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
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
26bdd9b2-ad0a-443a-9777-5a2bc531c4b5 | can-everybody-sign-now-exploring-sign | 2012.10941 | null | https://arxiv.org/abs/2012.10941v2 | https://arxiv.org/pdf/2012.10941v2.pdf | Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses | Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2... | ['Xavier Giro-i-Nieto', 'Amanda Duarte', 'Lucas Ventura'] | 2020-12-20 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [-2.72730827e-01 -3.94866476e-03 -5.51234782e-02 -1.40964046e-01
-5.87410212e-01 -6.12532794e-01 6.99451447e-01 -1.45087099e+00
-5.15699029e-01 7.09538400e-01 6.84772015e-01 -2.11188614e-01
2.13058740e-01 -2.15217456e-01 -8.30857515e-01 -5.02818763e-01
-1.75307870e-01 4.62331682e-01 5.19396126e-01 -3.99461180... | [9.19231128692627, -6.515130996704102] |
674bd818-2d4d-44cd-99d2-968865556007 | the-brain-tumor-segmentation-brats-challenge-2 | 2305.17033 | null | https://arxiv.org/abs/2305.17033v2 | https://arxiv.org/pdf/2305.17033v2.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) | Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment con... | ['Leonie Mikael', 'Maryam Fouladi', 'Michelle Deutsch', 'Peter de Blank', 'Miriam Bornhorst', 'Russel Taki Shinohara', 'Nakul Sheth', 'Lubdha M. Shah', 'Ibraheem Salman Shaikh', 'Andres Rodriguez', 'Zachary Reitman', 'Sanjay P Prabhu', 'Julija Pavaine', 'Khanak K Nandolia', 'Ahmed W Moawad', 'Aaron S McAllister', 'Naza... | 2023-05-26 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-5.79013769e-03 2.37428233e-01 -2.81694859e-01 -2.96508104e-01
-1.20785892e+00 -5.06456256e-01 3.71072739e-01 7.19828069e-01
-6.96700335e-01 4.02161717e-01 8.95069987e-02 -5.06043732e-01
-1.88260943e-01 -4.46287006e-01 -5.20298719e-01 -8.32568586e-01
-1.72392830e-01 1.25195551e+00 4.32787240e-01 2.51798719... | [14.568521499633789, -2.494520425796509] |
4792a885-a7d8-4bc9-8223-c8a2eff51ffb | top-two-algorithms-revisited | 2206.05979 | null | https://arxiv.org/abs/2206.05979v2 | https://arxiv.org/pdf/2206.05979v2.pdf | Top Two Algorithms Revisited | Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms. They select the next arm to sample from by randomizing among two candidate arms, a leader and a challenger. Despite their good empirical performance, the... | ['Emilie Kaufmann', 'Rianne de Heide', 'Dorian Baudry', 'Rémy Degenne', 'Marc Jourdan'] | 2022-06-13 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.32078022e-01 1.88703537e-01 -9.86640215e-01 -1.08001903e-01
-1.29811907e+00 -1.17134798e+00 5.14290035e-01 -1.68699846e-01
-8.48033577e-02 1.01767755e+00 -4.17359807e-02 -7.56738544e-01
-7.75252461e-01 -6.02584302e-01 -9.29336965e-01 -9.00573790e-01
-1.43452855e-02 1.12897575e+00 -3.75003159e-01 2.52733201... | [4.539419174194336, 3.2879409790039062] |
914f6e01-be49-45b7-8454-b4e4060403af | fusing-saliency-maps-with-region-proposals | 1804.03905 | null | http://arxiv.org/abs/1804.03905v1 | http://arxiv.org/pdf/1804.03905v1.pdf | Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization | In this paper we address the problem of unsupervised localization of objects
in single images. Compared to previous state-of-the-art method our method is
fully unsupervised in the sense that there is no prior instance level or
category level information about the image. Furthermore, we treat each image
individually and... | ['Patric Jensfelt', 'Hakan Karaoguz'] | 2018-04-11 | null | null | null | null | ['unsupervised-object-localization'] | ['computer-vision'] | [ 2.19586402e-01 1.01723202e-01 -2.29741022e-01 -4.89571780e-01
-7.70979166e-01 -4.40066546e-01 7.07788348e-01 4.04114217e-01
-5.48215568e-01 4.92359579e-01 1.45422682e-01 2.91041225e-01
1.58058479e-01 -4.52648759e-01 -7.79241979e-01 -4.80031431e-01
3.05187441e-02 3.45854998e-01 1.09518993e+00 -1.67890742... | [9.576698303222656, 0.22442106902599335] |
208cfa49-d3ac-4899-820d-f05a6c612700 | an-error-propagation-spiking-neural-network | 2104.05241 | null | https://arxiv.org/abs/2104.05241v1 | https://arxiv.org/pdf/2104.05241v1.pdf | An error-propagation spiking neural network compatible with neuromorphic processors | Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algorithms on such architectures is still an open challenge, especially for multi-layer networks that rely on the back-propagation algorithm. In t... | ['Giacomo Indiveri', 'Germain Haessig', 'Matteo Cartiglia'] | 2021-04-12 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 6.58835948e-01 -5.90135872e-01 4.62764233e-01 -2.97867358e-01
-4.73428331e-02 -2.50065327e-01 2.28913024e-01 3.17211479e-01
-8.21333826e-01 6.88659012e-01 -6.11517251e-01 -1.54558867e-01
1.68917865e-01 -9.97195899e-01 -8.71853948e-01 -6.92479730e-01
-8.44795853e-02 -7.99193699e-03 9.96419787e-01 -1.31778598... | [8.21544075012207, 2.475943088531494] |
88e01397-796c-4d89-ae8d-29e5c1b8817e | gsb-group-superposition-binarization-for | 2305.07931 | null | https://arxiv.org/abs/2305.07931v3 | https://arxiv.org/pdf/2305.07931v3.pdf | GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples | Affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In addition, ViT generally demands heavy computing resources, which limit its deployment on resource-constrained devices. As a type of model-compression method,model ... | ['Hui Kong', 'Le Zhang', 'Cheng-Zhong Xu', 'Tian Gao'] | 2023-05-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.50898987e-01 -4.52237815e-01 -3.21847707e-01 -2.44938031e-01
-4.52145308e-01 9.32954773e-02 4.02395397e-01 2.32146963e-01
-6.17766261e-01 6.56864643e-01 -2.81911731e-01 -3.04198772e-01
-1.44325286e-01 -8.16276312e-01 -4.99453515e-01 -9.65133905e-01
5.51115572e-01 1.89706579e-01 2.68629760e-01 -7.64031485... | [8.622784614562988, 2.986327886581421] |
dc63e9d6-4948-4d68-81f9-5ebe26836af5 | frequency-and-temporal-convolutional | 1910.07364 | null | https://arxiv.org/abs/1910.07364v2 | https://arxiv.org/pdf/1910.07364v2.pdf | Frequency and temporal convolutional attention for text-independent speaker recognition | Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end. In this paper, we propose methods of convolutional attention for independently modelling temporal and fr... | ['Sarthak Yadav', 'Atul Rai'] | 2019-10-16 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 1.95765808e-01 -1.62166134e-01 4.25672010e-02 -6.01394892e-01
-1.07666314e+00 -3.14985335e-01 7.09695458e-01 -1.16327226e-01
-5.81366122e-01 3.23109776e-01 4.98873383e-01 -2.75398493e-01
7.96367675e-02 -1.60176516e-01 -5.64029336e-01 -6.99262321e-01
-2.94020891e-01 -1.10061407e-01 -8.50003883e-02 -8.25143307... | [14.418307304382324, 6.0310378074646] |
536c4727-f2eb-4299-a58b-e5d1d0ef7dda | hybrid-machine-learning-models-for-crop-yield | 2005.04155 | null | https://arxiv.org/abs/2005.04155v1 | https://arxiv.org/pdf/2005.04155v1.pdf | Hybrid Machine Learning Models for Crop Yield Prediction | Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of the artificial neural networks-imperialist competitive algorithm (ANN... | ['Bertalan Beszedes', 'Saeed Nosratabadi', 'Karoly Szell', 'Felde Imre', 'Amir Mosavi', 'Sina Ardabili'] | 2020-03-08 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-1.65801436e-01 -1.90122962e-01 -6.62868023e-01 2.60864615e-01
1.00103855e-01 -3.78725737e-01 -1.28817767e-01 4.70528305e-01
-2.26597369e-01 1.11267090e+00 -2.69013017e-01 -8.04876983e-01
-2.64840722e-01 -1.13838696e+00 -2.09984645e-01 -9.26048338e-01
-1.43282458e-01 -1.23582415e-01 -3.96448344e-01 -3.95909458... | [9.3503999710083, -1.5936752557754517] |
4d446f20-d6c5-4e03-8579-39fb39b5fec1 | two-to-five-truths-in-non-negative-matrix | 2305.05389 | null | https://arxiv.org/abs/2305.05389v1 | https://arxiv.org/pdf/2305.05389v1.pdf | Two to Five Truths in Non-Negative Matrix Factorization | In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization. We present a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel thos... | ['Nicholas A. Lines', 'Ryan Kaliszewski', 'Rod Gomez', 'Brian Baughman', 'Neil P Molino', 'John M. Conroy'] | 2023-05-06 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering', 'topic-models'] | ['graphs', 'graphs', 'natural-language-processing'] | [-1.32861768e-03 3.45951080e-01 -9.83754098e-02 1.94137450e-02
-4.41173464e-01 -8.38168442e-01 9.21753168e-01 4.61285919e-01
-4.77314711e-01 4.80225921e-01 6.09793603e-01 -5.10459304e-01
-5.75757325e-01 -9.03805017e-01 -3.73870164e-01 -7.80972660e-01
-5.61904728e-01 5.99010468e-01 1.56172872e-01 -2.55436242... | [7.266862392425537, 5.1336870193481445] |
a61847b3-70cd-4693-a30a-76a63e3de9b5 | realtime-multi-person-2d-pose-estimation | 1611.08050 | null | http://arxiv.org/abs/1611.08050v2 | http://arxiv.org/pdf/1611.08050v2.pdf | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields | We present an approach to efficiently detect the 2D pose of multiple people
in an image. The approach uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. The architecture encodes global context, allowing a
greedy bottom-u... | ['Shih-En Wei', 'Zhe Cao', 'Tomas Simon', 'Yaser Sheikh'] | 2016-11-24 | realtime-multi-person-2d-pose-estimation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Cao_Realtime_Multi-Person_2D_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Cao_Realtime_Multi-Person_2D_CVPR_2017_paper.pdf | cvpr-2017-7 | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.37120178e-02 2.05726415e-01 -1.43395752e-01 -4.15035456e-01
-5.65017939e-01 -5.82655847e-01 6.42533004e-01 1.62538990e-01
-6.18038535e-01 2.65076399e-01 4.86355960e-01 3.94081205e-01
6.55243844e-02 -4.94038910e-01 -9.22173023e-01 -2.19856009e-01
-4.49645787e-01 1.04529452e+00 3.42041403e-01 4.93300669... | [7.1550188064575195, -0.7913190126419067] |
557072ca-8a10-4fbc-aa53-5c876bb64855 | deep-kernelized-dense-geometric-matching | 2202.00667 | null | https://arxiv.org/abs/2202.00667v3 | https://arxiv.org/pdf/2202.00667v3.pdf | DKM: Dense Kernelized Feature Matching for Geometry Estimation | Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously sh... | ['Mårten Wadenbäck', 'Ioannis Athanasiadis', 'Michael Felsberg', 'Johan Edstedt'] | 2022-02-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Edstedt_DKM_Dense_Kernelized_Feature_Matching_for_Geometry_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Edstedt_DKM_Dense_Kernelized_Feature_Matching_for_Geometry_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['geometric-matching'] | ['computer-vision'] | [-7.13204965e-02 3.80693525e-02 2.46148244e-01 -3.41957301e-01
-9.88435686e-01 -3.42331260e-01 5.59355199e-01 -2.19855811e-02
-3.50543499e-01 5.13743639e-01 3.78683925e-01 1.85484424e-01
-2.18216762e-01 -7.20160365e-01 -1.01018214e+00 -5.48986197e-01
-7.08396137e-02 5.04522502e-01 3.56818169e-01 6.23822026... | [8.407602310180664, -2.4740753173828125] |
db3bcecb-dea7-4d91-8823-48822f5dae09 | correcting-real-word-spelling-errors-a-new | 2302.06407 | null | https://arxiv.org/abs/2302.06407v1 | https://arxiv.org/pdf/2302.06407v1.pdf | Correcting Real-Word Spelling Errors: A New Hybrid Approach | Spelling correction is one of the main tasks in the field of Natural Language Processing. Contrary to common spelling errors, real-word errors cannot be detected by conventional spelling correction methods. The real-word correction model proposed by Mays, Damerau and Mercer showed a great performance in different evalu... | ['Vahid Khatibi Bardsiri', 'Amid Khatibi Bardsiri', 'Seyed MohammadSadegh Dashti'] | 2023-02-09 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 1.37087047e-01 -9.48885605e-02 4.80534919e-02 -2.64615536e-01
-4.90859777e-01 -3.05577695e-01 5.36119044e-01 8.82759631e-01
-8.91982138e-01 1.01299620e+00 1.49525508e-01 -5.58689713e-01
-2.62681007e-01 -6.86312616e-01 -2.87223488e-01 -2.37776279e-01
4.07560915e-01 4.66506481e-01 5.68829894e-01 -3.71768624... | [10.843220710754395, 10.580338478088379] |
f0aad60e-91c8-4002-9a0e-fca38fe46f9d | justifying-corpus-based-choices-in-referring | null | null | https://aclanthology.org/R13-1040 | https://aclanthology.org/R13-1040.pdf | Justifying Corpus-Based Choices in Referring Expression Generation | null | ['Helmut Horacek'] | 2013-09-01 | justifying-corpus-based-choices-in-referring-1 | https://aclanthology.org/R13-1040 | https://aclanthology.org/R13-1040.pdf | ranlp-2013-9 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.296783447265625, 3.758841037750244] |
90a52c84-641f-4186-8277-3800698d5bf7 | 3d-coded-3d-correspondences-by-deep-1 | 1806.05228 | null | http://arxiv.org/abs/1806.05228v2 | http://arxiv.org/pdf/1806.05228v2.pdf | 3D-CODED : 3D Correspondences by Deep Deformation | We present a new deep learning approach for matching deformable shapes by
