paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2e801aa0-d823-4b8f-96ca-ab90de8fc577 | matt-multimodal-attention-level-estimation | 2301.09174 | null | https://arxiv.org/abs/2301.09174v1 | https://arxiv.org/pdf/2301.09174v1.pdf | MATT: Multimodal Attention Level Estimation for e-learning Platforms | This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load such as faces gestures (e.g., blink r... | ['Javier Ortega-Garcia', 'Ruth Cobos', 'Ruben Tolosana', 'Julian Fierrez', 'Aythami Morales', 'Luis F. Gomez', 'Roberto Daza'] | 2023-01-22 | null | null | null | null | ['head-pose-estimation', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision'] | [-1.36071905e-01 1.77356660e-01 8.73400830e-03 -3.22421134e-01
-5.96907258e-01 -3.14686149e-01 1.23920359e-01 2.32843578e-01
-5.07369041e-01 4.39210534e-01 1.49817899e-01 3.23162556e-01
-1.31687909e-01 -2.71623820e-01 -4.46122110e-01 -7.36727178e-01
9.46587548e-02 -1.07519761e-01 -2.03562170e-01 -1.68885231... | [13.506632804870605, 2.3923935890197754] |
46616c8b-bd08-43ee-81e8-8b14fd538a24 | a-comprehensive-study-of-batch-construction | 1705.02414 | null | http://arxiv.org/abs/1705.02414v1 | http://arxiv.org/pdf/1705.02414v1.pdf | A comprehensive study of batch construction strategies for recurrent neural networks in MXNet | In this work we compare different batch construction methods for mini-batch
training of recurrent neural networks. While popular implementations like
TensorFlow and MXNet suggest a bucketing approach to improve the
parallelization capabilities of the recurrent training process, we propose a
simple ordering strategy tha... | ['Hermann Ney', 'Pavel Golik', 'Patrick Doetsch'] | 2017-05-05 | null | null | null | null | ['noisy-speech-recognition'] | ['speech'] | [ 7.33895227e-02 -1.12162121e-01 -7.97594115e-02 -8.25318336e-01
-2.19109848e-01 -3.86758238e-01 6.05795622e-01 -3.47109139e-01
-9.36227083e-01 6.26857102e-01 1.97886512e-01 -1.09693849e+00
3.17131132e-02 -4.84426111e-01 -3.70520949e-01 -7.52295554e-01
-3.41583081e-02 6.86424911e-01 3.20139050e-01 -2.09808603... | [10.903983116149902, 6.394798755645752] |
34addedd-1b3f-498a-a63d-a5c603f3fca8 | video-representation-learning-with-visual | 2006.15489 | null | https://arxiv.org/abs/2006.15489v2 | https://arxiv.org/pdf/2006.15489v2.pdf | Video Representation Learning with Visual Tempo Consistency | Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition. In this work, we demonstrate that visual tempo can also serve as a self-supervision signal for video representation learning. We propose to maximize the mutual information between representations of slow and... | ['Bolei Zhou', 'Ceyuan Yang', 'Yinghao Xu', 'Bo Dai'] | 2020-06-28 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.79870057e-01 -1.53366506e-01 -7.67902434e-01 -4.96735901e-01
-5.93026221e-01 -2.85667211e-01 6.78701639e-01 -7.65672252e-02
-2.38692909e-01 4.45564598e-01 6.70909464e-01 3.65869433e-01
5.84757794e-03 -4.27400708e-01 -7.03663528e-01 -7.24197388e-01
-4.40044880e-01 -1.35103511e-02 1.58121243e-01 -1.35605752... | [8.601305961608887, 0.7505678534507751] |
68472d2c-da46-4bd1-917f-e0405033bbd9 | frenlys-a-tool-for-the-automatic | null | null | https://aclanthology.org/2021.ranlp-main.135 | https://aclanthology.org/2021.ranlp-main.135.pdf | FrenLyS: A Tool for the Automatic Simplification of French General Language Texts | Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our... | ['Thomas François', 'Patrick Watrin', 'Quentin Langlois', 'Eva Rolin'] | null | null | https://aclanthology.org/2021.ranlp-1.135 | https://aclanthology.org/2021.ranlp-1.135.pdf | ranlp-2021-9 | ['lexical-simplification'] | ['natural-language-processing'] | [-1.64917275e-01 3.25402766e-01 2.00971738e-01 -3.36149007e-01
-7.24006951e-01 -8.04954946e-01 8.79299998e-01 4.96674389e-01
-6.95876598e-01 1.22161210e+00 6.04299724e-01 -1.28975376e-01
-2.59264857e-01 -7.45667100e-01 -2.92630136e-01 4.58441041e-02
7.28702068e-01 8.86188626e-01 2.98521459e-01 -8.26719820... | [10.795594215393066, 10.367788314819336] |
e321f44d-f6f0-4597-9869-b0a1e89aa9d6 | foundations-and-modelling-of-dynamic-networks | 2005.07496 | null | https://arxiv.org/abs/2005.07496v2 | https://arxiv.org/pdf/2005.07496v2.pdf | Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey | Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from... | ['Bogdan Gabrys', 'Katarzyna Musial', 'Joakim Skarding'] | 2020-05-13 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [ 1.08528554e-01 1.98004901e-01 -5.83483160e-01 -9.93032530e-02
8.07809591e-01 -6.25592172e-01 4.65888590e-01 3.15194935e-01
1.19418152e-01 4.01009321e-01 1.32536404e-02 -6.97477877e-01
-9.19971645e-01 -1.10699618e+00 -9.93601829e-02 -3.88042539e-01
-7.33090281e-01 2.12020054e-01 1.55890018e-01 -3.96788001... | [7.105871200561523, 6.032923221588135] |
3bf4f837-45ba-4b0b-ba90-185755ac9dca | temporal-view-synthesis-of-dynamic-scenes | 2208.09463 | null | https://arxiv.org/abs/2208.09463v1 | https://arxiv.org/pdf/2208.09463v1.pdf | Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images | The challenge of graphically rendering high frame-rate videos on low compute devices can be addressed through periodic prediction of future frames to enhance the user experience in virtual reality applications. This is studied through the problem of temporal view synthesis (TVS), where the goal is to predict the next f... | ['Rajiv Soundararajan', 'Pranali Sancheti', 'Nagabhushan Somraj'] | 2022-08-19 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.00022304e-01 -5.12401573e-02 2.44791895e-01 -1.47287220e-01
-3.45306188e-01 -3.70756656e-01 6.39957726e-01 -6.73145056e-01
-8.33088458e-02 5.57761550e-01 3.92754763e-01 1.13220491e-01
2.38913864e-01 -6.92513764e-01 -9.29061353e-01 -6.83078945e-01
-6.55109212e-02 1.68702692e-01 6.42891884e-01 -3.93019430... | [9.75572681427002, -2.098076343536377] |
a438af81-5131-4e6c-9979-6ac8d5aaae4b | taking-a-step-back-with-kcal-multi-class | 2202.07679 | null | https://arxiv.org/abs/2202.07679v3 | https://arxiv.org/pdf/2202.07679v3.pdf | Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks | Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. In high-risk applications like healthcare, practitioners require $\textit{fully calibrated}$ probability predictions for decision-making. That is, conditioned on the prediction $\textit{vector}$, $\textit{every}$... | ['Jimeng Sun', 'Shubhendu Trivedi', 'Zhen Lin'] | 2022-02-15 | null | null | null | null | ['supervised-dimensionality-reduction', 'network-embedding'] | ['computer-vision', 'methodology'] | [ 4.16343473e-02 5.71115434e-01 -5.66412389e-01 -9.41910744e-01
-1.09948003e+00 -4.97950345e-01 8.33990946e-02 7.20715746e-02
-5.83702683e-01 9.75522757e-01 -1.87047720e-01 -7.53017545e-01
-3.02529991e-01 -8.32531393e-01 -1.11511576e+00 -7.52108932e-01
1.32295489e-02 7.79450595e-01 -3.02598774e-01 5.97861648... | [8.010332107543945, 4.049944877624512] |
3faf3609-7576-4b69-a7c0-a68305680a5d | a-tale-of-color-variants-representation-and | 2112.0291 | null | https://arxiv.org/abs/2112.02910v1 | https://arxiv.org/pdf/2112.02910v1.pdf | A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce | In this paper, we address a crucial problem in fashion e-commerce (with respect to customer experience, as well as revenue): color variants identification, i.e., identifying fashion products that match exactly in their design (or style), but only to differ in their color. We propose a generic framework, that leverages ... | ['Abhinav Ravi', 'Maulik Parmar', 'Sandeep Repakula', 'Ujjal Kr Dutta'] | 2021-12-06 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.81552106e-01 -7.57349581e-02 -1.13788523e-01 -2.87064046e-01
-3.34723443e-01 -1.27863157e+00 3.88257295e-01 1.12924978e-01
-4.12742943e-02 2.76435643e-01 -1.44365519e-01 -3.91705066e-01
-1.25262573e-01 -5.84694386e-01 -8.82310748e-01 -5.73658168e-01
1.18710473e-01 4.01542932e-01 -3.51961851e-01 -5.48211038... | [11.058286666870117, 0.1167890876531601] |
4ddf2d6a-0dd4-42ec-ba6f-b2622d4b5c3c | weakly-supervised-video-summarization-using | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Sijia_Cai_Weakly-supervised_Video_Summarization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sijia_Cai_Weakly-supervised_Video_Summarization_ECCV_2018_paper.pdf | Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior | Video summarization is a challenging under-constrained problem because the underlying summary of a single video strongly depends on users' subjective understandings. Data-driven approaches, such as deep neural networks, can deal with the ambiguity inherent in this task to some extent, but it is extremely expensive to a... | ['WangMeng Zuo', 'Sijia Cai', 'Lei Zhang', 'Larry S. Davis'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['supervised-video-summarization'] | ['computer-vision'] | [ 2.86488622e-01 6.83792401e-03 -2.43233845e-01 -4.21864986e-01
-1.21081209e+00 -3.08284104e-01 5.15020072e-01 -1.21880680e-01
-8.73413533e-02 5.47912538e-01 7.46257305e-01 1.88056916e-01
2.93206125e-01 -3.00684154e-01 -9.94992733e-01 -6.75257862e-01
3.53094369e-01 -4.84824292e-02 3.00930351e-01 9.68787149... | [10.448873519897461, 0.5515821576118469] |
e3d567d8-53d9-46ba-857e-d5310c0bec92 | explaining-graph-neural-networks-via-non | 2301.0278 | null | https://arxiv.org/abs/2301.02780v1 | https://arxiv.org/pdf/2301.02780v1.pdf | Explaining Graph Neural Networks via Non-parametric Subgraph Matching | The great success in graph neural networks (GNNs) provokes the question about explainability: Which fraction of the input graph is the most determinant of the prediction? Particularly, parametric explainers prevail in existing approaches because of their stronger capability to decipher the black-box (i.e., the target G... | ['Stan Z. Li', 'Zhangming Niu', 'Xurui Jin', 'Yinghui Jiang', 'Dragomir Radev', 'Lirong Wu', 'Siyuan Li', 'Fang Wu'] | 2023-01-07 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.36262286e-01 6.56601131e-01 -3.99591267e-01 -2.21473455e-01
-2.46012732e-01 -4.47748035e-01 4.59038109e-01 -1.31115392e-01
2.53578246e-01 8.09145033e-01 7.55523145e-02 -4.88214880e-01
-3.44645768e-01 -8.84896636e-01 -1.20365453e+00 -7.25166380e-01
-9.55558419e-02 5.73189139e-01 5.68338595e-02 -9.97258052... | [7.40493106842041, 6.234894752502441] |
aed09d74-8783-4aef-959c-096d97dde9a1 | deepsat-v2-feature-augmented-convolutional | 1911.07747 | null | https://arxiv.org/abs/1911.07747v1 | https://arxiv.org/pdf/1911.07747v1.pdf | DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification | Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of sat... | ['Manohar Karki', 'Robert DiBiano', 'Supratik Mukhopadhyay', 'Qun Liu', 'Sangram Ganguly', 'Saikat Basu', 'Ramakrishna Nemani'] | 2019-11-15 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 1.31073162e-01 -1.74505889e-01 -8.05171859e-03 -5.49042106e-01
-7.72969902e-01 -5.44287145e-01 8.22335720e-01 4.04726267e-02
-5.34625173e-01 1.01220059e+00 -6.09050430e-02 -3.24550897e-01
-6.52792752e-01 -1.13461030e+00 -5.01844823e-01 -8.98020566e-01
-6.29168749e-01 4.39238638e-01 1.04650311e-01 -5.20108581... | [9.683403968811035, -1.5431785583496094] |
fb75761b-169b-468a-9b6f-f674661de96e | subjective-quality-assessment-for-images | 2206.05008 | null | https://arxiv.org/abs/2206.05008v1 | https://arxiv.org/pdf/2206.05008v1.pdf | Subjective Quality Assessment for Images Generated by Computer Graphics | With the development of rendering techniques, computer graphics generated images (CGIs) have been widely used in practical application scenarios such as architecture design, video games, simulators, movies, etc. Different from natural scene images (NSIs), the distortions of CGIs are usually caused by poor rending setti... | ['Guangtao Zhai', 'Wei Lu', 'Xiongkuo Min', 'Wei Sun', 'ZiCheng Zhang', 'Tao Wang'] | 2022-06-10 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 0.01248805 -0.6255926 0.22868577 -0.48040935 -0.6074113 -0.03752552
0.33964354 -0.11814548 -0.46556956 0.49885872 0.15807751 -0.08786611
-0.15900053 -0.90852743 -0.37838525 -0.6490233 -0.09047694 0.04089455
0.40666977 -0.4345676 0.3477907 0.3068731 -1.5851626 0.27512947
0.9769487 1.2206628 0.... | [11.794408798217773, -1.8545010089874268] |
