paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5f674d43-2bd2-49eb-b24d-fcc5e19241de | who-would-be-interested-in-services-an-entity | 2305.18780 | null | https://arxiv.org/abs/2305.18780v1 | https://arxiv.org/pdf/2305.18780v1.pdf | Who Would be Interested in Services? An Entity Graph Learning System for User Targeting | With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a fe... | ['Guannan Zhang', 'Jinjie Gu', 'Zhiqiang Zhang', 'Yue Shen', 'Xiaoyan Yang', 'Binbin Hu', 'Dan Yang'] | 2023-05-30 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-1.83865815e-01 -2.23498307e-02 -7.70115077e-01 -2.34047994e-01
-5.48727989e-01 -4.11932826e-01 2.28669465e-01 2.72201210e-01
-2.51116455e-01 3.29224259e-01 -1.52543366e-01 -6.21559978e-01
-2.78411210e-01 -9.67470348e-01 -1.49897754e-01 -3.74089271e-01
-1.89332455e-01 6.17894232e-01 5.81015170e-01 -2.80053854... | [10.095008850097656, 5.611320495605469] |
fbfd6f3d-bfd5-4a23-a753-31e3ac750af0 | spatially-and-color-consistent-environment | 2108.07903 | null | https://arxiv.org/abs/2108.07903v1 | https://arxiv.org/pdf/2108.07903v1.pdf | Spatially and color consistent environment lighting estimation using deep neural networks for mixed reality | The representation of consistent mixed reality (XR) environments requires adequate real and virtual illumination composition in real-time. Estimating the lighting of a real scenario is still a challenge. Due to the ill-posed nature of the problem, classical inverse-rendering techniques tackle the problem for simple lig... | ['Cristina Nader Vasconcelos', 'Anselmo Antunes Montenegro', 'Esteban Walter Gonzalez Clua', 'Bruno Augusto Dorta Marques'] | 2021-08-17 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.45105162e-01 -2.36105546e-01 7.49434650e-01 -6.42904997e-01
-3.34764421e-01 -3.90308142e-01 3.81610096e-01 -4.74727064e-01
-4.19652581e-01 7.10775197e-01 -2.53471702e-01 -2.69948632e-01
8.26086551e-02 -9.36344743e-01 -8.77250671e-01 -6.75038338e-01
1.84924319e-01 2.84177929e-01 -3.44030142e-01 -4.06176358... | [9.694299697875977, -2.9799106121063232] |
8b08521e-d7a0-4675-9627-11d14a283724 | delta-degradation-free-fully-test-time | 2301.13018 | null | https://arxiv.org/abs/2301.13018v1 | https://arxiv.org/pdf/2301.13018v1.pdf | DELTA: degradation-free fully test-time adaptation | Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution. Several efforts have been devoted to improving adaptation performance. However, we find that two unfavorable defect... | ['Shu-Tao Xia', 'Chen Chen', 'Bowen Zhao'] | 2023-01-30 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.37831223e-01 -5.01371980e-01 -3.35565835e-01 -5.60467303e-01
-6.79108143e-01 -5.53688049e-01 3.64971042e-01 1.77971587e-01
-3.33407342e-01 9.82653499e-01 -1.48008823e-01 -1.91941649e-01
-3.46582234e-01 -7.32834339e-01 -5.47496080e-01 -8.92906964e-01
1.43495440e-01 6.39014244e-01 3.51503938e-01 -1.57486260... | [9.770751953125, 3.3218913078308105] |
e5d32496-2c71-4fd5-aeb6-1ec7cfdcc872 | uavm-a-unified-model-for-audio-visual | 2208.00061 | null | https://arxiv.org/abs/2208.00061v2 | https://arxiv.org/pdf/2208.00061v2.pdf | UAVM: Towards Unifying Audio and Visual Models | Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification accuracy of 65.8% on VGGSound. More interestingly, we also find a ... | ['James Glass', 'Andrew Rouditchenko', 'Alexander H. Liu', 'Yuan Gong'] | 2022-07-29 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [-1.80351764e-01 -2.76919007e-01 -2.15081856e-01 -3.06036007e-02
-9.58058596e-01 -6.17144644e-01 6.96862698e-01 3.71438205e-01
1.08026564e-01 4.00301844e-01 2.21907839e-01 -1.33985832e-01
9.58837662e-03 -4.30928648e-01 -6.68194473e-01 -5.07043123e-01
-3.51957709e-01 -3.14155787e-01 5.60365021e-01 -1.23257093... | [14.582504272460938, 4.941234111785889] |
f3609091-a7d7-46fa-b2bf-aad3b02e4f34 | a-neural-layered-model-for-nested-named | null | null | https://aclanthology.org/N18-1131 | https://aclanthology.org/N18-1131.pdf | A Neural Layered Model for Nested Named Entity Recognition | Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel n... | ['Meizhi Ju', 'Sophia Ananiadou', 'Makoto Miwa'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-3.57790649e-01 5.41542292e-01 1.03399217e-01 -4.30310726e-01
-5.47778428e-01 -7.40418017e-01 3.37830245e-01 5.37889183e-01
-8.97625029e-01 6.25387371e-01 4.03656781e-01 -3.59892964e-01
3.06445658e-01 -1.13933301e+00 -8.31611156e-01 -2.70831287e-01
-2.77320474e-01 3.01016837e-01 4.19524491e-01 -1.40516698... | [9.573051452636719, 9.495382308959961] |
688e48a1-da4d-4d36-b3d7-4162b26dc5ad | robust-multilingual-named-entity-recognition | 1701.09123 | null | http://arxiv.org/abs/1701.09123v1 | http://arxiv.org/pdf/1701.09123v1.pdf | Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features | We present a multilingual Named Entity Recognition approach based on a robust
and general set of features across languages and datasets. Our system combines
shallow local information with clustering semi-supervised features induced on
large amounts of unlabeled text. Understanding via empirical experimentation
how to e... | ['Rodrigo Agerri', 'German Rigau'] | 2017-01-31 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.59398454e-01 -2.31781021e-01 -2.07093686e-01 -7.77367353e-01
-1.14388514e+00 -9.10965979e-01 1.00240982e+00 2.25401744e-01
-8.99618268e-01 8.64968717e-01 3.55068654e-01 -2.24511370e-01
6.14044145e-02 -2.78027803e-01 -3.79091263e-01 -4.25254792e-01
-2.26830974e-01 5.87250352e-01 1.54838130e-01 -1.34284511... | [10.253564834594727, 9.77231502532959] |
2172fecd-b9f8-4bc5-8832-fd98bb378b1b | trader-company-method-a-metaheuristic-for | 2012.10215 | null | https://arxiv.org/abs/2012.10215v1 | https://arxiv.org/pdf/2012.10215v1.pdf | Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction | Investors try to predict returns of financial assets to make successful investment. Many quantitative analysts have used machine learning-based methods to find unknown profitable market rules from large amounts of market data. However, there are several challenges in financial markets hindering practical applications o... | ['Kei Nakagawa', 'Kentaro Imajo', 'Kentaro Minami', 'Katsuya Ito'] | 2020-12-18 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-3.85042220e-01 1.42775318e-02 -2.83961296e-01 -2.13506356e-01
-3.48025739e-01 -8.82548213e-01 3.09316486e-01 -1.33561432e-01
-2.05520272e-01 8.76566350e-01 -3.30736130e-01 -6.29787147e-01
-2.37003148e-01 -1.18640435e+00 -7.35564768e-01 -3.72236848e-01
-1.95674390e-01 9.01013315e-01 2.52792805e-01 -2.38010839... | [4.547447681427002, 4.166967391967773] |
1fe431de-646e-4d30-b1f2-be6f5219c509 | object-region-video-transformers-1 | 2110.06915 | null | https://arxiv.org/abs/2110.06915v3 | https://arxiv.org/pdf/2110.06915v3.pdf | Object-Region Video Transformers | Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, although objects can be essential for recognizing actions. In this work, we present Object-Region Video Transformers (ORViT), an \emph{object-ce... | ['Amir Globerson', 'Trevor Darrell', 'Anna Rohrbach', 'Gal Chechik', 'Amir Bar', 'Karttikeya Mangalam', 'Elad Ben-Avraham', 'Roei Herzig'] | 2021-10-13 | object-region-video-transformers | http://openaccess.thecvf.com//content/CVPR2022/html/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 1.47555918e-01 -2.20138580e-01 -2.68305331e-01 -1.22217223e-01
-2.18504146e-01 -5.04596591e-01 9.57240403e-01 -2.46571779e-01
-2.27382705e-01 1.96697429e-01 5.34267545e-01 9.06984210e-02
9.96174961e-02 -7.56405950e-01 -1.01877558e+00 -6.40967429e-01
-2.11222265e-02 2.03671783e-01 9.01137650e-01 -1.82941243... | [8.771844863891602, 0.5971102714538574] |
0fa9f063-9095-4451-9a72-25596d43b385 | insta-beeer-explicit-error-estimation-and | 2306.16132 | null | https://arxiv.org/abs/2306.16132v1 | https://arxiv.org/pdf/2306.16132v1.pdf | INSTA-BEEER: Explicit Error Estimation and Refinement for Fast and Accurate Unseen Object Instance Segmentation | Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over- or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we pro... | ['Kyoobin Lee', 'Jaemo Maeng', 'Sungho Shin', 'Joosoon Lee', 'KangMin Kim', 'Sangbeom Lee', 'Seunghyeok Back'] | 2023-06-28 | null | null | null | null | ['unseen-object-instance-segmentation', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.63837004e-01 3.27765435e-01 -5.34192473e-02 -9.40782726e-02
-6.60209119e-01 -4.84058142e-01 2.56302357e-02 3.32230508e-01
-3.98797065e-01 6.79883003e-01 -5.83860040e-01 -3.72740701e-02
3.72866504e-02 -6.43628061e-01 -8.42747390e-01 -5.28960824e-01
1.27130866e-01 7.36881852e-01 9.82570827e-01 1.57310031... | [9.302469253540039, -0.36331555247306824] |
fa79eb1f-1edc-436c-9576-ac0e622ad71c | a-comparative-study-for-time-to-event | null | null | http://jeeemi.org/index.php/jeeemi/article/view/225 | http://jeeemi.org/index.php/jeeemi/article/view/225/94 | A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques | Heart Failure, an ailment in which the heart isn’t functioning as effectively as it should, causing in an insufficient cardiac output. The effectual functioning of the human body is dependent on how well the heart is able to pump oxygenated, and nutrient rich blood to the tissues and cells. Heart failure falls into the... | ['Saurav Mishra'] | 2022-07-25 | null | null | null | journal-of-electronics-electromedical-1 | ['survival-analysis'] | ['miscellaneous'] | [-1.67117849e-01 -9.68321413e-02 -2.33862251e-01 -1.06246345e-01
2.71178901e-01 -1.51617795e-01 6.72272891e-02 4.09048885e-01
-4.95163023e-01 9.93441463e-01 2.02931300e-01 -5.31415939e-01
-3.50615203e-01 -8.96995664e-01 4.33391705e-02 -7.82828808e-01
-4.64216113e-01 5.39092481e-01 4.63697463e-02 2.71981396... | [14.070279121398926, 3.137432336807251] |
8cc84344-acd0-4f89-84ba-519052edf2d5 | evolutionary-multi-objective-reinforcement | 2202.12028 | null | https://arxiv.org/abs/2202.12028v1 | https://arxiv.org/pdf/2202.12028v1.pdf | Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing | This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the ba... | ['Bowen Zhao', 'Zhiwen Xiao', 'Penglin Dai', 'Shouxi Luo', 'Xinhan Wang', 'Huanlai Xing', 'Fuhong Song'] | 2022-02-24 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 4.36156429e-02 -1.70778275e-01 -5.60717881e-01 2.42846578e-01
3.58114252e-03 -5.80738604e-01 -1.66459233e-01 -2.62626350e-01
-6.48658991e-01 1.08250117e+00 -3.95336419e-01 -4.10294414e-01
-6.65558279e-01 -6.32115364e-01 -3.17089498e-01 -1.11148036e+00
-1.34448513e-01 3.46613467e-01 1.71273410e-01 -2.80875206... | [5.864119529724121, 1.6605379581451416] |
37e6cad4-a25d-42a7-898b-7a8d0bb9c9a8 | semi-supervised-sparse-representation-based | 1609.03279 | null | http://arxiv.org/abs/1609.03279v2 | http://arxiv.org/pdf/1609.03279v2.pdf | Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples | This paper addresses the problem of face recognition when there is only few,
or even only a single, labeled examples of the face that we wish to recognize.