introducing {\it Shape Deformation Networks} which jointly encode 3D shapes and
correspondences. This is achieved by factoring the surface representation into
(i) a template, that parameterizes the surface, and (ii) a learnt global
feature vector... | ['Thibault Groueix', 'Bryan C. Russell', 'Matthew Fisher', 'Mathieu Aubry', 'Vladimir G. Kim'] | 2018-06-13 | null | null | null | null | ['3d-dense-shape-correspondence', '3d-surface-generation', '3d-point-cloud-matching'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.21025631e-01 3.93597364e-01 3.27740043e-01 -4.36056703e-01
-8.38918030e-01 -8.31778347e-01 7.96584487e-01 -9.63949971e-03
-7.90824518e-02 2.76867092e-01 2.51833797e-01 1.81072488e-01
2.76107434e-02 -9.19389546e-01 -1.15736771e+00 -4.82818961e-01
-1.84027180e-01 1.14422405e+00 2.47620985e-01 -2.67673314... | [8.754236221313477, -3.558008909225464] |
a9118cd9-3ad5-495c-b65d-dd72dc655c20 | multi-task-learning-for-mental-health-using | 1712.03538 | null | http://arxiv.org/abs/1712.03538v1 | http://arxiv.org/pdf/1712.03538v1.pdf | Multi-Task Learning for Mental Health using Social Media Text | We introduce initial groundwork for estimating suicide risk and mental health
in a deep learning framework. By modeling multiple conditions, the system
learns to make predictions about suicide risk and mental health at a low false
positive rate. Conditions are modeled as tasks in a multi-task learning (MTL)
framework, ... | ['Dirk Hovy', 'Margaret Mitchell', 'Adrian Benton'] | 2017-12-10 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 9.35357064e-02 4.86120433e-01 -5.32039404e-01 -6.64869189e-01
-1.40086102e+00 -1.66313145e-02 4.11261767e-01 5.57422876e-01
-6.93732738e-01 7.89239585e-01 4.71970022e-01 -1.16317468e-02
-5.01500852e-02 -5.04014075e-01 -2.48293608e-01 -2.60216475e-01
4.28281352e-02 8.99064183e-01 -4.01343048e-01 -4.06036824... | [8.741467475891113, 10.212217330932617] |
f8606d56-7560-4c85-9e9c-f583a2fe705d | stacked-capsule-autoencoders | 1906.06818 | null | https://arxiv.org/abs/1906.06818v2 | https://arxiv.org/pdf/1906.06818v2.pdf | Stacked Capsule Autoencoders | Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts to reason about objects. Since these relationships do not depend on the viewpoint, our model is robust to viewpoint changes. SCAE consists ... | ['Geoffrey E. Hinton', 'Sara Sabour', 'Adam R. Kosiorek', 'Yee Whye Teh'] | 2019-06-17 | stacked-capsule-autoencoders-1 | http://papers.nips.cc/paper/9684-stacked-capsule-autoencoders | http://papers.nips.cc/paper/9684-stacked-capsule-autoencoders.pdf | neurips-2019-12 | ['unsupervised-mnist'] | ['methodology'] | [-3.39213848e-01 2.18270570e-01 2.14409772e-02 -3.62592727e-01
-4.55309391e-01 -6.45783961e-01 5.64009786e-01 -5.10663018e-02
-4.72851694e-02 1.79726124e-01 4.25014317e-01 3.25471401e-01
-2.39700414e-02 -7.31760144e-01 -1.16160345e+00 -7.61642337e-01
-6.45760521e-02 8.78399551e-01 2.15335965e-01 1.12503864... | [8.14825439453125, -2.9956214427948] |
923eb631-17bf-4637-9dcd-c9feac494ab1 | on-the-use-of-bert-for-automated-essay | 2205.03835 | null | https://arxiv.org/abs/2205.03835v2 | https://arxiv.org/pdf/2205.03835v2.pdf | On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation | In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks. However, in the area of Automated Essay Scoring (AES), pre-trained models such as BERT have not been properly used to outperform other deep learning models such as LSTM. In this paper, we introduce a novel multi-sc... | ['Hui Lin', 'Ruobing Li', 'Chuan Wang', 'Yongjie Wang'] | 2022-05-08 | null | https://aclanthology.org/2022.naacl-main.249 | https://aclanthology.org/2022.naacl-main.249.pdf | naacl-2022-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.57731637e-01 -1.97141960e-01 -1.97968230e-01 -5.84811449e-01
-9.63274598e-01 -3.70281458e-01 4.55801576e-01 4.59752500e-01
-6.77543163e-01 9.57739115e-01 2.83425122e-01 -3.38370919e-01
-1.52460665e-01 -7.12657094e-01 -4.90392178e-01 -2.41600543e-01
5.51135778e-01 6.11066878e-01 4.22480628e-02 -5.01679599... | [11.329475402832031, 9.348170280456543] |
5a742783-284e-4d04-83c7-3b3f616f31db | deep-cardiosound-an-ensembled-deep-learning | 2204.07420 | null | https://arxiv.org/abs/2204.07420v2 | https://arxiv.org/pdf/2204.07420v2.pdf | Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling | Heart sound diagnosis and classification play an essential role in detecting cardiovascular disorders, especially when the remote diagnosis becomes standard clinical practice. Most of the current work is designed for single category based heard sound classification tasks. To further extend the landscape of the automati... | ['Yonghong Peng', 'Steven Davenport', 'Li Guo'] | 2022-04-15 | null | null | null | null | ['sound-classification'] | ['audio'] | [-4.33394015e-02 2.45066181e-01 -4.91996408e-02 -3.10942322e-01
-1.17694032e+00 -5.69809914e-01 -1.03527941e-01 4.50489938e-01
-1.48774371e-01 5.08147895e-01 6.00558743e-02 -4.24021691e-01
-1.82419971e-01 -4.63744044e-01 1.06853060e-01 -6.56086326e-01
-2.15002507e-01 3.33505452e-01 2.80100316e-01 3.27291518... | [14.360359191894531, 3.3690760135650635] |
31584e1c-edbc-4682-ad53-fc6f2aef51c4 | don-t-do-it-safer-reinforcement-learning-with | 2212.13819 | null | https://arxiv.org/abs/2212.13819v1 | https://arxiv.org/pdf/2212.13819v1.pdf | Don't do it: Safer Reinforcement Learning With Rule-based Guidance | During training, reinforcement learning systems interact with the world without considering the safety of their actions. When deployed into the real world, such systems can be dangerous and cause harm to their surroundings. Often, dangerous situations can be mitigated by defining a set of rules that the system should n... | ['Jochen Renz', 'Cheng Xue', 'Ekaterina Nikonova'] | 2022-12-28 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 1.27022276e-02 4.78945255e-01 1.06981479e-01 -2.31556460e-01
2.84055471e-01 -5.50100744e-01 6.35069847e-01 1.38209224e-01
-8.94885361e-01 1.19526088e+00 -2.33913735e-01 -4.20415252e-01
-3.32185239e-01 -1.13613188e+00 -7.65455067e-01 -8.89338493e-01
-4.59604114e-01 4.56301600e-01 5.61464429e-01 -4.71358895... | [4.527477264404297, 2.02911639213562] |
ac15e8db-0e9f-46ba-993d-3ff12654d22e | pugeo-net-a-geometry-centric-network-for-3d | 2002.10277 | null | https://arxiv.org/abs/2002.10277v2 | https://arxiv.org/pdf/2002.10277v2.pdf | PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling | This paper addresses the problem of generating uniform dense point clouds to describe the underlying geometric structures from given sparse point clouds. Due to the irregular and unordered nature, point cloud densification as a generative task is challenging. To tackle the challenge, we propose a novel deep neural netw... | ['Sam Kwong', 'Junhui Hou', 'Yue Qian', 'Ying He'] | 2020-02-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3338_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640732.pdf | eccv-2020-8 | ['point-cloud-super-resolution'] | ['computer-vision'] | [-7.13023469e-02 6.84602186e-02 1.95531264e-01 -1.58455759e-01
-7.10298240e-01 -1.13027014e-01 5.70863307e-01 -3.73952448e-01
5.11154607e-02 7.27214158e-01 -1.79868504e-01 1.81243941e-02
-1.33441776e-01 -1.55403864e+00 -1.30100095e+00 -5.71203232e-01
3.87524664e-02 1.15532255e+00 -1.26015693e-01 -3.40193480... | [8.594663619995117, -3.6473848819732666] |
63f8c5c5-92df-47cd-a110-e6baace3134d | lets-gzsl-a-latent-embedding-model-for-time | 2207.12007 | null | https://arxiv.org/abs/2207.12007v1 | https://arxiv.org/pdf/2207.12007v1.pdf | LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning | One of the recent developments in deep learning is generalized zero-shot learning (GZSL), which aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Over the past couple of years, GZSL has picked up traction and several models have been proposed to ... | ['Manik Gupta', 'Priyanka Gupta', 'Sathvik Bhaskarpandit'] | 2022-07-25 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 4.01341319e-01 3.91906798e-02 -6.15196452e-02 -4.18826312e-01
-7.25517929e-01 -1.72501132e-01 5.09222269e-01 4.33458894e-01
-2.65328050e-01 6.08899713e-01 5.21986596e-02 6.53721020e-02
-3.26266199e-01 -8.18803847e-01 -1.09277397e-01 -7.74347425e-01
-3.24191809e-01 1.94911346e-01 1.83625191e-01 -1.62176713... | [9.911816596984863, 3.1111841201782227] |
4f64e5e0-18c8-4800-88b2-45271970f776 | early-prediction-of-respiratory-failure-in | 2105.05728 | null | https://arxiv.org/abs/2105.05728v1 | https://arxiv.org/pdf/2105.05728v1.pdf | Early prediction of respiratory failure in the intensive care unit | The development of respiratory failure is common among patients in intensive care units (ICU). Large data quantities from ICU patient monitoring systems make timely and comprehensive analysis by clinicians difficult but are ideal for automatic processing by machine learning algorithms. Early prediction of respiratory s... | ['Gunnar Rätsch', 'Tobias M. Merz', 'Stephanie L. Hyland', 'Chris Barber', 'Xinrui Lyu', 'Martin Faltys', 'Matthias Hüser'] | 2021-05-12 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 4.14812624e-01 -8.19644108e-02 -3.31281088e-02 -3.84105295e-01
-1.66332006e-01 -4.79629159e-01 -9.57212523e-02 8.36826921e-01
-3.84721577e-01 7.01728463e-01 -5.41864224e-02 -1.15495312e+00
-4.76783127e-01 -6.54191196e-01 1.99680194e-01 -4.31878865e-01
-1.35899425e-01 1.01458287e+00 1.75823927e-01 3.05949032... | [7.988918781280518, 6.160658359527588] |
ed09331d-2052-4e4f-90af-0c7ed25705a0 | comparison-of-deep-learning-models-on-time | 1911.08414 | null | https://arxiv.org/abs/1911.08414v2 | https://arxiv.org/pdf/1911.08414v2.pdf | Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction | Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many researchers have developed hybrid models or variant models based on deep learning techniques, there is... | ['Hongqian Qin'] | 2019-11-17 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-3.13037068e-01 -6.65722013e-01 1.46227568e-01 -2.79753655e-01
-2.35778213e-01 -2.52099425e-01 7.68222213e-01 -1.94924586e-02
-3.64798039e-01 9.50403273e-01 2.27971748e-01 -8.99783492e-01
-3.89192402e-01 -1.02649426e+00 -3.30570281e-01 -1.04051137e+00
-6.34431005e-01 -1.79947644e-01 -9.13037881e-02 -3.89330298... | [6.513217449188232, 2.9082977771759033] |
91d5e2a2-3e92-4dff-acf8-ff25b000b57b | structural-encoding-and-pre-training-matter | null | null | https://aclanthology.org/2021.eacl-main.201 | https://aclanthology.org/2021.eacl-main.201.pdf | Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification | Growing concern with online misinformation has encouraged NLP research on fact verification. Since writers often base their assertions on structured data, we focus here on verifying textual statements given evidence in tables. Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al.... | ['David Smith', 'Rui Dong'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['table-to-text-generation', 'table-based-fact-verification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.22470528e-01 8.84915769e-01 -4.45963115e-01 -3.82799387e-01
-1.14939356e+00 -7.09860682e-01 6.22217298e-01 1.27272046e+00
-1.95971683e-01 9.66083586e-01 6.59633815e-01 -1.10966980e+00
2.19739843e-02 -1.32061625e+00 -1.10602236e+00 5.05176604e-01
2.18238682e-02 6.25272572e-01 4.07350250e-02 -4.41990256... | [9.66479206085205, 7.825282096862793] |
95fd253e-6416-42b0-ae9c-b4db75dc2749 | fusion-of-eeg-and-musical-features-in | 1611.10120 | null | http://arxiv.org/abs/1611.10120v1 | http://arxiv.org/pdf/1611.10120v1.pdf | Fusion of EEG and Musical Features in Continuous Music-emotion Recognition | Emotion estimation in music listening is confronting challenges to capture
the emotion variation of listeners. Recent years have witnessed attempts to
exploit multimodality fusing information from musical contents and
physiological signals captured from listeners to improve the performance of
emotion recognition. In th... | ['Ken-ichi Fukui', 'Nattapong Thammasan', 'Masayuki Numao'] | 2016-11-30 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 2.34386161e-01 -5.62632799e-01 5.38070023e-01 -3.30755234e-01
-9.07993913e-01 -4.56109941e-01 1.41257241e-01 -8.53024870e-02
-4.06188160e-01 7.72214293e-01 2.99834102e-01 5.48464715e-01
-5.68721473e-01 -3.24264824e-01 -5.53730987e-02 -8.03662717e-01
-4.11444247e-01 -3.67653787e-01 -5.56673050e-01 -1.59082994... | [13.221643447875977, 3.3581929206848145] |
