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ee2114e9-539a-41ff-8504-3d0e32c0df58 | a-fixed-viewpoint-approach-for-dense | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Han_A_Fixed_Viewpoint_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Han_A_Fixed_Viewpoint_2015_CVPR_paper.pdf | A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects | This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a... | ['Kwan-Yee K. Wong', 'Miaomiao Liu', 'Kai Han'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['transparent-objects'] | ['computer-vision'] | [ 6.38616681e-01 5.33009432e-02 7.61883616e-01 -2.40428805e-01
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2.18716562e-01 9.11201477e-01 6.14549160e-01 -1.35188147... | [9.630631446838379, -2.9986634254455566] |
c0e21506-5e9c-43a0-b4ef-acfcad2b09e1 | optimal-order-simple-regret-for-gaussian | 2108.09262 | null | https://arxiv.org/abs/2108.09262v1 | https://arxiv.org/pdf/2108.09262v1.pdf | Optimal Order Simple Regret for Gaussian Process Bandits | Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$. The problem can be cast as a Gaussian Process (GP) bandit where $f$ lives in a reproducing kernel Hilbert space (RKHS). The state of the art analysis of several learning algorithms shows a signif... | ['Da-Shan Shiu', 'Alberto Bernacchia', 'Sepehr Jalali', 'Nacime Bouziani', 'Sattar Vakili'] | 2021-08-20 | null | http://proceedings.neurips.cc/paper/2021/hash/b1300291698eadedb559786c809cc592-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/b1300291698eadedb559786c809cc592-Paper.pdf | neurips-2021-12 | ['art-analysis'] | ['computer-vision'] | [ 2.86267251e-01 6.16840541e-01 -8.84248987e-02 -2.54846126e-01
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6acc9f40-1093-4330-a8b4-e0b91fa4040d | frustum-voxnet-for-3d-object-detection-from | 1910.05483 | null | https://arxiv.org/abs/1910.05483v3 | https://arxiv.org/pdf/1910.05483v3.pdf | Frustum VoxNet for 3D object detection from RGB-D or Depth images | Recently, there have been a plethora of classification and detection systems from RGB as well as 3D images. In this work, we describe a new 3D object detection system from an RGB-D or depth-only point cloud. Our system first detects objects in 2D (either RGB or pseudo-RGB constructed from depth). The next step is to de... | ['Ioannis Stamos', 'Xiaoke Shen'] | 2019-10-12 | null | null | null | null | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [-7.00875446e-02 6.95876926e-02 4.52462643e-01 -4.71524149e-03
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603cd483-92c3-4228-ade6-4fe3d9eb12a1 | do-pedestrians-pay-attention-eye-contact | 2112.04212 | null | https://arxiv.org/abs/2112.04212v1 | https://arxiv.org/pdf/2112.04212v1.pdf | Do Pedestrians Pay Attention? Eye Contact Detection in the Wild | In urban or crowded environments, humans rely on eye contact for fast and efficient communication with nearby people. Autonomous agents also need to detect eye contact to interact with pedestrians and safely navigate around them. In this paper, we focus on eye contact detection in the wild, i.e., real-world scenarios f... | ['Alexandre Alahi', 'Taylor Mordan', 'Romain Caristan', 'Lorenzo Bertoni', 'Younes Belkada'] | 2021-12-08 | null | null | null | null | ['contact-detection'] | ['robots'] | [-3.36633623e-01 -3.74293700e-02 1.99723169e-01 -5.40596485e-01
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-2.26575479e-01 4.99268383e-01 6.26413763e-01 -2.56750315... | [6.109981060028076, 0.43113186955451965] |
ea102e7c-3eb3-43ec-9e64-3b1a83feec96 | lorral-facial-action-unit-detection-based-on | 2009.10892 | null | https://arxiv.org/abs/2009.10892v3 | https://arxiv.org/pdf/2009.10892v3.pdf | HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning | The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual \textbf{ex}clusion (COMEX) as... | ['Zhongling Liu', 'and Kentaro Murase', 'Rujie Liu', 'Ziqiang Shi', 'Xiaoyu Mi', 'Liu Liu'] | 2020-09-23 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.55053192e-01 2.06307188e-01 1.34383917e-01 -4.94651347e-01
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9e7b2f6f-d21a-4953-9d1b-8543a2bd1c7e | sciwing-a-software-toolkit-for-scientific-1 | null | null | https://aclanthology.org/2020.sdp-1.13 | https://aclanthology.org/2020.sdp-1.13.pdf | SciWING– A Software Toolkit for Scientific Document Processing | We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pi... | ['Min-Yen Kan', 'Abhinav Ramesh Kashyap'] | null | null | null | null | emnlp-sdp-2020-11 | ['citation-intent-classification'] | ['natural-language-processing'] | [-2.28576303e-01 -1.19031049e-01 -2.34474987e-01 -6.68024659e-01
-1.20383036e+00 -1.05975020e+00 8.62960398e-01 2.94397473e-01
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2.38434404e-01 6.27071202e-01 1.72137525e-02 1.73985898... | [9.69401741027832, 8.322304725646973] |
e79f9cd6-5ac8-49f5-89e9-2b97514240d3 | pointtrack-for-effective-online-multi-object | 2007.01549 | null | https://arxiv.org/abs/2007.01549v1 | https://arxiv.org/pdf/2007.01549v1.pdf | PointTrack++ for Effective Online Multi-Object Tracking and Segmentation | Multiple-object tracking and segmentation (MOTS) is a novel computer vision task that aims to jointly perform multiple object tracking (MOT) and instance segmentation. In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework. To ... | ['Wei zhang', 'Errui Ding', 'Xiangbo Su', 'Wei Yang', 'Shilei Wen', 'Zhenbo Xu', 'Hongwu Zhang', 'Yuchen Yuan', 'Xiao Tan', 'Liusheng Huang'] | 2020-07-03 | null | null | null | null | ['online-multi-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.60273534e-01 4.21502665e-02 -2.58367479e-01 -3.38950753e-01
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3.59835595e-01 7.78722703e-01 9.91832316e-01 1.80722296... | [8.839649200439453, -0.27810317277908325] |
c9763ba9-1e1d-473d-846f-b90da8410345 | an-extensible-framework-for-verification-of | null | null | https://aclanthology.org/E17-3010 | https://aclanthology.org/E17-3010.pdf | An Extensible Framework for Verification of Numerical Claims | In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge. We focused on simple numerical claims such as {``}population of Germany in 2015 was 80 million{''} which comprised a quarter of the test instances in the cha... | ['James Thorne', 'Andreas Vlachos'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['rumour-detection'] | ['natural-language-processing'] | [ 6.71206862e-02 6.48336232e-01 -5.40009379e-01 -1.39306709e-01
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-2.90438775e-02 6.63115382e-01 5.28266966e-01 -4.44308877... | [9.067280769348145, 9.0975980758667] |
cde20b25-8f0d-4c4d-9967-2907d7c24805 | geo-referencing-place-from-everyday-natural | 1710.03346 | null | http://arxiv.org/abs/1710.03346v1 | http://arxiv.org/pdf/1710.03346v1.pdf | Geo-referencing Place from Everyday Natural Language Descriptions | Natural language place descriptions in everyday communication provide a rich
source of spatial knowledge about places. An important step to utilize such
knowledge in information systems is geo-referencing all the places referred to
in these descriptions. Current techniques for geo-referencing places from text
documents... | ['Hao Chen', 'Stephan Winter', 'Maria Vasardani'] | 2017-10-09 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-1.51740566e-01 -1.37595132e-01 -1.18488565e-01 -3.77641082e-01
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b0aaeca3-86fb-4b97-82bd-90438b01a883 | deep-learning-of-fmri-big-data-a-novel | 1502.00093 | null | http://arxiv.org/abs/1502.00093v1 | http://arxiv.org/pdf/1502.00093v1.pdf | Deep learning of fMRI big data: a novel approach to subject-transfer decoding | As a technology to read brain states from measurable brain activities, brain
decoding are widely applied in industries and medical sciences. In spite of
high demands in these applications for a universal decoder that can be applied
to all individuals simultaneously, large variation in brain activities across
individual... | ['Sotetsu Koyamada', 'Shin Ishii', 'Yumi Shikauchi', 'Masanori Koyama', 'Ken Nakae'] | 2015-01-31 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 5.17273009e-01 -1.43801486e-02 7.39245415e-02 -3.54362100e-01
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-3.27707790e-02 2.16088578e-01 1.38912067e-01 1.94333401... | [12.606175422668457, 3.3854987621307373] |
f0502fe4-e83c-4a53-ab95-4527d5126f50 | ranking-and-sampling-in-open-domain-question | null | null | https://aclanthology.org/D19-1245 | https://aclanthology.org/D19-1245.pdf | Ranking and Sampling in Open-Domain Question Answering | Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs. Existing approaches always follow the distantly supervised setup where some of the paragraphs are wrong-labeled (noisy), and mainly utilize the paragraph-question relevance to denoise. However, the paragraph-para... | ['Weiping Wang', 'Dan Meng', 'Zheng Lin', 'Yanfu Xu', 'Rui Liu', 'Yuanxin Liu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['triviaqa'] | ['miscellaneous'] | [-2.47714520e-01 3.21355999e-01 1.24036092e-02 -4.61816102e-01
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5.31103671e-01 3.25799048e-01 7.17361689e-01 -3.15893382... | [11.422390937805176, 8.0520658493042] |
1c0f5b78-a3f8-4b7b-a1b4-1c1fa4b0a417 | task-specific-scene-structure-representations | 2301.00555 | null | https://arxiv.org/abs/2301.00555v1 | https://arxiv.org/pdf/2301.00555v1.pdf | Task-specific Scene Structure Representations | Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture ... | ['Hae-Gon Jeon', 'Seunghyun Shin', 'Jisu Shin'] | 2023-01-02 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 4.79727924e-01 9.26802680e-02 1.14057124e-01 -4.76527154e-01
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5.50398342e-02 1.47566065e-01 2.82697409e-01 -2.24034011... | [9.688889503479004, 0.5782443881034851] |
22a4f14e-5358-4e7d-a4a2-45d553133947 | mltr-multi-label-classification-with | 2106.06195 | null | https://arxiv.org/abs/2106.06195v1 | https://arxiv.org/pdf/2106.06195v1.pdf | MlTr: Multi-label Classification with Transformer | The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects with high conditional probability are still the main bottlenecks of previous convolutional neural network(CNN) based models, limited by convolu... | ['Honglin Liu', 'Nian Shi', 'Zhongyuan Wang', 'Dong Shen', 'Fan Yang', 'Xiangyu Wu', 'Hezheng Lin', 'Xing Cheng'] | 2021-06-11 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 3.05265844e-01 -2.60208428e-01 -3.22160065e-01 -3.96809310e-01
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4.10613984e-01 2.52565816e-02 4.40851599e-01 3.46098021... | [9.857475280761719, 3.993795871734619] |
d1794029-9180-423b-b095-246237840821 | il-net-using-expert-knowledge-to-guide-the | 1809.05127 | null | http://arxiv.org/abs/1809.05127v1 | http://arxiv.org/pdf/1809.05127v1.pdf | IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks | Deep neural networks (DNN) excel at extracting patterns. Through
representation learning and automated feature engineering on large datasets,
such models have been highly successful in computer vision and natural language
applications. Designing optimal network architectures from a principled or
rational approach howev... | ['Wesley Beckner', 'Jim Pfaendtner', 'Khushmeen Sakloth', 'Garrett B. Goh'] | 2018-09-13 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 3.35942358e-01 1.88272223e-01 -4.75366414e-01 -4.27134007e-01
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53309515-3c4c-4e77-81f1-c3bfb7e7b068 | why-does-zero-shot-cross-lingual-generation | 2305.17325 | null | https://arxiv.org/abs/2305.17325v1 | https://arxiv.org/pdf/2305.17325v1.pdf | Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution | Zero-shot cross-lingual transfer is when a multilingual model is trained to perform a task in one language and then is applied to another language. Although the zero-shot cross-lingual transfer approach has achieved success in various classification tasks, its performance on natural language generation tasks falls shor... | ['Kenton Murray', 'Tianjian Li'] | 2023-05-27 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.17740834e-01 1.58103719e-01 -4.03972030e-01 -4.16172981e-01
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3.91818613e-01 8.32486391e-01 -5.84663749e-02 -5.90933323... | [11.408146858215332, 10.120308876037598] |
b659e9ea-c2d7-40bf-afed-baadf1a0a587 | differentiable-dictionary-search-integrating | 2211.15524 | null | https://arxiv.org/abs/2211.15524v1 | https://arxiv.org/pdf/2211.15524v1.pdf | Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation | This paper describes several improvements to a new method for signal decomposition that we recently formulated under the name of Differentiable Dictionary Search (DDS). The fundamental idea of DDS is to exploit a class of powerful deep invertible density estimators called normalizing flows, to model the dictionary in a... | ['Gerhard Widmer', 'Rainer Kelz', 'Lukáš Samuel Marták'] | 2022-11-28 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 3.20901066e-01 1.45743310e-01 -8.42821971e-02 7.69132450e-02
