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c53f2873-fac7-4fc5-9957-55680c5e2b6a | zero-extremely-efficient-collective | 2306.10209 | null | https://arxiv.org/abs/2306.10209v1 | https://arxiv.org/pdf/2306.10209v1.pdf | ZeRO++: Extremely Efficient Collective Communication for Giant Model Training | Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limit... | ['Yuxiong He', 'Lei Yang', 'Feng Yan', 'Olatunji Ruwase', 'Samyam Rajbhandari', 'Connor Holmes', 'Sam Ade Jacobs', 'Heyang Qin', 'Guanhua Wang'] | 2023-06-16 | null | null | null | null | ['quantization'] | ['methodology'] | [-3.72391790e-01 -6.45110905e-01 -1.44864917e-01 -4.93658096e-01
-9.78165567e-01 -2.13244006e-01 4.29227054e-01 6.16366327e-01
-7.44390368e-01 4.78884488e-01 2.64187723e-01 -4.85964745e-01
2.13912353e-01 -1.00587821e+00 -6.54447854e-01 -6.22689247e-01
-1.49520159e-01 1.63511798e-01 3.57041299e-01 -1.70072496... | [8.571599960327148, 3.4237289428710938] |
85caffe9-096e-444f-8dfe-ab361cde0945 | docbed-a-multi-stage-ocr-solution-for | 2202.01414 | null | https://arxiv.org/abs/2202.01414v1 | https://arxiv.org/pdf/2202.01414v1.pdf | DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts | Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc. While digitization of documents such as scientific articles and magazines is prevalent in literature, one of the main challenges for digitization of newspaper lies in its complex layout (... | ['Suchitra Sathyanarayana', 'Sujitha Martin', 'Guang Yang', 'Negin Sokhandan', 'Wenzhen Zhu'] | 2022-02-03 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 4.15052146e-01 -3.53956044e-01 -1.17052125e-03 -1.23770282e-01
-6.67203724e-01 -1.16565812e+00 5.74976981e-01 4.41771716e-01
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1.56152591e-01 5.81881642e-01 2.77550012e-01 -4.06213030... | [11.81942367553711, 2.6613519191741943] |
734a3df1-cc97-4204-add3-9bc7e982a3ec | minimax-robust-landmine-detection-using | 2111.08379 | null | https://arxiv.org/abs/2111.08379v2 | https://arxiv.org/pdf/2111.08379v2.pdf | Minimax Robust Landmine Detection Using Forward-Looking Ground-Penetrating Radar | We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using a forward-looking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The... | ['Michael Fauß', 'Abdelhak M. Zoubir', 'Fauzia Ahmad', 'Afief Dias Pambudi'] | 2021-11-16 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 4.67230588e-01 -1.55193001e-01 1.23499744e-01 -3.06088597e-01
-9.24837530e-01 -5.22537887e-01 3.18616152e-01 -1.98046669e-01
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-4.28130448e-01 4.42960441e-01 2.55420774e-01 1.11346230... | [6.78205680847168, 1.246838092803955] |
1f103232-99a0-40f7-85a4-eea68dff4921 | dual-branched-spatio-temporal-fusion-network | 2202.13336 | null | https://arxiv.org/abs/2202.13336v1 | https://arxiv.org/pdf/2202.13336v1.pdf | Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast | Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data. Effective mining of these data is the key to accurate TCs track forecasting. However, existing methods face the problem that the model complexity is too high or it is difficult to effici... | ['Zhenwei Shi', 'Xiaoyi Geng', 'Kun Hao', 'Zili Liu'] | 2022-02-27 | null | null | null | null | ['tropical-cyclone-track-forecasting'] | ['time-series'] | [-4.00580525e-01 -8.15535843e-01 -3.00101668e-01 -6.42851889e-01
-9.56217647e-01 -4.15046245e-01 6.24868512e-01 -2.49404669e-01
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-6.93710506e-01 1.83991060e-01 3.33921820e-01 -7.62804747... | [6.6157941818237305, 2.817659854888916] |
2a8bd753-1db3-4162-9852-02dda2d4f6ce | neural-topic-models-with-survival-supervision | 2007.07796 | null | https://arxiv.org/abs/2007.07796v1 | https://arxiv.org/pdf/2007.07796v1.pdf | Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate | In time-to-event prediction problems, a standard approach to estimating an interpretable model is to use Cox proportional hazards, where features are selected based on lasso regularization or stepwise regression. However, these Cox-based models do not learn how different features relate. As an alternative, we present a... | ['Jeremy C. Weiss', 'Ren Zuo', 'Linhong Li', 'George H. Chen', 'Amanda Coston'] | 2020-07-15 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [ 2.82834917e-01 4.99121577e-01 -7.10472167e-01 -9.82512355e-01
-1.17692912e+00 -1.22041982e-02 1.31654546e-01 7.71796227e-01
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-6.26666963e-01 8.69448662e-01 -6.72040880e-01 1.37796134... | [7.898685932159424, 5.703148365020752] |
44bcc7cb-3e1d-419b-8055-8ac16eb9ebe7 | a-new-backbone-for-hyperspectral-image | 2108.07739 | null | https://arxiv.org/abs/2108.07739v3 | https://arxiv.org/pdf/2108.07739v3.pdf | A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging | The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way. It is generally implemented by two components: an optical encoder that compresses HD signals into a 2D measurement and an algorithm decoder that retrieves the HD data upon the hardware-encoded... | ['Zhiqiang Tao', 'Yun Fu', 'Xin Yuan', 'Yulun Zhang', 'Jiamian Wang'] | 2021-08-17 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 7.93067217e-01 -5.56411028e-01 7.23214000e-02 -2.90842857e-02
-7.52868712e-01 -3.47978100e-02 2.61668831e-01 -7.11911023e-01
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-5.99368632e-01 -7.13157296e-01 -7.21757531e-01 -1.17985415e+00
1.63597524e-01 -2.14188233e-01 -1.42459393e-01 4.98546883... | [10.732048988342285, -2.1461007595062256] |
ef3c3521-b792-4f9c-b3e4-942d42e7b27d | a-template-based-hybrid-model-for-chinese | null | null | https://aclanthology.org/W12-6323 | https://aclanthology.org/W12-6323.pdf | A Template Based Hybrid Model for Chinese Personal Name Disambiguation | null | ['Lidia S. Chao', 'Hao Zong', 'Derek F. Wong'] | 2012-12-01 | a-template-based-hybrid-model-for-chinese-1 | https://aclanthology.org/W12-6323 | https://aclanthology.org/W12-6323.pdf | ws-2012-12 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.233432769775391, 3.8435404300689697] |
574f2f40-4fe8-406e-bb86-f96660bdf0e2 | learning-to-influence-human-behavior-with | 2303.02265 | null | https://arxiv.org/abs/2303.02265v3 | https://arxiv.org/pdf/2303.02265v3.pdf | Learning to Influence Human Behavior with Offline Reinforcement Learning | When interacting with people, AI agents do not just influence the state of the world -- they also influence the actions people take in response to the agent, and even their underlying intentions and strategies. Accounting for and leveraging this influence has mostly been studied in settings where it is sufficient to as... | ['Sergey Levine', 'Anca Dragan', 'Joey Hong'] | 2023-03-03 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 8.19661543e-02 5.06973684e-01 1.92574486e-01 -7.11216554e-02
-9.98485908e-02 -5.43738782e-01 5.53754389e-01 -1.61985159e-01
-8.81326199e-01 9.96168613e-01 1.98538214e-01 -3.17609370e-01
-8.19622502e-02 -7.19998777e-01 -7.65897334e-01 -5.42620659e-01
-1.86835900e-01 9.23993707e-01 1.66431338e-01 -6.99968755... | [4.297763824462891, 1.9149256944656372] |
47d0ca3a-968f-4b7b-bdde-5d60cae1d52d | from-known-to-the-unknown-transferring | 1811.12772 | null | http://arxiv.org/abs/1811.12772v1 | http://arxiv.org/pdf/1811.12772v1.pdf | From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts | Current Visual Question Answering (VQA) systems can answer intelligent
questions about `Known' visual content. However, their performance drops
significantly when questions about visually and linguistically `Unknown'
concepts are presented during inference (`Open-world' scenario). A practical
VQA system should be able ... | ['Moshiur R. Farazi', 'Salman H. Khan', 'Nick Barnes'] | 2018-11-30 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-3.84786422e-03 1.68960367e-03 -4.73573431e-02 -4.81509447e-01
-1.26390576e+00 -9.03507054e-01 5.61730564e-01 4.41294014e-01
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-9.94120240e-02 -8.13619137e-01 -9.07296240e-01 -6.21281862e-01
3.53267729e-01 7.32539117e-01 6.01827145e-01 -4.11617726... | [10.81718635559082, 1.7069590091705322] |
585114d4-d7d8-409f-9b39-7d15dcaa8007 | towards-computationally-efficient | 2302.12676 | null | https://arxiv.org/abs/2302.12676v1 | https://arxiv.org/pdf/2302.12676v1.pdf | Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs | Responsibility attribution is a key concept of accountable multi-agent decision making. Given a sequence of actions, responsibility attribution mechanisms quantify the impact of each participating agent to the final outcome. One such popular mechanism is based on actual causality, and it assigns (causal) responsibility... | ['Goran Radanovic', 'Stelios Triantafyllou'] | 2023-02-24 | null | null | null | null | ['card-games'] | ['playing-games'] | [ 3.79711181e-01 6.02569461e-01 -3.39989603e-01 -1.37309924e-01
-4.28890616e-01 -4.28444564e-01 7.38142133e-01 4.97524142e-01
-5.79638064e-01 1.15119362e+00 2.16448501e-01 -2.95049071e-01
-7.74110675e-01 -9.24171865e-01 -4.14568037e-01 -7.26770341e-01
-2.57418692e-01 1.12950492e+00 2.60999501e-01 -9.36925337... | [8.241532325744629, 5.723372459411621] |
4e137572-4735-4558-bca3-205ca6c8f2b5 | greenhouse-gases-emissions-estimating | 2212.10844 | null | https://arxiv.org/abs/2212.10844v1 | https://arxiv.org/pdf/2212.10844v1.pdf | Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning | As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifical... | ['François Soupé', 'Laurent Carlier', 'Thibaut Heurtebize', 'Jeremi Assael'] | 2022-12-21 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-2.80690163e-01 6.14486635e-01 -5.44771910e-01 -7.51941800e-02
-7.43725121e-01 -7.14878619e-01 6.96542442e-01 2.01865777e-01
-1.78690076e-01 1.11567557e+00 8.06509927e-02 -7.27927327e-01
-4.37517941e-01 -1.19593716e+00 -5.74478626e-01 -4.89118934e-01
1.31191298e-01 6.37201846e-01 -2.88431704e-01 1.43981099... | [5.4125800132751465, 4.038724899291992] |
568c2d19-9381-4d16-8a7d-e5488aa71ca7 | poincare-glove-hyperbolic-word-embeddings-1 | null | null | https://openreview.net/forum?id=Ske5r3AqK7 | https://openreview.net/pdf?id=Ske5r3AqK7 | Poincare Glove: Hyperbolic Word Embeddings | Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian... | ['Octavian-Eugen Ganea*', 'Gary Becigneul*', 'Alexandru Tifrea*'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['learning-word-embeddings'] | ['methodology'] | [-3.36194217e-01 3.34123343e-01 -3.80632132e-02 -2.00147763e-01
-1.20610923e-01 -6.18079364e-01 8.94286036e-01 2.41506726e-01
-5.68007350e-01 -6.49389401e-02 4.55670983e-01 -4.30604279e-01
-3.74765277e-01 -8.70289981e-01 -2.34540924e-01 -7.21382856e-01
-1.46743640e-01 4.21716094e-01 -2.71687329e-01 -5.39127409... | [10.313300132751465, 8.494136810302734] |
c61c97e6-f620-4c67-b4df-e107dfdc142f | pyrca-a-library-for-metric-based-root-cause | 2306.11417 | null | https://arxiv.org/abs/2306.11417v1 | https://arxiv.org/pdf/2306.11417v1.pdf | PyRCA: A Library for Metric-based Root Cause Analysis | We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents. It offers a unified interface f... | ['Steven C. H. Hoi', 'Doyen Sahoo', 'Manpreet Singh', 'Himanshu Mittal', 'Wenzhuo Yang', 'Chenghao Liu'] | 2023-06-20 | null | null | null | null | ['graph-construction', 'causal-discovery'] | ['graphs', 'knowledge-base'] | [-1.45018503e-01 1.30937025e-01 -4.78370726e-01 -7.35711232e-02
-2.68006861e-01 -6.21048927e-01 6.11066461e-01 6.51209772e-01
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-9.02392924e-01 -9.94448364e-01 -2.38555431e-01 -4.06633466e-01
-8.25453937e-01 4.13865685e-01 -2.30947621e-02 -4.76190224... | [7.863565921783447, 5.386106491088867] |