Moreover, these examples are typically corrupted by nuisance variables, both
linear (i.e., additive nuisance variables such as bad lighting, wearing of
glasses) and... | ['Yuan Gao', 'Jiayi Ma', 'Alan L. Yuille'] | 2016-09-12 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.10553899e-01 -3.11766297e-01 -1.67826012e-01 -6.22790396e-01
-5.07534862e-01 -3.48837614e-01 4.10070747e-01 -7.30266452e-01
7.16757849e-02 8.86115968e-01 6.95429335e-04 2.46140450e-01
1.69763625e-01 -5.34072995e-01 -8.14390004e-01 -9.86705005e-01
-7.32688084e-02 3.09298635e-01 -5.25048614e-01 6.35818392... | [12.675947189331055, 0.35900911688804626] |
1217ac5b-6d77-447a-9c45-ea2d716ac2cc | salt-subspace-alignment-as-an-auxiliary | 1906.04338 | null | https://arxiv.org/abs/1906.04338v2 | https://arxiv.org/pdf/1906.04338v2.pdf | SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation | Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adaptation include: (a) maximizing performance on the target, and (b) aligning the source and target domains. Traditionally, these tasks have eith... | ['Pavan Turaga', 'Kowshik Thopalli', 'Jayaraman J. Thiagarajan', 'Rushil Anirudh'] | 2019-06-11 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.86383808e-01 4.23404723e-02 -2.43092448e-01 -5.83789825e-01
-1.04308856e+00 -8.16170275e-01 9.24791276e-01 1.34503037e-01
-4.73599464e-01 8.27882409e-01 3.24824601e-01 -6.85258061e-02
-3.16544026e-01 -4.70829219e-01 -8.63156796e-01 -1.02007067e+00
2.19559267e-01 8.55349064e-01 3.10691837e-02 -1.44664511... | [10.363542556762695, 3.137275457382202] |
57ee11c3-4c0d-4d1c-b80d-0a4ae9e0ab9b | expanding-synthetic-real-world-degradations | 2305.02660 | null | https://arxiv.org/abs/2305.02660v1 | https://arxiv.org/pdf/2305.02660v1.pdf | Expanding Synthetic Real-World Degradations for Blind Video Super Resolution | Video super-resolution (VSR) techniques, especially deep-learning-based algorithms, have drastically improved over the last few years and shown impressive performance on synthetic data. However, their performance on real-world video data suffers because of the complexity of real-world degradations and misaligned video ... | ['Sunil Jaiswal', 'Philipp Slusallek', 'Klaus Illgner-Fehns', 'Noshaba Cheema', 'Sadbhawna', 'Mehran Jeelani'] | 2023-05-04 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 4.41477686e-01 -5.30654192e-01 2.33827040e-01 -1.88027188e-01
-1.05639434e+00 -1.13045484e-01 4.64221895e-01 -6.58354700e-01
-4.40358400e-01 1.06059039e+00 2.97877938e-01 -9.58747119e-02
9.75835398e-02 -4.93359059e-01 -9.60319638e-01 -6.87010288e-01
-2.66521126e-01 -6.92977905e-02 2.76767552e-01 -3.35568845... | [11.097443580627441, -1.9686071872711182] |
8ebb9647-873c-4921-a505-bc69e80ebfa4 | uncertain-machine-ethical-decisions-using | 2305.01424 | null | https://arxiv.org/abs/2305.01424v1 | https://arxiv.org/pdf/2305.01424v1.pdf | Uncertain Machine Ethical Decisions Using Hypothetical Retrospection | We propose the use of the hypothetical retrospection argumentation procedure, developed by Sven Hansson, to improve existing approaches to machine ethical reasoning by accounting for probability and uncertainty from a position of Philosophy that resonates with humans. Actions are represented with a branching set of pot... | ['Mengwei Xu', 'Ramon Fraga Pereira', 'Louise Dennis', 'Simon Kolker'] | 2023-05-02 | null | null | null | null | ['ethics', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [ 2.35608891e-01 1.30091345e+00 -1.65291697e-01 -6.12660289e-01
1.01486675e-03 -6.53123617e-01 1.17988920e+00 2.85143286e-01
-4.28688437e-01 8.85187685e-01 6.97756410e-01 -8.27338099e-01
-5.05596042e-01 -6.52230024e-01 -3.02470446e-01 -2.94994682e-01
2.29619414e-01 6.21523619e-01 1.23201564e-01 -4.33346421... | [8.981452941894531, 6.2834672927856445] |
5da67698-7370-4dc3-9285-e2474e1bd21b | monocular-3d-object-detection-with-bounding | 2304.01289 | null | https://arxiv.org/abs/2304.01289v1 | https://arxiv.org/pdf/2304.01289v1.pdf | Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver | The main challenge of monocular 3D object detection is the accurate localization of 3D center. Motivated by a new and strong observation that this challenge can be remedied by a 3D-space local-grid search scheme in an ideal case, we propose a stage-wise approach, which combines the information flow from 2D-to-3D (3D bo... | ['Tianfu Wu', 'Guo-Jun Qi', 'Nan Xue', 'Kelvin Cheng', 'Ce Zheng', 'Xianpeng Liu'] | 2023-04-03 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-1.81745544e-01 1.76614989e-02 -6.16817735e-02 -3.43075901e-01
-1.22346795e+00 -6.33614838e-01 6.17317736e-01 -2.23273844e-01
-1.58700436e-01 3.20713483e-02 1.44061847e-02 -1.17054805e-01
3.04152369e-01 -4.58139181e-01 -8.49608779e-01 -5.78876495e-01
3.82833153e-01 5.86970389e-01 6.46748126e-01 -1.09340377... | [7.757711887359619, -2.608560085296631] |
47bda9d8-d3e0-4e34-a137-719816b4d220 | a-corpus-with-multi-level-annotations-of | 1806.04185 | null | http://arxiv.org/abs/1806.04185v1 | http://arxiv.org/pdf/1806.04185v1.pdf | A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature | We present a corpus of 5,000 richly annotated abstracts of medical articles
describing clinical randomized controlled trials. Annotations include
demarcations of text spans that describe the Patient population enrolled, the
Interventions studied and to what they were Compared, and the Outcomes measured
(the `PICO' elem... | ['Byron C. Wallace', 'Yinfei Yang', 'Roma Patel', 'Ani Nenkova', 'Junyi Jessy Li', 'Iain J. Marshall', 'Benjamin Nye'] | 2018-06-11 | a-corpus-with-multi-level-annotations-of-1 | https://aclanthology.org/P18-1019 | https://aclanthology.org/P18-1019.pdf | acl-2018-7 | ['participant-intervention-comparison-outcome', 'pico'] | ['medical', 'natural-language-processing'] | [ 6.28396928e-01 4.08797920e-01 -9.32650685e-01 -1.73811987e-01
-1.22798193e+00 -9.49183702e-01 3.61891091e-01 1.07914996e+00
-5.35674453e-01 9.46823716e-01 8.24792683e-01 -7.82924235e-01
-4.21244144e-01 -7.10318983e-02 -5.30415595e-01 -3.95971447e-01
-2.18039900e-01 7.47285664e-01 -2.57724941e-01 4.35775459... | [8.435112953186035, 8.671392440795898] |
959339c9-b00e-4742-bb86-3b656e594d26 | learning-physical-spatio-temporal-features | 2303.09370 | null | https://arxiv.org/abs/2303.09370v1 | https://arxiv.org/pdf/2303.09370v1.pdf | Learning Physical-Spatio-Temporal Features for Video Shadow Removal | Shadow removal in a single image has received increasing attention in recent years. However, removing shadows over dynamic scenes remains largely under-explored. In this paper, we propose the first data-driven video shadow removal model, termed PSTNet, by exploiting three essential characteristics of video shadows, i.e... | ['Huazhu Fu', 'Lei Zhu', 'Yefan Xiao', 'Liang Wan', 'Zhihao Chen'] | 2023-03-16 | null | null | null | null | ['shadow-removal', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 5.10693789e-01 -4.38828856e-01 3.04145336e-01 -1.46441385e-01
-1.68848634e-01 -1.04118757e-01 3.73736441e-01 -4.41615522e-01
-1.62070513e-01 7.13239968e-01 2.93885380e-01 -2.34367609e-01
-1.36623815e-01 -6.20398939e-01 -7.23524809e-01 -1.12446332e+00
2.62230802e-02 -2.61856258e-01 8.06615114e-01 -3.35336238... | [10.843023300170898, -4.090719699859619] |
bec6042f-8bba-412f-ac54-3be938e4017c | a-generalist-neural-algorithmic-learner | 2209.11142 | null | https://arxiv.org/abs/2209.11142v2 | https://arxiv.org/pdf/2209.11142v2.pdf | A Generalist Neural Algorithmic Learner | The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learn... | ['Petar Veličković', 'Charles Blundell', 'Yaroslav Ganin', 'Beatrice Bevilacqua', 'Andreea Deac', 'Yulia Rubanova', 'Alex Vitvitskyi', 'Matko Bošnjak', 'Andrew Dudzik', 'Róbert Csordás', 'Mehdi Bennani', 'Kyriacos Nikiforou', 'George Papamakarios', 'Vitaly Kurin', 'Borja Ibarz'] | 2022-09-22 | null | null | null | null | ['learning-to-execute'] | ['computer-code'] | [ 5.91158390e-01 1.70484841e-01 -3.13879224e-03 -1.58997253e-01
-6.19428158e-01 -9.17719901e-01 5.52767694e-01 4.88941610e-01
-5.53310156e-01 3.05897743e-01 1.12832882e-01 -7.08042443e-01
-2.90941060e-01 -1.10470390e+00 -1.31380785e+00 -4.59910393e-01
-1.73816562e-01 7.42458403e-01 3.44674945e-01 -2.74221987... | [9.159567832946777, 7.164095878601074] |
891dbcd0-cd61-4f9d-b372-56b41baef927 | learning-stiff-chemical-kinetics-using | 2302.12645 | null | https://arxiv.org/abs/2302.12645v1 | https://arxiv.org/pdf/2302.12645v1.pdf | Learning stiff chemical kinetics using extended deep neural operators | We utilize neural operators to learn the solution propagator for the challenging chemical kinetics equation. Specifically, we apply the deep operator network (DeepONet) along with its extensions, such as the autoencoder-based DeepONet and the newly proposed Partition-of-Unity (PoU-) DeepONet to study a range of example... | ['George Em Karniadakis', 'Bryan T. Susi', 'Hessam Babaee', 'Ameya D. Jagtap', 'Somdatta Goswami'] | 2023-02-23 | null | null | null | null | ['unity'] | ['computer-vision'] | [-3.34095120e-01 -2.86606938e-01 4.42419022e-01 4.65267032e-01
-1.47311240e-01 -4.44896549e-01 6.01828933e-01 5.85613474e-02
-4.79003340e-01 1.04420626e+00 -3.91153842e-01 -2.38109633e-01
-2.19145909e-01 -8.82840693e-01 -5.50398827e-01 -1.23703253e+00
-3.95304918e-01 7.71965981e-01 -2.00759083e-01 -4.22587335... | [6.4808783531188965, 3.4021923542022705] |
2b90afd9-1f90-4512-a6c7-97c32088f7f1 | scene-understanding-for-autonomous | 1903.09761 | null | http://arxiv.org/abs/1903.09761v1 | http://arxiv.org/pdf/1903.09761v1.pdf | Scene Understanding for Autonomous Manipulation with Deep Learning | Over the past few years, deep learning techniques have achieved tremendous
success in many visual understanding tasks such as object detection, image
segmentation, and caption generation. Despite this thriving in computer vision
and natural language processing, deep learning has not yet shown significant
impact in robo... | ['Anh Nguyen'] | 2019-03-23 | null | null | null | null | ['action-understanding', 'affordance-detection'] | ['computer-vision', 'computer-vision'] | [ 2.28154510e-01 1.59593537e-01 -1.78696096e-01 -1.45455554e-01
3.29620093e-02 -4.50797945e-01 6.49381340e-01 -4.43116166e-02
-2.73471028e-01 4.71895367e-01 -1.02003187e-01 -2.66524553e-01
-6.80689607e-03 -6.44755244e-01 -7.57960498e-01 -3.76852959e-01
-1.67163983e-02 3.33481699e-01 4.66419280e-01 -3.64477009... | [4.697975158691406, 0.6919640898704529] |
430dcbb9-81d3-43f0-b752-f998421eb6de | ergo-event-relational-graph-transformer-for | 2204.07434 | null | https://arxiv.org/abs/2204.07434v1 | https://arxiv.org/pdf/2204.07434v1.pdf | ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification | Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI, which improve... | ['Yan Zhang', 'Jing Shao', 'Kun Wang', 'Mukai Li', 'Kunquan Deng', 'Yixin Cao', 'Meiqi Chen'] | 2022-04-15 | null | https://aclanthology.org/2022.coling-1.185 | https://aclanthology.org/2022.coling-1.185.pdf | coling-2022-10 | ['relation-classification', 'event-causality-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.56831163e-01 9.82584432e-02 -5.19665182e-01 -1.94191635e-01
-6.22038364e-01 -5.01161516e-01 9.68812704e-01 6.86601520e-01
2.21025825e-01 7.96787620e-01 5.46651840e-01 -4.77456480e-01
-6.99339688e-01 -1.08629632e+00 -6.24749005e-01 -2.39510223e-01
-5.08216977e-01 2.72004455e-01 4.95753467e-01 -2.56324038... | [9.07274055480957, 9.110339164733887] |
86a2be65-0433-428a-b8a7-9e4aca628474 | towards-sampling-from-nondirected | 1905.00159 | null | http://arxiv.org/abs/1905.00159v1 | http://arxiv.org/pdf/1905.00159v1.pdf | Towards Sampling from Nondirected Probabilistic Graphical models using a D-Wave Quantum Annealer | A D-Wave quantum annealer (QA) having a 2048 qubit lattice, with no missing
qubits and couplings, allowed embedding of a complete graph of a Restricted
Boltzmann Machine (RBM). A handwritten digit OptDigits data set having 8x7
pixels of visible units was used to train the RBM using a classical Contrastive
Divergence. E... | ['M. A. Novotny', 'Yaroslav Koshka'] | 2019-05-01 | null | null | null | null | ['2048'] | ['playing-games'] | [ 3.26543897e-01 3.48231941e-01 1.82668626e-01 -1.70439541e-01
-1.01828241e+00 -3.18950266e-01 6.37236834e-01 -1.46981776e-01
-6.01145566e-01 9.26230431e-01 -1.04142085e-01 -3.59476656e-01
