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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
baf41ad0-c510-4f9d-9828-07cbdb38c593 | animatable-neural-radiance-fields-from | 2106.13629 | null | https://arxiv.org/abs/2106.13629v2 | https://arxiv.org/pdf/2106.13629v2.pdf | Animatable Neural Radiance Fields from Monocular RGB Videos | We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos. Our approach extends neural radiance fields (NeRF) to the dynamic scenes with human movements via introducing explicit pose-guided deformation while learning the scene representation network. In part... | ['Huchuan Lu', 'Xu Jia', 'Linchao Bao', 'Xuefei Zhe', 'Di Kang', 'Ying Zhang', 'Jianchuan Chen'] | 2021-06-25 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 8.11079666e-02 1.77091941e-01 4.02204335e-01 -2.13622853e-01
-2.54820466e-01 -4.08344686e-01 5.47958434e-01 -5.28563917e-01
-3.56100947e-01 7.63816535e-01 1.35490939e-01 4.37512219e-01
1.69573873e-01 -7.65317798e-01 -9.76812899e-01 -7.04632044e-01
1.38816327e-01 5.43246865e-01 6.33018985e-02 -2.14098871... | [7.21701192855835, -1.2745369672775269] |
7345cacd-0723-4451-bb61-c32131281431 | compartmental-models-for-covid-19-and-control | 2203.02860 | null | https://arxiv.org/abs/2203.02860v1 | https://arxiv.org/pdf/2203.02860v1.pdf | Compartmental Models for COVID-19 and Control via Policy Interventions | We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our goal is to study the impact of various modeling assumptions and motivate policy interventions enacted to limit the spread of infectious diseases. Usi... | ['Noah Kasmanoff', 'Swapneel Mehta'] | 2022-03-06 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 8.07958543e-02 -6.58731982e-02 -1.48390740e-01 -2.94102747e-02
-5.52726388e-01 -5.41265130e-01 7.31756687e-01 3.27360064e-01
-5.21107376e-01 9.08995450e-01 3.03481996e-01 -1.02623248e+00
-3.69307667e-01 -7.49130726e-01 -5.79917371e-01 -5.87326050e-01
-3.83031547e-01 1.17475927e+00 1.25244126e-01 3.51875299... | [6.034331321716309, 4.379302501678467] |
f6ff5763-0b99-4bd8-ae78-903703a818ae | two-stage-convolutional-neural-network-for | 1803.04054 | null | http://arxiv.org/abs/1803.04054v2 | http://arxiv.org/pdf/1803.04054v2.pdf | Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification | This paper explores the problem of breast tissue classification of microscopy
images. Based on the predominant cancer type the goal is to classify images
into four categories of normal, benign, in situ carcinoma, and invasive
carcinoma. Given a suitable training dataset, we utilize deep learning
techniques to address t... | ['Mehran Ebrahimi', 'Kamyar Nazeri', 'Azad Aminpour'] | 2018-03-11 | null | null | null | null | ['breast-cancer-histology-image-classification'] | ['medical'] | [ 4.66629356e-01 2.50875175e-01 -6.60335049e-02 -2.94020861e-01
-1.01505446e+00 -1.54372200e-01 4.63001609e-01 4.33263749e-01
-4.89184946e-01 5.21625996e-01 -1.80431649e-01 -4.57492054e-01
1.26686826e-01 -7.52424598e-01 -8.29559207e-01 -1.29642487e+00
1.92110557e-02 2.16485217e-01 1.01228349e-01 2.13795722... | [15.093503952026367, -2.9432520866394043] |
0a0bb4aa-fe18-46d4-a008-863606607f5d | liver-segmentation-from-multimodal-images | 1910.10504 | null | http://arxiv.org/abs/1910.10504v1 | http://arxiv.org/pdf/1910.10504v1.pdf | Liver Segmentation from Multimodal Images using HED-Mask R-CNN | Precise segmentation of the liver is critical for computer-aided diagnosis
such as pre-evaluation of the liver for living donor-based transplantation
surgery. This task is challenging due to the weak boundaries of organs,
countless anatomical variations, and the complexity of the background. Computed
tomography (CT) sc... | [] | 2019-10-23 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-4.37919721e-02 -1.66686177e-01 3.70693915e-02 -1.94977000e-01
-3.27023804e-01 -4.89906520e-01 1.06120773e-01 1.51724964e-01
-5.76217949e-01 3.12309295e-01 1.39893517e-01 -4.26801473e-01
-3.92512083e-02 -6.06898189e-01 -1.13743402e-01 -7.91221857e-01
-7.02115834e-01 5.11041224e-01 1.03469014e-01 1.11803293... | [14.477005958557129, -2.6889665126800537] |
e2403e54-b533-4c6d-897b-f744b84e91c6 | a-state-transition-model-for-mobile | 2207.03099 | null | https://arxiv.org/abs/2207.03099v1 | https://arxiv.org/pdf/2207.03099v1.pdf | A State Transition Model for Mobile Notifications via Survival Analysis | Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to users, they constantly face decisions on what to send, when and how. A lack of research and methodology commonly leads to heuristic decision... | ['Romer Rosales', 'Shipeng Yu', 'Shaunak Chatterjee', 'Jing Zhang', 'Yiping Yuan'] | 2022-07-07 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.00548255e-01 -1.73028689e-02 -7.02462614e-01 -4.90284443e-01
-5.82244277e-01 -5.68910599e-01 3.77559692e-01 2.15739533e-01
-3.85447353e-01 7.85328150e-01 2.45153040e-01 -9.70059812e-01
-3.11139941e-01 -6.61796212e-01 -1.80994734e-01 -1.72349960e-01
-2.03840863e-02 3.12007219e-01 2.23218232e-01 -9.80135873... | [10.222745895385742, 5.852547645568848] |
463fb70a-8e46-4805-b493-25b946184054 | repainting-and-imitating-learning-for-lane | 2210.05097 | null | https://arxiv.org/abs/2210.05097v1 | https://arxiv.org/pdf/2210.05097v1.pdf | Repainting and Imitating Learning for Lane Detection | Current lane detection methods are struggling with the invisibility lane issue caused by heavy shadows, severe road mark degradation, and serious vehicle occlusion. As a result, discriminative lane features can be barely learned by the network despite elaborate designs due to the inherent invisibility of lanes in the w... | ['Errui Ding', 'Xiao Tan', 'Wei zhang', 'Zhikang Zou', 'Liang Du', 'Xiaoqing Ye', 'Minyue Jiang', 'Yue He'] | 2022-10-11 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-1.50379553e-01 2.78137714e-01 -1.46447822e-01 -4.19817120e-01
-6.29577339e-01 -7.81490266e-01 4.10336196e-01 -5.71973801e-01
-2.11559221e-01 6.18307471e-01 -2.79797614e-01 -4.40987676e-01
1.87673524e-01 -7.46362984e-01 -9.54711556e-01 -8.31923544e-01
4.10208479e-02 1.18909925e-01 5.21091521e-01 -3.23557407... | [8.022945404052734, -1.4933087825775146] |
d01f2963-47e9-44e6-af33-0cc69791f530 | plot2api-recommending-graphic-api-from-plot | 2104.01032 | null | https://arxiv.org/abs/2104.01032v1 | https://arxiv.org/pdf/2104.01032v1.pdf | Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network | Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a ... | ['Dan Yang', 'Bei Wang', 'Xin Xia', 'Meng Yan', 'Zhongxin Liu', 'Sheng Huang', 'Zeyu Wang'] | 2021-04-02 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [-5.89345843e-02 -3.19998860e-01 -1.65501863e-01 -4.72294539e-01
-2.40199491e-01 -5.29698193e-01 2.54304856e-01 -1.14823140e-01
1.60858154e-01 -3.67371738e-02 -8.40913802e-02 -7.77809322e-01
-1.75401866e-01 -6.63162947e-01 -7.65002251e-01 -5.17477632e-01
2.51723796e-01 5.85857481e-02 3.30111645e-02 7.05128461... | [11.321520805358887, 2.192783832550049] |
7ec11269-83ac-4f38-9601-ad89370b05b7 | predicting-clinical-outcome-of-stroke | 1907.10419 | null | https://arxiv.org/abs/1907.10419v3 | https://arxiv.org/pdf/1907.10419v3.pdf | Predicting Clinical Outcome of Stroke Patients with Tractographic Feature | The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the t... | ['Po-Yu Kao', 'Jefferson W. Chen', 'B. S. Manjunath'] | 2019-07-22 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [-2.54709363e-01 -4.92669344e-01 -4.64473099e-01 -2.02406064e-01
-5.32515526e-01 -6.19656086e-01 4.56886798e-01 3.07344168e-01
-7.18626618e-01 7.90810466e-01 8.96927476e-01 -3.53355855e-01
-3.42267036e-01 -1.00174391e+00 -2.48670563e-01 -6.33577526e-01
-3.20714235e-01 5.44168949e-01 4.19017851e-01 8.87848362... | [14.192084312438965, -2.0177011489868164] |
557928d5-6c84-4537-9569-4747bd92b98e | layoutgan-generating-graphic-layouts-with | 1901.06767 | null | http://arxiv.org/abs/1901.06767v1 | http://arxiv.org/pdf/1901.06767v1.pdf | LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators | Layout is important for graphic design and scene generation. We propose a
novel Generative Adversarial Network, called LayoutGAN, that synthesizes
layouts by modeling geometric relations of different types of 2D elements. The
generator of LayoutGAN takes as input a set of randomly-placed 2D graphic
elements and uses se... | ['Jianming Zhang', 'Aaron Hertzmann', 'Tingfa Xu', 'Jimei Yang', 'Jianan Li'] | 2019-01-21 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.12361145e-01 2.42382854e-01 2.86149472e-01 -2.17720658e-01
-6.17002308e-01 -1.01305687e+00 7.42278755e-01 -5.20315826e-01
2.99871564e-01 4.97279346e-01 1.31134391e-01 -6.20523095e-01
2.30849072e-01 -1.23678529e+00 -1.14212573e+00 -2.76160389e-01
3.20256233e-01 4.66386229e-01 -4.06171024e-01 -2.57080466... | [11.609295845031738, -0.4226371943950653] |
03bcfa35-4013-400b-913c-69a96e84cb45 | normality-guided-distributional-reinforcement | 2208.13125 | null | https://arxiv.org/abs/2208.13125v2 | https://arxiv.org/pdf/2208.13125v2.pdf | Normality-Guided Distributional Reinforcement Learning for Continuous Control | Learning a predictive model of the mean return, or value function, plays a critical role in many reinforcement learning algorithms. Distributional reinforcement learning (DRL) methods instead model the value distribution, which has been shown to improve performance in many settings. In this paper, we model the value di... | ['Andrew Perrault', 'Ju-Seung Byun'] | 2022-08-28 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-7.54398331e-02 2.23216206e-01 -6.28599644e-01 -1.85477793e-01
-7.15379119e-01 -6.59092307e-01 6.70984626e-01 4.99875039e-01
-8.77010286e-01 1.41469169e+00 4.85651456e-02 -4.82667267e-01
-4.65337008e-01 -7.10688829e-01 -7.83663452e-01 -6.84279084e-01
-3.58965963e-01 6.30796671e-01 3.04301232e-01 -3.24491560... | [4.196836948394775, 2.4997730255126953] |
afdad2db-efc2-4fad-8362-5d0956c565c1 | monocular-3d-object-reconstruction-with-gan | 2207.10061 | null | https://arxiv.org/abs/2207.10061v1 | https://arxiv.org/pdf/2207.10061v1.pdf | Monocular 3D Object Reconstruction with GAN Inversion | Recovering a textured 3D mesh from a monocular image is highly challenging, particularly for in-the-wild objects that lack 3D ground truths. In this work, we present MeshInversion, a novel framework to improve the reconstruction by exploiting the generative prior of a 3D GAN pre-trained for 3D textured mesh synthesis. ... | ['Chen Change Loy', 'Bo Dai', 'Chai Kiat Yeo', 'Zhongang Cai', 'Daxuan Ren', 'Junzhe Zhang'] | 2022-07-20 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.43789768e-01 5.20459354e-01 -2.79490463e-02 -1.62337527e-01
-8.11854124e-01 -6.33828878e-01 5.54578125e-01 -4.25660133e-01
5.05798459e-01 4.81784046e-01 2.57719129e-01 2.41328135e-01
1.27409011e-01 -1.13403380e+00 -1.24053776e+00 -8.49918544e-01
5.41359186e-01 9.27425623e-01 -1.81406513e-01 -7.06083477... | [8.89989948272705, -3.285189628601074] |
53b71516-01ed-4b73-84ec-7e30b24c0eda | knowledge-graph-question-answering | 2201.08174 | null | https://arxiv.org/abs/2201.08174v1 | https://arxiv.org/pdf/2201.08174v1.pdf | Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis | Data-driven systems need to be evaluated to establish trust in the scientific approach and its applicability. In particular, this is true for Knowledge Graph (KG) Question Answering (QA), where complex data structures are made accessible via natural-language interfaces. Evaluating the capabilities of these systems has ... | ['Ricardo Usbeck', 'Andreas Both', 'Longquan Jiang', 'Liubov Kovriguina', 'Xi Yan', 'Aleksandr Perevalov'] | 2022-01-20 | null | https://aclanthology.org/2022.lrec-1.321 | https://aclanthology.org/2022.lrec-1.321.pdf | lrec-2022-6 | ['graph-question-answering'] | ['graphs'] | [-5.59594512e-01 2.92568922e-01 3.92007781e-03 -4.52456832e-01
