/abs/2306.11371v2 | https://arxiv.org/pdf/2306.11371v2.pdf | Visually grounded few-shot word learning in low-resource settings | We propose a visually grounded speech model that learns new words and their visual depictions from just a few word-image example pairs. Given a set of test images and a spoken query, we ask the model which image depicts the query word. Previous work has simplified this few-shot learning problem by either using an artif... | ['Herman Kamper', 'Dan Oneata', 'Leanne Nortje'] | 2023-06-20 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [ 3.65558267e-01 1.19908072e-01 4.04621884e-02 -4.53820229e-01
-1.19461274e+00 -2.23970011e-01 8.25277448e-01 -6.12071082e-02
-7.79538810e-01 6.50833607e-01 2.29082897e-01 -6.20289892e-03
4.29668903e-01 -6.68092310e-01 -7.62523532e-01 -5.93106627e-01
1.69827431e-01 5.39014220e-01 5.53553402e-01 -4.54771638... | [10.404559135437012, 1.9685567617416382] |
c060b0be-89d3-43bd-b34c-1e073503a430 | dynamic-message-propagation-network-for-rgb-d | 2206.09552 | null | https://arxiv.org/abs/2206.09552v1 | https://arxiv.org/pdf/2206.09552v1.pdf | Dynamic Message Propagation Network for RGB-D Salient Object Detection | This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects. To ... | ['Jing Qin', 'Mingqiang Wei', 'Haoran Xie', 'Jun Xu', 'Xiaowei Hu', 'Zhilei Chen', 'Baian Chen'] | 2022-06-20 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.94541857e-01 7.76503906e-02 1.80854678e-01 -4.84746128e-01
-4.11538661e-01 -1.70663908e-01 4.90905285e-01 4.46403384e-01
-4.28689152e-01 1.39295131e-01 2.52763212e-01 1.06052207e-02
-1.87365651e-01 -9.07394826e-01 -7.57216871e-01 -6.90246582e-01
-4.50905621e-01 1.26942590e-01 9.35457587e-01 -3.05992335... | [9.733587265014648, -0.7327616214752197] |
470ef5bc-52f8-44e7-a75e-d33940d9474c | information-theoretic-limits-of-exact | 2101.12369 | null | https://arxiv.org/abs/2101.12369v1 | https://arxiv.org/pdf/2101.12369v1.pdf | Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection | In this paper, we study the information theoretic bounds for exact recovery in sub-hypergraph models for community detection. We define a general model called the $m-$uniform sub-hypergraph stochastic block model ($m-$ShSBM). Under the $m-$ShSBM, we use Fano's inequality to identify the region of model parameters where... | ['Jean Honorio', 'Chuyang Ke', 'Jiajun Liang'] | 2021-01-29 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 4.63885516e-01 4.52449709e-01 -5.40559828e-01 3.05317074e-01
-7.55234063e-01 -5.69475234e-01 -5.70504367e-02 1.87152833e-01
9.34700891e-02 6.97417676e-01 -4.26049858e-01 -5.77777863e-01
-5.13186038e-01 -1.11378610e+00 -6.72025442e-01 -9.12750244e-01
-6.88765943e-01 8.75459790e-01 3.63898069e-01 1.27603874... | [6.8741912841796875, 5.109037399291992] |
7731c757-803d-45af-8bc3-7bbf0c88aa19 | mnemonic-descent-method-a-recurrent-process-1 | null | null | https://www.ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf | https://www.ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf | Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment | Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of casc... | ['P. Snape', 'E. Antonakos', 'S. Zafeiriou', 'M. A. Nicolaou', 'G. Trigeorgis'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['head-pose-estimation'] | ['computer-vision'] | [ 8.65965188e-02 2.81522661e-01 6.35323487e-03 -3.64072293e-01
-4.31787997e-01 -1.78455308e-01 6.81373894e-01 -2.21614823e-01
-6.08363330e-01 3.85959864e-01 6.10285960e-02 -6.11679964e-02
-7.63444304e-02 -2.71566361e-01 -8.56121123e-01 -7.84507751e-01
-1.14886329e-01 3.89506668e-01 -5.24707437e-02 -5.66568255... | [13.532952308654785, 0.30707037448883057] |
c0c426d7-6f9b-4c14-b429-be14114a1b3b | common-phone-a-multilingual-dataset-for | 2201.05912 | null | https://arxiv.org/abs/2201.05912v2 | https://arxiv.org/pdf/2201.05912v2.pdf | Common Phone: A Multilingual Dataset for Robust Acoustic Modelling | Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function. An ideal dataset is not necessarily large in size, but large with respect to the amount of unique sp... | ['Juan Rafael Orozco-Arroyave', 'Elmar Nöth', 'Paula Andrea Pérez-Toro', 'Tomás Arias-Vergara', 'Philipp Klumpp'] | 2022-01-15 | null | https://aclanthology.org/2022.lrec-1.81 | https://aclanthology.org/2022.lrec-1.81.pdf | lrec-2022-6 | ['acoustic-modelling'] | ['speech'] | [-1.83217779e-01 1.16652347e-01 -6.44180253e-02 -6.33096099e-01
-1.21787071e+00 -8.14190805e-01 3.21086049e-01 -2.46093258e-01
-4.25299972e-01 4.25148875e-01 9.45137665e-02 -4.85847682e-01
3.35382879e-01 -2.28818446e-01 -6.72932982e-01 -6.17284298e-01
4.60748896e-02 9.40278530e-01 -4.19020392e-02 -1.41096309... | [14.50281810760498, 6.745894908905029] |
acc3eb4f-20fb-4d6c-a9ea-4a8baa9b0a07 | nu-wave-a-diffusion-probabilistic-model-for | 2104.02321 | null | https://arxiv.org/abs/2104.02321v2 | https://arxiv.org/pdf/2104.02321v2.pdf | NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling | In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz. NU-Wave is the first diffusion probabilistic model for audio super-resolution which is engineered based on neural ... | ['Seungu Han', 'Junhyeok Lee'] | 2021-04-06 | null | null | null | null | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 1.21468179e-01 7.67061785e-02 -2.21565083e-01 -8.87954682e-02
-1.50065947e+00 -4.70373809e-01 3.74583691e-01 -2.95284718e-01
-6.79257661e-02 7.48242855e-01 7.11209476e-01 -2.57214487e-01
5.18840142e-02 -6.03561282e-01 -6.14162803e-01 -3.79784703e-01
-2.43117705e-01 -4.06844094e-02 3.08910757e-01 6.13559633... | [15.423030853271484, 5.836530685424805] |
266f4577-baf1-429b-892c-35c037341397 | 3d-pictorial-structures-for-multiple-human | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper.pdf | 3D Pictorial Structures for Multiple Human Pose Estimation | In this work, we address the problem of 3D pose estimation of multiple humans from multiple views. This is a more challenging problem than single human 3D pose estimation due to the much larger state space, partial occlusions as well as across view ambiguities when not knowing the identity of the humans in advance. To ... | ['Nassir Navab', 'Mykhaylo Andriluka', 'Vasileios Belagiannis', 'Slobodan Ilic', 'Sikandar Amin', 'Bernt Schiele'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-8.65033865e-02 1.04497001e-01 1.30057037e-01 -5.65280765e-02