-3.63673568e-02 -1.05438221e+00 -9.13153410e-01 -1.42829013e+00
3.84857580e-02 8.69606137e-01 2.99985439e-01 2.97956970... | [5.601771354675293, 4.9226460456848145] |
fee8e433-7d82-424d-9278-ced3978b5990 | deep-neural-network-solution-of-the-1 | null | null | https://www.nature.com/articles/s41557-020-0544-y | https://pub.hrmnn.net/4a/bc8d5cac/preprint.pdf | Deep-neural-network solution of the electronic Schrödinger equation | The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be paralleli... | ['Frank Noé', 'Zeno Schätzle', 'Jan Hermann'] | 2020-09-23 | null | null | null | nature-chemistry-2020-9 | ['total-energy'] | ['miscellaneous'] | [-2.21599579e-01 -2.11211175e-01 -9.93043035e-02 -7.32861385e-02
-1.03822434e+00 -4.03139800e-01 6.77506626e-01 1.69729531e-01
-7.22102404e-01 1.22499704e+00 1.98371299e-02 -6.50246441e-01
-2.92280260e-02 -9.92591798e-01 -6.37288332e-01 -1.26714218e+00
-3.03373914e-02 1.08005869e+00 -1.30042732e-01 -3.72274756... | [5.350881576538086, 5.187221050262451] |
14fddbcf-6571-4f90-89e5-46b8e349d630 | combining-fully-convolutional-and-recurrent | 1609.01006 | null | http://arxiv.org/abs/1609.01006v2 | http://arxiv.org/pdf/1609.01006v2.pdf | Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation | Segmentation of 3D images is a fundamental problem in biomedical image
analysis. Deep learning (DL) approaches have achieved state-of-the-art
segmentation perfor- mance. To exploit the 3D contexts using neural networks,
known DL segmentation methods, including 3D convolution, 2D convolution on
planes orthogonal to 2D i... | ['Danny Z. Chen', 'Yizhe Zhang', 'Mark Alber', 'Lin Yang', 'Jianxu Chen'] | 2016-09-05 | combining-fully-convolutional-and-recurrent-1 | http://papers.nips.cc/paper/6448-combining-fully-convolutional-and-recurrent-neural-networks-for-3d-biomedical-image-segmentation | http://papers.nips.cc/paper/6448-combining-fully-convolutional-and-recurrent-neural-networks-for-3d-biomedical-image-segmentation.pdf | neurips-2016-12 | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 3.62376988e-01 2.26927876e-01 4.99570370e-02 -4.47035968e-01
-6.83668435e-01 -5.11838496e-01 4.11115110e-01 -4.62352410e-02
-7.55485654e-01 2.98628330e-01 -3.93352658e-02 -5.87417543e-01
5.52397184e-02 -3.32443297e-01 -7.47207999e-01 -7.40514338e-01
-3.82153429e-02 6.12474203e-01 3.99602532e-01 1.59543604... | [14.418302536010742, -2.5942347049713135] |
f60cceb2-b311-4a0b-8e16-c4849b1a278f | translation-invariant-word-embeddings | null | null | https://aclanthology.org/D15-1127 | https://aclanthology.org/D15-1127.pdf | Translation Invariant Word Embeddings | null | ['Partha P. Talukdar', 'Xiao Fu', 'Christos Faloutsos', 'Tom Mitchell', 'Matt Gardner', 'Kejun Huang', 'Nikos Sidiropoulos', 'Evangelos Papalexakis'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['multilingual-word-embeddings'] | ['methodology'] | [-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... | [-7.372432708740234, 3.846893072128296] |
3ba65346-ce06-44b8-a9fe-6947967c9634 | barkour-benchmarking-animal-level-agility | 2305.14654 | null | https://arxiv.org/abs/2305.14654v1 | https://arxiv.org/pdf/2305.14654v1.pdf | Barkour: Benchmarking Animal-level Agility with Quadruped Robots | Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biological counterparts and show various agile skills to navigate complex environments quickly. Despite the interest, the field lacks systematic... | ['Jie Tan', 'Vincent Vanhoucke', 'Vikas Sindhwani', 'Carolina Parada', 'Jeff Seto', 'Francesco Nori', 'Nicolas Heess', 'Raia Hadsell', 'Michael Neunert', 'Daniel Zheng', 'Baruch Tabanpour', 'Ron Sloat', 'Feresteh Sadeghi', 'Francesco Romano', 'Diego Reyes', 'Jason Powell', 'Ken Oslund', 'Ofir Nachum', 'Linda Luu', 'Edw... | 2023-05-24 | null | null | null | null | ['navigate'] | ['reasoning'] | [-2.86021799e-01 8.86375681e-02 -4.80725132e-02 1.40432976e-02
-1.70962468e-01 -5.00787139e-01 2.71191716e-01 -2.39131659e-01
-4.79398072e-01 9.82285261e-01 -2.05712780e-01 2.19008420e-03
-2.31726933e-02 -1.00146306e+00 -7.91698754e-01 -6.87655568e-01
-5.32653272e-01 6.35069549e-01 7.35395491e-01 -1.19443142... | [4.544819355010986, 1.2980616092681885] |
6c370c0a-f1f9-4b54-86c5-2f6529c5276c | learning-from-mistakes-self-regularizing | 2301.11145 | null | https://arxiv.org/abs/2301.11145v1 | https://arxiv.org/pdf/2301.11145v1.pdf | Learning from Mistakes: Self-Regularizing Hierarchical Semantic Representations in Point Cloud Segmentation | Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accomplish fine-grained scene understanding by acting directly on raw content provided by sensors. Recent solutions showed how different learning t... | ['Simone Milani', 'Umberto Michieli', 'Elena Camuffo'] | 2023-01-26 | null | null | null | null | ['lidar-semantic-segmentation', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.73347515e-01 1.36293799e-01 -2.79041857e-01 -7.26130784e-01
-7.74525762e-01 -5.79446077e-01 7.26179838e-01 4.51976061e-01
-5.96306443e-01 6.29414260e-01 -2.07934514e-01 6.74118176e-02
-3.62303287e-01 -1.06242979e+00 -8.46341848e-01 -6.96105242e-01
1.29010022e-01 8.04176092e-01 7.02262998e-01 9.30135101... | [8.24416732788086, -2.5432093143463135] |
d66cc120-b836-47d7-a564-7d1c326fb625 | unsupervised-energy-based-adversarial-domain | null | null | https://aclanthology.org/2021.findings-acl.103 | https://aclanthology.org/2021.findings-acl.103.pdf | Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification | null | ['Xiaojian Wu', 'Jianfei Yang', 'Han Zou'] | null | null | null | null | findings-acl-2021-8 | ['cross-domain-text-classification'] | ['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... | [-7.267245769500732, 3.851016044616699] |
fa9a88e5-0bcf-4b24-b15f-4f8e20e91649 | relieving-triplet-ambiguity-consensus-network | 2306.02092 | null | https://arxiv.org/abs/2306.02092v1 | https://arxiv.org/pdf/2306.02092v1.pdf | Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image Retrieval | Language-guided image retrieval enables users to search for images and interact with the retrieval system more naturally and expressively by using a reference image and a relative caption as a query. Most existing studies mainly focus on designing image-text composition architecture to extract discriminative visual-lin... | ['Yi Yang', 'Xiaohan Wang', 'Zhedong Zheng', 'Xu Zhang'] | 2023-06-03 | null | null | null | null | ['multi-modal'] | ['miscellaneous'] | [ 2.53254116e-01 -2.98020273e-01 -2.35153392e-01 -6.79710627e-01
-7.70013094e-01 -5.59379041e-01 4.50118572e-01 -7.70931831e-03
-6.55146658e-01 2.50277549e-01 1.38356835e-01 3.14276367e-01
5.40085398e-02 -3.90478104e-01 -6.94644988e-01 -5.52715778e-01
2.39043996e-01 4.22921777e-01 -2.08511457e-01 -3.29995126... | [10.86137580871582, 1.3276658058166504] |
c4b13cf7-f74b-4e65-9fbd-7c71928776e6 | a-methodology-to-identify-cognition-gaps-in | 2110.02080 | null | https://arxiv.org/abs/2110.02080v1 | https://arxiv.org/pdf/2110.02080v1.pdf | A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks | Developing consistently well performing visual recognition applications based on convolutional neural networks, e.g. for autonomous driving, is very challenging. One of the obstacles during the development is the opaqueness of their cognitive behaviour. A considerable amount of literature has been published which descr... | ['Michael Weyrich', 'Nasser Jazdi', 'Andreas Löcklin', 'Tristan Rauch', 'Hannes Vietz'] | 2021-10-05 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.24596727e-01 9.15699124e-01 4.17899936e-01 -2.52504736e-01
8.98447111e-02 -6.26177251e-01 9.25899684e-01 -2.89610595e-01
-4.71410066e-01 7.86490142e-01 -3.09272140e-01 -5.84152281e-01
-6.62111863e-02 -8.32293451e-01 -9.30259466e-01 -5.78435719e-01
7.49331638e-02 1.56408653e-01 -2.95263473e-02 -6.10905826... | [5.643067359924316, 7.8331990242004395] |
1d3acf15-8610-4697-bae9-e3944e1e7a13 | posecontrast-class-agnostic-object-viewpoint | 2105.05643 | null | https://arxiv.org/abs/2105.05643v2 | https://arxiv.org/pdf/2105.05643v2.pdf | PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning | Motivated by the need for estimating the 3D pose of arbitrary objects, we consider the challenging problem of class-agnostic object viewpoint estimation from images only, without CAD model knowledge. The idea is to leverage features learned on seen classes to estimate the pose for classes that are unseen, yet that shar... | ['Renaud Marlet', 'Yuming Du', 'Yang Xiao'] | 2021-05-12 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 8.83458927e-02 1.44486532e-01 1.80291757e-02 -6.34434819e-01
-9.95601952e-01 -8.39629948e-01 8.65331113e-01 -4.71576527e-02
-4.64167267e-01 2.37093478e-01 1.11433111e-01 2.05782801e-01
1.02564991e-01 -5.03166795e-01 -1.07634985e+00 -4.22985107e-01
-3.14085633e-02 1.12184882e+00 6.31999969e-01 3.04374639... | [7.68296480178833, -2.7159180641174316] |
1bb6c117-5525-4795-a575-052b04ac7247 | 190910278 | 1909.10278 | null | https://arxiv.org/abs/1909.10278v1 | https://arxiv.org/pdf/1909.10278v1.pdf | Detection of Classifier Inconsistencies in Image Steganalysis | In this paper, a methodology to detect inconsistencies in classification-based image steganalysis is presented. The proposed approach uses two classifiers: the usual one, trained with a set formed by cover and stego images, and a second classifier trained with the set obtained after embedding additional random messages... | ['David Megías', 'Daniel Lerch-Hostalot'] | 2019-09-23 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 8.83884072e-01 4.00655478e-01 1.78635791e-01 -4.93145064e-02
-3.10046583e-01 -1.75622001e-01 5.32422543e-01 2.22120419e-01
-6.05383888e-02 7.67219365e-01 -4.47100967e-01 -5.74822545e-01
3.69530112e-01 -1.05334604e+00 -6.92997277e-01 -8.38364184e-01
3.46397944e-02 4.58016157e-01 5.87879717e-01 -2.08994135... | [4.318588733673096, 8.042418479919434] |
0b0a3efc-f485-466b-bd3a-e1258ba50939 | traffsign-multilingual-traffic-signboard-text | null | null | https://link.springer.com/chapter/10.1007/978-3-031-06555-2_50 | https://link.springer.com/chapter/10.1007/978-3-031-06555-2_50 | TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and English | Scene-text detection and recognition methods have demonstrated remarkable performance on standard benchmark datasets. These methods can be utilized in human-driven/self-driving cars to perform navigation assistance through traffic signboard text detection and recognition. Existing datasets include scripts in numerous l... | ['and Faisal Shafait', 'Adnan Ul-Hasan', 'Muhammad Atif Butt'] | 2022-05-18 | null | null | null | document-analysis-systems-2022-5 | ['scene-text-detection'] | ['computer-vision'] | [ 1.22300431e-01 -6.64125323e-01 -2.03433573e-01 -5.41616619e-01
-6.85686052e-01 -4.21037763e-01 9.33668077e-01 -4.60157692e-01
-5.58471680e-01 3.69101822e-01 1.09595358e-01 -8.10999215e-01
5.53604543e-01 -6.22828543e-01 -3.81310165e-01 -4.81213063e-01
8.58020425e-01 4.33539510e-01 5.11781812e-01 -5.15227199... | [8.014371871948242, -0.8011314272880554] |
a0935a9e-03d2-4a5d-b3f9-bf96a7b69086 | forecasting-loss-of-signal-in-optical | 2201.07089 | null | https://arxiv.org/abs/2201.07089v2 | https://arxiv.org/pdf/2201.07089v2.pdf | Forecasting Loss of Signal in Optical Networks with Machine Learning | Loss of Signal (LOS) represents a significant cost for operators of optical networks. By studying large sets of real-world Performance Monitoring (PM) data collected from six international optical networks, we find that it is possible to forecast LOS events with good precision 1-7 days before they occur, albeit at rela... | ['David Cote', 'Yan Liu', 'Chris Barber', 'Wenjie Du'] | 2022-01-08 | null | null | null | null | ['classification-on-time-series-with-missing'] | ['time-series'] | [-1.50672689e-01 -4.54509519e-02 -5.71020067e-01 -4.37438488e-01
-5.15802801e-01 -5.14470339e-01 -1.99215874e-01 -5.67354960e-03
3.84892225e-02 1.09778035e+00 -4.30590600e-01 -8.57864618e-01
-6.24559045e-01 -6.54437900e-01 -4.18729872e-01 -4.06710833e-01
-4.88343328e-01 7.08610177e-01 2.02347711e-01 2.70005584... | [6.107728481292725, 1.6225645542144775] |