badbbbf7-dced-42a9-8cd0-8f3dcda80b9f | non-contact-sensing-for-anomaly-detection-in | 2306.10808 | null | https://arxiv.org/abs/2306.10808v1 | https://arxiv.org/pdf/2306.10808v1.pdf | Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach | The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs and lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials.... | ['Olga Fink', 'David Flynn', 'Jamie Blanche', 'Daniel Mitchell', 'Gaëtan Frusque'] | 2023-06-19 | null | null | null | null | ['anomaly-detection', 'unsupervised-anomaly-detection'] | ['methodology', 'methodology'] | [ 3.47040653e-01 -3.44852358e-01 8.13128650e-01 -4.54353392e-02
-2.44414315e-01 -2.15449214e-01 4.51300442e-01 3.38582128e-01
-1.16363436e-01 4.54438090e-01 -1.46021470e-01 3.20236348e-02
-7.09476888e-01 -8.73622656e-01 -1.74656898e-01 -1.22685575e+00
-5.54411888e-01 1.98205382e-01 4.35527414e-03 -3.57433975... | [6.732947826385498, 2.3330180644989014] |
f8eff211-4945-41e9-bbbc-c2fbee21ae91 | pointwise-mutual-information-based-metric-and | 2305.12191 | null | https://arxiv.org/abs/2305.12191v1 | https://arxiv.org/pdf/2305.12191v1.pdf | Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs | A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not \textit{faithful} to the underlying document. Existing automated metrics used for evaluating the faithfulness of response with respect to the grounding document measure the ... | ['Luis A. Lastras', 'Sachindra Joshi', 'Dinesh Raghu', 'Vineet Kumar', 'Yatin Nandwani'] | 2023-05-20 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.03818589e-01 5.92719316e-01 2.31515802e-02 -8.67358208e-01
-9.28115189e-01 -5.91779649e-01 1.17090917e+00 1.78677782e-01
-1.44975722e-01 8.00975382e-01 1.16963804e+00 -7.66240135e-02
2.86211222e-01 -9.25670683e-01 -2.20251441e-01 -3.74392301e-01
4.96029705e-01 8.56353939e-01 -3.53397541e-02 -7.02301562... | [12.625585556030273, 8.286717414855957] |
41e2e919-f282-4733-ad14-de8896d3c911 | calibrating-for-class-weights-by-modeling | 2205.04613 | null | https://arxiv.org/abs/2205.04613v2 | https://arxiv.org/pdf/2205.04613v2.pdf | Calibrating for Class Weights by Modeling Machine Learning | A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities. Our starting point is that calibration is seemingly incompatible with class weighting, a technique often employed when one class is less common (class imbalance) or wit... | ['Philip Marx', 'Daniel Martin', 'Andrew Caplin'] | 2022-05-10 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 4.89878923e-01 4.29646730e-01 -5.51889181e-01 -3.89523566e-01
-9.36611950e-01 -4.92013603e-01 6.84946060e-01 4.69343662e-01
-3.16403925e-01 9.75939035e-01 2.27111891e-01 -5.16476095e-01
-4.55422461e-01 -7.12061405e-01 -6.05259299e-01 -4.92713362e-01
2.45229945e-01 8.91880155e-01 -2.17070081e-03 3.94076079... | [8.594366073608398, 4.663268566131592] |
6709621b-6775-49d1-888e-2dfec2f02e3d | explainability-of-predictive-process | 2202.08041 | null | https://arxiv.org/abs/2202.08041v1 | https://arxiv.org/pdf/2202.08041v1.pdf | Explainability of Predictive Process Monitoring Results: Can You See My Data Issues? | Predictive business process monitoring (PPM) has been around for several years as a use case of process mining. PPM enables foreseeing the future of a business process through predicting relevant information about how a running process instance might end, related performance indicators, and other predictable aspects. A... | ['Manfred Reichert', 'Mervat Abuelkheir', 'Ghada ElKhawaga'] | 2022-02-16 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.24854633e-01 5.51811457e-01 -1.91040143e-01 -5.16886711e-01
-1.55464828e-01 -3.53525519e-01 1.00846708e+00 5.98063588e-01
3.51031050e-02 5.80759525e-01 2.72130817e-01 -5.48323274e-01
-5.69775760e-01 -8.60358894e-01 -5.53753614e-01 -3.56014818e-01
-4.66536991e-02 7.74155438e-01 1.51782930e-01 2.94306427... | [8.594805717468262, 6.012921333312988] |
653dfed9-376d-43b3-a41c-34781f1ac6c5 | rlprompt-optimizing-discrete-text-prompts | 2205.12548 | null | https://arxiv.org/abs/2205.12548v3 | https://arxiv.org/pdf/2205.12548v3.pdf | RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning | Prompting has shown impressive success in enabling large pretrained language models (LMs) to perform diverse NLP tasks, especially when only few downstream data are available. Automatically finding the optimal prompt for each task, however, is challenging. Most existing work resorts to tuning soft prompt (e.g., embeddi... | ['Zhiting Hu', 'Eric P. Xing', 'Meng Song', 'Tianmin Shu', 'Han Guo', 'Yihan Wang', 'Cheng-Ping Hsieh', 'Jianyu Wang', 'Mingkai Deng'] | 2022-05-25 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.32593888e-01 1.18059367e-01 -3.71929467e-01 -3.30011368e-01
-1.01977837e+00 -9.57559705e-01 5.97155273e-01 -1.45788109e-02
-5.90799749e-01 8.41139197e-01 2.40126863e-01 -6.83327615e-01
3.44443917e-02 -5.21914363e-01 -5.42200565e-01 -5.42927682e-01
3.64589274e-01 5.31027615e-01 -1.49872243e-01 -4.81985480... | [11.355358123779297, 8.64217758178711] |
2b2e049d-0c19-4c61-b518-01bb9b59bcbc | discriminative-diffusion-models-as-few-shot | 2305.10722 | null | https://arxiv.org/abs/2305.10722v1 | https://arxiv.org/pdf/2305.10722v1.pdf | Discriminative Diffusion Models as Few-shot Vision and Language Learners | Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trai... | ['Xin Eric Wang', 'William Yang Wang', 'Sugato Basu', 'Pradyumna Narayana', 'Arjun Akula', 'Varun Jampani', 'Tsu-Jui Fu', 'Weixi Feng', 'Xuehai He'] | 2023-05-18 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 4.77586150e-01 -8.10557045e-03 -2.53624141e-01 -3.50579888e-01
-1.00833166e+00 -3.55797470e-01 1.28353691e+00 -2.83552818e-02
-2.33240828e-01 2.48259112e-01 6.95512831e-01 -1.44445136e-01
1.06612220e-01 -7.74904191e-01 -6.84759319e-01 -4.08638388e-01
3.87492031e-01 7.92353034e-01 3.58664125e-01 -2.71820247... | [11.188199996948242, 0.023235972970724106] |
160715ef-8ef9-49a6-8d30-e2bd83d0ca6f | unrolling-svt-to-obtain-computationally | 2212.08852 | null | https://arxiv.org/abs/2212.08852v1 | https://arxiv.org/pdf/2212.08852v1.pdf | Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography | Quantum state tomography aims to estimate the state of a quantum mechanical system which is described by a trace one, Hermitian positive semidefinite complex matrix, given a set of measurements of the state. Existing works focus on estimating the density matrix that represents the state, using a compressive sensing app... | ['Sheetal Kalyani', 'Siva Shanmugam'] | 2022-12-17 | null | null | null | null | ['compressive-sensing', 'quantum-state-tomography'] | ['computer-vision', 'medical'] | [ 4.48508203e-01 1.30238444e-01 1.55036002e-01 -4.16531295e-01
-7.15270042e-01 -5.53061306e-01 3.42445910e-01 -2.62056440e-01
-6.18606806e-01 7.72445261e-01 -1.68093108e-02 -5.96330762e-01
-1.20931208e-01 -8.61729026e-01 -8.70399058e-01 -7.47643411e-01
-1.24453558e-02 8.16540122e-01 -2.37556070e-01 -1.27301022... | [5.637819290161133, 4.911802291870117] |
bd5804fe-2df4-466c-8c63-aad6dc4abe03 | uc-owod-unknown-classified-open-world-object | 2207.11455 | null | https://arxiv.org/abs/2207.11455v1 | https://arxiv.org/pdf/2207.11455v1.pdf | UC-OWOD: Unknown-Classified Open World Object Detection | Open World Object Detection (OWOD) is a challenging computer vision problem that requires detecting unknown objects and gradually learning the identified unknown classes. However, it cannot distinguish unknown instances as multiple unknown classes. In this work, we propose a novel OWOD problem called Unknown-Classified... | ['Junzhi Yu', 'Liwen Kang', 'Zhengxing Wu', 'Xingyu Chen', 'Yue Lu', 'Zhiheng Wu'] | 2022-07-23 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [-2.90854406e-02 -3.70924473e-02 -1.76315233e-01 -2.28254929e-01
-7.87475586e-01 -7.29936659e-01 3.10720503e-01 1.84897706e-01
-8.02911744e-02 6.84062719e-01 -3.30939412e-01 4.85863425e-02
-2.13994414e-01 -6.38901353e-01 -3.77434999e-01 -6.69788122e-01
5.89443482e-02 7.59889305e-01 7.08873332e-01 4.66641158... | [9.263511657714844, 1.474398136138916] |
3f4adc3e-27f4-466b-8735-56ecc02fee8a | credence-counterfactual-explanations-for | 2302.04983 | null | https://arxiv.org/abs/2302.04983v1 | https://arxiv.org/pdf/2302.04983v1.pdf | CREDENCE: Counterfactual Explanations for Document Ranking | Towards better explainability in the field of information retrieval, we present CREDENCE, an interactive tool capable of generating counterfactual explanations for document rankers. Embracing the unique properties of the ranking problem, we present counterfactual explanations in terms of document perturbations, query p... | ['Jaroslaw Szlichta', 'Divesh Srivastava', 'Mehdi Kargar', 'Lukasz Golab', 'Parke Godfrey', 'Joel Rorseth'] | 2023-02-10 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 4.05224681e-01 7.71571457e-01 -3.92034769e-01 -1.29604757e-01
-6.59606874e-01 -9.97531414e-01 1.30846155e+00 3.30392361e-01
4.63359989e-02 1.04289591e+00 8.92616689e-01 -9.74435031e-01
-7.17913806e-01 -5.09817123e-01 -6.89862549e-01 -7.27054030e-02
-3.44389290e-01 8.93326163e-01 -1.00625895e-01 -3.70759279... | [8.784059524536133, 5.668107032775879] |
ffbcd780-926e-4e7e-aa3d-b4842fb0ca07 | classification-of-eye-state-using-eeg | 2209.01023 | null | https://arxiv.org/abs/2209.01023v1 | https://arxiv.org/pdf/2209.01023v1.pdf | Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure | The classification of electroencephalography (EEG) signals is useful in a wide range of applications such as seizure detection/prediction, motor imagery classification, emotion classification and drug effects diagnosis, amongst others. With the large number of EEG channels acquired, it has become vital that efficient d... | ['Hisham Ihshaish', 'Phoebe M Asquith'] | 2022-08-31 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 6.06552780e-01 -2.95753926e-01 1.24012515e-01 -6.25567675e-01
-5.34077883e-01 -3.55124593e-01 3.03243756e-01 6.23571455e-01
-5.61048508e-01 9.14355516e-01 9.29515064e-02 -2.39171475e-01
-6.25856340e-01 -2.86984682e-01 3.63679938e-02 -6.07007265e-01
-7.83018172e-01 2.43934318e-02 -1.21320914e-02 1.93758786... | [13.211723327636719, 3.4330949783325195] |
ba3b72d7-e3c4-4172-bd92-eea386ca2c76 | robust-object-detection-in-remote-sensing | 2210.12989 | null | https://arxiv.org/abs/2210.12989v1 | https://arxiv.org/pdf/2210.12989v1.pdf | Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version) | Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based object detectors achieve state-of-the-art performance. However, established object detectors require complete, precise, and correct bounding box... | ['Matthias Schubert', 'Maximilian Bernhard'] | 2022-10-24 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 1.87053606e-01 -2.25141793e-01 -1.10845484e-01 -4.09647375e-01
-1.16974902e+00 -5.87203622e-01 2.61170149e-01 2.97430784e-01
-4.96059120e-01 6.03058457e-01 -4.91633892e-01 -3.71965498e-01
-3.13637964e-02 -9.18301821e-01 -7.79839933e-01 -7.08724141e-01
9.10685584e-03 3.25379252e-01 5.44838905e-01 9.53553841... | [9.03646469116211, -0.9182347655296326] |
dfccd936-c6ab-47cb-9faf-1302c68da208 | unsupervised-learning-of-time-varying | null | null | https://openreview.net/forum?id=HkxIIaVKPB | https://openreview.net/pdf?id=HkxIIaVKPB | Unsupervised-Learning of time-varying features | We present an architecture based on the conditional Variational Autoencoder to learn a representation
of transformations in time-sequence data. The model is constructed in a way that allows to identify sub-spaces of features indicating changes between frames without learning features that are constant within a time-seq... | ['Oswin Krause', 'Matthias Brix', 'Henrik Høeg'] | 2019-09-25 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-1.33638561e-01 -5.65948337e-02 -5.33407591e-02 -3.15712512e-01
-2.46987030e-01 -6.40331686e-01 1.05243611e+00 -1.30153269e-01
-5.77321827e-01 3.37191463e-01 3.90417337e-01 2.38308180e-02
-9.12250131e-02 -7.24627316e-01 -9.91935313e-01 -8.40846002e-01
-1.93315685e-01 4.12131429e-01 3.66424084e-01 -4.84926581... | [8.633235931396484, 0.42483648657798767] |
631524f7-6518-4855-97e2-81031de0c536 | predicting-3d-shapes-masks-and-properties-of | 2109.07577 | null | https://arxiv.org/abs/2109.07577v1 | https://arxiv.org/pdf/2109.07577v1.pdf | Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset | We present TransProteus, a dataset, and methods for predicting the 3D structure, masks, and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in man... | ['Alan Aspuru-Guzik', 'Yi Ru Wang', 'Haoping Xu', 'Sagi Eppel'] | 2021-09-15 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 2.66167104e-01 1.33454889e-01 6.91990733e-01 -1.70892969e-01
-2.60259777e-01 -9.81065333e-01 5.65649271e-01 -1.56344905e-01
-2.67362595e-01 1.29256576e-01 -2.35712424e-01 -4.32363115e-02