-7.75721133e-01 -9.51112688e-01 5.74334860e-01 5.69844246e-01
-1.18556947e-01 6.82371914e-01 2.57309049e-01 -5.08559823e-01
-5.17862201e-01 -9.69679713e-01 -6.74768865e-01 -2.28035212e-01
6.36914819e-02 7.86092520e-01 4.05066967e-01 -6.78875327... | [10.11627197265625, 7.954028129577637] |
cdb652cd-dcfe-43a7-8a3d-be4ce2efd339 | achieving-stable-training-of-reinforcement | 2307.00923 | null | https://arxiv.org/abs/2307.00923v1 | https://arxiv.org/pdf/2307.00923v1.pdf | Achieving Stable Training of Reinforcement Learning Agents in Bimodal Environments through Batch Learning | Bimodal, stochastic environments present a challenge to typical Reinforcement Learning problems. This problem is one that is surprisingly common in real world applications, being particularly applicable to pricing problems. In this paper we present a novel learning approach to the tabular Q-learning algorithm, tailored... | ['G. Cevora', 'N. Peace', 'E. Hurwitz'] | 2023-07-03 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.11352962e-02 -1.24289557e-01 -2.63826877e-01 -8.55549797e-02
-9.19071198e-01 -4.30166513e-01 5.03122389e-01 1.08976439e-01
-5.69158375e-01 1.34621942e+00 -2.49663934e-01 -4.50362682e-01
-3.88120085e-01 -8.26607227e-01 -6.01925850e-01 -8.54106605e-01
-7.34119058e-01 8.80767047e-01 3.86671275e-01 -6.16648376... | [4.150516986846924, 2.5090363025665283] |
d15dd5cb-901f-4cd2-826c-691e57991286 | speech-enhanced-and-noise-aware-networks-for | 2203.13696 | null | https://arxiv.org/abs/2203.13696v3 | https://arxiv.org/pdf/2203.13696v3.pdf | Speech-enhanced and Noise-aware Networks for Robust Speech Recognition | Compensation for channel mismatch and noise interference is essential for robust automatic speech recognition. Enhanced speech has been introduced into the multi-condition training of acoustic models to improve their generalization ability. In this paper, a noise-aware training framework based on two cascaded neural st... | ['Hsin-Min Wang', 'Yao-Fei Cheng', 'Yu Tsao', 'Pin-Yuan Chen', 'Hung-Shin Lee'] | 2022-03-25 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.85338810e-01 -1.63111612e-01 3.32994044e-01 -4.65369374e-01
-1.09151518e+00 8.80195424e-02 3.10881555e-01 -3.61527383e-01
-6.98965907e-01 3.86376649e-01 4.42683727e-01 -5.21927059e-01
2.03431234e-01 -3.07108402e-01 -5.87874115e-01 -8.95755649e-01
6.47119954e-02 -2.89083928e-01 3.33112814e-02 -1.54727325... | [14.843406677246094, 6.01363468170166] |
2d8ba437-a203-4f04-aaf5-e3eba7f44530 | aspect-sentiment-quad-prediction-as | 2110.00796 | null | https://arxiv.org/abs/2110.00796v1 | https://arxiv.org/pdf/2110.00796v1.pdf | Aspect Sentiment Quad Prediction as Paraphrase Generation | Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of p... | ['Wai Lam', 'Lidong Bing', 'Yifei Yuan', 'Xin Li', 'Yang Deng', 'Wenxuan Zhang'] | 2021-10-02 | null | https://aclanthology.org/2021.emnlp-main.726 | https://aclanthology.org/2021.emnlp-main.726.pdf | emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.48227233e-01 -3.46537568e-02 2.81718597e-02 -6.86642289e-01
-1.03153968e+00 -7.05087900e-01 5.78842342e-01 3.68440658e-01
-8.74041691e-02 3.78740102e-01 4.64413136e-01 -2.20799059e-01
2.10406423e-01 -7.40514934e-01 -6.83445811e-01 -5.57786524e-01
7.33203232e-01 3.14775914e-01 -2.26849914e-01 -4.74611968... | [11.482150077819824, 6.6427812576293945] |
2d427244-9951-4bef-8046-2937c817f6ab | neural-document-embeddings-for-intensive-care | 1612.00467 | null | http://arxiv.org/abs/1612.00467v1 | http://arxiv.org/pdf/1612.00467v1.pdf | Neural Document Embeddings for Intensive Care Patient Mortality Prediction | We present an automatic mortality prediction scheme based on the unstructured
textual content of clinical notes. Proposing a convolutional document embedding
approach, our empirical investigation using the MIMIC-III intensive care
database shows significant performance gains compared to previously employed
methods such... | ['Florian Schmidt', 'Stephanie L. Hyland', 'Paulina Grnarova', 'Carsten Eickhoff'] | 2016-12-01 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-1.27771690e-01 3.19557816e-01 -1.41338944e-01 -4.33364175e-02
-9.85601783e-01 -6.69579506e-02 4.77038831e-01 1.11886966e+00
-7.06171274e-01 7.33313262e-01 1.15423071e+00 -4.18361664e-01
-6.56007588e-01 -5.95619500e-01 3.22328240e-01 -7.68556356e-01
-7.57477760e-01 8.86668026e-01 -3.98258895e-01 1.78885698... | [7.974947452545166, 6.8873114585876465] |
be6a8e4e-e65f-4f94-896e-0c16d3e1c498 | commonsense-aware-prompting-for-controllable | 2302.01441 | null | https://arxiv.org/abs/2302.01441v1 | https://arxiv.org/pdf/2302.01441v1.pdf | Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation | Improving the emotional awareness of pre-trained language models is an emerging important problem for dialogue generation tasks. Although prior studies have introduced methods to improve empathetic dialogue generation, few have discussed how to incorporate commonsense knowledge into pre-trained language models for cont... | ['Halil Kilicoglu', 'Yiren Liu'] | 2023-02-02 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 1.03453584e-01 9.19822156e-01 -9.18055773e-02 -3.54830712e-01
-3.53533834e-01 -4.82901126e-01 1.03484654e+00 2.45552063e-02
-2.56144524e-01 1.06598568e+00 9.72550750e-01 -3.46864536e-02
4.23850745e-01 -9.15360808e-01 -4.01022956e-02 -8.96626860e-02
3.77758831e-01 4.49640632e-01 -3.23123246e-01 -9.61909771... | [13.053296089172363, 7.737351894378662] |
c1f39625-9a1d-4711-97d6-b2890ca20393 | continuous-online-extrinsic-calibration-of | 2306.13240 | null | https://arxiv.org/abs/2306.13240v1 | https://arxiv.org/pdf/2306.13240v1.pdf | Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR | Automated driving systems use multi-modal sensor suites to ensure the reliable, redundant and robust perception of the operating domain, for example camera and LiDAR. An accurate extrinsic calibration is required to fuse the camera and LiDAR data into a common spatial reference frame required by high-level perception f... | ['Stefan Milz', 'Florian Ölsner', 'Jeremy Tschirner', 'Jack Borer'] | 2023-06-22 | null | null | null | null | ['depth-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.72388386e-02 -8.14064145e-02 2.67193206e-02 -9.10314620e-01
-5.90738058e-01 -6.26343012e-01 3.23311776e-01 3.37012261e-02
-5.40115237e-01 4.53094184e-01 -6.33823872e-01 -7.21638184e-03
2.99058985e-02 -8.36581767e-01 -8.38416517e-01 -5.43683708e-01
5.73947787e-01 5.77866375e-01 5.02207339e-01 -7.15800226... | [7.601121425628662, -2.232048273086548] |
7f6ed10f-dc9e-4847-b813-a488d8e7d6cc | kosign-sign-language-translation-project | null | null | https://aclanthology.org/2022.sltat-1.9 | https://aclanthology.org/2022.sltat-1.9.pdf | KoSign Sign Language Translation Project: Introducing The NIASL2021 Dataset | We introduce a new sign language production (SLP) and sign language translation (SLT) dataset, NIASL2021, consisting of 201,026 Korean-KSL data pairs. KSL translations of Korean source texts are represented in three formats: video recordings, keypoint position data, and time-aligned gloss annotations for each hand (usi... | ['Jun Woo Lee', 'Kang Suk Byun', 'Hye Jin Myung', 'Du Hui Lee', 'Mathew Huerta-Enochian'] | null | null | null | null | sltat-lrec-2022-6 | ['sign-language-translation', 'sign-language-production'] | ['computer-vision', 'natural-language-processing'] | [ 4.50494200e-01 -8.52550790e-02 -2.44181335e-01 -3.21007073e-01
-1.38835549e+00 -1.04486322e+00 6.99411035e-01 -7.07753241e-01
-5.64648807e-01 7.00119853e-01 1.03658307e+00 -4.09805924e-01
1.88384622e-01 -7.85569921e-02 -6.10318065e-01 -2.30180651e-01
6.15123451e-01 4.16719049e-01 1.95810422e-01 -2.31479146... | [9.179977416992188, -6.507266044616699] |
7e2688fd-4054-4f0d-b9e6-4ab525f5c2a5 | learning-facial-liveness-representation-for | 2208.07828 | null | https://arxiv.org/abs/2208.07828v1 | https://arxiv.org/pdf/2208.07828v1.pdf | Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing | Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones, which is typically approached by learning proper models for performing the associated classification task. In practice, one would expect such models to be generalized to FAS in different image domains. Moreover, it is not practi... | ['Yu-Chiang Frank Wang', 'Shang-Fu Chen', 'Chin-Lun Fu', 'Lin-Hsi Tsao', 'Zih-Ching Chen'] | 2022-08-16 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 4.50863391e-01 -2.57625699e-01 -2.88868695e-01 -2.19042644e-01
-3.78023684e-01 -6.69012368e-01 8.09701383e-01 -1.24256477e-01
-3.87353115e-02 4.70217437e-01 -2.48492900e-02 -2.79630214e-01
8.90897214e-02 -7.15150118e-01 -5.81595242e-01 -9.84938323e-01
-7.18576014e-02 2.72988409e-01 4.83472757e-02 -3.59653592... | [13.051520347595215, 1.1777169704437256] |
da3fce41-084b-46ae-972d-7f0560e2947b | feathers-dataset-for-fine-grained-visual | 2004.08606 | null | https://arxiv.org/abs/2004.08606v1 | https://arxiv.org/pdf/2004.08606v1.pdf | Feathers dataset for Fine-Grained Visual Categorization | This paper introduces a novel dataset FeatherV1, containing 28,272 images of feathers categorized by 595 bird species. It was created to perform taxonomic identification of bird species by a single feather, which can be applied in amateur and professional ornithology. FeatherV1 is the first publicly available bird's pl... | ['Konstantin Dobratulin', 'Andrey Kuznetsov', 'Alina Belko'] | 2020-04-18 | null | null | null | null | ['fine-grained-visual-recognition', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [-2.46656716e-01 -4.81305957e-01 1.37414753e-01 -5.58305025e-01
2.85983980e-01 -7.73031533e-01 5.38497090e-01 1.66577861e-01
-5.47277927e-01 3.82569909e-01 1.18964590e-01 2.29282290e-01
-3.03726465e-01 -9.54561114e-01 -4.99871224e-01 -5.29266179e-01
-3.72342736e-01 2.29294926e-01 1.45707903e-02 -2.82206714... | [9.790682792663574, 2.2142117023468018] |
68434a00-e67d-4be7-8678-86eab0fb2ae6 | type-prediction-systems | 2104.01207 | null | https://arxiv.org/abs/2104.01207v1 | https://arxiv.org/pdf/2104.01207v1.pdf | Type Prediction Systems | Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Knowledge Base Question Answering, etc. Prior works have mostly focused on supervised solutions that generally operate on relatively small-to-me... | ['Mustafa Canim', 'Alfio Gliozzo', 'Nandana Mihindukulasooriya', 'Sarthak Dash'] | 2021-04-02 | null | null | null | null | ['type-prediction', 'knowledge-base-question-answering'] | ['computer-code', 'natural-language-processing'] | [ 1.04717147e-02 6.82632148e-01 -4.81588334e-01 -4.78588283e-01
-5.91921151e-01 -8.11862171e-01 6.76979721e-01 7.91182637e-01
-5.40295482e-01 1.05930793e+00 6.37341151e-03 -5.61184168e-01
-1.62556529e-01 -1.19243228e+00 -5.11798561e-01 -3.57704580e-01
6.52786419e-02 9.89309371e-01 6.26577795e-01 -4.23968285... | [9.659411430358887, 8.75721549987793] |
d1176801-b1cb-4532-8d89-37c1bd5b53f1 | faceforensics-a-large-scale-video-dataset-for | 1803.09179 | null | http://arxiv.org/abs/1803.09179v1 | http://arxiv.org/pdf/1803.09179v1.pdf | FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces | With recent advances in computer vision and graphics, it is now possible to
generate videos with extremely realistic synthetic faces, even in real time.