-3.30719411e-01 -5.48665643e-01 4.42482144e-01 -2.42453635e-01
-5.78605294e-01 6.91673756e-01 7.42775798e-02 3.77858520e-01
1.61028564e-01 -2.21802771e-01 -8.07163477e-01 -2.51740456e-01
-1.05549254e-01 1.19451189e+00 6.41862631e-01 -2.33775839... | [7.017344951629639, -0.9885551333427429] |
06b87819-0c6f-47d6-be92-cb196678aa5f | personalized-food-recommendation-as | 2101.01775 | null | https://arxiv.org/abs/2101.01775v1 | https://arxiv.org/pdf/2101.01775v1.pdf | Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph | Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' explicit requirements, ii) ignore crucial health factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich food knowledge fo... | ['Mohammed J. Zaki', 'Ching-Hua Chen', 'Ananya Subburathinam', 'Yu Chen'] | 2021-01-05 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [-7.22405454e-03 2.89819539e-01 -6.85314357e-01 -7.43198037e-01
-6.28294468e-01 -7.27869213e-01 -3.48402232e-01 9.01895344e-01
-2.71929979e-01 4.19637203e-01 8.67276132e-01 -1.83648199e-01
-3.40396583e-01 -1.32376766e+00 -6.71271861e-01 -1.69645786e-01
1.52348697e-01 3.41022730e-01 3.79565537e-01 -7.65223145... | [11.526190757751465, 4.513442516326904] |
5f7ab7e9-b356-428a-aa14-8f9490d6d570 | defending-against-patch-based-backdoor | 2304.01482 | null | https://arxiv.org/abs/2304.01482v1 | https://arxiv.org/pdf/2304.01482v1.pdf | Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning | Recently, self-supervised learning (SSL) was shown to be vulnerable to patch-based data poisoning backdoor attacks. It was shown that an adversary can poison a small part of the unlabeled data so that when a victim trains an SSL model on it, the final model will have a backdoor that the adversary can exploit. This work... | ['Liang Tan', 'Hamed Pirsiavash', 'Hamed Firooz', 'Sinong Wang', 'Qifan Wang', 'Maziar Sanjabi', 'Ajinkya Tejankar'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tejankar_Defending_Against_Patch-Based_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tejankar_Defending_Against_Patch-Based_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-poisoning'] | ['adversarial'] | [-1.04481816e-01 5.05499765e-02 -3.63521159e-01 -3.68659832e-02
-1.20900393e+00 -1.17457962e+00 5.78958452e-01 2.91258097e-01
-5.12493789e-01 6.04172111e-01 -2.98211873e-01 -5.08863747e-01
4.12693590e-01 -7.96963632e-01 -1.12450945e+00 -9.41841125e-01
6.22511618e-02 3.39829624e-01 5.48961639e-01 -8.16821381... | [5.819960594177246, 7.566900730133057] |
49d6ac03-acb8-4bfc-a0d0-da99b45f8664 | one-configuration-to-rule-them-all-towards | 2202.07631 | null | https://arxiv.org/abs/2202.07631v1 | https://arxiv.org/pdf/2202.07631v1.pdf | One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization | Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constitute good document representations for downstream tasks (e.g. document classification). In this paper... | ['Elisabetta Fersini', 'Raphael Troncy', 'Pasquale Lisena', 'Ismail Harrando', 'Silvia Terragni'] | 2022-02-15 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.35196149e-02 5.15131280e-02 -5.32063067e-01 -4.48073655e-01
-8.08396995e-01 -4.86547619e-01 9.13581252e-01 5.10616243e-01
-8.26545954e-02 8.14990520e-01 3.80697638e-01 9.73732844e-02
-4.78265077e-01 -6.48753822e-01 -2.92359591e-01 -1.07007754e+00
-5.24120331e-02 7.66242027e-01 1.18154205e-01 -3.59107591... | [10.396595001220703, 6.979869365692139] |
beb38c1b-8f40-4e7c-9a2d-bee9584ef9fb | fooling-ocr-systems-with-adversarial-text | 1802.05385 | null | http://arxiv.org/abs/1802.05385v1 | http://arxiv.org/pdf/1802.05385v1.pdf | Fooling OCR Systems with Adversarial Text Images | We demonstrate that state-of-the-art optical character recognition (OCR)
based on deep learning is vulnerable to adversarial images. Minor modifications
to images of printed text, which do not change the meaning of the text to a
human reader, cause the OCR system to "recognize" a different text where
certain words chos... | ['Vitaly Shmatikov', 'Congzheng Song'] | 2018-02-15 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 8.36084008e-01 -1.26755506e-01 2.23253950e-01 -2.26557553e-01
-3.03431898e-01 -1.44470274e+00 6.23915970e-01 -2.27414295e-01
-7.61044443e-01 6.53785408e-01 -2.38069281e-01 -5.64609230e-01
6.05030775e-01 -7.76540220e-01 -1.14165473e+00 -6.07493520e-01
6.26517951e-01 2.44405106e-01 9.57162082e-02 -2.16766000... | [5.827850341796875, 8.031594276428223] |
8dde5546-5c8b-4549-93d2-6321d44d592f | recent-advances-in-direct-speech-to-text | 2306.11646 | null | https://arxiv.org/abs/2306.11646v1 | https://arxiv.org/pdf/2306.11646v1.pdf | Recent Advances in Direct Speech-to-text Translation | Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly. In this paper, we present a comprehensive survey on direct speech translation aiming to summarize the current state-of-the-art techniques. First, we categorize the existing research work into three directio... | ['Jingbo Zhu', 'Tong Xiao', 'Mingxuan Wang', 'Tom Ko', 'Chengqi Zhao', 'Qianqian Dong', 'Rong Ye', 'Chen Xu'] | 2023-06-20 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.45275810e-01 5.79032786e-02 -5.21955311e-01 -5.61787486e-01
-1.36136484e+00 -3.52041721e-01 6.10521197e-01 -2.88823575e-01
-3.55712891e-01 1.00253224e+00 2.49647081e-01 -6.76854253e-01
4.69038576e-01 -1.18920483e-01 -4.86181706e-01 -4.61487234e-01
5.05255997e-01 8.02809834e-01 -5.55428304e-02 -2.79793918... | [14.480711936950684, 7.267546653747559] |
30918fcc-8ab3-49dd-b1fb-4ab1eb09ed3f | an-empirical-investigation-of-cash-conversion | 2005.09482 | null | https://arxiv.org/abs/2005.09482v1 | https://arxiv.org/pdf/2005.09482v1.pdf | An Empirical Investigation of Cash Conversion Cycle of Manufacturing Firms and its Association with Firm Size and Profitability | The purpose of this empirical study is to investigate Cash Conversion Cycle of thirty manufacturing firms listed in Dhaka Stock Exchanges under six different categories, which are, Food and allied, Pharmaceuticals and chemical, Cement, Textile, Engineering and Miscellaneous. This paper sets industry average Cash Conver... | ['Nusrat Jahan'] | 2020-05-15 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-6.00563049e-01 1.16804995e-01 -4.81115192e-01 2.50910282e-01