264c172b-f66f-4f90-9c7e-c0708f0fb296 | tsup-speaker-diarization-system-for | 2210.14653 | null | https://arxiv.org/abs/2210.14653v1 | https://arxiv.org/pdf/2210.14653v1.pdf | TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge | This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typi... | ['Lei Xie', 'Qing Wang', 'Li Zhang', 'Yang Sun', 'Xiaoyue Yang', 'Gaosheng Zhang', 'Huan Zhao', 'Bowen Pang'] | 2022-10-26 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 6.32869229e-02 2.24358197e-02 1.54505640e-01 -4.93270248e-01
-1.32003331e+00 -5.88895321e-01 7.32007205e-01 -2.85230726e-01
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1.44332185e-01 1.73130721e-01 8.86953697e-02 -7.65196204e-01
-4.30607945e-02 5.78614712e-01 6.15258329e-02 -2.00743645... | [14.58634090423584, 6.1583123207092285] |
71361450-d7e9-4fae-b366-83b0e3df2fd3 | towards-self-supervised-gaze-estimation | 2203.10974 | null | https://arxiv.org/abs/2203.10974v2 | https://arxiv.org/pdf/2203.10974v2.pdf | Towards Self-Supervised Gaze Estimation | Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results... | ['Sergio Escalera', 'Simone Scardapane', 'Cristina Palmero', 'Arya Farkhondeh'] | 2022-03-21 | null | null | null | null | ['gaze-estimation', 'online-clustering'] | ['computer-vision', 'computer-vision'] | [ 3.05184931e-01 1.40931770e-01 -2.44527012e-01 -8.72126460e-01
-4.66944635e-01 -3.21971804e-01 4.47862357e-01 -3.31203133e-01
-4.29393113e-01 4.08915341e-01 1.73443928e-01 2.93718725e-01
-1.02925718e-01 -6.53800964e-02 -8.84954572e-01 -7.76803911e-01
3.48329619e-02 2.06478074e-01 -1.24481320e-01 3.50241400... | [14.111310958862305, 0.0540902353823185] |
ea17ea68-401d-4e93-a369-a9fcf1103d42 | predicting-retrosynthetic-pathways-using-a | 1910.08036 | null | https://arxiv.org/abs/1910.08036v1 | https://arxiv.org/pdf/1910.08036v1.pdf | Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy | We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts... | ['Valerio Zullo', 'Riccardo Petraglia', 'Anna Iuliano', 'Rico Andreas Haeuselmann', 'Costas Bekas', 'Teodoro Laino', 'Riccardo Pisoni', 'Philippe Schwaller', 'Vishnu H Nair'] | 2019-10-17 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.44270045e-01 5.40679157e-01 -3.88419896e-01 9.50664580e-02
-3.95422518e-01 -1.13506317e+00 7.87402391e-01 7.31548429e-01
-4.12691653e-01 1.06144679e+00 -6.42126128e-02 -5.88801920e-01
-3.88623506e-01 -7.52157092e-01 -4.59444404e-01 -8.52976620e-01
-5.76556660e-02 7.54200161e-01 3.19488525e-01 -4.42422688... | [4.478693962097168, 6.1194939613342285] |
a1abebc6-fd53-4aaa-9efa-b1a8751fa421 | a-dataset-for-audio-visual-sound-event | 2302.07315 | null | https://arxiv.org/abs/2302.07315v1 | https://arxiv.org/pdf/2302.07315v1.pdf | A dataset for Audio-Visual Sound Event Detection in Movies | Audio event detection is a widely studied audio processing task, with applications ranging from self-driving cars to healthcare. In-the-wild datasets such as Audioset have propelled research in this field. However, many efforts typically involve manual annotation and verification, which is expensive to perform at scale... | ['Shrikanth Narayanan', 'Veena Vijai', 'Krishna Somandepalli', 'Digbalay Bose', 'Rajat Hebbar'] | 2023-02-14 | null | null | null | null | ['sound-event-detection', 'sound-classification'] | ['audio', 'audio'] | [ 2.35406578e-01 -5.89522719e-01 7.17325211e-02 -4.33100045e-01
-1.44206667e+00 -8.66821766e-01 2.25359261e-01 4.71604317e-01
-2.11894929e-01 2.87410200e-01 5.17976463e-01 4.81326412e-03
7.02238753e-02 -4.36426431e-01 -5.06855667e-01 -4.35476094e-01
-1.87574089e-01 -1.75072595e-01 3.51236999e-01 1.26781678... | [15.171067237854004, 5.1837568283081055] |
f621b4da-1a21-4eb6-980b-f77d65a7a0d5 | imitation-learning-as-state-matching-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.pdf | Imitation Learning As State Matching via Differentiable Physics | Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually have a double-loop training process, alternating between learning a reward function and a policy and tend to suffer long training time and high variance. In this work, we identify the benefits of differentiable physics sim... | ['Zhongwen Xu', 'Xiao Ma', 'Siwei Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['continuous-control', 'deformable-object-manipulation'] | ['playing-games', 'robots'] | [-1.03131412e-02 -8.95083044e-03 -2.47122526e-01 1.69661418e-01
-3.29171240e-01 -6.29280686e-01 5.05339682e-01 -5.76280579e-02
-5.93717754e-01 8.40260088e-01 -4.17857438e-01 -3.59391868e-01
-4.37235177e-01 -6.13103330e-01 -1.14929605e+00 -7.83715069e-01
-2.28794098e-01 6.05865359e-01 4.46626097e-01 -3.41417938... | [4.4325032234191895, 1.4282480478286743] |
d52d00ce-cc87-4c4b-baba-47bf1b8beb0d | unsupervised-jpeg-domain-adaptation-for | null | null | https://hal.archives-ouvertes.fr/hal-03374780/ | https://hal.archives-ouvertes.fr/hal-03374780v1/document | Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics | Domain adaptation is a major issue for doing practical forensics. Since examined images are likely to come from a different development pipeline compared to the ones used for training our models, that may disturb them by a lot, degrading their performances. In this paper, we present a method enabling to make a forgery ... | ['Patrick Bas', 'Jérémie Boulanger', 'Vincent Itier', 'Rony Abecidan'] | 2021-12-09 | null | null | null | wifs-ieee-international-workshop-on | ['jpeg-forgery-localization', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 1.96307063e-01 -3.28216814e-02 -2.80115753e-02 -1.75014123e-01
-7.07349896e-01 -6.98603928e-01 8.12491357e-01 1.13698497e-01
-5.47555447e-01 8.11655521e-01 -2.82791518e-02 -6.83977380e-02
-1.49157792e-01 -6.44376516e-01 -7.09231079e-01 -7.84514546e-01
2.78846532e-01 7.42670238e-01 6.52590334e-01 -1.79069504... | [12.567466735839844, 1.1304576396942139] |
5588b6a7-3c47-4640-a088-9ca8060e5e66 | improving-generalizability-in-implicitly-1 | 2204.02261 | null | https://arxiv.org/abs/2204.02261v1 | https://arxiv.org/pdf/2204.02261v1.pdf | Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors | Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of abusive language continually emerge in online discussions in response to current events (e.g., COVID-19), and the deployed abuse detection s... | ['Svetlana Kiritchenko', 'Kathleen C. Fraser', 'Isar Nejadgholi'] | 2022-04-05 | null | https://aclanthology.org/2022.acl-long.378 | https://aclanthology.org/2022.acl-long.378.pdf | acl-2022-5 | ['abusive-language', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.14016545e-02 2.48382818e-02 -4.22458351e-01 -4.73256648e-01
-4.36682254e-01 -7.49853015e-01 5.37315309e-01 4.39218074e-01
-4.10234839e-01 9.70649779e-01 3.32223296e-01 -4.51348513e-01
8.10517184e-03 -3.62596810e-01 -3.84075791e-01 -3.62331063e-01
-4.49509323e-02 4.16867733e-01 -8.44686478e-02 -4.51781660... | [8.715778350830078, 10.486804008483887] |
b604042e-1eaa-482c-8ddf-462231cd84bb | does-knowledge-transfer-always-help-to-learn | 1912.02986 | null | https://arxiv.org/abs/1912.02986v2 | https://arxiv.org/pdf/1912.02986v2.pdf | How Does an Approximate Model Help in Reinforcement Learning? | One of the key approaches to save samples in reinforcement learning (RL) is to use knowledge from an approximate model such as its simulator. However, how much does an approximate model help to learn a near-optimal policy of the true unknown model? Despite numerous empirical studies of transfer reinforcement learning, ... | ['Lin F. Yang', 'Fei Feng', 'Wotao Yin'] | 2019-12-06 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-1.43738091e-02 2.87290543e-01 -4.18204367e-01 -1.47538912e-02
-9.30006266e-01 -7.20409393e-01 2.22979292e-01 5.59520610e-02
-9.02953744e-01 1.20412910e+00 -3.76596481e-01 -6.55784726e-01
-2.69555092e-01 -8.39204252e-01 -1.06185746e+00 -8.20654333e-01
-4.35071826e-01 6.42423272e-01 3.65172207e-01 -1.93072066... | [4.330489635467529, 2.7785613536834717] |
38937659-5d95-44fb-a821-dff969052093 | predictive-and-diagnosis-models-of-stroke | 2306.05289 | null | https://arxiv.org/abs/2306.05289v1 | https://arxiv.org/pdf/2306.05289v1.pdf | Predictive and diagnosis models of stroke from hemodynamic signal monitoring | This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitori... | ['José L. Ayala', 'Gemma Reig Roselló', 'José L. Risco-Martín', 'Luis García-Terriza'] | 2023-05-30 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-2.50746667e-01 -1.32068098e-01 -2.10551426e-01 -3.65761489e-01
-7.52700329e-01 -3.17763716e-01 -5.79480529e-02 4.06271517e-01
-6.82762504e-01 1.08040261e+00 8.90845060e-02 -1.00004363e+00
-6.25410616e-01 -9.37893331e-01 -1.83184862e-01 -3.10228467e-01
-8.66159797e-01 8.69240701e-01 2.13880673e-01 2.42642149... | [14.152853012084961, 3.0088353157043457] |
cf81fab3-1648-4f77-91e7-176990b14391 | on-the-origins-of-bias-in-nlp-through-the | 2305.09281 | null | https://arxiv.org/abs/2305.09281v1 | https://arxiv.org/pdf/2305.09281v1.pdf | On the Origins of Bias in NLP through the Lens of the Jim Code | In this paper, we trace the biases in current natural language processing (NLP) models back to their origins in racism, sexism, and homophobia over the last 500 years. We review literature from critical race theory, gender studies, data ethics, and digital humanities studies, and summarize the origins of bias in NLP mo... | ['Gavin Abercrombie', 'Fatma Elsafoury'] | 2023-05-16 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 2.07260042e-01 7.72275269e-01 -8.19376290e-01 -6.56405151e-01
3.04745380e-02 -6.89616203e-01 6.91432834e-01 8.55188012e-01
-9.41271305e-01 4.51214820e-01 1.29231465e+00 -8.00793946e-01
-1.52965412e-01 -4.14645791e-01 -4.19408679e-01 2.94633824e-02
7.00970769e-01 1.48369774e-01 -5.39998710e-01 -2.17974842... | [9.164240837097168, 9.909505844116211] |
61315081-7f2d-4e93-8c59-79757e50914a | proposal-tracking-and-segmentation-pts-a | 1907.01203 | null | https://arxiv.org/abs/1907.01203v2 | https://arxiv.org/pdf/1907.01203v2.pdf | Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation | Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame. Due to the large visual variations of objects in video and the lack of training samples, it remains a difficult task despite the upsurging development of deep learning. Toward solving the VOS problem, we b... | ['Chang Huang', 'Yongchao Gong', 'Han Shen', 'Wenyu Liu', 'Qiang Zhou', 'Xinggang Wang', 'Lichao Huang', 'Zilong Huang'] | 2019-07-02 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-3.52127217e-02 -1.18161537e-01 -4.96753097e-01 -2.60660082e-01
-5.70769131e-01 -4.53473389e-01 2.16255978e-01 -2.72728205e-01
-3.61255020e-01 4.00190562e-01 -2.34311685e-01 3.58459800e-02
3.07793319e-01 -3.75331283e-01 -9.29159701e-01 -5.88025451e-01
6.51617125e-02 3.61974210e-01 1.21405351e+00 1.14123136... | [9.149412155151367, -0.1759055256843567] |
6ee73d9a-0fce-426d-846e-8576c2165f7a | rcdt-relational-remote-sensing-change | 2212.04869 | null | https://arxiv.org/abs/2212.04869v1 | https://arxiv.org/pdf/2212.04869v1.pdf | RCDT: Relational Remote Sensing Change Detection with Transformer | Deep learning based change detection methods have received wide attentoion, thanks to their strong capability in obtaining rich features from images. However, existing AI-based CD methods largely rely on three functionality-enhancing modules, i.e., semantic enhancement, attention mechanisms, and correspondence enhancem... | ['Xiao Huang', 'Kaixuan Lu'] | 2022-12-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.57202673e-01 -3.37930679e-01 1.75289631e-01 -4.94691014e-01
-6.37608111e-01 -2.10838690e-01 9.16598678e-01 -4.13857587e-02
-3.76921177e-01 4.86598998e-01 2.96151966e-01 -2.06190962e-02
-2.65356958e-01 -1.01595128e+00 -5.73283792e-01 -8.65830183e-01
-9.81560722e-02 6.46705702e-02 4.87315893e-01 -4.17164028... | [9.70023250579834, -1.2933502197265625] |