Countless applications are possible, some of which raise a legitimate alarm,
calling for reliable detectors of fake videos. In fact, distinguishing between
original an... | ['Matthias Nießner', 'Christian Riess', 'Andreas Rössler', 'Luisa Verdoliva', 'Justus Thies', 'Davide Cozzolino'] | 2018-03-24 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 6.23513520e-01 -7.18902722e-02 2.36602336e-01 -1.93442330e-01
-5.35661817e-01 -7.40281940e-01 7.18296528e-01 -1.57885566e-01
-1.87812328e-01 6.81440115e-01 -2.88826942e-01 8.65982920e-02
2.07264721e-01 -6.89773679e-01 -9.52558160e-01 -5.93466759e-01
-1.42722219e-01 3.72052372e-01 2.30558261e-01 -1.87731609... | [12.548041343688965, 1.0790483951568604] |
901d7966-0513-45d1-907f-a96f6fb9f2c5 | a-comparison-of-multi-view-learning | 2105.04984 | null | https://arxiv.org/abs/2105.04984v1 | https://arxiv.org/pdf/2105.04984v1.pdf | A Comparison of Multi-View Learning Strategies for Satellite Image-Based Real Estate Appraisal | In the house credit process, banks and lenders rely on a fast and accurate estimation of a real estate price to determine the maximum loan value. Real estate appraisal is often based on relational data, capturing the hard facts of the property. Yet, models benefit strongly from including image data, capturing additiona... | ['Oliver Müller', 'Jan-Peter Kucklick'] | 2021-05-11 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.40219730e-01 -1.52444094e-01 -4.21523660e-01 -4.64753449e-01
-7.36399889e-01 -4.41629946e-01 3.88380587e-01 1.37007341e-01
-2.52863824e-01 3.94396126e-01 5.05773246e-01 -4.48070496e-01
-1.97212398e-01 -1.19629085e+00 -2.72911876e-01 -5.68441093e-01
4.86868620e-02 4.24274892e-01 -3.35281938e-01 -4.04026538... | [7.096523761749268, 2.3849501609802246] |
b26e551d-a964-43d7-adae-b6a56eff179e | multi-slice-net-a-novel-light-weight | 2108.03786 | null | https://arxiv.org/abs/2108.03786v1 | https://arxiv.org/pdf/2108.03786v1.pdf | Multi-Slice Net: A novel light weight framework for COVID-19 Diagnosis | This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans. Our system utilises a novel two-stage approach to generate robust and efficient diagnoses across heterogeneous patient level inputs. We use a powerful backbone network as a feature extractor to capture discriminative slice-level featur... | ['Clinton Fookes', 'Simon Denman', 'Sridha Sridharan', 'Tharindu Fernando', 'Harshala Gammulle'] | 2021-08-09 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.70914346e-01 1.71211705e-01 -6.06196485e-02 -6.21191680e-01
-1.26686919e+00 -4.66147721e-01 1.25531510e-01 2.21731871e-01
-6.02229834e-01 4.38311338e-01 1.21848881e-01 -5.30679643e-01
-3.59839112e-01 -7.45302558e-01 -2.65155166e-01 -7.20762014e-01
-3.26877773e-01 1.04480016e+00 3.55220467e-01 1.52593896... | [14.85074520111084, -2.2794744968414307] |
c865034a-0660-423b-a4b5-3f2ac77b5415 | semeval-2022-task-4-patronizing-and | null | null | https://aclanthology.org/2022.semeval-1.38 | https://aclanthology.org/2022.semeval-1.38.pdf | SemEval-2022 Task 4: Patronizing and Condescending Language Detection | This paper presents an overview of Task 4 at SemEval-2022, which was focused on detecting Patronizing and Condescending Language (PCL) towards vulnerable communities. Two sub-tasks were considered: a binary classification task, where participants needed to classify a given paragraph as containing PCL or not, and a mult... | ['Steven Schockaert', 'Luis Espinosa-Anke', 'Carla Perez-Almendros'] | null | null | null | null | semeval-naacl-2022-7 | ['semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection'] | ['miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing'] | [ 9.50709879e-02 -1.89489797e-01 -2.51681745e-01 -1.39241025e-01
-1.02818906e+00 -9.47191060e-01 7.23479033e-01 9.01369095e-01
-5.58382213e-01 6.37221813e-01 3.87703657e-01 -6.71422899e-01
1.63540587e-01 -4.18488920e-01 -8.66460800e-02 -3.68595511e-01
-2.42352098e-01 3.56321126e-01 -3.13360877e-02 2.13464722... | [8.790444374084473, 10.584943771362305] |
1d942457-4d32-42ff-8f1b-e97ebacce70d | high-fidelity-generalized-emotional-talking | 2305.02572 | null | https://arxiv.org/abs/2305.02572v2 | https://arxiv.org/pdf/2305.02572v2.pdf | High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning | Recently, emotional talking face generation has received considerable attention. However, existing methods only adopt one-hot coding, image, or audio as emotion conditions, thus lacking flexible control in practical applications and failing to handle unseen emotion styles due to limited semantics. They either ignore th... | ['Yong liu', 'Zhifeng Xie', 'Chengjie Wang', 'Ying Tai', 'Wenqing Chu', 'Yue Han', 'Jiangning Zhang', 'Junwei Zhu', 'Chao Xu'] | 2023-05-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_High-Fidelity_Generalized_Emotional_Talking_Face_Generation_With_Multi-Modal_Emotion_Space_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_High-Fidelity_Generalized_Emotional_Talking_Face_Generation_With_Multi-Modal_Emotion_Space_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.89951658e-01 7.21183186e-03 9.36208069e-02 -6.41538084e-01
-5.41837990e-01 -4.05069917e-01 4.13236350e-01 -1.06666780e+00
2.03454390e-01 6.19731009e-01 4.10254240e-01 3.89724344e-01
1.99523076e-01 -6.12136006e-01 -7.15383291e-01 -5.58898389e-01
3.90871257e-01 1.05046995e-01 -3.14710021e-01 -3.79130065... | [13.03292179107666, -0.3666633665561676] |
e333ad71-7efd-471f-a3c0-9047f956a864 | semi-supervised-object-detection-via-virtual | null | null | https://openreview.net/forum?id=HJWD_2bApjI | https://openreview.net/pdf?id=HJWD_2bApjI | Semi-supervised Object Detection via Virtual Category Learning | Due to the lack of large amounts of labelled data to learn rich-expressive features of objects, semi-supervised detectors powered by pseudo labelling techniques usually make a tentative decision for the pseudo labels of confusing samples. When dealing with confusing training samples, neither of the two recently adopted... | ['Anonymous'] | 2021-11-25 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.14945036e-01 6.50429904e-01 -3.47251773e-01 -5.42503774e-01
-5.79734087e-01 -4.13677216e-01 7.74341464e-01 6.00589752e-01
-7.77830005e-01 9.19175982e-01 -4.44068640e-01 -1.14233479e-01
-2.99713641e-01 -6.09181106e-01 -5.30253410e-01 -9.96132612e-01
3.49731073e-02 5.73326707e-01 4.89109039e-01 2.41235808... | [9.125629425048828, 3.8137247562408447] |
787b23b4-6cf1-429f-9ea4-7c517f3a9a39 | revisiting-rumour-stance-classification | null | null | https://aclanthology.org/2020.rdsm-1.4 | https://aclanthology.org/2020.rdsm-1.4.pdf | Revisiting Rumour Stance Classification: Dealing with Imbalanced Data | Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours. One major challenge is that there are considerably more non-relevant replies (comments) than informative ones (supports and denies), making the task highly imbalanced. In this paper we... | ['Carolina Scarton', 'Yue Li'] | null | null | null | null | rdsm-coling-2020-12 | ['rumour-detection'] | ['natural-language-processing'] | [-1.66593015e-01 6.83911666e-02 -8.21340919e-01 -3.87109071e-01
-6.67315423e-01 -3.52012724e-01 7.78652608e-01 7.14652359e-01
-2.65060425e-01 1.05647540e+00 6.72940433e-01 -2.52400637e-01
1.55855238e-01 -7.79622316e-01 -3.35406423e-01 -4.89105105e-01
-3.97197269e-02 8.96896839e-01 3.88876289e-01 -8.01976860... | [8.227649688720703, 10.116371154785156] |
78358021-7c86-48d6-baf6-0d47f995a3d6 | efficient-uncertainty-estimation-in-spiking | 2304.10191 | null | https://arxiv.org/abs/2304.10191v1 | https://arxiv.org/pdf/2304.10191v1.pdf | Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout | Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware. As with classical artificial neural networks (ANNs), predictive uncertainties are important... | ['Sander Bohte', 'Bojian Yin', 'Tao Sun'] | 2023-04-20 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 4.26125139e-01 -1.11383848e-01 1.62179962e-01 -3.91921818e-01
-7.58929849e-01 -2.86570638e-01 5.34993649e-01 2.31985196e-01
-8.95367563e-01 1.52397060e+00 -2.27154151e-01 -2.01378480e-01
-1.96696699e-01 -8.45982611e-01 -1.24546564e+00 -6.53457880e-01
-3.16361561e-02 4.90793824e-01 4.38442349e-01 6.39672577... | [8.207540512084961, 2.4647674560546875] |
a987a948-5486-48a8-aa44-2c9f11a404ad | graph-exploration-for-effective-multi-agent-q | 2304.09547 | null | https://arxiv.org/abs/2304.09547v1 | https://arxiv.org/pdf/2304.09547v1.pdf | Graph Exploration for Effective Multi-agent Q-Learning | This paper proposes an exploration technique for multi-agent reinforcement learning (MARL) with graph-based communication among agents. We assume the individual rewards received by the agents are independent of the actions by the other agents, while their policies are coupled. In the proposed framework, neighbouring ag... | ['Ali H. Sayed', 'Ainur Zhaikhan'] | 2023-04-19 | null | null | null | null | ['q-learning'] | ['methodology'] | [-2.44367212e-01 3.76693964e-01 -2.87642390e-01 1.61686748e-01
-1.59474149e-01 -5.45616150e-01 8.18827808e-01 5.48851728e-01
-9.11059618e-01 1.42473626e+00 -4.16486859e-01 -1.46578565e-01
-4.91529167e-01 -9.97548878e-01 -4.33547795e-01 -9.42304671e-01
-5.83276749e-01 8.19811165e-01 3.24679762e-01 -2.44080871... | [3.9602549076080322, 2.0619235038757324] |
4fe0c60d-d795-4d3f-ba16-b093a8157e95 | harnessing-the-power-of-infinitely-wide-deep-1 | 1910.01663 | null | https://arxiv.org/abs/1910.01663v3 | https://arxiv.org/pdf/1910.01663v3.pdf | Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks | Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under l2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with respect to so-called Neural Tangent Kernels (NTKs) (Jacot et al., 2018). An efficient algorithm to c... | ['Dingli Yu', 'Ruslan Salakhutdinov', 'Sanjeev Arora', 'Ruosong Wang', 'Zhiyuan Li', 'Simon S. Du'] | 2019-10-03 | null | https://openreview.net/forum?id=rkl8sJBYvH | https://openreview.net/pdf?id=rkl8sJBYvH | iclr-2020-1 | ['small-data'] | ['computer-vision'] | [ 5.42932637e-02 1.13510847e-01 -4.55494702e-01 -3.49177837e-01
-5.47697842e-01 -3.71790677e-01 6.36152446e-01 -3.31808150e-01
-9.21580732e-01 7.35620022e-01 -7.55270049e-02 -7.97650933e-01
-1.97054073e-01 -7.89546847e-01 -8.69312048e-01 -6.13655150e-01
-2.51616567e-01 -2.21313626e-01 4.41853434e-01 1.33418590... | [8.945683479309082, 2.888660430908203] |
b51966f7-57e6-450f-be2d-e1c287c35e9e | learning-video-stabilization-using-optical | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Learning_Video_Stabilization_Using_Optical_Flow_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_Learning_Video_Stabilization_Using_Optical_Flow_CVPR_2020_paper.pdf | Learning Video Stabilization Using Optical Flow | We propose a novel neural network that infers the per-pixel warp fields for video stabilization from the optical flow fields of the input video. While previous learning based video stabilization methods attempt to implicitly learn frame motions from color videos, our method resorts to optical flow for motion analysis a... | [' Ravi Ramamoorthi', 'Jiyang Yu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['video-stabilization'] | ['computer-vision'] | [-3.25181752e-01 -4.51089591e-01 -4.38480258e-01 -2.60907244e-02
-4.93225247e-01 -4.34132874e-01 4.02900815e-01 -2.78520077e-01
-2.81780452e-01 7.94089258e-01 5.55625379e-01 -1.43555313e-01
4.70349550e-01 -1.91213548e-01 -1.05397570e+00 -7.27944314e-01
-2.26768269e-03 -1.61444739e-01 3.64424139e-01 -9.04017612... | [10.619760513305664, -1.4273602962493896] |
6aacaa3d-783f-4ff5-a4b8-9dfe0d58c922 | dial2vec-self-guided-contrastive-learning-of | 2210.15332 | null | https://arxiv.org/abs/2210.15332v1 | https://arxiv.org/pdf/2210.15332v1.pdf | Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings | In this paper, we introduce the task of learning unsupervised dialogue embeddings. Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained language models (PLMs) have been shown to be feasible for this task. However, these approaches typically ignore the conversatio... | ['Fei Huang', 'Yongbin Li', 'Junfeng Jiang', 'Rui Wang', 'Che Liu'] | 2022-10-27 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-4.51766044e-01 2.48473004e-01 -3.65188755e-02 -3.69082242e-01
-6.50133014e-01 -8.09726894e-01 1.09269965e+00 4.50298905e-01
-4.25987840e-01 6.12108767e-01 8.51716578e-01 -4.84007858e-02
8.10990483e-02 -4.95945990e-01 -6.74925372e-02 -5.89975953e-01
-2.74904817e-02 6.58720434e-01 -7.38524124e-02 -6.85489595... | [12.641233444213867, 7.837474822998047] |
f70f15bb-0b8d-48f1-850c-01514a4bf4de | ultra-low-bitrate-video-conferencing-using | 2012.00346 | null | https://arxiv.org/abs/2012.00346v1 | https://arxiv.org/pdf/2012.00346v1.pdf | Ultra-low bitrate video conferencing using deep image animation | In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to e... | ['Stéphane Lathuilière', 'Giuseppe Valenzise', 'Goluck Konuko'] | 2020-12-01 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 4.45733339e-01 -1.72519051e-02 -4.09747541e-01 -2.40578413e-01
-7.76426017e-01 2.37106040e-01 2.55209327e-01 -2.00019419e-01
-2.43664473e-01 7.33675599e-01 2.46614829e-01 -4.38921720e-01
-1.34209961e-01 -6.79181218e-01 -7.11906850e-01 -3.90001059e-01