cbf34b46-9e8c-4b8c-a4ee-9ff8837b5b13 | introduction-to-protein-structure | 2307.02169 | null | https://arxiv.org/abs/2307.02169v2 | https://arxiv.org/pdf/2307.02169v2.pdf | Introduction to Protein Structure | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['Sanne Abeln', 'K. Anton Feenstra', 'Laura Hoekstra', 'Jose Gavaldá-Garciá', 'Olga Ivanova', 'Bas Stringer', 'Halima Mouhib', 'Erik van Dijk', 'Annika Jacobsen'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction', 'protein-folding'] | ['miscellaneous', 'natural-language-processing'] | [ 3.40722710e-01 -9.73070189e-02 -2.46383294e-01 -1.89455971e-01
-1.09504862e-02 -6.64771199e-01 1.92964301e-02 3.60015094e-01
-2.23519519e-01 1.23232865e+00 -1.42063290e-01 -5.79270661e-01
1.31502435e-01 -4.31641310e-01 -6.23378336e-01 -1.30053473e+00
-1.81336015e-01 5.26887596e-01 2.73586690e-01 -3.43329370... | [4.743171215057373, 5.304063320159912] |
af11b2fc-030b-4b04-9ee2-af9b01ab0419 | a-residual-encoder-decoder-network-for | 2201.05963 | null | https://arxiv.org/abs/2201.05963v1 | https://arxiv.org/pdf/2201.05963v1.pdf | A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening | Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision problem through continuous screening and treatment. In traditional clinical practice, the involved le... | ['Syed S. Naqvi', 'Muhammad Arsalan', 'Ahsan Saadat', 'Tariq M. Khan', 'Malik A. Manan'] | 2022-01-16 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 1.16404213e-01 -2.48240635e-01 1.67626649e-01 -1.84744924e-01
-7.98772499e-02 -1.62981823e-01 4.47546244e-02 -9.75891668e-03
-5.44507205e-01 6.69005692e-01 -8.01387355e-02 -5.19809663e-01
-1.20851561e-01 -6.89789653e-01 -2.49510482e-01 -7.92746186e-01
1.29608810e-01 -1.52890861e-01 3.94881487e-01 1.23689637... | [15.829453468322754, -3.9905219078063965] |
29db7105-d0bd-4d51-b327-e083bf0591b6 | dgcnn-disordered-graph-convolutional-neural | 1712.03563 | null | http://arxiv.org/abs/1712.03563v1 | http://arxiv.org/pdf/1712.03563v1.pdf | DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model | Convolutional neural networks (CNNs) can be applied to graph similarity
matching, in which case they are called graph CNNs. Graph CNNs are attracting
increasing attention due to their effectiveness and efficiency. However, the
existing convolution approaches focus only on regular data forms and require
the transfer of ... | ['Yang Liu', 'Lei Huang', 'Bo Wu', 'Bo Lang'] | 2017-12-10 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.10984351e-02 7.51842260e-02 4.31339592e-02 -2.51500040e-01
2.68629730e-01 -3.07377785e-01 4.27401572e-01 2.97956973e-01
-3.62647235e-01 7.36332983e-02 -1.32718161e-01 -3.41257423e-01
4.78290804e-02 -1.53347647e+00 -7.36310363e-01 -6.81834102e-01
2.83230871e-01 1.82942688e-01 4.09722567e-01 -1.01086564... | [7.20604133605957, 6.2462053298950195] |
89817829-e129-488d-b203-4272fd236d20 | learning-segmentation-masks-with-the | 1811.04682 | null | http://arxiv.org/abs/1811.04682v2 | http://arxiv.org/pdf/1811.04682v2.pdf | Learning Segmentation Masks with the Independence Prior | An instance with a bad mask might make a composite image that uses it look
fake. This encourages us to learn segmentation by generating realistic
composite images. To achieve this, we propose a novel framework that exploits a
new proposed prior called the independence prior based on Generative
Adversarial Networks (GAN... | ['Xiaoqiang Li', 'Weiqin Tong', 'Pin Wu', 'Yimin Chen', 'Songmin Dai', 'Lu Wang'] | 2018-11-12 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 9.10300434e-01 1.02875519e+00 -1.02098778e-01 -3.90805751e-01
-9.56474364e-01 -8.11047256e-01 6.14140153e-01 -2.97250807e-01
-1.27633318e-01 6.54667377e-01 -4.01685297e-01 6.80722529e-03
3.64592820e-01 -9.76409495e-01 -1.30943251e+00 -9.05392468e-01
3.50470036e-01 8.93534243e-01 5.33905685e-01 1.40392616... | [10.959456443786621, -0.2004670947790146] |
20ae0dc5-9d6d-449e-99d5-5eb868a7c3c5 | unsupervised-image-classification-through | 2009.08309 | null | http://arxiv.org/abs/2009.08309v1 | http://arxiv.org/pdf/2009.08309v1.pdf | Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network | We present results of a deep photonic spiking convolutional neural network,
based on two-section VCSELs, targeting image classification. Training is based
on unsupervised spike-timing dependent plasticity, whereas neuron
time-multiplexing and ultra-fast response are exploited towards a a reduction
of the physical neuro... | [] | 2020-09-16 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 4.22659963e-01 1.55676026e-02 5.20038068e-01 1.89268161e-02
9.02883634e-02 -5.71935356e-01 1.60111517e-01 -1.19964212e-01
-1.12588549e+00 1.19498014e+00 -6.27046645e-01 -3.14464658e-01
-1.71467602e-01 -7.51691639e-01 -7.78622270e-01 -1.35382020e+00
6.09843107e-03 1.77811645e-02 7.45070159e-01 -2.58199722... | [8.217429161071777, 2.4768898487091064] |
06fe55d1-97b4-42e5-ae71-569671ff3393 | an-adaptive-artificial-neural-network-based | 2101.1241 | null | https://arxiv.org/abs/2101.12410v1 | https://arxiv.org/pdf/2101.12410v1.pdf | An adaptive artificial neural network-based generative design method for layout designs | Layout designs are encountered in a variety of fields. For problems with many design degrees of freedom, efficiency of design methods becomes a major concern. In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process. A main issue of many suc... | ['Wenjing Ye', 'Renkai Tan', 'Chao Qian'] | 2021-01-29 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 2.21089348e-01 -9.73978862e-02 1.26033351e-01 5.10827219e-03
-4.00673032e-01 -2.97312498e-01 3.83224875e-01 -7.13709742e-02
-1.74326092e-01 9.77693141e-01 -2.47535452e-01 -1.84289739e-01
-3.31390172e-01 -1.06140363e+00 -3.80837917e-01 -8.19153488e-01
2.69508749e-01 2.76356816e-01 -2.24203259e-01 -1.65569872... | [5.92638635635376, 3.3302972316741943] |
0e1c7dc7-20c6-4e4b-b75f-3fc32826eb8b | revisiting-embodiedqa-a-simple-baseline-and | 1904.04166 | null | https://arxiv.org/abs/1904.04166v2 | https://arxiv.org/pdf/1904.04166v2.pdf | Revisiting EmbodiedQA: A Simple Baseline and Beyond | In Embodied Question Answering (EmbodiedQA), an agent interacts with an environment to gather necessary information for answering user questions. Existing works have laid a solid foundation towards solving this interesting problem. But the current performance, especially in navigation, suggests that EmbodiedQA might be... | ['Yu Wu', 'Yi Yang', 'Lu Jiang'] | 2019-04-08 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [ 3.62269282e-02 3.68966311e-01 4.05079663e-01 -4.73286659e-01
-1.06322527e+00 -8.24466825e-01 6.43044591e-01 -8.32434967e-02
-7.61578798e-01 6.37858272e-01 3.36002558e-01 -5.31661510e-01
-3.26131582e-02 -8.58242095e-01 -8.96660388e-01 -6.14187121e-01
-7.29412585e-02 7.46382475e-01 5.07219613e-01 -8.30850065... | [4.417631149291992, 0.6066211462020874] |
4ad00b37-a4da-4a0f-b001-59220bc9cd98 | local2global-scaling-global-representation | 2107.12224 | null | https://arxiv.org/abs/2107.12224v1 | https://arxiv.org/pdf/2107.12224v1.pdf | Local2Global: Scaling global representation learning on graphs via local training | We propose a decentralised "local2global" approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.... | ['Mihai Cucuringu', 'Marya Bazzi', 'Xiaowen Dong', 'Giovanni Colavizza', 'Lucas G. S. Jeub'] | 2021-07-26 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-2.65230536e-01 5.80196798e-01 -5.31603634e-01 -8.50137174e-02
-5.65646589e-01 -5.51637113e-01 4.40041393e-01 6.95595503e-01
1.93538338e-01 4.20406580e-01 1.12041287e-01 -3.50199699e-01
-2.41758585e-01 -1.03046978e+00 -6.91264749e-01 -7.81363785e-01
-4.81753260e-01 6.68500841e-01 3.69477868e-01 -1.00134172... | [7.101648807525635, 6.074704170227051] |
c2d06c66-0405-4d1c-a389-e0f43df4348e | low-frequency-image-deep-steganography | 2303.13713 | null | https://arxiv.org/abs/2303.13713v1 | https://arxiv.org/pdf/2303.13713v1.pdf | Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness | Image deep steganography (IDS) is a technique that utilizes deep learning to embed a secret image invisibly into a cover image to generate a container image. However, the container images generated by convolutional neural networks (CNNs) are vulnerable to attacks that distort their high-frequency components. To address... | ['Wanlei Zhou', 'Xin Yu', 'Bo Liu', 'Yuan Zhao', 'Tianqing Zhu', 'Huajie Chen'] | 2023-03-23 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 4.69181269e-01 -4.77143936e-02 1.11144491e-01 3.10504258e-01
-4.12528127e-01 -4.99874890e-01 3.25136304e-01 -4.47808951e-01
-3.61937195e-01 1.99047536e-01 7.01769604e-04 -2.98300743e-01
2.46054724e-01 -1.24246073e+00 -8.76791120e-01 -1.29692614e+00
-9.41880643e-02 -8.40059280e-01 9.98800471e-02 -4.44249719... | [4.319727897644043, 8.047130584716797] |
42b9507a-746d-422d-8d4a-f83cf7c976a2 | generative-zero-shot-prompt-learning-for | 2307.0283 | null | https://arxiv.org/abs/2307.02830v1 | https://arxiv.org/pdf/2307.02830v1.pdf | Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting | Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Existing models either encode slot descriptions and examples or design handcrafted question templates using heuristic rules, suffering from poor generalization capability or robustness. In this ... | ['Weiran Xu', 'Jiachi Liu', 'Hao Lei', 'Jinzheng Zhao', 'Keqing He', 'Guanting Dong', 'LiWen Wang', 'Xuefeng Li'] | 2023-07-06 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 2.18586698e-01 5.15060246e-01 -6.05031312e-01 -4.93158102e-01
-1.04532504e+00 -3.98082495e-01 5.87099731e-01 -6.86792517e-03
-3.81838977e-01 9.32231545e-01 1.49373501e-03 -3.31498474e-01
-9.53712985e-02 -9.27159905e-01 -3.51174831e-01 -3.93585205e-01
4.53665435e-01 7.51556337e-01 6.23208046e-01 -3.59693825... | [12.556131362915039, 7.340592384338379] |
17c95988-7c19-47c2-ba21-a0bb036967d9 | tiny-word-embeddings-using-globally-informed | null | null | https://aclanthology.org/2020.coling-main.103 | https://aclanthology.org/2020.coling-main.103.pdf | Tiny Word Embeddings Using Globally Informed Reconstruction | We reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. Previous studies in this direction created a smaller word embedding model by reconstructing pre-trained word representations from those of subwords, which allows to store only a smaller number of subword embeddings ... | ['Yuki Arase', 'Tomoyuki Kajiwara', 'Mao Isogawa', 'Sora Ohashi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['word-similarity'] | ['natural-language-processing'] | [-2.14281693e-01 -2.36850232e-02 -5.67180634e-01 -1.99561909e-01
-3.42889547e-01 -2.10049808e-01 5.97817540e-01 5.17713904e-01
-8.57250690e-01 1.68741211e-01 7.71756172e-01 -4.86435980e-01
1.71149790e-01 -1.11860812e+00 -5.45427203e-01 -5.10531366e-01
2.17865944e-01 1.83367953e-01 3.24163586e-01 -2.87840486... | [10.547735214233398, 8.668899536132812] |
9498c628-8166-46ea-b113-acbfec1c6ff0 | the-gh-exin-neural-network-for-hierarchical | null | null | https://www.sciencedirect.com/science/article/pii/S0893608019302060 | https://www.sciencedirect.com/science/article/pii/S0893608019302060 | The GH-EXIN neural network for hierarchical clustering | Hierarchical clustering is an important tool for extracting information from data in a multi-resolution way. It is more meaningful if driven by data, as in the case of divisive algorithms, which split data until no more division is allowed. However, they have the drawback of the splitting threshold setting. The neural ... | ['Gabriele Ciravegna', 'Vincenzo Randazzo', 'Pietro Barbiero', 'Giansalvo Cirrincione', 'Eros Pasero'] | 2020-01-01 | null | null | null | neural-networks-2020-1 | ['self-organized-clustering'] | ['miscellaneous'] | [ 1.46738410e-01 8.65511671e-02 5.61399423e-02 -3.90548825e-01
-1.75586089e-01 -1.55524388e-01 2.76953518e-01 6.48622096e-01
-8.01279485e-01 4.57989424e-01 1.00231305e-01 5.80661483e-02
-6.93284750e-01 -1.04669917e+00 -3.82442832e-01 -1.30147636e+00
-3.05283368e-01 8.84387553e-01 3.41604918e-01 -1.36644721... | [7.658005237579346, 4.571566104888916] |
ac0936c4-adc0-4380-8686-bcae1527029c | unsupervised-domain-adaptation-for-semantic-5 | 2305.05789 | null | https://arxiv.org/abs/2305.05789v2 | https://arxiv.org/pdf/2305.05789v2.pdf | Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching | Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classified into one or more predefined semantically meaningful classes. Deep learning approaches for semantic segmentation rely on harnessing the power of annotated images to learn features indicative of these seman... | ['Shireen Elhabian', 'Beatrice Knudsen', 'Tushar Kataria'] | 2023-05-09 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 7.13950813e-01 3.70648466e-02 -3.34323794e-01 -8.39939594e-01