f8a122d2-8f74-4d63-b069-4f66f6fd3a5e | multi-objective-conflict-based-search-for | 2101.03805 | null | https://arxiv.org/abs/2101.03805v5 | https://arxiv.org/pdf/2101.03805v5.pdf | A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding | Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readi... | ['Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren'] | 2021-01-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.22083151e-03 -4.31604944e-02 -5.16706347e-01 2.21650541e-01
-8.42536747e-01 -8.17734122e-01 1.49354994e-01 5.79110086e-01
-4.86219823e-01 1.26619565e+00 -5.58969416e-02 -2.81968504e-01
-9.88786757e-01 -8.81130159e-01 -3.30472916e-01 -6.07048690e-01
-6.44593954e-01 1.11265099e+00 2.54354268e-01 -5.15997171... | [4.928719997406006, 1.8675615787506104] |
e892718b-077e-4b5a-9edc-dc1be51dfc7a | old-is-gold-linguistic-driven-approach-for | null | null | https://aclanthology.org/N19-1243 | https://aclanthology.org/N19-1243.pdf | Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text | Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively ma... | ['S{\\"o}ren Auer', 'Maria Esther Vidal', "Isaiah o Mulang{'}", 'on', 'Saeedeh Shekarpour', 'Ahmad Sakor', 'Kuldeep Singh', 'Jens Lehmann'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['implicit-relations'] | ['natural-language-processing'] | [-1.88320279e-01 3.79468739e-01 -5.21645963e-01 -1.05402626e-01
-5.84096193e-01 -1.03176451e+00 7.80638099e-01 1.00130296e+00
-9.12626922e-01 1.03218174e+00 2.12536231e-01 -4.64143276e-01
-3.24449509e-01 -1.13499177e+00 -7.54920900e-01 9.65573341e-02
1.19100012e-01 9.15663898e-01 6.13586187e-01 -5.24452090... | [9.443821907043457, 8.734537124633789] |
2448d569-6969-46a6-bdf0-2f5da182d921 | apt-36k-a-large-scale-benchmark-for-animal | 2206.05683 | null | https://arxiv.org/abs/2206.05683v2 | https://arxiv.org/pdf/2206.05683v2.pdf | APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking | Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames. Previous animal-related datasets focus either on animal tracking or single-frame animal pose estimation, and never on both aspects. The lack of APT datasets hinders the developmen... | ['DaCheng Tao', 'Long Lan', 'Jing Zhang', 'Yufei Xu', 'Junjie Yang', 'Yuxiang Yang'] | 2022-06-12 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [-1.62785634e-01 -5.60460865e-01 -3.46226543e-01 -2.67331958e-01
-4.00731534e-01 -7.46138215e-01 1.20279416e-01 2.26359770e-01
-7.98248649e-01 5.14847398e-01 -1.37532294e-01 2.54982680e-01
3.05275619e-02 -3.79776120e-01 -1.06151998e+00 -4.76377338e-01
-7.19013691e-01 2.13898569e-01 5.60735762e-01 6.58976510... | [7.617290019989014, -0.9591038227081299] |
c20e55fb-eda9-4119-a365-5c0b2b4e4892 | vision-models-are-more-robust-and-fair-when | 2202.08360 | null | https://arxiv.org/abs/2202.08360v2 | https://arxiv.org/pdf/2202.08360v2.pdf | Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision | Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images. Applied to ImageNet, this leads to object centric features that perform on par with supervised features on most object-centric down... | ['Piotr Bojanowski', 'Armand Joulin', 'Levent Sagun', 'Ishan Misra', 'Mathilde Caron', 'Isaac Seessel', 'Quentin Duval', 'Priya Goyal'] | 2022-02-16 | vision-models-are-more-robust-and-fair-when-1 | https://arxiv.org/abs/2202.08360 | https://arxiv.org/pdf/2202.08360.pdf | null | ['traffic-sign-recognition', 'self-supervised-image-classification', 'semi-supervised-image-classification', 'fine-grained-image-classification', 'multilingual-word-embeddings', 'meme-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'natural-language-processing'] | [-2.03603003e-02 -7.36477822e-02 -5.37161112e-01 -4.86459106e-01
-6.04209423e-01 -6.95578158e-01 9.85595286e-01 3.75273198e-01
-6.21620655e-01 6.07911825e-01 5.92090666e-01 4.67915684e-02
-1.42378524e-01 -8.00137401e-01 -1.04608762e+00 -5.24869323e-01
-2.22241282e-01 3.36428076e-01 5.27493022e-02 -1.91650420... | [10.098209381103516, 1.8492321968078613] |
bb894aa8-92b4-417d-a802-523a92f872e7 | provably-convergent-policy-optimization-via | 2306.14133 | null | https://arxiv.org/abs/2306.14133v1 | https://arxiv.org/pdf/2306.14133v1.pdf | Provably Convergent Policy Optimization via Metric-aware Trust Region Methods | Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible metrics and examine two natural extensions of policy optimization with Wasserstein and Sinkhorn trust regions, namely Wasserstein policy opti... | ['Chaoyue Zhao', 'Lijun Ding', 'Niao He', 'Jun Song'] | 2023-06-25 | null | null | null | null | ['policy-gradient-methods', 'continuous-control'] | ['methodology', 'playing-games'] | [-2.50778317e-01 4.15935628e-02 -6.99109137e-01 -3.04532405e-02
-6.87648892e-01 -7.21902549e-01 4.52084184e-01 1.63933560e-01
-8.56473565e-01 1.45410168e+00 1.86182767e-01 -3.36068362e-01
-4.42023784e-01 -3.87181878e-01 -9.09845889e-01 -8.66312504e-01
-3.52947146e-01 2.12354705e-01 7.64110982e-02 -3.06442022... | [4.1664347648620605, 2.4065134525299072] |
d63483b8-d1ab-4ad9-944b-c822c6d1a036 | diacritics-restoration-using-neural-networks | null | null | https://aclanthology.org/L18-1247 | https://aclanthology.org/L18-1247.pdf | Diacritics Restoration Using Neural Networks | null | ['Jan Haji{\\v{c}}', "Pavel Stra{\\v{n}}{\\'a}k", "Jakub N{\\'a}plava", 'Milan Straka'] | 2018-05-01 | diacritics-restoration-using-neural-networks-1 | https://aclanthology.org/L18-1247 | https://aclanthology.org/L18-1247.pdf | lrec-2018-5 | ['irish-text-diacritization', 'croatian-text-diacritization', 'french-text-diacritization', 'romanian-text-diacritization', 'turkish-text-diacritization', 'hungarian-text-diacritization', 'slovak-text-diacritization', 'spanish-text-diacritization', 'latvian-text-diacritization', 'czech-text-diacritization', 'vietnamese... | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-... | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3394975662231445, 3.6458990573883057] |
3df37b65-0298-4d46-af20-c5e82b268c80 | universal-image-manipulation-detection-using | 1808.06323 | null | http://arxiv.org/abs/1808.06323v2 | http://arxiv.org/pdf/1808.06323v2.pdf | Universal Image Manipulation Detection using Deep Siamese Convolutional Neural Network | Detection of different types of image editing operations carried out on an
image is an important problem in image forensics. It gives the information
about the processing history of an image, and also can expose forgeries present
in an image. There have been few methods proposed to detect different types of
image editi... | ['Yosha Singh Tomar', 'Jaya Singh', 'Aniruddha Mazumdar', 'Prabin Kumar Bora'] | 2018-08-20 | null | null | null | null | ['image-manipulation-detection', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 5.54055393e-01 -6.96770608e-01 2.55216926e-01 -2.92403340e-01
-2.96750456e-01 -6.23662174e-01 6.69508636e-01 4.65562224e-01
-5.48004150e-01 3.68940681e-02 -3.31811935e-01 -2.15215474e-01
1.57268178e-02 -9.67019796e-01 -7.26307571e-01 -9.09798801e-01
-1.54208289e-02 2.61781216e-01 2.90628463e-01 -1.35352373... | [12.376590728759766, 0.9888411164283752] |
5331a5c4-c532-4c71-8b7d-c9340f4af07c | learning-audio-text-agreement-for-open | 2206.15400 | null | https://arxiv.org/abs/2206.15400v2 | https://arxiv.org/pdf/2206.15400v2.pdf | Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting | In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences. Unlike previous approaches requiring speech keyword enrollment, our method compares input queries with an enrolled text keyword sequence. To place the ... | ['Hong-Goo Kang', 'Soo-Whan Chung', 'Doyeon Kim', 'Hyewon Han', 'Hyeon-Kyeong Shin'] | 2022-06-30 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.22677255e-01 -2.35570788e-01 -3.76367778e-01 -4.73403543e-01
-1.97463334e+00 -7.57925570e-01 6.28947020e-01 9.52106342e-02
-7.90603042e-01 9.64878052e-02 5.89505255e-01 -2.49365613e-01
-1.01908907e-01 -2.68037707e-01 -7.20134795e-01 -3.91455114e-01
3.58840227e-01 2.99496204e-01 9.07299593e-02 1.03864916... | [14.24906063079834, 6.387243270874023] |
19f60af7-bd47-4289-aa93-4e5065851ff5 | unsupervised-deep-learning-for-bayesian-brain | 1904.11319 | null | https://arxiv.org/abs/1904.11319v2 | https://arxiv.org/pdf/1904.11319v2.pdf | Unsupervised Deep Learning for Bayesian Brain MRI Segmentation | Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning ... | ['Mert R. Sabuncu', 'Evan Yu', 'Polina Golland', 'Juan Eugenio Iglesias', 'Adrian V. Dalca', 'Bruce Fischl'] | 2019-04-25 | null | null | null | null | ['zero-shot-segmentation', 'brain-image-segmentation'] | ['computer-vision', 'medical'] | [ 3.26841474e-01 -1.31607637e-01 1.62322715e-01 -6.32305562e-01
-1.08329213e+00 -4.76547509e-01 3.14333379e-01 3.08188647e-01
-8.65717053e-01 5.60463011e-01 -3.06358159e-01 -3.11776131e-01
1.48496956e-01 -7.35479355e-01 -6.30313098e-01 -8.51561725e-01
3.44666280e-02 8.55839491e-01 5.84943712e-01 2.45367989... | [14.385137557983398, -2.3020551204681396] |
3a3bc3b4-0db5-423c-bd24-e2869434bd50 | nuaa-qmul-aiit-at-memotion-3-multi-modal | 2302.08326 | null | https://arxiv.org/abs/2302.08326v1 | https://arxiv.org/pdf/2302.08326v1.pdf | NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis | This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal fusion method, Squeeze-and-Excitation Fusion (SEFusion), and embed it into our system for emotion classification in memes. SEFusion is a simple fusion method that empl... | ['Arkaitz Zubiaga', 'Jing Ma', 'XIAOYU GUO'] | 2023-02-16 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-3.78018498e-01 -3.78997326e-01 1.80367395e-01 -4.40289706e-01
-8.20470095e-01 -3.69165599e-01 6.49324954e-01 1.24090053e-01
-8.50126088e-01 7.02554107e-01 4.87245530e-01 2.73047835e-01
3.09257925e-01 -3.31789970e-01 -4.28912818e-01 -5.34552515e-01
1.24519676e-01 1.46045819e-01 -4.61414814e-01 -4.85003084... | [13.24276065826416, 5.1729936599731445] |
b22b34c0-75fb-4e81-aff1-9344ba094731 | relation-matters-foreground-aware-graph-based | 2206.02355 | null | https://arxiv.org/abs/2206.02355v1 | https://arxiv.org/pdf/2206.02355v1.pdf | Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection | Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods. However, both the global and local... | ['Yizhou Yu', 'Xinghao Ding', 'Yue Huang', 'Xiaoguang Han', 'Hong-Yu Zhou', 'Jiongcheng Li', 'Chaoqi Chen'] | 2022-06-06 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 2.36077905e-01 -2.76920311e-02 -1.41073808e-01 -4.00453091e-01
-2.27343023e-01 -4.04371172e-01 6.27807558e-01 1.38495356e-01
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-3.12389970e-01 -1.14831090e+00 -7.57649601e-01 -7.09525049e-01
2.64016658e-01 4.64270383e-01 9.79074240e-01 -1.63882956... | [10.072761535644531, 1.8229345083236694] |
a42b7630-295a-44ac-98eb-acf99c0a61d7 | long-short-range-context-neural-networks-for | 1708.06555 | null | http://arxiv.org/abs/1708.06555v1 | http://arxiv.org/pdf/1708.06555v1.pdf | Long-Short Range Context Neural Networks for Language Modeling | The goal of language modeling techniques is to capture the statistical and
structural properties of natural languages from training corpora. This task
typically involves the learning of short range dependencies, which generally
model the syntactic properties of a language and/or long range dependencies,
which are seman... | ['Mittul Singh', 'Clayton Greenberg', 'Dietrich Klakow', 'Youssef Oualil'] | 2017-08-22 | long-short-range-context-neural-networks-for-1 | https://aclanthology.org/D16-1154 | https://aclanthology.org/D16-1154.pdf | emnlp-2016-11 | ['text-compression'] | ['natural-language-processing'] | [ 6.09574020e-02 -9.59881619e-02 -3.88156176e-01 -7.51464367e-01
-4.50228304e-01 -1.40228346e-01 8.53487492e-01 4.13418740e-01
-8.56759846e-01 7.71009564e-01 4.30722743e-01 -6.87044024e-01
1.38700530e-01 -7.82132983e-01 -6.32113159e-01 -5.89838743e-01
-2.61285841e-01 6.02227211e-01 3.01710457e-01 -2.55227268... | [10.718241691589355, 8.910235404968262] |