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-2.17735827e-01 3.60982478e-01 -3.92621905e-01 -7.71115646... | [15.457072257995605, 5.573808670043945] |
d8571362-1ac4-400c-bb33-18acf1d7f354 | icurb-imitation-learning-based-detection-of | 2103.17118 | null | https://arxiv.org/abs/2103.17118v1 | https://arxiv.org/pdf/2103.17118v1.pdf | iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving | Detection of road curbs is an essential capability for autonomous driving. It can be used for autonomous vehicles to determine drivable areas on roads. Usually, road curbs are detected on-line using vehicle-mounted sensors, such as video cameras and 3-D Lidars. However, on-line detection using video cameras may suffer ... | ['Ming Liu', 'Yuxiang Sun', 'Zhenhua Xu'] | 2021-03-31 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.30646208e-01 -2.73148477e-01 -2.20706448e-01 -2.86674291e-01
-5.32280952e-02 -5.98192334e-01 3.18889260e-01 -3.33477646e-01
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-4.00155187e-02 -1.25853276e+00 -9.42992032e-01 -5.19686699e-01
1.59010202e-01 2.42443770e-01 6.64977729e-01 -4.27680731... | [8.148786544799805, -1.71361243724823] |
1b8a2d1a-eb9f-47f8-81fe-183c84a89bed | an-iterative-clustering-algorithm-for-the | 2112.10467 | null | https://arxiv.org/abs/2112.10467v2 | https://arxiv.org/pdf/2112.10467v2.pdf | An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees | Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used... | ['Christophe Biernacki', 'Hemant Tyagi', 'Guillaume Braun'] | 2021-12-20 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.02777728e-01 1.66048288e-01 -5.17855763e-01 -3.24014336e-01
-2.91717708e-01 -7.73656964e-01 6.06479526e-01 2.00736970e-01
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5bc87e05-c4cf-49c2-af9b-5200e2e6d118 | pos-bert-point-cloud-one-stage-bert-pre | 2204.00989 | null | https://arxiv.org/abs/2204.00989v1 | https://arxiv.org/pdf/2204.00989v1.pdf | POS-BERT: Point Cloud One-Stage BERT Pre-Training | Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT. However, directly extending BERT from NLP to point clouds requires training a fixed discrete Variational AutoEncoder (dVAE) before pre-training, which re... | ['Manning Wang', 'Yu Qiao', 'Renrui Zhang', 'Shaolei Liu', 'Peng Gao', 'Kexue Fu'] | 2022-04-03 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [-6.68343678e-02 2.85795659e-01 -2.26036340e-01 -3.65530908e-01
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2.41378546e-01 7.35500932e-01 2.14041561e-01 -1.41984951... | [8.08509635925293, -3.388658046722412] |
2870185a-ac0a-4a99-9014-65bc1bd8eaa1 | theme-driven-keyphrase-extraction-from-social | 2301.11508 | null | https://arxiv.org/abs/2301.11508v2 | https://arxiv.org/pdf/2301.11508v2.pdf | Theme-driven Keyphrase Extraction to Analyze Social Media Discourse | Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyph... | ['Sarah Preum', 'Joseph Gatto', 'Madhusudan Basak', 'Omar Sharif', 'William Romano'] | 2023-01-27 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 4.31278944e-01 7.56185412e-01 -8.53595614e-01 2.14070320e-01
-1.18704689e+00 -5.33218622e-01 5.37417531e-01 1.43510485e+00
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-3.55900824e-01 1.74349084e-01 -2.09866792e-01 -2.79565513... | [8.546022415161133, 9.195648193359375] |
b28ddc1d-daad-4164-9c94-68207182022c | sf2se3-clustering-scene-flow-into-se-3 | 2209.08532 | null | https://arxiv.org/abs/2209.08532v2 | https://arxiv.org/pdf/2209.08532v2.pdf | SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection | We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is obtained by application of existing optical flow and depth estimation algorithms... | ['Thomas Brox', 'Philipp Schröppel', 'Leonhard Sommer'] | 2022-09-18 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.91964114e-01 -2.09740117e-01 -2.01216519e-01 -1.34699866e-01
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3.24383266e-02 8.02913427e-01 9.87353623e-01 1.45508111... | [8.60582447052002, -1.8161295652389526] |
bc90fa8f-ab04-45ea-89d0-ec4127f25ca5 | mmnet-multi-collaboration-and-multi | 2307.02733 | null | https://arxiv.org/abs/2307.02733v1 | https://arxiv.org/pdf/2307.02733v1.pdf | MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection | Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged face images. In response, various deepfake detection methods have been proposed to assess image authenticity. Sequential deepfake detection, ... | ['Xinbo Gao', 'Nannan Wang', 'Lin Yuan', 'Jie Li', 'Decheng Liu', 'Ruiyang Xia'] | 2023-07-06 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 5.06956220e-01 -3.87208581e-01 -2.21878644e-02 -7.30438530e-02
-6.24346316e-01 -7.10999370e-01 5.80151975e-01 -1.96529314e-01
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-7.23678991e-02 -9.60106999e-02 -2.78714281e-02 -2.86468059... | [12.620223999023438, 1.0623643398284912] |
e79588af-4852-4b4b-aece-bbdc47a464e5 | whose-hands-are-these-hand-detection-and-hand | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.pdf | Whose Hands Are These? Hand Detection and Hand-Body Association in the Wild | We study a new problem of detecting hands and finding the location of the corresponding person for each detected hand. This task is helpful for many downstream tasks such as hand tracking and hand contact estimation. Associating hands with people is challenging in unconstrained conditions since multiple people can ... | ['Minh Hoai', 'Mingzhen Huang', 'Thanh Nguyen', 'Supreeth Narasimhaswamy'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['hand-detection'] | ['computer-vision'] | [-4.32817310e-01 -3.48305672e-01 -2.06833869e-01 -2.83861578e-01
-3.58813912e-01 -7.03613281e-01 2.22301051e-01 -4.60752964e-01
-4.01145458e-01 4.99761552e-01 1.65400222e-01 2.74628580e-01
3.74447368e-02 -2.12285832e-01 -6.01657331e-01 -5.13540268e-01
-1.53659418e-01 1.03344738e+00 5.15854239e-01 1.43630356... | [6.624588966369629, -0.6696982979774475] |
6d384604-1fde-487b-8d45-36d466b98adb | an-internal-cluster-validity-index-based-on | 2009.01328 | null | https://arxiv.org/abs/2009.01328v2 | https://arxiv.org/pdf/2009.01328v2.pdf | An Internal Cluster Validity Index Using a Distance-based Separability Measure | To evaluate clustering results is a significant part of cluster analysis. There are no true class labels for clustering in typical unsupervised learning. Thus, a number of internal evaluations, which use predicted labels and data, have been created. They are also named internal cluster validity indices (CVIs). Without ... | ['Shuyue Guan', 'Murray Loew'] | 2020-09-02 | null | null | null | null | ['clustering-algorithms-evaluation'] | ['methodology'] | [-3.33827198e-01 -2.71116495e-01 -4.56529967e-02 -5.49688756e-01
-4.54006344e-01 -6.11201704e-01 6.32694781e-01 5.08438885e-01
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-5.49323738e-01 -7.12442279e-01 -1.76582783e-01 -7.34237850e-01
-1.49573743e-01 9.21364546e-01 3.19199592e-01 2.74234086... | [7.636549472808838, 4.571020603179932] |
ca1ff7e3-d4b1-41c9-9b01-9fce8ddb95fa | partially-view-aligned-representation | null | null | http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-MvCLNwith-supp.pdf | http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-MvCLNwith-supp.pdf | Partially View-aligned Representation Learning with Noise-robust Contrastive Loss | In real-world applications, it is common that only a portion of data is aligned across views due to spatial, temporal, or spatiotemporal asynchronism, thus leading to socalled Partially View-aligned Problem (PVP). To solve such a less-touched problem without the help of labels, we propose simultaneously learning repres... | ['Xi Peng', 'Peng Hu', 'Zitao Liu', 'Zhenyu Huang', 'Yunfan Li', 'Mouxing Yang'] | 2021-03-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.pdf | cvpr-2021-1 | ['partially-view-aligned-multi-view-learning'] | ['computer-vision'] | [-1.06071219e-01 -2.03819051e-01 -1.39976263e-01 -4.27613556e-01
-8.85234773e-01 -6.86155438e-01 4.59687293e-01 -1.69189014e-02
-3.10340315e-01 6.12676919e-01 -1.28612876e-01 2.01186970e-01
-2.50697017e-01 -6.24119222e-01 -6.85207486e-01 -1.03348660e+00
1.14835963e-01 5.97777426e-01 1.65855229e-01 1.00617662... | [8.282139778137207, 4.514838218688965] |
f39034aa-bd9a-43f0-961d-6a99f06fd37a | dvcflow-modeling-information-flow-towards | 2111.10146 | null | https://arxiv.org/abs/2111.10146v1 | https://arxiv.org/pdf/2111.10146v1.pdf | DVCFlow: Modeling Information Flow Towards Human-like Video Captioning | Dense video captioning (DVC) aims to generate multi-sentence descriptions to elucidate the multiple events in the video, which is challenging and demands visual consistency, discoursal coherence, and linguistic diversity. Existing methods mainly generate captions from individual video segments, lacking adaptation to th... | ['Qi Tian', 'Qingming Huang', 'Shuhui Wang', 'Zhengcong Fei', 'Xu Yan'] | 2021-11-19 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 2.01830864e-01 -1.62178069e-01 -3.52370054e-01 -2.84981310e-01
-5.75140476e-01 -5.86301565e-01 8.60985756e-01 -1.39636964e-01
8.37701838e-03 7.86222756e-01 9.33660150e-01 1.31146580e-01
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2.09418871e-02 1.37728214e-01 3.05635273e-01 -1.06089279... | [10.65777587890625, 0.6842981576919556] |
c592d290-96aa-4bbf-885d-86e996ea7284 | embedding-logical-queries-on-knowledge-graphs | 1806.01445 | null | https://arxiv.org/abs/1806.01445v4 | https://arxiv.org/pdf/1806.01445v4.pdf | Embedding Logical Queries on Knowledge Graphs | Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple un... | ['Payal Bajaj', 'Marinka Zitnik', 'William L. Hamilton', 'Jure Leskovec', 'Dan Jurafsky'] | 2018-06-05 | embedding-logical-queries-on-knowledge-graphs-1 | http://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs | http://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs.pdf | neurips-2018-12 | ['complex-query-answering'] | ['knowledge-base'] | [ 9.00814161e-02 6.88183784e-01 -2.90560246e-01 -2.11970091e-01
-2.45471314e-01 -7.16563404e-01 1.90912843e-01 7.94791758e-01
-6.47844896e-02 8.91779840e-01 4.81607579e-03 -7.72058308e-01
-5.72598279e-01 -1.29477048e+00 -1.07553506e+00 -1.81414813e-01
-7.80329227e-01 9.64785993e-01 6.49612844e-02 -3.86206210... | [8.644543647766113, 7.723686218261719] |
f565a826-9da6-4e29-aa79-96de696aa73c | disp-r-cnn-stereo-3d-object-detection-via | 2004.03572 | null | https://arxiv.org/abs/2004.03572v1 | https://arxiv.org/pdf/2004.03572v1.pdf | Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation | In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage c... | ['Siyu Zhang', 'Hujun Bao', 'Xiaowei Zhou', 'Qinhong Jiang', 'Jiaming Sun', 'Yiming Xie', 'Linghao Chen'] | 2020-04-07 | disp-r-cnn-stereo-3d-object-detection-via-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.pdf | cvpr-2020-6 | ['vehicle-pose-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [ 1.80611819e-01 -1.82367899e-02 -5.18103875e-02 -4.22035038e-01
-7.51185656e-01 -3.95369053e-01 3.74879807e-01 -1.88348114e-01
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4.26957160e-01 5.85154295e-01 7.14200616e-01 -1.32645303... | [8.004793167114258, -2.6581056118011475] |
4f58c37e-6246-4adc-965a-4aa313ffad66 | autonomous-overtaking-in-gran-turismo-sport | 2103.14666 | null | https://arxiv.org/abs/2103.14666v2 | https://arxiv.org/pdf/2103.14666v2.pdf | Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning | Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online. When the vehicle approaches its physical limits, existing mod... | ['Davide Scaramuzza', 'Peter Duerr', 'Elia Kaufmann', 'HaoChih Lin', 'Yunlong Song'] | 2021-03-26 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-3.21219623e-01 2.18084961e-01 -3.95846575e-01 7.74549171e-02
-4.76660520e-01 -4.79315758e-01 5.42931259e-01 -3.27986747e-01
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-1.06021035e-02 -4.43601698e-01 -8.03977191e-01 -4.30618703e-01
6.82058260e-02 6.55110598e-01 5.19580424e-01 -8.26541126... | [5.151772499084473, 1.2531284093856812] |
213d9c04-aee8-4726-96ad-35aa9cc21901 | der-dynamically-expandable-representation-for | 2103.16788 | null | https://arxiv.org/abs/2103.16788v1 | https://arxiv.org/pdf/2103.16788v1.pdf | DER: Dynamically Expandable Representation for Class Incremental Learning | We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a novel two-stage learning ... | ['Xuming He', 'Jiangwei Xie', 'Shipeng Yan'] | 2021-03-31 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['novel-concepts'] | ['reasoning'] | [ 4.23037231e-01 -5.36075272e-02 -3.36861193e-01 -2.42844880e-01
-5.61231136e-01 -6.92908943e-01 4.91744816e-01 1.95151299e-01
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-3.13714594e-01 -6.40153289e-01 -8.25800717e-01 -7.24598169e-01