9b9220bc-a427-44d2-a48a-67ecfbe6bf97 | ji-yu-kuang-jia-yu-yi-ying-she-he-lei-xing | null | null | https://aclanthology.org/2022.ccl-1.22 | https://aclanthology.org/2022.ccl-1.22.pdf | 基于框架语义映射和类型感知的篇章事件抽取(Document-Level Event Extraction Based on Frame Semantic Mapping and Type Awareness) | “篇章事件抽取是从给定的文本中识别其事件类型和事件论元。目前篇章事件普遍存在数据稀疏和多值论元耦合的问题。基于此,本文将汉语框架网(CFN)与中文篇章事件建立映射,同时引入滑窗机制和触发词释义改善了事件检测的数据稀疏问题;使用基于类型感知标签的多事件分离策略缓解了论元耦合问题。为了提升模型的鲁棒性,进一步引入对抗训练。本文提出的方法在DuEE-Fin和CCKS2021数据集上实验结果显著优于现有方法。” | ['Jiaxing Chen', 'Zhichao Yan', 'Xuefeng Su', 'Ru Li', 'Jiang Lu'] | null | null | null | null | ccl-2022-10 | ['event-extraction', 'document-level-event-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-6.74544752e-01 -1.17104435e+00 6.30947709e-01 5.67468703e-01
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1.87848825e-02 9.45893407e-01 2.45181724e-01 -1.24320403... | [-3.3159375190734863, 6.907866954803467] |
8233e89d-e3e0-49f4-bfcc-21ba7c88ef2a | chasing-the-tail-in-monocular-3d-human | 2012.14739 | null | https://arxiv.org/abs/2012.14739v1 | https://arxiv.org/pdf/2012.14739v1.pdf | Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory | Deep neural networks have achieved great progress in single-image 3D human reconstruction. However, existing methods still fall short in predicting rare poses. The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare pos... | ['Chen Change Loy', 'Ziwei Liu', 'Yu Rong'] | 2020-12-29 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-2.28644431e-01 -1.66860312e-01 -4.45247442e-01 -1.77383497e-01
-5.18966258e-01 1.29109934e-01 5.26535511e-01 -2.38106906e-01
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5.78968748e-02 -6.50308609e-01 -8.31916213e-01 -4.36943710e-01
-8.64015743e-02 1.08696020e+00 7.14016780e-02 -2.26473123... | [7.107000827789307, -0.8464437127113342] |
e9fb1552-6191-4988-b1a3-1d437ed6e9c8 | fast-graspnext-a-fast-self-attention-neural | 2304.11196 | null | https://arxiv.org/abs/2304.11196v1 | https://arxiv.org/pdf/2304.11196v1.pdf | Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge | Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic ... | ['Mohammad Javad Shafiee', 'Yuhao Chen', 'Saeejith Nair', 'Saad Abbasi', 'Yifan Wu', 'Alexander Wong'] | 2023-04-21 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.01105593e-01 1.87197141e-02 -1.38118878e-01 -3.18758219e-01
-5.16440272e-01 -4.39265847e-01 4.00132686e-01 -2.46358603e-01
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-6.76796913e-01 -4.94869262e-01 -9.07044172e-01 -9.24705029e-01
-1.18899383e-01 5.92932403e-01 -3.34739126e-02 2.69669108... | [5.770719528198242, -0.8687095046043396] |
e8261423-f6d7-4241-b554-fed0e1e8597a | topic-driven-and-knowledge-aware-transformer | 2106.01071 | null | https://arxiv.org/abs/2106.01071v1 | https://arxiv.org/pdf/2106.01071v1.pdf | Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection | Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the c... | ['Yulan He', 'Deyu Zhou', 'Lin Gui', 'Gabriele Pergola', 'Lixing Zhu'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-long.125 | https://aclanthology.org/2021.acl-long.125.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.06608886e-01 2.66474217e-01 -1.23321332e-01 -6.54209912e-01
-7.97920287e-01 -3.79487276e-01 7.97577739e-01 2.00459272e-01
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8.87385756e-02 4.38285619e-01 2.83919368e-02 -4.93502051... | [12.915342330932617, 6.225044250488281] |
36042faf-a76d-4d30-8942-3f5871858d8e | deformable-vistr-spatio-temporal-deformable | 2203.06318 | null | https://arxiv.org/abs/2203.06318v1 | https://arxiv.org/pdf/2203.06318v1.pdf | Deformable VisTR: Spatio temporal deformable attention for video instance segmentation | Video instance segmentation (VIS) task requires classifying, segmenting, and tracking object instances over all frames in a video clip. Recently, VisTR has been proposed as end-to-end transformer-based VIS framework, while demonstrating state-of-the-art performance. However, VisTR is slow to converge during training, r... | ['Junsong Yuan', 'Yi Xu', 'Pan Ji', 'Jialian Wu', 'Sudhir Yarram'] | 2022-03-12 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-8.31966773e-02 -1.80730030e-01 -1.45101488e-01 -1.61525756e-01
-1.02788949e+00 -5.60439467e-01 1.48571283e-01 -3.70222405e-02
-4.67359751e-01 2.80400544e-01 -9.21962783e-02 -2.94788212e-01
1.06661052e-01 -6.84198916e-01 -9.58636343e-01 -4.92649347e-01
9.19915140e-02 3.71083558e-01 5.97600102e-01 1.78637728... | [9.166115760803223, 0.01609838753938675] |
24bf8efc-da07-46af-9618-a18462314007 | tactile-image-to-image-disentanglement-of | 2109.03615 | null | https://arxiv.org/abs/2109.03615v1 | https://arxiv.org/pdf/2109.03615v1.pdf | Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear | Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the geometry of the stimulus. In this work, we propose a supervised convolutional deep neural ... | ['Nathan F. Lepora', 'Laurence Aitchison', 'Anupam K. Gupta'] | 2021-09-08 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 5.66839576e-01 8.03738087e-02 1.32187620e-01 -1.99187398e-01
-5.23271203e-01 -7.21787393e-01 5.79661191e-01 -2.38080636e-01
-4.73210573e-01 2.74684668e-01 1.18449204e-01 3.25938731e-01
-2.51474619e-01 -6.49625301e-01 -1.16806185e+00 -9.71577704e-01
3.64439368e-01 7.15887308e-01 2.27091372e-01 -2.66836137... | [5.8874006271362305, -0.859358012676239] |
207741f1-0ad0-4798-b0a2-c1a6703b8103 | are-you-robert-or-roberta-deceiving-online | 2203.09813 | null | https://arxiv.org/abs/2203.09813v1 | https://arxiv.org/pdf/2203.09813v1.pdf | Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators | Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, ... | ['Shujun Li', 'Jason R. C. Nurse', 'Keenan Jones'] | 2022-03-18 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.66399106e-01 7.64874518e-01 2.18471721e-01 1.67921588e-01
-7.61881411e-01 -6.92261755e-01 1.39536369e+00 2.69587398e-01
-2.60049224e-01 7.01544464e-01 5.39672494e-01 -4.49249983e-01
8.15533698e-02 -6.98864222e-01 -4.11566883e-01 -2.77681388e-02
1.45036161e-01 7.08919168e-01 -1.37516052e-01 -4.86176103... | [8.468404769897461, 9.994772911071777] |
618f57f0-528f-46a0-9bd0-f30c75fe01d4 | self-supervised-high-fidelity-and-re | 2111.08282 | null | https://arxiv.org/abs/2111.08282v2 | https://arxiv.org/pdf/2111.08282v2.pdf | Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image | Reconstructing high-fidelity 3D facial texture from a single image is a quite challenging task due to the lack of complete face information and the domain gap between the 3D face and 2D image. Further, obtaining re-renderable 3D faces has become a strongly desired property in many applications, where the term 're-rende... | ['Dong-Ming Yan', 'Xiaopeng Zhang', 'Zhanglin Cheng', 'Jianwei Guo', 'Mingxin Yang'] | 2021-11-16 | null | null | null | null | ['3d-face-reconstruction'] | ['computer-vision'] | [ 3.58472407e-01 5.45413941e-02 2.12749720e-01 -6.52281880e-01
-6.19451344e-01 -3.50816309e-01 5.07188678e-01 -8.35844517e-01
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7.93498475e-03 -9.53003109e-01 -7.00897276e-01 -1.14348531e+00
5.40982187e-01 2.55881041e-01 -5.37048519e-01 -1.63783669... | [12.823260307312012, -0.16604742407798767] |
50406879-b7e2-4cd0-9495-84e76e0ea0ea | improved-churn-causal-analysis-through-1 | 2304.11503 | null | https://arxiv.org/abs/2304.11503v1 | https://arxiv.org/pdf/2304.11503v1.pdf | Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions | Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a custom... | ['Guandong Xu', 'Huan Huo', 'David Hason Rudd'] | 2023-04-23 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-2.44559839e-01 5.10050617e-02 -5.38051009e-01 -5.34113050e-01
-9.01694968e-02 -2.84608066e-01 3.13537836e-01 2.28260562e-01
3.90353836e-02 9.99307871e-01 3.86432260e-01 -5.94606042e-01
-8.03986192e-01 -1.28487456e+00 -4.40584183e-01 -6.94693148e-01
-3.61240923e-01 9.95666921e-01 -6.74296856e-01 -1.73054129... | [9.061005592346191, 5.772404670715332] |
210d3e00-745b-4077-ad77-fdc51284082d | covost-2-a-massively-multilingual-speech-to | 2007.10310 | null | https://arxiv.org/abs/2007.10310v3 | https://arxiv.org/pdf/2007.10310v3.pdf | CoVoST 2 and Massively Multilingual Speech-to-Text Translation | Speech translation has recently become an increasingly popular topic of research, partly due to the development of benchmark datasets. Nevertheless, current datasets cover a limited number of languages. With the aim to foster research in massive multilingual speech translation and speech translation for low resource la... | ['Anne Wu', 'Juan Pino', 'Changhan Wang'] | 2020-07-20 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [-1.46331098e-02 -3.23464796e-02 -8.00700903e-01 -2.32710123e-01
-1.55539489e+00 -8.11051667e-01 9.25384521e-01 -1.59051970e-01
-4.55449045e-01 9.43942964e-01 7.43814230e-01 -7.77810097e-01
4.49021369e-01 -2.14751512e-01 -6.98238850e-01 -2.16358200e-01
5.19795537e-01 9.13041234e-01 -1.01649486e-01 -3.33993107... | [14.338574409484863, 7.311089515686035] |
69a51256-ae47-4166-bc87-9040d89aeb60 | the-second-place-solution-for-eccv-2022 | 2211.13509 | null | https://arxiv.org/abs/2211.13509v2 | https://arxiv.org/pdf/2211.13509v2.pdf | The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge | This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. Our C-BIoU tracker ad... | ['Shan Jiang', 'Shoichi Masui', 'Shigeyuki Odashima', 'Fan Yang'] | 2022-11-24 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-1.93543106e-01 -2.49560997e-01 6.36787042e-02 2.54765183e-01
-4.72098380e-01 -5.94285607e-01 2.67799020e-01 2.83139378e-01
-6.89668715e-01 5.97989321e-01 1.17018998e-01 3.49848777e-01
2.49432191e-01 -7.07781911e-01 -6.19463921e-01 -3.87270570e-01
-1.92329869e-01 5.62186539e-01 1.31638849e+00 3.30580957... | [6.441183090209961, -1.9420164823532104] |
23572c1c-610a-4fae-bb0c-d6c855dda4b5 | real-time-action-recognition-for-fine-grained | 2210.07400 | null | https://arxiv.org/abs/2210.07400v1 | https://arxiv.org/pdf/2210.07400v1.pdf | Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset | In this paper we present a three-stream algorithm for real-time action recognition and a new dataset of handwash videos, with the intent of aligning action recognition with real-world constraints to yield effective conclusions. A three-stream fusion algorithm is proposed, which runs both accurately and efficiently, in ... | ['Gowri Srinivasa', 'Chetna Sureka', 'Mukund Sood', 'Akash Nagaraj'] | 2022-10-13 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.55842352e-01 -4.87188578e-01 2.15092003e-02 -1.17399424e-01
-2.65282005e-01 -4.30995613e-01 5.83597064e-01 -8.26792419e-02
-5.67807376e-01 6.50331020e-01 1.12683788e-01 1.95962399e-01
-3.91563088e-01 -4.80737239e-01 -3.30837488e-01 -8.49980295e-01
-2.19339103e-01 3.36020768e-01 4.95662451e-01 -1.71316981... | [8.032539367675781, 0.2266845703125] |
5e5f2105-afac-4e69-a675-6701f37f929f | informed-machine-learning-centrality-cnn | 2303.14475 | null | https://arxiv.org/abs/2303.14475v1 | https://arxiv.org/pdf/2303.14475v1.pdf | Informed Machine Learning, Centrality, CNN, Relevant Document Detection, Repatriation of Indigenous Human Remains | Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating informa... | ['Cressida Fforde', 'Paul Turnbull', 'Gareth Knapman', 'Richi Nayak', 'Md Abul Bashar'] | 2023-03-25 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.79236609e-01 2.15466261e-01 -3.11564982e-01 -1.33070216e-01
-6.79157555e-01 -7.31460690e-01 9.31678832e-01 5.52067280e-01
-9.61613417e-01 5.93104839e-01 9.09518898e-01 -9.40119803e-01
-6.48453653e-01 -7.56183267e-01 -4.20064658e-01 -1.94426611e-01
9.16457623e-02 4.96284693e-01 -1.41749084e-01 -3.11178923... | [9.903979301452637, 9.022194862365723] |
6398ff67-5bb5-4566-b877-75bba48763ad | boba-byzantine-robust-federated-learning-with | 2208.12932 | null | https://arxiv.org/abs/2208.12932v1 | https://arxiv.org/pdf/2208.12932v1.pdf | BOBA: Byzantine-Robust Federated Learning with Label Skewness | In federated learning, most existing techniques for robust aggregation against Byzantine attacks are designed for the IID setting, i.e., the data distributions for clients are independent and identically distributed. In this paper, we address label skewness, a more realistic and challenging non-IID setting, where each ... | ['Jingrui He', 'Wenxuan Bao'] | 2022-08-27 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-2.20199004e-01 -3.13828707e-01 -2.96392202e-01 -3.16843659e-01