3.42922419e-01 -1.07777452e+00 -1.16228783e+00 -6.72014475e-01
1.78252697e-01 4.51418072e-01 5.34574151e-01 1.82969049... | [9.413822174072266, -3.0035767555236816] |
ce4ac318-c439-40fb-af6c-acf711b4f5cf | weakly-supervised-discriminative-feature | 2002.11939 | null | https://arxiv.org/abs/2002.11939v1 | https://arxiv.org/pdf/2002.11939v1.pdf | Weakly supervised discriminative feature learning with state information for person identification | Unsupervised learning of identity-discriminative visual feature is appealing in real-world tasks where manual labelling is costly. However, the images of an identity can be visually discrepant when images are taken under different states, e.g. different camera views and poses. This visual discrepancy leads to great dif... | ['Wei-Shi Zheng', 'Hong-Xing Yu'] | 2020-02-27 | weakly-supervised-discriminative-feature-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Weakly_Supervised_Discriminative_Feature_Learning_With_State_Information_for_Person_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_Weakly_Supervised_Discriminative_Feature_Learning_With_State_Information_for_Person_CVPR_2020_paper.pdf | cvpr-2020-6 | ['robust-face-recognition', 'person-identification', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.11235031e-02 -1.12488337e-01 -2.13641033e-01 -7.74690092e-01
-4.20501590e-01 -8.55018020e-01 6.50141060e-01 -3.94645691e-01
-5.10517716e-01 6.84822857e-01 1.98778823e-01 2.17432022e-01
1.39099762e-01 -1.75084725e-01 -7.09786713e-01 -7.74208724e-01
3.34631354e-01 6.84650838e-01 -2.74208248e-01 6.73021451... | [14.608723640441895, 1.0699182748794556] |
81f9d40a-85f6-4166-b291-5c99ad273448 | optimal-clustering-with-bandit-feedback | 2202.04294 | null | https://arxiv.org/abs/2202.04294v1 | https://arxiv.org/pdf/2202.04294v1.pdf | Optimal Clustering with Bandit Feedback | This paper considers the problem of online clustering with bandit feedback. A set of arms (or items) can be partitioned into various groups that are unknown. Within each group, the observations associated to each of the arms follow the same distribution with the same mean vector. At each time step, the agent queries or... | ['Vincent Y. F. Tan', 'Zixin Zhong', 'Junwen Yang'] | 2022-02-09 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 4.72388603e-02 -2.69448552e-02 -9.40813363e-01 -1.76681876e-01
-9.65226591e-01 -9.94185150e-01 -4.49308269e-02 2.23917007e-01
-3.20000440e-01 7.55144954e-01 -3.28032613e-01 -6.43836379e-01
-8.59256446e-01 -5.90161383e-01 -9.32403862e-01 -1.04559374e+00
-2.77131706e-01 1.42900920e+00 1.94334537e-01 6.53329849... | [4.566433906555176, 3.3497769832611084] |
172b1e75-71ad-4c19-9fea-9e4865e9c779 | relationprompt-leveraging-prompts-to-generate | 2203.09101 | null | https://arxiv.org/abs/2203.09101v1 | https://arxiv.org/pdf/2203.09101v1.pdf | RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction | Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods. Given an i... | ['Luo Si', 'Soujanya Poria', 'Lidong Bing', 'Yew Ken Chia'] | 2022-03-17 | null | https://aclanthology.org/2022.findings-acl.5 | https://aclanthology.org/2022.findings-acl.5.pdf | findings-acl-2022-5 | ['zero-shot-relation-triplet-extraction', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.05840147e-01 9.26529288e-01 -5.02075315e-01 -3.69896948e-01
-8.91573846e-01 -3.45470130e-01 7.31887698e-01 3.16608846e-01
-6.92681819e-02 9.50187802e-01 3.85784328e-01 -6.29761755e-01
-5.87973557e-02 -1.00139213e+00 -6.22793317e-01 -1.97636962e-01
2.47241035e-01 8.47481012e-01 -2.17633788e-02 -4.03473526... | [9.464255332946777, 8.513602256774902] |
344f3725-2d8b-40ea-b82d-e6396ace8823 | towards-more-transparent-and-accurate-cancer | 2305.11728 | null | https://arxiv.org/abs/2305.11728v1 | https://arxiv.org/pdf/2305.11728v1.pdf | Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach | Digital pathology has revolutionized cancer diagnosis by leveraging Content-Based Medical Image Retrieval (CBMIR) for analyzing histopathological Whole Slide Images (WSIs). CBMIR enables searching for similar content, enhancing diagnostic reliability and accuracy. In 2020, breast and prostate cancer constituted 11.7% a... | ['Valery Naranjo', 'Javier Oliver Moll', 'Adrian colomer', 'Zahra Tabatabaei'] | 2023-05-19 | null | null | null | null | ['whole-slide-images', 'medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [ 1.57493487e-01 9.55968350e-02 -2.24793464e-01 3.14945877e-01
-1.43294764e+00 -5.83791137e-01 5.42553067e-01 8.45791221e-01
-7.20824301e-01 3.10631603e-01 1.76790711e-02 -3.55162621e-01
-3.54210883e-01 -8.85597348e-01 -2.99907416e-01 -1.11878932e+00
-3.36337239e-02 2.63395220e-01 1.29702061e-01 4.80370708... | [15.137126922607422, -2.9589176177978516] |
9a15dee4-b671-4d0b-98aa-8ec6028021a9 | improving-task-adaptation-for-cross-domain | 2107.00358 | null | https://arxiv.org/abs/2107.00358v4 | https://arxiv.org/pdf/2107.00358v4.pdf | Cross-domain Few-shot Learning with Task-specific Adapters | In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. Recent approaches broadly solve this problem by parameterizing their few-shot classifiers with task-agnostic and task-specific weights where ... | ['Hakan Bilen', 'Xialei Liu', 'Wei-Hong Li'] | 2021-07-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Cross-Domain_Few-Shot_Learning_With_Task-Specific_Adapters_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Cross-Domain_Few-Shot_Learning_With_Task-Specific_Adapters_CVPR_2022_paper.pdf | cvpr-2022-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.99579132e-01 6.04835264e-02 -3.87557328e-01 -5.23770630e-01
-7.79215157e-01 -4.51569945e-01 7.44882703e-01 2.28644330e-02
-5.83074212e-01 6.63437843e-01 -1.45111814e-01 1.69599831e-01
-1.81560501e-01 -5.93539476e-01 -7.62765944e-01 -4.02505606e-01
1.25893205e-01 9.11136627e-01 4.79363859e-01 -3.22942197... | [9.99753189086914, 3.0009000301361084] |
ef92c397-5374-4987-a31e-bef35445f7cd | mmocr-a-comprehensive-toolbox-for-text | 2108.06543 | null | https://arxiv.org/abs/2108.06543v1 | https://arxiv.org/pdf/2108.06543v1.pdf | MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding | We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction. MMOCR implements 14 state-of-the-art algorithms, which is significantly more than all the existing open-s... | ['Dahua Lin', 'Wayne Zhang', 'Kai Chen', 'Wenwei Zhang', 'Tong Gao', 'Yiqin Zhu', 'Huaqiang Wei', 'Jianyong Chen', 'Tsui Hin Lin', 'Xiaoyu Yue', 'Zhizhong Li', 'Hongbin Sun', 'Zhanghui Kuang'] | 2021-08-14 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.49639711e-01 -3.39102417e-01 -7.32826740e-02 -1.97888479e-01
-1.01636934e+00 -9.45026457e-01 6.91649079e-01 2.26601601e-01
-4.88312334e-01 2.82481909e-01 3.37415487e-01 -3.91784012e-01
5.22530556e-01 -5.79156756e-01 -2.22188741e-01 -2.71890491e-01
4.77117777e-01 5.13973892e-01 1.04152821e-01 1.25839919... | [11.95984172821045, 2.3208062648773193] |
0507e110-bad7-42ea-8ad2-0cc073b39865 | unbiased-scene-graph-generation-using | 2210.00920 | null | https://arxiv.org/abs/2210.00920v1 | https://arxiv.org/pdf/2210.00920v1.pdf | Unbiased Scene Graph Generation using Predicate Similarities | Scene Graphs are widely applied in computer vision as a graphical representation of relationships between objects shown in images. However, these applications have not yet reached a practical stage of development owing to biased training caused by long-tailed predicate distributions. In recent years, many studies have ... | ['Yusuke Matsui', 'Misaki Ohashi'] | 2022-10-03 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 6.21891141e-01 -1.68348048e-02 -2.91347831e-01 -5.15469432e-01
-4.26813960e-01 -4.14210558e-01 6.47099614e-01 4.97959465e-01
6.07599355e-02 7.28396475e-01 6.27644882e-02 -1.46308392e-01
-2.44937539e-01 -8.05342436e-01 -9.65577483e-01 -8.72355640e-01
-2.27597523e-02 5.00294983e-01 7.98683286e-01 6.75107837... | [10.291558265686035, 1.7857413291931152] |
6fb17ad8-51a8-499a-b47e-ee143869a050 | fast-l1-minimization-algorithms-for-robust-1 | 1007.3753 | null | https://arxiv.org/abs/1007.3753v4 | https://arxiv.org/pdf/1007.3753v4.pdf | Fast L1-Minimization Algorithms For Robust Face Recognition | L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax. Under certain conditions as described in compressive sensing theory, the minimum L1-norm solution is also the sparsest solution. In this paper, our study addresses the speed and scalability of its algorithms. In par... | ['Yi Ma', 'S. Shankar Sastry', 'Arvind Ganesh', 'Zihan Zhou', 'Allen Y. Yang'] | 2010-07-21 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.63462687e-01 -1.21395171e-01 -9.57698934e-03 -1.49442479e-01
-6.91440642e-01 -2.33510956e-01 2.37883344e-01 -5.08312464e-01
7.91455582e-02 8.62378240e-01 1.69873700e-01 -3.95384245e-02
-4.50470865e-01 -1.97279572e-01 -7.93483496e-01 -8.41366947e-01
-1.40017018e-01 3.28979284e-01 -8.04593801e-01 -1.81359261... | [12.510961532592773, 0.3726765215396881] |
99f8f411-1c12-4840-88c7-2c293d35deb9 | a-physics-informed-machine-learning-approach | 2010.02011 | null | https://arxiv.org/abs/2010.02011v1 | https://arxiv.org/pdf/2010.02011v1.pdf | A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications | A physics-informed neural network is developed to solve conductive heat transfer partial differential equation (PDE), along with convective heat transfer PDEs as boundary conditions (BCs), in manufacturing and engineering applications where parts are heated in ovens. Since convective coefficients are typically unknown,... | ['Keith D. Humfeld', 'Navid Zobeiry'] | 2020-09-28 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 3.63541573e-01 3.26100662e-02 2.58053783e-02 -4.91382867e-01
-8.83184820e-02 -7.53149688e-02 1.13312081e-01 3.45632255e-01
2.45784566e-01 7.61707127e-01 -4.81935352e-01 -1.11001618e-02
-4.17093933e-01 -8.49309444e-01 -7.38181114e-01 -7.40213215e-01
-1.44610330e-01 3.30206484e-01 -4.26504821e-01 -3.01950812... | [6.2967963218688965, 3.3331828117370605] |
dbda35f7-3aa6-4eb7-b7e7-30daa3629c5c | unifying-large-language-models-and-knowledge | 2306.08302 | null | https://arxiv.org/abs/2306.08302v2 | https://arxiv.org/pdf/2306.08302v2.pdf | Unifying Large Language Models and Knowledge Graphs: A Roadmap | Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, ... | ['Xindong Wu', 'Jiapu Wang', 'Chen Chen', 'YuFei Wang', 'Linhao Luo', 'Shirui Pan'] | 2023-06-14 | null | null | null | null | ['knowledge-graphs', 'text-generation'] | ['knowledge-base', 'natural-language-processing'] | [-1.59683645e-01 9.67971087e-01 -5.39791822e-01 -6.54289126e-02
-2.37047240e-01 -6.31287098e-01 8.55303943e-01 4.60022599e-01
1.05780903e-02 9.41524208e-01 6.07764184e-01 -6.22383296e-01
-3.58080238e-01 -1.55814159e+00 -8.87614906e-01 -1.15522936e-01
3.41189541e-02 4.91656452e-01 1.03885569e-01 -3.83260220... | [9.467732429504395, 8.093534469604492] |
d96eaf39-b48d-48e5-ab1b-3ce2f0bdcd99 | financial-dynamics-economic-state | 2203.15911 | null | https://arxiv.org/abs/2203.15911v4 | https://arxiv.org/pdf/2203.15911v4.pdf | Economic state classification and portfolio optimisation with application to stagflationary environments | Motivated by the current fears of a potentially stagflationary global economic environment, this paper uses new and recently introduced mathematical techniques to study multivariate time series pertaining to country inflation (CPI), economic growth (GDP) and equity index behaviours. We begin by assessing the temporal e... | ['Max Menzies', 'Kevin Chin', 'Nick James'] | 2022-03-29 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.87731707e-01 -4.73117493e-02 -1.10700667e-01 1.14579983e-02
-4.04767960e-01 -7.32137740e-01 1.17687225e+00 2.20216006e-01
-3.00200284e-01 7.99698830e-01 5.30743122e-01 -1.06741774e+00
-6.98125184e-01 -9.81344819e-01 2.97135245e-02 -7.76707292e-01
-3.39337617e-01 4.15631503e-01 -1.55693218e-01 -2.97070563... | [5.437546730041504, 4.022642612457275] |
99c30ffe-aa15-47af-9538-e0f332994009 | aspect-sentiment-multiple-opinion-triplet | 2110.07303 | null | https://arxiv.org/abs/2110.07303v1 | https://arxiv.org/pdf/2110.07303v1.pdf | Aspect-Sentiment-Multiple-Opinion Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i.e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment. ASTE is a charming task, however, one triplet extracte... | ['Yancheng He', 'Cunxiang Yin', 'Sheng-hua Zhong', 'Yuncong Li', 'Fang Wang'] | 2021-10-14 | null | null | null | null | ['extract-aspect', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.77605733e-01 4.55548614e-02 -1.24488249e-01 -6.89934254e-01
-5.46934724e-01 -7.35022604e-01 6.43180072e-01 3.59049797e-01
5.80441169e-02 6.02417648e-01 6.44276381e-01 -2.96122521e-01