-5.18851504e-02 9.93049815e-02 4.38980281e-01 7.41899461... | [9.813409805297852, 3.379843235015869] |
8aec11a0-731f-44e2-9fc7-fa64729517d3 | indl-a-new-datasets-and-benchmark-for-in | 2305.17716 | null | https://arxiv.org/abs/2305.17716v4 | https://arxiv.org/pdf/2305.17716v4.pdf | InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion | This paper introduces a novel approach to evaluating deep learning models' capacity for in-diagram logic interpretation. Leveraging the intriguing realm of visual illusions, we establish a unique dataset, InDL, designed to rigorously test and benchmark these models. Deep learning has witnessed remarkable progress in do... | ['Xuchen Liu', 'Zhekai Duan', 'Ze Cao', 'Wenyu Wang', 'Haobo Yang'] | 2023-05-28 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 6.68266490e-02 3.81335244e-02 -9.44040418e-02 -4.34572935e-01
-3.35813552e-01 -8.08002234e-01 7.70939171e-01 9.49587077e-02
-1.07620865e-01 3.16012353e-01 3.68300974e-01 -7.52658010e-01
-1.51104495e-01 -6.47063076e-01 -7.31186688e-01 -2.27396533e-01
-3.07995155e-02 3.58043432e-01 -2.11808085e-02 -2.64780551... | [10.653966903686523, 1.9965671300888062] |
d650784c-02d8-4b29-94c8-525ed1ae59c3 | content-based-image-retrieval-and-the | 2011.06490 | null | https://arxiv.org/abs/2011.06490v1 | https://arxiv.org/pdf/2011.06490v1.pdf | Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era | Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image. This scenario is called instance or object retrieval and requires matching fine-grained visual patterns between images. Semantics, howeve... | ['Joachim Denzler', 'Björn Barz'] | 2020-11-12 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 5.38731098e-01 -2.50424147e-01 -3.44501138e-01 -3.15135866e-01
-8.80720556e-01 -7.87135065e-01 8.79811466e-01 4.64087158e-01
-4.17987674e-01 3.67780089e-01 2.09906790e-02 1.47574954e-02
-5.51781714e-01 -5.26113331e-01 -4.60509896e-01 -5.53736866e-01
2.32150823e-01 3.58085603e-01 4.32414860e-01 -3.77967000... | [10.86926555633545, 0.9163470268249512] |
4c77a4f0-ba52-4b15-9ed9-ca478be0c5a2 | joint-learning-of-self-representation-and | 1905.04432 | null | https://arxiv.org/abs/1905.04432v1 | https://arxiv.org/pdf/1905.04432v1.pdf | Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering | Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure. Although the spectral clustering based methods achieve promotion in multi-view clustering, their utility is limited by the separate learning manner in which affinity matrix const... | ['Xiao-Yuan Jing', 'Zuoyong Li', 'Songsong Wu', 'Yan Yan', 'Hao Tang', 'Zhiqiang Lu', 'Songhao Zhu'] | 2019-05-11 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-5.23162603e-01 -5.93846619e-01 -5.03228784e-01 -1.65510669e-01
-9.66717124e-01 -9.53246772e-01 3.49878132e-01 -2.40588382e-01
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-3.44313651e-01 -2.03083903e-01 -1.48261622e-01 -1.17634976e+00
-5.12445755e-02 7.19777822e-01 -2.77605541e-02 3.83036852... | [8.241517066955566, 4.581013202667236] |
f49dfa52-908b-49be-a60e-9b46d0549a40 | meta-pu-an-arbitrary-scale-upsampling-network-1 | null | null | https://ieeexplore.ieee.org/document/9351772/ | https://ieeexplore.ieee.org/document/9351772/ | Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud | Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus... | ['Shuquan Ye; Dongdong Chen; Songfang Han; Ziyu Wan; Jing Liao'] | 2021-02-09 | null | null | null | null | ['point-cloud-super-resolution'] | ['computer-vision'] | [-2.06310555e-01 -1.94190353e-01 -1.19488537e-01 -1.24329589e-01
-5.38659036e-01 6.40718713e-02 2.84447730e-01 -7.65481070e-02
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1.87490508e-01 -1.25723398e+00 -1.05809069e+00 -6.05631292e-01
2.23046124e-01 4.60013837e-01 5.53330243e-01 -1.55715957... | [8.254898071289062, -3.5252885818481445] |
1d58611a-1e50-478f-9462-e4e881123767 | a-cross-document-coreference-dataset-for | null | null | https://aclanthology.org/2022.lrec-1.393 | https://aclanthology.org/2022.lrec-1.393.pdf | A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports | This paper proposes a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient’s radiology reports. Our annotated corpus contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, cove... | ['Kirk Roberts', 'Sunitha Mogalla', 'Atieh Pajouhi', 'Hio Cheng Lam', 'Surabhi Datta'] | null | null | null | null | lrec-2022-6 | ['cross-document-coreference-resolution', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.21496856e-01 5.47496259e-01 -5.28821588e-01 -2.83022881e-01
-1.80319738e+00 -7.42305040e-01 5.73017359e-01 8.69003713e-01
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-4.30038244e-01 -1.75171554e-01 -4.48571146e-01 -1.68337867e-01
-1.61742613e-01 8.46076906e-01 1.65691972e-01 -1.27627715... | [8.439163208007812, 8.722953796386719] |
0a76939a-6ad4-4d41-af58-83773b7adb0c | debate-dynamics-for-human-comprehensible-fact | 2001.03436 | null | https://arxiv.org/abs/2001.03436v1 | https://arxiv.org/pdf/2001.03436v1.pdf | Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs | We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (... | ['Jorge Andres Quintero Serna', 'Yunpu Ma', 'Mitchell Joblin', 'Martin Ringsquandl', 'Marcel Hildebrandt', 'Volker Tresp'] | 2020-01-09 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 1.50776520e-01 8.04868996e-01 -4.99415249e-01 -3.59983742e-01
-3.24974746e-01 -6.88627660e-01 5.73212028e-01 7.10274756e-01
-2.72203714e-01 8.69021952e-01 2.61923168e-02 -8.42038691e-01
-3.34482640e-01 -1.28173435e+00 -5.22156179e-01 -6.20945275e-01
9.20540020e-02 5.32279730e-01 1.81430891e-01 -1.09795719... | [9.524627685546875, 7.820497035980225] |
d7cf2a9a-f5eb-412e-8538-ca0aaef980f0 | improving-the-classification-of-rare-chords | 2012.07055 | null | https://arxiv.org/abs/2012.07055v2 | https://arxiv.org/pdf/2012.07055v2.pdf | Improving the Classification of Rare Chords with Unlabeled Data | In this work, we explore techniques to improve performance for rare classes in the task of Automatic Chord Recognition (ACR). We first explored the use of the focal loss in the context of ACR, which was originally proposed to improve the classification of hard samples. In parallel, we adapted a self-learning technique ... | ['Claudio R. Jung', 'Rodrigo Schramm', 'Marcelo Bortolozzo'] | 2020-12-13 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 5.23615599e-01 -5.51436320e-02 2.04298988e-01 -5.14719561e-02
-9.21606779e-01 -6.45794213e-01 5.36849499e-01 -6.44858479e-02
-6.44257009e-01 5.47356784e-01 3.79924141e-02 5.33897318e-02
-1.93278059e-01 -5.75181425e-01 -3.46933872e-01 -6.29546821e-01
-2.74926484e-01 2.96612054e-01 5.69268644e-01 -3.74943018... | [15.821442604064941, 5.255793571472168] |
03005733-dd48-415a-b5b8-ce62eda2f806 | learning-with-contrastive-examples-for-data | null | null | https://aclanthology.org/2020.coling-main.213 | https://aclanthology.org/2020.coling-main.213.pdf | Learning with Contrastive Examples for Data-to-Text Generation | Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., {``}Nikkei gains{''} is generated when {``}Nikkei drops{''} is expected. We investigate models trained on contrastive examples i.e., incorrect sentences or terms, in addition to correct ones to reduce such errors. We first cr... | ['Yusuke Miyao', 'Hiroya Takamura', 'Ichiro Kobayashi', 'Keiichi Goshima', 'Hiroshi Noji', 'Kasumi Aoki', 'Tatsuya Ishigaki', 'Yui Uehara'] | 2020-12-01 | null | null | null | coling-2020-8 | ['comment-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.82807225e-01 3.27496767e-01 -8.85596313e-03 -5.29345751e-01
-6.76458180e-01 -4.39987868e-01 7.98816502e-01 3.93569946e-01
-6.91836357e-01 1.26979411e+00 4.22511071e-01 -3.14208776e-01
-2.29978248e-01 -7.31843472e-01 -7.55517602e-01 -5.50000608e-01
2.84410566e-01 5.80160201e-01 1.46613047e-01 -6.05595410... | [11.60914421081543, 9.06697940826416] |
a429be23-7e68-48ff-9a99-c4fd04f59a56 | unlimited-sampling-radar-a-real-time-end-to | 2306.17684 | null | https://arxiv.org/abs/2306.17684v1 | https://arxiv.org/pdf/2306.17684v1.pdf | Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator | In this paper, the trade-off between the quantization noise and the dynamic range of ADCs used to acquire radar signals is revisited using the Unlimited Sensing Framework (USF) in a practical setting. Trade-offs between saturation and resolution arise in many applications, like radar, where sensors acquire signals whic... | ['Ayush Bhandari', 'Bhavani Shankar MRR', 'Thomas Feuillen'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 6.82309389e-01 -1.73385575e-01 -1.68391839e-02 -2.59687454e-01
-8.56882632e-01 -9.21965182e-01 5.67987323e-01 -3.38639051e-01
-4.40961063e-01 7.01935410e-01 -2.71413978e-02 -4.35871124e-01
-4.11782622e-01 -6.79431200e-01 -3.72538388e-01 -7.13507473e-01
-4.71442223e-01 4.60148901e-02 1.99547723e-01 -1.66824862... | [6.492488861083984, 1.2348252534866333] |
ce02d0bc-50f8-4e8e-933f-01bb4fd6a02d | scaling-evidence-based-instructional-design | 2306.01006 | null | https://arxiv.org/abs/2306.01006v2 | https://arxiv.org/pdf/2306.01006v2.pdf | Scaling Evidence-based Instructional Design Expertise through Large Language Models | This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implement... | ['Gautam Yadav'] | 2023-05-31 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [-2.28083543e-02 1.93134710e-01 -1.14138432e-01 -6.61800578e-02
-7.62552440e-01 -9.08210516e-01 3.53328109e-01 2.30409563e-01
-1.53795034e-01 2.33976439e-01 9.31464016e-01 -1.06786847e+00
-5.97139716e-01 -6.39478743e-01 -6.88416123e-01 7.41304308e-02
3.05408269e-01 1.21347405e-01 1.09874703e-01 -4.69676793... | [10.685524940490723, 7.6111578941345215] |
581c8c8a-76e4-4fde-8eb8-ed5dfb4536f9 | coarse-grained-decomposition-and-fine-grained | 2101.05988 | null | https://arxiv.org/abs/2101.05988v1 | https://arxiv.org/pdf/2101.05988v1.pdf | Coarse-grained decomposition and fine-grained interaction for multi-hop question answering | Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning. However, in actual scenarios, lots of complex queries require multi-hop reasoning. The ... | ['Yun Liu', 'Xing Cao'] | 2021-01-15 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 4.01388146e-02 1.60298586e-01 2.90103674e-01 -5.03212154e-01
-9.57651675e-01 -6.28975213e-01 5.96297264e-01 4.44699079e-01
-4.91112977e-01 6.06903195e-01 5.93962967e-01 -5.14213443e-01
-3.75917763e-01 -1.03032827e+00 -6.65227830e-01 -7.72460625e-02
5.61863899e-01 5.93702614e-01 9.36455190e-01 -7.13072360... | [11.091657638549805, 7.969547271728516] |
248cd04e-f706-4426-8425-80604347e231 | clothfit-cloth-human-attribute-guided-virtual | 2306.13908 | null | https://arxiv.org/abs/2306.13908v1 | https://arxiv.org/pdf/2306.13908v1.pdf | ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset | Online clothing shopping has become increasingly popular, but the high rate of returns due to size and fit issues has remained a major challenge. To address this problem, virtual try-on systems have been developed to provide customers with a more realistic and personalized way to try on clothing. In this paper, we prop... | ['Paul Lukowicz', 'Sungho Suh', 'Kundan Sai Prabhu Thota', 'Lala Shakti Swarup Ray', 'Yunmin Cho'] | 2023-06-24 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [-1.11267015e-01 -2.33425684e-02 -2.22738236e-01 -7.61437893e-01
7.05469400e-02 -1.63569868e-01 -2.63438046e-01 -2.46033609e-01
4.16601859e-02 4.06258970e-01 7.95535296e-02 1.04947090e-01
3.40356320e-01 -1.24911857e+00 -9.24527466e-01 -2.03175500e-01
2.99142331e-01 1.12736456e-01 -1.95105880e-01 -5.99640250... | [11.89603328704834, -0.8884181380271912] |
b37e7e4d-80d9-4e9a-bc8d-25bf6909cb57 | strass-a-light-and-effective-method-for | 1907.07323 | null | https://arxiv.org/abs/1907.07323v1 | https://arxiv.org/pdf/1907.07323v1.pdf | STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings | This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the docume... | ['Cécile Pereira', 'Léo Bouscarrat', 'Thomas Peel', 'Antoine Bonnefoy'] | 2019-07-16 | strass-a-light-and-effective-method-for-1 | https://aclanthology.org/P19-2034 | https://aclanthology.org/P19-2034.pdf | acl-2019-7 | ['document-embedding', 'extractive-document-summarization'] | ['methodology', 'natural-language-processing'] | [ 4.64630902e-01 6.38322175e-01 -2.80277252e-01 -1.86937854e-01
-1.29587281e+00 -7.44953096e-01 7.56669819e-01 6.32254720e-01
-5.27277887e-01 7.12256193e-01 1.15822923e+00 6.49998561e-02
-1.97300464e-02 -6.74396217e-01 -6.50782108e-01 -4.04105514e-01
4.01066035e-01 6.77986324e-01 3.44303027e-02 -3.26137662... | [12.533712387084961, 9.511524200439453] |
2aa32576-2e4b-4196-ad2f-8404a5ff3b32 | predictive-business-process-monitoring-with | 1612.02130 | null | http://arxiv.org/abs/1612.02130v2 | http://arxiv.org/pdf/1612.02130v2.pdf | Predictive Business Process Monitoring with LSTM Neural Networks | Predictive business process monitoring methods exploit logs of completed
cases of a process in order to make predictions about running cases thereof.