2.31832802e-01 -6.45695686e-01 4.76401836e-01 9.13901925e-02
-1.20303892e-01 8.92350793e-01 3.49252194e-01 -6.82012379e-01
-3.85533690e-01 -1.10356140e+00 -1.37469769e-01 -6.56554163e-01
2.28761226e-01 2.81272158e-02 -3.15648198e-01 -2.34228566... | [9.033390998840332, 6.1504621505737305] |
57b56834-9d79-46e1-9000-1f7b3d5c3e24 | evaluating-text-coherence-at-sentence-and-1 | 2006.03221 | null | https://arxiv.org/abs/2006.03221v1 | https://arxiv.org/pdf/2006.03221v1.pdf | Evaluating Text Coherence at Sentence and Paragraph Levels | In this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering. We collected four distinct corpora from different domains on which we investigate the adaptation of existing sentence ordering methods to a paragraph ordering task. We also compare the learnability... | ['Sujian Li', 'Shuang Zeng', 'Sennan Liu'] | 2020-06-05 | evaluating-text-coherence-at-sentence-and | https://aclanthology.org/2020.lrec-1.210 | https://aclanthology.org/2020.lrec-1.210.pdf | lrec-2020-5 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.64872909e-02 4.46925089e-02 -9.62112173e-02 -4.42634672e-01
-8.19903612e-01 -5.15742302e-01 7.88841188e-01 5.45794666e-01
-5.52808225e-01 9.43186522e-01 7.69653499e-01 -2.08981812e-01
-4.19171005e-01 -4.48392421e-01 -2.57600665e-01 -3.15794587e-01
-1.15749784e-01 5.16485631e-01 2.89373815e-01 -3.23819399... | [12.03528881072998, 9.368117332458496] |
a7907ce4-3f99-4b8c-9cb1-368c78510ade | can-generative-large-language-models-perform | 2307.04172 | null | https://arxiv.org/abs/2307.04172v1 | https://arxiv.org/pdf/2307.04172v1.pdf | Can Generative Large Language Models Perform ASR Error Correction? | ASR error correction continues to serve as an important part of post-processing for speech recognition systems. Traditionally, these models are trained with supervised training using the decoding results of the underlying ASR system and the reference text. This approach is computationally intensive and the model needs ... | ['Kate Knill', 'Mark Gales', 'Potsawee Manakul', 'Mengjie Qian', 'Rao Ma'] | 2023-07-09 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 5.01927137e-01 1.71045080e-01 1.68650493e-01 -5.42862535e-01
-1.32021177e+00 -3.31527442e-01 5.48490584e-01 1.60186991e-01
-7.11128294e-01 2.25845397e-01 3.99218231e-01 -7.26878166e-01
4.52882439e-01 -1.68072030e-01 -4.43852276e-01 -2.27833942e-01
2.30266169e-01 8.54698598e-01 3.87369633e-01 -6.97880447... | [14.37130069732666, 6.826721668243408] |
8694ed63-c3a0-4106-a4a7-3072cf3ae4d6 | distinguishing-noise-from-chaos-objective | 1401.2139 | null | http://arxiv.org/abs/1401.2139v1 | http://arxiv.org/pdf/1401.2139v1.pdf | Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph | A recently proposed methodology called the Horizontal Visibility Graph (HVG)
[Luque {\it et al.}, Phys. Rev. E., 80, 046103 (2009)] that constitutes a
geometrical simplification of the well known Visibility Graph algorithm [Lacasa
{\it et al.\/}, Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to
study the dis... | ['Osvaldo A. Rosso', 'Bruna Amin Gonçalves', 'Martín Gómez Ravetti', 'Alejandro C. Frery', 'Laura C. Carpi'] | 2014-01-09 | null | null | null | null | ['information-plane'] | ['methodology'] | [-1.30256817e-01 2.38521546e-02 3.27625990e-01 9.73670557e-02
-3.48673649e-02 -7.16034472e-01 7.86047339e-01 2.98937052e-01
-3.05240422e-01 8.35828543e-01 -6.28167570e-01 -6.98521495e-01
-5.41028917e-01 -1.23254049e+00 -2.51430899e-01 -1.03324878e+00
-5.52725613e-01 3.49025935e-01 1.54297262e-01 -3.71189743... | [6.938035488128662, 4.496188163757324] |
21a3c8b1-4dbc-4187-a5a0-00156130e7e5 | robust-tracking-of-respiratory-rate-in-high | 1705.06628 | null | http://arxiv.org/abs/1705.06628v2 | http://arxiv.org/pdf/1705.06628v2.pdf | Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging | The ability to monitor respiratory rate is extremely important for medical
treatment, healthcare and fitness sectors. In many situations, mobile methods,
which allow users to undertake every day activities, are required. However,
current monitoring systems can be obtrusive, requiring users to wear
respiration belts or ... | ['Nadia Bianchi-Berthouze', 'Simon J. Julier', 'Youngjun Cho', 'Nicolai Marquardt'] | 2017-05-08 | null | null | null | null | ['physiological-computing', 'thermal-infrared-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 5.86601794e-01 -5.45010626e-01 -2.46284697e-02 -1.29375577e-01
-5.46622276e-01 -5.24194658e-01 -1.01593859e-01 -1.74101800e-01
-3.65555555e-01 5.80573797e-01 -1.38398275e-01 2.88648438e-02
1.16373993e-01 -5.64463973e-01 4.81072254e-02 -9.93857622e-01
2.72736579e-01 -2.17731565e-01 4.32395488e-01 2.17412710... | [13.900979995727539, 2.8118507862091064] |
e8a72f4b-c664-474f-98ec-4adccd1cb251 | peek-inside-the-closed-world-evaluating | 1912.05590 | null | https://arxiv.org/abs/1912.05590v3 | https://arxiv.org/pdf/1912.05590v3.pdf | Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud | Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have used only limited data and provided minimal insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existin... | ['Anh Cao', 'John Heidemann', 'Hang Guo', 'Geoff Outhred', 'Xun Fan'] | 2019-12-11 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-5.61765611e-01 -2.57729977e-01 -5.22222295e-02 -2.01985806e-01
-1.99345082e-01 -7.46721148e-01 5.34449518e-01 7.43945986e-02
2.72473525e-02 4.92596447e-01 -1.10279039e-01 -9.68778074e-01
-7.98293799e-02 -1.00394917e+00 -5.40483832e-01 -3.73307377e-01
-7.33169317e-01 6.48782551e-01 1.85412034e-01 1.65319408... | [5.3961873054504395, 7.386047840118408] |
6d98fd08-cd64-4cbf-b27d-6c140eb4a886 | std-stable-triangle-descriptor-for-3d-place | 2209.12435 | null | https://arxiv.org/abs/2209.12435v2 | https://arxiv.org/pdf/2209.12435v2.pdf | STD: Stable Triangle Descriptor for 3D place recognition | In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, ... | ['Fu Zhang', 'Xiaoping Hong', 'Zuhao Zou', 'Jiarong Lin', 'Chongjian Yuan'] | 2022-09-26 | null | null | null | null | ['3d-place-recognition'] | ['computer-vision'] | [-2.55153716e-01 -5.45961261e-01 -2.11748868e-01 -4.31471199e-01
-7.09745944e-01 -6.63600624e-01 5.94834208e-01 2.12071046e-01
-2.18312070e-01 3.32396001e-01 -1.78351954e-01 -2.08138838e-01