-9.33192551e-01 -9.97786641e-01 5.94841778e-01 2.67416149e-01
-3.64484876e-01 7.17156589e-01 5.78747727e-02 -4.66135859e-01
3.16324420e-02 -5.85432529e-01 -7.22178280e-01 -1.00978744e+00
-2.65448868e-01 4.92504299e-01 8.18111971e-02 1.19126186... | [5.836976051330566, 6.753190517425537] |
639f6105-2b34-4f9d-a493-457877092bad | sts-surround-view-temporal-stereo-for-multi | 2208.10145 | null | https://arxiv.org/abs/2208.10145v1 | https://arxiv.org/pdf/2208.10145v1.pdf | STS: Surround-view Temporal Stereo for Multi-view 3D Detection | Learning accurate depth is essential to multi-view 3D object detection. Recent approaches mainly learn depth from monocular images, which confront inherent difficulties due to the ill-posed nature of monocular depth learning. Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view... | ['Di Huang', 'Hongyu Yang', 'Zeming Li', 'Yinhao Li', 'Zheng Ge', 'Chen Min', 'Zengran Wang'] | 2022-08-22 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-6.55172691e-02 -3.20011020e-01 -2.67861009e-01 -2.77497709e-01
-4.83508050e-01 -7.63621807e-01 6.36552155e-01 -5.84496200e-01
-3.84920895e-01 3.47334832e-01 2.15207785e-01 -3.15516227e-04
3.41061085e-01 -8.52505386e-01 -7.13591695e-01 -6.24942482e-01
4.39494133e-01 3.40386443e-02 6.51896834e-01 6.66952552... | [8.294414520263672, -2.359398603439331] |
79ab9bc0-54aa-4188-90c4-df3ae7ef6ffe | masked-bayesian-neural-networks-theoretical | 2305.14765 | null | https://arxiv.org/abs/2305.14765v1 | https://arxiv.org/pdf/2305.14765v1.pdf | Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference | Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of having better generalization ability as well as better uncertainty quantification. For the success of BNN, search an appropriate architectu... | ['Yongdai Kim', 'Gyuseung Baek', 'Ilsang Ohn', 'Jongjin Lee', 'Dongyoon Yang', 'Insung Kong'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.53574552e-02 6.49137050e-02 -2.26725072e-01 -5.91114998e-01
-4.69493240e-01 1.55877499e-02 3.43069434e-01 -2.51738071e-01
-1.74178764e-01 8.82128894e-01 -4.95334566e-02 -6.08457029e-02
-8.13323557e-01 -7.61756003e-01 -8.68075371e-01 -1.06345356e+00
7.53108272e-03 6.10766053e-01 3.31980497e-01 1.90751627... | [7.313090801239014, 3.76324200630188] |
b72bf202-131a-4e85-9d50-f559d2293398 | self-supervised-document-clustering-based-on | 2011.08523 | null | https://arxiv.org/abs/2011.08523v3 | https://arxiv.org/pdf/2011.08523v3.pdf | Self-supervised Document Clustering Based on BERT with Data Augment | Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers, we propose self-supervised contrastive learning (SCL) as well as... | ['Cen Wang', 'Haoxiang Shi'] | 2020-11-17 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-1.28422186e-01 -2.62450185e-02 -8.64849091e-02 -4.82233256e-01
-7.48323143e-01 -2.79023737e-01 1.20232534e+00 6.06205761e-01
-5.46474397e-01 2.67199218e-01 6.26219571e-01 -1.15503840e-01
-2.89132088e-01 -5.47452927e-01 -4.78264540e-01 -7.84680128e-01
-7.35246763e-03 9.40238774e-01 1.16355471e-01 -1.61693439... | [10.445768356323242, 6.971776962280273] |
d9c08670-e88d-4f02-a791-4bc2290985c0 | deep-1d-convnet-for-accurate-parkinson | 1910.11509 | null | https://arxiv.org/abs/1910.11509v4 | https://arxiv.org/pdf/1910.11509v4.pdf | Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait | Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians... | ['Guillaume-Alexandre Bilodeau', 'Imanne El Maachi', 'Wassim Bouachir'] | 2019-10-25 | deep-1d-convnet-for-accurate-parkinson-1 | null | null | expert-systems-with-applications-2020-4 | ['severity-prediction'] | ['computer-vision'] | [-9.49036479e-02 -8.30944330e-02 -6.52295724e-02 -1.87159047e-01
-2.31838167e-01 1.68219343e-01 -7.71826059e-02 2.66084610e-03
-7.59755492e-01 7.85112262e-01 -1.11553468e-01 -1.07336789e-01
-7.98642263e-02 -7.96797991e-01 -2.56781518e-01 -6.55421495e-01
-4.37374353e-01 7.40050912e-01 4.33286220e-01 -2.93512106... | [7.140610694885254, 0.3465273082256317] |
f081a4b1-a650-4b2a-b9f5-c58874678609 | august-an-automatic-generation-understudy-for | 2306.09631 | null | https://arxiv.org/abs/2306.09631v1 | https://arxiv.org/pdf/2306.09631v1.pdf | AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets | High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existing works contribute heavy human efforts to manually labeling or designing and extending recommender dialogue templates. However, they suffer ... | ['Xiaodong He', 'Shuguang Cui', 'Youzheng Wu', 'Xiaoguang Han', 'Zichen Ma', 'Junwei Bao', 'Yu Lu'] | 2023-06-16 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [ 3.54599692e-02 4.93093669e-01 -1.65795967e-01 -3.14939290e-01
-4.37531859e-01 -6.32238150e-01 7.86547840e-01 -3.60541195e-01
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-4.30729359e-01 -7.73979366e-01 -1.93530425e-01 -2.82328963e-01
3.44754994e-01 7.83082187e-01 5.15614040e-02 -7.53666818... | [12.307169914245605, 7.490447998046875] |
676204f9-8979-43d1-813c-3820a1ee1ac1 | robust-product-classification-with-instance-1 | 2209.06946 | null | https://arxiv.org/abs/2209.06946v1 | https://arxiv.org/pdf/2209.06946v1.pdf | Robust Product Classification with Instance-Dependent Noise | Noisy labels in large E-commerce product data (i.e., product items are placed into incorrect categories) are a critical issue for product categorization task because they are unavoidable, non-trivial to remove and degrade prediction performance significantly. Training a product title classification model which is robus... | ['Devashish Khatwani', 'Huy Nguyen'] | 2022-09-14 | robust-product-classification-with-instance | https://aclanthology.org/2022.ecnlp-1.20 | https://aclanthology.org/2022.ecnlp-1.20.pdf | ecnlp-acl-2022-5 | ['product-categorization'] | ['miscellaneous'] | [ 4.86821383e-01 -4.24868762e-01 -7.75444461e-03 -6.42059505e-01
-4.14585829e-01 -5.50255775e-01 -7.11819297e-03 3.42142671e-01
-2.16136873e-01 4.68735576e-01 -1.02568552e-01 -1.09602146e-01
-2.51888394e-01 -9.98946607e-01 -7.23927915e-01 -9.20895994e-01
2.85095036e-01 2.36366555e-01 -8.96135196e-02 -2.62789130... | [9.314871788024902, 3.856586456298828] |
e90ebf6a-94c4-41fa-80aa-5795c8aec251 | exploring-multimodal-approaches-for-alzheimer | 2307.02514 | null | https://arxiv.org/abs/2307.02514v1 | https://arxiv.org/pdf/2307.02514v1.pdf | Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data | Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease. This study investigates various methods for detecting AD using patients' speech and ... | ['Xiang Li', 'Tianming Liu', 'Quanzheng Li', 'Hui Ren', 'Dajiang Zhu', 'Zihao Wu', 'Haixing Dai', 'Wenxiong Liao', 'Zhengliang Liu', 'Xiaoke Huang', 'Hongmin Cai'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'alzheimer-s-disease-detection', 'contrastive-learning'] | ['computer-vision', 'medical', 'methodology'] | [ 4.75320667e-01 4.34296191e-01 1.16680376e-01 -4.64208037e-01
-1.07041717e+00 -4.25351560e-02 4.72408712e-01 3.74459803e-01
-5.18534243e-01 8.46559942e-01 8.81096184e-01 -2.90127605e-01
9.25771669e-02 -7.00555325e-01 -1.26615819e-02 -2.28476688e-01
-5.29214561e-01 4.94086027e-01 4.40848656e-02 -1.66270748... | [13.917306900024414, 5.365899562835693] |
c854b019-3eee-4787-88e0-299183584eeb | layered-depth-refinement-with-mask-guidance-1 | 2206.03048 | null | https://arxiv.org/abs/2206.03048v1 | https://arxiv.org/pdf/2206.03048v1.pdf | Layered Depth Refinement with Mask Guidance | Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtai... | ['Munchurl Kim', 'Zhe Lin', 'Simon Chen', 'Yifei Fan', 'Simon Niklaus', 'Jianming Zhang', 'Soo Ye Kim'] | 2022-06-07 | layered-depth-refinement-with-mask-guidance | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-matting'] | ['computer-vision'] | [ 7.79950023e-01 4.02863383e-01 -8.70717019e-02 -3.20140779e-01
-5.99252164e-01 -4.19875026e-01 4.26350236e-01 -1.19132102e-01
-8.94984901e-02 5.57820141e-01 -1.02353372e-01 -1.04549043e-01
2.54442871e-01 -8.28803420e-01 -5.56529045e-01 -4.57165003e-01
3.11374545e-01 5.90192974e-01 7.11713016e-01 6.88528344... | [8.932079315185547, -2.940671443939209] |
44736dd2-0566-47ff-97be-dbfe36a534d2 | hiring-now-a-skill-aware-multi-attention | null | null | https://aclanthology.org/2020.acl-main.281 | https://aclanthology.org/2020.acl-main.281.pdf | Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation | Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per the company information and job description. To this end, we propose a novel task of Job Posting Gene... | ['YaLou Huang', 'Ziming Chi', 'Jie Liu', 'Wenzheng Zhang', 'Wenxuan Shi', 'Liting Liu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 7.66121209e-01 3.22324127e-01 4.42296006e-02 -4.98096198e-01
-4.36776608e-01 -2.88699120e-01 6.64358675e-01 6.67086542e-02
-2.65253425e-01 7.05468476e-01 4.60411489e-01 -2.02402696e-01
-2.80181110e-01 -8.07321906e-01 -4.13964093e-01 -3.73880535e-01
1.05236495e+00 7.92497337e-01 -8.29897821e-02 -3.40316415... | [11.845379829406738, 8.852474212646484] |
6dffe8f4-95e2-417d-b05f-af2319bbfb0f | rotational-subgroup-voting-and-pose | 1709.02142 | null | http://arxiv.org/abs/1709.02142v1 | http://arxiv.org/pdf/1709.02142v1.pdf | Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition | It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to hand... | ['Dirk Kraft', 'Anders Glent Buch', 'Lilita Kiforenko'] | 2017-09-07 | rotational-subgroup-voting-and-pose-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Buch_Rotational_Subgroup_Voting_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Buch_Rotational_Subgroup_Voting_ICCV_2017_paper.pdf | iccv-2017-10 | ['3d-object-recognition'] | ['computer-vision'] | [-1.55906649e-02 -2.77097523e-01 -1.41875565e-01 -1.91430494e-01
-1.01280928e+00 -6.55402243e-01 6.49799585e-01 4.21405956e-02
-2.34497070e-01 1.84867993e-01 -2.05093205e-01 -2.78848652e-02
-2.06527010e-01 -5.55584311e-01 -7.91497350e-01 -5.45427740e-01
1.30056545e-01 1.31898499e+00 5.93938649e-01 6.92173019... | [7.733535289764404, -2.8151257038116455] |
c5d86a25-1d48-44d3-af89-f0fdfed5ee40 | apes-audiovisual-person-search-in-untrimmed | 2106.01667 | null | https://arxiv.org/abs/2106.01667v1 | https://arxiv.org/pdf/2106.01667v1.pdf | APES: Audiovisual Person Search in Untrimmed Video | Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person reidentification and retrieval domains, few work... | ['Fabian Caba Heilbron', 'Bernard Ghanem', 'Pablo Arbelaez', 'Joon-Young Lee', 'Federico Perazzi', 'Long Mai', 'Juan Leon Alcazar'] | 2021-06-03 | null | null | null | null | ['person-retrieval', 'person-search'] | ['computer-vision', 'computer-vision'] | [-0.03824044 -0.32337388 -0.20153074 -0.33802474 -0.890442 -0.80429494
1.0300903 0.14793576 -0.5392068 0.52922964 0.59950745 0.43303183
0.06907801 -0.11973775 -0.5260478 -0.37407488 -0.15807272 0.37393296
-0.05359513 0.2655612 0.04490134 0.50845957 -2.048713 0.36379972
0.23451532 1.2169 -0.... | [14.384830474853516, 1.0807918310165405] |
3ec24ea5-b07c-4638-90a5-ed127d3cd22b | scene-relighting-with-illumination-estimation | 2006.02333 | null | https://arxiv.org/abs/2006.02333v1 | https://arxiv.org/pdf/2006.02333v1.pdf | Scene relighting with illumination estimation in the latent space on an encoder-decoder scheme | The image relighting task of transferring illumination conditions between two images offers an interesting and difficult challenge with potential applications in photography, cinematography and computer graphics. In this report we present methods that we tried to achieve that goal. Our models are trained on a rendered ... | ['Jakub Jan Gwizdała', 'Martin Nicolas Everaert', 'Alexandre Pierre Dherse'] | 2020-06-03 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 5.48424125e-01 -1.59539610e-01 3.69829714e-01 -5.68665504e-01
-1.87083825e-01 -4.86479402e-01 7.78034389e-01 -5.68041980e-01
-1.67334184e-01 6.87423468e-01 7.62691125e-02 -4.83178943e-02
3.02702546e-01 -6.41991019e-01 -7.81606138e-01 -8.73108804e-01
2.89659500e-01 -1.16865784e-01 -5.81699889e-03 -2.86536273... | [9.953207015991211, -2.773599147796631] |