2.57243037e-01 -8.08545470e-01 -5.37843585e-01 -7.07366168e-01
6.19399786e-01 2.22690776e-01 -3.72222066e-02 -5.99919736... | [11.483474731445312, 6.640137195587158] |
218ec99d-3182-4161-a353-15c845885d80 | recognition-and-prediction-of-surgical | 2212.01683 | null | https://arxiv.org/abs/2212.01683v1 | https://arxiv.org/pdf/2212.01683v1.pdf | Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery | Surgical activity recognition and prediction can help provide important context in many Robot-Assisted Surgery (RAS) applications, for example, surgical progress monitoring and estimation, surgical skill evaluation, and shared control strategies during teleoperation. Transformer models were first developed for Natural ... | ['Ann Majewicz Fey', 'Yi Zheng', 'Chang Shi'] | 2022-12-03 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.07246530e-01 2.59528518e-01 -6.20010138e-01 -1.90893114e-01
-9.94850218e-01 -3.93222392e-01 4.74617213e-01 -7.36629544e-03
-8.99011493e-01 4.09128934e-01 6.64883554e-01 -6.05396152e-01
-4.57317948e-01 -1.46857426e-01 -2.15948015e-01 -5.32727599e-01
-3.84925306e-01 6.09392822e-01 1.90938413e-01 -2.05345631... | [14.047775268554688, -3.368760585784912] |
8ddd5c6c-e97f-4339-9cc0-1b05e89be5a2 | neural-machine-translation-by-jointly | 1409.0473 | null | http://arxiv.org/abs/1409.0473v7 | http://arxiv.org/pdf/1409.0473v7.pdf | Neural Machine Translation by Jointly Learning to Align and Translate | Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural ma... | ['Kyunghyun Cho', 'Yoshua Bengio', 'Dzmitry Bahdanau'] | 2014-09-01 | null | null | null | null | ['bangla-spelling-error-correction'] | ['natural-language-processing'] | [ 6.51026607e-01 4.78096902e-01 -4.92339015e-01 -4.84126985e-01
-1.29237151e+00 -6.00202262e-01 6.61758065e-01 -1.72751203e-01
-2.85340011e-01 1.03990149e+00 5.10267317e-01 -8.67818177e-01
5.11374593e-01 -7.66668260e-01 -1.20130384e+00 -4.04237807e-01
4.06114161e-01 8.00173223e-01 -2.28457525e-01 -5.44910550... | [11.630714416503906, 10.28522777557373] |
efd32e57-a005-4d6c-807d-70cc41df5982 | convolutional-neural-network-compression | 2109.14710 | null | https://arxiv.org/abs/2109.14710v2 | https://arxiv.org/pdf/2109.14710v2.pdf | Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition | Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory usage and floating-point operations required by convolutional layers in CNNs. We compress these lay... | ['Vahid Partovi Nia', 'Ali Mosleh', 'Marzieh S. Tahaei', 'Marawan Gamal Abdel Hameed'] | 2021-09-29 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-2.26592690e-01 -1.12891428e-01 -5.83876781e-02 -3.56207401e-01
1.30847588e-01 -2.99355417e-01 2.17067137e-01 -1.61181599e-01
-9.17064250e-01 6.28111362e-01 -1.74807549e-01 -8.38002980e-01
-4.09605682e-01 -9.17193353e-01 -8.34203660e-01 -5.34692705e-01
-1.34289786e-01 -3.86993513e-02 4.39804465e-01 -1.53111741... | [8.5014066696167, 3.0536675453186035] |
4aa4c810-5804-4882-94c9-abba3c6b71ca | zero-shot-learning-for-joint-intent-and-slot | 2212.07922 | null | https://arxiv.org/abs/2212.07922v1 | https://arxiv.org/pdf/2212.07922v1.pdf | Zero-Shot Learning for Joint Intent and Slot Labeling | It is expensive and difficult to obtain the large number of sentence-level intent and token-level slot label annotations required to train neural network (NN)-based Natural Language Understanding (NLU) components of task-oriented dialog systems, especially for the many real world tasks that have a large and growing num... | ['Balakrishnan Narayanaswamy', 'Rashmi Gangadharaiah'] | 2022-11-29 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 3.62120152e-01 6.52817369e-01 -3.73302341e-01 -7.08037615e-01
-7.30047464e-01 -4.28221345e-01 7.85427630e-01 2.61451393e-01
-6.46703184e-01 5.49655855e-01 7.03920960e-01 -7.50098944e-01
1.81312054e-01 -6.52779818e-01 -4.79801707e-02 -1.76807478e-01
-1.47866309e-01 6.04641795e-01 8.00123736e-02 -5.56804836... | [12.380186080932617, 7.671482563018799] |
31565532-5872-450e-b5c0-2048ce88dca1 | a-deep-neural-network-algorithm-for-linear | 2301.10869 | null | https://arxiv.org/abs/2301.10869v2 | https://arxiv.org/pdf/2301.10869v2.pdf | A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs | We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal portfolio mean-variance preferences in the setting of multivariate generalized autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on trading. A numerical solution is obtained using a neural network (NN) architecture w... | ['Farshad Khorrami', 'Prashanth Krishnamurthy', 'Hao Fu', 'Andrew Papanicolaou'] | 2023-01-25 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.20163631e-01 2.36346394e-01 -5.32271974e-02 -3.45359832e-01
-6.98145747e-01 -6.09700620e-01 5.67890257e-02 -2.08185390e-01
-6.99842095e-01 6.97008669e-01 -1.09515823e-01 -7.95400143e-01
-5.55289268e-01 -8.59836698e-01 -9.66531754e-01 -8.25490415e-01
-3.95514756e-01 5.18354714e-01 -6.01792559e-02 -2.32776478... | [4.93118953704834, 3.933338165283203] |
978719d4-bef2-42ab-abe6-73b551c455de | a-fast-keypoint-based-hybrid-method-for-copy | 1612.03989 | null | http://arxiv.org/abs/1612.03989v1 | http://arxiv.org/pdf/1612.03989v1.pdf | A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection | Copy move forgery detection in digital images has become a very popular
research topic in the area of image forensics. Due to the availability of
sophisticated image editing tools and ever increasing hardware capabilities, it
has become an easy task to manipulate the digital images. Passive forgery
detection techniques... | ['Sunil Kumar', 'J. V. Desai', 'Shaktidev Mukherjee'] | 2016-12-11 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.62967104e-01 -6.96416318e-01 2.02634230e-01 1.30617663e-01
-6.41387999e-01 -7.17146695e-01 6.25715852e-01 7.02526629e-01
-6.36259675e-01 3.00366849e-01 -1.20458841e-01 -1.35981381e-01
-2.10834444e-01 -7.03378439e-01 -4.12802756e-01 -7.44965374e-01
-1.55511303e-02 -1.69984266e-01 8.64237070e-01 -2.94506401... | [12.363049507141113, 0.9572150111198425] |
a3e0d500-859b-4d15-b591-fcc2721c3b9b | machine-learning-for-predicting-epileptic | 2002.01925 | null | https://arxiv.org/abs/2002.01925v1 | https://arxiv.org/pdf/2002.01925v1.pdf | Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review | With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. Thi... | ["Terence O'Brien", 'Shobi Sivathamboo', 'Adnan Qayyum', 'Junaid Qadir', 'Levin Kuhlmann', 'Khansa Rasheed', 'Patrick Kwan', 'Adeel Razi'] | 2020-02-04 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.52419680e-01 -7.56605789e-02 6.94610775e-02 -3.97602946e-01
-8.89524519e-01 -5.91907278e-02 1.00425355e-01 4.69808847e-01
-3.47167522e-01 7.51836181e-01 1.70621485e-01 -2.71077454e-01
-4.79364187e-01 -2.15205252e-01 -8.61664563e-02 -7.86541224e-01
-7.70881832e-01 3.96173596e-01 -1.28234074e-01 1.95059888... | [13.2317533493042, 3.52274227142334] |
e65145ac-ffdb-49b1-90c8-1ae8490772d4 | cbr-ikb-a-case-based-reasoning-approach-for | 2204.08554 | null | https://arxiv.org/abs/2204.08554v1 | https://arxiv.org/pdf/2204.08554v1.pdf | CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases | Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked. To this end, we propose a case-based reasoning approach, CBR-iKB, for knowledge base question answering (KBQA) with inc... | ['Andrew McCallum', 'Achille Fokoue', 'Pavan Kapanipathi', 'Rajarshi Das', 'Tahira Naseem', 'Nandana Mihindukulasooriya', 'Mudit Chaudhary', 'Ibrahim Abdelaziz', 'Srinivas Ravishankar', 'Dung Thai'] | 2022-04-18 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-5.85637629e-01 2.66580701e-01 -3.87995005e-01 -3.76998395e-01
-1.38167763e+00 -7.30893314e-01 6.00400344e-02 1.32563874e-01
-1.96594536e-01 1.34319043e+00 1.61782235e-01 -4.97974515e-01
-4.36613142e-01 -1.03864157e+00 -9.58827436e-01 -1.32223889e-01
4.44600731e-01 1.22465181e+00 9.99287009e-01 -7.49367833... | [10.495563507080078, 7.936329364776611] |
f7ba79ba-71d3-46a4-900b-182fd22229e4 | chinese-grammatical-error-diagnosis-using-1 | null | null | https://aclanthology.org/W15-4415 | https://aclanthology.org/W15-4415.pdf | Chinese Grammatical Error Diagnosis Using Ensemble Learning | null | ['Wenying Han', 'Xiaolong Wang', 'Yang Xiang', 'Qinghua Hong'] | 2015-07-01 | null | null | null | ws-2015-7 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.45961856842041, 3.7029452323913574] |
edbc1cc3-5f6e-4c52-a799-4ccb82b233f6 | jokr-joint-keypoint-representation-for | 2106.09679 | null | https://arxiv.org/abs/2106.09679v1 | https://arxiv.org/pdf/2106.09679v1.pdf | JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting | The task of unsupervised motion retargeting in videos has seen substantial advancements through the use of deep neural networks. While early works concentrated on specific object priors such as a human face or body, recent work considered the unsupervised case. When the source and target videos, however, are of differe... | ['Daniel Cohen-Or', 'Amit H. Bermano', 'Sagie Benaim', 'Rotem Tzaban', 'Ron Mokady'] | 2021-06-17 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 1.31862983e-01 -2.43674085e-01 -2.98203051e-01 1.36832008e-02
-3.76554996e-01 -1.04481506e+00 8.55564177e-01 -3.51619810e-01
-8.99326578e-02 4.49186862e-01 3.83367270e-01 1.33199692e-01
7.28175938e-02 -4.16799664e-01 -6.47496879e-01 -7.53120899e-01
1.34403659e-02 8.03255737e-02 3.64604264e-01 -1.47128582... | [10.624373435974121, -0.8155587911605835] |
19e8e18f-2e14-4b14-b423-51f56cefc298 | mapping-it-differently-a-solution-to-the | null | null | https://aclanthology.org/2016.gwc-1.57 | https://aclanthology.org/2016.gwc-1.57.pdf | Mapping it differently: A solution to the linking challenges | This paper reports the work of creating bilingual mappings in English for certain synsets of Hindi wordnet, the need for doing this, the methods adopted and the tools created for the task. Hindi wordnet, which forms the foundation for other Indian language wordnets, has been linked to the English WordNet. To maximize l... | ['Pushpak Bhattacharyya', 'Diptesh Kanojia', 'Laxmi Kashyap', 'Jaya Saraswati', 'Rajita Shukla', 'Meghna Singh'] | null | null | null | null | gwc-2016-1 | ['transliteration'] | ['natural-language-processing'] | [-1.83449507e-01 -1.93348210e-02 -4.38435912e-01 -2.00726658e-01
-3.01075667e-01 -7.27606237e-01 9.66831923e-01 4.03660208e-01
-7.77120352e-01 1.15162480e+00 4.76707935e-01 -3.26402336e-01
-3.46100748e-01 -1.05217350e+00 -4.57549319e-02 -3.19208413e-01
3.39907557e-01 7.62472510e-01 1.10394076e-01 -8.94791722... | [10.495444297790527, 9.65776252746582] |
cc23efb6-f448-438b-ac69-04880947e8c1 | memory-selection-network-for-video | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2319_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600171.pdf | Memory Selection Network for Video Propagation | Video propagation is a fundamental problem in video processing where guidance frame predictions are propagated to guide predictions of the target frame. Previous research mainly treats the previous adjacent frame as guidance, which, however, could make the propagation vulnerable to occlusion, large motion, and inaccura... | ['Xiaojuan Qi', 'Jiaya Jia', 'Huaijia Lin', 'Ruizheng Wu'] | null | null | null | null | eccv-2020-8 | ['video-propagation'] | ['computer-vision'] | [ 2.87820011e-01 -3.94546717e-01 -4.83031631e-01 -5.00701964e-01
-4.27123368e-01 -4.05948788e-01 5.80034442e-02 -9.45088342e-02
-5.39115012e-01 6.26816392e-01 2.91499514e-02 -2.01423585e-01
4.80265826e-01 -7.47555435e-01 -7.97586143e-01 -7.15867460e-01
-1.33983299e-01 -1.82782024e-01 1.10661018e+00 2.92253476... | [9.192179679870605, -0.14194990694522858] |
b14d38a9-2fbe-4a15-969c-e3359b8e0b1b | findings-of-the-shared-task-on-multilingual | 2209.07841 | null | https://arxiv.org/abs/2209.07841v1 | https://arxiv.org/pdf/2209.07841v1.pdf | Findings of the Shared Task on Multilingual Coreference Resolution | This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, ... | ['YIlun Zhu', 'Daniel Zeman', 'Jakub Sido', 'Ondřej Pražák', 'Martin Popel', 'Maciej Ogrodniczuk', 'Michal Novák', 'Anna Nedoluzhko', 'Miloslav Konopík', 'Zdeněk Žabokrtský'] | 2022-09-16 | null | https://aclanthology.org/2022.crac-mcr.1 | https://aclanthology.org/2022.crac-mcr.1.pdf | crac-acl-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [-1.65208936e-01 3.06502610e-01 -3.77875388e-01 -4.21815425e-01
-1.57320273e+00 -9.30987716e-01 9.09166515e-01 2.36383274e-01
-6.40417099e-01 1.01000774e+00 7.36670434e-01 -1.97833017e-01
-6.76831678e-02 -3.25999171e-01 -4.93707120e-01 -4.05247748e-01
2.19692122e-02 1.34800148e+00 3.53054971e-01 -6.61294341... | [9.300512313842773, 9.591814041137695] |