-1.08247459e+00 -8.33265424e-01 2.76772708e-01 6.08868539e-01
-6.66157424e-01 6.04364336e-01 -7.96908811e-02 2.22621430e-02
-2.49264121e-01 -5.86859882e-01 -6.08535588e-01 -9.09496486e-01
1.32730260e-01 7.69852102e-01 2.38171741e-01 3.88212055... | [14.615788459777832, -2.0215003490448] |
4ab363d2-9b31-443a-9b7a-31f994261616 | knowledge-restore-and-transfer-for-multi | 2302.13334 | null | https://arxiv.org/abs/2302.13334v2 | https://arxiv.org/pdf/2302.13334v2.pdf | Knowledge Restore and Transfer for Multi-label Class-Incremental Learning | Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in class... | ['Yihong Gong', 'Xing Wei', 'Yuhang He', 'Haoyu Luo', 'Songlin Dong'] | 2023-02-26 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 6.64414883e-01 -2.75629293e-02 -3.78618926e-01 -5.60137391e-01
-6.97011173e-01 -2.48087451e-01 2.73679018e-01 2.14047253e-01
-4.86338705e-01 9.41483200e-01 -1.48701683e-01 -2.08516255e-01
-3.82305384e-02 -2.48886198e-01 -7.00595200e-01 -7.53553569e-01
5.21789908e-01 3.57639432e-01 2.86203712e-01 3.31241310... | [9.823328018188477, 3.3397717475891113] |
336eac41-33fd-4926-8e94-844fef7d4f3d | unsupervised-writer-retrieval-using-netrvlad | 2305.05358 | null | https://arxiv.org/abs/2305.05358v2 | https://arxiv.org/pdf/2305.05358v2.pdf | Towards Writer Retrieval for Historical Datasets | This paper presents an unsupervised approach for writer retrieval based on clustering SIFT descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those cluster labels, a residual network followed by our proposed NetRVLAD, an encoding layer with reduced complexity compared to NetVLAD, is tra... | ['Robert Sablatnig', 'Florian Kleber', 'Marco Peer'] | 2023-05-09 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.95256725e-01 -4.61192966e-01 -5.26486039e-01 -1.37588054e-01
-9.98226762e-01 -6.72202110e-01 9.16876554e-01 7.47346342e-01
-5.23587465e-01 1.61863714e-01 4.52725559e-01 -1.43002167e-01
-7.38964081e-01 -6.97560310e-01 -5.90881109e-01 -2.95713216e-01
-7.44154632e-01 6.07955635e-01 5.49610436e-01 -2.25643754... | [10.7545804977417, 0.5406612753868103] |
36a93646-1698-431c-90f1-eedf663e7890 | motif-difference-field-a-simple-and-effective | 2001.07582 | null | https://arxiv.org/abs/2001.07582v1 | https://arxiv.org/pdf/2001.07582v1.pdf | Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification | Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif diffe... | ['Xin Chen', 'Yadong Zhang'] | 2020-01-21 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 6.19270317e-02 -7.26100326e-01 1.68141574e-01 -2.34489694e-01
1.47977278e-01 -4.02755380e-01 5.24315298e-01 2.26410806e-01
-2.97311872e-01 3.30659717e-01 9.55514312e-02 -2.31307775e-01
-6.88497245e-01 -8.39205205e-01 -6.10412896e-01 -7.60379195e-01
-1.06321430e+00 -1.68850869e-01 1.40899308e-02 -2.98672587... | [7.164590358734131, 3.011826276779175] |
7d7bff70-15c2-4ddb-9fbe-6ae1fab182be | concept-representation-learning-with | 2112.05677 | null | https://arxiv.org/abs/2112.05677v2 | https://arxiv.org/pdf/2112.05677v2.pdf | Concept Representation Learning with Contrastive Self-Supervised Learning | Concept-oriented deep learning (CODL) is a general approach to meet the future challenges for deep learning: (1) learning with little or no external supervision, (2) coping with test examples that come from a different distribution than the training examples, and (3) integrating deep learning with symbolic AI. In CODL,... | ['Daniel T. Chang'] | 2021-12-10 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 4.40384716e-01 4.27177221e-01 -2.57349819e-01 -5.38607478e-01
-1.97179317e-01 -6.79547429e-01 9.35596228e-01 7.09869385e-01
-3.31760764e-01 7.90174484e-01 1.90912351e-01 -2.15195403e-01
-8.25380862e-01 -9.43986714e-01 -5.97207665e-01 -4.99008119e-01
-5.32144368e-01 9.42112386e-01 1.14583507e-01 -4.89204913... | [10.14940357208252, 2.4974546432495117] |
3e0b9c75-5a6b-49a8-b0d9-d6b2ee8b8594 | when-did-it-happen-duration-informed-temporal | 2202.08138 | null | https://arxiv.org/abs/2202.08138v2 | https://arxiv.org/pdf/2202.08138v2.pdf | When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs | We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and... | ['Rada Mihalcea', 'Dandan Shan', 'Jiajun Bao', 'YuHang Zhou', 'Santiago Castro', 'Oana Ignat'] | 2022-02-16 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.91390717e-01 -6.72567338e-02 -4.80557859e-01 -2.28730038e-01
-5.19095600e-01 -7.72865951e-01 8.46793473e-01 9.94543508e-02
-4.91271406e-01 6.14561081e-01 8.97400916e-01 2.35501006e-01
1.33332804e-01 -2.41295010e-01 -5.12146056e-01 -5.53941607e-01
-7.50480831e-01 -1.34148285e-01 6.10394657e-01 4.25149649... | [8.377942085266113, 0.5068516135215759] |
b435fba1-7b54-4f4d-b282-d1647c158fba | data-augmented-3d-semantic-scene-completion | 2111.13309 | null | https://arxiv.org/abs/2111.13309v1 | https://arxiv.org/pdf/2111.13309v1.pdf | Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors | Semantic scene completion (SSC) is a challenging Computer Vision task with many practical applications, from robotics to assistive computing. Its goal is to infer the 3D geometry in a field of view of a scene and the semantic labels of voxels, including occluded regions. In this work, we present SPAwN, a novel lightwei... | ['Teofilo de Campos', 'Frederico Guth', 'Aloisio Dourado'] | 2021-11-26 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 4.84772503e-01 3.94365519e-01 2.39328116e-01 -4.83281910e-01
-5.35930395e-01 -5.78834116e-01 3.28801274e-01 5.27433194e-02
-5.31287372e-01 3.40741068e-01 -7.77418762e-02 -2.94796109e-01
8.28787684e-02 -4.39153671e-01 -7.06961811e-01 -4.24967140e-01
2.49842331e-01 6.22919917e-01 5.60144305e-01 -2.60537326... | [8.411423683166504, -2.8275582790374756] |
8893bb71-819b-4e5f-8921-614ddf8263b4 | mattica-smm4h22-leveraging-sentiment-for | null | null | https://aclanthology.org/2022.smm4h-1.22 | https://aclanthology.org/2022.smm4h-1.22.pdf | mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning | This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team (mattica) participated in detecting stances and premises in tweets about health mandates related to COVID-19 (Task 2). Our approach was based on using an in-domain Pretrained Language Model, which ... | ['Ljiljana Dolamic', 'Fabio Rinaldi', 'Joseph Cornelius', 'Oscar Lithgow-Serrano'] | null | null | null | null | smm4h-coling-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [ 3.78714859e-01 8.10441077e-01 -4.41473335e-01 -6.11304879e-01
-1.06921244e+00 -5.15899658e-01 7.54949033e-01 8.66627634e-01
-6.82850778e-01 7.64359474e-01 6.78784251e-01 -4.99690026e-01
1.59787670e-01 -6.44275129e-01 -6.40226483e-01 -1.71172470e-01
-1.50033340e-01 7.12562442e-01 2.56130010e-01 -5.04383862... | [8.543702125549316, 9.313679695129395] |
f91d6fc0-d4fd-4259-81e6-9c8b7d8521ea | deception-detection-in-text-and-its-relation | 2105.1253 | null | https://arxiv.org/abs/2105.12530v1 | https://arxiv.org/pdf/2105.12530v1.pdf | Deception detection in text and its relation to the cultural dimension of individualism/collectivism | Deception detection is a task with many applications both in direct physical and in computer-mediated communication. Our focus is on automatic deception detection in text across cultures. We view culture through the prism of the individualism/collectivism dimension and we approximate culture by using country as a proxy... | ['Dimitris Plexousakis', 'Ion Androutsopoulos', 'Giorgos Flouris', 'Theodore Patkos', 'Panagiotis Papadakos', 'Katerina Papantoniou'] | 2021-05-26 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.41812661e-01 -6.08503759e-01 -1.15450449e-01 -3.96163911e-01
-2.15701416e-01 -8.19360077e-01 1.20811582e+00 2.49351338e-01
-7.90052772e-01 7.74460077e-01 6.67475760e-01 -2.35980824e-01
-9.59445164e-02 -3.74409378e-01 -1.80279747e-01 -5.76114595e-01
2.91401237e-01 1.57362342e-01 -4.29432988e-01 -3.91531229... | [8.303926467895508, 10.43237590789795] |
71c4be01-5140-41dc-a35a-bd9a85fe5a9c | generalized-earley-parser-bridging-symbolic | 1806.03497 | null | http://arxiv.org/abs/1806.03497v1 | http://arxiv.org/pdf/1806.03497v1.pdf | Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction | Future predictions on sequence data (e.g., videos or audios) require the
algorithms to capture non-Markovian and compositional properties of high-level
semantics. Context-free grammars are natural choices to capture such
properties, but traditional grammar parsers (e.g., Earley parser) only take
symbolic sentences as i... | ['Song-Chun Zhu', 'Siyuan Qi', 'Baoxiong Jia'] | 2018-06-09 | generalized-earley-parser-bridging-symbolic-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=1920 | http://proceedings.mlr.press/v80/qi18a/qi18a.pdf | icml-2018-7 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 5.46561539e-01 4.78294045e-01 -5.18316448e-01 -6.72601342e-01
-4.30756629e-01 -6.55567706e-01 4.93914604e-01 1.69005960e-01
-1.11418463e-01 7.81716883e-01 2.73124397e-01 -4.43904132e-01
3.58062297e-01 -9.73638296e-01 -5.96744180e-01 -7.80209303e-02
-2.21324444e-01 2.47387215e-01 7.38348007e-01 -4.52002995... | [10.377405166625977, 9.499640464782715] |
92400ad5-69c5-4d02-9236-f815c8b87df1 | imaginenet-target-speaker-extraction-with | 2211.00109 | null | https://arxiv.org/abs/2211.00109v2 | https://arxiv.org/pdf/2211.00109v2.pdf | ImagineNET: Target Speaker Extraction with Intermittent Visual Cue through Embedding Inpainting | The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the ... | ['Haizhou Li', 'Marvin Borsdorf', 'Wupeng Wang', 'Zexu Pan'] | 2022-10-31 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.39219016e-01 -1.29574180e-01 -1.63032725e-01 1.38142258e-02
-8.81460369e-01 -3.70163918e-01 4.56610441e-01 -1.20967574e-01
-2.26474196e-01 5.03927112e-01 4.34273064e-01 7.93436356e-03
8.98889601e-02 -9.57310200e-02 -5.43381274e-01 -9.17590618e-01
2.36343384e-01 -3.07357848e-01 1.15477696e-01 2.02929646... | [14.5238618850708, 5.1998395919799805] |
1de20229-e86a-4bf2-866e-da4df136bd95 | computation-with-sequences-in-the-brain | 2306.03812 | null | https://arxiv.org/abs/2306.03812v1 | https://arxiv.org/pdf/2306.03812v1.pdf | Computation with Sequences in the Brain | Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How cognition arises from neural activity is a central open question in neuroscience, inextricable from the study of intelligence itself. A simpl... | ['Santosh S. Vempala', 'Christos H. Papadimitriou', 'Max Dabagia'] | 2023-06-06 | null | null | null | null | ['mathematical-proofs', 'memorization', 'open-question', 'temporal-sequences'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [ 5.79262793e-01 -5.42303035e-03 1.80695757e-01 4.30493616e-02
2.78620750e-01 -9.19864476e-01 1.09941018e+00 3.42094570e-01
-5.40191293e-01 8.27582002e-01 -9.68314186e-02 -3.75986934e-01
-3.49315524e-01 -8.81309748e-01 -7.31576502e-01 -9.89131749e-01
-3.29602629e-01 2.05165818e-01 4.31850195e-01 -3.74594420... | [8.143631935119629, 3.168800115585327] |
f07308f2-76d1-44e2-bafb-0e88302c1cd4 | aigciqa2023-a-large-scale-image-quality | 2307.00211 | null | https://arxiv.org/abs/2307.00211v1 | https://arxiv.org/pdf/2307.00211v1.pdf | AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence | In this paper, in order to get a better understanding of the human visual preferences for AIGIs, a large-scale IQA database for AIGC is established, which is named as AIGCIQA2023. We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a w... | ['Guangtao Zhai', 'Xiongkuo Min', 'Shi Chen', 'Jing Liu', 'Huiyu Duan', 'Jiarui Wang'] | 2023-07-01 | null | null | null | null | ['image-quality-assessment', 'image-generation'] | ['computer-vision', 'computer-vision'] | [-1.72180280e-01 -2.29719311e-01 1.58428669e-01 -3.43051523e-01
-8.65685821e-01 -5.93840301e-01 6.36135399e-01 -1.08861923e-01
-2.68688321e-01 4.59948987e-01 2.02960506e-01 -1.51372716e-01
1.59339681e-01 -8.92879486e-01 -4.88441527e-01 -3.15046400e-01
1.08316623e-01 3.53154123e-01 1.77212432e-01 -1.05153598... | [11.738749504089355, -1.4124592542648315] |