Existing methods in this space are tailor-made for specific prediction tasks.
Moreover, their relative accuracy is highly sensitive to the dataset at hand,
thus requiring... | ['Niek Tax', 'Marcello La Rosa', 'Ilya Verenich', 'Marlon Dumas'] | 2016-12-07 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.08961421e-01 -2.82936729e-02 -4.17684019e-01 -3.50645542e-01
-6.82011187e-01 -2.27769285e-01 8.20050001e-01 3.72664183e-01
-2.55546123e-01 5.26415646e-01 3.58285047e-02 -6.60909295e-01
-3.33876252e-01 -8.91871452e-01 -5.04637599e-01 -3.99064273e-01
-3.80401105e-01 7.53085971e-01 2.40847558e-01 2.66508430... | [8.545812606811523, 5.881584167480469] |
85555573-563b-4e49-9a40-6cd730c5f06a | optimal-transport-vs-fisher-rao-distance | 1604.08634 | null | http://arxiv.org/abs/1604.08634v2 | http://arxiv.org/pdf/1604.08634v2.pdf | Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series | We present a methodology for clustering N objects which are described by
multivariate time series, i.e. several sequences of real-valued random
variables. This clustering methodology leverages copulas which are
distributions encoding the dependence structure between several random
variables. To take fully into account ... | ['Sébastien Andler', 'Philippe Donnat', 'Gautier Marti', 'Frank Nielsen'] | 2016-04-28 | null | null | null | null | ['clustering-multivariate-time-series'] | ['time-series'] | [-6.02714479e-01 -5.95334888e-01 -2.50073359e-03 -4.78326887e-01
-6.43388629e-01 -1.19332850e+00 4.98225987e-01 1.75426185e-01
-8.91612843e-02 5.66089988e-01 -1.41353279e-01 -2.98020571e-01
-7.75353849e-01 -7.34522700e-01 -3.35046709e-01 -9.30050671e-01
-6.47488594e-01 8.87146294e-01 -1.90401524e-02 1.70640275... | [7.155595302581787, 3.8454694747924805] |
e6a9a828-7d72-45b3-a709-f162e75b80a5 | deep-sequence-learning-for-accurate | 2012.00553 | null | https://arxiv.org/abs/2012.00553v1 | https://arxiv.org/pdf/2012.00553v1.pdf | Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device | Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively. In this work, we propose a low-cost one-dimensional Doppler-based method for estima... | ['Gari D. Clifford', 'Reza Sameni', 'Nasim Katebi'] | 2020-11-24 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.65490612e-01 -3.74509469e-02 1.42820001e-01 -4.97834593e-01
-2.42801577e-01 -3.79897237e-01 -3.31221428e-03 1.08036436e-01
-3.68595988e-01 6.04099333e-01 -3.01028579e-01 -5.26803434e-01
-2.92079747e-01 -1.01688457e+00 -6.84210181e-01 -7.09755301e-01
-8.64755630e-01 -2.76371464e-02 -1.92605689e-01 2.76156545... | [14.042360305786133, -2.3551957607269287] |
ce3e32f1-6fe0-45b9-90f9-89ec983932a2 | textgraphs-16-natural-language-premise | null | null | https://aclanthology.org/2022.textgraphs-1.15 | https://aclanthology.org/2022.textgraphs-1.15.pdf | TextGraphs-16 Natural Language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative Language Models | Automated theorem proving can benefit a lot from methods employed in natural language processing, knowledge graphs and information retrieval: this non-trivial task combines formal languages understanding, reasoning, similarity search. We tackle this task by enhancing semantic similarity ranking with prompt engineering,... | ['Robert Wardenga', 'Roman Teucher', 'Liubov Kovriguina'] | null | null | null | null | coling-textgraphs-2022-10 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.21371442e-01 6.81645334e-01 -2.02115729e-01 -5.56345358e-02
-5.60952783e-01 -8.28082323e-01 1.18511915e+00 8.31823647e-01
-6.87628463e-02 9.47534204e-01 5.61677618e-03 -8.87901247e-01
-9.30682421e-01 -1.00975740e+00 -7.41139710e-01 2.17118233e-01
2.11022748e-03 8.80153954e-01 8.60403001e-01 -6.43071711... | [9.14687728881836, 7.185277938842773] |
e0fa9190-71ea-43d4-be71-612643e75f0f | robust-learning-based-incipient-slip | 2307.04011 | null | https://arxiv.org/abs/2307.04011v1 | https://arxiv.org/pdf/2307.04011v1.pdf | Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping | The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In th... | ['Stephen J. Redmond', 'David Cordova Bulens', 'Robert Burke', 'Pablo Martinez Ulloa', 'Qiang Wang'] | 2023-07-08 | null | null | null | null | ['data-augmentation'] | ['methodology'] | [ 3.10547531e-01 -1.53958634e-01 -2.36321449e-01 1.13711454e-01
-6.70804620e-01 -7.31534362e-01 -1.95807680e-01 -1.61655441e-01
-1.57829180e-01 4.21796769e-01 -3.92477095e-01 -8.27036425e-02
4.92776483e-02 -5.24384797e-01 -9.72965658e-01 -5.38896978e-01
-2.83001661e-01 -3.05178724e-02 4.52778578e-01 -2.09779432... | [5.854954242706299, -0.8181201815605164] |
8469b7cf-d3d6-44a9-a7fd-272edfdb7c6c | casein-cascading-explicit-and-implicit | 2307.00020 | null | https://arxiv.org/abs/2307.00020v1 | https://arxiv.org/pdf/2307.00020v1.pdf | CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation | Existing fine-grained intensity regulation methods rely on explicit control through predicted emotion probabilities. However, these high-level semantic probabilities are often inaccurate and unsmooth at the phoneme level, leading to bias in learning. Especially when we attempt to mix multiple emotion intensities for sp... | ['Haiqing Chen', 'Wei Zhou', 'Zhongzhou Zhao', 'Xiongwei Wang', 'Yuhao Cui'] | 2023-06-27 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 4.04241800e-01 3.98286670e-01 -4.19932812e-01 -4.23039079e-01
-7.90248811e-01 -7.45053887e-01 7.80980527e-01 -1.23910055e-01
-1.71644077e-01 5.91895759e-01 6.48233294e-01 2.38274217e-01
1.65893901e-02 -7.53341258e-01 -5.38905919e-01 -6.13870978e-01
4.08100873e-01 1.34842634e-01 -3.34407836e-01 -2.91335851... | [14.824446678161621, 6.6487274169921875] |
5bcb263c-29ae-4430-a041-15dcf71c39b2 | steganogan-high-capacity-image-steganography | 1901.03892 | null | http://arxiv.org/abs/1901.03892v2 | http://arxiv.org/pdf/1901.03892v2.pdf | SteganoGAN: High Capacity Image Steganography with GANs | Image steganography is a procedure for hiding messages inside pictures. While
other techniques such as cryptography aim to prevent adversaries from reading
the secret message, steganography aims to hide the presence of the message
itself. In this paper, we propose a novel technique for hiding arbitrary binary
data in i... | ['Kalyan Veeramachaneni', 'Alfredo Cuesta-Infante', 'Kevin Alex Zhang', 'Lei Xu'] | 2019-01-12 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 0.89813614 0.3657292 0.24577469 0.11848558 -0.5895426 -0.7304073
0.5484978 -0.27623686 -0.27255332 0.44056302 -0.22708546 -0.7927703
0.5409282 -0.92833656 -0.92794186 -0.9720089 -0.641804 -0.24865168
0.25283137 -0.10357568 0.44492796 0.37385795 -1.2036797 0.44257298
0.31815225 0.7952904 -0.12... | [4.360043048858643, 8.037175178527832] |
cf4cc925-0ba0-43b2-9aac-c0a5e55b0e60 | alternative-semantic-representations-for-zero | 1706.09317 | null | http://arxiv.org/abs/1706.09317v1 | http://arxiv.org/pdf/1706.09317v1.pdf | Alternative Semantic Representations for Zero-Shot Human Action Recognition | A proper semantic representation for encoding side information is key to the
success of zero-shot learning. In this paper, we explore two alternative
semantic representations especially for zero-shot human action recognition:
textual descriptions of human actions and deep features extracted from still
images relevant t... | ['Ke Chen', 'Qian Wang'] | 2017-06-28 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 6.33875728e-01 1.17512770e-01 -4.62706923e-01 -3.46940219e-01
-8.23174119e-01 8.11671838e-02 1.10505176e+00 -1.07302153e-02
-5.49273193e-01 7.36414671e-01 6.77444398e-01 3.07776958e-01
-1.05725445e-01 -8.03628802e-01 -3.96521032e-01 -7.25170195e-01
1.43252209e-01 1.81282982e-01 4.28881854e-01 -2.47225657... | [8.598367691040039, 0.9591240882873535] |
b05eec2b-83f6-4b88-b25b-2aa499801d9e | context-aware-saliency-detection-for-image | 1910.08071 | null | https://arxiv.org/abs/1910.08071v1 | https://arxiv.org/pdf/1910.08071v1.pdf | Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks | Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual information and low-level features such as texture remain as intact as possible to the human visual system, while the output image may have different dimensions. ... | ['Shadrokh Samavi', 'Nader Karimi', 'Mahdi Ahmadi'] | 2019-10-17 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 5.09422541e-01 -1.43369168e-01 -1.13072589e-01 -3.52052301e-01
-7.43600726e-02 -2.58743584e-01 3.10734242e-01 4.17501241e-01
-4.86788392e-01 5.08598983e-01 -1.35554641e-01 -1.25177458e-01
1.77488685e-01 -7.79220164e-01 -5.46008766e-01 -5.39533496e-01
5.98282635e-01 -1.45695001e-01 1.26092672e+00 -4.21984226... | [10.774490356445312, -0.9535187482833862] |
7ce8997c-3e5c-4410-adba-9f0823db6b55 | argument-mining-a-survey | null | null | https://aclanthology.org/J19-4006 | https://aclanthology.org/J19-4006.pdf | Argument Mining: A Survey | Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, ... | ['Chris Reed', 'John Lawrence'] | 2019-12-01 | null | null | null | cl-2019-12 | ['public-relations'] | ['miscellaneous'] | [ 1.37835607e-01 9.65901852e-01 -8.48492920e-01 -6.26623690e-01
-2.14957193e-01 -1.03621650e+00 8.37113380e-01 9.62572396e-01
-3.64719659e-01 9.74232078e-01 7.79824138e-01 -1.42655373e+00
-2.88127184e-01 -1.01350689e+00 -4.35579091e-01 -1.80417046e-01
1.52740553e-01 7.53817916e-01 9.15871188e-02 -4.11568195... | [9.491507530212402, 9.573378562927246] |
9f9545e8-be68-447d-b4d6-bb847f4f4192 | a-survey-on-deep-neural-network-partition | 2304.10020 | null | https://arxiv.org/abs/2304.10020v1 | https://arxiv.org/pdf/2304.10020v1.pdf | A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices | Deep neural network (DNN) partition is a research problem that involves splitting a DNN into multiple parts and offloading them to specific locations. Because of the recent advancement in multi-access edge computing and edge intelligence, DNN partition has been considered as a powerful tool for improving DNN inference ... | ['Zhongjie Wang', 'Tonghua Su', 'Xiang He', 'Di Xu'] | 2023-04-20 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-2.06129864e-01 -2.87587047e-01 -5.88780701e-01 -2.10211441e-01
1.72887295e-01 -6.88190937e-01 -5.13456687e-02 -3.89657378e-01
-9.48117748e-02 9.50621843e-01 -2.56589770e-01 -5.75601757e-01
-6.67382181e-01 -9.56292093e-01 -2.76277184e-01 -6.38427019e-01