811edacc-87b5-4eaf-8f08-aa87d61cc905 | a-meta-probabilistic-programming-language-for | 2203.15970 | null | https://arxiv.org/abs/2203.15970v3 | https://arxiv.org/pdf/2203.15970v3.pdf | A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems | We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded. We are motivated here by the desire to allow an AGI to learn not only relevant knowledge (programs/proofs), but also appropriate ways of reasoning (logics/type systems)... | ['Ben Goertzel', 'Adam Vandervorst', 'Alexey Potapov', 'Jonathan Warrell'] | 2022-03-30 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 3.91175151e-02 4.48343396e-01 -8.51327479e-02 -2.50614762e-01
-7.37698376e-01 -8.60886753e-01 1.03724277e+00 1.57699451e-01
1.27331540e-01 6.55409217e-01 -2.11172685e-01 -7.49459624e-01
-3.96309167e-01 -1.60710371e+00 -1.06376970e+00 -5.92710197e-01
-3.99810493e-01 5.93990147e-01 5.67121625e-01 -2.81813234... | [8.587276458740234, 6.7471208572387695] |
3a42a94f-aab0-4f11-ad6a-69a5f8dceba9 | two-stream-network-for-sign-language | 2211.01367 | null | https://arxiv.org/abs/2211.01367v2 | https://arxiv.org/pdf/2211.01367v2.pdf | Two-Stream Network for Sign Language Recognition and Translation | Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existing approaches directly encode RGB videos into hidden representations. RGB videos, however, are raw signals with substantial visual redundancy... | ['Brian Mak', 'Shujie Liu', 'Yu Wu', 'Fangyun Wei', 'Ronglai Zuo', 'Yutong Chen'] | 2022-11-02 | null | null | null | null | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 1.12448208e-01 -1.57819420e-01 -4.23223794e-01 -4.16407585e-01
-7.05457926e-01 -4.24101651e-01 5.27356625e-01 -8.39479804e-01
-3.82223010e-01 3.98904979e-01 6.71951950e-01 -2.05814794e-01
5.13347208e-01 -3.00140470e-01 -8.66705656e-01 -5.47235072e-01
2.72088200e-01 -1.26870319e-01 3.13541859e-01 -2.02636316... | [9.152552604675293, -6.53400182723999] |
fceb7e3b-cffb-43a8-b82d-2980947eab39 | andi-at-semeval-2021-task-1-predicting | null | null | https://aclanthology.org/2021.semeval-1.84 | https://aclanthology.org/2021.semeval-1.84.pdf | ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources | In this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context. Our approach relies on a combination of distributional models, both co... | ['Armand Rotaru'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-3.14397961e-01 -2.08735704e-01 -1.82910711e-01 -6.41974807e-01
-4.07334864e-01 -6.16379082e-01 7.58788645e-01 7.34210968e-01
-1.12134361e+00 6.06863320e-01 8.71990561e-01 -2.76948988e-01
-7.73663744e-02 -5.25160134e-01 1.24363609e-01 9.28263888e-02
-5.55483513e-02 1.34406671e-01 4.91336659e-02 -3.53280902... | [10.64912223815918, 10.446139335632324] |
659331cf-5cc0-43e2-8ccd-52ab6380ff9c | fusion-of-detected-objects-in-text-for-visual | 1908.05054 | null | https://arxiv.org/abs/1908.05054v2 | https://arxiv.org/pdf/1908.05054v2.pdf | Fusion of Detected Objects in Text for Visual Question Answering | To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The "Bounding Boxes in Text Transformer" (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is high... | ['Michael Collins', 'Chris Alberti', 'Jeffrey Ling', 'David Reitter'] | 2019-08-14 | fusion-of-detected-objects-in-text-for-visual-1 | https://aclanthology.org/D19-1219 | https://aclanthology.org/D19-1219.pdf | ijcnlp-2019-11 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 1.11505133e-03 1.24009758e-01 -7.58841038e-02 -3.88833582e-01
-1.04788458e+00 -8.22442472e-01 8.95943046e-01 1.21192873e-01
-3.94165277e-01 2.67428249e-01 7.99398780e-01 -5.79599023e-01
2.78594017e-01 -5.14862418e-01 -8.34593832e-01 -1.33336008e-01
5.49215794e-01 2.11929321e-01 4.51223180e-02 -3.92207533... | [10.821282386779785, 1.7898143529891968] |
9e4ca1a8-a43a-41c5-8163-e422acee888f | twitter-topic-summarization-by-ranking-tweets | null | null | https://aclanthology.org/C12-1047 | https://aclanthology.org/C12-1047.pdf | Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality | null | ['Heung-Yeung Shum', 'Furu Wei', 'Yajuan Duan', 'Ming Zhou', 'Zhumin Chen'] | 2012-12-01 | twitter-topic-summarization-by-ranking-tweets-1 | https://aclanthology.org/C12-1047 | https://aclanthology.org/C12-1047.pdf | coling-2012-12 | ['extractive-document-summarization'] | ['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... | [-7.175817012786865, 3.831111431121826] |
d4fba237-0b5e-4013-83bf-484bbacdfffc | scalable-spectral-clustering-using-random | 1805.11048 | null | https://arxiv.org/abs/1805.11048v3 | https://arxiv.org/pdf/1805.11048v3.pdf | Scalable Spectral Clustering Using Random Binning Features | Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods h... | ['Ian En-Hsu Yen', 'Pin-Yu Chen', 'Lingfei Wu', 'Charu Aggarwal', 'Fangli Xu', 'Yinglong Xia'] | 2018-05-25 | null | null | null | null | ['imagedocument-clustering', 'graph-similarity'] | ['computer-vision', 'graphs'] | [-3.00314724e-01 -4.91502345e-01 4.19921987e-02 -1.26264364e-01
-8.87381554e-01 -6.96826339e-01 2.03772053e-01 3.22759122e-01
-1.81878194e-01 -4.95776162e-02 5.37357926e-02 -1.72815293e-01
-3.59834433e-01 -6.53446317e-01 -5.27960062e-01 -8.94081235e-01
-3.35285991e-01 7.13967443e-01 3.48158926e-01 1.36565328... | [7.445282936096191, 4.758256912231445] |
e2c33079-b1e3-4606-b7b8-fa64e458d9a4 | generative-adversarial-network-for-future | 2203.11305 | null | https://arxiv.org/abs/2203.11305v2 | https://arxiv.org/pdf/2203.11305v2.pdf | Generative Adversarial Network for Future Hand Segmentation from Egocentric Video | We introduce the novel problem of anticipating a time series of future hand masks from egocentric video. A key challenge is to model the stochasticity of future head motions, which globally impact the head-worn camera video analysis. To this end, we propose a novel deep generative model -- EgoGAN, which uses a 3D Fully... | ['James M. Rehg', 'Miao Liu', 'Wenqi Jia'] | 2022-03-21 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 1.72676325e-01 3.29762697e-01 2.32219383e-01 -5.73834777e-01
-4.59028035e-01 -5.09805322e-01 5.00141680e-01 -1.19221771e+00
-1.41383439e-01 5.25631905e-01 6.73670292e-01 -5.87885641e-02
4.64920044e-01 -3.24798495e-01 -1.04430032e+00 -4.97569025e-01
-1.26881540e-01 1.29857451e-01 -1.60196006e-01 5.43422438... | [10.807650566101074, -0.6064101457595825] |
0cd2e63d-0990-4551-bf7c-3708ad86e495 | learning-to-push-the-limits-of-efficient-fft | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Kruse_Learning_to_Push_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Kruse_Learning_to_Push_ICCV_2017_paper.pdf | Learning to Push the Limits of Efficient FFT-Based Image Deconvolution | This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize... | ['Uwe Schmidt', 'Jakob Kruse', 'Carsten Rother'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['image-deconvolution'] | ['computer-vision'] | [ 2.58634925e-01 -3.53983551e-01 2.00459555e-01 -1.47254646e-01
-7.61459172e-01 -6.59268260e-01 6.05979919e-01 -4.36289638e-01
-7.94238329e-01 7.16087341e-01 5.17766416e-01 -4.20030773e-01
-4.26286133e-04 -3.48148972e-01 -6.49287224e-01 -6.06635034e-01
6.05064854e-02 1.26045361e-01 2.43424535e-01 6.23416379... | [11.657476425170898, -2.6386873722076416] |
31e24640-e74f-49f2-9a67-59e75c228862 | learning-word-embeddings-without-context | null | null | https://aclanthology.org/W19-4329 | https://aclanthology.org/W19-4329.pdf | Learning Word Embeddings without Context Vectors | Most word embedding algorithms such as word2vec or fastText construct two sort of vectors: for words and for contexts. Naive use of vectors of only one sort leads to poor results. We suggest using indefinite inner product in skip-gram negative sampling algorithm. This allows us to use only one sort of vectors without l... | ['Evgenia Elistratova', 'Alexey Zobnin'] | 2019-08-01 | null | null | null | ws-2019-8 | ['learning-word-embeddings'] | ['methodology'] | [-4.92075179e-03 -1.88945960e-02 -2.83940226e-01 -3.64893466e-01
-5.41352332e-01 -7.94410288e-01 1.06853330e+00 5.66122472e-01
-1.11290312e+00 5.69603443e-01 5.03012300e-01 -1.08695579e+00
2.08722189e-01 -1.05447185e+00 9.22113098e-03 -7.45257139e-01
-1.67748496e-01 2.98458546e-01 5.48117757e-01 -6.07663810... | [10.49528694152832, 8.669921875] |
16a050d2-d5f0-4e42-a0f9-a3a5a849a865 | pushing-the-boundaries-of-audiovisual-word | 1811.01194 | null | http://arxiv.org/abs/1811.01194v1 | http://arxiv.org/pdf/1811.01194v1.pdf | Pushing the boundaries of audiovisual word recognition using Residual Networks and LSTMs | Visual and audiovisual speech recognition are witnessing a renaissance which
is largely due to the advent of deep learning methods. In this paper, we
present a deep learning architecture for lipreading and audiovisual word
recognition, which combines Residual Networks equipped with spatiotemporal
input layers and Bidir... | ['Georgios Tzimiropoulos', 'Themos Stafylakis', 'Muhammad Haris Khan'] | 2018-11-03 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.45685115e-01 6.06833212e-03 -1.98578879e-01 7.25364238e-02
-1.23917174e+00 -2.58317798e-01 6.44286513e-01 -1.13439180e-01
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2.29771689e-01 -1.39841095e-01 -1.05447061e-01 -5.08084819... | [14.34532356262207, 5.041101455688477] |
68320548-0b44-4542-999e-4ca128e2239c | can-self-supervised-neural-networks-pre | 2305.14035 | null | https://arxiv.org/abs/2305.14035v3 | https://arxiv.org/pdf/2305.14035v3.pdf | Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers? | Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is not limited to modeling human speech alone. Building on this under... | ['Mathew Magimai. -Doss', 'Eklavya Sarkar'] | 2023-05-23 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 4.77740556e-01 1.53219223e-01 4.07450408e-01 -5.76110184e-01
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-6.70178413e-01 3.50368202e-01 -8.59726742e-02 -2.18048155... | [15.0018310546875, 5.833489894866943] |
2f285ca2-8ad8-4d72-9b18-3c0c0d40ffd8 | all-are-worth-words-a-vit-backbone-for-score | 2209.12152 | null | https://arxiv.org/abs/2209.12152v4 | https://arxiv.org/pdf/2209.12152v4.pdf | All are Worth Words: A ViT Backbone for Diffusion Models | Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion models. We design a simple and general ViT-based architecture (named U-ViT) for image generation with diffusion models. U-ViT is characterized by treating all ... | ['Jun Zhu', 'Hang Su', 'Yue Cao', 'Kaiwen Xue', 'Shen Nie', 'Chongxuan Li', 'Fan Bao'] | 2022-09-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 2.76914567e-01 3.01171243e-01 -2.40416657e-02 -1.61489695e-01
-6.76642358e-01 -4.20568973e-01 1.11477971e+00 -4.67964292e-01
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3.00611943e-01 3.06203961e-01 2.38008007e-01 -2.88166013... | [11.375862121582031, -0.21779748797416687] |
ae269813-ab62-4101-9ebb-aa6f76d351ed | beta-embeddings-for-multi-hop-logical | 2010.11465 | null | https://arxiv.org/abs/2010.11465v1 | https://arxiv.org/pdf/2010.11465v1.pdf | Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs | One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop logical reasoning over the facts captured by a knowledge graph (KG). This problem is challenging, because KGs can be massive and incomplete. Recent approaches embed KG entities in a low dimensional space and then use these embedd... | ['Jure Leskovec', 'Hongyu Ren'] | 2020-10-22 | null | http://proceedings.neurips.cc/paper/2020/hash/e43739bba7cdb577e9e3e4e42447f5a5-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e43739bba7cdb577e9e3e4e42447f5a5-Paper.pdf | neurips-2020-12 | ['complex-query-answering'] | ['knowledge-base'] | [-3.58958185e-01 5.17234504e-01 -2.63885140e-01 -4.94419336e-01
-6.47774518e-01 -6.15744293e-01 1.81862295e-01 4.35266435e-01
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-5.91796160e-01 -1.52293062e+00 -1.02003133e+00 -1.81825951e-01
-2.90988296e-01 1.12316513e+00 4.04373527e-01 -2.96649069... | [9.002423286437988, 7.62980318069458] |
a6075b3e-cdf6-428d-b6e3-bd30f860c5c1 | 3d-solid-spherical-bispectrum-cnns-for | 2004.13371 | null | https://arxiv.org/abs/2004.13371v2 | https://arxiv.org/pdf/2004.13371v2.pdf | 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis | Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can be obtained by computing the responses to the discrete rotation of local descriptors, such as Local Binary Patterns (LBP) or the Scale Invari... | ['Julien Fageot', 'Valentin Oreiller', 'Adrien Depeursinge', 'Vincent Andrearczyk', 'John O. Prior'] | 2020-04-28 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 4.33514267e-01 -2.35001385e-01 -5.73835224e-02 -2.19966352e-01