83069fc0-1430-4a6d-9a14-570ad4702720 | quiko-a-quantum-beat-generation-application | 2204.0437 | null | https://arxiv.org/abs/2204.04370v2 | https://arxiv.org/pdf/2204.04370v2.pdf | QuiKo: A Quantum Beat Generation Application | In this chapter a quantum music generation application called QuiKo will be discussed. It combines existing quantum algorithms with data encoding methods from quantum machine learning to build drum and audio sample patterns from a database of audio tracks. QuiKo leverages the physical properties and characteristics of ... | ['Scott Oshiro'] | 2022-04-09 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.79777521e-01 -1.25040904e-01 2.10188270e-01 1.56633973e-01
-8.57094109e-01 -1.03952670e+00 4.64763701e-01 -1.30395532e-01
8.11762959e-02 6.08951688e-01 1.43850118e-01 1.96636900e-01
-1.59190208e-01 -1.24936295e+00 -5.44905186e-01 -9.53786075e-01
3.59255299e-02 3.34065974e-01 4.31770496e-02 -5.10988235... | [5.585755825042725, 4.951233863830566] |
a1a2a3c8-2456-4fbe-83a5-d8dc1441cd29 | autolycus-exploiting-explainable-ai-xai-for | 2302.02162 | null | https://arxiv.org/abs/2302.02162v2 | https://arxiv.org/pdf/2302.02162v2.pdf | AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against White-Box Models | Explainable Artificial Intelligence (XAI) encompasses a range of techniques and procedures aimed at elucidating the decision-making processes of AI models. While XAI is valuable in understanding the reasoning behind AI models, the data used for such revelations poses potential security and privacy vulnerabilities. Exis... | ['Erman Ayday', 'Anisa Halimi', 'Abdullah Caglar Oksuz'] | 2023-02-04 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 4.39113647e-01 7.41196990e-01 -3.96026522e-01 -3.59127522e-01
-6.65929019e-01 -1.15585041e+00 7.64455974e-01 1.47939295e-01
8.64166543e-02 4.77742255e-01 -3.76435846e-01 -1.04749680e+00
-2.07204923e-01 -7.56063461e-01 -9.88452852e-01 -4.29794461e-01
-7.92094991e-02 5.26252389e-01 -3.90446275e-01 5.59403062... | [5.9478278160095215, 7.187702655792236] |
17838e90-78e1-4721-840c-ddc282167fb8 | correcting-comma-errors-in-learner-essays-and | null | null | https://aclanthology.org/N12-1029 | https://aclanthology.org/N12-1029.pdf | Correcting Comma Errors in Learner Essays, and Restoring Commas in Newswire Text | null | ['Martin Chodorow', 'Joel Tetreault', 'Ross Israel'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['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... | [-7.184088230133057, 3.728224754333496] |
2bceab27-7f67-4c32-83f5-db2483bc8240 | a-privacy-preserving-content-based-image | 2011.0027 | null | https://arxiv.org/abs/2011.00270v1 | https://arxiv.org/pdf/2011.00270v1.pdf | A Privacy-Preserving Content-Based Image Retrieval Scheme Allowing Mixed Use Of Encrypted And Plain Images | In this paper, we propose a novel content based-image retrieval scheme allowing the mixed use of encrypted and plain images for the first time. In the proposed scheme, images are encrypted by a block-scrambling method developed for encryption-then-compression (EtC) systems. The encrypted images, referred to as EtC imag... | ['Hitoshi Kiya', 'Kenta Iida'] | 2020-10-31 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 6.28964484e-01 -5.25315225e-01 1.93051472e-02 -2.11965919e-01
-5.21633863e-01 -5.03022492e-01 8.34372222e-01 2.60336578e-01
-9.28994894e-01 5.38512290e-01 -1.27155632e-01 3.34941037e-02
-2.27309451e-01 -1.10003722e+00 -2.94012517e-01 -9.04617667e-01
1.95250273e-01 -2.06933007e-01 9.44491699e-02 -1.34485856... | [10.714630126953125, -0.14587631821632385] |
3a80f81a-4a6e-49e1-ad07-afc96631bac5 | an-adaptive-gmm-approach-to-background | 1307.58 | null | http://arxiv.org/abs/1307.5800v1 | http://arxiv.org/pdf/1307.5800v1.pdf | An Adaptive GMM Approach to Background Subtraction for Application in Real Time Surveillance | Efficient security management has become an important parameter in todays
world. As the problem is growing, there is an urgent need for the introduction
of advanced technology and equipment to improve the state-of art of
surveillance. In this paper we propose a model for real time background
subtraction using AGMM. The... | ['Subra Mukherjee', 'Karen Das'] | 2013-07-22 | null | null | null | null | ['detecting-shadows'] | ['computer-vision'] | [ 4.27714735e-01 -5.04221976e-01 3.78623337e-01 -1.03384189e-01
3.03776264e-01 -5.26488543e-01 6.06547177e-01 2.90042341e-01
-7.23233461e-01 7.39476264e-01 -3.28124672e-01 -3.51042241e-01
1.06600970e-01 -8.51155519e-01 -1.37282223e-01 -7.46836960e-01
1.30939439e-01 1.73310921e-01 1.03707409e+00 -3.36541027... | [8.908577919006348, -0.9914636611938477] |
1f335660-14b1-4c62-8535-f61cbf6f3936 | semi-supervised-classification-for-dynamic | 1704.05948 | null | http://arxiv.org/abs/1704.05948v1 | http://arxiv.org/pdf/1704.05948v1.pdf | Semi-supervised classification for dynamic Android malware detection | A growing number of threats to Android phones creates challenges for malware
detection. Manually labeling the samples into benign or different malicious
families requires tremendous human efforts, while it is comparably easy and
cheap to obtain a large amount of unlabeled APKs from various sources.
Moreover, the fast-p... | ['Chih-Yuan Yang', 'Ravi Sahita', 'Mingwei Zhang', 'Li Chen'] | 2017-04-19 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 8.50041434e-02 -3.70217830e-01 -7.72036135e-01 -2.30263010e-01
-7.36254454e-01 -7.95440495e-01 4.96788353e-01 -1.34139001e-01
-5.18963560e-02 7.21465826e-01 -7.13182271e-01 -8.54614437e-01
1.72189310e-01 -6.85939252e-01 -5.54903507e-01 -6.35056496e-01
-1.87439889e-01 3.82062733e-01 6.80230379e-01 1.44894481... | [14.419934272766113, 9.67808723449707] |
a4541dee-2b80-43b8-99cb-bea13c26230b | hipode-enhancing-offline-reinforcement | 2306.06329 | null | https://arxiv.org/abs/2306.06329v1 | https://arxiv.org/pdf/2306.06329v1.pdf | HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach | Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data. To address the issue of limited data and improve downstream ORL performance, recent work has attempted to expand the dataset's coverage through data augmentation. However, most... | ['Zhaopeng Meng', 'Yan Zheng', 'Jinyi Liu', 'Yi Ma', 'Shixi Lian'] | 2023-06-10 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.49672508e-01 5.51038049e-02 -1.05485058e+00 -9.29408967e-02
-8.16371441e-01 -4.57170069e-01 7.13407397e-01 3.37104172e-01
-5.87646842e-01 1.19997156e+00 2.49840692e-01 -7.96967149e-01
-7.76182413e-02 -8.66176963e-01 -5.66613495e-01 -8.96905303e-01
1.47072211e-01 7.13136017e-01 1.74405232e-01 -4.28169399... | [4.064946174621582, 2.1746792793273926] |
2367bd51-9fa8-4812-8411-173dc14670b5 | uadam-unified-adam-type-algorithmic-framework | 2305.05675 | null | https://arxiv.org/abs/2305.05675v1 | https://arxiv.org/pdf/2305.05675v1.pdf | UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization | Adam-type algorithms have become a preferred choice for optimisation in the deep learning setting, however, despite success, their convergence is still not well understood. To this end, we introduce a unified framework for Adam-type algorithms (called UAdam). This is equipped with a general form of the second-order mom... | ['Danilo P. Mandic', 'Dongpo Xu', 'Jinlan Liu', 'Yiming Jiang'] | 2023-05-09 | null | null | null | null | ['stochastic-optimization', 'type'] | ['methodology', 'speech'] | [-5.36787271e-01 1.24453388e-01 -9.57925245e-03 -1.39354527e-01
-6.46191955e-01 -3.44439417e-01 2.97890902e-01 3.07426274e-01
-7.74615049e-01 8.96889031e-01 -1.71031207e-01 -2.37255186e-01
-3.75802577e-01 -6.69554710e-01 -9.59917784e-01 -1.21909869e+00
-3.15155506e-01 4.12349552e-01 2.59751175e-02 -3.82804781... | [7.156538009643555, 4.0512261390686035] |
2d7ae4c2-4cfd-4029-a68b-c9cb73feea6e | fusing-structure-from-motion-and-simulation | 2304.0725 | null | https://arxiv.org/abs/2304.07250v1 | https://arxiv.org/pdf/2304.07250v1.pdf | Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments | The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pos... | ['Christopher Mutschler', 'Bernd Bischl', 'David Rügamer', 'Lucas Heublein', 'Felix Ott'] | 2023-04-14 | null | null | null | null | ['pose-prediction'] | ['computer-vision'] | [-3.88645977e-02 -3.83529186e-01 1.27600849e-01 -4.69610244e-01
-3.36591214e-01 -5.22441745e-01 6.91708267e-01 -2.33922809e-01
-3.67573589e-01 4.14930761e-01 2.06250343e-02 4.10531498e-02
-7.03267083e-02 -6.66676044e-01 -1.21733487e+00 -6.75898433e-01
-6.32373169e-02 4.51587975e-01 1.55020371e-01 -4.09414619... | [7.709527492523193, -2.165611743927002] |
22a5f63c-b6f7-4aef-b4cc-8b5e62a9e1cf | graph-property-prediction-on-open-graph | 2207.06027 | null | https://arxiv.org/abs/2207.06027v1 | https://arxiv.org/pdf/2207.06027v1.pdf | Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search | Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search). At the same time, we improve it based on the GNN topology design method F2GNN to... | ['Quanming Yao', 'Lanning Wei', 'Huan Zhao', 'Xu Wang'] | 2022-07-13 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 6.92865774e-02 1.60911262e-01 -3.38145435e-01 -1.62016526e-01
1.11239001e-01 -1.62317380e-01 1.85763091e-01 4.25428480e-01
-6.62921891e-02 7.16112077e-01 -2.06199467e-01 -5.36836624e-01
-5.06406784e-01 -1.31852221e+00 -5.09603322e-01 -8.06643844e-01
-3.44099134e-01 2.93784529e-01 4.60763961e-01 -3.59227747... | [7.072296619415283, 6.2035932540893555] |
6aa45bcb-4aa8-416d-94ca-a9bd009de181 | calibrated-predictive-distributions-via | 2205.14568 | null | https://arxiv.org/abs/2205.14568v3 | https://arxiv.org/pdf/2205.14568v3.pdf | Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting | Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. Much research has been devoted to describing the predictive distribution (PD) $F(y|\mathbf{x})$ of a target variable $y \in \mathbb{R}$ given complex input features $\mathbf{x} \in \mathcal{X}$. However, off-the-shelf PDs (from... | ['Ann B. Lee', 'Rafael Izbicki', 'Brett H. Andrews', 'Jeffrey A. Newman', 'David Zhao', 'Biprateep Dey'] | 2022-05-29 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [ 6.16093874e-02 3.87560017e-02 1.03929915e-01 -6.15599513e-01
-1.29792118e+00 -7.67619193e-01 6.77769363e-01 -1.38067126e-01
-1.50758311e-01 1.08338773e+00 -3.59318495e-01 -5.02914846e-01
-6.50281966e-01 -1.16936076e+00 -1.11104572e+00 -1.12162745e+00
-8.44692439e-02 9.88385975e-01 2.78182030e-01 3.46473932... | [7.258779048919678, 3.613044500350952] |
d76f8c1f-5e59-4bb1-b8e0-c217496af888 | comparing-well-and-geophysical-data-for | null | null | https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR033045 | https://www.researchgate.net/publication/364419032_Comparing_Well_and_Geophysical_Data_for_Temperature_Monitoring_within_a_Bayesian_Experimental_Design_Framework | Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework | Temperature logs are an important tool in the geothermal industry. Temperature measurements from boreholes are used for exploration, system design, and monitoring. The number of observations, however, is not always sufficient to fully determine the temperature field or explore the entire parameter space of interest. Dr... | ['Thomas Hermans', 'Eric Laloy', 'Maximilian Ramgraber', 'Nolwenn Lesparre', 'Nicolas Compaire', 'Robin Thibaut'] | 2022-10-19 | null | null | null | water-resources-research-2022-10 | ['time-series-regression'] | ['time-series'] | [ 5.88244200e-03 -3.47261906e-01 -5.01320623e-02 -1.05787173e-01
-5.99108458e-01 -6.00414202e-02 5.13094008e-01 2.75831044e-01
-4.85317051e-01 9.74129856e-01 -2.58133084e-01 -5.38945436e-01
-6.03938520e-01 -7.63945103e-01 -3.56230021e-01 -1.11410952e+00
-2.14655846e-01 7.76418447e-01 1.88954145e-01 2.61459351... | [6.3514485359191895, 3.401796817779541] |
ca59358f-4c70-40bf-b08b-c6e08f64913f | learning-multimodal-graph-to-graph-1 | 1812.0107 | null | http://arxiv.org/abs/1812.01070v3 | http://arxiv.org/pdf/1812.01070v3.pdf | Learning Multimodal Graph-to-Graph Translation for Molecular Optimization | We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph... | ['Wengong Jin', 'Regina Barzilay', 'Kevin Yang', 'Tommi Jaakkola'] | 2018-12-03 | null | null | null | null | ['graph-to-graph-translation'] | ['graphs'] | [ 6.73045456e-01 2.75560647e-01 -5.47006071e-01 -1.12806886e-01
-1.07046247e+00 -9.77311432e-01 6.57315433e-01 1.98470175e-01
-1.16531037e-01 1.26646376e+00 3.72336984e-01 -4.41164643e-01
3.94588828e-01 -7.94809759e-01 -1.45030451e+00 -8.12831998e-01
6.91675991e-02 8.55755806e-01 -2.49887511e-01 -3.55993718... | [4.869469165802002, 5.852307319641113] |
