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f514d546-5d01-44bc-ab77-848209685456 | beyond-word2vec-embedding-words-and-phrases | null | null | https://cdn.iiit.ac.in/cdn/ltrc.iiit.ac.in/icon2017/proceedings/icon2017/pdf/W17-7526.pdf | https://cdn.iiit.ac.in/cdn/ltrc.iiit.ac.in/icon2017/proceedings/icon2017/pdf/W17-7526.pdf | Beyond Word2Vec: Embedding Words and Phrases in Same Vector Space | Word embeddings are being used for several linguistic problems and NLP tasks. Improvements in solutions to such problems are great because of the recent breakthroughs in vector representation of words and research in vector space models. However, vector embeddings of phrases keeping semantics intact with words has been... | ['Manish Shrivastava', 'Vijay Prakash Dwivedi'] | 2017-12-18 | beyond-word2vec-embedding-words-and-phrases-1 | https://aclanthology.org/W17-7526 | https://aclanthology.org/W17-7526.pdf | international-conference-on-natural-language | ['phrase-vector-embedding'] | ['natural-language-processing'] | [-3.25111747e-01 -2.08909005e-01 -5.69092870e-01 -4.06702161e-01
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0b3334fa-e439-4a1b-ac72-7bf01209b172 | deshadowgan-a-deep-learning-approach-to | 1910.02844 | null | https://arxiv.org/abs/1910.02844v1 | https://arxiv.org/pdf/1910.02844v1.pdf | DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images | Purpose: To remove retinal shadows from optical coherence tomography (OCT) images of the optic nerve head(ONH). Methods:2328 OCT images acquired through the center of the ONH using a Spectralis OCT machine for both eyes of 13 subjects were used to train a generative adversarial network (GAN) using a custom loss functio... | ['Shamira Perera', 'Xiaofei Wang', 'Zhang Liang', 'Sripad Krishna Devalla', 'Haris Cheong', 'Alexandre H. Thiery', 'Tin Aung Tun', 'Tan Hung Pham', 'Michael J. A. Girard', 'Leopold Schmetterer', 'Craig Boote', 'Aung Tin'] | 2019-10-07 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 4.43789721e-01 3.00085604e-01 4.33752477e-01 2.09378615e-01
-3.64251614e-01 -5.18939137e-01 -5.03849089e-02 -2.44027033e-01
-4.29017395e-01 1.02314985e+00 2.96498323e-03 -5.37753761e-01
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-9.36144218e-02 -1.40532568e-01 3.29017580e-01 3.33538264... | [15.810975074768066, -3.990570306777954] |
6336dad1-8126-4d48-9ea8-cc8e145898af | multi-layered-semantic-representation-network | 2106.11596 | null | https://arxiv.org/abs/2106.11596v1 | https://arxiv.org/pdf/2106.11596v1.pdf | Multi-layered Semantic Representation Network for Multi-label Image Classification | Multi-label image classification (MLIC) is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have been proposed which model label correlations to discover semantics of labels and learn semantic repr... | ['Xiao Zheng', 'Linchuan Xu', 'Jun Huang', 'Hao Che', 'Xiwen Qu'] | 2021-06-22 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.80911177e-01 -1.70894220e-01 -4.81246501e-01 -8.27465057e-01
-2.56064028e-01 -3.04118752e-01 3.96341503e-01 4.17194337e-01
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3.83599818e-01 1.64573923e-01 3.86491120e-01 -7.12207099... | [9.783952713012695, 4.051229953765869] |
5561b607-2afa-4ae0-a057-8f4182f46b2e | from-open-set-to-closed-set-supervised | 2001.01886 | null | https://arxiv.org/abs/2001.01886v2 | https://arxiv.org/pdf/2001.01886v2.pdf | From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting | Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0, inf) in theory. However, collected data and labeled instances are limited in reality, which means that only a small closed set is observed. Existing m... | ['Liang Liu', 'Hao Lu', 'Chunhua Shen', 'Chengxin Liu', 'Zhiguo Cao', 'Haipeng Xiong'] | 2020-01-07 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.06482863e-01 -2.67806113e-01 -1.50305396e-02 -1.70813084e-01
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-1.43982366e-01 5.48381984e-01 4.61684287e-01 1.94146380... | [8.835429191589355, 0.2173265516757965] |
2c5d7877-92a7-4f22-bcb9-5f7687ccd5a3 | fast-and-robust-video-based-exercise | 2210.00507 | null | https://arxiv.org/abs/2210.00507v1 | https://arxiv.org/pdf/2210.00507v1.pdf | Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers | Technological advancements have spurred the usage of machine learning based applications in sports science. Physiotherapists, sports coaches and athletes actively look to incorporate the latest technologies in order to further improve performance and avoid injuries. While wearable sensors are very popular, their use is... | ['Georgiana Ifrim', 'Brian Caulfield', 'Darragh Whelan', 'Martin OReilly', 'Kevin McGuinness', 'Feiyan Hu', 'Thach Le Nguyen', 'Antonio Bevilacqua', 'Ashish Singh'] | 2022-10-02 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [ 2.98908323e-01 -2.89448202e-01 -3.88777852e-01 1.56573541e-02