-7.32883289e-02 -1.02236295e+00 -6.10380709e-01 -5.21282852e-01
-1.12029977e-01 5.45132637e-01 5.28966486e-01 -3.05183321... | [7.640600204467773, -2.5790302753448486] |
741e7053-ce4d-438d-8998-f755582c299f | enforcing-mutual-consistency-of-hard-regions | 2109.09960 | null | https://arxiv.org/abs/2109.09960v4 | https://arxiv.org/pdf/2109.09960v4.pdf | Mutual Consistency Learning for Semi-supervised Medical Image Segmentation | In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classif... | ['Jianfei Cai', 'Yong Xia', 'Lei Zhang', 'Minfeng Xu', 'Donghao Zhang', 'ZongYuan Ge', 'Yicheng Wu'] | 2021-09-21 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.32607889e-01 8.37137759e-01 -4.97515082e-01 -6.20025694e-01
-1.13377917e+00 -2.67506570e-01 1.64333418e-01 -2.00001583e-01
-3.10833186e-01 6.98637605e-01 -6.32284582e-02 -2.90025175e-01
3.15764725e-01 -5.39143622e-01 -8.46786380e-01 -8.69291306e-01
3.63571525e-01 6.67877734e-01 3.88017356e-01 3.23846549... | [14.6248779296875, -2.0580923557281494] |
54f3e4dc-c840-4f9b-a477-6e6c98d2a4c3 | open-domain-question-answering-goes | 2010.04898 | null | https://arxiv.org/abs/2010.04898v3 | https://arxiv.org/pdf/2010.04898v3.pdf | Open-Domain Question Answering Goes Conversational via Question Rewriting | We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversa... | ['Srinivas Chappidi', 'Stephen Pulman', 'Shayne Longpre', 'Zhucheng Tu', 'Svitlana Vakulenko', 'Raviteja Anantha'] | 2020-10-10 | null | https://aclanthology.org/2021.naacl-main.44 | https://aclanthology.org/2021.naacl-main.44.pdf | naacl-2021-4 | ['question-rewriting'] | ['natural-language-processing'] | [ 6.64544329e-02 5.39636672e-01 5.90451777e-01 -2.87425786e-01
-1.83560395e+00 -1.13124502e+00 7.52958655e-01 4.38242331e-02
-4.40275788e-01 6.50658429e-01 8.51879835e-01 -6.23606145e-01
2.43175253e-02 -3.21182102e-01 -6.22086048e-01 -3.83180939e-02
1.79328844e-01 8.13769877e-01 4.92619872e-01 -8.35390985... | [11.925176620483398, 8.017422676086426] |
e305aec1-b5ae-4dad-9e0f-574d09b18d26 | molecular-dynamics | 2307.02176 | null | https://arxiv.org/abs/2307.02176v2 | https://arxiv.org/pdf/2307.02176v2.pdf | Molecular Dynamics | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['K. Anton Feenstra', 'Sanne Abeln', 'Jocelyne Vreede', 'Arriën Symon Rauh', 'Ali May', 'Qingzhen Hou', 'Jose Gavaldá-Garciá', 'Juami H. M. van Gils', 'Halima Mouhib'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 2.84753233e-01 5.33944555e-02 8.34074616e-02 -1.75151438e-01
-8.81903693e-02 -3.40693176e-01 1.66503236e-01 4.30620074e-01
-3.37856889e-01 1.30621469e+00 -2.01401561e-01 -4.89661664e-01
-1.20785862e-01 -3.77715290e-01 -5.72887123e-01 -1.43847978e+00
-3.16596806e-01 6.44216716e-01 3.49335164e-01 -5.75509548... | [4.735240459442139, 5.274872779846191] |
478be648-6043-4d4c-9c1f-11bb13c99cda | a-new-framework-for-experimental-design-using | 2105.05539 | null | https://arxiv.org/abs/2105.05539v1 | https://arxiv.org/pdf/2105.05539v1.pdf | A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area | In this contribution, we predict the wellhead protection area (WHPA, target), the shape and extent of which is influenced by the distribution of hydraulic conductivity (K), from a small number of tracing experiments (predictor). Our first objective is to make stochastic predictions of the WHPA within the Bayesian Evide... | ['Thomas Hermans', 'Eric Laloy', 'Robin Thibaut'] | 2021-05-12 | a-new-framework-for-experimental-design-using-1 | https://www.sciencedirect.com/science/article/pii/S0022169421009537 | https://www.sciencedirect.com/science/article/pii/S0022169421009537 | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.80686612e-02 3.71638387e-02 6.47987872e-02 5.16890921e-02
-8.67974997e-01 -5.60776591e-01 7.02737808e-01 4.64043945e-01
-3.68793368e-01 9.16698277e-01 7.55151808e-02 -4.39088494e-01
-8.89919698e-01 -1.02380526e+00 -8.04267883e-01 -9.62636590e-01
-2.49494433e-01 4.53365088e-01 4.45046067e-01 2.42112726... | [6.464364051818848, 3.3578972816467285] |
8cd48ccb-a025-4780-8b7a-0e760a4eb341 | self-supervised-video-representation-learning-5 | 2008.13426 | null | https://arxiv.org/abs/2008.13426v2 | https://arxiv.org/pdf/2008.13426v2.pdf | Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics | This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and do... | ['Yun-hui Liu', 'Jianbo Jiao', 'Wei Liu', 'Linchao Bao', 'Jiangliu Wang', 'Shengfeng He'] | 2020-08-31 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-1.07695095e-01 -6.18366182e-01 -4.97776330e-01 -2.07603425e-01
-3.05719048e-01 -4.93137598e-01 2.35142872e-01 -1.08230844e-01
-2.70216942e-01 4.63195860e-01 4.91696775e-01 -1.23676755e-01
-1.08407997e-01 -4.85700876e-01 -6.02742255e-01 -6.65832400e-01
-3.60045224e-01 -2.30142310e-01 3.97961408e-01 2.01177910... | [8.761964797973633, 0.5932644605636597] |
00a8f193-9838-49e8-91c2-b4b5398a75c2 | self-positioning-point-based-transformer-for | 2303.16450 | null | https://arxiv.org/abs/2303.16450v1 | https://arxiv.org/pdf/2303.16450v1.pdf | Self-positioning Point-based Transformer for Point Cloud Understanding | Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positionin... | ['Hyunwoo J. Kim', 'Yunyang Xiong', 'Sihyeon Kim', 'Sanghyeok Lee', 'Jinyoung Park'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-segmentation', '3d-point-cloud-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.11847781e-01 2.49307859e-03 6.90577552e-02 -5.73696375e-01
-8.75674188e-01 -4.84304368e-01 3.84219259e-01 2.20619757e-02
-6.28875047e-02 -7.07305372e-02 -1.03382409e-01 -2.51426011e-01
-4.20416445e-01 -8.03383768e-01 -1.12521541e+00 -5.53690314e-01
1.49874568e-01 8.00866902e-01 4.35546100e-01 -2.09994122... | [7.890538692474365, -3.4414405822753906] |
720e3be6-d523-43f5-8de4-b09d827461c9 | deep-head-pose-estimation-using-synthetic | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Kuhnke_Deep_Head_Pose_Estimation_Using_Synthetic_Images_and_Partial_Adversarial_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kuhnke_Deep_Head_Pose_Estimation_Using_Synthetic_Images_and_Partial_Adversarial_ICCV_2019_paper.pdf | Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces | Head pose estimation aims at predicting an accurate pose from an image. Current approaches rely on supervised deep learning, which typically requires large amounts of labeled data. Manual or sensor-based annotations of head poses are prone to errors. A solution is to generate synthetic training data by rendering 3D fac... | [' Jorn Ostermann', 'Felix Kuhnke'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['head-pose-estimation'] | ['computer-vision'] | [ 3.18599105e-01 3.19700927e-01 -9.17691961e-02 -8.24137807e-01