-3.28406960e-01 -3.38037312e-01 7.04074144e-01 -2.09355131e-01
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-4.88690764e-01 -7.74371684e-01 -5.80479681e-01 -1.03928125e+00
-3.88955057e-01 2.18547076e-01 3.44691426e-02 -5.84577285... | [9.103982925415039, 2.1281540393829346] |
69dca4ed-3828-4b71-9244-d0d45b13cd40 | neumesh-learning-disentangled-neural-mesh | 2207.11911 | null | https://arxiv.org/abs/2207.11911v1 | https://arxiv.org/pdf/2207.11911v1.pdf | NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing | Very recently neural implicit rendering techniques have been rapidly evolved and shown great advantages in novel view synthesis and 3D scene reconstruction. However, existing neural rendering methods for editing purposes offer limited functionality, e.g., rigid transformation, or not applicable for fine-grained editing... | ['Guofeng Zhang', 'Zhaopeng Cui', 'yinda zhang', 'Hujun Bao', 'Junyi Zeng', 'Chong Bao', 'Bangbang Yang'] | 2022-07-25 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.12382078e-01 -1.02857001e-01 6.84316531e-02 -4.30472076e-01
-3.22100878e-01 -4.58949506e-01 6.37360573e-01 -4.14805233e-01
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-2.21157938e-01 -1.09310281e+00 -7.16465592e-01 -6.81032181e-01
1.56451270e-01 4.50300485e-01 -1.18952364e-01 -3.48314434... | [9.145861625671387, -3.345402956008911] |
3c18ad70-eb41-458d-987d-43b818191440 | covered-collaborative-robot-environment | 2302.12656 | null | https://arxiv.org/abs/2302.12656v2 | https://arxiv.org/pdf/2302.12656v2.pdf | COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentation | Safe human-robot collaboration (HRC) has recently gained a lot of interest with the emerging Industry 5.0 paradigm. Conventional robots are being replaced with more intelligent and flexible collaborative robots (cobots). Safe and efficient collaboration between cobots and humans largely relies on the cobot's comprehens... | ['Hans Wernher van de Venn', 'Davide Scaramuzza', 'Fatemeh Mohammadi Amin', 'Charith Munasinghe'] | 2023-02-24 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-5.92543706e-02 2.47250885e-01 4.35450137e-01 -4.14377600e-01
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-1.42706707e-01 1.14546752e+00 6.84895396e-01 -5.23854256... | [5.13912296295166, 0.32238081097602844] |
b71d04cc-1bd4-448a-9fb1-d7af8044b672 | deep-neural-models-for-color-discrimination | 2012.14402 | null | https://arxiv.org/abs/2012.14402v1 | https://arxiv.org/pdf/2012.14402v1.pdf | Deep Neural Models for color discrimination and color constancy | Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance with varying cues. Inputs to the networks consisted of the cone excitations in 3D-rendered images of 2115 different 3D-shapes, with spectral... | ['Karl R. Gegenfurtner', 'Felix A. Wichmann', 'Roland W. Fleming', 'Heiko H. Schütt', 'Arash Akbarinia', 'Alban Flachot'] | 2020-12-28 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 3.80473733e-02 -5.68661809e-01 3.75737309e-01 -2.72638530e-01
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-5.68824708e-01 6.13252103e-01 -1.83462307e-01 -4.09923643e-01
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6.44208193e-02 -1.18102647e-01 2.79892802e-01 -1.75057888... | [10.430401802062988, -2.511734962463379] |
6f393da3-61c4-4ff4-b9b6-e5798f60cd0b | pointhop-a-lightweight-learning-model-on | 2002.03281 | null | https://arxiv.org/abs/2002.03281v2 | https://arxiv.org/pdf/2002.03281v2.pdf | PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification | The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing ... | ['C. -C. Jay Kuo', 'Yifan Wang', 'Shan Liu', 'Pranav Kadam', 'Min Zhang'] | 2020-02-09 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 7.74917798e-03 -2.36311436e-01 -2.33337834e-01 -3.67585152e-01
-6.80259109e-01 -2.44081482e-01 4.44951475e-01 5.42378902e-01
-3.89789879e-01 3.74531150e-01 -3.22825760e-01 -3.08699369e-01
-3.63518029e-01 -6.47139788e-01 -4.93635267e-01 -5.29954433e-01
-4.66716647e-01 5.44539094e-01 1.52511418e-01 -8.65510106... | [7.914428234100342, -3.4765255451202393] |
c4eda250-e0ed-4739-ba83-044f458d32fc | divide-and-contrast-self-supervised-learning | 2105.08054 | null | https://arxiv.org/abs/2105.08054v1 | https://arxiv.org/pdf/2105.08054v1.pdf | Divide and Contrast: Self-supervised Learning from Uncurated Data | Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data such as ImageNet. We explore the effects of contrastive learning from larger, less-curated image datasets such as YFCC, and find there is indee... | ['Aaron van den Oord', 'Olivier J. Henaff', 'Yonglong Tian'] | 2021-05-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Tian_Divide_and_Contrast_Self-Supervised_Learning_From_Uncurated_Data_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Tian_Divide_and_Contrast_Self-Supervised_Learning_From_Uncurated_Data_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.41304445e-01 3.32719207e-01 -3.96157742e-01 -5.15207171e-01
-8.39301884e-01 -6.05700672e-01 6.09162867e-01 1.62581936e-01
-8.82411003e-01 8.04028332e-01 3.02978963e-01 -3.12705398e-01
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2.66589951e-02 6.19920433e-01 2.11903706e-01 -7.94232190... | [9.518661499023438, 2.9697227478027344] |
2bb15a97-91ed-4079-b48a-9005f3bd5f2a | on-the-influence-of-masking-policies-in | 2104.08840 | null | https://arxiv.org/abs/2104.08840v2 | https://arxiv.org/pdf/2104.08840v2.pdf | On the Influence of Masking Policies in Intermediate Pre-training | Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline. Prior work has shown that inserting an intermediate pre-training stage, using heuristic masking policies for masked language modeling (MLM), can significantly improve final performance. However, it is still unclear (1)... | ['Madian Khabsa', 'Xiang Ren', 'Wen-tau Yih', 'Hao Ma', 'Benjamin Bolte', 'Sinong Wang', 'Belinda Z. Li', 'Qinyuan Ye'] | 2021-04-18 | null | https://aclanthology.org/2021.emnlp-main.573 | https://aclanthology.org/2021.emnlp-main.573.pdf | emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [ 4.39933538e-01 2.47877195e-01 -2.93342829e-01 -5.41827261e-01
-1.06208766e+00 -6.01812065e-01 6.35882258e-01 2.34651923e-01
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2.14023992e-01 5.43498933e-01 1.23531945e-01 -2.36058623... | [10.593392372131348, 9.122764587402344] |
44af1e59-7c61-4818-a031-8b17081b60ad | self-adaptive-matrix-completion-for-heart | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper.pdf | Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions | Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain op... | ['Xavier Alameda-Pineda', 'Jeffrey F. Cohn', 'Elisa Ricci', 'Sergey Tulyakov', 'Lijun Yin', 'Nicu Sebe'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['heart-rate-estimation'] | ['medical'] | [ 2.90607154e-01 1.26426771e-01 -1.45498544e-01 -3.98014635e-01
-2.13449761e-01 -1.67054996e-01 2.64328390e-01 -2.44119555e-01
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-2.54278541e-01 -4.62318331e-01 -2.09760264e-01 -4.35389876... | [13.885655403137207, 2.722783327102661] |
1a59a1b2-b958-43fb-b4f9-a159fdc9ca93 | hard-net-hardness-aware-discrimination | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1360_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560409.pdf | HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction | Predicting the class label from the partially observed activity sequence is a very hard task, as the observed early segments of different activities can be very similar. In this paper, we propose a novel Hardness-AwaRe Discrimination Network (HARD-Net) to specifically investigate the relationships between the similar a... | ['Ling-Yu Duan', 'Wei zhang', 'Tianjiao Li', 'Jun Liu'] | null | null | null | null | eccv-2020-8 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 6.21209741e-01 -1.45469718e-02 -5.39814413e-01 -3.82130891e-01
-7.35461056e-01 -6.75511420e-01 3.41369927e-01 -9.63957831e-02
-7.78413936e-02 8.92552972e-01 6.94321990e-02 -1.91121638e-01
-1.97440192e-01 -6.71095252e-01 -7.92561829e-01 -7.81812370e-01
-4.34099942e-01 4.71996188e-01 6.04354799e-01 1.92607000... | [8.261638641357422, 0.8878798484802246] |
3bfeb63f-c294-4400-b025-11abf1189281 | compositional-zero-shot-learning-via-fine | null | null | http://proceedings.neurips.cc/paper/2020/hash/e58cc5ca94270acaceed13bc82dfedf7-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e58cc5ca94270acaceed13bc82dfedf7-Paper.pdf | Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition | We develop a novel generative model for zero-shot learning to recognize fine-grained unseen classes without training samples. Our observation is that generating holistic features of unseen classes fails to capture every attribute needed to distinguish small differences among classes. We propose a feature composition fr... | ['Ehsan Elhamifar', 'Dat Huynh'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.66348749e-01 -3.14846665e-01 2.34100297e-02 -7.38745332e-01
-9.57456768e-01 -7.26096690e-01 6.39089286e-01 1.37391791e-01
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-4.66897339e-02 -1.37117279e+00 -6.17018998e-01 -1.05378735e+00
3.42315555e-01 5.48147202e-01 2.97213823e-01 -9.19264928... | [9.93409252166748, 2.4726362228393555] |
6a89000f-605e-4efc-b751-2c2e73a85bfc | copent-estimating-copula-entropy-in-r | 2005.14025 | null | https://arxiv.org/abs/2005.14025v3 | https://arxiv.org/pdf/2005.14025v3.pdf | copent: Estimating Copula Entropy and Transfer Entropy in R | Statistical independence and conditional independence are two fundamental concepts in statistics and machine learning. Copula Entropy is a mathematical concept defined by Ma and Sun for multivariate statistical independence measuring and testing, and also proved to be closely related to conditional independence (or tra... | ['Jian Ma'] | 2020-05-27 | null | null | null | null | ['mutual-information-estimation', 'statistical-independence-testing'] | ['methodology', 'methodology'] | [-4.50339288e-01 -2.41041169e-01 -4.29850638e-01 -4.37578261e-01
-3.07979435e-01 -4.37849879e-01 2.40907028e-01 2.41343215e-01
-1.01054378e-01 1.29200721e+00 -1.73299745e-01 -4.51094657e-01
-5.35131335e-01 -7.45777249e-01 -3.99429739e-01 -7.65801966e-01
-9.41416800e-01 5.40470600e-01 -2.82124847e-01 2.93037802... | [7.518985748291016, 4.5362935066223145] |
01d2b5c0-1221-4536-a68e-7a876a522ed8 | corruption-is-not-all-bad-incorporating | 2010.06137 | null | https://arxiv.org/abs/2010.06137v1 | https://arxiv.org/pdf/2010.06137v1.pdf | Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring | Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this ... | ['Kentaro Inui', 'Hiroki Ouchi', 'Paul Reisert', 'Naoya Inoue', 'Farjana Sultana Mim'] | 2020-10-13 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 2.36991003e-01 4.74685580e-01 -4.42545980e-01 -5.03160000e-01
-1.07730520e+00 -6.00505471e-01 7.14913845e-01 9.04772103e-01
-2.83707380e-01 9.23669100e-01 9.32710826e-01 -4.56688017e-01
1.03021137e-01 -7.75381267e-01 -2.69045293e-01 -1.42413363e-01
4.69998300e-01 1.86738566e-01 1.28250226e-01 -3.12457949... | [11.132820129394531, 9.380640029907227] |
e566690f-ea44-447c-8c4b-1d6109d084a5 | the-diabetic-buddy-a-diet-regulator | 2101.03203 | null | https://arxiv.org/abs/2101.03203v1 | https://arxiv.org/pdf/2101.03203v1.pdf | The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics | The prevalence of Diabetes mellitus (DM) in the Middle East is exceptionally high as compared to the rest of the world. In fact, the prevalence of diabetes in the Middle East is 17-20%, which is well above the global average of 8-9%. Research has shown that food intake has strong connections with the blood glucose leve... | ['Marwa Qaraqe', 'Amir Sohail', 'Kashif Ahmad', 'Muhammad Usman'] | 2021-01-08 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-2.86436945e-01 -4.04640645e-01 -6.45407796e-01 -8.04891706e-01
-3.37456018e-01 -1.29521757e-01 -7.49491304e-02 8.04259181e-01
-3.23693097e-01 4.04850841e-01 3.51613849e-01 -1.56368986e-01
6.62659854e-02 -1.16396177e+00 -5.46085596e-01 -6.06737912e-01
-3.68676245e-01 2.92433411e-01 -5.11663616e-01 -1.94634721... | [11.557198524475098, 4.411873817443848] |