-1.63611189e-01 -1.42498106e-01 -7.28767812e-02 -4.11374383... | [11.384016036987305, -1.6105942726135254] |
a45c90f6-b62e-49ec-bcc9-742941e5e9c5 | deep-learning-based-parameter-mapping-for | null | null | https://openreview.net/forum?id=wthvY6Y9e | https://openreview.net/pdf?id=wthvY6Y9e | Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting | Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting... | ['Marion I. Menzel', 'Bjoern H. Menze', 'Derek K. Jones', 'Michela Tosetti', 'Valentina Tomassini', 'Joseph R. Whittaker', 'Alberto Merola', 'Sebastian Endt', 'Jonathan Dannenberg', 'Diana Waldmannstetter', 'Anjany Sekuboyina', 'Miguel Molina-Romero', 'Guido Buonincontri', 'Ilona Lipp', 'Pedro A. Gómez', 'Carolin M. Pi... | 2020-01-25 | null | null | null | midl-2019-7 | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.89382780e-01 -7.21540973e-02 -7.06104562e-02 -3.45489293e-01
-6.23044968e-01 -4.50666130e-01 5.35259426e-01 1.69663325e-01
-6.10722423e-01 6.73752427e-01 2.69712001e-01 9.41815972e-02
-6.40011847e-01 -4.42607015e-01 -2.88759589e-01 -9.66790259e-01
-6.99989974e-01 6.21825933e-01 4.71269429e-01 2.40559448... | [13.542041778564453, -2.398016929626465] |
fb8ee329-3c32-4a60-9577-c6f68ac78f29 | convolutional-neural-network-with-pruning | 2101.05996 | null | https://arxiv.org/abs/2101.05996v1 | https://arxiv.org/pdf/2101.05996v1.pdf | Convolutional Neural Network with Pruning Method for Handwritten Digit Recognition | CNN model is a popular method for imagery analysis, so it could be utilized to recognize handwritten digits based on MNIST datasets. For higher recognition accuracy, various CNN models with different fully connected layer sizes are exploited to figure out the relationship between the CNN fully connected layer size and ... | ['Mengyu Chen'] | 2021-01-15 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.57224059e-01 4.46434096e-02 -2.31137857e-01 -1.49787232e-01
8.26034844e-01 -1.12149335e-01 4.21934612e-02 -1.25834554e-01
-6.20740891e-01 5.97891986e-01 -1.19347580e-01 -2.16227695e-01
-2.90690005e-01 -1.21229374e+00 -3.39794636e-01 -6.64659619e-01
9.22726234e-04 1.32445507e-02 4.97928441e-01 -8.20417255... | [8.602825164794922, 2.917962074279785] |
011f1ceb-b483-475c-a99c-2ba24eb7d35b | precise-facial-landmark-detection-by | 2303.07840 | null | https://arxiv.org/abs/2303.07840v1 | https://arxiv.org/pdf/2303.07840v1.pdf | Precise Facial Landmark Detection by Reference Heatmap Transformer | Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature represen... | ['Wenwen Min', 'Ping Xiong', 'Hang Sun', 'Linlin Shen', 'Zhihui Lai', 'Jie zhou', 'Jun Liu', 'Jun Wan'] | 2023-03-14 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 1.08845443e-01 5.65169845e-03 -8.38154405e-02 -7.38625467e-01
-6.44219398e-01 8.89943819e-03 5.54350615e-01 -3.46134394e-01
-1.03637442e-01 2.12375537e-01 -3.91021296e-02 3.92509729e-01
-8.97179693e-02 -6.93276823e-01 -4.87234622e-01 -9.07624483e-01
3.76702249e-01 3.05387020e-01 1.23826303e-01 -7.11692497... | [13.515851974487305, 0.4081331789493561] |
5ac82749-5791-47d4-b692-caf7821e1e77 | fake-news-detection-and-behavioral-analysis | 2305.16057 | null | https://arxiv.org/abs/2305.16057v1 | https://arxiv.org/pdf/2305.16057v1.pdf | Fake News Detection and Behavioral Analysis: Case of COVID-19 | While the world has been combating COVID-19 for over three years, an ongoing "Infodemic" due to the spread of fake news regarding the pandemic has also been a global issue. The existence of the fake news impact different aspect of our daily lives, including politics, public health, economic activities, etc. Readers cou... | ['James Geller', 'Soon Ae Chun', 'Navya Martin Kollapally', 'Chih-Yuan Li'] | 2023-05-25 | null | null | null | null | ['sentence-embeddings', 'fake-news-detection', 'sentence-embeddings'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-4.33813542e-01 1.36847660e-01 -1.18268549e-01 -7.52981901e-02
-4.25061166e-01 -5.71029246e-01 9.01007533e-01 4.15112495e-01
-2.39302784e-01 8.28211784e-01 5.98449528e-01 -2.30265081e-01
6.67038858e-01 -1.15107417e+00 -6.56537116e-01 -2.87321717e-01
3.50423992e-01 2.59662747e-01 2.71010160e-01 -8.65449905... | [8.148965835571289, 10.259276390075684] |
7daeef49-9b05-45e7-b79c-35a70bfb1aff | diffstyler-controllable-dual-diffusion-for | 2211.10682 | null | https://arxiv.org/abs/2211.10682v1 | https://arxiv.org/pdf/2211.10682v1.pdf | DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization | Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into the stylized one according to textual descriptions of the target style provided by the user. Unlike previous image-to-image transfer approaches,... | ['Changsheng Xu', 'WeiMing Dong', 'Yong Zhang', 'Haibin Huang', 'Chongyang Ma', 'Fan Tang', 'Yuxin Zhang', 'Nisha Huang'] | 2022-11-19 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.51880437e-01 1.12507999e-01 -4.02832031e-03 -6.45656526e-01
-4.80057955e-01 -5.69613218e-01 8.66371930e-01 -1.50945917e-01
-3.60930055e-01 3.95174176e-01 4.19513822e-01 6.07494591e-03
2.55889714e-01 -8.06042850e-01 -7.67060816e-01 -7.43088782e-01
7.50983477e-01 3.00554454e-01 5.08761108e-02 -2.95254171... | [11.421856880187988, -0.4735671579837799] |
ac24ab07-6634-4123-9b95-d67ffbec3a18 | stack-sentence-ordering-with-temporal | 2109.02247 | null | https://arxiv.org/abs/2109.02247v1 | https://arxiv.org/pdf/2109.02247v1.pdf | STaCK: Sentence Ordering with Temporal Commonsense Knowledge | Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document. Correctly ordering the sentences requires an understanding of coherence with respect to the chronological sequence of events described in the text. Document-level contextual understanding and commonsense know... | ['Soujanya Poria', 'Rada Mihalcea', 'Navonil Majumder', 'Deepanway Ghosal'] | 2021-09-06 | null | https://aclanthology.org/2021.emnlp-main.683 | https://aclanthology.org/2021.emnlp-main.683.pdf | emnlp-2021-11 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.17061579e-01 1.73441678e-01 -4.07236338e-01 -7.27391779e-01
2.70168334e-01 -5.27000248e-01 7.24355698e-01 8.14313710e-01
-1.58003658e-01 5.69375098e-01 7.90637791e-01 -6.18438900e-01
-3.03276777e-01 -8.87850702e-01 -6.02506459e-01 1.45753980e-01
-4.89612550e-01 5.15698075e-01 2.10751057e-01 -3.78358096... | [11.587079048156738, 9.145392417907715] |
4cacc6dd-6a7a-467a-97fe-1c51dbac0a96 | blind-room-parameter-estimation-using | 2107.13832 | null | https://arxiv.org/abs/2107.13832v1 | https://arxiv.org/pdf/2107.13832v1.pdf | Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordings | Knowing the geometrical and acoustical parameters of a room may benefit applications such as audio augmented reality, speech dereverberation or audio forensics. In this paper, we study the problem of jointly estimating the total surface area, the volume, as well as the frequency-dependent reverberation time and mean su... | ['Emmanuel Vincent', 'Antoine Deleforge', 'Prerak Srivastava'] | 2021-07-29 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 2.13228568e-01 -2.09425926e-01 1.00009203e+00 -2.06684977e-01
-1.26074982e+00 -5.25156736e-01 2.93569863e-01 3.41315091e-01
-3.13092679e-01 5.83157957e-01 4.52838004e-01 -2.33074129e-01
-2.43068561e-01 -5.07921517e-01 -6.93431497e-01 -9.73245800e-01
-4.59772289e-01 7.53906071e-02 -3.75564694e-01 1.64701015... | [15.152462005615234, 5.771498680114746] |
356bc62e-2ce1-4fda-9f3c-9c09cdd47a5a | refu-refine-and-fuse-the-unobserved-view-for | 2211.04753 | null | https://arxiv.org/abs/2211.04753v1 | https://arxiv.org/pdf/2211.04753v1.pdf | ReFu: Refine and Fuse the Unobserved View for Detail-Preserving Single-Image 3D Human Reconstruction | Single-image 3D human reconstruction aims to reconstruct the 3D textured surface of the human body given a single image. While implicit function-based methods recently achieved reasonable reconstruction performance, they still bear limitations showing degraded quality in both surface geometry and texture from an unobse... | ['Jaegul Choo', 'Minsoo Lee', 'Gyumin Shim'] | 2022-11-09 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 7.74000466e-01 4.19686258e-01 4.64643717e-01 -3.11780602e-01
-9.16827083e-01 -4.19924408e-03 4.87791985e-01 -5.10197699e-01
-8.79645068e-03 4.77374852e-01 4.52107430e-01 3.14435303e-01
3.11545521e-01 -8.90907764e-01 -8.43760133e-01 -4.58729535e-01
3.55634391e-01 1.06226885e+00 4.53586102e-01 -1.23412773... | [7.21945858001709, -1.3019931316375732] |
50b2ba86-6ed9-4a03-babb-b0a14bbaf811 | rssod-bench-a-large-scale-benchmark-dataset | 2306.02351 | null | https://arxiv.org/abs/2306.02351v1 | https://arxiv.org/pdf/2306.02351v1.pdf | RSSOD-Bench: A large-scale benchmark dataset for Salient Object Detection in Optical Remote Sensing Imagery | We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning, research in SOD for remote sensing imagery (RSSOD) is still in its early stages. Existing RSSOD datasets have limitations in terms of scale,... | ['Xiao Xiang Zhu', 'Qi Wang', 'Yanfeng Liu', 'Zhitong Xiong'] | 2023-06-04 | null | null | null | null | ['salient-object-detection-1'] | ['computer-vision'] | [ 2.55248755e-01 -2.02537075e-01 -5.31760566e-02 -1.75866261e-01
-4.52398032e-01 -2.51993507e-01 4.30752963e-01 1.29170820e-01
-9.98823643e-02 5.05599797e-01 3.56642723e-01 -1.67469174e-01
-8.72786045e-02 -8.59262884e-01 -2.05583200e-01 -7.42259920e-01
-2.35678419e-01 -1.23861626e-01 6.95611358e-01 -6.76252306... | [9.119061470031738, -0.8726456761360168] |
f94b1dad-6162-427b-a098-3c702d131c02 | acceptance-of-covid-19-vaccine-and-its | 2103.15206 | null | https://arxiv.org/abs/2103.15206v2 | https://arxiv.org/pdf/2103.15206v2.pdf | Knowledge, beliefs, attitudes and perceived risk about COVID-19 vaccine and determinants of COVID-19 vaccine acceptance in Bangladesh | A total of 605 eligible respondents took part in this survey (population size 1630046161 and required sample size 591) with an age range of 18 to 100. A large proportion of the respondents are aged less than 50 (82%) and male (62.15%). The majority of the respondents live in urban areas (60.83%). A total of 61.16% (370... | ['Miah Akib Zaman', 'Ashraf Uddin Mian', 'Ijaz Ahmed Khan', 'Md. Mohsin', 'Sultan Mahmud'] | 2021-03-28 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-2.35581622e-01 5.60753932e-03 -5.09432495e-01 -4.22685981e-01
-1.09118335e-02 -6.86739504e-01 -4.22327258e-02 6.45408392e-01
-6.66134238e-01 7.11858332e-01 3.45172852e-01 -8.76569390e-01
1.32323101e-01 -9.80394006e-01 -7.15737581e-01 -6.26387477e-01
1.27197415e-01 1.54297292e-01 -1.34726912e-01 -3.16383421... | [5.723084926605225, 4.718062400817871] |
2cf46bbb-9c87-4563-8dcf-76ac65900516 | deep-integro-difference-equation-models-for | 1910.13524 | null | https://arxiv.org/abs/1910.13524v3 | https://arxiv.org/pdf/1910.13524v3.pdf | Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting | Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary tempor... | ['Andrew Zammit-Mangion', 'Christopher K. Wikle'] | 2019-10-29 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-1.09452799e-01 -4.01346087e-01 5.10337651e-01 -2.53565967e-01
-4.76264387e-01 -6.35746717e-01 8.60719979e-01 -6.35879859e-02
-3.65178257e-01 7.83463776e-01 1.72355935e-01 -6.46305323e-01
-4.25556451e-01 -7.99504101e-01 -5.53628385e-01 -8.84867013e-01
-5.30125260e-01 5.12217820e-01 -1.71770621e-02 -7.38462433... | [6.565573215484619, 3.2887582778930664] |
e932902d-2d75-45f6-88d1-ffedf1a17fca | micro-video-tagging-via-jointly-modeling | 2303.08318 | null | https://arxiv.org/abs/2303.08318v1 | https://arxiv.org/pdf/2303.08318v1.pdf | Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation | The last decade has witnessed the proliferation of micro-videos on various user-generated content platforms. According to our statistics, around 85.7\% of micro-videos lack annotation. In this paper, we focus on annotating micro-videos with tags. Existing methods mostly focus on analyzing video content, neglecting user... | ['Liqiang Nie', 'Dai Meng', 'Jianlong Wu', 'Yinwei Wei', 'Tian Gan', 'Xiao Wang'] | 2023-03-15 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [-1.67917207e-01 1.53139725e-01 -6.40150368e-01 -2.07681611e-01
-2.55799890e-01 -4.07891572e-01 5.88385940e-01 3.50234389e-01
-1.23422906e-01 5.45529246e-01 5.69543064e-01 2.52772003e-01
-2.10174397e-01 -6.93320692e-01 -3.54037076e-01 -5.64325094e-01
-6.06889278e-02 1.28917858e-01 7.87920475e-01 8.92502517... | [9.836834907531738, 0.8725608587265015] |
e4956b70-2e1e-402d-ae53-8a8ee29ec691 | context-aware-6d-pose-estimation-of-known | 2212.05560 | null | https://arxiv.org/abs/2212.05560v1 | https://arxiv.org/pdf/2212.05560v1.pdf | Context-aware 6D Pose Estimation of Known Objects using RGB-D data | 6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navigation etc, require the correct pose of objects present in a scene to perform their specific task. It becomes even harder when the objects ar... | ['G. C. Nandi', 'Vandana Kushwaha', 'Priya Shukla', 'Ankit Kumar'] | 2022-12-11 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.02731414e-01 -5.72465323e-02 1.41850933e-01 -5.03771305e-01
-3.99635524e-01 -2.91699678e-01 4.78779316e-01 2.33573839e-01
-6.84698462e-01 6.44782603e-01 -1.74816787e-01 1.38558358e-01