1.83632985e-01 8.94932687e-01 9.20677930e-02 3.77249211... | [8.156311988830566, 2.9290030002593994] |
ba702fad-9d96-4d4d-ba87-2643b24120ca | red-reinforced-encoder-decoder-networks-for | 1707.04818 | null | http://arxiv.org/abs/1707.04818v1 | http://arxiv.org/pdf/1707.04818v1.pdf | RED: Reinforced Encoder-Decoder Networks for Action Anticipation | Action anticipation aims to detect an action before it happens. Many real
world applications in robotics and surveillance are related to this predictive
capability. Current methods address this problem by first anticipating visual
representations of future frames and then categorizing the anticipated
representations to... | ['Jiyang Gao', 'Ram Nevatia', 'Zhenheng Yang'] | 2017-07-16 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.76354682e-01 7.04579830e-01 -4.93342489e-01 -6.26780093e-01
-3.14628392e-01 -1.32928938e-01 8.93526614e-01 -4.13747923e-03
-3.82040203e-01 4.83015001e-01 6.81309521e-01 -5.37140667e-02
4.20091897e-01 -4.52017397e-01 -9.09856498e-01 -4.26555485e-01
-4.93571967e-01 1.06577232e-01 6.08330846e-01 -1.74282134... | [7.945503234863281, 0.4721047878265381] |
f31e9956-e52a-47d5-9131-2b3c2e240cf2 | learning-to-generate-product-reviews-from | null | null | https://aclanthology.org/E17-1059 | https://aclanthology.org/E17-1059.pdf | Learning to Generate Product Reviews from Attributes | Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews fo... | ['Li Dong', 'Furu Wei', 'Shaohan Huang', 'Ming Zhou', 'Mirella Lapata', 'Ke Xu'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['review-generation'] | ['natural-language-processing'] | [ 4.50436562e-01 2.98710823e-01 -4.14761841e-01 -1.03517497e+00
-1.07019842e+00 -7.23379195e-01 9.64709580e-01 -4.43503894e-02
-3.11679214e-01 7.57209122e-01 5.51649272e-01 -2.09015653e-01
6.67480350e-01 -8.20598781e-01 -7.82834947e-01 -2.99215764e-01
7.10476220e-01 6.63390458e-01 -7.09016263e-01 -4.22732741... | [11.866084098815918, 8.791191101074219] |
86a25ae5-783a-4d55-904d-69d99c70da7b | graph-neural-networks-for-temperature | 2206.11776 | null | https://arxiv.org/abs/2206.11776v1 | https://arxiv.org/pdf/2206.11776v1.pdf | Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids | Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high accuracy in predicting ACs of binary mixtures, superior to well-establis... | ['Alexander Mitsos', 'Manuel Dahmen', 'Artur M. Schweidtmann', 'Karim Ben Hicham', 'Jan G. Rittig'] | 2022-06-23 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.81837732e-01 -4.23709214e-01 -3.33076417e-02 -8.59355256e-02
-3.85647714e-01 -5.36932826e-01 5.70147455e-01 6.71092987e-01
-2.69671381e-01 1.21602964e+00 -1.47745386e-01 -9.38970029e-01
-2.95065343e-01 -6.56767309e-01 -7.72111595e-01 -9.41554546e-01
-3.13435674e-01 8.61101270e-01 -1.09803514e-03 -1.77445620... | [5.10114860534668, 5.606948375701904] |
0ab80aca-97d0-42e3-9063-e7d0219644da | neural-additive-models-for-location-scale-and | 2301.11862 | null | https://arxiv.org/abs/2301.11862v1 | https://arxiv.org/pdf/2301.11862v1.pdf | Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean | Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretabili... | ['Benjamin Säfken', 'Thomas Kneib', 'René-Marcel Kruse', 'Anton Thielmann'] | 2023-01-27 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.28456900e-01 2.61753559e-01 -1.16544232e-01 -6.69467986e-01
-9.32143480e-02 -6.18253291e-01 9.48965430e-01 2.64686853e-01
-3.13769847e-01 5.85687339e-01 5.95989287e-01 -6.99216783e-01
-2.82282621e-01 -5.86944282e-01 -7.34443545e-01 -5.58933258e-01
1.09745733e-01 4.26416457e-01 -1.40151069e-01 -2.39892572... | [8.846667289733887, 5.587249279022217] |
a4be9cc2-ba39-4a93-8403-6ef880811ab7 | unique-unsupervised-image-quality-estimation | 1810.06631 | null | http://arxiv.org/abs/1810.06631v2 | http://arxiv.org/pdf/1810.06631v2.pdf | UNIQUE: Unsupervised Image Quality Estimation | In this paper, we estimate perceived image quality using sparse
representations obtained from generic image databases through an unsupervised
learning approach. A color space transformation, a mean subtraction, and a
whitening operation are used to enhance descriptiveness of images by reducing
spatial redundancy; a lin... | ['M. Prabhushankar', 'D. Temel', 'G. AlRegib'] | 2018-10-15 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 4.10872400e-01 -4.00756598e-01 -1.61705077e-01 -3.27342629e-01
-6.73228621e-01 5.49712516e-02 1.87426746e-01 -1.48772523e-02
-2.50405341e-01 6.22647345e-01 2.53261447e-01 2.42093086e-01
-3.04238498e-02 -6.51125848e-01 -6.59503698e-01 -8.09231639e-01
-1.60379216e-01 -3.11197817e-01 1.53141111e-01 6.91756383... | [11.754158973693848, -1.9718486070632935] |
cbec830d-fbca-41db-9695-67868065d605 | integrating-multi-type-aberrations-from-dna | 2212.05064 | null | https://arxiv.org/abs/2212.05064v1 | https://arxiv.org/pdf/2212.05064v1.pdf | Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery | Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. Especially, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. still, most of the existing computational driver discovery studies mainly exploit the inf... | ['Wen Shi', 'Qian Wang', 'Yang Liu', 'Zhen Deng', 'Jianing Xi'] | 2022-12-09 | null | null | null | null | ['type'] | ['speech'] | [ 2.15707302e-01 -4.16891485e-01 -5.98775327e-01 -3.46164316e-01
-5.41713119e-01 -7.78717399e-01 2.67122507e-01 6.58164620e-01
-2.37737335e-02 5.80822349e-01 3.32649648e-01 -5.82263529e-01
-6.81938350e-01 -1.03975177e+00 -4.77864623e-01 -1.12450290e+00
2.51122713e-01 3.42852563e-01 6.20240234e-02 -3.28541964... | [5.95910120010376, 5.695948600769043] |
f953d91e-65bd-49a4-a972-24966c90e6bb | divide-and-conquer-a-deep-casa-approach-to | 1904.11148 | null | http://arxiv.org/abs/1904.11148v1 | http://arxiv.org/pdf/1904.11148v1.pdf | Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation | We address talker-independent monaural speaker separation from the
perspectives of deep learning and computational auditory scene analysis (CASA).
Specifically, we decompose the multi-speaker separation task into the stages of
simultaneous grouping and sequential grouping. Simultaneous grouping is first
performed in ea... | ['Yuzhou Liu', 'DeLiang Wang'] | 2019-04-25 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 1.04276791e-01 -6.11452043e-01 5.40353477e-01 -2.99469203e-01
-1.29927576e+00 -4.62311685e-01 2.05918774e-01 1.53815016e-01
-3.84087056e-01 1.38661191e-01 1.79687619e-01 5.11762053e-02
-1.96002036e-01 3.00841089e-02 -4.66621995e-01 -1.26547050e+00
-1.63820744e-01 1.91313297e-01 2.49205008e-01 -3.43682021... | [14.951070785522461, 5.8514227867126465] |
bee7413f-b52b-4a89-a2ff-4d6595e8de11 | borm-bayesian-object-relation-model-for | 2108.00397 | null | https://arxiv.org/abs/2108.00397v1 | https://arxiv.org/pdf/2108.00397v1.pdf | BORM: Bayesian Object Relation Model for Indoor Scene Recognition | Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we prop... | ['Yangsheng Xu', 'Tin Lun Lam', 'Zhenglong Sun', 'Xingchao Wang', 'Jun Cen', 'Liguang Zhou'] | 2021-08-01 | null | null | null | null | ['scene-parsing', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.84034604e-01 -1.27818346e-01 -1.50893837e-01 -9.56730545e-01
-3.03259939e-01 -3.46880108e-01 6.61721051e-01 -6.28754199e-02
-4.00483847e-01 3.08732122e-01 2.42767856e-01 -1.55612081e-01
-4.04657871e-01 -8.74777913e-01 -9.59748089e-01 -6.19826674e-01
5.89935303e-01 2.48643130e-01 4.10339028e-01 3.76410745... | [9.403485298156738, -0.7840469479560852] |
563e71a6-bf52-4da7-9f05-639d2cf78816 | knowledge-detection-by-relevant-question-and | 2306.04938 | null | https://arxiv.org/abs/2306.04938v1 | https://arxiv.org/pdf/2306.04938v1.pdf | Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering | Visual question answering (VQA) is a Multidisciplinary research problem that pursued through practices of natural language processing and computer vision. Visual question answering automatically answers natural language questions according to the content of an image. Some testing questions require external knowledge to... | ['Dr. Hiteishi Diwanji', 'Param Ahir'] | 2023-06-08 | null | null | null | null | ['visual-question-answering-1'] | ['computer-vision'] | [ 2.45204985e-01 2.38905683e-01 2.40419716e-01 -3.26420873e-01
-8.10032487e-01 -1.09952486e+00 4.40506816e-01 5.21238208e-01
-3.26491356e-01 8.47240746e-01 5.47775403e-02 -5.01224935e-01
-1.46028608e-01 -1.13156772e+00 -5.59371173e-01 -2.22937420e-01
7.42468059e-01 3.61920685e-01 7.70974696e-01 -3.75371605... | [10.946371078491211, 1.7423896789550781] |
53c50948-da68-4a82-aa89-b22a0b79c041 | neural-tensor-contractions-and-the-expressive | 2103.10293 | null | https://arxiv.org/abs/2103.10293v3 | https://arxiv.org/pdf/2103.10293v3.pdf | Neural tensor contractions and the expressive power of deep neural quantum states | We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction of neural-network layers that efficiently perform tensor contractions, and that use commonly adopted non-linear activation functions. The resulting deep networks... | ['Giuseppe Carleo', 'Amnon Shashua', 'Or Sharir'] | 2021-03-18 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-7.54576921e-02 5.29518545e-01 -1.17369846e-01 -1.28101021e-01
-9.19480324e-02 -5.50685287e-01 5.62645495e-01 -1.63096026e-01
-3.53258967e-01 6.87980354e-01 1.66409224e-01 -7.12783515e-01
-2.52719730e-01 -1.05422378e+00 -9.60988104e-01 -9.44938838e-01
-5.64181447e-01 6.41505420e-01 -1.70381859e-01 -7.31530249... | [5.77336311340332, 5.011919021606445] |
03eab59d-de1d-4138-8135-9862bed600a8 | one-shot-face-swapping-on-megapixels | 2105.04932 | null | https://arxiv.org/abs/2105.04932v2 | https://arxiv.org/pdf/2105.04932v2.pdf | One Shot Face Swapping on Megapixels | Face swapping has both positive applications such as entertainment, human-computer interaction, etc., and negative applications such as DeepFake threats to politics, economics, etc. Nevertheless, it is necessary to understand the scheme of advanced methods for high-quality face swapping and generate enough and represen... | ['Zhenan Sun', 'Chengzhong Xu', 'Jian Wang', 'Qi Li', 'Yuhao Zhu'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.pdf | cvpr-2021-1 | ['face-transfer'] | ['computer-vision'] | [ 3.55641693e-01 2.71230787e-01 -1.40608445e-01 -4.45676416e-01