d04594f9-1c58-457e-847f-99501fa5e530 | multi-modal-visual-place-recognition-in | 2105.07800 | null | https://arxiv.org/abs/2105.07800v2 | https://arxiv.org/pdf/2105.07800v2.pdf | Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space | Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first de... | ['Changyin Sun', 'Teng Wang', 'Lin Wu'] | 2021-05-17 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 2.39935607e-01 -2.71983802e-01 4.14030254e-02 -3.78925264e-01
-7.47731686e-01 -6.53102398e-01 8.12255323e-01 -1.02962004e-02
-4.97204363e-01 1.35194927e-01 1.39757439e-01 1.14323296e-01
-3.51827264e-01 -7.64537513e-01 -8.05543482e-01 -8.55483472e-01
2.60540664e-01 5.06132655e-03 2.13765562e-01 -1.08802140... | [7.645900249481201, -1.97385835647583] |
752f3077-e8aa-4443-975a-9af1d79dd315 | scaling-novel-object-detection-with-weakly | 2207.05205 | null | https://arxiv.org/abs/2207.05205v3 | https://arxiv.org/pdf/2207.05205v3.pdf | Scaling Novel Object Detection with Weakly Supervised Detection Transformers | A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect. Weakly supervised object detection (WSOD) offers an appealing alternative, where object detectors can be trained using ... | ['Neel Joshi', 'Vibhav Vineet', 'Xin Wang', 'Yale Song', 'Tyler LaBonte'] | 2022-07-11 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 2.57337898e-01 1.29634393e-02 -3.29795539e-01 -2.32493252e-01
-8.56529117e-01 -6.90346718e-01 5.67646265e-01 3.99699539e-01
-6.42523110e-01 4.27199990e-01 -1.65971741e-01 1.49066616e-02
1.25347152e-01 -6.30208015e-01 -1.02131975e+00 -5.52987933e-01
6.32710233e-02 5.33883274e-01 8.71184349e-01 7.78499246... | [9.375312805175781, 1.3139145374298096] |
df1739c4-f534-4a18-a3da-2a5c05b7af0c | bayesian-risk-averse-q-learning-with | 2305.11300 | null | https://arxiv.org/abs/2305.11300v1 | https://arxiv.org/pdf/2305.11300v1.pdf | Bayesian Risk-Averse Q-Learning with Streaming Observations | We consider a robust reinforcement learning problem, where a learning agent learns from a simulated training environment. To account for the model mis-specification between this training environment and the real environment due to lack of data, we adopt a formulation of Bayesian risk MDP (BRMDP) with infinite horizon, ... | ['Enlu Zhou', 'Yuhao Wang'] | 2023-05-18 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 3.15565802e-02 5.87324202e-01 -2.19750419e-01 -1.53589249e-01
-1.10265231e+00 -2.64221579e-01 3.01868051e-01 1.78768083e-01
-8.06502461e-01 9.55003977e-01 -1.30605519e-01 -3.05777580e-01
-4.96462017e-01 -8.34092796e-01 -9.23016071e-01 -8.28409672e-01
-4.44432139e-01 5.14572740e-01 1.55433387e-01 2.15401158... | [4.44673490524292, 2.501689910888672] |
0948e59f-a307-45e8-a53e-964af4097a44 | differential-machine-learning | 2005.02347 | null | https://arxiv.org/abs/2005.02347v4 | https://arxiv.org/pdf/2005.02347v4.pdf | Differential Machine Learning | Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our m... | ['Brian Huge', 'Antoine Savine'] | 2020-05-05 | differential-machine-learning-1 | null | null | arxiv-2020-5 | ['mathematical-proofs'] | ['miscellaneous'] | [-7.77741134e-01 -6.20477349e-02 2.86647381e-04 -1.54328033e-01
-9.56985772e-01 -8.62158656e-01 6.24326408e-01 6.82676882e-02
-1.93825826e-01 7.59657145e-01 -9.61641222e-02 -1.09883356e+00
-3.81359279e-01 -8.31578672e-01 -4.92816240e-01 -4.71427888e-01
-5.05694270e-01 6.85634315e-01 -4.35042739e-01 -3.35515201... | [4.837160587310791, 3.9984641075134277] |
b81034a4-4dda-416e-bd36-dabb1e697809 | the-lottery-ticket-hypothesis-for-vision | 2211.01484 | null | https://arxiv.org/abs/2211.01484v3 | https://arxiv.org/pdf/2211.01484v3.pdf | Data Level Lottery Ticket Hypothesis for Vision Transformers | The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision t... | ['Yanzhi Wang', 'Xiaolong Ma', 'Hao Tang', 'Xin Meng', 'Geng Yuan', 'Peiyan Dong', 'Minghai Qin', 'Zhenglun Kong', 'Xuan Shen'] | 2022-11-02 | null | null | null | null | ['analogical-similarity'] | ['reasoning'] | [ 1.10436983e-01 2.84067899e-01 -1.93209857e-01 -2.74422050e-01
-3.16728145e-01 -1.96857363e-01 4.48625118e-01 -5.26353300e-01
-1.82347130e-02 5.77732623e-01 2.94261463e-02 -1.06288850e-01
-1.14304706e-01 -1.11767316e+00 -1.04398453e+00 -9.66248870e-01
4.18371886e-01 4.20073509e-01 4.11152303e-01 -2.31841519... | [9.403544425964355, 2.7496886253356934] |
824037a2-0789-4be8-803d-b1a43a5946b3 | distilling-multi-step-reasoning-capabilities | 2212.00193 | null | https://arxiv.org/abs/2212.00193v2 | https://arxiv.org/pdf/2212.00193v2.pdf | Distilling Reasoning Capabilities into Smaller Language Models | Step-by-step reasoning approaches like chain of thought (CoT) have proved to be very effective in inducing reasoning capabilities in large language models. However, the success of the CoT approach is fundamentally tied to the model size, and billion parameter-scale models are often needed to get CoT to work. In this pa... | ['Mrinmaya Sachan', 'Alessandro Stolfo', 'Kumar Shridhar'] | 2022-12-01 | null | null | null | null | ['problem-decomposition', 'gsm8k', 'strategyqa'] | ['miscellaneous', 'natural-language-processing', 'reasoning'] | [-2.70161241e-01 5.06140828e-01 -1.35748647e-02 -2.57422149e-01
-1.01671863e+00 -9.19076860e-01 4.23764467e-01 6.41443431e-02
-2.54204363e-01 6.45421922e-01 2.51426607e-01 -9.59518015e-01
-7.00455233e-02 -7.73597836e-01 -8.31282973e-01 -3.83772373e-01
3.20022523e-01 1.06341040e+00 1.08607627e-01 -5.49253285... | [9.719385147094727, 7.43944787979126] |
be23ed2e-43c1-4358-88c1-caaf4c0f6d9f | planning-for-goal-oriented-dialogue-systems | 1910.08137 | null | https://arxiv.org/abs/1910.08137v1 | https://arxiv.org/pdf/1910.08137v1.pdf | Planning for Goal-Oriented Dialogue Systems | Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviou... | ['Luis A. Lastras-Montano', 'Shubham Agarwal', 'Tathagata Chakraborti', 'Miroslav Vodolan', 'Arunima Chaudhary', 'Ondrej Bajgar', 'Charlie Wiecha', 'Josef Ondrej', 'Christian Muise'] | 2019-10-17 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.08686179e-01 8.56070101e-01 4.29734662e-02 -4.52857882e-01
-5.53249955e-01 -7.67237782e-01 9.77578104e-01 5.23161590e-02
-2.07037553e-01 9.12040353e-01 4.13108617e-01 -5.68153858e-01
-1.57707166e-02 -1.03953528e+00 7.87159242e-03 -1.55629575e-01
6.86503202e-02 1.13011611e+00 3.95325571e-01 -9.06944156... | [12.866419792175293, 7.9528279304504395] |
416b7c83-2bd0-428b-b30e-a60ceea90a73 | minimizing-energy-consumption-in-mu-mimo-via | 2306.05162 | null | https://arxiv.org/abs/2306.05162v1 | https://arxiv.org/pdf/2306.05162v1.pdf | Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss | Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizing only a subset of antennas in the BS. In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to... | ['Nandana Rajatheva', 'Thorsten Wild', 'Stefan Wesemann', 'Jafar Mohammadi', 'Nuwanthika Rajapaksha'] | 2023-06-08 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 5.69789410e-01 5.47175288e-01 -1.69163764e-01 -9.43158641e-02
-5.34689844e-01 -2.91859150e-01 -4.84976679e-01 -2.78923400e-02
-4.45741683e-01 1.10957599e+00 -6.03402913e-01 -1.01567030e+00
-4.75826085e-01 -1.18319070e+00 -9.29693997e-01 -1.12838352e+00
-4.33890313e-01 -1.86200708e-01 -3.00612003e-01 -1.02212997... | [6.134462833404541, 1.461717963218689] |
bed2b0e3-b9d5-488a-8a84-3fe032ccb857 | learning-diverse-policies-in-moba-games-via | 2110.14221 | null | https://arxiv.org/abs/2110.14221v1 | https://arxiv.org/pdf/2110.14221v1.pdf | Learning Diverse Policies in MOBA Games via Macro-Goals | Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings. Even though these AI systems have achieved or even exceeded human-level performance, they still suffer from the lack of policy diversity. In t... | ['Lanxiao Huang', 'Wei Yang', 'Qiang Fu', 'Deheng Ye', 'Weixuan Wang', 'Guoan Han', 'Fuhao Qiu', 'Zhenjie Lian', 'Guangwei Chen', 'Liang Wang', 'Xueying Du', 'Bei Shi', 'Yiming Gao'] | 2021-10-27 | null | http://proceedings.neurips.cc/paper/2021/hash/86dba86754c0ad93997a11fa947d97b2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/86dba86754c0ad93997a11fa947d97b2-Paper.pdf | neurips-2021-12 | ['dota-2'] | ['playing-games'] | [-6.25677109e-01 -1.80689365e-01 -2.30598703e-01 -1.14791520e-01
-5.18317461e-01 -3.68901908e-01 5.26656449e-01 -4.20236826e-01
-6.14856005e-01 1.17791760e+00 -8.03675205e-02 -2.02874422e-01
-2.10848182e-01 -7.18343973e-01 -5.57092667e-01 -4.89937007e-01
-3.89025658e-01 9.05222356e-01 5.33507824e-01 -9.18057501... | [3.6298179626464844, 1.6030700206756592] |
e397e030-49a3-471c-85d3-6624318b97ea | toward-a-logical-theory-of-fairness-and-bias | 2306.13659 | null | https://arxiv.org/abs/2306.13659v1 | https://arxiv.org/pdf/2306.13659v1.pdf | Toward A Logical Theory Of Fairness and Bias | Fairness in machine learning is of considerable interest in recent years owing to the propensity of algorithms trained on historical data to amplify and perpetuate historical biases. In this paper, we argue for a formal reconstruction of fairness definitions, not so much to replace existing definitions but to ground th... | ['Vaishak Belle'] | 2023-06-08 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 1.89082533e-01 6.76172137e-01 -3.10604692e-01 -3.93740118e-01
-4.91593219e-02 -4.65521455e-01 1.26826549e+00 4.65055496e-01
-9.44957972e-01 1.23801780e+00 6.68741584e-01 -6.47161603e-01
-4.81548220e-01 -8.69299412e-01 -5.25745571e-01 -5.27041316e-01
-1.77869171e-01 1.37959406e-01 -3.29000264e-01 -1.71956554... | [8.641676902770996, 5.544895648956299] |
098311aa-8a2a-4345-b8b8-5f045f7eccbb | genomic-interpreter-a-hierarchical-genomic | 2306.05143 | null | https://arxiv.org/abs/2306.05143v2 | https://arxiv.org/pdf/2306.05143v2.pdf | Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer | Given the increasing volume and quality of genomics data, extracting new insights requires interpretable machine-learning models. This work presents Genomic Interpreter: a novel architecture for genomic assay prediction. This model outperforms the state-of-the-art models for genomic assay prediction tasks. Our model ca... | ['Guy-Bart Stan', 'Yiren Zhao', 'William A V Beardall', 'Akashaditya Das', 'Zehui Li'] | 2023-06-08 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.40596402e-01 4.43508863e-01 -5.78486443e-01 -5.11318088e-01
-9.84954178e-01 -1.03159189e+00 2.22544685e-01 5.88572204e-01
1.18459783e-01 1.06950676e+00 4.34232891e-01 -7.69455552e-01
-5.26833057e-01 -4.98831928e-01 -1.00187576e+00 -1.00608635e+00
-2.97511727e-01 8.73193741e-01 4.01423872e-01 4.99185100... | [4.843075275421143, 5.665994644165039] |
b01e2112-5db9-4e7d-a59e-63aab2b85a60 | decoding-strategies-for-neural-referring | null | null | https://aclanthology.org/W18-6563 | https://aclanthology.org/W18-6563.pdf | Decoding Strategies for Neural Referring Expression Generation | RNN-based sequence generation is now widely used in NLP and NLG (natural language generation). Most work focusses on how to train RNNs, even though also decoding is not necessarily straightforward: previous work on neural MT found seq2seq models to radically prefer short candidates, and has proposed a number of beam se... | ['Sina Zarrie{\\ss}', 'David Schlangen'] | 2018-11-01 | null | null | null | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [ 5.92875421e-01 5.48238277e-01 -1.45969152e-01 -3.82579654e-01
-9.82697666e-01 -7.40514278e-01 5.95472634e-01 -2.78043896e-01
-4.65723008e-01 1.26229632e+00 5.89171112e-01 -7.52266467e-01
1.27317908e-03 -8.08339536e-01 -5.56175590e-01 -6.60788536e-01
3.66628200e-01 6.07170522e-01 -5.37238829e-02 -5.04196823... | [11.746225357055664, 9.131495475769043] |
1bba59d2-4816-49d4-8b1c-e6ed48ff7cd4 | order-aware-generative-modeling-using-the-3d | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Order-Aware_Generative_Modeling_Using_the_3D-Craft_Dataset_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Order-Aware_Generative_Modeling_Using_the_3D-Craft_Dataset_ICCV_2019_paper.pdf | Order-Aware Generative Modeling Using the 3D-Craft Dataset | In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete... | [' C. Lawrence Zitnick', ' Arthur Szlam', ' Shubham Tulsiani', ' Charles R. Qi', ' Jerry Ma', ' Haoqi Fan', ' Kavya Srinet', ' Jonathan Gray', ' Haonan Yu', ' Xinlei Chen', ' Saining Xie', ' Tong Xiao', ' Demi Guo', 'Zhuoyuan Chen'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['house-generation'] | ['computer-vision'] | [ 1.67062894e-01 3.10151845e-01 1.50593445e-01 -1.88192949e-01