d7cb76ef-f04e-479e-966c-98a5c8195a4a | towards-multilingual-conversations-in-the | null | null | https://aclanthology.org/L14-1556 | https://aclanthology.org/L14-1556.pdf | Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System | This paper outlines the recent development on multilingual medical data and multilingual speech recognition system for network-based speech-to-speech translation in the medical domain. The overall speech-to-speech translation (S2ST) system was designed to translate spoken utterances from a given source language into a ... | ['Ryosuke Isotani', 'Keigo Kubo', 'Fumihiro Adachi', 'Tomoki Toda', 'Sho Matsumiya', 'Satoshi Nakamura', 'Sakriani Sakti', 'Graham Neubig'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['speech-to-speech-translation'] | ['speech'] | [ 1.98806763e-01 4.15039152e-01 -2.41236404e-01 -4.77735668e-01
-1.47779822e+00 -4.27951515e-02 3.41494054e-01 -5.61278947e-02
-4.15084988e-01 9.57719743e-01 5.84349990e-01 -1.06500936e+00
4.66464162e-01 -2.36131102e-01 -3.24063841e-03 -5.34093678e-01
6.82330355e-02 7.93199360e-01 2.54126370e-01 -4.28177863... | [14.420988082885742, 7.180373191833496] |
220312cf-c24a-477e-854d-37375d8cc802 | phase-aware-single-stage-speech-denoising-and-1 | 2006.00687 | null | https://arxiv.org/abs/2006.00687v1 | https://arxiv.org/pdf/2006.00687v1.pdf | Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net | In this work, we tackle a denoising and dereverberation problem with a single-stage framework. Although denoising and dereverberation may be considered two separate challenging tasks, and thus, two modules are typically required for each task, we show that a single deep network can be shared to solve the two problems. ... | [] | 2020-06-01 | phase-aware-single-stage-speech-denoising-and | https://arxiv.org/abs/2006.00687 | https://arxiv.org/pdf/2006.00687 | interspeech-2020-6 | ['speech-denoising'] | ['speech'] | [ 1.81178555e-01 -2.08712861e-01 4.14085984e-01 -4.05556887e-01
-8.78496051e-01 -1.87959284e-01 1.31978512e-01 -2.32407048e-01
-5.03595352e-01 7.21190393e-01 2.07461603e-02 -2.39043072e-01
-1.47625450e-02 -6.20270312e-01 -6.64333463e-01 -1.08457232e+00
7.32758269e-02 -3.86804521e-01 4.19507250e-02 -1.33237675... | [14.966816902160645, 5.9065070152282715] |
e3aeb223-bb5e-45ad-948b-0a554732a4ae | the-ethical-ambiguity-of-ai-data-enrichment | 2306.018 | null | https://arxiv.org/abs/2306.01800v1 | https://arxiv.org/pdf/2306.01800v1.pdf | The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices | The technical progression of artificial intelligence (AI) research has been built on breakthroughs in fields such as computer science, statistics, and mathematics. However, in the past decade AI researchers have increasingly looked to the social sciences, turning to human interactions to solve the challenges of model d... | ['Brent Mittelstadt', 'Will Hawkins'] | 2023-06-01 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 2.16826145e-02 5.37242055e-01 -1.15169346e-01 -5.40316582e-01
-5.62165916e-01 -6.84326053e-01 5.15311062e-01 5.75923443e-01
-9.63721395e-01 7.44489014e-01 6.84564173e-01 -4.77114290e-01
-1.34015922e-02 -1.61740094e-01 -4.25328851e-01 -2.43574470e-01
4.76538777e-01 4.49687243e-01 -1.60224006e-01 -1.24966964... | [9.288463592529297, 6.539114475250244] |
10fa5ad9-fc1e-46fc-80ef-c03c70fe1aa3 | event-camera-based-visual-odometry-for | 2305.08962 | null | https://arxiv.org/abs/2305.08962v1 | https://arxiv.org/pdf/2305.08962v1.pdf | Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface | Our paper proposes a direct sparse visual odometry method that combines event and RGB-D data to estimate the pose of agile-legged robots during dynamic locomotion and acrobatic behaviors. Event cameras offer high temporal resolution and dynamic range, which can eliminate the issue of blurred RGB images during fast move... | ['Donghyun Kim', 'Erik Learned-Miller', 'Michael Yang', 'Zhipeng Tang', 'Shifan Zhu'] | 2023-05-15 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 1.46916822e-01 -3.52068841e-01 1.73312187e-01 -9.73848850e-02
-6.00801945e-01 -5.84965765e-01 1.61498576e-01 -1.11114308e-01
-6.79783642e-01 5.65444350e-01 -1.93011656e-01 3.13316166e-01
3.78820696e-03 -9.08708513e-01 -9.59829628e-01 -5.24001598e-01
-4.97062296e-01 5.46090066e-01 6.47073984e-01 -5.03650367... | [7.547848224639893, -1.6077483892440796] |
59c1c474-567c-4898-8adb-8e144d45ab61 | approximately-optimal-domain-adaptation-with | 2302.14186 | null | https://arxiv.org/abs/2302.14186v2 | https://arxiv.org/pdf/2302.14186v2.pdf | Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis | We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average hypothesis representing previously seen source tasks and ii) a hypothesis trained on a new target task. For a particular generative setting w... | ['Carey E. Priebe', 'Joshua T. Vogelstein', 'Ashwin De Silva', 'Weiwei Yang', 'Hayden S. Helm'] | 2023-02-27 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 5.84522545e-01 3.11899602e-01 -1.84546784e-01 -5.38380682e-01
-1.10445511e+00 -4.12270010e-01 5.99926412e-01 1.98062181e-01
-6.16110206e-01 9.33203161e-01 1.31205752e-01 -1.78699911e-01
-4.78171080e-01 -2.18626097e-01 -5.79950869e-01 -8.65626276e-01
-4.19288129e-01 3.97853732e-01 1.34849206e-01 3.07019591... | [8.785208702087402, 4.091343402862549] |
90c2a33c-89f4-4524-9b07-7c32e9294b89 | weakly-supervised-domain-adaptive-semantic | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Das_Weakly-Supervised_Domain_Adaptive_Semantic_Segmentation_With_Prototypical_Contrastive_Learning_CVPR_2023_paper.pdf | Weakly-Supervised Domain Adaptive Semantic Segmentation With Prototypical Contrastive Learning | There has been a lot of effort in improving the performance of unsupervised domain adaptation for semantic segmentation task, however there is still a huge gap in performance when compared with supervised learning. In this work, we propose a common framework to use different weak labels, e.g. image, point and coars... | ['Bernt Schiele', 'Dengxin Dai', 'Yongqin Xian', 'Anurag Das'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 4.53990102e-01 2.18450144e-01 -3.54524851e-01 -6.07236981e-01
-5.48890173e-01 -5.10545671e-01 5.88051617e-01 2.38350719e-01
-6.10789835e-01 6.05629265e-01 2.30261367e-02 1.99699402e-01
-7.38616809e-02 -7.88813591e-01 -5.91737628e-01 -7.46837676e-01
3.83891642e-01 5.01163304e-01 9.04620826e-01 1.76804569... | [9.642324447631836, 1.316994547843933] |
63ca721a-a4bb-4b7f-8586-7451c80859af | probabilistic-attention-based-on-gaussian | 2302.04061 | null | https://arxiv.org/abs/2302.04061v1 | https://arxiv.org/pdf/2302.04061v1.pdf | Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning | Multiple Instance Learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised methods. This is especially interesting for areas where the creation of large annotated datasets remains challenging, as in medicine. Although re... | ['Rafael Molina', 'Pablo Morales-Álvarez', 'Arne Schmidt'] | 2023-02-08 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 6.22902177e-02 4.45915163e-01 -3.71264637e-01 -5.17072618e-01
-1.28573835e+00 -7.60256052e-02 5.51545143e-01 6.78794265e-01
-5.27208209e-01 1.15549374e+00 -8.15105885e-02 -1.61760643e-01
-3.65881205e-01 -6.83171988e-01 -9.25524890e-01 -1.08335114e+00
1.11647155e-02 1.11438227e+00 1.11834720e-01 3.72372955... | [14.377866744995117, -2.022735118865967] |
60691639-9620-489e-9103-59240f4fec21 | prodmps-a-unified-perspective-on-dynamic-and | 2210.01531 | null | https://arxiv.org/abs/2210.01531v1 | https://arxiv.org/pdf/2210.01531v1.pdf | ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives | Movement Primitives (MPs) are a well-known concept to represent and generate modular trajectories. MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that captu... | ['Gerhard Neumann', 'Rudolf Lioutikov', 'Fabian Otto', 'Michael Volpp', 'Zeqi Jin', 'Ge Li'] | 2022-10-04 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.19933206e-01 -2.65709013e-01 -1.53270677e-01 5.68673527e-03
-8.65582347e-01 -6.54778957e-01 7.81097591e-01 1.34033978e-01
-2.87290394e-01 7.29428232e-01 2.66209602e-01 -2.04983532e-01
-5.35625160e-01 -9.27641869e-01 -9.55957532e-01 -8.89775097e-01
-2.81760633e-01 4.03541863e-01 3.06359947e-01 -1.10132515... | [6.353351593017578, 0.877469539642334] |
95c1f32e-5236-430c-ad38-84c8f5064cf7 | cross-lingual-speaker-identification-from | null | null | https://openreview.net/forum?id=jCqESRWnumE | https://openreview.net/pdf?id=jCqESRWnumE | Cross-Lingual Speaker Identification from Weak Local Evidence | Speaker identification, determining which character said each utterance in text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-based features to directly approach this task, but these approaches come with significant drawbacks, such as lack of contextual reasoning and poor cr... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['speaker-identification'] | ['speech'] | [ 9.11092311e-02 -9.28579196e-02 -3.71749490e-01 -9.09647226e-01
-1.44279480e+00 -6.77720606e-01 5.12551129e-01 -1.60050154e-01
-4.04631406e-01 7.14155793e-01 2.52643615e-01 -5.49747109e-01
1.41443342e-01 -2.69969761e-01 -5.82048416e-01 -4.83422250e-01
1.87024698e-01 5.21982074e-01 1.28055200e-01 -1.41916901... | [14.197402000427246, 6.692615985870361] |
f0885ad6-880c-45a3-95b0-59de99eadcec | prosit-latent-variable-discovery-with | 2210.14763 | null | https://arxiv.org/abs/2210.14763v1 | https://arxiv.org/pdf/2210.14763v1.pdf | ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds | The most common ways to explore latent document dimensions are topic models and clustering methods. However, topic models have several drawbacks: e.g., they require us to choose the number of latent dimensions a priori, and the results are stochastic. Most clustering methods have the same issues and lack flexibility in... | ['Federico Bianchi', 'Dirk Hovy', 'Tommaso Fornaciari'] | 2022-10-26 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.15713544e-01 -8.73775110e-02 -4.23370242e-01 -2.07350746e-01
-7.44016707e-01 -9.12381470e-01 9.83118594e-01 2.12703586e-01
-1.51726902e-01 2.36835539e-01 5.32267809e-01 -2.03719288e-01
-6.28843606e-01 -7.21382201e-01 3.41683701e-02 -1.01352870e+00
-1.46750525e-01 1.01998734e+00 4.57730889e-01 1.93600997... | [10.363126754760742, 6.982141017913818] |
41bef87f-24d7-41a4-88b7-dfc07516681f | query-based-named-entity-recognition | 1908.09138 | null | https://arxiv.org/abs/1908.09138v2 | https://arxiv.org/pdf/1908.09138v2.pdf | Query-Based Named Entity Recognition | In this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of extracting entities with PER is formalized as answering the question of "which person is mentioned in the text ?". Such a strategy comes with t... | ['Zijun Sun', 'Yuxian Meng', 'Jiwei Li', 'Xiaoya Li'] | 2019-08-24 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-3.38103212e-02 3.34948272e-01 1.50361940e-01 -3.73459458e-01
-8.76625121e-01 -7.32851326e-01 4.55790520e-01 5.95057964e-01
-1.07410502e+00 1.11748981e+00 5.66880584e-01 -3.25179875e-01
5.15917875e-02 -9.19039190e-01 -5.30078471e-01 -2.10363358e-01
2.84749717e-01 3.89840633e-01 2.38374770e-01 -1.85180560... | [9.636527061462402, 9.514569282531738] |
a3fae9d2-e89f-4eb4-aa3e-a17c70aa2964 | joint-distribution-matters-deep-brownian | 2204.04567 | null | https://arxiv.org/abs/2204.04567v1 | https://arxiv.org/pdf/2204.04567v1.pdf | Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification | Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically... | ['Peihua Li', 'Qilong Wang', 'Jiaming Lv', 'Fei Long', 'Jiangtao Xie'] | 2022-04-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-image-classification'] | ['computer-vision'] | [-8.15383065e-03 -3.63038838e-01 -2.32260004e-01 -5.48403144e-01
-8.27742994e-01 -1.86305359e-01 9.11366522e-01 1.14463851e-01
-4.45438862e-01 3.45094144e-01 -8.44154228e-03 1.84335053e-01
-2.63711363e-01 -8.47765386e-01 -6.99717581e-01 -7.81179547e-01
1.33968741e-01 2.00745597e-01 2.85647303e-01 -7.58925080... | [9.892231941223145, 2.733476400375366] |
55a3571f-deb5-4a62-a774-ed60287dc4d4 | a-comparative-study-of-fruit-detection-and | 1810.09499 | null | http://arxiv.org/abs/1810.09499v2 | http://arxiv.org/pdf/1810.09499v2.pdf | A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards | We present new methods for apple detection and counting based on recent deep
learning approaches and compare them with state-of-the-art results based on
classical methods. Our goal is to quantify performance improvements by neural
network-based methods compared to methods based on classical approaches.