-8.70945871e-01 -6.04449451e-01 6.26958191e-01 -1.96800381e-01
-4.95704383e-01 9.38982606e-01 3.73727590e-01 2.77094722e-01
3.23383600e-01 -5.33662736e-01 -8.21867645e-01 -3.86314601e-01
1.10699952e-01 8.06560934e-01 2.37962097e-01 -2.16414630... | [13.401111602783203, 0.1380205601453781] |
14e76535-5c42-4471-b228-fefd294ce7eb | why-is-this-misleading-detecting-news | 2302.05852 | null | https://arxiv.org/abs/2302.05852v1 | https://arxiv.org/pdf/2302.05852v1.pdf | "Why is this misleading?": Detecting News Headline Hallucinations with Explanations | Automatic headline generation enables users to comprehend ongoing news events promptly and has recently become an important task in web mining and natural language processing. With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by... | ['Marc Najork', 'Michael Bendersky', 'Negar Rahmati', 'Dan Finnie', 'Jialu Liu', 'Jiaming Shen'] | 2023-02-12 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 1.83058120e-02 4.71394569e-01 -1.22294098e-01 -3.46826881e-01
-9.01618600e-01 -3.93708438e-01 8.03174615e-01 3.29142541e-01
-5.17894253e-02 9.45348680e-01 1.03649747e+00 -6.10715300e-02
1.64696276e-01 -6.00008726e-01 -6.36117935e-01 5.98445162e-03
3.04629356e-01 5.44820011e-01 1.26989871e-01 -5.08127034... | [12.237536430358887, 9.315686225891113] |
0a5518f3-d3f3-4f4e-8c96-824509b4e50d | scaling-speech-technology-to-1000-languages | null | null | https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/ | https://research.facebook.com/micro_site/url/?click_from_context_menu=true&country=US&destination=https%3A%2F%2Fresearch.facebook.com%2Ffile%2F6486163864750413%2FScaling-Speech-Technology-to-1%2C000%2B-Languages.pdf&event_type=click&last_nav_impression_id=09L70osMgoDOIV1W3&max_percent_page_viewed=71&max_viewport_height... | Scaling Speech Technology to 1,000+ Languages | Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (... | ['Michael Auli', 'Alexis Conneau', 'Wei-Ning Hsu', 'Xiaohui Zhang', 'Yossi Adi', 'Alexei Baevski', 'Maryam Fazel-Zarandi', 'Apoorv Vyas', 'Zhaoheng Ni', 'Ali Elkahky', 'Sayani Kundu', 'Arun Babu', 'Paden Tomasello', 'Bowen Shi', 'Andros Tjandra', 'Vineel Pratap'] | 2023-05-23 | null | null | null | arxiv-2023-5 | ['speech-synthesis'] | ['speech'] | [-4.98242825e-01 1.18107021e-01 -3.93799633e-01 -3.89031291e-01
-1.38148916e+00 -7.15821624e-01 7.52986848e-01 -5.27949594e-02
-6.22684300e-01 6.40827298e-01 9.22380745e-01 -7.31779099e-01
7.87507296e-01 -4.52437699e-01 -5.65143645e-01 -2.60493785e-01
1.37350783e-01 7.56999552e-01 2.09490433e-02 -5.98681629... | [14.278338432312012, 6.939574241638184] |
8f5355c8-54d1-49e0-925a-fea5e0a4c46d | heater-an-efficient-and-unified-network-for | 2205.15448 | null | https://arxiv.org/abs/2205.15448v3 | https://arxiv.org/pdf/2205.15448v3.pdf | FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER | Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and human mesh reconstruction (HMR) tasks. In these tasks, feature map representations of the human structural information are often extracted first... | ['Chen Chen', 'Guo-Jun Qi', 'Taojiannan Yang', 'Matias Mendieta', 'Ce Zheng'] | 2022-05-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_FeatER_An_Efficient_Network_for_Human_Reconstruction_via_Feature_Map-Based_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_FeatER_An_Efficient_Network_for_Human_Reconstruction_via_Feature_Map-Based_CVPR_2023_paper.pdf | cvpr-2023-1 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.80354431e-01 2.12075874e-01 1.52093619e-01 -2.88420141e-01
-5.91907561e-01 -2.03603152e-02 2.83991724e-01 -2.27290913e-01
-3.97735864e-01 3.36263150e-01 2.92475641e-01 1.45703107e-01
9.28289816e-02 -9.63311613e-01 -9.65352833e-01 -1.74482584e-01
7.96505809e-02 8.53327751e-01 3.51289511e-01 -3.45362753... | [7.081048488616943, -1.0579566955566406] |
2519f1b7-2d38-4e86-968f-611a8b0e46c0 | when-to-read-documents-or-qa-history-on | 2306.04176 | null | https://arxiv.org/abs/2306.04176v1 | https://arxiv.org/pdf/2306.04176v1.pdf | When to Read Documents or QA History: On Unified and Selective Open-domain QA | This paper studies the problem of open-domain question answering, with the aim of answering a diverse range of questions leveraging knowledge resources. Two types of sources, QA-pair and document corpora, have been actively leveraged with the following complementary strength. The former is highly precise when the parap... | ['Moontae Lee', 'Seung-won Hwang', 'Sang-eun Han', 'Kyungjae Lee'] | 2023-06-07 | null | null | null | null | ['natural-questions', 'triviaqa', 'open-domain-question-answering'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 6.41786158e-02 4.60062027e-01 8.84953421e-03 -3.86552334e-01
-1.41430616e+00 -9.49172378e-01 8.62594843e-01 3.81839365e-01
-4.79644179e-01 8.73405516e-01 3.11243355e-01 -4.97949302e-01
-4.87102211e-01 -8.26203287e-01 -5.08391142e-01 -3.01251948e-01
3.32657248e-01 8.93150091e-01 6.79904461e-01 -7.85649538... | [11.330581665039062, 8.018545150756836] |
3b9d4a1f-3772-4366-8f2f-2c88a45fb170 | sequential-latent-variable-models-for-few | null | null | https://openreview.net/forum?id=7C9aRX2nBf2 | https://openreview.net/pdf?id=7C9aRX2nBf2 | Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting | Modern applications increasingly require learning and forecasting latent dynamics from high-dimensional time-series. Compared to univariate time-series forecasting, this adds a new challenge of reasoning about the latent dynamics of an unobserved abstract state. Sequential latent variable models (LVMs) present an attra... | ['Linwei Wang', 'Zhiyuan Li', 'Ryan Missel', 'Xiajun Jiang'] | 2023-05-05 | null | null | null | iclr-2023-5 | ['univariate-time-series-forecasting'] | ['time-series'] | [-2.55242772e-02 -1.39360830e-01 -4.63871777e-01 -2.14863464e-01
-6.88681960e-01 -5.07740378e-01 1.25440347e+00 -1.18809320e-01
7.38108456e-02 5.65191031e-01 3.18047941e-01 -1.74786046e-01