-4.28288639e-01 -7.43360937e-01 9.74874258e-01 -5.48817813e-01
-7.91329294e-02 6.76292777e-01 6.22406363e-01 -1.27313644e-01
1.03545532e-01 -1.23791742e+00 -8.73541653e-01 -5.30015469e-01
7.51657337e-02 8.45982909e-01 3.90950628e-02 -4.16740566... | [10.956110000610352, -0.3672347366809845] |
b7be33d1-3ee2-4d15-b122-8d52ae67d57c | investigating-the-nature-of-3d-generalization | 2304.09358 | null | https://arxiv.org/abs/2304.09358v1 | https://arxiv.org/pdf/2304.09358v1.pdf | Investigating the Nature of 3D Generalization in Deep Neural Networks | Visual object recognition systems need to generalize from a set of 2D training views to novel views. The question of how the human visual system can generalize to novel views has been studied and modeled in psychology, computer vision, and neuroscience. Modern deep learning architectures for object recognition generali... | ['Thomas Breuel', 'David Krueger', 'Shoaib Ahmed Siddiqui'] | 2023-04-19 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-1.17880926e-01 -1.13783605e-01 -1.13512479e-01 -6.84519768e-01
-1.25929695e-02 -1.03452218e+00 6.62064970e-01 -5.03764749e-01
-6.56143948e-02 4.84728515e-01 7.14428648e-02 -4.33292300e-01
-3.56792621e-02 -5.65835595e-01 -9.03043389e-01 -4.95180815e-01
8.24656487e-02 3.64088416e-01 1.71437576e-01 -8.41741189... | [8.47904109954834, -3.0551509857177734] |
c5091ed4-5557-4020-88ea-2ac1350dbee9 | learning-probabilistic-temporal-safety | 2211.03461 | null | https://arxiv.org/abs/2211.03461v1 | https://arxiv.org/pdf/2211.03461v1.pdf | Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains | We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relational Markov Decision Processes with pCTL model-checking. More specifically, we assume that there is... | ['Luc De Raedt', 'Jean-François Raskin', 'Wen-Chi Yang', 'Gavin Rens'] | 2022-11-07 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 2.33614430e-01 8.11582744e-01 -3.54774266e-01 -3.98623884e-01
-1.03836894e+00 -6.58053935e-01 7.13390529e-01 2.12691769e-01
-1.08750373e-01 8.52972865e-01 -8.37396309e-02 -9.06341553e-01
-1.16695188e-01 -1.38859499e+00 -8.81726921e-01 -5.91013849e-01
-3.97772640e-01 8.42203677e-01 7.35681891e-01 9.62750465... | [4.72896146774292, 2.28243088722229] |
853e7748-42fa-4452-8267-5fce18834be6 | supervised-topological-data-analysis-for | 2302.13948 | null | https://arxiv.org/abs/2302.13948v1 | https://arxiv.org/pdf/2302.13948v1.pdf | Supervised topological data analysis for MALDI imaging applications | We propose a new algebraic topological framework, which obtains intrinsic information from the MALDI data and transforms it to reflect topological persistence in the data. Our framework has two main advantages. First, the topological persistence helps us to distinguish the signal from noise. Second, it compresses the M... | ['Anastasios Stefanou', 'Vladimir Vutov', 'Gideon Klaila'] | 2023-02-27 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 4.29935813e-01 -2.20163435e-01 -4.49860871e-01 -2.10786387e-01
-6.32310688e-01 -5.33898950e-01 5.39189339e-01 5.16726375e-01
-3.84391457e-01 6.65811360e-01 -1.42994717e-01 -5.43090105e-01
-6.60740554e-01 -1.00181222e+00 -5.61420023e-01 -9.83325362e-01
-5.89049935e-01 5.66371500e-01 4.43435311e-01 1.96700022... | [7.461863040924072, 4.210243225097656] |
2fed1ae9-f7dd-4d01-aa96-5186540d1c04 | local-activity-tuned-image-filtering-for | 1707.02637 | null | http://arxiv.org/abs/1707.02637v4 | http://arxiv.org/pdf/1707.02637v4.pdf | Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing | In this paper, two local activity-tuned filtering frameworks are proposed for
noise removal and image smoothing, where the local activity measurement is
given by the clipped and normalized local variance or standard deviation. The
first framework is a modified anisotropic diffusion for noise removal of
piece-wise smoot... | ['Huihui Bai', 'Lili Meng', 'Yao Zhao', 'Lijun Zhao', 'Jie Liang', 'Anhong Wang'] | 2017-07-09 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 5.16085625e-01 -4.80602264e-01 1.48575559e-01 -1.78243175e-01
-5.32008886e-01 -8.63471925e-02 4.74305242e-01 -1.71243697e-01
-4.20173824e-01 5.13421834e-01 6.34785771e-01 1.68335795e-01
-2.47982755e-01 -6.52365506e-01 6.12432649e-03 -1.31390929e+00
2.52570361e-01 -7.91591942e-01 6.42586768e-01 -4.97457422... | [11.156822204589844, -2.563786745071411] |
b225abdc-f1f7-4171-b9a1-f107aec8b0e4 | data-augmentation-and-multimodal-learning-for | 2204.11678 | null | https://arxiv.org/abs/2204.11678v1 | https://arxiv.org/pdf/2204.11678v1.pdf | Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images | Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. We investigate multimodal neural network (MM-Net) and data augmentation fo... | ['Rick L. Stevens', 'James H. Doroshow', 'Yvonne A. Evrard', 'Maulik Shukla', 'Alexander T. Pearson', 'Sara Kochanny', 'James M. Dolezal', 'Yitan Zhu', 'Thomas Brettin', 'Alexander Partin'] | 2022-04-25 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.36327016e-01 -3.54418635e-01 -6.62006736e-01 -1.24403805e-01
-8.81060719e-01 -4.76744652e-01 8.07470620e-01 5.78216195e-01
-5.13045430e-01 9.76008236e-01 3.75939399e-01 -5.39231241e-01
-2.75968611e-01 -7.65485227e-01 -5.57312727e-01 -1.14013672e+00
-8.31777521e-04 6.00474060e-01 -1.97919250e-01 -3.29431534... | [5.705982208251953, 5.684578895568848] |
236cc115-5b79-4758-8ee4-5bfb001d1e7a | tokenized-graph-transformer-with-neighborhood | 2305.12677 | null | https://arxiv.org/abs/2305.12677v1 | https://arxiv.org/pdf/2305.12677v1.pdf | Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs | Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of token... | ['Kun He', 'Gaichao Li', 'Kaiyuan Gao', 'Chang Liu', 'Jinsong Chen'] | 2023-05-22 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [-1.46097645e-01 3.93886179e-01 -2.25316897e-01 -1.58899471e-01
-2.46668026e-01 -4.09545660e-01 5.95835447e-01 5.86940527e-01
-4.75437678e-02 5.06344557e-01 2.64036179e-01 -4.46803719e-01
-1.35151492e-02 -1.43869174e+00 -7.60407388e-01 -7.29681194e-01
-2.34935477e-01 2.46788323e-01 2.31372133e-01 -3.95714760... | [7.221351146697998, 6.301760196685791] |
7dccc7f8-cf05-4dba-b14c-60c5b9f57879 | scalable-causal-structure-learning-new | 2110.07785 | null | https://arxiv.org/abs/2110.07785v2 | https://arxiv.org/pdf/2110.07785v2.pdf | Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine | Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care. This paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help health c... | ['Yejin Kim', 'Xiaoqian Jiang', 'Can Li', 'Kai Zhang', 'Pulakesh Upadhyaya'] | 2021-10-15 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.61564982e-01 2.96685874e-01 -6.73247993e-01 -4.24506783e-01
-5.27186930e-01 -2.28123575e-01 4.97390509e-01 7.67174184e-01
-7.10115805e-02 1.14721906e+00 7.77496219e-01 -7.34791577e-01
-1.08562326e+00 -7.75223494e-01 -7.46708989e-01 -8.30188096e-01
-9.06386673e-01 6.22604251e-01 2.04906538e-02 -3.59272622... | [7.867162704467773, 5.402318954467773] |
8a9dd8d1-786d-4ab2-a02b-e52f8504010d | interweaved-graph-and-attention-network-for | 2304.14045 | null | https://arxiv.org/abs/2304.14045v1 | https://arxiv.org/pdf/2304.14045v1.pdf | Interweaved Graph and Attention Network for 3D Human Pose Estimation | Despite substantial progress in 3D human pose estimation from a single-view image, prior works rarely explore global and local correlations, leading to insufficient learning of human skeleton representations. To address this issue, we propose a novel Interweaved Graph and Attention Network (IGANet) that allows bidirect... | ['Xia Li', 'Yingxuan You', 'Wenhao Li', 'Runwei Ding', 'Hong Liu', 'Ti Wang'] | 2023-04-27 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-2.55955100e-01 2.91032910e-01 -2.36526087e-01 -2.38485023e-01
-3.70169640e-01 -7.36567541e-04 4.15227294e-01 -2.20897540e-01
-3.41588914e-01 4.51026976e-01 4.27389830e-01 1.44316927e-02
2.11652860e-01 -7.23722935e-01 -1.01141644e+00 -2.68639207e-01
-2.34878689e-01 5.69477201e-01 3.18209916e-01 -1.50454164... | [7.077125072479248, -0.6599253416061401] |
4fb97b81-87d3-4ea3-95da-6d7ef3a90f41 | en-compactness-self-distillation-embedding | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kong_En-Compactness_Self-Distillation_Embedding__Contrastive_Generation_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kong_En-Compactness_Self-Distillation_Embedding__Contrastive_Generation_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.pdf | En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning | Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can recognize objects from both seen and unseen classes. Due to the absence of unseen training samples, the classifier tends to bias towards seen classes. To mitigate this problem, feature generation based models are proposed t... | ['Yanyun Qu', 'Yuan Xie', 'Chengjie Wang', 'Jun Liu', 'Ming Hong', 'Xiaofan Li', 'Zuodong Gao', 'Xia Kong'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.17770463e-01 6.65758029e-02 -9.50008556e-02 -3.40723872e-01
-2.29941875e-01 -3.65520656e-01 7.35318959e-01 1.08874924e-01
-2.21640170e-01 3.33535165e-01 9.31141824e-02 1.36484191e-01
-2.40616798e-01 -1.20324624e+00 -2.81359434e-01 -9.63198185e-01
3.02516669e-01 5.33103235e-02 4.99312222e-01 -1.64113000... | [9.896726608276367, 2.4027037620544434] |
057eb181-45ba-4973-87eb-a8d845867138 | size-generalizability-of-graph-neural | 2305.15611 | null | https://arxiv.org/abs/2305.15611v1 | https://arxiv.org/pdf/2305.15611v1.pdf | Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective | We investigate the question of whether the knowledge learned by graph neural networks (GNNs) from small graphs is generalizable to large graphs in the same domain. Prior works suggest that the distribution shift, particularly in the degree distribution, between graphs of different sizes can lead to performance degradat... | ['Danai Koutra', 'Gaotang Li', 'Yujun Yan'] | 2023-05-24 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 1.07383154e-01 1.51757509e-01 -2.67280996e-01 6.51234835e-02
2.73245037e-01 -8.58664215e-01 3.35998774e-01 3.74490440e-01
2.28230399e-03 6.57888651e-01 -2.86950404e-03 -4.85776544e-01
-6.00220263e-01 -1.09327459e+00 -8.15903544e-01 -9.26883042e-01
-6.16854191e-01 3.52025211e-01 2.87273914e-01 -3.40469927... | [6.891356468200684, 6.120457172393799] |
6d93973a-ae19-4cfa-a62c-99091872a235 | non-lexical-neural-architecture-for-fine | null | null | https://aclanthology.org/D15-1025 | https://aclanthology.org/D15-1025.pdf | Non-lexical neural architecture for fine-grained POS Tagging | null | ['re', 'Kevin L{\\"o}ser', 'Alex Allauzen', 'Matthieu Labeau'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.255837440490723, 3.7783403396606445] |
ae838745-f0b7-44ae-8665-c75dd270db6d | lddmm-face-large-deformation-diffeomorphic-1 | null | null | https://openreview.net/forum?id=iy2b91gvZpf | https://openreview.net/pdf?id=iy2b91gvZpf | LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face Alignment | We innovatively propose a flexible and consistent cross-annotation face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate the face a... | ['Xiaoying Tang', 'Roger Tam', 'Pujin Cheng', 'Junyan Lyu', 'Huilin Yang'] | 2021-09-29 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-4.75260556e-01 2.63645321e-01 -1.28788009e-01 -7.01336384e-01
-8.44738126e-01 -5.49872577e-01 5.56489408e-01 -6.00180805e-01
-7.94929937e-02 2.67375886e-01 2.34859675e-01 4.54452902e-01
-2.18218621e-02 -4.65499878e-01 -5.36331117e-01 -5.43450773e-01