Additionally, we... | ['Nicolai Häni', 'Volkan Isler', 'Pravakar Roy'] | 2018-10-22 | null | null | null | null | ['yield-mapping-in-apple-orchards'] | ['computer-vision'] | [ 1.14324987e-01 -7.09635675e-01 -8.81746560e-02 4.40631062e-02
-3.19495142e-01 -8.88494134e-01 4.92394090e-01 4.61613983e-01
-5.59748411e-01 3.51987220e-02 -6.82514191e-01 -1.08524024e-01
2.50913441e-01 -1.16854012e+00 -6.42767370e-01 -6.46557570e-01
-1.15654700e-01 2.70939380e-01 4.12388474e-01 1.53222576... | [9.109281539916992, -1.4667738676071167] |
1c80341a-3629-4ee4-9ed3-edf93b0b47ef | semeval-2016-task-11-complex-word | null | null | https://aclanthology.org/S16-1085 | https://aclanthology.org/S16-1085.pdf | SemEval 2016 Task 11: Complex Word Identification | null | ['Lucia Specia', 'Gustavo Paetzold'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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... | [-7.408107757568359, 3.6139261722564697] |
c6dba10b-6a15-448f-9ce8-f479c4f561dc | multi-level-sequence-gan-for-group-activity | 1812.07124 | null | http://arxiv.org/abs/1812.07124v1 | http://arxiv.org/pdf/1812.07124v1.pdf | Multi-Level Sequence GAN for Group Activity Recognition | We propose a novel semi-supervised, Multi-Level Sequential Generative
Adversarial Network (MLS-GAN) architecture for group activity recognition. In
contrast to previous works which utilise manually annotated individual human
action predictions, we allow the models to learn it's own internal
representations to discover ... | ['Harshala Gammulle', 'Clinton Fookes', 'Sridha Sridharan', 'Simon Denman'] | 2018-12-18 | null | null | null | null | ['group-activity-recognition', 'activity-prediction', 'activity-prediction'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 6.14403665e-01 3.04376364e-01 -1.96454972e-01 -3.76571625e-01
-8.54197383e-01 -2.30342939e-01 1.10797858e+00 -2.08498091e-01
-4.07509238e-01 9.56665516e-01 4.82156098e-01 8.10618699e-02
1.62765294e-01 -8.49027038e-01 -9.32172060e-01 -8.84700537e-01
-4.66088951e-01 3.44576776e-01 4.08614635e-01 -2.23934293... | [8.29609203338623, 0.4551445245742798] |
2e3c723c-beb3-499e-8144-2c0be9a67744 | complex-hyperbolic-knowledge-graph-embeddings | 2211.03635 | null | https://arxiv.org/abs/2211.03635v1 | https://arxiv.org/pdf/2211.03635v1.pdf | Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform | The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to capture the hierarchical patterns due to its tree-like metrics, which addressed the limitations of the Euclidean embedding models. Recent ex... | ['Simon See', 'Ginny Y. Wong', 'Yangqiu Song', 'Xin Liu', 'Huiru Xiao'] | 2022-11-07 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.62709755e-01 3.68134081e-01 -3.47531997e-02 -6.98862374e-02
-2.18157753e-01 -4.50012863e-01 5.18730700e-01 1.94540530e-01
-3.00128251e-01 1.26671374e-01 5.42064607e-01 -3.80949706e-01
-6.91264212e-01 -1.17187333e+00 -4.25194591e-01 -8.35221708e-01
-4.33187753e-01 5.30674517e-01 3.16021711e-01 -3.88465583... | [8.661627769470215, 7.760256290435791] |
c670f79c-a806-4500-8c4f-34d7b80d64e6 | domain-adaptation-for-semg-based-gesture | 1901.06958 | null | https://arxiv.org/abs/1901.06958v2 | https://arxiv.org/pdf/1901.06958v2.pdf | Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks | Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approxima... | ['Krisztián Zsolt Varga', 'Ferenc Kovács', 'István Ketykó'] | 2019-01-21 | null | null | null | null | ['emg-gesture-recognition'] | ['medical'] | [ 6.55820549e-01 -8.30210671e-02 -4.90243286e-01 -2.37557590e-01
-1.02841675e+00 -1.65748551e-01 1.53037101e-01 -9.13794816e-01
-6.13374531e-01 9.51155007e-01 4.42958444e-01 4.94314134e-01
-2.80179709e-01 -1.99551687e-01 -8.28565896e-01 -6.59466267e-01
-2.94045866e-01 2.47852132e-01 -1.40954882e-01 2.28924438... | [6.823986053466797, 0.15220455825328827] |
938e713a-c2d6-4abc-80b6-7274f0de74f2 | neural-separation-of-observed-and-unobserved | 1811.12739 | null | https://arxiv.org/abs/1811.12739v2 | https://arxiv.org/pdf/1811.12739v2.pdf | Neural separation of observed and unobserved distributions | Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separat... | ['Ariel Ephrat', 'Yedid Hoshen', 'Tavi Halperin'] | 2018-11-30 | neural-separation-of-observed-and-unobserved-1 | https://openreview.net/forum?id=SkelJnRqt7 | https://openreview.net/pdf?id=SkelJnRqt7 | iclr-2019-5 | ['speaker-separation'] | ['speech'] | [ 4.45621461e-01 2.81863343e-02 -8.41121972e-02 -2.77419955e-01
-1.20872736e+00 -4.47951555e-01 4.82037157e-01 -2.72253633e-01
-1.96768135e-01 6.93020463e-01 2.57983580e-02 1.88311655e-02
-1.25031337e-01 -1.63119420e-01 -8.42197299e-01 -1.11839592e+00
-1.88411862e-01 4.86276478e-01 3.96271311e-02 1.81797102... | [15.333200454711914, 5.640718936920166] |
fc8ae82c-b799-42c7-92fe-cacfd6505b10 | emotion-aware-human-attention-prediction | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Cordel_Emotion-Aware_Human_Attention_Prediction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cordel_Emotion-Aware_Human_Attention_Prediction_CVPR_2019_paper.pdf | Emotion-Aware Human Attention Prediction | Despite the recent success in face recognition and object classification, in the field of human gaze prediction, computer models are still struggling to accurately mimic human attention. One main reason is that visual attention is a complex human behavior influenced by multiple factors, ranging from low-level features ... | [' Mohan S. Kankanhalli', ' Zhiqi Shen', ' Shaojing Fan', 'Macario O. Cordel II'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['eye-tracking'] | ['computer-vision'] | [ 1.31454349e-01 -3.66322458e-01 -2.04207703e-01 -3.21346045e-01
4.50198740e-01 -1.18433222e-01 2.93789893e-01 8.65370557e-02
-3.69628489e-01 3.81478399e-01 2.08144054e-01 -7.71256015e-02
-1.42159150e-03 -3.53091806e-01 -5.62207222e-01 -6.36784971e-01
5.88439628e-02 -1.93533614e-01 8.13517645e-02 -1.90720588... | [10.268671989440918, 2.029336452484131] |
769c6706-c2c3-4ecf-b6dd-da0b49ae446d | confident-anchor-induced-multi-source-free | null | null | http://proceedings.neurips.cc/paper/2021/hash/168908dd3227b8358eababa07fcaf091-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/168908dd3227b8358eababa07fcaf091-Paper.pdf | Confident Anchor-Induced Multi-Source Free Domain Adaptation | Unsupervised domain adaptation has attracted appealing academic attentions by transferring knowledge from labeled source domain to unlabeled target domain. However, most existing methods assume the source data are drawn from a single domain, which cannot be successfully applied to explore complementarily transferable k... | ['Tongliang Liu', 'Gan Sun', 'Anjin Liu', 'Zhen Fang', 'Jiahua Dong'] | 2021-12-01 | null | https://openreview.net/forum?id=EAdJEN8xKUl | https://openreview.net/pdf?id=EAdJEN8xKUl | neurips-2021-12 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.71417844e-01 2.12934762e-01 -5.45930445e-01 -5.37469864e-01
-1.15878630e+00 -8.76353145e-01 4.26590115e-01 1.36364549e-02
-1.16260670e-01 1.14837182e+00 -8.26419964e-02 4.92567662e-03
-1.19311221e-01 -6.34967148e-01 -9.63102579e-01 -7.68773675e-01
3.76027942e-01 5.56156278e-01 5.85936084e-02 2.25192383... | [10.401406288146973, 3.126574754714966] |
fa15350a-cead-4436-afaf-dfb41e62aa87 | adaptive-window-pruning-for-efficient-local | 2306.14268 | null | https://arxiv.org/abs/2306.14268v1 | https://arxiv.org/pdf/2306.14268v1.pdf | Adaptive Window Pruning for Efficient Local Motion Deblurring | Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure. Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecess... | ['Chen Change Loy', 'Chongyi Li', 'Huajun Feng', 'Shangchen Zhou', 'Jixin Zhao', 'Haoying Li'] | 2023-06-25 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 5.31876683e-01 -4.46077973e-01 1.80677608e-01 -1.63878635e-01
-6.52805030e-01 -3.82577568e-01 3.19141746e-01 -6.20938480e-01
-4.34712052e-01 7.56043375e-01 5.79882860e-01 -3.45768422e-01
2.30666772e-02 -2.83865809e-01 -6.94741011e-01 -7.87565231e-01
1.48827419e-01 -3.85148168e-01 4.48259413e-01 4.12835538... | [11.545602798461914, -2.669739246368408] |
0b018087-55a5-4cb6-a9c1-c06003020012 | bebold-exploration-beyond-the-boundary-of-1 | 2012.08621 | null | https://arxiv.org/abs/2012.08621v1 | https://arxiv.org/pdf/2012.08621v1.pdf | BeBold: Exploration Beyond the Boundary of Explored Regions | Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR). There are many heuristics for IR, including visitation counts, curiosity, and state-difference. In this paper, we analyze the pros and cons... | ['Yuandong Tian', 'Joseph E. Gonzalez', 'Kurt Keutzer', 'Yi Wu', 'Xiaolong Wang', 'Huazhe Xu', 'Tianjun Zhang'] | 2020-12-15 | bebold-exploration-beyond-the-boundary-of | https://openreview.net/forum?id=_ptUyYP19mP | https://openreview.net/pdf?id=_ptUyYP19mP | null | ['nethack'] | ['playing-games'] | [-2.77263612e-01 1.07550390e-01 -2.06648454e-01 5.05306870e-02
-7.38850057e-01 -7.42204249e-01 3.42303187e-01 1.30675241e-01
-8.22379887e-01 1.21003354e+00 1.75827872e-02 -4.85118747e-01
-4.05728400e-01 -7.27527857e-01 -7.44229555e-01 -7.86614060e-01
-4.46517289e-01 5.67798197e-01 1.38598472e-01 -7.25120008... | [3.904327630996704, 1.7408944368362427] |
333c61d5-be7a-4872-ba49-3b4211016af8 | shiva-a-framework-for-graph-based-ontology | 1403.7465 | null | http://arxiv.org/abs/1403.7465v1 | http://arxiv.org/pdf/1403.7465v1.pdf | Shiva: A Framework for Graph Based Ontology Matching | Since long, corporations are looking for knowledge sources which can provide
structured description of data and can focus on meaning and shared
understanding. Structures which can facilitate open world assumptions and can
be flexible enough to incorporate and recognize more than one name for an
entity. A source whose m... | ['Nisheeth Joshi', 'Hemant Darbari', 'Iti Mathur', 'Ajai Kumar'] | 2014-03-28 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-3.88204038e-01 1.25242919e-01 -2.22858638e-01 -4.61944759e-01
-1.74109504e-01 -5.88432610e-01 7.31398880e-01 5.97393632e-01
-3.45489889e-01 5.39677858e-01 1.95706546e-01 -3.18938226e-01
-5.64013302e-01 -1.23807645e+00 -1.24788873e-01 -1.30578637e-01
1.61651582e-01 7.22686589e-01 5.85611582e-01 -6.00218952... | [9.165251731872559, 7.926514148712158] |
24111f8f-63d5-4399-8e32-69fa41a8a9f1 | omega-a-probabilistic-approach-to-referring | null | null | https://aclanthology.org/2020.inlg-1.36 | https://aclanthology.org/2020.inlg-1.36.pdf | OMEGA : A probabilistic approach to referring expression generation in a virtual environment | In recent years, referring expression genera- tion algorithms were inspired by game theory and probability theory. In this paper, an al- gorithm is designed for the generation of re- ferring expressions (REG) that base on both models by integrating maximization of utilities into the content determination process. It im... | ['Maurice Langner'] | null | null | null | null | inlg-acl-2020-12 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.42746070e-01 3.43829244e-01 -2.55387556e-02 -3.48352581e-01
-4.28775102e-01 -7.00900733e-01 5.84678888e-01 5.38468182e-01
-6.28095925e-01 7.38008142e-01 3.72973084e-01 -3.78624290e-01
-7.96440005e-01 -1.09058011e+00 6.48084730e-02 -1.28267799e-02
-2.88901199e-02 2.63707548e-01 -1.55864909e-01 -2.31230006... | [9.429643630981445, 6.942612648010254] |
5200fcb9-6ca3-49d0-a9e2-c59224fa7b34 | tspnet-hierarchical-feature-learning-via | 2010.05468 | null | https://arxiv.org/abs/2010.05468v1 | https://arxiv.org/pdf/2010.05468v1.pdf | TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation | Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with no clear boundaries in between. Existing SLT models usually represent sign visual features in a frame-wise manner so as to avoid needing to... | ['Hongdong Li', 'Hanna Suominen', 'Ben Swift', 'Kaihao Zhang', 'Xin Yu', 'Chenchen Xu', 'Dongxu Li'] | 2020-10-12 | null | http://proceedings.neurips.cc/paper/2020/hash/8c00dee24c9878fea090ed070b44f1ab-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/8c00dee24c9878fea090ed070b44f1ab-Paper.pdf | neurips-2020-12 | ['sign-language-translation'] | ['computer-vision'] | [ 3.98427635e-01 -4.57475185e-01 -4.83121097e-01 -4.64827955e-01
-7.50546515e-01 -6.48902953e-01 4.64105994e-01 -5.34548998e-01
-4.65481728e-01 4.99078602e-01 4.98098582e-01 -9.47323143e-02
3.85721698e-02 -3.25667053e-01 -5.82633376e-01 -6.32057667e-01