-5.11759281e-01 -6.39809549e-01 -6.62682474e-01 -9.04691279e-01
-6.39906406e-01 8.18541229e-01 1.66974902e-01 -2.80987769... | [6.952516078948975, 3.300468683242798] |
2201cd36-b6bf-4898-a9be-0b0940b8c30f | words-hk-a-comprehensive-cantonese-dictionary | null | null | https://aclanthology.org/2022.dclrl-1.7 | https://aclanthology.org/2022.dclrl-1.7.pdf | Words.hk: A Comprehensive Cantonese Dictionary Dataset with Definitions, Translations and Transliterated Examples | This paper discusses the compilation of the words.hk Cantonese dictionary dataset, which was compiled through manual annotation over a period of 7 years. Cantonese is a low-resource language with limited tagged or manually checked resources, especially at the sentential level, and this dataset is an attempt to fill the... | ['Lilian Suet-ying Chan', 'Raymond Ka-wai Tse', 'Grace Wing-yan Chan', 'Chaak-ming Lau'] | null | null | null | null | dclrl-lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [ 2.32781976e-01 -1.51794270e-01 -4.56231326e-01 -4.17797565e-01
-5.95360875e-01 -8.54047835e-01 4.51463193e-01 9.02010575e-02
-8.39102209e-01 1.03953803e+00 2.90386856e-01 -7.60322988e-01
8.52920339e-02 -4.10601735e-01 -1.83631733e-01 -3.62189561e-01
5.69326282e-01 1.02657461e+00 1.69149816e-01 -5.41800737... | [10.93042278289795, 10.279072761535645] |
7ee610ad-937e-4ceb-8b4b-5a4e672dc6d2 | unseen-object-amodal-instance-segmentation | 2109.11103 | null | https://arxiv.org/abs/2109.11103v2 | https://arxiv.org/pdf/2109.11103v2.pdf | Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling | Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limited to segmenting the only visible regions of unseen objects. For robotic manipulation in a cluttered scene, amodal perception is required t... | ['Kyoobin Lee', 'Seongho Bak', 'Raeyoung Kang', 'Sangjun Noh', 'Taewon Kim', 'Joosoon Lee', 'Seunghyeok Back'] | 2021-09-23 | null | null | null | null | ['amodal-instance-segmentation', 'unseen-object-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.22345963e-01 3.56772751e-01 -5.24287820e-02 -4.02401417e-01
-4.03629929e-01 -7.45658934e-01 4.21656698e-01 2.34638616e-01
8.22673962e-02 4.64567482e-01 -2.75930345e-01 5.40362634e-02
-2.00828776e-01 -4.01221037e-01 -8.85179758e-01 -6.23988867e-01
-1.19598009e-01 1.00657964e+00 6.20185196e-01 -8.77595246... | [6.1657795906066895, -1.0615936517715454] |
d7257a37-fcd3-41cd-a18f-1262bf2e2348 | augmenting-hessians-with-inter-layer | 2306.04879 | null | https://arxiv.org/abs/2306.04879v1 | https://arxiv.org/pdf/2306.04879v1.pdf | Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization | Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution which simultaneously reduces memory footprint and compute requirements. However, aggressive quantization may lead to an unacceptable loss i... | ['Yu Emma Wang', 'Siddharth Joshi', 'Caitlin Stanton', 'Elfie Guo', 'Jian Li', 'Tom Jablin', 'Chiachen Chou', 'Xiaofan Zhang', 'Navid Lambert-Shirzad', 'Clemens JS Schaefer'] | 2023-06-08 | null | null | null | null | ['quantization'] | ['methodology'] | [-1.74604841e-02 -4.91260231e-01 -2.41271004e-01 -5.33376336e-01
-1.04929423e+00 -6.54796183e-01 4.67558717e-03 2.24528134e-01
-7.15243101e-01 5.91722548e-01 -1.78760156e-01 -6.60294950e-01
-3.14927310e-01 -5.35325587e-01 -7.16595471e-01 -3.17984313e-01
-3.09932411e-01 2.58425415e-01 2.76598990e-01 1.35777071... | [8.659713745117188, 2.983760118484497] |
94c3d672-f5f8-48a2-a422-f4373868d5d1 | dsfnet-dual-space-fusion-network-for-1 | 2305.11522 | null | https://arxiv.org/abs/2305.11522v1 | https://arxiv.org/pdf/2305.11522v1.pdf | DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment | Sensitivity to severe occlusion and large view angles limits the usage scenarios of the existing monocular 3D dense face alignment methods. The state-of-the-art 3DMM-based method, directly regresses the model's coefficients, underutilizing the low-level 2D spatial and semantic information, which can actually offer cues... | ['Robby T. Tan', 'Mohan Kankanhalli', 'Yu Cheng', 'Bo wang', 'Heyuan Li'] | 2023-05-19 | dsfnet-dual-space-fusion-network-for | http://openaccess.thecvf.com//content/CVPR2023/html/Li_DSFNet_Dual_Space_Fusion_Network_for_Occlusion-Robust_3D_Dense_Face_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_DSFNet_Dual_Space_Fusion_Network_for_Occlusion-Robust_3D_Dense_Face_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-alignment'] | ['computer-vision'] | [-2.12459445e-01 7.38039985e-02 -1.02877930e-01 -4.46357667e-01
-3.25764447e-01 -2.60653675e-01 4.33288753e-01 -7.83081949e-01
8.76636878e-02 2.25321189e-01 2.33849645e-01 4.20869663e-02
2.48478413e-01 -5.78682840e-01 -5.75218499e-01 -7.12946594e-01
4.53586876e-01 4.14506555e-01 -2.27500096e-01 -2.13541448... | [13.257974624633789, 0.17709293961524963] |
ac6509b8-7938-4a4a-8d21-8cb2eb58b698 | iitk-at-semeval-2021-task-10-source-free | null | null | https://aclanthology.org/2021.semeval-1.53 | https://aclanthology.org/2021.semeval-1.53.pdf | IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes | Recent progress in deep learning has primarily been fueled by the availability of large amounts of annotated data that is obtained from highly expensive manual annotating pro-cesses. To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to g... | ['Ashutosh Modi', 'Vaibhav Jindal', 'Priyanshu Gupta', 'Nidhi Hegde', 'Jinang Shah', 'Harshit Kumar'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation', 'negation-detection'] | ['computer-vision', 'natural-language-processing'] | [ 4.83942091e-01 7.66966939e-01 -2.41238654e-01 -7.74321914e-01
-8.66679072e-01 -2.26099372e-01 5.52036941e-01 6.61656737e-01
-8.78864646e-01 1.17098737e+00 1.33268237e-01 2.79689468e-02
1.91518337e-01 -7.45498776e-01 -5.33701420e-01 -6.54091358e-01
3.36236209e-01 8.65573943e-01 2.16599941e-01 -2.73943752... | [10.309295654296875, 3.3009631633758545] |
4b8d5c11-dbc3-4578-aa7f-44a4f63be66a | end-to-end-attention-based-distant-speech | 1610.05361 | null | http://arxiv.org/abs/1610.05361v1 | http://arxiv.org/pdf/1610.05361v1.pdf | End-to-end attention-based distant speech recognition with Highway LSTM | End-to-end attention-based models have been shown to be competitive
alternatives to conventional DNN-HMM models in the Speech Recognition Systems.