6.00509234e-02 8.79659235e-01 -2.38889217e-01 -2.01044500... | [13.372685432434082, 0.2617430090904236] |
42d820ae-6a88-4c5c-a3fd-3baf9dd6f19a | adapting-discriminative-reranking-to-grounded | null | null | https://aclanthology.org/P13-1022 | https://aclanthology.org/P13-1022.pdf | Adapting Discriminative Reranking to Grounded Language Learning | null | ['Raymond Mooney', 'Joohyun Kim'] | 2013-08-01 | null | null | null | acl-2013-8 | ['grounded-language-learning'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.420624256134033, 3.6111667156219482] |
61774cc2-83fb-40db-9294-4285a5137e9e | kuaisar-a-unified-search-and-recommendation | 2306.07705 | null | https://arxiv.org/abs/2306.07705v3 | https://arxiv.org/pdf/2306.07705v3.pdf | KuaiSAR: A Unified Search And Recommendation Dataset | The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within academia, prim... | ['Jun Xu', 'Xiao Zhang', 'Yang song', 'Yanan Niu', 'Dewei Leng', 'Xiaoxue Zang', 'Zihua Si', 'Zhongxiang Sun'] | 2023-06-13 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [-3.33634764e-02 -7.30439425e-01 -1.06407082e+00 -2.39065647e-01
-8.86503100e-01 -5.31615257e-01 4.41466600e-01 -4.02318507e-01
-2.44427416e-02 -6.87492117e-02 4.84982103e-01 -6.91712856e-01
-4.38930631e-01 -2.65524119e-01 -5.20791471e-01 -2.18913198e-01
2.06585843e-02 3.29585522e-01 -2.54768312e-01 -3.37922961... | [10.131698608398438, 5.649380683898926] |
d168d054-5cb3-43f0-9313-031d3e6accc5 | path-aware-siamese-graph-neural-network-for | 2208.05781 | null | https://arxiv.org/abs/2208.05781v2 | https://arxiv.org/pdf/2208.05781v2.pdf | Path-aware Siamese Graph Neural Network for Link Prediction | In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks. First, PSG captures both nodes and edge features for given two nodes, namely the structure information of k-neighborhoods and relay paths information of the nodes. Furthermore, a novel multi-task GNN framework with self-... | ['Chunqi Wu', 'Yao Qi', 'Hongyang Chen', 'Zhao Li', 'Jingsong Lv'] | 2022-08-10 | null | null | null | null | ['link-property-prediction'] | ['graphs'] | [-6.11426115e-01 -7.56860971e-02 -9.99392688e-01 -1.98359981e-01
-1.59679905e-01 -3.69554222e-01 3.19355220e-01 2.29534373e-01
4.13891859e-03 1.20690107e+00 -1.22761456e-02 -4.63254690e-01
-9.13628519e-01 -1.02719057e+00 -6.71034038e-01 -2.62664497e-01
-9.75230813e-01 7.09258914e-01 5.20198762e-01 -1.23030677... | [7.2903594970703125, 6.308441162109375] |
bc5a128a-609c-4d04-afe7-e58f93f5c473 | prd-peer-rank-and-discussion-improve-large | 2307.02762 | null | https://arxiv.org/abs/2307.02762v1 | https://arxiv.org/pdf/2307.02762v1.pdf | PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations | Nowadays, the quality of responses generated by different modern large language models (LLMs) are hard to evaluate and compare automatically. Recent studies suggest and predominantly use LLMs as a reference-free metric for open-ended question answering. More specifically, they use the recognized "strongest" LLM as the ... | ['Xinya Du', 'Teerth Patel', 'Ruosen Li'] | 2023-07-06 | null | null | null | null | ['question-answering', 'open-question'] | ['natural-language-processing', 'natural-language-processing'] | [-1.72194362e-01 2.21773222e-01 -2.80134112e-01 -6.74654305e-01
-1.18870413e+00 -7.93904662e-01 5.52762032e-01 6.83612406e-01
-4.94282901e-01 5.66958129e-01 4.65362340e-01 -3.34033400e-01
-2.50990629e-01 -6.12851560e-01 -6.88427806e-01 -1.83036715e-01
3.59594405e-01 4.97720599e-01 3.02123040e-01 -2.30466709... | [11.519058227539062, 8.130666732788086] |
f59affca-850c-4259-8d35-decb1e898cc3 | learning-algebraic-representation-for-2 | 2111.12990 | null | https://arxiv.org/abs/2111.12990v2 | https://arxiv.org/pdf/2111.12990v2.pdf | Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning | Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate between connectionist and cl... | ['Yixin Zhu', 'Song-Chun Zhu', 'Ying Nian Wu', 'Baoxiong Jia', 'Sirui Xie', 'Chi Zhang'] | 2021-11-25 | learning-algebraic-representation-for-1 | https://openreview.net/forum?id=gehXu3kDU1P | https://openreview.net/pdf?id=gehXu3kDU1P | null | ['abstract-algebra', 'systematic-generalization'] | ['reasoning', 'reasoning'] | [ 0.19066758 0.47343105 0.05542615 -0.21675238 0.23598644 -0.5454501
0.84867585 0.29771328 -0.1938598 0.1857712 0.21377936 -0.6292369
-0.7731846 -0.8714844 -0.5022221 -0.5511246 0.09037403 0.86430365
0.18093969 -0.60789603 0.6715214 0.76339996 -1.5559889 0.6104165
1.0568206 1.0004901 0.486... | [10.605350494384766, 2.2728829383850098] |
52815484-74ff-4556-aa29-6f6175d278d7 | constrained-environment-optimization-for | 2305.11260 | null | https://arxiv.org/abs/2305.11260v1 | https://arxiv.org/pdf/2305.11260v1.pdf | Constrained Environment Optimization for Prioritized Multi-Agent Navigation | Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the influence of spatial constraints on agents' performance. Yet hand-designing conducive environment layouts is inefficient and potentially expensive. The goal of this paper is to consider ... | ['Amanda Prorok', 'Zhan Gao'] | 2023-05-18 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 8.06452259e-02 -7.01333210e-02 -2.48016477e-01 1.75617300e-02
-1.95887521e-01 -8.39875579e-01 2.87889630e-01 1.58908039e-01
-8.49751234e-01 9.90140975e-01 -4.00541686e-02 -4.57361579e-01
-8.22317839e-01 -8.79590750e-01 -6.56507254e-01 -8.19580674e-01
-3.39397997e-01 5.00109255e-01 -1.01047471e-01 -4.82141137... | [4.7897748947143555, 2.009540557861328] |
87890072-4568-4493-a014-8868ecbc0153 | automated-diabetic-retinopathy-grading-using | 2004.06334 | null | https://arxiv.org/abs/2004.06334v1 | https://arxiv.org/pdf/2004.06334v1.pdf | Automated Diabetic Retinopathy Grading using Deep Convolutional Neural Network | Diabetic Retinopathy is a global health problem, influences 100 million individuals worldwide, and in the next few decades, these incidences are expected to reach epidemic proportions. Diabetic Retinopathy is a subtle eye disease that can cause sudden, irreversible vision loss. The early-stage Diabetic Retinopathy diag... | ['Prakash. S. Prasad', 'Vaishali Ninawe', 'Saket S. Chaturvedi', 'Kajol Gupta'] | 2020-04-14 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 7.39419609e-02 -1.75352588e-01 -1.14163563e-01 -4.03753966e-01
-3.62160087e-01 -1.77726015e-01 1.38899311e-01 8.89637992e-02
-6.20658755e-01 8.84525836e-01 3.73554714e-02 -2.70704567e-01
-2.09480748e-01 -6.54540837e-01 -1.25002906e-01 -7.72984385e-01
1.26684353e-01 1.78072602e-01 2.13301927e-01 2.27528006... | [15.82994556427002, -3.9909508228302] |
2c407067-2291-4456-8e9e-b0d7582f9add | ubiwear-an-end-to-end-data-driven-framework | 2212.14731 | null | https://arxiv.org/abs/2212.14731v2 | https://arxiv.org/pdf/2212.14731v2.pdf | UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions | It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to cre... | ['Athena Vakali', 'Sofia Yfantidou', 'Asterios Bampakis'] | 2022-12-30 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.91085780e-01 2.19778508e-01 -9.58073199e-01 -2.37751901e-01
-4.74532396e-01 -1.38948869e-03 5.85156441e-01 3.93556237e-01
-2.98234463e-01 8.57291758e-01 9.44426537e-01 -2.35927388e-01
-3.17156821e-01 -1.11445296e+00 -6.29960775e-01 -2.99544871e-01
-1.52629107e-01 2.75658220e-02 -1.21099986e-01 -1.72666639... | [13.59363842010498, 3.362592935562134] |
62e9007a-a3bd-4227-a746-dcede72dbf8d | cogintac-modeling-the-relationships-between | 2205.03540 | null | https://arxiv.org/abs/2205.03540v2 | https://arxiv.org/pdf/2205.03540v2.pdf | CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective | Intention, emotion and action are important psychological factors in human activities, which play an important role in the interaction between individuals. How to model the interaction process between individuals by analyzing the relationship of their intentions, emotions, and actions at the cognitive level is challeng... | ['Yajing Sun', 'Luxi Xing', 'Yuqiang Xie', 'Yue Hu', 'Wei Peng'] | 2022-05-07 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 3.75334658e-02 4.78772342e-01 -1.78948298e-01 -5.71677029e-01
5.18007219e-01 4.39012721e-02 8.50428224e-01 -2.10787565e-01
-1.81830265e-02 4.21060801e-01 7.60881901e-01 2.74676114e-01
-8.06025341e-02 -1.01044810e+00 -2.87207097e-01 -3.95315915e-01
2.61178881e-01 1.02280267e-01 -2.95983434e-01 -4.16090876... | [12.818678855895996, 7.288086414337158] |
fb119a22-ea80-4dc1-91fc-13363fd6c598 | ordered-and-binary-speaker-embedding | 2305.16043 | null | https://arxiv.org/abs/2305.16043v1 | https://arxiv.org/pdf/2305.16043v1.pdf | Ordered and Binary Speaker Embedding | Modern speaker recognition systems represent utterances by embedding vectors. Conventional embedding vectors are dense and non-structural. In this paper, we propose an ordered binary embedding approach that sorts the dimensions of the embedding vector via a nested dropout and converts the sorted vectors to binary codes... | ['Dong Wang', 'Lantian Li', 'Namin Wang', 'Xianglong Wang', 'Jiaying Wang'] | 2023-05-25 | null | null | null | null | ['speaker-recognition', 'speaker-identification'] | ['speech', 'speech'] | [-1.84949309e-01 -6.43613636e-02 -2.90115744e-01 -7.28626370e-01
-6.07054949e-01 -4.13061172e-01 5.78053772e-01 1.30269557e-01
-3.74878079e-01 4.41634178e-01 3.79301488e-01 -3.93960476e-01
-2.60355920e-01 -4.29340988e-01 -4.62425016e-02 -6.56460404e-01
-2.53491968e-01 5.49966335e-01 -1.18286438e-01 1.25631243... | [14.355448722839355, 6.117115020751953] |
d3b4fd05-f516-4b56-8521-73ac532da8f2 | roma-run-time-object-detection-to-maximize | 2210.16083 | null | https://arxiv.org/abs/2210.16083v1 | https://arxiv.org/pdf/2210.16083v1.pdf | ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy | This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in r... | ['Hans Vandierendonck', 'Blesson Varghese', 'JunKyu Lee'] | 2022-10-28 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.44797015e-01 -9.14685905e-01 -1.51771605e-01 -1.65091917e-01
-4.63655800e-01 -6.45177424e-01 2.60574669e-01 2.49231681e-01
-7.26578057e-01 4.59507629e-02 -5.74966311e-01 -3.41358602e-01
1.62165061e-01 -5.06991863e-01 -5.92348874e-01 -2.84899116e-01
-2.46040881e-01 4.70041484e-01 1.14273310e+00 2.02404544... | [8.35452938079834, -0.5287566781044006] |
96287837-a82d-4391-a9ec-c49679279065 | adapting-to-label-shift-with-bias-corrected | null | null | https://openreview.net/forum?id=rkx-wA4YPS | https://openreview.net/pdf?id=rkx-wA4YPS | Adapting to Label Shift with Bias-Corrected Calibration | Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. This is relevant in settings such as medical diagnosis, where a classifier trained to predict disease based ... | ['Anshul Kundaje', 'Amr M. Alexandari', 'Avanti Shrikumar'] | 2019-09-25 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 6.40078485e-01 -1.70269795e-02 -4.06680346e-01 -7.32410192e-01
-6.55070484e-01 -3.50041240e-01 4.06489223e-01 4.08492088e-01
-6.12378776e-01 9.23216999e-01 -2.79525723e-02 -4.96604621e-01
-1.83502764e-01 -6.68473721e-01 -8.14540505e-01 -1.00985336e+00
3.63527745e-01 5.08434832e-01 1.60941437e-01 3.77205253... | [8.631362915039062, 4.461163520812988] |
05652613-15a7-45a9-815d-6534d2d7dbfd | attention-u-net-based-adversarial | 2003.10304 | null | https://arxiv.org/abs/2003.10304v1 | https://arxiv.org/pdf/2003.10304v1.pdf | Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation | Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, g... | ['András Lukács', 'Balázs Maga', 'Gusztáv Gaál'] | 2020-03-23 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 4.02162299e-02 -2.21998561e-02 -1.52339667e-01 -5.55413477e-02
-9.36263323e-01 -5.88132024e-01 1.21511936e-01 8.52991939e-02
-7.16083825e-01 6.18573904e-01 -1.90359786e-01 -9.76973772e-01
-2.87310630e-02 -6.34538770e-01 -3.93778741e-01 -7.22536445e-01
2.72252589e-01 1.09545052e+00 4.30333257e-01 3.73692900... | [15.235322952270508, -2.002157211303711] |