-9.59279835e-02 -5.86181343e-01 -8.48047137e-01 -6.53460741e-01
-4.43288311e-02 1.03884494e+00 7.12224782e-01 4.43560667... | [7.403671741485596, -2.398400068283081] |
e425735a-9fe2-4349-84e0-8553929d98ee | icolorit-towards-propagating-local-hint-to | 2207.06831 | null | https://arxiv.org/abs/2207.06831v4 | https://arxiv.org/pdf/2207.06831v4.pdf | iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer | Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with min... | ['Jaegul Choo', 'Minho Park', 'Jooyeol Yun', 'Sanghyeon Lee'] | 2022-07-14 | null | null | null | null | ['colorization', 'point-interactive-image-colorization'] | ['computer-vision', 'computer-vision'] | [ 1.72157630e-01 -2.84137309e-01 -3.43939774e-02 -2.28278801e-01
-7.63152301e-01 -7.69189537e-01 2.01792374e-01 -1.37975588e-01
-3.58265340e-01 5.19054890e-01 5.79896159e-02 -4.45806026e-01
4.60677743e-01 -6.73373878e-01 -8.37018788e-01 -5.56783020e-01
4.13073897e-01 -2.85214245e-01 3.31588477e-01 -3.05963252... | [11.386664390563965, -0.9991265535354614] |
70dfdc7f-01db-4ae3-a550-1449800c8572 | in2i-unsupervised-multi-image-to-image | 1711.09334 | null | http://arxiv.org/abs/1711.09334v1 | http://arxiv.org/pdf/1711.09334v1.pdf | In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks | In unsupervised image-to-image translation, the goal is to learn the mapping
between an input image and an output image using a set of unpaired training
images. In this paper, we propose an extension of the unsupervised
image-to-image translation problem to multiple input setting. Given a set of
paired images from mult... | ['Pramuditha Perera', 'Vishal M. Patel', 'Mahdi Abavisani'] | 2017-11-26 | null | null | null | null | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 9.91326034e-01 2.49851197e-01 -1.97971910e-01 -5.39891124e-01
-1.22677183e+00 -7.33382761e-01 8.27859700e-01 -4.99899626e-01
-2.59438038e-01 7.41001844e-01 6.42236918e-02 1.93685293e-02
3.60852063e-01 -7.36588836e-01 -1.12406445e+00 -8.29424083e-01
7.15898395e-01 4.71894443e-01 -2.50635922e-01 1.78845376... | [11.66956901550293, -0.3979678750038147] |
72238fb8-b280-430a-99e5-d64bab8f9c0e | relative-positional-encoding-for-transformers | 2105.08399 | null | https://arxiv.org/abs/2105.08399v2 | https://arxiv.org/pdf/2105.08399v2.pdf | Relative Positional Encoding for Transformers with Linear Complexity | Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity. In the meantime, relative positional encoding (RPE) was proposed as beneficial for classical Transformers and consists in exploiting lags instead of absolute positions for inference. Still, RPE is no... | ['Gaël Richard', 'Yi-Hsuan Yang', 'Umut Şimşekli', 'Shih-Lun Wu', 'Ondřej Cífka', 'Antoine Liutkus'] | 2021-05-18 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.12517315e-01 7.17456937e-02 3.40155542e-01 2.55370587e-01
-6.82260871e-01 -7.13355124e-01 7.85597324e-01 1.78888872e-01
-3.69425684e-01 9.32380795e-01 2.02026054e-01 -3.14720511e-01
-6.55904055e-01 -7.21543431e-01 -7.24542618e-01 -8.67399931e-01
-2.53394842e-01 4.87718016e-01 1.39381364e-01 -3.09738606... | [15.608009338378906, 5.5897955894470215] |
94c28084-fb24-4c46-b913-ac236eabfd27 | wasserstein-cnn-learning-invariant-features | 1708.02412 | null | http://arxiv.org/abs/1708.02412v1 | http://arxiv.org/pdf/1708.02412v1.pdf | Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition | Heterogeneous face recognition (HFR) aims to match facial images acquired
from different sensing modalities with mission-critical applications in
forensics, security and commercial sectors. However, HFR is a much more
challenging problem than traditional face recognition because of large
intra-class variations of heter... | ['Xiang Wu', 'Zhenan Sun', 'Tieniu Tan', 'Ran He'] | 2017-08-08 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.68918625e-01 -2.41316780e-01 1.02122188e-01 -6.38005555e-01
-7.69286752e-01 -3.49739902e-02 2.40952507e-01 -7.97521412e-01
-2.34249592e-01 3.50519270e-01 8.08674395e-02 1.19042955e-01
-4.16115582e-01 -7.75922775e-01 -4.79665786e-01 -1.13723147e+00
1.77084118e-01 7.88533986e-02 -5.41428745e-01 -1.19635716... | [13.14852237701416, 0.4679422080516815] |
f10bc8f8-4922-43bd-9c3a-790587cf4082 | joint-action-loss-for-proximal-policy | 2301.10919 | null | https://arxiv.org/abs/2301.10919v1 | https://arxiv.org/pdf/2301.10919v1.pdf | Joint action loss for proximal policy optimization | PPO (Proximal Policy Optimization) is a state-of-the-art policy gradient algorithm that has been successfully applied to complex computer games such as Dota 2 and Honor of Kings. In these environments, an agent makes compound actions consisting of multiple sub-actions. PPO uses clipping to restrict policy updates. Alth... | ['Simon Lucas', 'Greg Slabaugh', 'Yizhao Jin', 'Xiulei Song'] | 2023-01-26 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-3.11289966e-01 -1.84241176e-01 -7.12505400e-01 5.55640385e-02
-6.76052094e-01 -3.39596242e-01 4.49209511e-01 7.66534135e-02
-1.14451408e+00 1.29994202e+00 1.32200763e-01 -4.67224389e-01
-9.72143933e-02 -6.50580704e-01 -7.48265386e-01 -6.15646899e-01
-4.28451210e-01 6.03879154e-01 6.78168297e-01 -3.60379934... | [4.061579704284668, 2.3138651847839355] |
a7886223-2c6f-4168-9bf0-40ae025b6c62 | tncr-table-net-detection-and-classification | 2106.15322 | null | https://arxiv.org/abs/2106.15322v1 | https://arxiv.org/pdf/2106.15322v1.pdf | TNCR: Table Net Detection and Classification Dataset | We present TNCR, a new table dataset with varying image quality collected from free websites. The TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 high-quality labeled images. In this paper, we have implemented state-of-the-art... | ['Daniyar Nurseitov', 'Islam Nuradin', 'Alexander Berendeyev', 'Abdelrahman Abdallah'] | 2021-06-19 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 1.32950008e-01 -2.39433795e-01 -4.67221320e-01 -1.45935416e-01
-1.36031163e+00 -7.76153386e-01 2.61053443e-01 3.74848425e-01
-5.70542291e-02 3.07247847e-01 3.10805976e-01 -1.55655727e-01
1.66953593e-01 -1.09037173e+00 -7.13810027e-01 -3.78184289e-01
3.23206410e-02 5.21045566e-01 2.34502122e-01 -3.88587832... | [11.69363784790039, 3.006596088409424] |
1d0ed87f-b212-449c-9e4c-5ed5cd56e9d6 | extreme-multi-label-classification-from | 2004.00198 | null | https://arxiv.org/abs/2004.00198v1 | https://arxiv.org/pdf/2004.00198v1.pdf | Extreme Multi-label Classification from Aggregated Labels | Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-inst... | ['Hsiang-Fu Yu', 'Yanyao Shen', 'Sujay Sanghavi', 'Inderjit Dhillon'] | 2020-04-01 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1388-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1388-Paper.pdf | icml-2020-1 | ['extreme-multi-label-classification'] | ['methodology'] | [ 6.80315435e-01 7.62011409e-02 -3.97496790e-01 -8.42544556e-01
-1.60567629e+00 -6.68845594e-01 2.72112101e-01 2.51356333e-01
-3.19143951e-01 8.29200923e-01 -2.71081746e-01 -2.43942767e-01
-4.22562093e-01 -1.53114006e-01 -5.84930837e-01 -7.63153255e-01
1.74408495e-01 9.88456011e-01 -2.28095949e-01 5.67279398... | [9.470165252685547, 4.34206485748291] |
c2ccc2de-99de-4430-b0be-ad42fd5db523 | docred-fe-a-document-level-fine-grained | 2303.11141 | null | https://arxiv.org/abs/2303.11141v2 | https://arxiv.org/pdf/2303.11141v2.pdf | DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset | Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction. However, most existing works focus on sentence-level coarse-grained JERE, which have limitations in real-world scenarios. In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE... | ['Sujian Li', 'Yu Xia', 'Dawei Zhu', 'YiFan Song', 'Weimin Xiong', 'Hongbo Wang'] | 2023-03-20 | null | null | null | null | ['relation-classification', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-4.34748411e-01 1.43449321e-01 -5.10747313e-01 -3.83676082e-01
-9.67389405e-01 -6.84958696e-01 6.07115746e-01 3.83753538e-01
-4.64044660e-01 1.04699695e+00 6.00651920e-01 -2.57336855e-01
-1.56423375e-01 -1.01583302e+00 -6.20404959e-01 1.58980843e-02
1.06023103e-01 6.10276222e-01 1.86542511e-01 -3.40903342... | [9.402658462524414, 8.691110610961914] |
89c8ecbd-5bfd-4201-bcaf-a268f1bf9ada | advpicker-effectively-leveraging-unlabeled | 2106.02300 | null | https://arxiv.org/abs/2106.02300v2 | https://arxiv.org/pdf/2106.02300v2.pdf | AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER | Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. While previous works have shown that unlabeled data in a target language can be used to improve cross-lingual model per... | ['Yi Guan', 'Börje F. Karlsson', 'Qianhui Wu', 'Huiqiang Jiang', 'WEILE CHEN'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.61 | https://aclanthology.org/2021.acl-long.61.pdf | acl-2021-5 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-4.52110805e-02 -4.14062515e-02 -3.97440463e-01 -6.71135843e-01
-1.11579096e+00 -9.55969810e-01 7.24027753e-01 -2.78923452e-01
-8.31845343e-01 9.18399274e-01 1.87966675e-01 -2.88171202e-01
5.00737786e-01 -7.20134377e-01 -8.66565347e-01 -3.28952521e-01
1.84172988e-01 4.80733037e-01 -2.34376073e-01 -3.41674387... | [10.022905349731445, 9.653237342834473] |
48da4b32-4991-453a-bb99-6a9825bebfad | compound-prototype-matching-for-few-shot | 2207.05515 | null | https://arxiv.org/abs/2207.05515v5 | https://arxiv.org/pdf/2207.05515v5.pdf | Compound Prototype Matching for Few-shot Action Recognition | Few-shot action recognition aims to recognize novel action classes using only a small number of labeled training samples. In this work, we propose a novel approach that first summarizes each video into compound prototypes consisting of a group of global prototypes and a group of focused prototypes, and then compares vi... | ['Lijin Yang', 'Yifei HUANG', 'Yoichi Sato'] | 2022-07-12 | null | null | null | null | ['few-shot-action-recognition', 'video-similarity'] | ['computer-vision', 'computer-vision'] | [ 1.68258280e-01 -3.83406490e-01 -4.37582046e-01 -3.94062191e-01
-5.32338381e-01 -5.04607737e-01 5.51449418e-01 3.35597456e-01
-2.59433717e-01 5.25158525e-01 3.44467461e-01 3.63858819e-01
-8.41447040e-02 -3.98018718e-01 -5.23013949e-01 -7.58920014e-01
-2.62185037e-01 2.53542334e-01 8.67626727e-01 2.03672975... | [8.533966064453125, 0.6552910208702087] |
64e3796b-6e31-43d5-b5e8-b292151304c4 | unsupervised-learning-of-fine-structure-1 | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_Unsupervised_Learning_of_Fine_Structure_Generation_for_3D_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_Unsupervised_Learning_of_Fine_Structure_Generation_for_3D_Point_Clouds_ICCV_2021_paper.pdf | Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching | Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the generated 3D point clouds onto a 2D image plane, and train deep neural networks using the per-pixel difference with 2D ground truth images. ... | ['Matthias Zwicker', 'Yu-Shen Liu', 'Zhizhong Han', 'Chao Chen'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['point-cloud-generation'] | ['computer-vision'] | [-2.27558389e-02 1.69240370e-01 2.57405676e-02 -1.80135533e-01
-8.22767794e-01 -6.78530633e-01 6.71007693e-01 -3.55156153e-01
7.66514018e-02 3.11732680e-01 -1.35140836e-01 -3.13171089e-01
3.69230598e-01 -1.11406326e+00 -1.22361732e+00 -4.59243029e-01
3.14002037e-01 9.97162879e-01 1.24069884e-01 -1.61360174... | [8.532735824584961, -3.5607972145080566] |
7aacac16-4659-47db-8b77-802418e44022 | a-novel-two-stream-decision-level-fusion-of | 2306.15765 | null | https://arxiv.org/abs/2306.15765v1 | https://arxiv.org/pdf/2306.15765v1.pdf | A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System | This paper presents a novel multimodal human activity recognition system. It uses a two-stream decision level fusion of vision and inertial sensors. In the first stream, raw RGB frames are passed to a part affinity field-based pose estimation network to detect the keypoints of the user. These keypoints are then pre-pro... | ['Shaik Ali Akbara', 'Hari Mohan Pandey', 'Kamlesh Tiwari', 'Egna Praneeth Gummana', 'Muhtashim Rafiqi', 'Santosh Kumar Yadav'] | 2023-06-27 | null | null | null | null | ['pose-estimation', 'activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [ 2.31639087e-01 -4.58644748e-01 -1.19852118e-01 -3.70178163e-01
-7.25375116e-01 1.56062126e-01 3.82198930e-01 1.12979412e-01
-9.40474391e-01 7.93734968e-01 1.29502803e-01 1.62806436e-02
1.18275456e-01 -6.90322638e-01 -6.68344438e-01 -7.24792063e-01
-4.31868434e-01 -2.54148338e-03 2.79098630e-01 -6.02221899... | [7.822396278381348, 0.48544570803642273] |
363da4ba-6222-4158-b213-e8f5b17fa89a | vspw-a-large-scale-dataset-for-video-scene | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.pdf | VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild | In this paper, we present a new dataset with the target of advancing the scene parsing task from images to videos. Our dataset aims to perform Video Scene Parsing in the Wild (VSPW), which covers a wide range of real-world scenarios and categories. To be specific, our VSPW is featured from the following aspects: 1)... | ['Yi Yang', 'Guangrui Li', 'Chen Liang', 'Yu Wu', 'Yunchao Wei', 'Jiaxu Miao'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['scene-parsing'] | ['computer-vision'] | [ 4.93252665e-01 -1.57182008e-01 -2.89380819e-01 -2.76139051e-01