9.59286094e-02 1.21576205e-01 5.76392889e-01 2.93513015... | [9.251214027404785, -6.541750907897949] |
d2e36998-9118-4b68-b279-ccb1f07deab6 | multilayer-deep-feature-extraction-for-visual | 2208.10044 | null | https://arxiv.org/abs/2208.10044v1 | https://arxiv.org/pdf/2208.10044v1.pdf | Multilayer deep feature extraction for visual texture recognition | Convolutional neural networks have shown successful results in image classification achieving real-time results superior to the human level. However, texture images still pose some challenge to these models due, for example, to the limited availability of data for training in several problems where these images appear,... | ['Joao B. Florindo', 'Antonio Elias Fabris', 'Lucas O. Lyra'] | 2022-08-22 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.55259216e-01 -2.18469903e-01 -1.07771665e-01 -3.25079829e-01
-3.09122860e-01 -5.46914518e-01 6.32782042e-01 4.39124227e-01
-4.70459521e-01 4.11854953e-01 -1.33946195e-01 5.32606877e-02
-4.89548773e-01 -9.29232180e-01 -5.08288205e-01 -9.06448007e-01
-2.78935194e-01 3.09061408e-01 1.70795575e-01 -3.26092124... | [10.225089073181152, -0.3243653476238251] |
a6a6b447-ce17-4d41-9be2-ad9216117c75 | pareto-policy-adaptation | null | null | https://openreview.net/forum?id=wfZGut6e09 | https://openreview.net/pdf?id=wfZGut6e09 | Pareto Policy Adaptation | We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a simple policy gradient algorithm that approximates the Pareto front and infers the unknown preference... | ['Paul Bogdan', 'Jyotirmoy Deshmukh', 'Panagiotis Kyriakis'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.70690352e-01 -2.14865757e-03 -3.39171052e-01 -9.71433967e-02
-8.04394484e-01 -8.26616943e-01 3.15322846e-01 3.69925685e-02
-6.10304952e-01 1.41463947e+00 2.09293738e-01 -4.87557560e-01
-3.79027933e-01 -4.71459806e-01 -6.66047752e-01 -6.34400129e-01
1.89599693e-02 7.68403411e-01 -1.48101822e-01 -4.20662940... | [4.2037224769592285, 2.382455348968506] |
a7d7a443-c4bf-4011-b2de-b2932f5a66b2 | a-bayesian-approach-to-graph-partitioning | 2204.12927 | null | https://arxiv.org/abs/2204.12927v1 | https://arxiv.org/pdf/2204.12927v1.pdf | A Bayesian Approach To Graph Partitioning | A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provided to learn the bahavior of conductance when traversing the ... | ['Farshad Noravesh'] | 2022-04-24 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-7.86733925e-02 8.82051513e-02 -1.89199090e-01 -1.83338553e-01
-8.56384933e-01 -2.21905947e-01 6.27394855e-01 4.14930791e-01
-3.65907520e-01 8.49173725e-01 -8.86740908e-02 -4.38540488e-01
-2.77976871e-01 -1.12542307e+00 -5.53961754e-01 -1.03692245e+00
-4.42890882e-01 6.49447739e-01 3.57822210e-01 5.37803054... | [6.9614033699035645, 4.152979373931885] |
9ea3741c-f875-4ba3-90af-4479c87d9922 | gps-an-optimised-hybrid-mpnn-transformer-for | 2212.02229 | null | https://arxiv.org/abs/2212.02229v2 | https://arxiv.org/pdf/2212.02229v2.pdf | GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction | This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer model t... | ['Dominique Beaini', 'Ladislav Rampášek', 'Deniz Beker', 'Hatem Helal', 'Adam Sanders', 'Sam Maddrell-Mander', 'Zhiyi Li', 'Kerstin Klaser', 'Josef Dean', 'Dominic Masters'] | 2022-11-18 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 1.84761122e-01 2.06577152e-01 -1.49291247e-01 -2.44929567e-01
-9.87693787e-01 -3.71148139e-01 3.74048769e-01 4.50214684e-01
-3.72378170e-01 1.25175250e+00 -1.42850950e-01 -7.62204587e-01
-1.09069012e-01 -6.88261032e-01 -1.20819056e+00 -8.11664641e-01
-3.97240460e-01 7.46403813e-01 -3.85392196e-02 -1.04006179... | [5.215849876403809, 5.773880481719971] |
4da10412-63fa-46e0-984c-286acde2013b | what-matters-for-neural-cross-lingual-named | 1909.03598 | null | https://arxiv.org/abs/1909.03598v1 | https://arxiv.org/pdf/1909.03598v1.pdf | What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis | Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages to low-resource languages, it is unclear what knowledge is transferred. In this paper, we first pro... | ['Xiaolei Huang', 'Nanyun Peng', 'Jonathan May'] | 2019-09-09 | what-matters-for-neural-cross-lingual-named-1 | https://aclanthology.org/D19-1672 | https://aclanthology.org/D19-1672.pdf | ijcnlp-2019-11 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-5.92792451e-01 -1.57376096e-01 -3.64804685e-01 -6.13840878e-01
-1.12402463e+00 -8.40668321e-01 4.71348524e-01 4.74398509e-02
-1.16225135e+00 7.41683125e-01 6.03951633e-01 -5.58060050e-01
4.54634637e-01 -7.95193970e-01 -9.29301739e-01 -4.92248647e-02
-1.16093159e-02 2.73762256e-01 -5.69063202e-02 -2.41613016... | [10.216711044311523, 9.763360023498535] |
fad28d7d-925a-4206-9bfd-b6911354bc68 | microscopic-fine-grained-instance | 2010.02818 | null | https://arxiv.org/abs/2010.02818v1 | https://arxiv.org/pdf/2010.02818v1.pdf | Microscopic fine-grained instance classification through deep attention | Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly deal with high number of low-resolution images, whereas subtle detail in biomedical images require higher resolution. To bridge this gap, we ... | ['Jens Rittscher', 'Yan Xu', 'Eric I-Chao Chang', 'Tapabrata Chakrabort', 'Mengran Fan'] | 2020-10-06 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 4.78458226e-01 6.32316023e-02 1.28997058e-01 -5.65326810e-01
-1.14489830e+00 -4.40105528e-01 7.26753116e-01 5.95773637e-01
-6.86174512e-01 8.81903350e-01 -2.69066811e-01 -1.40810370e-01
-2.47663289e-01 -6.44767642e-01 -7.03980684e-01 -1.17414939e+00
-6.98007829e-03 5.98978877e-01 2.94563740e-01 1.38084814... | [15.055379867553711, -2.9617836475372314] |
4b9a3b21-03d4-4664-bf4d-197f12b4e2a7 | multi-view-gradient-consistency-for-svbrdf | 2202.13017 | null | https://arxiv.org/abs/2202.13017v1 | https://arxiv.org/pdf/2202.13017v1.pdf | Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination | This paper presents a process for estimating the spatially varying surface reflectance of complex scenes observed under natural illumination. In contrast to previous methods, our process is not limited to scenes viewed under controlled lighting conditions but can handle complex indoor and outdoor scenes viewed under ar... | ['Charalambos Poullis', 'Alen Joy'] | 2022-02-25 | null | null | null | null | ['svbrdf-estimation'] | ['computer-vision'] | [ 4.88707334e-01 -3.70772183e-01 6.87786996e-01 -4.34799224e-01
-1.03534877e+00 -6.87055528e-01 2.33611122e-01 -5.01679301e-01
3.05769350e-02 5.10813713e-01 1.42677464e-02 -1.50157496e-01
5.65687977e-02 -6.28299415e-01 -7.34201550e-01 -6.98255956e-01
2.92222261e-01 3.03930849e-01 1.21679120e-01 6.25966266... | [9.759852409362793, -3.0328726768493652] |
74bbbdac-b62e-4d00-95ee-cc529a3d9705 | infocse-information-aggregated-contrastive | 2210.06432 | null | https://arxiv.org/abs/2210.06432v3 | https://arxiv.org/pdf/2210.06432v3.pdf | InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings | Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constraint brought by this assumption is weak, and a good sentence representation should also be able to reconstruct the original sentence fragments... | ['Songlin Hu', 'Zhongyuan Wang', 'Jizhong Han', 'Zijia Lin', 'Chaochen Gao', 'Xing Wu'] | 2022-10-08 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 9.87985730e-02 1.92482740e-01 -1.76740453e-01 -6.37635767e-01
-7.42458344e-01 -3.42440993e-01 7.77636051e-01 7.20510483e-01
-7.04440653e-01 4.56644654e-01 8.17677557e-01 -6.30927384e-02
1.30743369e-01 -6.68343306e-01 -6.15042746e-01 -5.30313075e-01
1.25571892e-01 1.48925886e-01 1.83805361e-01 -3.49428862... | [10.955759048461914, 8.65303897857666] |
6668c651-f722-46f5-8c99-3aa045180b5e | exploring-fine-grained-audiovisual | 2207.10664 | null | https://arxiv.org/abs/2207.10664v1 | https://arxiv.org/pdf/2207.10664v1.pdf | Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset | We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on images, the counterparts in audio and video fine-grained categorization are relatively unexplored. To enc... | ['Serge Belongie', 'Oisin Mac Aodha', 'Hartwig Adam', 'Kimberly Wilber', 'Rui Qian', 'Grant van Horn'] | 2022-07-21 | null | null | null | null | ['video-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 3.36563140e-01 -5.91758132e-01 -8.39322731e-02 -2.18344525e-01
-9.81391549e-01 -9.13355947e-01 8.96736860e-01 6.90132007e-02
-4.80081081e-01 3.92058790e-01 5.73258519e-01 -1.07532591e-01
-8.11907873e-02 -3.01878095e-01 -4.44152772e-01 -5.62910557e-01
-2.14580283e-01 -1.18000478e-01 3.43463987e-01 -2.69327372... | [10.025888442993164, 1.1515223979949951] |
5e27b29a-c386-4f46-9a99-4b03c0e0f2b2 | unexpected-effects-of-online-k-means | 1908.06818 | null | https://arxiv.org/abs/1908.06818v2 | https://arxiv.org/pdf/1908.06818v2.pdf | Unexpected Effects of Online no-Substitution k-means Clustering | Offline k-means clustering was studied extensively, and algorithms with a constant approximation are available. However, online clustering is still uncharted. New factors come into play: the ordering of the dataset and whether the number of points, n, is known in advance or not. Their exact effects are unknown. In this... | ['Michal Moshkovitz'] | 2019-08-09 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-3.30247641e-01 -1.89169779e-01 -1.63180158e-01 -1.93451107e-01
-5.16628027e-01 -1.16418386e+00 -1.32878810e-01 9.01574314e-01
-7.25051880e-01 4.05200899e-01 -3.60579431e-01 -4.60462898e-01
-4.58700210e-01 -8.63775313e-01 -8.92447352e-01 -1.20755410e+00
-3.87158483e-01 9.42682505e-01 5.31160831e-01 3.66661549... | [6.7162604331970215, 4.95934534072876] |
61a322d8-01f8-42b9-922a-0ee46363f88a | to-adapt-or-to-annotate-challenges-and | 2212.10381 | null | https://arxiv.org/abs/2212.10381v1 | https://arxiv.org/pdf/2212.10381v1.pdf | To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering | Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work ... | ['Pat Verga', 'Sameer Singh', 'Emma Strubell', 'Dheeru Dua'] | 2022-12-20 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 9.69004110e-02 1.21355392e-02 -1.51928857e-01 -5.44019520e-01
-1.43194044e+00 -1.03158677e+00 5.44426858e-01 3.72071743e-01
-4.84800041e-01 7.96660662e-01 4.18278873e-01 -4.62977797e-01
-3.91514510e-01 -6.78970635e-01 -7.42253065e-01 3.20713315e-03
2.44334519e-01 1.06431258e+00 5.63979208e-01 -5.69876134... | [11.340867042541504, 8.011509895324707] |
ea759863-23db-49e1-8831-82fbccc463eb | assessing-cross-cultural-alignment-between | 2303.17466 | null | https://arxiv.org/abs/2303.17466v2 | https://arxiv.org/pdf/2303.17466v2.pdf | Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study | The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like responses in dialogue. Given its usage by users from various nations and its training on a vast multilingual corpus that incorporates diverse cultural and societal norms, it is crucial to evaluate its ef... | ['Daniel Hershcovich', 'Min Chen', 'Laura Cabello', 'Seolhwa Lee', 'Li Zhou', 'Yong Cao'] | 2023-03-30 | null | null | null | null | ['culture'] | ['speech'] | [-2.43424729e-01 4.54039797e-02 -4.80632186e-02 -2.90334195e-01
-4.07609522e-01 -8.39620948e-01 7.04025447e-01 7.06394538e-02
-4.83182043e-01 7.49473393e-01 9.59577441e-01 -4.54681486e-01
2.66019344e-01 -3.58251303e-01 -7.63961524e-02 -1.58896789e-01
4.68949258e-01 2.84136474e-01 -1.91559672e-01 -7.99884200... | [9.737759590148926, 9.973832130432129] |
d7f0f984-73a5-4394-bc88-5b8d80bbe987 | neural-preset-for-color-style-transfer | 2303.13511 | null | https://arxiv.org/abs/2303.13511v2 | https://arxiv.org/pdf/2303.13511v2.pdf | Neural Preset for Color Style Transfer | In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. First, we propose Deterministic Neural Color Mapping (DNCM) to consistent... | ['Rynson W. H. Lau', 'Nanxuan Zhao', 'Lei Zhu', 'Yuhao Liu', 'Zhanghan Ke'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-dehazing', 'image-enhancement', 'low-light-image-enhancement', 'image-harmonization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.41849196e-01 -4.60603327e-01 3.07840675e-01 -3.18587482e-01
-5.12917280e-01 -6.42410219e-01 1.79831520e-01 -4.42366511e-01
-5.55176735e-01 6.70131624e-01 -6.84947744e-02 -2.56515682e-01
1.31953776e-01 -6.89813256e-01 -9.80318010e-01 -5.74203372e-01
4.87853527e-01 -1.68864056e-01 9.02249739e-02 -4.40905541... | [11.269197463989258, -1.0695786476135254] |