c77cff73-9aa9-49b2-b5bd-99357963efca | nlu-for-game-based-learning-in-real-initial | 2205.13754 | null | https://arxiv.org/abs/2205.13754v1 | https://arxiv.org/pdf/2205.13754v1.pdf | NLU for Game-based Learning in Real: Initial Evaluations | Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment... | ['Lama Nachman', 'Saurav Sahay', 'Eda Okur'] | 2022-05-27 | null | https://aclanthology.org/2022.games-1.4 | https://aclanthology.org/2022.games-1.4.pdf | games-lrec-2022-6 | ['intent-recognition', 'spoken-dialogue-systems'] | ['natural-language-processing', 'speech'] | [ 1.23987995e-01 4.82514232e-01 3.98868531e-01 -4.51599181e-01
-7.31170654e-01 -7.92460382e-01 6.85279906e-01 1.72692239e-01
-4.52281654e-01 3.49297136e-01 2.86227226e-01 -3.84290934e-01
-2.51435280e-01 -7.96486855e-01 -3.28119576e-01 -1.30553052e-01
-3.68419588e-01 1.00270987e+00 6.67396247e-01 -9.33682203... | [12.627395629882812, 7.954371452331543] |
005a57e4-c58a-4453-a511-e9d450e35f26 | modelling-multi-relations-for-convolutional | 2210.11711 | null | https://arxiv.org/abs/2210.11711v1 | https://arxiv.org/pdf/2210.11711v1.pdf | Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding | Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and w... | ['Chun Che Fung', 'Dengya Zhu', 'Kok Wai Wong', 'Sirui Li'] | 2022-10-21 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.72875309e-01 5.59958577e-01 -5.50463021e-01 -4.56452101e-01
-1.39540702e-01 -4.09292966e-01 3.35187078e-01 5.86671174e-01
-3.49641800e-01 6.44107580e-01 6.07513666e-01 -3.28613728e-01
-5.73567986e-01 -1.46858680e+00 -7.41395533e-01 -1.16348580e-01
-3.27883333e-01 7.27967083e-01 1.99613720e-01 -5.94249845... | [8.86294174194336, 8.02209758758545] |
9253d710-615f-4517-887a-a48caa9149a5 | deep-sinogram-completion-with-image-prior-for | 2009.07469 | null | https://arxiv.org/abs/2009.07469v1 | https://arxiv.org/pdf/2009.07469v1.pdf | Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images | Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. I... | ['Xiaomeng Li', 'Zhicheng Zhang', 'Lequan Yu', 'Lei Xing'] | 2020-09-16 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.40423870e-01 6.98953718e-02 6.60576373e-02 -2.70389646e-01
-7.57087350e-01 1.29264630e-02 1.31976813e-01 -3.62692297e-01
-1.57588556e-01 6.12534344e-01 5.24348080e-01 -3.84496063e-01
-1.89593613e-01 -7.33256757e-01 -5.80895126e-01 -8.96338344e-01
3.27547312e-01 3.30639929e-01 2.36628979e-01 1.31139502... | [13.516698837280273, -2.5348141193389893] |
9768b305-f450-4846-895b-8bd848acb1b3 | end-to-end-memristive-htm-system-for-pattern | 2006.11958 | null | https://arxiv.org/abs/2006.11958v1 | https://arxiv.org/pdf/2006.11958v1.pdf | End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction | Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this paper, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that ... | ['Dhireesha Kudithipudi', 'Kevin Gomez', 'Abdullah M. Zyarah'] | 2020-06-22 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 3.82386178e-01 -2.47179836e-01 -6.32707775e-02 -1.65007725e-01
-1.89180486e-02 -2.26727262e-01 3.36763829e-01 1.46068811e-01
-5.46999216e-01 7.82716036e-01 -1.30414084e-01 -1.61824718e-01
-2.41674289e-01 -6.73860252e-01 -9.99554217e-01 -6.92625284e-01
-3.84717107e-01 -1.18445814e-01 7.24332154e-01 -4.89600748... | [8.255464553833008, 2.4775288105010986] |
8a15a4f3-b9a1-453c-aa4f-40982e02d0d3 | csts-conditional-semantic-textual-similarity | 2305.15093 | null | https://arxiv.org/abs/2305.15093v1 | https://arxiv.org/pdf/2305.15093v1.pdf | CSTS: Conditional Semantic Textual Similarity | Semantic textual similarity (STS) has been a cornerstone task in NLP that measures the degree of similarity between a pair of sentences, with applications in information retrieval, question answering, and embedding methods. However, it is an inherently ambiguous task, with the sentence similarity depending on the speci... | ['Karthik Narasimhan', 'Danqi Chen', 'Ashwin Kalyan', 'Tanmay Rajpurohit', 'Victoria Graf', 'Vishvak Murahari', 'Howard Chen', 'Carlos E. Jimenez', 'Ameet Deshpande'] | 2023-05-24 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.74982697e-01 -3.43325257e-01 -3.21964659e-02 -3.98404777e-01
-7.98354328e-01 -7.96587169e-01 7.87436664e-01 7.01034606e-01
-6.13348007e-01 4.58354861e-01 5.95850587e-01 -3.95675212e-01
-2.67034382e-01 -5.81782997e-01 -2.70900041e-01 -3.90504628e-01
7.59501457e-02 5.75726688e-01 3.24810266e-01 -7.40194917... | [10.894889831542969, 8.894818305969238] |
6a3bac85-4be5-4876-a3ab-1b53a49d9177 | learning-the-finer-things-bayesian-structure | 2303.04339 | null | https://arxiv.org/abs/2303.04339v1 | https://arxiv.org/pdf/2303.04339v1.pdf | Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level | Successful machine learning methods require a trade-off between memorization and generalization. Too much memorization and the model cannot generalize to unobserved examples. Too much over-generalization and we risk under-fitting the data. While we commonly measure their performance through cross validation and accurac... | ['Eugene Santos Jr', 'Chase Yakaboski'] | 2023-03-08 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 4.24606562e-01 5.50440371e-01 -5.94659269e-01 -5.95425129e-01
-9.71815944e-01 -4.91267979e-01 4.88953263e-01 4.26937908e-01
-1.66055858e-01 1.45017910e+00 3.86623410e-03 -6.58688605e-01
-8.33922744e-01 -8.26825082e-01 -1.02478099e+00 -7.92818546e-01
-3.51106077e-01 8.47946286e-01 5.21609001e-02 2.10200101... | [8.73215103149414, 6.195498943328857] |
eaf04b4d-5e4b-4b66-ba9a-5d6fa96c62e9 | a-brief-review-of-contrastive-learning | 2306.05528 | null | https://arxiv.org/abs/2306.05528v1 | https://arxiv.org/pdf/2306.05528v1.pdf | A brief review of contrastive learning applied to astrophysics | Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical datasets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multi-dimensional datasets, which has become in... | ['Johan Knapen', 'Regina Sarmiento', 'Marc Huertas-Company'] | 2023-06-08 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 3.41838449e-01 -6.59608766e-02 -5.28791070e-01 -4.24129725e-01
-7.14205384e-01 -8.26994181e-01 1.00735784e+00 -9.09754857e-02
-4.27886784e-01 7.00743318e-01 1.63663000e-01 1.14555415e-02
-7.97462523e-01 -4.30145144e-01 -2.85820276e-01 -1.09631193e+00
4.81125973e-02 5.53643227e-01 -2.38506794e-01 1.59362987... | [7.79077672958374, 3.1715784072875977] |
63961805-f458-4105-9e2e-c5ad22551858 | function-driven-diffusion-for-personalized | 1610.10025 | null | http://arxiv.org/abs/1610.10025v5 | http://arxiv.org/pdf/1610.10025v5.pdf | Function Driven Diffusion for Personalized Counterfactual Inference | We consider the problem of constructing diffusion operators high dimensional
data $X$ to address counterfactual functions $F$, such as individualized
treatment effectiveness. We propose and construct a new diffusion metric $K_F$
that captures both the local geometry of $X$ and the directions of variance of
$F$. The res... | ['Alexander Cloninger'] | 2016-10-31 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-1.44576877e-01 2.31969222e-01 -6.45926714e-01 -1.95958346e-01
-5.10459363e-01 -6.32475913e-01 3.79028559e-01 1.67549223e-01
-5.85172474e-01 1.13245404e+00 5.17795205e-01 -5.72621346e-01
-8.81756306e-01 -9.89543319e-01 -4.16160494e-01 -6.24030054e-01
-7.80627012e-01 4.17538077e-01 -5.77147126e-01 5.05493768... | [8.040497779846191, 5.279520511627197] |
c09b9eb6-1240-4c12-8611-f4b99815d578 | hierarchical-reinforcement-learning-based | 2208.11529 | null | https://arxiv.org/abs/2208.11529v1 | https://arxiv.org/pdf/2208.11529v1.pdf | Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation | The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into th... | ['Zhibo Chen', 'Yue Li', 'Kai Zhang', 'Li Zhang', 'Shiqi Lin', 'Xin Li', 'Guangqi Xie'] | 2022-08-24 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 3.67970556e-01 -2.07959771e-01 -2.61036843e-01 -3.50015283e-01
-3.45769674e-01 1.22287031e-02 6.09890074e-02 -1.35557994e-01
-5.04033387e-01 4.32578921e-01 -5.82246892e-02 -2.85901159e-01
-2.10726038e-01 -9.08494592e-01 -4.53446090e-01 -7.74162233e-01
-1.02351494e-02 1.71628550e-01 7.38988459e-01 1.68762729... | [11.233055114746094, -1.5690338611602783] |
d0444c1a-0698-432f-bb5b-e5e5c5470a4b | seq2rel-a-sequence-to-sequence-based-approach | null | null | https://openreview.net/forum?id=JUrlIlxIEun | https://openreview.net/pdf?id=JUrlIlxIEun | Seq2rel: A sequence-to-sequence-based approach for document-level relation extraction | Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (RE). Document-level RE requires integrating information within and across sentences, capturing complex interactions between mentions of interacting entities. Most document-lev... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.68617034e-01 3.89704287e-01 -1.52156636e-01 -4.62267786e-01
-1.24390304e+00 -9.30313349e-01 7.12404430e-01 5.38506508e-01
-5.41817546e-01 9.10629749e-01 6.48424447e-01 -3.61063123e-01
-2.04293057e-01 -5.03885984e-01 -6.21048808e-01 6.23541363e-02
-3.80424351e-01 7.98609912e-01 4.05000478e-01 -3.44602287... | [9.412043571472168, 8.883505821228027] |
5c522aeb-655d-4764-b5ee-88d1b092fa32 | dual-teacher-integrating-intra-domain-and | 2007.06279 | null | https://arxiv.org/abs/2007.06279v1 | https://arxiv.org/pdf/2007.06279v1.pdf | Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation | Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g.,semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learnin... | ['Pheng-Ann Heng', 'Shujun Wang', 'Lequan Yu', 'Kang Li'] | 2020-07-13 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 3.34949225e-01 4.73734289e-01 -6.52856588e-01 -4.35867369e-01
-1.26126909e+00 -5.38604081e-01 2.64218271e-01 5.58736399e-02
-5.45815051e-01 9.41676080e-01 1.38319448e-01 -7.36339986e-02
1.08070321e-01 -4.06734079e-01 -5.55252969e-01 -8.64582658e-01
4.27843571e-01 7.20677137e-01 2.64883995e-01 1.65166169... | [14.64262580871582, -2.0361151695251465] |
3d1e73c5-7d82-4843-9079-a0f6bcb14eaa | high-fidelity-image-inpainting-with-gan | 2208.11850 | null | https://arxiv.org/abs/2208.11850v1 | https://arxiv.org/pdf/2208.11850v1.pdf | High-Fidelity Image Inpainting with GAN Inversion | Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of a hard constraint in thes... | ['Tiejian Luo', 'Heng Fan', 'Libo Zhang', 'Yongsheng Yu'] | 2022-08-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.38910389e-01 1.88002199e-01 -6.00745901e-03 -3.09560329e-01
-8.78413916e-01 -4.27030116e-01 5.10204256e-01 -8.17921281e-01
3.05356354e-01 8.07519913e-01 3.61988753e-01 6.63387105e-02
3.04667294e-01 -9.53946054e-01 -1.11073935e+00 -7.25156009e-01
7.84564972e-01 7.44486153e-02 -2.47953698e-01 -3.76976252... | [11.43864631652832, -1.0613371133804321] |
38f97498-ec3a-4352-9832-7c6ea0a30861 | trex-learning-execution-semantics-from-micro | 2012.08680 | null | https://arxiv.org/abs/2012.08680v3 | https://arxiv.org/pdf/2012.08680v3.pdf | Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity | Detecting semantically similar functions -- a crucial analysis capability with broad real-world security usages including vulnerability detection, malware lineage, and forensics -- requires understanding function behaviors and intentions. This task is challenging as semantically similar functions can be implemented dif... | ['Baishakhi Ray', 'Suman Jana', 'Junfeng Yang', 'Zhou Xuan', 'Kexin Pei'] | 2020-12-16 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-7.62833357e-02 -6.47214532e-01 -6.63759410e-01 -5.54696798e-01
-7.20744133e-01 -1.08274436e+00 2.85698295e-01 2.27344915e-01
-2.05226049e-01 2.08617032e-01 2.35727459e-01 -8.51310432e-01
4.97238457e-01 -8.81033599e-01 -1.12368238e+00 -1.51025519e-01
-3.85571986e-01 6.79637939e-02 3.60429198e-01 -1.95965961... | [7.2763519287109375, 7.833011150360107] |