-7.24027038e-01 -4.97296154e-01 4.01136488e-01 -2.29851082e-01
-4.36009288e-01 3.60909522e-01 -3.94179457e-04 -4.82879654e-02
1.84468716e-01 -6.05556786e-01 -9.70849395e-01 -6.24689996e-01
-1.18371025e-01 -1.50643274e-01 9.94504750e-01 6.04082793... | [9.264372825622559, -0.004658365622162819] |
b4d6a7d2-ed80-4e53-8ce5-1f76620a7ae0 | deep-image-homography-estimation | 1606.03798 | null | http://arxiv.org/abs/1606.03798v1 | http://arxiv.org/pdf/1606.03798v1.pdf | Deep Image Homography Estimation | We present a deep convolutional neural network for estimating the relative
homography between a pair of images. Our feed-forward network has 10 layers,
takes two stacked grayscale images as input, and produces an 8 degree of
freedom homography which can be used to map the pixels from the first image to
the second. We p... | ['Tomasz Malisiewicz', 'Daniel DeTone', 'Andrew Rabinovich'] | 2016-06-13 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.72057682e-01 1.11382768e-01 -1.76108345e-01 -3.41722786e-01
-6.39421880e-01 -5.09948075e-01 7.88462162e-01 -6.55596018e-01
-1.74327463e-01 2.21682295e-01 1.90948486e-01 -9.18460116e-02
1.97241887e-01 -8.02238286e-01 -1.25110698e+00 -5.47972262e-01
-2.27445085e-02 6.22444093e-01 6.04831167e-02 -2.25449100... | [8.447640419006348, -2.1880438327789307] |
71cfb4ab-2caf-481e-91bc-93a10a7c6eee | superpixel-meshes-for-fast-edge-preserving | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Bodis-Szomoru_Superpixel_Meshes_for_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Bodis-Szomoru_Superpixel_Meshes_for_2015_CVPR_paper.pdf | Superpixel Meshes for Fast Edge-Preserving Surface Reconstruction | Multi-View-Stereo (MVS) methods aim for the highest detail possible, however, such detail is often not required. In this work, we propose a novel surface reconstruction method based on image edges, superpixels and second-order smoothness constraints, producing meshes comparable to classic MVS surfaces in quality but or... | ['Luc van Gool', 'Andras Bodis-Szomoru', 'Hayko Riemenschneider'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.92067367e-01 8.75484198e-02 1.63713261e-01 -1.94109395e-01
-8.22634339e-01 -4.39536870e-01 4.76496994e-01 5.81667619e-03
-1.88495278e-01 5.69595575e-01 3.64100486e-02 3.34806442e-02
2.15470418e-01 -9.30144370e-01 -8.27390075e-01 -4.25737709e-01
1.78479061e-01 4.93218839e-01 6.22308552e-01 -2.60472707... | [9.048332214355469, -2.9267799854278564] |
7be328a3-99b4-46fa-a407-63655b497345 | temporal-inductive-logic-reasoning | 2206.05051 | null | https://arxiv.org/abs/2206.05051v1 | https://arxiv.org/pdf/2206.05051v1.pdf | Temporal Inductive Logic Reasoning | Inductive logic reasoning is one of the fundamental tasks on graphs, which seeks to generalize patterns from the data. This task has been studied extensively for traditional graph datasets such as knowledge graphs (KGs), with representative techniques such as inductive logic programming (ILP). Existing ILP methods typi... | ['Faramarz Fekri', 'James C Kerce', 'Siheng Xiong', 'Yuan Yang'] | 2022-06-09 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 1.14662826e-01 5.27011514e-01 -7.83463538e-01 -5.55653453e-01
1.92675993e-01 -5.74472547e-01 5.02029836e-01 7.62385428e-01
9.14982185e-02 8.25254142e-01 -2.10790619e-01 -9.63513196e-01
-7.21796930e-01 -1.50544882e+00 -1.18699241e+00 -2.28914097e-01
-7.86671221e-01 6.58497036e-01 8.77354145e-01 -1.76971838... | [8.855167388916016, 7.680927276611328] |
c97042e6-0c81-43a7-98ec-d35a07335e2d | nlp-workbench-efficient-and-extensible | 2303.01410 | null | https://arxiv.org/abs/2303.01410v1 | https://arxiv.org/pdf/2303.01410v1.pdf | NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools | NLP Workbench is a web-based platform for text mining that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. The platform is built upon latest pre-trained models and open source systems from academia that provide semantic analysis functionalities,... | ['Denilson Barbosa', 'Natalie Hervieux', 'Kostyantyn Guzhva', 'Abeer Waheed', 'Matej Kosmajac', 'Peiran Yao'] | 2023-03-02 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-1.84609473e-01 3.39921832e-01 -3.82708490e-01 -6.17357671e-01
-5.48725188e-01 -6.73003614e-01 3.48020971e-01 3.85359347e-01
-3.48748028e-01 5.55940330e-01 9.50975493e-02 -5.17568707e-01
1.43724948e-01 -8.21392238e-01 -3.82729441e-01 -1.25397697e-01
4.45192724e-01 8.23617756e-01 5.25292695e-01 -3.25202078... | [9.507044792175293, 8.794059753417969] |
58fb5467-5851-4033-9956-e0665bc9c64b | lbmt-team-at-vlsp2022-abmusu-hybrid-method | 2304.05205 | null | https://arxiv.org/abs/2304.05205v1 | https://arxiv.org/pdf/2304.05205v1.pdf | LBMT team at VLSP2022-Abmusu: Hybrid method with text correlation and generative models for Vietnamese multi-document summarization | Multi-document summarization is challenging because the summaries should not only describe the most important information from all documents but also provide a coherent interpretation of the documents. This paper proposes a method for multi-document summarization based on cluster similarity. In the extractive method we... | ['Thi-Hai-Yen Vuong', 'Ha-Thanh Nguyen', 'Tam Doan Thanh', 'Hai-Long Nguyen', 'Hoang-Trung Nguyen', 'Thai-Binh Nguyen', 'Tan-Minh Nguyen'] | 2023-04-11 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.96898782e-01 2.52257921e-02 -1.64207757e-01 -1.76457852e-01
-1.05453849e+00 -5.64378142e-01 6.76809251e-01 8.33820343e-01
-2.84904420e-01 1.12261605e+00 1.12490869e+00 2.21813560e-01
-4.50978279e-01 -4.70292866e-01 -1.58244491e-01 -5.33688188e-01
4.00656015e-02 6.72579050e-01 3.91963243e-01 -1.34895980... | [12.60445499420166, 9.581603050231934] |
1c281b29-48d4-49bd-8fd2-6945f9205d70 | community-detection-exact-recovery-in | 2102.04439 | null | https://arxiv.org/abs/2102.04439v1 | https://arxiv.org/pdf/2102.04439v1.pdf | Community Detection: Exact Recovery in Weighted Graphs | In community detection, the exact recovery of communities (clusters) has been mainly investigated under the general stochastic block model with edges drawn from Bernoulli distributions. This paper considers the exact recovery of communities in a complete graph in which the graph edges are drawn from either a set of Gau... | ['Aria Nosratinia', 'Mohammad Esmaeili'] | 2021-02-08 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.27486408e-01 6.18720278e-02 -9.94012132e-02 6.52569309e-02
-2.82888919e-01 -5.73522151e-01 3.35578203e-01 2.88926184e-01
-1.43195987e-01 7.97030270e-01 -1.35379493e-01 -2.19305754e-01
-4.03586149e-01 -8.19832504e-01 -3.21359158e-01 -1.05340421e+00
-5.96001387e-01 9.53144431e-01 3.57161343e-01 8.72225985... | [6.94626522064209, 5.168193340301514] |
62f9964c-82a6-4d29-a0fe-15d970b4c906 | mix-and-reason-reasoning-over-semantic | 2210.07571 | null | https://arxiv.org/abs/2210.07571v1 | https://arxiv.org/pdf/2210.07571v1.pdf | Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization | Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one. The general objective of DG methods is to learn semantic representations that are independent of domain labels, which is theoretically sound but empirically challenged due to the complex mixture... | ['Yizhou Yu', 'Yue Huang', 'Gangming Zhao', 'Feng Liu', 'Luyao Tang', 'Chaoqi Chen'] | 2022-10-14 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.88710856e-01 4.87602621e-01 -9.94192958e-02 -5.46793163e-01
-1.88726217e-01 -7.41303802e-01 1.02224123e+00 1.58691034e-01
6.97429404e-02 4.71117914e-01 1.20044261e-01 1.03074469e-01
-5.80619812e-01 -9.42868710e-01 -7.66463339e-01 -8.15075815e-01
1.00185156e-01 6.37807906e-01 4.21966195e-01 -3.47008228... | [10.080403327941895, 2.687117099761963] |
513d69ff-5fd6-4b7d-8320-e020d5cb7abf | relay-hindsight-experience-replay-continual | 2208.00843 | null | https://arxiv.org/abs/2208.00843v2 | https://arxiv.org/pdf/2208.00843v2.pdf | Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards | Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL). Especially for sequential object manipulation tasks, the RL agent always receives negative rewards until completing all sub-tasks, which results in low exploration efficiency. To solve these tasks efficiently, we prop... | ['Bo Song', 'Zhiyong Sun', 'Erkang Cheng', 'Qiang Zhang', 'Kun Dong', 'Yuxin Wang', 'Yongle Luo'] | 2022-08-01 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-1.61723092e-01 8.02158266e-02 -2.99461842e-01 1.10276394e-01
-5.91404378e-01 -2.33762130e-01 9.73217636e-02 -3.09169620e-01
-7.63450980e-01 1.22948980e+00 -5.82821369e-02 -1.87354565e-01
-4.55449522e-01 -5.49591482e-01 -7.47839749e-01 -8.10620606e-01
-4.82278764e-01 5.81433058e-01 3.98593694e-01 -6.64638460... | [4.219226837158203, 1.619389295578003] |
7e692b4c-ba2d-4ef8-9532-72e5ef6e218c | evaluation-of-medium-large-language-models-at | 2305.11991 | null | https://arxiv.org/abs/2305.11991v2 | https://arxiv.org/pdf/2305.11991v2.pdf | Evaluation of medium-large Language Models at zero-shot closed book generative question answering | Large language models (LLMs) have garnered significant attention, but the definition of "large" lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answerin... | ['Johannes Wirth', 'René Peinl'] | 2023-05-19 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [-3.65872175e-01 3.59949350e-01 1.12546086e-01 -2.13233888e-01
-1.45085800e+00 -6.09505892e-01 6.82671428e-01 8.02108645e-02
-6.57112002e-01 7.02577472e-01 3.03281099e-01 -4.88322347e-01
1.61717981e-01 -6.54607236e-01 -4.28799629e-01 -3.04381698e-01
5.35957515e-01 9.60932612e-01 4.27456409e-01 -3.55060875... | [11.455549240112305, 8.306624412536621] |
e4d06a75-a182-4909-aba4-a2f81e313ae0 | context-aware-mixup-for-domain-adaptive | 2108.03557 | null | https://arxiv.org/abs/2108.03557v3 | https://arxiv.org/pdf/2108.03557v3.pdf | Context-Aware Mixup for Domain Adaptive Semantic Segmentation | Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependenc... | ['Lizhuang Ma', 'Jianping Shi', 'Xuequan Lu', 'Guangliang Cheng', 'Jiangmiao Pang', 'Qiqi Gu', 'Zhengyang Feng', 'Qianyu Zhou'] | 2021-08-08 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.90555322e-01 -4.87517267e-02 -1.49813548e-01 -6.60251260e-01
-7.60022044e-01 -5.29572368e-01 3.45131069e-01 9.93954986e-02
-4.91806090e-01 5.60073972e-01 -3.88840847e-02 -6.72636479e-02
-2.77205277e-02 -8.21579516e-01 -6.52783990e-01 -8.35440934e-01
6.97340906e-01 3.20671499e-01 8.00360382e-01 -7.82932043... | [9.681595802307129, 1.4299215078353882] |
5a6c32ff-7d03-4f2d-a041-aba6b4d5950a | protein-secondary-structure-prediction-with | 1412.7828 | null | http://arxiv.org/abs/1412.7828v2 | http://arxiv.org/pdf/1412.7828v2.pdf | Protein Secondary Structure Prediction with Long Short Term Memory Networks | Prediction of protein secondary structure from the amino acid sequence is a
classical bioinformatics problem. Common methods use feed forward neural
networks or SVMs combined with a sliding window, as these models does not
naturally handle sequential data. Recurrent neural networks are an
generalization of the feed for... | ['Søren Kaae Sønderby', 'Ole Winther'] | 2014-12-25 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 4.17108566e-01 -5.76072112e-02 -3.36509556e-01 -3.70393336e-01
-2.69092977e-01 -2.59470880e-01 2.97533065e-01 2.31697112e-01
-5.54072261e-01 8.36911142e-01 1.99020028e-01 -1.07911432e+00
1.47595406e-01 -7.10343540e-01 -9.35927689e-01 -9.41337228e-01
-2.72465914e-01 4.32233602e-01 4.61195290e-01 -4.42957938... | [4.717435359954834, 5.618133068084717] |
8dda4c4b-5992-439b-95a6-737efd488bed | on-large-scale-dynamic-topic-modeling-with | 2001.00631 | null | https://arxiv.org/abs/2001.00631v2 | https://arxiv.org/pdf/2001.00631v2.pdf | On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition | There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primaril... | ['Deanna Needell', 'Kathryn Leonard', 'Alona Kryshchenko', 'Miju Ahn', 'R. W. M. A. Madushani', 'Chuntian Wang', 'Nicole Eikmeier', 'Jamie Haddock', 'Lara Kassab', 'Elena Sizikova'] | 2020-01-02 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-1.45507067e-01 -3.98190409e-01 -1.85820028e-01 1.07785888e-01
9.98444390e-03 -6.44027591e-01 7.87063658e-01 2.89330650e-02
-1.86816528e-01 3.95243376e-01 2.29641825e-01 -3.37899804e-01
-2.63657331e-01 -5.11655867e-01 -1.96921870e-01 -8.43873501e-01
-4.48780477e-01 4.04916078e-01 8.88433978e-02 -1.26680434... | [9.353681564331055, 5.584170341491699] |
9359a38d-6d5f-419a-8121-20bbaee4b36c | do-we-really-need-to-access-the-source-data | 2002.08546 | null | https://arxiv.org/abs/2002.08546v6 | https://arxiv.org/pdf/2002.08546v6.pdf | Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation | Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model, making them risky and inefficient for decentralized private data. Th... | ['Dapeng Hu', 'Jiashi Feng', 'Jian Liang'] | 2020-02-20 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/194-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/194-Paper.pdf | icml-2020-1 | ['universal-domain-adaptation', 'partial-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 3.34518403e-01 3.00120831e-01 -7.37210691e-01 -7.30701447e-01