-5.19391060e-01 -5.04766345e-01 6.80863559e-01 -1.28152657e+00
1.91166312e-01 8.36590052e-01 2.71270685e-02 1.08089574e-01
2.60821939e-01 -7.92210937e-01 -7.80691981e-01 -9.77210999e-01
2.55071640e-01 1.99449316e-01 -2.62655377e-01 -3.07424158... | [12.701362609863281, -0.060117319226264954] |
10a79d76-3d76-443a-a2e0-154df4c19189 | ot-filter-an-optimal-transport-filter-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.pdf | OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels | The success of deep learning is largely attributed to the training over clean data. However, data is often coupled with noisy labels in practice. Learning with noisy labels is challenging because the performance of the deep neural networks (DNN) drastically degenerates, due to confirmation bias caused by the networ... | ['Xike Xie', 'Yilong Ren', 'Chuanwen Feng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['learning-with-noisy-labels', 'memorization', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-4.58820090e-02 -1.93769768e-01 1.75319180e-01 -6.67177260e-01
-7.17873454e-01 -2.38597691e-01 4.33794826e-01 8.26895162e-02
-5.83059907e-01 9.12873983e-01 -3.11107412e-02 4.75323200e-02
-3.00964385e-01 -6.91259205e-01 -6.92490876e-01 -1.22971630e+00
1.48473650e-01 1.96382061e-01 7.21756518e-02 6.45727813... | [9.336294174194336, 3.829272508621216] |
2d988118-974b-4148-a409-8ec2045ab0b2 | lipreading-using-temporal-convolutional | 2001.08702 | null | https://arxiv.org/abs/2001.08702v1 | https://arxiv.org/pdf/2001.08702v1.pdf | Lipreading using Temporal Convolutional Networks | Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild consists of a residual network and Bidirectional Gated Recurrent Unit (BGRU) layers. In this work, we address the limitations of this model a... | ['Pingchuan Ma', 'Brais Martinez', 'Maja Pantic', 'Stavros Petridis'] | 2020-01-23 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 5.02631009e-01 -2.42962949e-02 -3.31880897e-01 -2.10750461e-01
-1.12814593e+00 -2.42614731e-01 5.02808750e-01 -4.08330202e-01
-6.89409077e-01 7.60739744e-01 2.71478593e-01 -4.77736622e-01
6.06217384e-01 -2.26815328e-01 -8.38163733e-01 -6.99343324e-01
2.34338447e-01 -1.28714889e-01 3.31239194e-01 -4.08117510... | [14.318989753723145, 5.123528957366943] |
c1b8cd6a-0f48-4f3d-bde9-83a5654ba993 | fifo-learning-fog-invariant-features-for-1 | 2204.01587 | null | https://arxiv.org/abs/2204.01587v1 | https://arxiv.org/pdf/2204.01587v1.pdf | FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation | Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images wit... | ['Suha Kwak', 'Taeyoung Son', 'Sohyun Lee'] | 2022-04-04 | fifo-learning-fog-invariant-features-for | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-segmentation'] | ['computer-vision'] | [ 1.80741325e-01 -4.16842163e-01 5.34709096e-01 -4.66939688e-01
1.30281404e-01 -8.05258334e-01 2.47037530e-01 -3.02839547e-01
-3.23611110e-01 5.01615644e-01 -7.00512081e-02 7.86235183e-02
2.04552516e-01 -9.61731613e-01 -6.40483618e-01 -1.01491761e+00
1.23412542e-01 1.44712642e-01 4.27183956e-01 -5.11006474... | [10.901630401611328, -3.2150230407714844] |
b6f45660-7edd-40cf-b0a1-3357c57677b0 | harnessing-the-power-of-large-language-models | 2305.15541 | null | https://arxiv.org/abs/2305.15541v1 | https://arxiv.org/pdf/2305.15541v1.pdf | Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation | Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaMA, a LLaMA-7B model fine-tuned for NL-FOL translation using LoRA on a single GPU. LogicLLaMA is capable of directly translating natural lang... | ['Faramarz Fekri', 'Ehsan Shareghi', 'Ali Payani', 'Siheng Xiong', 'Yuan Yang'] | 2023-05-24 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 5.25325052e-02 2.08665997e-01 -2.70692825e-01 -4.09105778e-01
-1.23413289e+00 -8.91782343e-01 2.51260132e-01 2.61507388e-02
4.86185178e-02 1.07377875e+00 1.78155124e-01 -8.38714540e-01
-1.38781546e-02 -1.10720587e+00 -1.49880016e+00 2.72519179e-02
1.97798878e-01 8.50776255e-01 -8.61198977e-02 -6.08205676... | [9.61962604522705, 7.415600299835205] |
f0a920de-7df1-4ffb-ac84-4503d244e47b | interactive-video-corpus-moment-retrieval | 2302.09522 | null | https://arxiv.org/abs/2302.09522v1 | https://arxiv.org/pdf/2302.09522v1.pdf | Interactive Video Corpus Moment Retrieval using Reinforcement Learning | Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually... | ['Chong-Wah Ngo', 'Zhixin Ma'] | 2023-02-19 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-2.76305974e-01 -4.76457477e-01 -5.29905379e-01 1.34582119e-02
-1.27169502e+00 -7.53385603e-01 3.64019126e-01 1.07706249e-01
-6.07213855e-01 3.17616493e-01 3.35491121e-01 -1.95959508e-01
-4.84370053e-01 -3.17756087e-01 -8.04091394e-01 -5.04333973e-01
-6.45184815e-01 3.70386273e-01 6.38746738e-01 -1.69201538... | [10.033806800842285, 0.7221654653549194] |
8eacd040-bfdb-403e-be60-9ed831056e37 | representation-and-correlation-enhanced | 2106.06960 | null | https://arxiv.org/abs/2106.06960v2 | https://arxiv.org/pdf/2106.06960v2.pdf | Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition | Attention-based encoder-decoder framework is widely used in the scene text recognition task. However, for the current state-of-the-art(SOTA) methods, there is room for improvement in terms of the efficient usage of local visual and global context information of the input text image, as well as the robust correlation be... | ['Liang Wang', 'Jinjin Zhang', 'Wei Wang', 'Mengmeng Cui'] | 2021-06-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.89331573e-01 -5.48180938e-01 1.73038721e-01 -4.13454503e-01
-3.96410257e-01 4.19130474e-02 6.15906119e-01 -5.73608745e-03
-6.26628280e-01 2.48112515e-01 5.38861871e-01 2.46971622e-02
2.79902339e-01 -5.31019807e-01 -4.80970860e-01 -8.91029179e-01
7.65821874e-01 8.12282786e-02 2.47064665e-01 -1.23808146... | [11.885041236877441, 2.155763626098633] |
68ba9460-2262-44bc-9343-f0a445c033ad | gqfedwavg-optimization-based-quantized | 2306.07497 | null | https://arxiv.org/abs/2306.07497v1 | https://arxiv.org/pdf/2306.07497v1.pdf | GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems | The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources ... | ['Vincent Lau', 'Ying Cui', 'Yangchen Li'] | 2023-06-13 | null | null | null | null | ['quantization', 'edge-computing'] | ['methodology', 'time-series'] | [-4.39858824e-01 -4.08049256e-01 -2.19996929e-01 -1.55638173e-01
-1.09292805e+00 -1.16414279e-01 -1.05839908e-01 -3.43312249e-02
-2.02071518e-01 7.21403182e-01 -4.51751202e-02 -2.07554027e-01
-4.19659078e-01 -8.64068687e-01 -6.97287381e-01 -9.77109730e-01
-8.22646171e-02 2.81395793e-01 -2.62230128e-01 -1.40777379... | [6.209682464599609, 5.147902011871338] |
83afac37-ce4a-4a03-8f48-1beffa43f819 | incorporating-structured-commonsense | 1811.00625 | null | http://arxiv.org/abs/1811.00625v1 | http://arxiv.org/pdf/1811.00625v1.pdf | Incorporating Structured Commonsense Knowledge in Story Completion | The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous appro... | ['Zhou Yu', 'Jianshu Chen', 'Jiaao Chen'] | 2018-11-01 | null | null | null | null | ['story-completion'] | ['natural-language-processing'] | [ 2.18314648e-01 -2.11455643e-01 -4.89867151e-01 -4.72978115e-01
-6.42860055e-01 -8.36817086e-01 8.20536196e-01 4.12279785e-01
-3.51306319e-01 9.34584439e-01 1.04380417e+00 1.79751124e-02
1.11430667e-01 -9.76636469e-01 -4.53917742e-01 1.64607074e-02
4.17065501e-01 2.92683810e-01 2.92698920e-01 -8.70696068... | [11.246016502380371, 8.830438613891602] |
75172067-7480-4b58-90ef-fd8b3a348b29 | blpnet-a-new-dnn-model-for-automatic-license | 2112.04752 | null | https://arxiv.org/abs/2112.04752v2 | https://arxiv.org/pdf/2112.04752v2.pdf | Modelling Lips-State Detection Using CNN for Non-Verbal Communications | Vision-based deep learning models can be promising for speech-and-hearing-impaired and secret communications. While such non-verbal communications are primarily investigated with hand-gestures and facial expressions, no research endeavour is tracked so far for the lips state (i.e., open/close)-based interpretation/tran... | ['Hossain Nyeem', 'Md. Akiful Haque Akif', 'Md. Saif Hassan Onim', 'Abtahi Ishmam', 'Mahmudul Hasan', 'Koushik Roy'] | 2021-12-09 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-1.34854212e-01 1.40512735e-01 -5.16081214e-01 -1.84087604e-01
-6.42223716e-01 -2.58192897e-01 6.89797044e-01 -3.54474336e-02
-5.97405553e-01 4.16288882e-01 -4.82297987e-02 -3.96602869e-01
4.31131601e-01 -3.73504817e-01 -2.49390125e-01 -7.06845343e-01
1.60600409e-01 4.64715436e-02 1.15201563e-01 3.60221863... | [14.2708101272583, 4.9601216316223145] |
1c1a90c7-ba1f-494f-b9a9-f8a0810ea6e4 | a-case-study-in-bootstrapping-ontology-graphs | null | null | https://openreview.net/forum?id=nDe2D8DDXKR | https://openreview.net/pdf?id=nDe2D8DDXKR | A Case Study in Bootstrapping Ontology Graphs from Textbooks | Ontology graphs are graphs in which the nodes are generic classes and edges have labels that specify the relationships between the classes. In this paper, we address the question:to what extent can automated extraction and crowdsourcing techniques be combined to boostrap the creation of comprehensive and accurat... | ['Richard Baraniuk', 'Andrew C Waters', 'Debshila Basu Mallick', 'Han Lin Aung', 'Matthew Boggess', 'Vinay K. Chaudhri'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['term-extraction'] | ['natural-language-processing'] | [ 1.29689351e-02 7.49826014e-01 -1.75801232e-01 -2.28017762e-01
-2.28032932e-01 -8.23802173e-01 7.42148280e-01 7.43581414e-01
-2.00001404e-01 7.33193934e-01 1.50163636e-01 -2.11580530e-01
-5.10972500e-01 -1.04460013e+00 -3.54324937e-01 2.39985064e-02
-1.99033901e-01 9.40101445e-01 8.10391605e-01 -5.57134390... | [9.252897262573242, 8.070655822753906] |
5ef29e87-13f6-405d-ab4d-5265937c2908 | real-time-mine-road-boundary-detection-and | null | null | https://www.semanticscholar.org/search?q=Real-Time%20Mine%20Road%20Boundary%20Detection%20and%20Tracking%20for%20Autonomous%20Truck&sort=relevance | https://www.semanticscholar.org/search?q=Real-Time%20Mine%20Road%20Boundary%20Detection%20and%20Tracking%20for%20Autonomous%20Truck&sort=relevance | Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck | Abstract: Road boundary detection is an important part of the perception of autonomous driving.