495ff350-bf9c-41b4-9f8b-b1c3209d8a17 | self-supervised-spatiotemporal-representation | 2112.05883 | null | https://arxiv.org/abs/2112.05883v3 | https://arxiv.org/pdf/2112.05883v3.pdf | Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity | Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. This work exploits an essential yet under-explored property of videos, the video continuity, to obtain supervision signals for self-supervised represe... | ['Yang Wang', 'Juwei Lu', 'Peng Dai', 'Lizhe Chen', 'Zhixiang Chi', 'Niamul Quader', 'Hanwen Liang'] | 2021-12-11 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 3.97563875e-01 -1.71483517e-01 -9.83216047e-01 -3.30814689e-01
-4.31833595e-01 -2.38138050e-01 7.50902057e-01 -8.23733285e-02
1.14584044e-02 5.45202732e-01 6.88977122e-01 7.43209571e-02
-2.07055017e-01 -3.10159296e-01 -8.66940558e-01 -7.69524992e-01
-1.58036709e-01 -1.44210324e-01 2.30564743e-01 6.77038741... | [8.67231559753418, 0.6995766758918762] |
17e8a2f4-a879-4179-acdf-05e60f661162 | no-regret-learning-in-unknown-games-with | 1909.08540 | null | https://arxiv.org/abs/1909.08540v2 | https://arxiv.org/pdf/1909.08540v2.pdf | No-Regret Learning in Unknown Games with Correlated Payoffs | We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable in many real-world scenarios. Bandit feedback alone, i.e., observ... | ['Andreas Krause', 'Maryam Kamgarpour', 'Pier Giuseppe Sessa', 'Ilija Bogunovic'] | 2019-09-18 | no-regret-learning-in-unknown-games-with-1 | http://papers.nips.cc/paper/9514-no-regret-learning-in-unknown-games-with-correlated-payoffs | http://papers.nips.cc/paper/9514-no-regret-learning-in-unknown-games-with-correlated-payoffs.pdf | neurips-2019-12 | ['movie-recommendation'] | ['miscellaneous'] | [-8.98908451e-02 -9.39285681e-02 -4.75115120e-01 1.45156682e-01
-1.17313313e+00 -7.36713231e-01 2.31426895e-01 1.93413794e-01
-6.52909279e-01 1.16611385e+00 2.19257064e-02 -4.86607760e-01
-7.60829508e-01 -8.55647326e-01 -9.10864294e-01 -9.15752411e-01
-2.87146926e-01 8.26579213e-01 2.54810959e-01 -2.26619124... | [4.478400230407715, 3.2064638137817383] |
a8587880-5afa-40d2-b54f-fd1f552ef6b5 | cat-localization-and-identification-cascade-1 | 2301.01970 | null | https://arxiv.org/abs/2301.01970v6 | https://arxiv.org/pdf/2301.01970v6.pdf | CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection | Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling me... | ['Fanbing Lv', 'Hongli Liu', 'Thomas H. Li', 'Ying WEI', 'Jiaqi Fan', 'Yuefeng Wang', 'Shuailei Ma'] | 2023-01-05 | cat-localization-and-identification-cascade | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-world-object-detection'] | ['computer-vision'] | [ 2.01240301e-01 -7.45800510e-02 3.40473801e-02 -2.26917297e-01
-6.43066108e-01 -7.19820261e-01 4.58253771e-01 -6.13052472e-02
-5.04125059e-01 4.76696730e-01 -2.88011581e-01 -4.57650460e-02
3.24217677e-01 -6.28019750e-01 -7.84105122e-01 -7.51873136e-01
3.45745742e-01 5.59644163e-01 8.81177366e-01 4.34377976... | [9.386089324951172, 1.3499833345413208] |
c7c7085a-048f-4f12-99c8-7f47a52e9452 | event-detection-in-coarsely-annotated-sports | 2004.06172 | null | https://arxiv.org/abs/2004.06172v1 | https://arxiv.org/pdf/2004.06172v1.pdf | Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions | In problems such as sports video analytics, it is difficult to obtain accurate frame level annotations and exact event duration because of the lengthy videos and sheer volume of video data. This issue is even more pronounced in fast-paced sports such as ice hockey. Obtaining annotations on a coarse scale can be much mo... | ['John Zelek', 'Pascale Walters', 'Mehrnaz Fani', 'Kanav Vats', 'David A. Clausi'] | 2020-04-13 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 8.80495161e-02 -3.67759764e-01 6.24532104e-02 -2.48550981e-01
-9.75048006e-01 -6.16341531e-01 2.46092379e-01 3.18905801e-01
-9.08360183e-01 5.62429667e-01 2.66521066e-01 3.15163374e-01
6.45220745e-04 -4.96765971e-01 -9.09608364e-01 -4.94852841e-01
-4.73998129e-01 1.84806794e-01 8.69608700e-01 -1.32162377... | [8.034278869628906, 0.20550447702407837] |
6ab9f551-4f78-450c-a33d-1f60d4475155 | diffusion-based-signal-refiner-for-speech | 2305.05857 | null | https://arxiv.org/abs/2305.05857v2 | https://arxiv.org/pdf/2305.05857v2.pdf | Diffusion-based Signal Refiner for Speech Separation | We have developed a diffusion-based speech refiner that improves the reference-free perceptual quality of the audio predicted by preceding single-channel speech separation models. Although modern deep neural network-based speech separation models have show high performance in reference-based metrics, they often produce... | ['Yuki Mitsufuji', 'Kazuki Shimada', 'Shusuke Takahashi', 'Yuichiro Koyama', 'Masato Hirano'] | 2023-05-10 | null | null | null | null | ['speech-separation', 'speech-enhancement'] | ['speech', 'speech'] | [ 4.01540361e-02 5.76606467e-02 3.77801329e-01 -2.77649760e-01
-1.13793838e+00 -3.40120971e-01 5.69387436e-01 -2.41057217e-01
-1.00272983e-01 3.89337927e-01 7.63205469e-01 6.84465617e-02
-2.27819815e-01 -4.10748214e-01 -3.91762257e-01 -9.30279136e-01
2.64106184e-01 6.60983026e-02 1.98507950e-01 -2.53181428... | [15.051956176757812, 5.996153354644775] |
44734bef-75ec-4616-84df-d8907bb96a2c | an-effective-two-branch-model-based-deep | 1905.05404 | null | https://arxiv.org/abs/1905.05404v2 | https://arxiv.org/pdf/1905.05404v2.pdf | An Effective Two-Branch Model-Based Deep Network for Single Image Deraining | Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing. Conventional methods rely on some heuristics to handcraft various priors to remove or separate the rain effects from an image. Recent deep learning models are proposed to learn en... | ['Anton Van Den Hengel', 'Jie Yang', 'Dehua Xie', 'Dong Gong', 'Yinglong Wang', 'Qinfeng Shi', 'Bing Zeng'] | 2019-05-14 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.28935337e-01 -3.82977217e-01 4.76940513e-01 -6.05886221e-01
-2.67997533e-01 -1.43572703e-01 2.49863982e-01 -1.97548479e-01
-3.45109373e-01 8.80519569e-01 -1.57185912e-01 -1.98882282e-01
1.94996536e-01 -7.88073897e-01 -8.10104430e-01 -1.11659539e+00
5.51230684e-02 6.55759498e-02 3.31023484e-01 -4.18221265... | [10.911825180053711, -3.2311511039733887] |
80a3f901-e5e9-4887-97bd-fa0d50d41c94 | dynamic-em-ray-tracing-for-large-urban-scenes | 2303.10521 | null | https://arxiv.org/abs/2303.10521v2 | https://arxiv.org/pdf/2303.10521v2.pdf | Dynamic EM Ray Tracing for Large Urban Scenes with Multiple Receivers | Radio applications are increasingly being used in urban environments for cellular radio systems and safety applications that use vehicle-vehicle, and vehicle-to-infrastructure. We present a novel ray tracing-based radio propagation algorithm that can handle large urban scenes with hundreds or thousands of dynamic objec... | ['Dinesh Manocha', 'Ruichen Wang'] | 2023-03-19 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-2.54532695e-01 -2.37836644e-01 3.81957799e-01 9.79268402e-02
-7.23550141e-01 -2.03114778e-01 4.63187099e-01 1.08677156e-01
-5.82711816e-01 1.23536026e+00 -1.88212633e-01 -8.59296024e-01
-1.09355912e-01 -1.27589178e+00 -4.16554660e-01 -6.90535963e-01
-9.56288636e-01 6.81332946e-01 1.00543427e+00 -3.55815113... | [6.238800048828125, 1.1701035499572754] |
1ba548da-8547-4715-88c8-0c1f89ceb601 | voice-conversion-based-speaker-normalization | 2105.01786 | null | https://arxiv.org/abs/2105.01786v1 | https://arxiv.org/pdf/2105.01786v1.pdf | Voice Conversion Based Speaker Normalization for Acoustic Unit Discovery | Discovering speaker independent acoustic units purely from spoken input is known to be a hard problem. In this work we propose an unsupervised speaker normalization technique prior to unit discovery. It is based on separating speaker related from content induced variations in a speech signal with an adversarial contras... | ['Reinhold Häb-Umbach', 'Janek Ebbers', 'Thomas Glarner'] | 2021-05-04 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 5.35110950e-01 2.99722552e-01 4.41899821e-02 -5.95810592e-01
-1.12597477e+00 -6.05179429e-01 5.87054908e-01 -1.46489263e-01
-5.31104982e-01 7.08002567e-01 4.27987725e-01 -1.93398193e-01
2.84138024e-01 -3.05668145e-01 -6.92248762e-01 -9.67276454e-01
1.34544045e-01 7.11621106e-01 1.95426345e-02 -3.98465186... | [14.508567810058594, 6.555363655090332] |
dd686aed-88e7-45ec-a848-dd96bb31da79 | a-predicate-function-argument-annotation-of | null | null | https://aclanthology.org/2020.emnlp-main.167 | https://aclanthology.org/2020.emnlp-main.167.pdf | A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression | Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is pr... | ['Ping Li', 'Kangjie Zheng', 'Xin Wang', 'Zoey Liu', 'Wenyue Hua', 'Mingming Sun'] | null | null | null | null | emnlp-2020-11 | ['open-information-extraction'] | ['natural-language-processing'] | [ 0.07548906 0.51571226 0.0525011 -0.6738369 -0.41833875 -0.513302
0.42613953 0.03851383 -0.10488481 0.7438418 0.21748506 -0.14223172
-0.3892251 -0.9645936 -0.63819605 0.01389639 0.15275995 0.47230887
0.38094798 -0.6204056 0.15781455 -0.02391677 -1.6855751 0.88226074
1.065783 0.9266774 0.45... | [9.671782493591309, 8.686400413513184] |
d82fcca7-c8ba-4b3d-998f-bca5d4c7bd32 | will-my-robot-achieve-my-goals-predicting-the | 2211.16462 | null | https://arxiv.org/abs/2211.16462v1 | https://arxiv.org/pdf/2211.16462v1.pdf | Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target | As an autonomous system performs a task, it should maintain a calibrated estimate of the probability that it will achieve the user's goal. If that probability falls below some desired level, it should alert the user so that appropriate interventions can be made. This paper considers settings where the user's goal is sp... | ['Thomas G. Dietterich', 'Alexander Guyer'] | 2022-11-29 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 7.23517612e-02 3.70339096e-01 -4.34800476e-01 -3.74135256e-01
-1.07830667e+00 -4.69665945e-01 3.45112383e-01 1.70576230e-01
-4.32972699e-01 1.05382884e+00 -4.78894979e-01 -6.59713686e-01
-4.37985003e-01 -9.41627443e-01 -8.09027374e-01 -6.84102833e-01
-3.20306689e-01 3.47888112e-01 2.96358347e-01 -7.97464177... | [4.476988792419434, 2.5625369548797607] |
f6e77686-c852-402a-8121-7a5c4ac2001c | memvit-memory-augmented-multiscale-vision | 2201.08383 | null | https://arxiv.org/abs/2201.08383v2 | https://arxiv.org/pdf/2201.08383v2.pdf | MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition | While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new st... | ['Christoph Feichtenhofer', 'Jitendra Malik', 'Bo Xiong', 'Haoqi Fan', 'Karttikeya Mangalam', 'Yanghao Li', 'Chao-yuan Wu'] | 2022-01-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-anticipation'] | ['computer-vision'] | [ 7.01915547e-02 -3.10045749e-01 -3.91772181e-01 -2.49820113e-01
-7.91484594e-01 -3.69404346e-01 5.41133940e-01 -2.37681210e-01
-5.48672140e-01 2.45727375e-01 3.69673610e-01 -2.15987802e-01
3.29440236e-01 -4.90424752e-01 -6.38141394e-01 -4.16386843e-01
4.10532057e-02 1.99143797e-01 6.19882405e-01 1.89536959... | [8.832599639892578, 0.41801217198371887] |
0cb30041-f611-4fc0-b597-ddab4c8ce22d | semantic-novelty-detection-via-relational | 2207.08699 | null | https://arxiv.org/abs/2207.08699v2 | https://arxiv.org/pdf/2207.08699v2.pdf | Semantic Novelty Detection via Relational Reasoning | Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving or healthcare, where it is crucial to recognize unknown objects at deployment time and issue a warning to the user accordingly. Despite the i... | ['Tatiana Tommasi', 'Silvia Bucci', 'Francesco Cappio Borlino'] | 2022-07-18 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.57520252e-01 3.17869484e-01 -2.28654504e-01 -5.01776695e-01
-3.45909297e-01 -4.84932959e-01 5.01427889e-01 6.05261624e-01
-4.13771272e-01 6.03496432e-01 -2.65371829e-01 -4.85094160e-01
-4.88310695e-01 -1.15448034e+00 -7.86579788e-01 -4.55858201e-01
-7.94708058e-02 5.79284489e-01 4.97142255e-01 -2.16669232... | [9.687284469604492, 3.0115294456481934] |
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