In this paper, we extend existing end-to-end attention-based models that can be
applied for Distant Speech Recognition (DSR) task. Specifically, we propose an
end-to-end att... | ['Hassan Taherian'] | 2016-10-17 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.64399505e-01 1.74916700e-01 4.31169346e-02 -7.07794309e-01
-1.08268893e+00 -4.57034893e-02 5.05232155e-01 -3.91892284e-01
-5.34286737e-01 4.89114970e-01 5.30859649e-01 -8.87756467e-01
4.92904097e-01 -1.16452426e-01 -4.95122910e-01 -5.06635308e-01
2.05663234e-01 3.86422962e-01 3.52796763e-01 -1.71493649... | [14.426094055175781, 6.925102233886719] |
b3a6ab75-4b19-40cd-b810-9fea1b2e6b9a | sherf-generalizable-human-nerf-from-a-single | 2303.12791 | null | https://arxiv.org/abs/2303.12791v1 | https://arxiv.org/pdf/2303.12791v1.pdf | SHERF: Generalizable Human NeRF from a Single Image | Existing Human NeRF methods for reconstructing 3D humans typically rely on multiple 2D images from multi-view cameras or monocular videos captured from fixed camera views. However, in real-world scenarios, human images are often captured from random camera angles, presenting challenges for high-quality 3D human reconst... | ['Ziwei Liu', 'Lei Yang', 'Haiyi Mei', 'Liang Pan', 'Fangzhou Hong', 'Shoukang Hu'] | 2023-03-22 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-1.26125380e-01 -3.19033682e-01 -2.23636981e-02 -3.40556383e-01
-3.81079167e-01 -4.01587158e-01 5.43284476e-01 -3.07572126e-01
-2.29631573e-01 2.88763881e-01 5.78967273e-01 7.05972970e-01
2.18038127e-01 -6.23212039e-01 -5.85933745e-01 -4.46325183e-01
1.57906920e-01 4.50564444e-01 6.93932697e-02 -3.24512124... | [7.277997016906738, -0.7095832824707031] |
f11e5361-30d5-4f49-adca-5faffcd63692 | towards-interpretable-math-word-problem | null | null | https://openreview.net/forum?id=euGE2v_UZgT | https://openreview.net/pdf?id=euGE2v_UZgT | Towards Interpretable Math Word Problem Solving with Grounded Linguistic Logic Reasoning | Automatically math word problem (MWP) solving is a challenging artificial intelligence task since a machine should be able to not only understand problem comprehensively on linguistics but also the grounded math logic entailed in problem. Recently, lots of deep learning models have made great progress in MWP solving o... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-2.81406678e-02 8.26469183e-01 -3.54019970e-01 -6.67965412e-01
-6.96984887e-01 -6.86304688e-01 -9.16474238e-02 1.52364448e-01
2.32030258e-01 7.94404566e-01 2.37687945e-01 -7.65704513e-01
-3.21761549e-01 -1.62777352e+00 -1.09168279e+00 8.02901313e-02
2.45622873e-01 1.14782667e+00 9.05409381e-02 -5.79105377... | [9.505708694458008, 7.495360374450684] |
94d5be59-1da5-4598-97a0-70e150d2ece7 | adopting-the-multi-answer-questioning-task | 2303.01064 | null | https://arxiv.org/abs/2303.01064v1 | https://arxiv.org/pdf/2303.01064v1.pdf | Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy | Extreme multi-label text classification utilizes the label hierarchy to partition extreme labels into multiple label groups, turning the task into simple multi-group multi-label classification tasks. Current research encodes labels as a vector with fixed length which needs establish multiple classifiers for different l... | ['Mohammed Ali Al-Garadi', 'Ying Wah Teh', 'Li Wang'] | 2023-03-02 | null | null | null | null | ['multi-label-text-classification', 'extreme-multi-label-classification', 'multi-label-text-classification'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 2.05428332e-01 2.53754914e-01 -3.54521513e-01 -8.14255238e-01
-9.03185606e-01 -5.49927354e-01 8.71141627e-02 3.80592376e-01
-6.61557615e-01 6.30933940e-01 -1.21755578e-01 -1.98167995e-01
-5.24925053e-01 -5.34131110e-01 3.01319122e-01 -5.93017638e-01
6.07340455e-01 7.08157957e-01 3.50557566e-01 -3.14192623... | [9.592610359191895, 4.387002944946289] |
32af5def-c3c8-40c4-a969-022555801420 | warped-convolution-networks-for-homography | 2206.11657 | null | https://arxiv.org/abs/2206.11657v2 | https://arxiv.org/pdf/2206.11657v2.pdf | Warped Convolutional Networks: Bridge Homography to sl(3) algebra by Group Convolution | Homography has an essential relationship with the special linear group and the embedding Lie algebra structure. Although the Lie algebra representation is elegant, few researchers have established the connection between homography and algebra expression in neural networks. In this paper, we propose Warped Convolution N... | ['Jianke Zhu', 'Wenyu Liu', 'Yang Li', 'Xinrui Zhan'] | 2022-06-23 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.77601874e-01 -8.34293514e-02 -2.07135618e-01 -3.03797662e-01
1.57709464e-01 -4.36012506e-01 8.54582548e-01 -6.30877376e-01
-9.89684388e-02 3.74408633e-01 1.26650974e-01 -2.44394362e-01
-5.36358580e-02 -1.05199170e+00 -1.08919823e+00 -7.55567014e-01
-6.28815964e-02 1.84585035e-01 1.30166665e-01 -2.49446124... | [8.621180534362793, -2.2042086124420166] |
f6d69605-1504-483e-80ff-e9c1d7031e9e | semi-supervised-node-splitting-for-random | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf | Semi-supervised Node Splitting for Random Forest Construction | Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples. Existing solutions fail to properly partition the feature space if there are insufficient training data. In this paper, we present semi-supervised splitting to overcome this limitation by splitting no... | ['Luming Zhang', 'Zicheng Liu', 'DaCheng Tao', 'Xiao Liu', 'Mingli Song', 'Chun Chen', 'Jiajun Bu'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['object-categorization'] | ['computer-vision'] | [ 1.45290479e-01 -1.75376255e-02 -7.59834945e-01 -7.17896521e-01
-6.87292457e-01 -4.13644791e-01 1.45895511e-01 -2.64644474e-01
-3.48362774e-01 8.11215043e-01 -1.00323096e-01 -2.06894740e-01
-2.49447986e-01 -6.21260166e-01 -3.92974496e-01 -9.70518947e-01
4.26086754e-01 6.86036110e-01 1.78632215e-01 4.37889934... | [14.7891263961792, -1.9806690216064453] |
f696785d-ae39-4087-9d7c-603e4edbbe41 | a-dual-source-attention-transformer-for-multi | 2306.05807 | null | https://arxiv.org/abs/2306.05807v1 | https://arxiv.org/pdf/2306.05807v1.pdf | A Dual-Source Attention Transformer for Multi-Person Pose Tracking | Multi-person pose tracking is an important element for many applications and requires to estimate the human poses of all persons in a video and to track them over time. The association of poses across frames remains an open research problem, in particular for online tracking methods, due to motion blur, crowded scenes ... | ['Juergen Gall', 'Andreas Doering'] | 2023-06-09 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [-1.75578028e-01 -3.96821499e-01 1.89886149e-02 -1.78872794e-01
-3.62460226e-01 -5.58598876e-01 6.62651896e-01 -1.71458900e-01
-6.84773684e-01 5.01984119e-01 5.43269694e-01 6.34279668e-01
-5.52538745e-02 -2.69482344e-01 -8.36256087e-01 -1.48231789e-01
-2.35063788e-02 6.49450004e-01 3.06027830e-01 -6.12587072... | [6.896444320678711, -1.0988194942474365] |