It is difficult to detect road boundaries of unstructured roads because there are no curbs. There are no
clear boundaries on mine roads to distinguish areas within the road boundary line and areas outside
the road boundar... | ['*', 'Yunfeng Ai1and Bin Tian3', '2', 'Xiaowei Lu1'] | 2020-01-01 | null | null | null | https-www-mdpi-com-journal-sensors-2020-1 | ['boundary-detection'] | ['computer-vision'] | [ 3.69209528e-01 -4.62411754e-02 9.03131962e-02 -4.71270144e-01
1.37365088e-01 -2.47828111e-01 3.08398247e-01 6.37016073e-02
-5.26958287e-01 5.86409926e-01 -4.75415409e-01 -4.82733667e-01
-4.28327739e-01 -1.36257768e+00 -4.89849031e-01 -1.93596333e-01
7.04503581e-02 6.60162330e-01 9.33465242e-01 -3.79421830... | [7.948750019073486, -1.6511772871017456] |
cbdd4944-b20f-49da-84f3-12a39f79e7f5 | point-discriminative-learning-for | 2108.02104 | null | https://arxiv.org/abs/2108.02104v3 | https://arxiv.org/pdf/2108.02104v3.pdf | Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis | 3D point cloud analysis has drawn a lot of research attention due to its wide applications. However, collecting massive labelled 3D point cloud data is both time-consuming and labor-intensive. This calls for data-efficient learning methods. In this work we propose PointDisc, a point discriminative learning method to le... | ['Jie Lin', 'Chaitanya K. Joshi', 'Chuan-Sheng Foo', 'Guosheng Lin', 'Fayao Liu'] | 2021-08-04 | null | null | null | null | ['3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.50055021e-01 -1.01382144e-01 -4.34847921e-01 -5.35649061e-01
-9.79236126e-01 -5.01461983e-01 3.69381309e-01 3.89718264e-01
-4.08271663e-02 1.57891482e-01 -5.08464217e-01 -5.10193687e-03
-1.21976033e-01 -7.49097407e-01 -8.93634737e-01 -6.15936339e-01
-2.27475092e-01 1.06219018e+00 4.11301792e-01 3.20129186... | [8.02712345123291, -3.232180118560791] |
432a622a-7857-47fa-93d2-993f0e6dfc2b | few-shot-transfer-learning-for-device-free | 2201.12656 | null | https://arxiv.org/abs/2201.12656v1 | https://arxiv.org/pdf/2201.12656v1.pdf | Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization | Device-free wireless indoor localization is an essential technology for the Internet of Things (IoT), and fingerprint-based methods are widely used. A common challenge to fingerprint-based methods is data collection and labeling. This paper proposes a few-shot transfer learning system that uses only a small amount of l... | ['Ronald Y. Chang', 'Bing-Jia Chen'] | 2022-01-29 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 6.76984489e-02 -2.47010365e-01 -3.67969275e-01 -6.66891813e-01
-5.96893191e-01 -3.64361048e-01 6.89405128e-02 -5.87727642e-03
-5.59354782e-01 1.01630235e+00 -1.75445184e-01 -3.94985467e-01
-1.96204126e-01 -1.24789929e+00 -7.23743320e-01 -3.62509161e-01
-3.99286784e-02 3.36218804e-01 3.70365500e-01 1.36591271... | [6.413232803344727, 0.9045594930648804] |
b4d2e13f-0de4-4f27-90be-aa9804df5f65 | distributed-scheduling-in-non-signalized | 2208.00141 | null | https://arxiv.org/abs/2208.00141v2 | https://arxiv.org/pdf/2208.00141v2.pdf | Distributed Scheduling at Non-Signalized Intersections with Mixed Cooperative and Non-Cooperative Vehicles | Intersection management with mixed cooperative and non-cooperative vehicles is crucial in next-generation transportation systems. For fully non-cooperative systems, a minimax scheduling framework was established, while it is inefficient in mixed systems as the benefit of cooperation is not exploited. This letter focuse... | ['Yuan Shen', 'Feihong Yang'] | 2022-07-30 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-5.73433280e-01 3.38801146e-01 -6.58365011e-01 -1.31437391e-01
-4.15151209e-01 -4.27980423e-01 1.65953144e-01 -3.41072232e-02
-3.84534150e-01 1.24021649e+00 -3.00012201e-01 -6.36299074e-01
-6.48992121e-01 -9.44331229e-01 -5.31058133e-01 -1.05882645e+00
-2.70309150e-01 2.33823642e-01 2.93382645e-01 -5.37338972... | [5.598873138427734, 1.5960140228271484] |
d382bae4-8270-4268-a167-a4b447bbb5cb | rethinking-atrous-convolution-for-semantic | 1706.05587 | null | http://arxiv.org/abs/1706.05587v3 | http://arxiv.org/pdf/1706.05587v3.pdf | Rethinking Atrous Convolution for Semantic Image Segmentation | In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scale... | ['Liang-Chieh Chen', 'Florian Schroff', 'Hartwig Adam', 'George Papandreou'] | 2017-06-17 | null | null | null | null | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.66725951e-01 -4.39311936e-02 1.16567560e-01 -6.06207550e-01
-5.42150021e-01 -8.45384359e-01 4.64341253e-01 -7.27367774e-02
-8.30074668e-01 2.41215020e-01 2.02816408e-02 -1.27240121e-01
2.05931410e-01 -9.15940881e-01 -1.04621506e+00 -4.27131623e-01
2.50239551e-01 3.97144742e-02 9.38316226e-01 -2.95628756... | [9.548810958862305, 0.29489463567733765] |
5056f42b-0819-4340-a6e1-44b952436a53 | human-level-reinforcement-learning-through | 2107.12544 | null | https://arxiv.org/abs/2107.12544v1 | https://arxiv.org/pdf/2107.12544v1.pdf | Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning | Reinforcement learning (RL) studies how an agent comes to achieve reward in an environment through interactions over time. Recent advances in machine RL have surpassed human expertise at the world's oldest board games and many classic video games, but they require vast quantities of experience to learn successfully -- ... | ['Joshua B. Tenenbaum', 'Samuel J. Gershman', 'Thomas Pouncy', 'Andres Campero', 'Nathan Foss', 'Jake Burga', 'Joao Loula', 'Pedro A. Tsividis'] | 2021-07-27 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.81685346e-01 3.82780164e-01 2.46620364e-02 1.67639241e-01
-5.15264869e-01 -3.56331825e-01 8.85213673e-01 -2.62352705e-01
-4.63385612e-01 9.17239010e-01 8.22023079e-02 -4.09718513e-01
-5.37646770e-01 -1.05696392e+00 -4.16393369e-01 -4.60217357e-01
-5.29082358e-01 1.22618389e+00 5.43783724e-01 -6.62600815... | [3.931255578994751, 1.3289799690246582] |
1de0f373-fd80-4e62-ae4c-0b3922204cd9 | achieving-reliable-human-assessment-of-open-1 | 2203.05899 | null | https://arxiv.org/abs/2203.05899v1 | https://arxiv.org/pdf/2203.05899v1.pdf | Achieving Reliable Human Assessment of Open-Domain Dialogue Systems | Evaluation of open-domain dialogue systems is highly challenging and development of better techniques is highlighted time and again as desperately needed. Despite substantial efforts to carry out reliable live evaluation of systems in recent competitions, annotations have been abandoned and reported as too unreliable t... | ['Qun Liu', 'Chenyang Lyu', 'Gareth J. F. Jones', 'Yvette Graham', 'Tianbo Ji'] | 2022-03-11 | null | https://aclanthology.org/2022.acl-long.445 | https://aclanthology.org/2022.acl-long.445.pdf | acl-2022-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-7.55537674e-02 2.87946135e-01 3.26565117e-01 -5.61136901e-01
-9.47480142e-01 -6.95885420e-01 1.07429683e+00 3.15702021e-01
-8.14694047e-01 1.12872362e+00 6.61730826e-01 -2.70932585e-01
-1.15990080e-01 -3.45661640e-01 -1.68673739e-01 -3.74213427e-01
1.86774004e-02 6.65667653e-01 1.86704755e-01 -5.95227420... | [12.87084674835205, 8.065065383911133] |
1006791e-8858-4264-b7ca-2cbd6f722ab0 | deep-multi-instance-networks-with-sparse-1 | 1705.08550 | null | http://arxiv.org/abs/1705.08550v1 | http://arxiv.org/pdf/1705.08550v1.pdf | Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification | Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labelin... | ['Wentao Zhu', 'Yeeleng Scott Vang', 'Xiaohui Xie', 'Qi Lou'] | 2017-05-23 | null | null | null | null | ['whole-mammogram-classification'] | ['medical'] | [ 5.91484725e-01 7.41681635e-01 -3.77051502e-01 -8.88821423e-01
-1.08284664e+00 4.26746011e-02 3.22877467e-01 4.34747905e-01
-5.67274868e-01 6.29814208e-01 -1.91537872e-01 -5.04056096e-01
-1.94663346e-01 -1.11210871e+00 -8.72331500e-01 -5.61391294e-01
-2.40129516e-01 5.69157720e-01 2.95399487e-01 1.44579904... | [15.153777122497559, -2.4333839416503906] |
1e5303a2-40b6-418f-9515-167aea895c44 | dynamic-conceptional-contrastive-learning-for | 2303.17393 | null | https://arxiv.org/abs/2303.17393v1 | https://arxiv.org/pdf/2303.17393v1.pdf | Dynamic Conceptional Contrastive Learning for Generalized Category Discovery | Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional n... | ['Nicu Sebe', 'Zhun Zhong', 'Nan Pu'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.pdf | cvpr-2023-1 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.90834885e-02 -3.01426761e-02 -2.22570866e-01 -4.00814116e-01
-6.55177712e-01 -6.49489105e-01 5.87280393e-01 2.90853351e-01
-1.16973661e-01 5.76262891e-01 -1.08243190e-01 -1.47116542e-01
-1.66492820e-01 -7.50356078e-01 -4.43933576e-01 -9.19556499e-01
1.35348111e-01 6.86585963e-01 1.44328728e-01 2.04089940... | [9.635714530944824, 2.900211811065674] |
829a0086-7905-4a15-b23c-0c4f63e755c5 | revisiting-transformer-for-point-cloud-based | 2303.11048 | null | https://arxiv.org/abs/2303.11048v2 | https://arxiv.org/pdf/2303.11048v2.pdf | Revisiting Transformer for Point Cloud-based 3D Scene Graph Generation | In this paper, we propose the semantic graph Transformer (SGT) for 3D scene graph generation. The task aims to parse a cloud point-based scene into a semantic structural graph, with the core challenge of modeling the complex global structure. Existing methods based on graph convolutional networks (GCNs) suffer from the... | ['Huadong Ma', 'Zhengyuan Yang', 'Xia Li', 'Mengshi Qi', 'Changsheng Lv'] | 2023-03-20 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.31215522e-01 4.38548118e-01 -1.27896583e-02 -4.09555912e-01
-3.88248503e-01 -4.26568598e-01 6.94645345e-01 2.46381387e-01
1.38743460e-01 1.84206530e-01 2.27360606e-01 -4.25660580e-01
-2.87467893e-02 -1.27798450e+00 -7.98902988e-01 -3.03564310e-01
-6.68845028e-02 3.45945150e-01 3.87794912e-01 -1.87106892... | [10.341608047485352, 1.6039198637008667] |
47689cef-2254-4b01-aa96-318cdc2ca2a9 | simplekt-a-simple-but-tough-to-beat-baseline | 2302.06881 | null | https://arxiv.org/abs/2302.06881v2 | https://arxiv.org/pdf/2302.06881v2.pdf | simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing | Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augment... | ['Weiqi Luo', 'Shuyan Huang', 'Jiahao Chen', 'Qiongqiong Liu', 'Zitao Liu'] | 2023-02-14 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-2.74874538e-01 1.88198298e-01 -1.95575222e-01 -3.67652714e-01
-5.41080952e-01 -6.34164155e-01 3.39536339e-01 1.62892804e-01
-4.91956621e-01 1.00298321e+00 2.75125727e-03 -7.87912190e-01
-3.94729853e-01 -8.82742107e-01 -1.01438737e+00 -4.33691323e-01
5.04473865e-01 2.88753659e-01 2.72069991e-01 -5.53130269... | [10.125115394592285, 7.184086322784424] |
58e54f22-aafe-45f9-923b-455bcd89beb6 | combining-ocr-models-for-reading-early-modern | 2305.07131 | null | https://arxiv.org/abs/2305.07131v1 | https://arxiv.org/pdf/2305.07131v1.pdf | Combining OCR Models for Reading Early Modern Printed Books | In this paper, we investigate the usage of fine-grained font recognition on OCR for books printed from the 15th to the 18th century. We used a newly created dataset for OCR of early printed books for which fonts are labeled with bounding boxes. We know not only the font group used for each character, but the locations ... | ['Vincent Christlein', 'Anguelos Nicolau', 'Florian Kordon', 'Tatjana Hass', 'Janina Molnar', 'Martin Mayr', 'Nikolaus Weichselbaumer', 'Janne van der Loop', 'Mathias Seuret'] | 2023-05-11 | null | null | null | null | ['optical-character-recognition', 'font-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.43150583e-01 -5.52179754e-01 2.82200307e-01 -3.00182968e-01
-9.79299471e-02 -1.30990148e+00 1.03069270e+00 2.86185324e-01
-4.88216043e-01 6.39977276e-01 1.87625840e-01 -3.23202103e-01
-3.09722163e-02 -6.59544826e-01 -7.00195193e-01 -4.12467003e-01
3.76245588e-01 5.67207873e-01 6.20039999e-01 -5.24720490... | [11.784977912902832, 2.6147868633270264] |
1ee6bcb7-764f-4ef2-8ba4-bf7d470fcbce | generating-molecules-via-chemical-reactions | null | null | https://openreview.net/forum?id=BJlQEILY_N | https://openreview.net/pdf?id=BJlQEILY_N | Generating Molecules via Chemical Reactions | Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the dif... | ['José Miguel Hernández-Lobato', 'Marwin H. S. Segler', 'Brooks Paige', 'Matt J. Kusner', 'John Bradshaw'] | 2019-03-27 | null | null | null | iclr-workshop-deepgenstruct-2019 | ['retrosynthesis'] | ['medical'] | [ 6.13499045e-01 3.26938510e-01 -2.25911677e-01 -9.69069973e-02