edf68c79-4018-4854-a092-6c172b0fe51d | using-semantic-role-labeling-to-improve | null | null | https://aclanthology.org/2022.lrec-1.329 | https://aclanthology.org/2022.lrec-1.329.pdf | Using Semantic Role Labeling to Improve Neural Machine Translation | Despite impressive progress in machine translation in recent years, it has occasionally been argued that current systems are still mainly based on pattern recognition and that further progress may be possible by using text understanding techniques, thereby e.g. looking at semantics of the type “Who is doing what to who... | ['Reinhard Rapp'] | null | null | null | null | lrec-2022-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 6.60931945e-01 4.64027107e-01 -3.22764754e-01 -5.05235136e-01
-8.48893821e-01 -1.02798486e+00 1.02841437e+00 2.62697577e-01
-5.18756807e-01 1.21705234e+00 5.51096439e-01 -7.49509752e-01
1.84202299e-01 -7.11180985e-01 -6.53292298e-01 -4.58468229e-01
6.65582776e-01 1.18255460e+00 1.97903350e-01 -7.49772727... | [10.524209022521973, 9.538413047790527] |
c8897d1b-9e3c-4259-8472-052184b505aa | alba-reinforcement-learning-for-video-object | 2005.13039 | null | https://arxiv.org/abs/2005.13039v2 | https://arxiv.org/pdf/2005.13039v2.pdf | ALBA : Reinforcement Learning for Video Object Segmentation | We consider the challenging problem of zero-shot video object segmentation (VOS). That is, segmenting and tracking multiple moving objects within a video fully automatically, without any manual initialization. We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping... | ['Laura Sevilla-Lara', 'Timothy Hospedales', 'Shreyank N Gowda', 'Panagiotis Eustratiadis'] | 2020-05-26 | null | null | null | null | ['one-shot-visual-object-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.89119303e-02 -9.49210580e-03 -3.87912989e-01 -3.02376837e-01
-8.39069963e-01 -6.91826046e-01 4.57329929e-01 -1.80744246e-01
-8.03871453e-01 4.92336273e-01 -7.59590715e-02 -1.41927958e-01
8.26798826e-02 -3.06478769e-01 -1.08163095e+00 -6.15658641e-01
-5.18922508e-01 5.74267387e-01 5.81345618e-01 3.98071289... | [9.139674186706543, -0.07904629409313202] |
9c2cd868-a097-417e-9fea-4e419726ff79 | chinese-word-sense-embedding-with-sememewsd | 2206.14388 | null | https://arxiv.org/abs/2206.14388v1 | https://arxiv.org/pdf/2206.14388v1.pdf | Chinese Word Sense Embedding with SememeWSD and Synonym Set | Word embedding is a fundamental natural language processing task which can learn feature of words. However, most word embedding methods assign only one vector to a word, even if polysemous words have multi-senses. To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to eve... | ['Zeli Guan', 'Ang Li', 'Zhe Xue', 'Junping Du', 'Yangxi Zhou'] | 2022-06-29 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-2.04891726e-01 -2.86173612e-01 -2.14025661e-01 -2.58491695e-01
1.09755903e-01 -5.68289161e-01 4.94018346e-01 6.61482990e-01
-9.59086835e-01 4.44016874e-01 7.21949220e-01 -1.24835864e-01
-1.95798546e-01 -1.06318188e+00 3.62663388e-01 -3.50540936e-01
4.02027011e-01 2.54499733e-01 3.25438470e-01 -8.52572441... | [10.224637031555176, 8.840865135192871] |
da24208a-8697-4ea4-a8ec-18f351bd0f83 | deepfakeson-phys-deepfakes-detection-based-on | 2010.00400 | null | https://arxiv.org/abs/2010.00400v3 | https://arxiv.org/pdf/2010.00400v3.pdf | DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation | This work introduces a novel DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of huma... | ['Ruben Tolosana', 'Javier Hernandez-Ortega', 'Julian Fierrez', 'Aythami Morales'] | 2020-10-01 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [-2.30242431e-01 -1.90978572e-02 5.74306175e-02 1.70620829e-01
-1.85898781e-01 -3.48922282e-01 6.70379877e-01 -2.84966439e-01
-4.66227531e-01 5.06594837e-01 1.80777505e-01 1.97875977e-01
3.71600658e-01 -4.30398762e-01 -6.42207265e-01 -7.61416852e-01
-1.97537810e-01 -2.29764089e-01 1.11752056e-01 -5.22097386... | [12.660526275634766, 1.1719478368759155] |
75985bd7-87cc-43a7-ab14-5696c94cd9be | cdlt-a-dataset-with-concept-drift-and-long | 2306.02346 | null | https://arxiv.org/abs/2306.02346v1 | https://arxiv.org/pdf/2306.02346v1.pdf | CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for Fine-Grained Visual Categorization | Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC). In the existing FGVC datasets used in computer vision, it is generally assumed that each collected instance has fixed charact... | ['Xinge You', 'Chuanwu Yang', 'Jiamiao Xu', 'Yu Wang', 'Ruxin Wang', 'Yufeng Shi', 'Shuo Ye'] | 2023-06-04 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-7.97310546e-02 -6.53421938e-01 -8.70082248e-03 -7.63602197e-01
1.56061873e-01 -7.60138333e-01 7.37738550e-01 1.59500256e-01
-5.25396585e-01 7.33650386e-01 -1.72868207e-01 5.24501279e-02
-1.79036453e-01 -6.61874175e-01 -5.20649374e-01 -1.15326512e+00
1.40237194e-02 4.70844060e-01 4.61189181e-01 1.72789656... | [9.684576034545898, 2.102139949798584] |
d50f521b-b03e-4bc3-9c8c-cb7fd20d6ca8 | sample-efficient-deep-reinforcement-learning-3 | 2301.12579 | null | https://arxiv.org/abs/2301.12579v2 | https://arxiv.org/pdf/2301.12579v2.pdf | Sample Efficient Deep Reinforcement Learning via Local Planning | The focus of this work is sample-efficient deep reinforcement learning (RL) with a simulator. One useful property of simulators is that it is typically easy to reset the environment to a previously observed state. We propose an algorithmic framework, named uncertainty-first local planning (UFLP), that takes advantage o... | ['Csaba Szepesvari', 'Botao Hao', 'Nived Rajaraman', 'Nevena Lazic', 'Sridhar Thiagarajan', 'Dong Yin'] | 2023-01-29 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-8.03561136e-02 4.54564303e-01 -2.78473884e-01 4.90381382e-02
-1.12744749e+00 -6.33235157e-01 6.74780846e-01 6.29754215e-02
-8.68080497e-01 1.26322186e+00 7.45406076e-02 -3.89350235e-01
-2.90035129e-01 -9.14708376e-01 -1.06830823e+00 -8.51669610e-01
-6.14594877e-01 1.15593946e+00 1.25041276e-01 -2.53421307... | [4.155027389526367, 2.054785966873169] |
1b5a36af-8909-41a7-8140-66c3b86e7223 | noiser-noise-is-all-you-need-for-enhancing | 2211.04700 | null | https://arxiv.org/abs/2211.04700v2 | https://arxiv.org/pdf/2211.04700v2.pdf | NoiSER: Noise is All You Need for Low-Light Image Enhancement | In this paper, we present an embarrassingly simple yet effective solution to a seemingly impossible mission, low-light image enhancement (LLIE) without access to any task-related data. The proposed solution, Noise SElf-Regression (NoiSER), simply learns a convolutional neural network equipped with a instance-normalizat... | ['Shuicheng Yan', 'Yi Yang', 'Mingliang Xu', 'Xiaojie Jin', 'Suiyi Zhao', 'Zhao Zhang'] | 2022-11-09 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 5.37545562e-01 -2.02062324e-01 3.85145247e-01 -5.03132224e-01
-5.10843575e-01 -1.98203370e-01 1.01024829e-01 -1.91188514e-01
-7.15444863e-01 7.30421782e-01 -6.23950303e-01 -3.99447411e-01
-1.52504118e-02 -9.64631855e-01 -8.77691031e-01 -1.00630176e+00
3.39577764e-01 -3.08639944e-01 1.73641518e-01 -2.55476922... | [10.778270721435547, -2.301703929901123] |
f547b8dd-13eb-444b-ad72-7a9eddfff3cb | internal-video-inpainting-by-implicit-long | 2108.01912 | null | https://arxiv.org/abs/2108.01912v3 | https://arxiv.org/pdf/2108.01912v3.pdf | Internal Video Inpainting by Implicit Long-range Propagation | We propose a novel framework for video inpainting by adopting an internal learning strategy. Unlike previous methods that use optical flow for cross-frame context propagation to inpaint unknown regions, we show that this can be achieved implicitly by fitting a convolutional neural network to known regions. Moreover, to... | ['Qifeng Chen', 'Tengfei Wang', 'Hao Ouyang'] | 2021-08-04 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ouyang_Internal_Video_Inpainting_by_Implicit_Long-Range_Propagation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ouyang_Internal_Video_Inpainting_by_Implicit_Long-Range_Propagation_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-inpainting'] | ['computer-vision'] | [ 3.33581269e-01 -6.52055070e-02 -5.99621795e-02 -3.26755643e-01
-8.24180901e-01 -4.39318568e-01 2.87996352e-01 -4.42109734e-01
-4.15516883e-01 9.78793323e-01 4.02544588e-02 4.44020331e-02
1.24520093e-01 -4.39447224e-01 -1.25135708e+00 -4.68701690e-01
2.55118776e-03 -7.57622123e-02 1.73914447e-01 1.73451826... | [10.753185272216797, -1.4257851839065552] |
0a8a0bf4-2e9a-4998-8fdf-951763477ccd | identifying-causal-structure-in-dynamical | 2006.03906 | null | https://arxiv.org/abs/2006.03906v2 | https://arxiv.org/pdf/2006.03906v2.pdf | Identifying Causal Structure in Dynamical Systems | Mathematical models are fundamental building blocks in the design of dynamical control systems. As control systems are becoming increasingly complex and networked, approaches for obtaining such models based on first principles reach their limits. Data-driven methods provide an alternative. However, without structural k... | ['Dominik Baumann', 'Karl H. Johansson', 'Sebastian Trimpe', 'Friedrich Solowjow'] | 2020-06-06 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.23651254e-01 6.41176999e-02 -5.33531308e-01 5.42145148e-02
-4.16124575e-02 -5.37632942e-01 7.15261459e-01 1.20415032e-01
2.33934388e-01 9.77771342e-01 -1.64990261e-01 -6.61704123e-01
-1.03781545e+00 -6.81550384e-01 -7.88503289e-01 -9.12519276e-01
-4.61233228e-01 1.84158936e-01 1.11599892e-01 -1.77099794... | [4.902138710021973, 2.3609161376953125] |
9b2440e8-0422-4bb9-bad4-6fd79226fc1e | an-application-of-pseudo-log-likelihoods-to | 2201.09377 | null | https://arxiv.org/abs/2201.09377v1 | https://arxiv.org/pdf/2201.09377v1.pdf | An Application of Pseudo-Log-Likelihoods to Natural Language Scoring | Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding. In this paper we apply a zero-shot approach independently developed by a number of researchers now gaining recognition as a significa... | ['Ali Emami', 'Darren Abramson'] | 2022-01-23 | null | null | null | null | ['timedial'] | ['natural-language-processing'] | [ 5.57699144e-01 4.23408777e-01 -3.54845315e-01 -3.09872836e-01
-1.11721361e+00 -6.42488122e-01 1.20683408e+00 1.39289916e-01
-7.94262588e-01 9.60299313e-01 4.68318462e-01 -7.94707835e-01
-2.83066630e-01 -8.61676693e-01 -4.85341698e-01 -4.28739607e-01
2.88634777e-01 8.69978368e-01 2.79963762e-01 -9.43197906... | [10.723957061767578, 8.514583587646484] |
b6ee53dd-8fb7-4559-bff9-d5c6d9090dd5 | blood-pressure-estimation-from | null | null | https://doi.org/10.3390/s19153420 | https://www.mdpi.com/1424-8220/19/15/3420/pdf | Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network | Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP ... | ['Mitja Luštrek', 'Nejc Mlakar', 'Gašper Slapničar'] | 2019-08-04 | null | null | null | sensors-2019-8 | ['blood-pressure-estimation', 'photoplethysmography-ppg'] | ['medical', 'medical'] | [-5.35632558e-02 -1.55670587e-02 3.02977622e-01 -8.70124876e-01
-5.93267441e-01 -3.23528647e-01 -3.80235910e-02 9.22114402e-02
-5.47905028e-01 9.84153450e-01 2.00127438e-01 -3.99703950e-01
-1.75514221e-01 -6.15812540e-01 -3.67594004e-01 -5.71701169e-01
-7.84411132e-01 1.55644059e-01 -1.07326724e-01 1.07975956... | [14.096305847167969, 3.001804828643799] |
f8875770-00e1-4fb3-9c99-fbbf014ffcb6 | adn-artifact-disentanglement-network-for | 1908.01104 | null | https://arxiv.org/abs/1908.01104v4 | https://arxiv.org/pdf/1908.01104v4.pdf | ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction | Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT imaging, the supervised methods often ge... | ['Wei-An Lin', 'S. Kevin Zhou', 'Jiebo Luo', 'Haofu Liao'] | 2019-08-03 | null | null | null | null | ['medical-image-generation', 'metal-artifact-reduction'] | ['medical', 'medical'] | [ 4.25968856e-01 1.62082687e-01 1.90274920e-02 -3.50648016e-01
-1.18131506e+00 -1.34818302e-02 1.90764859e-01 -7.40872994e-02
-7.99569711e-02 7.25429058e-01 3.55162352e-01 -2.73774236e-01
-1.47284493e-01 -6.35181606e-01 -7.54683375e-01 -7.27892935e-01
1.00456655e-01 4.30537969e-01 -2.42502149e-02 1.29319593... | [13.554051399230957, -2.5182135105133057] |
56e12a85-55e7-4e1e-b2fb-c8713e639e3f | padl-language-directed-physics-based | 2301.13868 | null | https://arxiv.org/abs/2301.13868v1 | https://arxiv.org/pdf/2301.13868v1.pdf | PADL: Language-Directed Physics-Based Character Control | Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be useful for downstream applications, they need not only produce high-quality motions, but must also provide an accessible and versatile interfac... | ['Xue Bin Peng', 'Sanja Fidler', 'Yunrong Guo', 'Jordan Juravsky'] | 2023-01-31 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.02453935e-01 1.44580938e-02 -1.69985324e-01 -2.05255806e-01