d6ce39a4-f081-4d84-937c-81b7bd9f151f | icdar-2021-competition-on-integrated-circuit | 2107.05279 | null | https://arxiv.org/abs/2107.05279v1 | https://arxiv.org/pdf/2107.05279v1.pdf | ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment | With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of ... | ['Lixin Fan', 'Yipeng Sun', 'Lianwen Jin', 'Chee Seng Chan', 'Yuliang Liu', 'Xinyu Wang', 'Yeong Khang Lee', 'Akmalul Khairi Bin Nazaruddin', 'Chun Chet Ng'] | 2021-07-12 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.43045121e-01 1.04381107e-01 2.12554246e-01 -3.36457103e-01
-7.59130120e-01 -6.21820807e-01 4.43954468e-01 6.30746856e-02
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-1.36212200e-01 -5.49022019e-01 -4.65956420e-01 -2.06708044e-01
5.21699965e-01 2.77403712e-01 1.24128098e-02 -2.15075970... | [11.797093391418457, 2.273937463760376] |
ff3b4c20-ee56-4525-9a60-97586a82fdf5 | quantum-data-center-theories-and-applications | 2207.14336 | null | https://arxiv.org/abs/2207.14336v2 | https://arxiv.org/pdf/2207.14336v2.pdf | Quantum Data Center: Theories and Applications | In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantu... | ['Liang Jiang', 'Connor T. Hann', 'Junyu Liu'] | 2022-07-28 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 8.58120471e-02 2.96557527e-02 -2.33749539e-01 -3.23997378e-01
-1.08789563e+00 -6.86743736e-01 2.85091609e-01 9.64929760e-02
-4.59256887e-01 6.33267283e-01 1.19889021e-01 -5.77211797e-01
-2.49881580e-01 -1.66021097e+00 -4.38551545e-01 -6.11490071e-01
-2.21119240e-01 6.63591564e-01 9.39875990e-02 -4.33069378... | [5.577793121337891, 4.953103542327881] |
5cb74183-bb78-41bd-9fd6-dea19b359960 | a-system-for-real-time-interactive-analysis | 2001.01215 | null | https://arxiv.org/abs/2001.01215v2 | https://arxiv.org/pdf/2001.01215v2.pdf | A System for Real-Time Interactive Analysis of Deep Learning Training | Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before... | ['Steven Drucker', 'Shital Shah', 'Roland Fernandez'] | 2020-01-05 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.67765240e-02 1.97967160e-02 4.05583590e-01 -4.89900529e-01
-1.49213478e-01 -7.66948283e-01 6.68659925e-01 4.95905399e-01
-9.54817161e-02 2.51514971e-01 -1.96143791e-01 -8.16834450e-01
-3.90583545e-01 -1.03032482e+00 -3.72313350e-01 -5.91879427e-01
-2.04765618e-01 8.50974739e-01 3.66729259e-01 1.71808109... | [8.633519172668457, 4.670372486114502] |
86f72dff-b6ba-4a78-bc71-16479cb621d1 | from-motor-control-to-team-play-in-simulated | 2105.12196 | null | https://arxiv.org/abs/2105.12196v1 | https://arxiv.org/pdf/2105.12196v1.pdf | From Motor Control to Team Play in Simulated Humanoid Football | Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals defined on much longer timescales, and in terms of relations that extend... | ['Nicolas Heess', 'Thore Graepel', 'Karl Tuyls', 'Brendan D. Tracey', 'Tuomas Haarnoja', 'Paul Muller', 'Markus Wulfmeier', 'H. Francis Song', 'Saran Tunyasuvunakool', 'Luke Marris', 'Leonard Hasenclever', 'Noah Y. Siegel', 'Abbas Abdolmaleki', 'Shayegan Omidshafiei', 'Yuval Tassa', 'Wojciech M. Czarnecki', 'Daniel Hen... | 2021-05-25 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 3.88673320e-02 9.28151682e-02 -1.22926950e-01 2.82936215e-01
-4.86192852e-01 -5.14724731e-01 7.08355248e-01 2.23665118e-01
-5.50928056e-01 9.40336585e-01 1.28947392e-01 -4.54746224e-02
-6.12362385e-01 -6.11139953e-01 -7.54111052e-01 -7.96997249e-01
-5.64191163e-01 8.06224048e-01 3.54866832e-01 -8.66470456... | [4.4611735343933105, 1.1390409469604492] |
151fc439-8298-4179-88f8-1988b4c194c2 | loc-vae-learning-structurally-localized | 2210.00506 | null | https://arxiv.org/abs/2210.00506v1 | https://arxiv.org/pdf/2210.00506v1.pdf | Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval | Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the r... | ['Kenichi Oishi', 'Hitoshi Iyatomi', 'Yuto Onga', 'Kumpei Ikuta', 'Kei Nishimaki'] | 2022-10-02 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-1.09513260e-01 2.51829196e-02 -2.53128279e-02 -2.47799993e-01
-8.06871593e-01 -4.27472852e-02 2.01680064e-01 3.87642890e-01
-5.52535117e-01 4.76020753e-01 6.32213116e-01 -1.86217323e-01
-5.94611824e-01 -8.22774410e-01 -3.53892267e-01 -8.16665411e-01
-4.16487694e-01 6.74483001e-01 5.29295132e-02 -9.09536779... | [14.284826278686523, -1.6713104248046875] |
b672616f-5879-4d96-8888-d59cf740ef70 | a-joint-model-for-semantic-sequences-frames | null | null | https://aclanthology.org/K17-1019 | https://aclanthology.org/K17-1019.pdf | A Joint Model for Semantic Sequences: Frames, Entities, Sentiments | Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task. These events typically carry multiple aspects of semantics including actions, entities and emotions. Not only does each individual aspect contribute to the meaning of the story, so does the interaction ... | ['Haoruo Peng', 'Snigdha Chaturvedi', 'Dan Roth'] | 2017-08-01 | null | null | null | conll-2017-8 | ['cloze-test'] | ['natural-language-processing'] | [ 4.18846428e-01 4.93365467e-01 -3.31042588e-01 -7.32692838e-01
-7.29500830e-01 -6.72365487e-01 8.72703612e-01 5.57960689e-01
-2.14312047e-01 6.26347601e-01 1.06472301e+00 -5.14057949e-02
1.69720232e-01 -9.34090436e-01 -9.58303154e-01 -1.50753394e-01
1.16660178e-01 4.48543280e-01 1.69134408e-01 -5.61835527... | [10.809053421020508, 8.985486030578613] |
6f69ddb9-aacf-403e-b513-5de78511d389 | covfefe-a-computer-vision-approach-for | 1809.09293 | null | http://arxiv.org/abs/1809.09293v1 | http://arxiv.org/pdf/1809.09293v1.pdf | Covfefe: A Computer Vision Approach For Estimating Force Exertion | Cumulative exposure to repetitive and forceful activities may lead to
musculoskeletal injuries which not only reduce workers' efficiency and
productivity, but also affect their quality of life. Thus, widely accessible
techniques for reliable detection of unsafe muscle force exertion levels for
human activity is necessa... | ['Denny Yu', 'Mayank Gupta', 'Vaneet Aggarwal', 'Jae Joong Lee', 'Hamed Asadi'] | 2018-09-25 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 4.58483309e-01 -3.54128741e-02 -1.03331305e-01 1.36822656e-01
-2.19672486e-01 -3.75958383e-01 1.02646425e-01 -4.48874414e-01
-2.00151592e-01 7.24198401e-01 -8.63927975e-02 2.47654513e-01
-5.47344267e-01 -4.30528462e-01 -2.32905552e-01 -7.21449733e-01
-1.25406519e-01 -2.23509267e-01 -3.70494425e-02 -9.47819501... | [13.777997016906738, 2.900695323944092] |
1056fc67-5654-4139-88b0-6509a08f33f9 | deer-a-data-efficient-language-model-for | 2012.15283 | null | https://arxiv.org/abs/2012.15283v3 | https://arxiv.org/pdf/2012.15283v3.pdf | ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning | While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event tempora... | ['Nanyun Peng', 'Xiang Ren', 'Rujun Han'] | 2020-12-30 | null | https://aclanthology.org/2021.emnlp-main.436 | https://aclanthology.org/2021.emnlp-main.436.pdf | emnlp-2021-11 | ['continual-pretraining'] | ['methodology'] | [ 3.04261059e-01 4.72468466e-01 -2.43325949e-01 -5.39505005e-01
-1.23027647e+00 -4.17343467e-01 1.10653365e+00 7.57450283e-01
-5.75295508e-01 7.72110462e-01 3.15203786e-01 -5.76220810e-01
-3.55264693e-01 -7.96875954e-01 -7.02277541e-01 -1.92639574e-01
-3.73833746e-01 4.76363093e-01 4.86846596e-01 -1.53862223... | [9.061918258666992, 9.131800651550293] |
b4b0e855-0abd-4e03-8eff-1753ae197f3e | transducer-based-language-embedding-for | 2204.03888 | null | https://arxiv.org/abs/2204.03888v2 | https://arxiv.org/pdf/2204.03888v2.pdf | Transducer-based language embedding for spoken language identification | The acoustic and linguistic features are important cues for the spoken language identification (LID) task. Recent advanced LID systems mainly use acoustic features that lack the usage of explicit linguistic feature encoding. In this paper, we propose a novel transducer-based language embedding approach for LID tasks by... | ['Hisashi Kawai', 'Xugang Lu', 'Peng Shen'] | 2022-04-08 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-0.45604488 -0.14864185 -0.05721155 -0.35952166 -1.3895006 -0.32705575
0.510879 -0.3085734 -0.69046634 0.34087345 0.56099945 -0.0902933
0.20147899 -0.16534609 -0.22646286 -0.69022346 0.30916643 0.08173372
-0.18444379 0.02291152 -0.23253016 0.1339875 -1.5353473 -0.03449308
0.91716695 1.1043777 0.2... | [14.291160583496094, 6.33777379989624] |
43c4127b-b696-4caa-88e7-3feab53f5b62 | assume-augment-and-learn-unsupervised-few | 1902.09884 | null | http://arxiv.org/abs/1902.09884v3 | http://arxiv.org/pdf/1902.09884v3.pdf | Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation | The field of few-shot learning has been laboriously explored in the
supervised setting, where per-class labels are available. On the other hand,
the unsupervised few-shot learning setting, where no labels of any kind are
required, has seen little investigation. We propose a method, named Assume,
Augment and Learn or AA... | ['Amos Storkey', 'Antreas Antoniou'] | 2019-02-26 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.60765982e-01 1.25847116e-01 -5.07860184e-01 -5.54179788e-01
-6.92349136e-01 -2.60326564e-01 7.12512434e-01 1.85695458e-02
-5.39105535e-01 8.34215760e-01 2.38656662e-02 5.45032471e-02
8.62609297e-02 -8.67757857e-01 -5.80208898e-01 -7.56363750e-01
1.88499331e-01 5.98299444e-01 3.48612785e-01 -4.70746942... | [10.021219253540039, 3.009561777114868] |
b01e6e14-07ae-4484-8d6d-0f47b9d67ee2 | 190807721 | 1908.07721 | null | https://arxiv.org/abs/1908.07721v2 | https://arxiv.org/pdf/1908.07721v2.pdf | Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text | Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natur... | ['Tong Ruan', 'Yangming Zhou', 'Kui Xue', 'Huanhuan Zhang', 'Zhiyuan Ma', 'Ping He'] | 2019-08-21 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.15772046e-01 2.05549583e-01 -5.83626390e-01 -4.05027032e-01
-6.89713836e-01 9.64320228e-02 2.25260198e-01 3.16299349e-01
-7.48170793e-01 8.22163701e-01 3.70465338e-01 -3.30498517e-01
-2.75879651e-01 -7.13249147e-01 -2.70852953e-01 -5.66733062e-01
-2.96773046e-01 3.83669406e-01 1.83284178e-01 -7.63703808... | [8.727280616760254, 8.953407287597656] |
6c356133-9a01-4da3-9ce8-b33d3284a535 | understanding-the-world-to-solve-social | 2305.11358 | null | https://arxiv.org/abs/2305.11358v1 | https://arxiv.org/pdf/2305.11358v1.pdf | Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning | Social dilemmas are situations where groups of individuals can benefit from mutual cooperation but conflicting interests impede them from doing so. This type of situations resembles many of humanity's most critical challenges, and discovering mechanisms that facilitate the emergence of cooperative behaviors is still an... | ['Luis Felipe Giraldo', 'Nicanor Quijano', 'Manuel Rios'] | 2023-05-19 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.48906752e-01 4.63093042e-01 3.12247217e-01 1.59028634e-01
2.22282335e-01 -4.47901368e-01 5.67585289e-01 3.32715750e-01
-8.07544231e-01 1.30015969e+00 -2.04139188e-01 1.40867963e-01
-5.55226624e-01 -9.14798498e-01 -4.11315203e-01 -1.08711994e+00
-8.86893094e-01 5.80884635e-01 2.77500480e-01 -9.59343791... | [3.8394436836242676, 2.168729543685913] |
fcd921c8-9105-43c7-a20c-fe74da85950f | sosr-source-free-image-super-resolution-with | 2303.17783 | null | https://arxiv.org/abs/2303.17783v2 | https://arxiv.org/pdf/2303.17783v2.pdf | SOSR: Source-Free Image Super-Resolution with Wavelet Augmentation Transformer | Real-world images taken by different cameras with different degradation kernels often result in a cross-device domain gap in image super-resolution. A prevalent attempt to this issue is unsupervised domain adaptation (UDA) that needs to access source data. Considering privacy policies or transmission restrictions of da... | ['Ran He', 'Lei Zhang', 'Huaibo Huang', 'Xiaoqiang Zhou', 'Yuang Ai'] | 2023-03-31 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 7.70311415e-01 2.00795442e-01 -4.82511520e-01 -5.48421681e-01
-1.26617980e+00 -3.32872242e-01 2.14231297e-01 -3.93748254e-01
-9.55912769e-02 9.59038973e-01 3.41327220e-01 4.19549681e-02
-9.18419659e-02 -7.19672203e-01 -7.42037654e-01 -7.16221690e-01
4.81938988e-01 -1.14799075e-01 1.05003424e-01 -5.45739233... | [11.04179573059082, -2.070509195327759] |
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