-9.81219411e-01 -8.40953529e-01 7.01305270e-01 -9.88365486e-02
-2.68436581e-01 9.86711860e-01 1.00775279e-01 -1.00877509e-01
1.71242997e-01 -6.00804746e-01 -8.56366038e-01 -7.50644684e-01
2.52602875e-01 8.86918783e-01 -3.06420252e-02 -1.86276406... | [10.380387306213379, 3.129263162612915] |
ede4a625-25d4-4c10-b178-0b246102f381 | when-bert-fails-the-limits-of-ehr | 2208.10245 | null | https://arxiv.org/abs/2208.10245v1 | https://arxiv.org/pdf/2208.10245v1.pdf | When BERT Fails -- The Limits of EHR Classification | Transformers are powerful text representation learners, useful for all kinds of clinical decision support tasks. Although they outperform baselines on readmission prediction, they are not infallible. Here, we look into one such failure case, and report patterns that lead to inferior predictive performance. | ['Carsten Eickhoff', 'Augusto Garcia-Agundez'] | 2022-07-26 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 3.00655544e-01 4.02780056e-01 -8.50074232e-01 -3.72922748e-01
-9.67740178e-01 -1.78652167e-01 5.08359015e-01 9.39563453e-01
-5.04576087e-01 7.89602816e-01 9.50109899e-01 -1.36635864e+00
-6.07857704e-01 -4.73574311e-01 -1.77471682e-01 -3.19171250e-01
-2.79513687e-01 7.97005236e-01 -1.69170424e-01 -2.32695490... | [8.046751022338867, 6.635904788970947] |
173e1285-4003-4dc1-8738-842c2a96c1bd | cd2-combined-distances-of-contrast | 1911.07995 | null | https://arxiv.org/abs/1911.07995v2 | https://arxiv.org/pdf/1911.07995v2.pdf | CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing | The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lacks redundancy regarding safety. We propose a novel image comparison method called the Combined Distan... | ['Sascha Xu', 'Jan Bauer', 'Benjamin Axmann'] | 2019-11-18 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 2.98438281e-01 -6.07881188e-01 2.71307886e-01 -6.09838784e-01
-4.68562543e-01 -4.92457926e-01 5.89487553e-01 6.48805082e-01
-6.09931827e-01 5.33964157e-01 -1.82527021e-01 -1.26806468e-01
-1.11782206e-02 -8.09122503e-01 -3.35172862e-01 -4.93071169e-01
6.01478852e-02 -4.01198179e-01 6.70548379e-01 -2.08530545... | [11.739117622375488, -1.968567967414856] |
1592a8c3-bc95-4179-9be8-df8219be0f8a | learning-unsupervised-multilingual-word | null | null | https://aclanthology.org/N19-1188 | https://aclanthology.org/N19-1188.pdf | Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs | Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervision. However, it has also been shown that for distant language pairs such fully unsupervised self-lea... | ['Marie-Francine Moens', "Ivan Vuli{\\'c}", 'Geert Heyman', 'Bregt Verreet'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['multilingual-word-embeddings'] | ['methodology'] | [-2.83582211e-01 4.29791696e-02 -6.28502548e-01 -3.71194512e-01
-1.01205778e+00 -9.90656078e-01 7.27730930e-01 1.82297856e-01
-8.29248130e-01 7.89530039e-01 6.14806771e-01 -7.04699814e-01
1.07803851e-01 -4.52428609e-01 -7.29231536e-01 -5.98609149e-01
-1.11328073e-01 7.13542342e-01 -1.49586409e-01 -4.28081989... | [11.010897636413574, 10.068422317504883] |
8cdf53b9-a54b-4287-8797-58745d42de7d | who-should-go-first-a-self-supervised-concept | 2104.03682 | null | https://arxiv.org/abs/2104.03682v2 | https://arxiv.org/pdf/2104.03682v2.pdf | Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion | Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks. One critical challenge is that, as data and business scope grow in real applications, existing taxonomies need to be expanded to incorporate new concepts. Previous works on taxonom... | ['Jiawei Han', 'Jieyu Zhang', 'Jiaming Shen', 'Xiangchen Song'] | 2021-04-08 | null | null | null | null | ['taxonomy-expansion'] | ['natural-language-processing'] | [ 6.66867495e-02 1.57553442e-02 -4.35321450e-01 -2.43459523e-01
4.57393169e-01 -7.43785203e-01 3.13388646e-01 6.13097787e-01
-4.48695272e-01 5.26193261e-01 1.16622047e-02 -5.19759715e-01
-6.35048687e-01 -1.25539207e+00 -2.21530460e-02 -3.04497153e-01
-1.63698848e-02 1.13024056e+00 1.71334833e-01 -4.17928010... | [9.225075721740723, 7.954920768737793] |
69bb5692-ea20-4539-9042-e404373e7c18 | with-measured-words-simple-sentence-selection | 2101.10096 | null | https://arxiv.org/abs/2101.10096v1 | https://arxiv.org/pdf/2101.10096v1.pdf | With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms | Sentence Compression is the task of generating a shorter, yet grammatical version of a given sentence, preserving the essence of the original sentence. This paper proposes a Black-Box Optimizer for Compression (B-BOC): given a black-box compression algorithm and assuming not all sentences need be compressed -- find the... | ['Oren Tsur', 'Meir Kalech', 'Yotam Shichel'] | 2021-01-25 | null | https://aclanthology.org/2021.eacl-main.139 | https://aclanthology.org/2021.eacl-main.139.pdf | eacl-2021-2 | ['sentence-compression'] | ['natural-language-processing'] | [ 7.47934461e-01 3.78585964e-01 -9.28457007e-02 -4.41557020e-01
-9.84330833e-01 -5.25934160e-01 1.87777802e-02 5.89939654e-01
-5.71111739e-01 5.25092483e-01 3.88798535e-01 -4.95368987e-01
-1.21931732e-01 -8.40715528e-01 -8.84215474e-01 -4.47713405e-01
2.65191458e-02 5.57461500e-01 -1.30012274e-01 -1.38978243... | [12.173003196716309, 9.190479278564453] |
1a134823-5b8e-45ea-a6ae-330daae16fa2 | focal-loss-for-dense-object-detection | 1708.02002 | null | http://arxiv.org/abs/1708.02002v2 | http://arxiv.org/pdf/1708.02002v2.pdf | Focal Loss for Dense Object Detection | The highest accuracy object detectors to date are based on a two-stage
approach popularized by R-CNN, where a classifier is applied to a sparse set of
candidate object locations. In contrast, one-stage detectors that are applied
over a regular, dense sampling of possible object locations have the potential
to be faster... | ['Piotr Dollár', 'Tsung-Yi Lin', 'Priya Goyal', 'Ross Girshick', 'Kaiming He'] | 2017-08-07 | focal-loss-for-dense-object-detection-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Lin_Focal_Loss_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Lin_Focal_Loss_for_ICCV_2017_paper.pdf | iccv-2017-10 | ['dense-object-detection'] | ['computer-vision'] | [ 1.80551827e-01 4.20612469e-02 -2.24547848e-01 -2.84700036e-01
-7.00673759e-01 -2.67953068e-01 6.11318529e-01 1.66595921e-01
-6.62768662e-01 3.66626918e-01 -1.93207204e-01 -2.34187797e-01
2.72507668e-01 -6.54035926e-01 -7.47452676e-01 -4.20514733e-01
2.30971184e-02 4.28905308e-01 7.76907504e-01 1.59406513... | [9.176859855651855, 1.1036165952682495] |
42947ce2-3140-4072-898f-93c8ee86cb3d | multiple-kernel-k-means-clustering-using-min | 1803.02458 | null | https://arxiv.org/abs/1803.02458v2 | https://arxiv.org/pdf/1803.02458v2.pdf | Robust Multiple Kernel k-means Clustering using Min-Max Optimization | Multiple kernel learning is a type of multiview learning that combines different data modalities by capturing view-specific patterns using kernels. Although supervised multiple kernel learning has been extensively studied, until recently, only a few unsupervised approaches have been proposed. In the meanwhile, adversar... | ['Yao-Liang Yu', 'Wei Wu', 'Seojin Bang'] | 2018-03-06 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 1.27048030e-01 -1.64827839e-01 -1.85259044e-01 -2.13774100e-01
-8.64484549e-01 -8.18163037e-01 5.53994536e-01 2.43863225e-01
-1.10477749e-02 4.92644250e-01 8.89447704e-02 1.25443414e-01
-2.30893955e-01 -4.87005681e-01 -8.15724850e-01 -1.02406824e+00
-1.24773189e-01 -4.09958884e-02 3.97020221e-01 -1.08762175... | [5.65727424621582, 7.814080715179443] |
b081431f-2e7a-4166-9393-643b26795043 | transductive-few-shot-learning-with-prototype | 2304.11598 | null | https://arxiv.org/abs/2304.11598v1 | https://arxiv.org/pdf/2304.11598v1.pdf | Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement | Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they leverage all samples of the query set. The two existing classes of methods, prototype-based and graph-based, have the disadvantages of inaccura... | ['Piotr Koniusz', 'Hao Zhu'] | 2023-04-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_Transductive_Few-Shot_Learning_With_Prototype-Based_Label_Propagation_by_Iterative_Graph_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_Transductive_Few-Shot_Learning_With_Prototype-Based_Label_Propagation_by_Iterative_Graph_CVPR_2023_paper.pdf | cvpr-2023-1 | ['graph-construction'] | ['graphs'] | [-2.93528233e-02 2.24834308e-01 -3.26955527e-01 -7.01551259e-01
-5.27150035e-01 -2.43209258e-01 5.49329996e-01 4.66729939e-01
-3.93305659e-01 8.48890543e-01 -4.65273969e-02 3.26635182e-01
-1.49088949e-01 -9.18312371e-01 -7.51545787e-01 -5.50279558e-01
-5.86757474e-02 8.83009911e-01 7.14314938e-01 -5.11723645... | [9.980371475219727, 3.048858404159546] |
2b6cdfe7-31d3-4b4a-980d-cf79effe8157 | drag-your-gan-interactive-point-based | 2305.10973 | null | https://arxiv.org/abs/2305.10973v1 | https://arxiv.org/pdf/2305.10973v1.pdf | Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold | Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which ... | ['Christian Theobalt', 'Abhimitra Meka', 'Lingjie Liu', 'Thomas Leimkühler', 'Ayush Tewari', 'Xingang Pan'] | 2023-05-18 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 2.44113013e-01 3.61251235e-01 1.62559628e-01 1.02844298e-01
-3.30622613e-01 -1.16037834e+00 8.14836919e-01 -4.59522486e-01
3.79517078e-02 4.76253510e-01 1.08209170e-01 1.09167598e-01
2.98541963e-01 -8.98550928e-01 -1.09090245e+00 -7.90440738e-01
3.44771534e-01 4.78308916e-01 8.48262906e-02 -5.32630026... | [11.864034652709961, -0.4648391604423523] |
d9bacc63-a8bf-4b20-89e6-dcd88f981309 | probabilistic-dag-search | 2106.08717 | null | https://arxiv.org/abs/2106.08717v1 | https://arxiv.org/pdf/2106.08717v1.pdf | Probabilistic DAG Search | Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search -- most famously, the game of Go. Interestingly, the state-space underlying these sequential decision-making problems often posses a more general latent structure than can be captured by a tree. In this wo... | ['Philipp Hennig', 'Cheng Zhang', 'Julia Grosse'] | 2021-06-16 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 3.33585590e-01 2.41598010e-01 -5.20679295e-01 -2.30098844e-01
-8.53450000e-01 -6.54768467e-01 7.51716316e-01 3.86167038e-03
-3.93786967e-01 8.04667413e-01 3.03061884e-02 -6.60666049e-01
-6.72743618e-01 -7.06832230e-01 -4.40020077e-02 -7.66141415e-01
-1.82069227e-01 9.22988772e-01 5.54531753e-01 -1.75700158... | [4.10031795501709, 1.8857680559158325] |
c8b9cf9c-4741-41ba-9e4c-c7542a885d0a | comprehensive-benchmark-datasets-for-amharic | 2203.12165 | null | https://arxiv.org/abs/2203.12165v1 | https://arxiv.org/pdf/2203.12165v1.pdf | Comprehensive Benchmark Datasets for Amharic Scene Text Detection and Recognition | Ethiopic/Amharic script is one of the oldest African writing systems, which serves at least 23 languages (e.g., Amharic, Tigrinya) in East Africa for more than 120 million people. The Amharic writing system, Abugida, has 282 syllables, 15 punctuation marks, and 20 numerals. The Amharic syllabic matrix is derived from 3... | ['Xiang Bai', 'Minghui Liao', 'Dingkang Liang', 'Wondimu Dikubab'] | 2022-03-23 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-5.93745708e-02 -7.03139603e-01 2.73469388e-01 -9.50398110e-03
-4.72474098e-01 -7.24823952e-01 8.58402550e-01 -2.28001294e-03
-3.70435476e-01 5.25121272e-01 2.30575547e-01 -2.85985887e-01
3.26906532e-01 -6.90177202e-01 -1.80991098e-01 -8.42076600e-01
2.62320161e-01 6.46944344e-01 3.12712610e-01 -3.95557225... | [11.85122299194336, 2.5887222290039062] |
37d95336-8980-4ec1-8f9c-3ca672ecf99f | markov-switching-model-for-driver-behavior | 2108.12801 | null | https://arxiv.org/abs/2108.12801v1 | https://arxiv.org/pdf/2108.12801v1.pdf | Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones | Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situation... | ['Walid Gomaa', 'Mohamed A. Khamis', 'Ahmed B. Zaky'] | 2021-08-29 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-2.21477170e-02 -4.08714145e-01 -3.82490695e-01 -6.75212681e-01
-6.48177743e-01 -1.94096133e-01 3.59146923e-01 2.12962076e-01
-5.59002578e-01 4.95860279e-01 -1.25407711e-01 -7.39834964e-01
-4.73714501e-01 -5.24580359e-01 -2.69548029e-01 -7.83015966e-01
4.76508498e-01 3.01349878e-01 4.24546152e-01 -3.44112247... | [5.871513366699219, 1.0351743698120117] |
03bcd330-dabc-4f9f-a1bb-20c50d19d73c | meta-learning-the-step-size-in-policy | null | null | https://openreview.net/forum?id=zRn12do9p0 | https://openreview.net/pdf?id=zRn12do9p0 | Meta Learning the Step Size in Policy Gradient Methods | Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces. Unfortunately, these methods require precise and problem-specific hyperparameter tuning to achieve good performance and, as a consequence,... | ['Marcello Restelli', 'Francesco Corda', 'Luca Sabbioni'] | 2021-05-20 | null | null | null | icml-workshop-automl-2021-7 | ['policy-gradient-methods'] | ['methodology'] | [ 2.01903507e-01 -1.84231594e-01 -3.36167246e-01 7.48072639e-02
-7.59158552e-01 -3.75951380e-01 6.68134749e-01 2.75004655e-01
-8.72940779e-01 1.04426908e+00 -2.47446537e-01 -2.77033877e-02
-5.33616364e-01 -4.50723380e-01 -4.60561395e-01 -1.08494210e+00