-2.65762359e-01 -1.04914117e+00 1.04022443e+00 4.29360747e-01
8.14709142e-02 1.15906787e+00 2.12959379e-01 -4.93865490e-01
2.81676024e-01 -1.33639061e+00 -8.29145491e-01 -6.77007318e-01
-4.79625119e-03 6.67636395e-01 -2.73981839e-02 -5.33705473... | [4.56606388092041, 6.045938491821289] |
444b4732-d17f-4403-82a2-468d3edc0a78 | a-structured-approach-to-predicting-image | 1704.01249 | null | http://arxiv.org/abs/1704.01249v1 | http://arxiv.org/pdf/1704.01249v1.pdf | A Structured Approach to Predicting Image Enhancement Parameters | Social networking on mobile devices has become a commonplace of everyday
life. In addition, photo capturing process has become trivial due to the
advances in mobile imaging. Hence people capture a lot of photos everyday and
they want them to be visually-attractive. This has given rise to automated,
one-touch enhancemen... | ['Baoxin Li', 'Parag S. Chandakkar'] | 2017-04-05 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 5.38514972e-01 -6.57839850e-02 9.23225135e-02 -4.04601693e-01
-5.36864579e-01 -3.37139994e-01 4.88945216e-01 1.70515060e-01
-3.83115649e-01 7.45298386e-01 2.34969724e-02 1.83819905e-02
-3.80077332e-01 -7.38622308e-01 -5.31468272e-01 -6.46194518e-01
1.72481649e-02 3.51135843e-02 3.63406599e-01 -2.11763099... | [11.62317943572998, -1.8568364381790161] |
855e943c-ed2e-4964-bd6c-6e7731ba3c97 | energy-management-of-multi-mode-plug-in | 2303.09658 | null | https://arxiv.org/abs/2303.09658v1 | https://arxiv.org/pdf/2303.09658v1.pdf | Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning | The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multiple-output (MIMO) control. At the present, the existing methods usually decouple the MIMO control into single-outp... | ['Quan Zhou', 'Hongming Xu', 'Xiaoli Yu', 'Zhi Li', 'Fanggang Zhang', 'Cetengfei Zhang', 'Min Hua'] | 2023-03-16 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.67811251e-01 1.59222677e-01 -3.38631868e-01 2.03308091e-01
-3.40114385e-01 -3.58751476e-01 5.90198398e-01 2.26572677e-02
-3.77483577e-01 8.76687407e-01 -4.50650692e-01 -3.63962382e-01
-6.37685716e-01 -9.10346627e-01 -6.73636436e-01 -1.27976680e+00
2.90388372e-02 3.69348794e-01 -1.58191115e-01 -3.21179897... | [5.501340389251709, 2.2970943450927734] |
32d441ba-69ec-42d0-b0f4-1e7ca9c7b409 | deep-motion-prior-for-weakly-supervised | 2108.05607 | null | https://arxiv.org/abs/2108.05607v2 | https://arxiv.org/pdf/2108.05607v2.pdf | Deep Motion Prior for Weakly-Supervised Temporal Action Localization | Weakly-Supervised Temporal Action Localization (WSTAL) aims to localize actions in untrimmed videos with only video-level labels. Currently, most state-of-the-art WSTAL methods follow a Multi-Instance Learning (MIL) pipeline: producing snippet-level predictions first and then aggregating to the video-level prediction. ... | ['Yuexian Zou', 'Mike Zheng Shou', 'Long Chen', 'Can Zhang', 'Meng Cao'] | 2021-08-12 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 3.80234659e-01 -2.08434597e-01 -8.03278327e-01 -1.75944880e-01
-6.62106931e-01 -2.15381578e-01 6.14714086e-01 -3.60508233e-01
-3.88014913e-01 6.95601761e-01 5.65457821e-01 2.61700451e-02
-2.06125647e-01 -2.63865739e-01 -7.28641510e-01 -8.54021788e-01
-2.13527709e-01 -3.06177109e-01 4.64352936e-01 -2.01822743... | [8.427390098571777, 0.5745623707771301] |
1940e604-bfe9-4464-9575-6ca5f7cf45d7 | intra-extra-source-exemplar-based-style | 2307.00648 | null | https://arxiv.org/abs/2307.00648v1 | https://arxiv.org/pdf/2307.00648v1.pdf | Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization | The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an exemplar-based style synthesis pipeline to improve domain generalization in semantic segmentation. Our method ... | ['Anna Khoreva', 'Margret Keuper', 'Dan Zhang', 'Yumeng Li'] | 2023-07-02 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 1.51154682e-01 -3.78663652e-02 -1.38295041e-02 -4.62397188e-01
-4.94145125e-01 -7.97826767e-01 5.88920951e-01 -5.40180683e-01
-3.66917551e-01 7.06477165e-01 -2.29653075e-01 -4.60102171e-01
9.95517597e-02 -9.84039664e-01 -1.07205534e+00 -6.33495450e-01
4.45484430e-01 2.86645919e-01 3.10458809e-01 -5.63200176... | [9.674208641052246, 1.1544915437698364] |
28b676f1-3d44-4699-8e55-c9c68bf49a28 | a-novel-empirical-bayes-with-reversible-jump | 1808.05480 | null | http://arxiv.org/abs/1808.05480v1 | http://arxiv.org/pdf/1808.05480v1.pdf | A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system | In this article we select the unknown dimension of the feature by re-
versible jump MCMC inside a simulated annealing in bayesian set up of
collaborative filter. We implement the same in MovieLens small dataset. We also
tune the hyper parameter by using a modified empirical bayes. It can also be
used to guess an initia... | ['Himanshu Jhamb', 'Arabin Kumar Dey'] | 2018-08-15 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-3.16229343e-01 -2.81457633e-01 2.07172468e-01 -4.79482234e-01
-6.61759555e-01 -9.05232668e-01 7.39203513e-01 1.25836963e-02
-9.96481478e-01 1.03865433e+00 1.59527510e-01 -1.92153186e-01
-4.58560795e-01 -9.90035117e-01 -3.42058569e-01 -1.00879991e+00
9.99380201e-02 1.23240459e+00 7.89872587e-01 -5.10212034... | [6.652161121368408, 3.972287893295288] |
7bb5b1cb-c00a-4a1a-9ee8-a004e6296002 | provably-calibrated-regression-under | null | null | https://openreview.net/forum?id=bOcUqfdH3S8 | https://openreview.net/pdf?id=bOcUqfdH3S8 | Provably Calibrated Regression Under Distribution Drift | Accurate uncertainty quantification is a key building block of trustworthy machine learning systems. Uncertainty is typically represented by probability distributions over the possible outcomes, and these probabilities should be calibrated, \textit{e.g}. the 90\% credible interval should contain the true outcome 90\% o... | ['Stefano Ermon', 'Danny Tse', 'Yusuke Tashiro', 'Shengjia Zhao'] | 2021-09-29 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.48421720e-01 5.86131155e-01 -6.99031413e-01 -6.30016625e-01
-1.34458971e+00 -9.62769449e-01 4.66629028e-01 3.22876245e-01
-5.11526875e-03 1.19996464e+00 4.69944254e-03 -8.29318345e-01
-4.21487659e-01 -8.40927124e-01 -1.03802109e+00 -6.16159976e-01
-1.73677966e-01 6.68123424e-01 9.84325856e-02 5.04805744... | [7.949704170227051, 4.019186496734619] |
11acf9aa-0708-49eb-848e-a0f6142a97fb | an-iterative-co-saliency-framework-for-rgbd | 1711.01371 | null | http://arxiv.org/abs/1711.01371v1 | http://arxiv.org/pdf/1711.01371v1.pdf | An Iterative Co-Saliency Framework for RGBD Images | As a newly emerging and significant topic in computer vision community,
co-saliency detection aims at discovering the common salient objects in
multiple related images. The existing methods often generate the co-saliency
map through a direct forward pipeline which is based on the designed cues or
initialization, but la... | ['Xiaochun Cao', 'Huazhu Fu', 'Runmin Cong', 'Qingming Huang', 'Weisi Lin', 'Jianjun Lei', 'Chunping Hou'] | 2017-11-04 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 3.47334415e-01 -1.24449380e-01 -2.99218856e-02 -7.73520991e-02
-2.94803321e-01 -6.51944056e-02 4.95454282e-01 3.38264048e-01
-3.18567663e-01 2.54355669e-01 3.18438858e-01 7.18064830e-02
-7.26187453e-02 -7.00724781e-01 -3.91190857e-01 -7.09496081e-01
4.71425474e-01 -3.06010693e-01 1.19530261e+00 -3.71267527... | [9.775506019592285, -0.564618706703186] |
2adba1c7-e3df-40d2-ba3f-b78630b6b749 | eurnet-efficient-multi-range-relational | 2211.12941 | null | https://arxiv.org/abs/2211.12941v1 | https://arxiv.org/pdf/2211.12941v1.pdf | EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data | Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Previous works often use a unified solution like relative positional encoding. However, there exists different kinds of spatial relations, incl... | ['Yuandong Tian', 'Xinlei Chen', 'Jian Tang', 'Yi Xu', 'Yuanfan Guo', 'Minghao Xu'] | 2022-11-23 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.33752102e-02 1.03678403e-03 -5.21242559e-01 -6.72849178e-01
-1.86836064e-01 -3.65953922e-01 1.75672531e-01 3.38645518e-01
-4.02678818e-01 5.51803410e-01 -1.36497125e-01 -4.30052817e-01
-4.18123633e-01 -1.14274001e+00 -9.68798339e-01 -6.97070539e-01
-7.65794441e-02 7.38369584e-01 9.01616752e-01 -2.20528081... | [9.630227088928223, 0.4890669584274292] |
c53dd138-3627-4b6b-8a81-94adff2afaa8 | uncertainty-aware-time-to-event-prediction | 2107.12250 | null | https://arxiv.org/abs/2107.12250v1 | https://arxiv.org/pdf/2107.12250v1.pdf | Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models | Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data. However, most works focus on prediction accuracy and neglect prediction uncertainty. We propose Deep Kernel Accelerated Failure Time models for the time-to-event prediction task, enabling ... | ['Volker Tresp', 'Peter A. Fasching', 'Yinchong Yang', 'Zhiliang Wu'] | 2021-07-26 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-1.47566095e-01 1.75336748e-01 -3.17768097e-01 -7.55860388e-01
-1.30847228e+00 2.35198691e-01 2.70717382e-01 3.54069054e-01
-3.01893204e-01 8.09365988e-01 5.09627879e-01 -3.91713440e-01
-3.78506690e-01 -7.62612224e-01 -8.19131374e-01 -5.04844189e-01
-3.05761516e-01 7.13316560e-01 -6.06923401e-02 4.41355318... | [7.781330108642578, 5.442653656005859] |
5f3ebd99-219f-4106-831d-76d2eb55a33d | knowledge-aware-attentional-neural-network | null | null | https://link.springer.com/article/10.1007/s00521-022-07689-1 | https://link.springer.com/content/pdf/10.1007/s00521-022-07689-1.pdf | Knowledge-aware attentional neural network for review-based movie recommendation with explanations | In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation
tasks by extracting knowledge entities from movie reviews and capturing understandable interactions between users and
movies at the knowledge level. In most recommendation systems, review information is... | ['Jun Miyazaki', 'Yun Liu'] | 2022-09-04 | null | null | null | neural-computing-and-applications-2022-9 | ['movie-recommendation'] | ['miscellaneous'] | [-0.06284636 0.04006381 -0.7103017 -0.40405864 0.21371123 -0.44747725
0.33179235 0.09656295 -0.09268454 0.38201863 0.6756802 -0.31887263
-0.41769063 -0.7666784 -0.5993361 -0.28943956 0.09107869 0.26430553
0.05722374 -0.2879662 0.2710999 -0.02016436 -1.5761645 0.67976904
0.8480571 1.0823753 0.... | [10.159708976745605, 5.630829334259033] |
3713e153-6d6b-4cfb-83ba-4da78c865020 | keras-gpt-copilot-integrating-the-power-of | null | null | https://doi.org/10.5281/zenodo.7935183 | https://doi.org/10.5281/zenodo.7935183 | Keras GPT Copilot: Integrating the Power of Large Language Models in Deep Learning Model Development | Keras GPT Copilot is the first Python package designed to integrate an LLM copilot within the model development workflow, offering iterative feedback options for enhancing the performance of your Keras deep learning models. Utilizing the power of OpenAI's GPT models, Keras GPT Copilot can use any of the compatible mode... | ['Fabi Prezja'] | 2023-05-15 | null | null | null | zenodo-github-2023-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [-9.82215106e-01 -2.19160016e-03 -8.19542855e-02 -3.92484248e-01
-5.19710898e-01 -6.88072622e-01 6.14860952e-01 5.36090694e-03
-2.18229622e-01 1.74678221e-01 2.85287201e-02 -1.24834037e+00
-8.87654722e-02 -6.88500524e-01 -4.81323093e-01 -1.62257571e-02
2.18334258e-01 4.50438261e-01 3.57694961e-02 -1.59742221... | [9.583963394165039, 7.494960784912109] |
ba850d98-ca92-4534-956e-c2f3593a3d26 | urbanfm-inferring-fine-grained-urban-flows | 1902.05377 | null | http://arxiv.org/abs/1902.05377v1 | http://arxiv.org/pdf/1902.05377v1.pdf | UrbanFM: Inferring Fine-Grained Urban Flows | Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devic... | ['Yu Zheng', 'Lin Jing', 'Yuxuan Liang', 'Ye Liu', 'Sijie Ruan', 'Kun Ouyang', 'David S. Rosenblum', 'Junbo Zhang'] | 2019-02-06 | null | null | null | null | ['fine-grained-urban-flow-inference'] | ['miscellaneous'] | [-2.90319800e-01 -3.87568235e-01 -1.97620749e-01 -4.41796213e-01
-4.17059660e-01 -1.11436702e-01 8.38296056e-01 6.48392411e-03
-1.96702108e-01 9.84574139e-01 6.69463336e-01 -5.55213451e-01
-2.13001817e-01 -1.49632668e+00 -3.49113643e-01 -5.58433294e-01
5.39284460e-02 3.91764522e-01 4.47759092e-01 -2.86460727... | [6.422889709472656, 2.0320215225219727] |
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