e99ddfd5-6d88-478e-a040-08924c00eced | thousand-to-one-semantic-prior-modeling-for | 2103.07131 | null | https://arxiv.org/abs/2103.07131v2 | https://arxiv.org/pdf/2103.07131v2.pdf | Thousand to One: Semantic Prior Modeling for Conceptual Coding | Conceptual coding has been an emerging research topic recently, which encodes natural images into disentangled conceptual representations for compression. However, the compression performance of the existing methods is still sub-optimal due to the lack of comprehensive consideration of rate constraint and reconstructio... | ['Siwei Ma', 'Jian Zhang', 'Chuanmin Jia', 'Lingbo Yang', 'Zhenghui Zhao', 'Jianhui Chang'] | 2021-03-12 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.87531567e-01 6.23094104e-02 -3.71375322e-01 -3.80213052e-01
-8.08648944e-01 -1.52399406e-01 4.41320240e-01 3.31458412e-02
-4.23650518e-02 6.09565914e-01 6.49450064e-01 1.33490656e-02
-2.83593297e-01 -8.23078036e-01 -5.40989399e-01 -9.12120521e-01
2.64199108e-01 1.72933284e-02 -1.30827427e-01 1.81070536... | [11.290245056152344, -1.6645078659057617] |
e1b5e413-1cfe-4902-98c4-b4f0bfc14d0a | combining-stereo-disparity-and-optical-flow | 1801.0472 | null | http://arxiv.org/abs/1801.04720v1 | http://arxiv.org/pdf/1801.04720v1.pdf | Combining Stereo Disparity and Optical Flow for Basic Scene Flow | Scene flow is a description of real world motion in 3D that contains more
information than optical flow. Because of its complexity there exists no
applicable variant for real-time scene flow estimation in an automotive or
commercial vehicle context that is sufficiently robust and accurate. Therefore,
many applications ... | ['Oliver Wasenmüller', 'René Schuster', 'Didier Stricker', 'Christian Bailer'] | 2018-01-15 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.03697145e-01 -7.37656355e-01 -4.35207337e-02 -4.23027277e-01
-2.30331883e-01 -5.29959023e-01 6.49736643e-01 -5.09554386e-01
-3.89810920e-01 9.66163695e-01 -1.18769594e-01 -5.39191425e-01
1.09217905e-01 -5.79301953e-01 -4.13048655e-01 -3.45246047e-01
1.83739841e-01 2.35276207e-01 7.04508424e-01 -3.80858570... | [8.706781387329102, -1.7970738410949707] |
26003976-4508-453c-a70e-7fc7e6de1d2c | 2305-14387 | 2305.14387 | null | https://arxiv.org/abs/2305.14387v1 | https://arxiv.org/pdf/2305.14387v1.pdf | AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback | Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their ability to follow user instructions well. Developing these LLMs involves a complex yet poorly understood workflow requiring training with human feedback. Replicating and understanding this instruction-following process faces three m... | ['Tatsunori B. Hashimoto', 'Percy Liang', 'Carlos Guestrin', 'Jimmy Ba', 'Ishaan Gulrajani', 'Tianyi Zhang', 'Rohan Taori', 'Xuechen Li', 'Yann Dubois'] | 2023-05-22 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-3.50969672e-01 1.45698816e-01 2.16672912e-01 -6.58390284e-01
-1.01731181e+00 -7.05093384e-01 5.93497217e-01 2.24104747e-01
-6.72128081e-01 7.32896626e-01 3.29921275e-01 -5.99804521e-01
1.03976861e-01 -1.38100952e-01 -8.57773364e-01 3.17734741e-02
3.45403031e-02 8.38828802e-01 4.22798753e-01 -4.96228039... | [12.487785339355469, 8.075278282165527] |
6572aa5c-adec-4b60-ad1e-325e448c7b8a | visual-analysis-of-ontology-matching-results | 2004.12628 | null | https://arxiv.org/abs/2004.12628v1 | https://arxiv.org/pdf/2004.12628v1.pdf | Visual Analysis of Ontology Matching Results with the MELT Dashboard | In this demo, we introduce MELT Dashboard, an interactive Web user interface for ontology alignment evaluation which is created with the existing Matching EvaLuation Toolkit (MELT). Compared to existing, static evaluation interfaces in the ontology matching domain, our dashboard allows for interactive self-service anal... | ['Sven Hertling', 'Jan Portisch', 'Heiko Paulheim'] | 2020-04-27 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-1.21224783e-01 4.64734137e-01 -1.24090470e-01 -6.27986729e-01
-3.59847128e-01 -3.90357524e-01 5.45227170e-01 7.84705162e-01
-1.45793065e-01 1.57584786e-01 6.39737666e-01 -4.43772703e-01
-7.36842930e-01 -1.04506767e+00 -2.94197481e-02 1.93657964e-01
-6.31858930e-02 9.79608774e-01 6.09407365e-01 -6.88765705... | [9.207497596740723, 8.023167610168457] |
43120b82-65ef-4049-a0e6-36a4a01bc083 | a-closed-loop-sleep-modulation-system-with | 2211.13128 | null | https://arxiv.org/abs/2211.13128v1 | https://arxiv.org/pdf/2211.13128v1.pdf | A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning | Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively... | ['Xilin Liu', 'Yuhan Hou', 'Yaqian Xu', 'Naize Yang', 'Aaron Zhou', 'Mingzhe Sun'] | 2022-11-19 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 3.15020651e-01 -4.99164045e-01 -2.02208564e-01 -4.45278466e-01
-3.18861067e-01 -9.71038640e-02 -2.23428339e-01 2.67051369e-01
-7.85280585e-01 7.55940020e-01 -3.60338449e-01 -3.35626513e-01
1.44516647e-01 -4.88047719e-01 -1.01708487e-01 -6.20589197e-01
-1.96305946e-01 -1.42655000e-01 1.61190182e-01 -6.84663504... | [13.533016204833984, 3.5119614601135254] |
11a2314e-fe13-4643-8141-a6a17424848f | xcodeeval-a-large-scale-multilingual | 2303.03004 | null | https://arxiv.org/abs/2303.03004v3 | https://arxiv.org/pdf/2303.03004v3.pdf | xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval | AI systems that can create codes as solutions to problems or assist developers in writing codes can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, trans... | ['Shafiq Joty', 'Md Rizwan Parvez', 'Weishi Wang', 'Xuan Long Do', 'M Saiful Bari', 'Mohammad Abdullah Matin Khan'] | 2023-03-06 | null | null | null | null | ['program-repair', 'program-synthesis', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning'] | [-1.93724018e-02 -2.06426084e-01 -2.90916443e-01 -2.10734919e-01
-1.15825903e+00 -6.36340201e-01 4.82759297e-01 5.28865039e-01
1.01937070e-01 4.88788754e-01 3.97720095e-03 -8.21111977e-01
-3.79294194e-02 -5.36495090e-01 -9.47880208e-01 -1.06615700e-01
-3.09805840e-01 6.34161890e-01 7.86138773e-02 -3.15567434... | [7.601531982421875, 7.955700874328613] |
3f4b3794-bbac-4798-9e8a-5602b4489676 | the-importance-of-open-endedness-for-the-sake | 2006.03079 | null | https://arxiv.org/abs/2006.03079v1 | https://arxiv.org/pdf/2006.03079v1.pdf | The Importance of Open-Endedness (for the Sake of Open-Endedness) | A paper in the recent Artificial Life journal special issue on open-ended evolution (OEE) presents a simple evolving computational system that, it is claimed, satisfies all proposed requirements for OEE (Hintze, 2019). Analysis and discussion of the system are used to support the further claims that complexity and dive... | ['Tim Taylor'] | 2020-06-04 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-6.16644956e-02 3.42268646e-01 -1.32133946e-01 -3.56581546e-02
2.35363424e-01 -7.54120767e-01 5.45640349e-01 1.38881817e-01
-3.10388416e-01 6.63225770e-01 2.97015250e-01 -5.54940999e-01
-8.14541876e-01 -3.37690026e-01 -2.41363376e-01 -3.10953438e-01
1.18715316e-01 -1.71955582e-02 -2.99326897e-01 -8.83949816... | [5.563313007354736, 4.185122966766357] |
8a5b151b-2e99-4c6e-8aa7-c8c635344c97 | semicontour-a-semi-supervised-learning | 1605.04996 | null | http://arxiv.org/abs/1605.04996v1 | http://arxiv.org/pdf/1605.04996v1.pdf | SemiContour: A Semi-supervised Learning Approach for Contour Detection | Supervised contour detection methods usually require many labeled training
images to obtain satisfactory performance. However, a large set of annotated
data might be unavailable or extremely labor intensive. In this paper, we
investigate the usage of semi-supervised learning (SSL) to obtain competitive
detection accura... | ['Zizhao Zhang', 'Fuyong Xing', 'Xiaoshuang Shi', 'Lin Yang'] | 2016-05-17 | semicontour-a-semi-supervised-learning-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_SemiContour_A_Semi-Supervised_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_SemiContour_A_Semi-Supervised_CVPR_2016_paper.pdf | cvpr-2016-6 | ['contour-detection'] | ['computer-vision'] | [ 7.28976071e-01 2.36414634e-02 -4.63870764e-01 -4.53855693e-01
-9.18519318e-01 -4.43159133e-01 1.45269707e-01 -8.31396133e-02
-1.50293916e-01 6.30588353e-01 1.82877406e-02 -2.13674828e-01
7.21932799e-02 -7.09693789e-01 -5.09976447e-01 -8.93860817e-01
1.82165474e-01 3.49815845e-01 5.06303251e-01 2.91457444... | [14.753399848937988, -2.110962152481079] |
ab3a62a6-69b1-4a5d-b1f5-176c0f5d5f6e | 3d-gan-inversion-for-controllable-portrait | 2203.13441 | null | https://arxiv.org/abs/2203.13441v1 | https://arxiv.org/pdf/2203.13441v1.pdf | 3D GAN Inversion for Controllable Portrait Image Animation | Millions of images of human faces are captured every single day; but these photographs portray the likeness of an individual with a fixed pose, expression, and appearance. Portrait image animation enables the post-capture adjustment of these attributes from a single image while maintaining a photorealistic reconstructi... | ['Gordon Wetzstein', 'Eric R. Chan', 'David B. Lindell', 'Connor Z. Lin'] | 2022-03-25 | null | null | null | null | ['pose-transfer', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 4.72723752e-01 3.38328809e-01 2.36993865e-03 -4.08951610e-01
-2.04302460e-01 -8.56984854e-01 6.71191037e-01 -4.24934119e-01
-1.06092617e-01 5.47279775e-01 -1.28088519e-01 3.21026564e-01
3.59675795e-01 -8.11053634e-01 -8.88609946e-01 -6.94942653e-01
4.39557046e-01 3.95076245e-01 -2.24789843e-01 -2.19749019... | [12.667744636535645, -0.3598661422729492] |
cf56adba-11cf-4e3d-9a3e-edc7480d9479 | look-ma-only-400-samples-revisiting-the | 2210.02675 | null | https://arxiv.org/abs/2210.02675v2 | https://arxiv.org/pdf/2210.02675v2.pdf | Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino | With 84.75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications. To this end, spelling correction is a crucial preprocessing step for downstream processing. However, the lack of data prevents the use of language models for this task. In this paper, ... | ['Dragomir Radev', 'Lorenzo Jaime Yu Flores'] | 2022-10-06 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [-1.33843437e-01 -1.09037690e-01 -3.25679451e-01 -1.38462558e-01
-8.21894169e-01 -1.04766941e+00 7.46159434e-01 6.29670322e-01
-7.67647922e-01 8.49863648e-01 -3.54103558e-03 -1.11897790e+00
-1.35961518e-01 -6.69848204e-01 -6.67124212e-01 -2.64377624e-01
5.09164073e-02 6.59129143e-01 -1.78446025e-01 -2.76689380... | [11.00686264038086, 9.053417205810547] |
8d9ae447-3263-442e-be48-e91abe5eb284 | accelerating-and-compressing-deep-neural | 2304.01914 | null | https://arxiv.org/abs/2304.01914v1 | https://arxiv.org/pdf/2304.01914v1.pdf | Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback | The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression. However, most of these neural networks are large and inefficient making it a barrier... | ['Hatem Abou-zeid', 'Omar Erak'] | 2023-01-20 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.08936185e-01 -1.89064413e-01 -3.85640204e-01 -4.60670412e-01
-4.36256140e-01 -3.03285997e-02 -3.70803173e-03 1.32425666e-01
-6.06479228e-01 7.65097022e-01 -8.73059686e-03 -8.44606459e-01
-3.06909174e-01 -6.98467016e-01 -8.54679644e-01 -6.30550921e-01
-6.25699341e-01 6.19303137e-02 -8.47640336e-02 -9.14306119... | [8.490293502807617, 2.9368550777435303] |
76bdd3cd-1ad7-4aa1-8a63-43fb6fe46c6f | entitybert-entity-centric-masking-strategy | null | null | https://aclanthology.org/2021.bionlp-1.21 | https://aclanthology.org/2021.bionlp-1.21.pdf | EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain | Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain data. We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representatio... | ['Guergana Savova', 'Steven Bethard', 'Dmitriy Dligach', 'Timothy Miller', 'Chen Lin'] | null | null | null | null | naacl-bionlp-2021-6 | ['temporal-relation-extraction', 'negation-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.29877007e-01 4.76847649e-01 -4.12866563e-01 -4.38739151e-01
-9.88565743e-01 -3.77273798e-01 5.06491601e-01 6.92203343e-01
-8.78048301e-01 9.26550567e-01 3.65080804e-01 -7.81080663e-01
-1.90158516e-01 -5.56597054e-01 -6.00640595e-01 -3.07326347e-01
-1.81248203e-01 7.33404160e-01 1.95730194e-01 -1.56664833... | [8.514509201049805, 8.832256317138672] |
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