2.67508686e-01 5.54942667e-01 2.07673043e-01 -2.46275440... | [4.305161476135254, 2.3530526161193848] |
1a06d11d-d55d-4755-b284-32d04f6f4f1b | using-implicit-feedback-to-improve-question | 2304.13664 | null | https://arxiv.org/abs/2304.13664v1 | https://arxiv.org/pdf/2304.13664v1.pdf | Using Implicit Feedback to Improve Question Generation | Question Generation (QG) is a task of Natural Language Processing (NLP) that aims at automatically generating questions from text. Many applications can benefit from automatically generated questions, but often it is necessary to curate those questions, either by selecting or editing them. This task is informative on i... | ['Luisa Coheur', 'Eric Nyberg', 'Hugo Rodrigues'] | 2023-04-26 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 5.91013014e-01 4.05977577e-01 4.34940875e-01 -3.28653157e-01
-9.10667717e-01 -7.79724956e-01 6.99553072e-01 7.48663068e-01
-5.74272871e-01 9.17580605e-01 3.29068750e-01 -3.91402662e-01
7.22380867e-03 -9.08789933e-01 -5.02325118e-01 -3.49687755e-01
3.63457471e-01 7.97757089e-01 6.11477673e-01 -5.27060032... | [11.604025840759277, 8.343402862548828] |
b5e86f47-155b-4a9b-b7a5-b0aaf1a44b10 | hierarchical-convolutional-deconvolutional | 1710.04540 | null | http://arxiv.org/abs/1710.04540v1 | http://arxiv.org/pdf/1710.04540v1.pdf | Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation | Automatic segmentation of liver and its tumors is an essential step for
extracting quantitative imaging biomarkers for accurate tumor detection,
diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017
Liver Tumor Segmentation Challenge (LiTS) provides a common platform for
comparing different au... | ['Yading Yuan'] | 2017-10-12 | null | null | null | null | ['liver-segmentation', 'automatic-liver-and-tumor-segmentation'] | ['medical', 'medical'] | [-2.11284488e-01 -1.36136532e-01 -2.14640573e-01 -2.60859281e-01
-7.15388775e-01 -3.82392764e-01 5.11835575e-01 4.26733196e-01
-5.35196781e-01 4.37446803e-01 2.93505579e-01 -4.90072370e-01
1.63836867e-01 -5.41590691e-01 -2.16365665e-01 -1.13915801e+00
-2.54760921e-01 5.71964145e-01 1.33684412e-01 4.41323847... | [14.480391502380371, -2.7060933113098145] |
9ed5e42c-0998-4dcd-b735-b9e9395611bb | learning-robust-visual-semantic-embedding-for | 2304.09498 | null | https://arxiv.org/abs/2304.09498v1 | https://arxiv.org/pdf/2304.09498v1.pdf | Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification | Generalizable person re-identification (Re-ID) is a very hot research topic in machine learning and computer vision, which plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. However, previous methods mainly focus on the visual representation learni... | ['Yuzhuo Fu', 'Dahong Qian', 'Ting Liu', 'Chengfeng Zhou', 'Jiacheng Ruan', 'Mengyuan Guan', 'Jingsheng Gao', 'Suncheng Xiang'] | 2023-04-19 | null | null | null | null | ['person-re-identification', 'generalizable-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 8.05654377e-02 -2.47245476e-01 -2.26874679e-01 -2.35665455e-01
-5.23141503e-01 -3.72390836e-01 7.73258924e-01 -5.26393540e-02
-3.04868281e-01 3.67091477e-01 5.28926790e-01 5.19921258e-02
1.09951749e-01 -5.67932248e-01 -3.95971119e-01 -7.55136847e-01
4.63490933e-01 6.58149868e-02 7.31537268e-02 -1.63902864... | [14.67236614227295, 0.9798128604888916] |
652fcf17-03ab-4bd4-a03d-0e16deff3653 | few-shot-action-recognition-with-prototype | 2101.08085 | null | https://arxiv.org/abs/2101.08085v4 | https://arxiv.org/pdf/2101.08085v4.pdf | Few-shot Action Recognition with Prototype-centered Attentive Learning | Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode, the few samples in a meta-training task are split into support and query sets. The former is used to build a classifier, which is then eva... | ['Juan-Manuel Perez-Rua', 'Tao Xiang', 'Brais Martinez', 'Li Zhang', 'Antoine Toisoul', 'Xiatian Zhu'] | 2021-01-20 | null | null | null | null | ['few-shot-action-recognition', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.61723149e-01 -1.08805902e-01 -7.33064353e-01 -3.68184060e-01
-1.09319687e+00 3.53976756e-01 5.04118919e-01 2.50374556e-01
-6.08500063e-01 8.76701176e-01 2.35486254e-01 4.82750952e-01
-3.65871757e-01 -5.85947216e-01 -5.35320818e-01 -8.76295328e-01
3.06589622e-02 4.11133945e-01 6.46417975e-01 -1.30058587... | [8.496225357055664, 0.8858839273452759] |
c9c4a376-de73-411f-a18d-785a90e4e4a2 | automatic-renal-segmentation-in-dce-mri-using | 1712.07022 | null | http://arxiv.org/abs/1712.07022v1 | http://arxiv.org/pdf/1712.07022v1.pdf | Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks | Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI)
images could help in diagnosis and treatment of kidney diseases of children.
Automatic segmentation of renal parenchyma is an important step in this
process. In this paper, we propose a time and memory efficient fully automated
segmentation method... | ['Simon K. Warfield', 'Marzieh Haghighi', 'Sila Kurugol'] | 2017-12-19 | null | null | null | null | ['kidney-function'] | ['medical'] | [-9.02858227e-02 -2.63252586e-01 2.95112967e-01 -7.13155031e-01
-1.31072044e-01 -6.48477197e-01 2.24497944e-01 2.88339406e-01
-6.68953001e-01 5.22142231e-01 -1.79067224e-01 -2.31870130e-01
-2.36984327e-01 -8.87766421e-01 -2.15144128e-01 -7.30699778e-01
-4.76883799e-01 9.11914766e-01 2.97289610e-01 4.21399921... | [14.264305114746094, -2.5941662788391113] |
08f68735-0add-4428-9c13-d1bcbda78b79 | lung-nodule-classification-using-biomarkers | 2010.11682 | null | https://arxiv.org/abs/2010.11682v1 | https://arxiv.org/pdf/2010.11682v1.pdf | Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs | We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist's annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and vol... | ['David R. Chapman', 'Phuong Nguyen', 'Sumeet Menon', 'Jayalakshmi Mangalagiri', 'Arshita Jain', 'Kushal Mehta'] | 2020-10-19 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 3.17989498e-01 7.03043044e-02 -5.56861639e-01 -2.27598831e-01
-9.66370344e-01 -4.64788258e-01 5.36846936e-01 4.37222391e-01
-7.39762664e-01 4.14618522e-01 4.23372924e-01 -8.39358449e-01
-4.32276398e-01 -9.02904809e-01 -6.12652183e-01 -7.40288615e-01
-7.18707889e-02 8.61121118e-01 3.17440242e-01 4.08248991... | [15.35405158996582, -2.182542324066162] |
0ea6aa81-6381-4a64-91a2-41b0226ecedc | a-physics-informed-neural-network-for-wind | null | null | http://www.phmsociety.org/node/2736 | http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2019/ijphm_20_003.pdf | A physics-informed neural network for wind turbine main bearing fatigue | Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have poin... | ['Yigit A. Yucesan', 'Felipe A. C. Viana'] | 2020-05-05 | null | null | null | international-journal-of-prognostics-and | ['physics-informed-machine-learning', 'graph-regression', 'graph-to-sequence'] | ['graphs', 'graphs', 'natural-language-processing'] | [-4.37480360e-01 -2.38066792e-01 2.65471935e-01 3.33286732e-01
3.02153151e-03 -6.57930970e-02 1.45024061e-01 3.14190209e-01
2.03990310e-01 7.39083052e-01 -1.17019452e-01 -1.85633332e-01
-7.06165075e-01 -9.62882817e-01 -7.06626534e-01 -7.85032630e-01
-2.35827610e-01 5.91901720e-01 1.32390216e-01 -3.50490868... | [6.757068157196045, 2.493236780166626] |
8684aec0-6fc9-4c28-a995-298659a32522 | hierarchical-stochastic-neighbor-embedding-as | 1910.02696 | null | https://arxiv.org/abs/1910.02696v1 | https://arxiv.org/pdf/1910.02696v1.pdf | Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries | In Magnetic Resonance Fingerprinting (MRF) the quality of the estimated parameter maps depends on the encoding capability of the variable flip angle train. In this work we show how the dimensionality reduction technique Hierarchical Stochastic Neighbor Embedding (HSNE) can be used to obtain insight into the encoding ca... | ['Peter Börnert', 'Kirsten Koolstra', 'Oleh Dzyubachyk', 'Boudewijn Lelieveldt', 'Andrew Webb'] | 2019-10-07 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.00214440e-01 -3.83010685e-01 -1.52869001e-01 -3.76155138e-01
-6.13444209e-01 -6.64779663e-01 3.84194106e-01 1.27949804e-01
-4.35450703e-01 6.53059721e-01 6.21321738e-01 -5.97692840e-02
-5.84698856e-01 -4.40335870e-01 -3.57474416e-01 -1.08254111e+00
-6.31325662e-01 4.18660700e-01 1.31706327e-01 -2.06797004... | [13.491772651672363, -2.3802764415740967] |
130846e8-3ba0-466c-9cd5-8ab765a1ed64 | biometric-signature-verification-using | 2205.02934 | null | https://arxiv.org/abs/2205.02934v1 | https://arxiv.org/pdf/2205.02934v1.pdf | Biometric Signature Verification Using Recurrent Neural Networks | Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-the-art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios.... | ['Javier Ortega-Garcia', 'Julian Fierrez', 'Ruben Vera-Rodriguez', 'Ruben Tolosana'] | 2022-05-03 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 6.13596797e-01 -3.89848202e-01 3.30728203e-01 -4.79395926e-01
-6.12686276e-01 -7.10154176e-02 6.50431037e-01 -1.64988980e-01
-7.92410254e-01 5.52847445e-01 -1.12771302e-01 -4.58184719e-01
-3.37107062e-01 -2.56724745e-01 -5.19716144e-01 -8.01219523e-01
-2.88830996e-01 3.91623288e-01 -1.74619332e-01 -5.15229225... | [11.983776092529297, 2.502734899520874] |
532ca085-e9c1-4da8-8fbb-3aac09b6f53e | bottlenet-an-end-to-end-approach-for-feature | 1910.14315 | null | https://arxiv.org/abs/1910.14315v5 | https://arxiv.org/pdf/1910.14315v5.pdf | BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems | The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance... | ['Jun Zhang', 'Jiawei Shao'] | 2019-10-31 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 2.29120910e-01 -4.64582182e-02 -4.58297729e-01 -2.71377228e-02
-5.88553548e-01 4.42514047e-02 3.30284536e-02 -4.49978560e-02
-4.37785506e-01 4.17661518e-01 2.55709440e-02 -4.87768143e-01
5.79098053e-02 -9.84762132e-01 -1.09790826e+00 -6.13846779e-01
-1.97556168e-01 1.14482611e-01 -8.13872665e-02 1.89055681... | [8.445035934448242, 2.878067970275879] |
764ca703-0b91-4e17-9b2f-424be2dc9811 | a-large-scale-chinese-short-text-conversation | 2008.03946 | null | https://arxiv.org/abs/2008.03946v2 | https://arxiv.org/pdf/2008.03946v2.pdf | A Large-Scale Chinese Short-Text Conversation Dataset | The advancements of neural dialogue generation models show promising results on modeling short-text conversations. However, training such models usually needs a large-scale high-quality dialogue corpus, which is hard to access. In this paper, we present a large-scale cleaned Chinese conversation dataset, LCCC, which co... | ['Yinhe Zheng', 'Minlie Huang', 'Yida Wang', 'Xiaoyan Zhu', 'Yong Jiang', 'Pei Ke', 'Kaili Huang'] | 2020-08-10 | null | null | null | null | ['short-text-conversation'] | ['natural-language-processing'] | [-1.15555391e-01 4.32570934e-01 1.94776133e-01 -6.01255655e-01
-8.47731531e-01 -4.58208293e-01 7.95920253e-01 -1.47210568e-01
-3.00822198e-01 1.20083737e+00 7.91177869e-01 -3.72541100e-01
5.01544833e-01 -7.40592718e-01 -1.81280926e-01 -4.99416471e-01
9.95115414e-02 7.34642386e-01 -2.06740290e-01 -6.85044467... | [12.788250923156738, 8.077248573303223] |
9b84b9d4-8d1c-4bac-84bb-4e7373ca1ce4 | multi-task-determinantal-point-processes-for | 1805.09916 | null | http://arxiv.org/abs/1805.09916v2 | http://arxiv.org/pdf/1805.09916v2.pdf | Multi-Task Determinantal Point Processes for Recommendation | Determinantal point processes (DPPs) have received significant attention in
the recent years as an elegant model for a variety of machine learning tasks,
due to their ability to elegantly model set diversity and item quality or
popularity. Recent work has shown that DPPs can be effective models for product
recommendati... | ['Jérémie Mary', 'Mike Gartrell', 'Romain Warlop'] | 2018-05-24 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.72603923e-01 -7.76060998e-01 -8.98862243e-01 -1.79674625e-01
-8.42586100e-01 -6.37849271e-01 5.84105790e-01 2.80786306e-01
2.08409220e-01 3.89091372e-01 6.64636433e-01 -4.96029735e-01
-3.12503636e-01 -5.72691202e-01 -7.24956572e-01 -4.26592827e-01
-1.66486070e-01 7.02223837e-01 -9.74235088e-02 -3.65047425... | [9.731524467468262, 5.510112762451172] |
a9c96c24-6b9d-4a0e-8335-7896fc2d8197 | seamlessgan-self-supervised-synthesis-of | 2201.05120 | null | https://arxiv.org/abs/2201.05120v1 | https://arxiv.org/pdf/2201.05120v1.pdf | SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps | We present SeamlessGAN, a method capable of automatically generating tileable texture maps from a single input exemplar. In contrast to most existing methods, focused solely on solving the synthesis problem, our work tackles both problems, synthesis and tileability, simultaneously. Our key idea is to realize that tilin... | ['Elena Garces', 'Carlos Rodriguez-Pardo'] | 2022-01-13 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 7.60813236e-01 4.83786881e-01 1.29420489e-01 9.02088080e-03
-7.88332641e-01 -8.23844016e-01 8.60283017e-01 -5.04392385e-01
1.79310322e-01 8.44544828e-01 1.99710563e-01 -9.99686718e-02
2.23699287e-01 -1.14717054e+00 -9.42049205e-01 -9.11642849e-01
4.43482101e-02 3.42744619e-01 3.00733838e-02 -2.57594347... | [11.593559265136719, -0.4848795533180237] |
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