-8.50162685e-01 -8.43172669e-01 7.88238049e-01 -9.35726538e-02
-3.52387071e-01 6.45577729e-01 4.44550142e-02 -4.49350208e-01
2.72449732e-01 -7.79180288e-01 -7.65955210e-01 -4.35307950e-01
1.16526857e-02 4.55050409e-01 3.23022246e-01 -6.26392663... | [4.894867897033691, 0.7266707420349121] |
55b12ccd-9408-470f-a665-a31c1bad3643 | revisiting-low-resource-neural-machine | 1905.11901 | null | https://arxiv.org/abs/1905.11901v1 | https://arxiv.org/pdf/1905.11901v1.pdf | Revisiting Low-Resource Neural Machine Translation: A Case Study | It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these result... | ['Biao Zhang', 'Rico Sennrich'] | 2019-05-28 | revisiting-low-resource-neural-machine-1 | https://aclanthology.org/P19-1021 | https://aclanthology.org/P19-1021.pdf | acl-2019-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.18679661e-01 -1.52792661e-02 -3.58271807e-01 -2.43383452e-01
-1.55700970e+00 -6.94153130e-01 7.80923724e-01 -2.11752206e-01
-1.05557668e+00 1.35215712e+00 3.80010724e-01 -1.08901668e+00
2.71538496e-01 -2.41746306e-01 -8.45294058e-01 -4.78354007e-01
2.28201300e-01 9.93760705e-01 -9.80725735e-02 -4.51063246... | [11.562766075134277, 10.348076820373535] |
1ec1b417-9066-42d6-ba55-8b28270377cf | semeval-2020-task-5-counterfactual | 2008.00563 | null | https://arxiv.org/abs/2008.00563v1 | https://arxiv.org/pdf/2008.00563v1.pdf | SemEval-2020 Task 5: Counterfactual Recognition | We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of t... | ['Stan Matwin', 'Xiaodan Zhu', 'Xiaoyu Yang', 'Stephen Obadinma', 'Qiong Zhang', 'Huasha Zhao'] | 2020-08-02 | null | https://aclanthology.org/2020.semeval-1.40 | https://aclanthology.org/2020.semeval-1.40.pdf | semeval-2020 | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.81707191e-01 5.50683439e-01 -2.70305336e-01 -5.91207981e-01
-7.67610073e-01 -5.64778030e-01 1.41371906e+00 2.23477021e-01
-2.46990457e-01 1.42701530e+00 9.25712824e-01 -6.14061236e-01
-2.86843106e-02 -5.92092514e-01 -9.28921103e-01 -8.57754722e-02
-4.29846764e-01 3.44216377e-01 -1.85283110e-01 -1.60533324... | [9.995975494384766, 8.068673133850098] |
33a7b431-54f0-4462-be14-041fc4fb7a7a | waveform-boundary-detection-for-partially | 2211.00226 | null | https://arxiv.org/abs/2211.00226v1 | https://arxiv.org/pdf/2211.00226v1.pdf | Waveform Boundary Detection for Partially Spoofed Audio | The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of the utterance is replaced, either with synthetic or natural audio clips, has recently been reported as one scenario of audio deepfakes. As dee... | ['Ming Li', 'Weiqing Wang', 'Zexin Cai'] | 2022-11-01 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 3.57970029e-01 -9.70329270e-02 -7.51335472e-02 2.67012775e-01
-1.18449056e+00 -6.72720909e-01 3.08727413e-01 1.76624745e-01
-1.27451241e-01 2.76755452e-01 1.13700703e-01 -2.33813480e-01
4.91098106e-01 -3.11881810e-01 -7.50487447e-01 -6.49188638e-01
-2.57604212e-01 -1.74156308e-01 5.07884085e-01 -1.38531625... | [14.130279541015625, 5.810825347900391] |
94e82b5b-a32b-4732-a193-3f0c1807a5ec | atst-audio-representation-learning-with | 2204.12076 | null | https://arxiv.org/abs/2204.12076v3 | https://arxiv.org/pdf/2204.12076v3.pdf | ATST: Audio Representation Learning with Teacher-Student Transformer | Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data. SSL has achieved promising results in various domains. This work addresses the problem of segment-level general audio SSL, and proposes a n... | ['Xiaofei Li', 'Xian Li'] | 2022-04-26 | null | null | null | null | ['instrument-recognition', 'speaker-identification', 'spoken-command-recognition'] | ['audio', 'speech', 'speech'] | [ 3.88749212e-01 2.46274322e-01 -5.94928861e-01 -6.79895401e-01
-1.29049051e+00 -3.75218481e-01 3.46691608e-01 -1.21779703e-01
-8.71723071e-02 7.13363290e-01 3.04415405e-01 -3.20239872e-01
1.94071412e-01 -4.63558584e-01 -6.94381714e-01 -5.74183404e-01
1.86354578e-01 3.31392616e-01 5.15989900e-01 -2.20184550... | [15.214885711669922, 5.187015533447266] |
808fc97a-09d4-4e4e-8d52-52d66762366e | an-attention-free-long-short-term-memory-for | 2209.09548 | null | https://arxiv.org/abs/2209.09548v1 | https://arxiv.org/pdf/2209.09548v1.pdf | An Attention Free Long Short-Term Memory for Time Series Forecasting | Deep learning is playing an increasingly important role in time series analysis. We focused on time series forecasting using attention free mechanism, a more efficient framework, and proposed a new architecture for time series prediction for which linear models seem to be unable to capture the time dependence. We propo... | ['Ludovic De Villelongue', 'Hugo Inzirillo'] | 2022-09-20 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.31631118e-01 -6.76543638e-02 4.14311774e-02 -3.63042563e-01
-1.84773728e-01 -1.71635985e-01 6.59836173e-01 -1.18673742e-01
-3.87526363e-01 6.04095697e-01 2.49104649e-01 -6.75599217e-01
-3.33211541e-01 -7.02843070e-01 -6.12497866e-01 -6.24700487e-01
-4.75442111e-01 2.30668053e-01 1.15057692e-01 -3.01628053... | [6.841998100280762, 3.0286407470703125] |
ebeffcb6-58ca-47b4-8911-d7d0d538eac5 | detection-based-defense-against-adversarial | 1806.09186 | null | http://arxiv.org/abs/1806.09186v3 | http://arxiv.org/pdf/1806.09186v3.pdf | Detection based Defense against Adversarial Examples from the Steganalysis Point of View | Deep Neural Networks (DNNs) have recently led to significant improvements in
many fields. However, DNNs are vulnerable to adversarial examples which are
samples with imperceptible perturbations while dramatically misleading the
DNNs. Moreover, adversarial examples can be used to perform an attack on
various kinds of DN... | ['Nenghai Yu', 'Yujia Liu', 'Yiwei Zhang', 'Weiming Zhang', 'Jiayang Liu', 'Hongyue Zha', 'Dongdong Hou'] | 2018-06-21 | detection-based-defense-against-adversarial-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Detection_Based_Defense_Against_Adversarial_Examples_From_the_Steganalysis_Point_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Detection_Based_Defense_Against_Adversarial_Examples_From_the_Steganalysis_Point_CVPR_2019_paper.pdf | cvpr-2019-6 | ['steganalysis'] | ['computer-vision'] | [ 5.90716124e-01 1.13785286e-02 3.92422646e-01 -3.91684808e-02
-2.73413777e-01 -9.27134931e-01 7.91101694e-01 -5.03237128e-01
-3.99717957e-01 7.00511515e-01 -2.40362808e-01 -5.13109863e-01
3.10127527e-01 -1.15651536e+00 -8.63425970e-01 -9.20124829e-01
-1.42547235e-01 -1.29562438e-01 4.76391345e-01 -4.14632648... | [5.525314807891846, 7.907380104064941] |
05c4c217-b85e-49c2-873e-ccbd1439492d | unsupervised-speech-recognition-via-segmental | 1812.09323 | null | http://arxiv.org/abs/1812.09323v1 | http://arxiv.org/pdf/1812.09323v1.pdf | Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching | We consider the problem of training speech recognition systems without using
any labeled data, under the assumption that the learner can only access to the
input utterances and a phoneme language model estimated from a non-overlapping
corpus. We propose a fully unsupervised learning algorithm that alternates
between so... | ['Chih-Kuan Yeh', 'Jianshu Chen', 'Chengzhu Yu', 'Dong Yu'] | 2018-12-23 | unsupervised-speech-recognition-via-segmental-1 | https://openreview.net/forum?id=Bylmkh05KX | https://openreview.net/pdf?id=Bylmkh05KX | iclr-2019-5 | ['unsupervised-speech-recognition'] | ['speech'] | [ 6.42636716e-01 5.05919158e-01 -2.30611473e-01 -5.85358620e-01
-1.33850050e+00 -7.10715473e-01 3.77308697e-01 -7.73627013e-02
-5.09023368e-01 5.18450081e-01 -1.35656316e-02 -5.92466533e-01
2.25970849e-01 -4.12162393e-01 -8.86684537e-01 -6.08882546e-01
1.54321432e-01 6.95288122e-01 1.69795454e-01 1.74626634... | [14.540863990783691, 6.725465774536133] |
9aa87e6a-5d45-4813-be84-08465ff0ef5b | a-graph-matching-perspective-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Qin_A_Graph_Matching_Perspective_With_Transformers_on_Video_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Qin_A_Graph_Matching_Perspective_With_Transformers_on_Video_Instance_Segmentation_CVPR_2022_paper.pdf | A Graph Matching Perspective With Transformers on Video Instance Segmentation | Video Instance Segmentation (VIS) needs to automatically track and segment multiple objects in videos that rely on modeling the spatial-temporal interactions of the instances. This paper presents a graph matching-based method to formulate VIS. Unlike traditional tracking-by-detection paradigm or bottom-up generativ... | ['Jianbing Shen', 'Yilong Yin', 'Xiushan Nie', 'Xiankai Lu', 'Zheyun Qin'] | 2022-01-01 | a-graph-matching-perspective-with-1 | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_A_Graph_Matching_Perspective_With_Transformers_on_Video_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Qin_A_Graph_Matching_Perspective_With_Transformers_on_Video_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-6 | ['video-instance-segmentation', 'graph-matching'] | ['computer-vision', 'graphs'] | [-3.54561768e-02 1.31675079e-01 -3.60458583e-01 -4.41713482e-01
-6.83554590e-01 -4.17603642e-01 3.33963662e-01 6.56392332e-03
-2.72512525e-01 4.59434241e-01 -8.16099271e-02 5.86094223e-02
-2.35634178e-01 -7.29741156e-01 -1.05830646e+00 -6.27834320e-01
-4.09394711e-01 4.37622011e-01 5.73786318e-01 1.92935556... | [9.212193489074707, -0.08724682778120041] |
aa17b875-794e-4186-a494-5c45cb7e2bb6 | extraction-of-diagnostic-reasoning-relations | null | null | https://aclanthology.org/2022.acl-srw.33 | https://aclanthology.org/2022.acl-srw.33.pdf | Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs | Clinical knowledge graphs lack meaningful diagnostic relations (e.g. comorbidities, sign/symptoms), limiting their ability to represent real-world diagnostic processes. Previous methods in biomedical relation extraction have focused on concept relations, such as gene-disease and disease-drug, and largely ignored clinic... | ['Vimig Socrates'] | null | null | null | null | acl-2022-5 | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.20641375e-01 9.92479980e-01 -6.66400194e-01 -2.06161112e-01
-6.78167999e-01 -4.30291325e-01 4.77660894e-01 1.07943976e+00
1.38377538e-03 1.03030837e+00 5.80233812e-01 -8.09527814e-01
-8.08459878e-01 -9.57370281e-01 -2.74124652e-01 -1.52791187e-01
-8.87305215e-02 1.00354469e+00 1.49919137e-01 8.30831900... | [8.475911140441895, 8.650739669799805] |
710ac821-d76e-40dc-9bd7-2c4dae2bc324 | rlogist-fast-observation-strategy-on-whole | 2212.01737 | null | https://arxiv.org/abs/2212.01737v2 | https://arxiv.org/pdf/2212.01737v2.pdf | RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning | Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that req... | ['Wei Yang', 'Qiang Fu', 'Xiao Han', 'Jian Cao', 'Deheng Ye', 'Jun Zhang', 'Boxuan Zhao'] | 2022-12-04 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.23680407e-01 3.38093102e-01 -6.05711341e-01 1.10889629e-01
-1.44950414e+00 -5.65416455e-01 1.57833453e-02 4.64930326e-01
-2.57232577e-01 7.93577850e-01 -2.73035854e-01 -6.55148566e-01
-4.23284948e-01 -8.65777850e-01 -5.95475495e-01 -1.32126021e+00
-1.20018005e-01 5.62716007e-01 3.20987552e-01 1.53891101... | [15.113755226135254, -2.9529941082000732] |
c3c1d2fc-ff0d-4786-b241-190ce0c99751 | management-and-detection-system-for-medical | 2211.02351 | null | https://arxiv.org/abs/2211.02351v1 | https://arxiv.org/pdf/2211.02351v1.pdf | Management and Detection System for Medical Surgical Equipment | Retained surgical bodies (RSB) are any foreign bodies left inside the patient after a medical procedure. RSB is often caused by human mistakes or miscommunication between medical staff during the procedure. Infection, medical complications, and even death are possible consequences of RSB, and it is a significant risk f... | ['Michael Winokur', 'Natan Levy', 'Alexandra Hadar'] | 2022-11-04 | null | null | null | null | ['medical-procedure'] | ['medical'] | [-2.33550612e-02 6.28360748e-01 -8.56183283e-03 5.44688106e-01
1.47428885e-01 -4.92747962e-01 1.45403847e-01 2.95174122e-01
-3.48446608e-01 1.02843368e+00 2.29376391e-01 -6.10540569e-01
-5.02768874e-01 -5.35762370e-01 -3.74354750e-01 -6.95002258e-01
2.05547005e-01 -1.50295906e-02 1.69076130e-01 -4.66043651... | [13.839810371398926, -2.955263137817383] |
d4f682fc-2ba0-4854-8a3a-586cf76b1dfb | influence-of-multiple-sequence-alignment | 1812.04162 | null | http://arxiv.org/abs/1812.04162v2 | http://arxiv.org/pdf/1812.04162v2.pdf | Influence of Multiple Sequence Alignment Depth on Potts Statistical Models of Protein Covariation | Potts statistical models have become a popular and promising way to analyze