e0f34ad8-12c3-42f5-a6d5-8749817199d8 | entailment-relation-aware-paraphrase | 2203.10483 | null | https://arxiv.org/abs/2203.10483v1 | https://arxiv.org/pdf/2203.10483v1.pdf | Entailment Relation Aware Paraphrase Generation | We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward entailing, or reverse entailing) with respect to a given input. We propose a reinforcement learning-based weakly-supervised paraphrasing sy... | ['Rachel Rudinger', 'Balaji Vasan Srinivasan', 'Abhilasha Sancheti'] | 2022-03-20 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 7.48875380e-01 4.80749100e-01 -2.37081915e-01 -6.23481095e-01
-1.10685802e+00 -8.17078233e-01 8.47762406e-01 2.13745877e-01
-2.89597154e-01 9.54140484e-01 8.36529791e-01 -7.17903912e-01
1.19014770e-01 -8.23079169e-01 -1.15496302e+00 1.59834340e-01
7.45688260e-01 7.48792470e-01 -1.85202077e-01 -5.88574052... | [11.59582805633545, 9.209705352783203] |
047eded3-4192-4ec8-a416-0ee428e626e0 | a-generative-approach-to-question-answering | 1711.06238 | null | http://arxiv.org/abs/1711.06238v2 | http://arxiv.org/pdf/1711.06238v2.pdf | A Generative Approach to Question Answering | Question Answering has come a long way from answer sentence selection,
relational QA to reading and comprehension. We shift our attention to
generative question answering (gQA) by which we facilitate machine to read
passages and answer questions by learning to generate the answers. We frame the
problem as a generative ... | ['Rajarshee Mitra'] | 2017-11-16 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 4.20407921e-01 7.51792192e-01 5.68987846e-01 -5.07242501e-01
-1.29062045e+00 -8.36403668e-01 7.23219335e-01 1.84501290e-01
-1.26213446e-01 9.31981742e-01 8.75886440e-01 -6.26453698e-01
9.62592736e-02 -1.07838726e+00 -9.74394262e-01 -1.54176988e-02
3.04786026e-01 5.20115674e-01 2.12614775e-01 -6.00606978... | [11.422576904296875, 8.160077095031738] |
953f3620-2817-4a75-8dd2-72f7806d8101 | feanet-feature-enhanced-attention-network-for | 2110.08988 | null | https://arxiv.org/abs/2110.08988v1 | https://arxiv.org/pdf/2110.08988v1.pdf | FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation | The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we p... | ['Tin Lun Lam', 'Xiyue Guo', 'Junjie Hu', 'Junfeng Chen', 'Yuan Gao', 'Yong Yang', 'Hongmin Wang', 'Mingjian Liang', 'Hua Feng', 'Fuqin Deng'] | 2021-10-18 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 3.27485442e-01 -3.09398770e-01 2.39754274e-01 -3.64911288e-01
-7.31412351e-01 -1.16248190e-01 1.13913551e-01 -4.24734265e-01
-6.83154821e-01 4.16993260e-01 -2.30293944e-01 -2.99026310e-01
-1.11422613e-01 -1.04378974e+00 -5.54478943e-01 -7.70914078e-01
2.98404515e-01 -7.86381960e-02 6.00505650e-01 -2.62789756... | [9.461923599243164, -1.0434285402297974] |
f4651463-e13b-443c-a06b-fa7381a66c42 | mpox-aism-ai-mediated-super-monitoring-for | 2303.09780 | null | https://arxiv.org/abs/2303.09780v2 | https://arxiv.org/pdf/2303.09780v2.pdf | Mpox-AISM: AI-Mediated Super Monitoring for Forestalling Monkeypox Spread | The challenge on forestalling monkeypox (Mpox) spread is the timely, convenient and accurate diagnosis for earlystage infected individuals. Here, we propose a remote and realtime online visualization strategy, called "Super Monitoring" to construct a low cost, convenient, timely and unspecialized diagnosis of early-sta... | ['Yang Li', 'Jinbao Liu', 'Jialong Xu', 'Xinyue Zhang', 'Zhenzhang Li', 'Yubiao Yue'] | 2023-03-17 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-8.83841664e-02 -5.20538509e-01 -2.11614698e-01 -1.32625341e-01
-2.12005809e-01 -4.34976757e-01 4.55740631e-01 2.40088925e-01
-2.40790263e-01 7.36168742e-01 -4.19805557e-01 -6.75698459e-01
-6.11285031e-01 -8.53177667e-01 4.60428894e-02 -6.59579158e-01
-8.31838548e-01 6.05491817e-01 1.00384735e-01 -5.92951998... | [15.576133728027344, -1.6934728622436523] |
2f62645d-88be-4f88-b8db-72432e7bd38b | consistent-multimodal-generation-via-a | 2307.01425 | null | https://arxiv.org/abs/2307.01425v1 | https://arxiv.org/pdf/2307.01425v1.pdf | Consistent Multimodal Generation via A Unified GAN Framework | We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. The challenge is to produce outputs that are realistic, and also consistent with each other. Our solution builds on the StyleGAN3 architecture, with a shared backbone and modality-specific b... | ['Derek Hoiem', 'Soeren Pirk', 'Zhixin Shu', 'Krishna Kumar Singh', 'Weijie Lyu', 'Yijun Li', 'Zhen Zhu'] | 2023-07-04 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 3.47012550e-01 2.74889916e-01 1.26532644e-01 -5.63999951e-01
-1.24687505e+00 -8.34698737e-01 8.65344584e-01 -5.48799694e-01
-1.44523545e-03 8.05006087e-01 3.75087947e-01 -8.44263658e-02
4.14233655e-01 -8.13676298e-01 -8.68022442e-01 -6.82072937e-01
3.24650615e-01 4.66725171e-01 -3.52257304e-02 -6.13410287... | [11.619135856628418, -0.48022836446762085] |
a4b7e0c8-b672-4ed9-a981-9d1b45b23e43 | how-are-policy-gradient-methods-affected-by | 2206.06863 | null | https://arxiv.org/abs/2206.06863v1 | https://arxiv.org/pdf/2206.06863v1.pdf | How are policy gradient methods affected by the limits of control? | We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inevitably lead to noisy gradient estimates. We also give an example of a class of stable systems in which policy gradient methods suffer from t... | ['Nikolai Matni', 'Henrik Sandberg', 'Anastasios Tsiamis', 'Ingvar Ziemann'] | 2022-06-14 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.00619607e-01 1.53368473e-01 -5.60557902e-01 5.79439327e-02
-5.23357451e-01 -4.92961586e-01 6.59136713e-01 -2.60807604e-01
-6.11884177e-01 1.39976954e+00 9.41247270e-02 -6.52582645e-01
-9.54517573e-02 -2.05530554e-01 -6.31797075e-01 -7.30278134e-01
-2.86643624e-01 -1.21381171e-01 7.62627795e-02 -5.82616508... | [4.429690361022949, 2.567796468734741] |
a87c3c2c-78b7-48c4-aa15-75df06ea3189 | out-of-distribution-detection-for-generalized | 1904.08703 | null | https://arxiv.org/abs/1904.08703v2 | https://arxiv.org/pdf/1904.08703v2.pdf | Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition | Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categorie... | ['Saikumar Dwivedi', 'Fahad Shahbaz Khan', 'Devraj Mandal', 'Vikram Gupta', 'Shuaib Ahmed', 'Sanath Narayan', 'Ling Shao'] | 2019-04-18 | out-of-distribution-detection-for-generalized-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mandal_Out-Of-Distribution_Detection_for_Generalized_Zero-Shot_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mandal_Out-Of-Distribution_Detection_for_Generalized_Zero-Shot_Action_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 7.50361741e-01 2.21174419e-01 -2.74823993e-01 -2.82063663e-01
-8.39377880e-01 -4.24456984e-01 7.25460768e-01 -4.38786596e-01
-2.02192381e-01 6.57118499e-01 2.08619863e-01 1.30557850e-01
2.04913244e-01 -6.64725780e-01 -1.00979841e+00 -9.28218424e-01
2.58896738e-01 2.68529862e-01 4.59512889e-01 1.38584236... | [8.473299026489258, 0.8916239142417908] |
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