mutational covariation in protein Multiple Sequence Alignments (MSAs) in order
to understand protein structure, function and fitness. But the statistical
limitations of these models, which can have millions of parameters and are fit
to MSAs of ... | [] | 2019-01-19 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 6.27693534e-01 -1.55479521e-01 -3.15934569e-02 -2.68881500e-01
-4.88719404e-01 -5.20038903e-01 1.43590048e-01 4.97012615e-01
-5.56917131e-01 1.15745032e+00 -4.73595224e-02 -7.74581432e-01
-4.95681390e-02 -4.29841906e-01 -1.23940206e+00 -8.90887260e-01
-7.15504959e-02 6.21394634e-01 4.49207485e-01 -3.83647591... | [4.775681972503662, 5.36406135559082] |
f85ca0f8-39af-41f7-82dc-5e269db9581b | learning-to-estimate-3d-human-pose-from-point | 2212.12910 | null | https://arxiv.org/abs/2212.12910v1 | https://arxiv.org/pdf/2212.12910v1.pdf | Learning to Estimate 3D Human Pose from Point Cloud | 3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimat... | ['Abdulmotaleb El Saddik', 'Haiwei Dong', 'Yufan Zhou'] | 2022-12-25 | null | null | null | null | ['3d-pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.03488696e-01 -4.24389291e-04 5.31455278e-02 -3.23752642e-01
-4.02394235e-01 -1.84738085e-01 2.88661957e-01 -4.88639235e-01
-8.11825752e-01 2.33599558e-01 1.53932303e-01 2.55463332e-01
5.17346382e-01 -5.24851680e-01 -8.49276543e-01 -6.31797537e-02
1.36157917e-02 1.19614935e+00 2.34158665e-01 -3.43523026... | [6.952374458312988, -0.9257334470748901] |
436d364f-a0a8-419b-bb2a-e07d080211c9 | intelligent-resource-allocation-in-joint | null | null | https://ieeexplore.ieee.org/document/9921194 | https://ieeexplore.ieee.org/document/9921194 | Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks | Autonomous vehicles produce high data rates of sensory information from sensing systems. To achieve the advantages of sensor fusion among different vehicles in a cooperative driving scenario, high data-rate communication becomes essential. Current strategies for joint radar-communication (JRC) often rely on specialized... | ['David González G.', 'Yong Liang Guan', 'Dusit Niyato', 'Yanyu Cheng', 'Joash Lee'] | 2022-10-17 | null | null | null | ieee-transactions-on-vehicular-technology-3 | ['distributional-reinforcement-learning', 'joint-radar-communication'] | ['methodology', 'robots'] | [ 3.45577449e-01 3.61077815e-01 -2.88867503e-01 -2.55214572e-01
-6.23778462e-01 -3.33419383e-01 8.52761030e-01 -4.07457445e-03
-6.31079614e-01 8.32658052e-01 -4.38188553e-01 -6.80232525e-01
-4.11641657e-01 -9.63320971e-01 -7.95481503e-01 -9.88618016e-01
-5.72179437e-01 5.07947922e-01 2.37863839e-01 -4.75935370... | [5.222797870635986, 1.4087061882019043] |
39e19fdd-c146-4e7b-93da-5dd80a87fa41 | styleheat-one-shot-high-resolution-editable | 2203.04036 | null | https://arxiv.org/abs/2203.04036v2 | https://arxiv.org/pdf/2203.04036v2.pdf | StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN | One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. One challenging quality factor is the resolution of the output video: higher resolution conveys more details. In this work, we investigate the latent feature s... | ['Yujiu Yang', 'Jue Wang', 'Baoyuan Wu', 'Qingyan Bai', 'Xuan Wang', 'Yanbo Fan', 'Mingdeng Cao', 'Xiaodong Cun', 'Yong Zhang', 'Fei Yin'] | 2022-03-08 | null | null | null | null | ['facial-editing', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 6.13550663e-01 3.45829964e-01 -4.48492914e-02 -7.25437477e-02
-4.99545842e-01 -6.86591506e-01 6.99498236e-01 -1.06960475e+00
9.79961455e-02 5.88125527e-01 2.42919892e-01 1.55946046e-01
7.46100843e-02 -9.33164775e-01 -9.89931524e-01 -9.48227823e-01
4.35017914e-01 -6.46340400e-02 -6.82295263e-02 -3.52570444... | [12.593626022338867, -0.3636786639690399] |
37988d3d-7365-4346-968a-636b66da8cc6 | hyperparameter-tricks-in-multi-agent | 2102.03479 | null | https://arxiv.org/abs/2102.03479v11 | https://arxiv.org/pdf/2102.03479v11.pdf | Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning | Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms. QMIX, a widely popular MARL algorithm, has been used to solve cooperative tasks, e.g. Starcraft Multi-Agent Challenge (SMAC), Difficult... | ['Haibin Wu', 'Siyang Jiang', 'Shih-wei Liao', 'Seth Austin Harding', 'Jian Hu'] | 2021-02-06 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.00192976e-01 -2.77945220e-01 -1.31628424e-01 4.80283201e-01
-4.48757380e-01 -4.65650350e-01 5.23031771e-01 3.50267053e-01
-6.56633317e-01 1.19141746e+00 -2.00364530e-01 -2.09241092e-01
-4.41680908e-01 -5.42250156e-01 -6.43446028e-01 -9.93045151e-01
-7.32738733e-01 4.65667099e-01 5.65195382e-01 -9.60248590... | [3.7646656036376953, 1.9176326990127563] |
61aae092-9aad-44f6-83d6-ed19bf23663f | learning-with-label-noise-for-image-retrieval | 2112.10453 | null | https://arxiv.org/abs/2112.10453v2 | https://arxiv.org/pdf/2112.10453v2.pdf | Learning with Label Noise for Image Retrieval by Selecting Interactions | Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propose a noise-resistant method for image retrieval named Teacher-based Selection of Interactions, T-SINT, which identifies noisy interactions, ... | ['Stéphane Clinchant', 'Rafael Sampaio de Rezende', 'Arnaud Sors', 'Sarah Ibrahimi'] | 2021-12-20 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 4.25917119e-01 -6.16160512e-01 -4.06550854e-01 -4.10947561e-01
-1.27463126e+00 -5.45158923e-01 6.28497899e-01 2.48428166e-01
-6.20521545e-01 6.22649014e-01 -1.54942915e-01 1.77414328e-01
-4.81058896e-01 -4.61667657e-01 -5.93596935e-01 -1.11948848e+00
3.33764330e-02 3.36865127e-01 2.50075340e-01 5.78714535... | [9.433987617492676, 3.960584878921509] |
7a899998-cbdf-4af5-88db-0b35719cdc8f | identity-aware-cyclegan-for-face-photo-sketch | 2103.16019 | null | https://arxiv.org/abs/2103.16019v1 | https://arxiv.org/pdf/2103.16019v1.pdf | Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition | Face photo-sketch synthesis and recognition has many applications in digital entertainment and law enforcement. Recently, generative adversarial networks (GANs) based methods have significantly improved the quality of image synthesis, but they have not explicitly considered the purpose of recognition. In this paper, we... | ['Weihong Deng', 'Jiani Hu', 'Yuke Fang'] | 2021-03-30 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 5.64484835e-01 6.20134622e-02 5.03926128e-02 -2.54024982e-01
-5.36550701e-01 -3.84133935e-01 8.95719111e-01 -9.57470000e-01
3.83563153e-02 6.21633589e-01 3.75069641e-02 1.29287392e-01
2.55929917e-01 -8.50712836e-01 -8.17029715e-01 -9.38484967e-01
5.81690133e-01 6.71404824e-02 -3.59954298e-01 -7.56977051... | [12.4877347946167, -0.07272697240114212] |
9909093b-9b70-4665-9ec5-1b5ef331bdc4 | an-iterative-unbiased-geometric-approach-to | 2212.02421 | null | https://arxiv.org/abs/2212.02421v3 | https://arxiv.org/pdf/2212.02421v3.pdf | Score-based denoising for atomic structure identification | We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but oth... | ['Fei Zhou', 'Vasily Bulatov', 'James Chapman', 'Cheol Woo Park', 'Nicolas Bertin', 'Babak Sadigh', 'Tim Hsu'] | 2022-12-05 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 6.33576751e-01 -2.43061453e-01 2.65235424e-01 -2.22870439e-01
-9.64317441e-01 -7.38371491e-01 7.80143559e-01 6.60637617e-02
-4.30637509e-01 1.08371449e+00 3.20617467e-01 -1.22395366e-01
1.94635615e-02 -6.99918151e-01 -8.46053481e-01 -1.40881670e+00
-3.33112516e-02 1.08651245e+00 1.92287713e-01 -5.62849939... | [5.024406433105469, 5.31587553024292] |
7cf63430-bf8f-44b6-8c60-2f0227101f1e | diversity-encouraged-learning-of-unsupervised | 1611.04899 | null | http://arxiv.org/abs/1611.04899v2 | http://arxiv.org/pdf/1611.04899v2.pdf | Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction | Being able to predict the neural signal in the near future from the current
and previous observations has the potential to enable real-time responsive
brain stimulation to suppress seizures. We have investigated how to use an
auto-encoder model consisting of LSTM cells for such prediction. Recog- nizing
that there exis... | ['Yao Wang', 'Yilin Song', 'Jonathan Viventi'] | 2016-11-15 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 7.68003523e-01 2.24730954e-01 6.32437989e-02 -5.98605871e-01
-6.58689260e-01 -3.23775530e-01 4.20587003e-01 -2.39936903e-01
-4.95595485e-01 8.86124671e-01 2.52577126e-01 8.32453892e-02
-2.45429575e-01 -2.85558552e-01 -8.43303978e-01 -9.16258395e-01
-4.42238241e-01 6.25928342e-01 6.51189610e-02 2.18283474... | [13.191075325012207, 3.539456367492676] |
623b3893-ede0-4406-b961-1db206dcfe38 | multiple-people-tracking-using-body-and-joint | null | null | http://openaccess.thecvf.com/content_CVPRW_2019/html/BMTT/Henschel_Multiple_People_Tracking_Using_Body_and_Joint_Detections_CVPRW_2019_paper.html | http://openaccess.thecvf.com/content_CVPRW_2019/papers/BMTT/Henschel_Multiple_People_Tracking_Using_Body_and_Joint_Detections_CVPRW_2019_paper.pdf | Multiple People Tracking using Body and Joint Detections | Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However, despite an enormous progress in recent years, partially occluded people are still often not recognized... | ['Bodo Rosenhahn', 'Yunzhe Zou', 'Roberto Henschel'] | 2019-06-01 | null | null | null | the-ieee-conference-on-computer-vision-and-3 | ['multiple-people-tracking'] | ['computer-vision'] | [-4.43272889e-02 -2.69593775e-01 -6.89116567e-02 -7.54368380e-02
-6.55218065e-01 -5.54847062e-01 4.79861259e-01 2.45377526e-01
-7.04890311e-01 7.34388769e-01 -1.54397026e-01 1.57368943e-01
-6.99589252e-02 -6.42069817e-01 -7.15598702e-01 -5.79916239e-01
-4.64909188e-02 8.77807736e-01 6.66186869e-01 8.22528526... | [6.521662712097168, -1.8310952186584473] |
3138074a-07b0-4442-9103-8299c6f4da81 | cliper-a-unified-vision-language-framework | 2303.00193 | null | https://arxiv.org/abs/2303.00193v1 | https://arxiv.org/pdf/2303.00193v1.pdf | CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition | Facial expression recognition (FER) is an essential task for understanding human behaviors. As one of the most informative behaviors of humans, facial expressions are often compound and variable, which is manifested by the fact that different people may express the same expression in very different ways. However, most ... | ['Feng Zhao', 'Zhaoqing Zhu', 'Hongjing Niu', 'Hanting Li'] | 2023-03-01 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 9.00417417e-02 -4.35052216e-01 -4.00382757e-01 -1.04013240e+00
-1.56631321e-01 -2.48401538e-01 5.81628382e-01 -3.37263674e-01
-2.29259759e-01 5.40100038e-01 2.20662087e-01 3.81987810e-01
3.13709974e-01 -3.73885334e-01 -4.23377603e-01 -7.41637468e-01
3.27684492e-01 3.37511338e-02 -1.53337955e-01 -4.65697885... | [13.66100025177002, 1.6928826570510864] |
f55ad8b4-76c5-49da-95c9-7f2c27bce820 | simple-real-time-qrs-detector-with-the-mamemi | null | null | https://www.sciencedirect.com/science/article/pii/S1746809415001032 | https://www.sciencedirect.com/science/article/pii/S1746809415001032 | Simple real-time QRS detector with the MaMeMi filter | Detection of QRS complexes in ECG signals is required to determine heart rate, and it is an important step in the study of cardiac disorders. ECG signals are usually affected by noise of low and high frequency. To improve the accuracy of QRS detectors several methods have been proposed to filter out the noise and detec... | ['David Castells-Rufas', 'Jordi Carrabina'] | 2015-08-01 | null | null | null | null | ['qrs-complex-detection'] | ['medical'] | [ 3.40010554e-01 -5.75492859e-01 1.90632075e-01 -2.88329184e-01
-1.93887800e-01 -4.66381013e-01 -4.06942546e-01 6.16309643e-01
-5.72306156e-01 6.57217681e-01 -4.25031215e-01 -1.48332953e-01
-6.23322167e-02 -7.98117697e-01 2.53684908e-01 -4.46740627e-01
4.84264269e-03 7.06860721e-02 4.76084024e-01 -1.18514104... | [14.099815368652344, 3.1734707355499268] |
8dead145-a7f0-4f22-a305-862800f778d6 | combining-subgoal-graphs-with-reinforcement | 1811.01700 | null | http://arxiv.org/abs/1811.01700v1 | http://arxiv.org/pdf/1811.01700v1.pdf | Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder | In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth fo... | ['Long Qin', 'Cong Hu', 'Yue Hu', 'Junjie Zeng', 'Quanjun Yin'] | 2018-11-05 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [-1.50485441e-01 3.78122002e-01 -2.54355490e-01 1.25234872e-01
-6.26278877e-01 -4.59656358e-01 2.20933959e-01 -2.44747400e-02
-4.50966537e-01 1.26452887e+00 9.33707952e-02 -5.33250093e-01
-4.33566153e-01 -1.02091360e+00 -7.63363957e-01 -7.42806554e-01
-6.83333397e-01 5.42367101e-01 6.26843929e-01 -6.12901211... | [4.844281196594238, 1.4902842044830322] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.