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187a4965-75c6-42c6-b594-6483b32a613a | entity-aware-and-motion-aware-transformers | 2205.05854 | null | https://arxiv.org/abs/2205.05854v1 | https://arxiv.org/pdf/2205.05854v1.pdf | Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos | Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language query to video segments, but estimating precise boundaries is still under-explored. In this paper, we pro... | ['Xinxiao wu', 'Shuo Yang'] | 2022-05-12 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 1.55362397e-01 -4.01616454e-01 -7.44561732e-01 -2.86292106e-01
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-4.13844764e-01 7.02306256e-02 9.65779126e-01 2.61065483... | [9.714305877685547, 0.7145808339118958] |
ba2d6854-25a0-480f-a37b-dfb9c09b382d | entropy-environment-transformer-and-offline | 2303.03811 | null | https://arxiv.org/abs/2303.03811v1 | https://arxiv.org/pdf/2303.03811v1.pdf | ENTROPY: Environment Transformer and Offline Policy Optimization | Model-based methods provide an effective approach to offline reinforcement learning (RL). They learn an environmental dynamics model from interaction experiences and then perform policy optimization based on the learned model. However, previous model-based offline RL methods lack long-term prediction capability, result... | ['Shaojie Shen', 'Meixin Zhu', 'Pengqin Wang'] | 2023-03-07 | null | null | null | null | ['trajectory-prediction', 'offline-rl', 'continuous-control'] | ['computer-vision', 'playing-games', 'playing-games'] | [-5.00916421e-01 -1.29286990e-01 -4.44251180e-01 6.12408072e-02
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16c8cc77-d1d6-4e67-99c8-8258443d1667 | representation-learning-for-tablet-and-paper | 2301.06293 | null | https://arxiv.org/abs/2301.06293v1 | https://arxiv.org/pdf/2301.06293v1.pdf | Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition | The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant r... | ['Christopher Mutschler', 'Bernd Bischl', 'Lucas Heublein', 'David Rügamer', 'Felix Ott'] | 2023-01-16 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.69531262e-01 -3.68091136e-01 -3.20916802e-01 -3.73778582e-01
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4.29792941e-01 5.32526195e-01 -2.34226622e-02 -1.81170866... | [11.646872520446777, 2.6737663745880127] |
a1ef0ced-c88a-466f-932d-094582507d68 | refining-a-nearest-neighbor-graph-for-a | null | null | https://doi.org/10.1016/j.patcog.2021.107869 | https://github.com/mashaan14/Spectral-Clustering/blob/master/Refining%20a%20k-nearest%20neighbor%20graph%20for%20a%20computationally%20efficient%20spectral%20clustering/PR-Preprint.pdf | Refining a -nearest neighbor graph for a computationally efficient spectral clustering | Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational demands. One of the effective ways to bypass these computational demands is to perform spectral clusteri... | ['Masahiro Takatsuka', 'John Stavrakakis', 'Mashaan Alshammari'] | 2021-02-06 | null | null | null | pattern-recognition-2021-2 | ['graph-clustering', 'spectral-graph-clustering', 'graph-partitioning'] | ['graphs', 'graphs', 'graphs'] | [ 7.85379112e-02 -1.86246440e-01 -1.82597954e-02 -2.82904923e-01
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ab5ce8b8-f74e-4014-8e12-2d17f74160be | motionrec-a-unified-deep-framework-for-moving | null | null | https://ieeexplore.ieee.org/abstract/document/9093324 | https://openaccess.thecvf.com/content_WACV_2020/papers/Mandal_MotionRec_A_Unified_Deep_Framework_for_Moving_Object_Recognition_WACV_2020_paper.pdf | MotionRec: A Unified Deep Framework for Moving Object Recognition | In this paper we present a novel deep learning framework to perform online moving object recognition(MOR) in streaming videos. The existing methods for moving object detection (MOD) only computes class-agnostic pixel-wise binary segmentation of video frames. On the other hand, the object detection techniques do not dif... | ['Santosh Kumar Vipparthi', 'Mahipal Singh Saran', 'Lav Kush Kumar', 'Murari Mandal'] | 2020-04-14 | null | null | null | wacv-2020-4 | ['moving-object-detection'] | ['computer-vision'] | [ 1.32386148e-01 -3.92207503e-01 -1.64838508e-01 -8.42238218e-02
-7.54142404e-01 -3.91960442e-01 5.59085250e-01 -1.10440217e-01
-8.28585982e-01 5.95006585e-01 -1.51624752e-03 1.07949309e-01
2.27655768e-01 -5.97567976e-01 -1.03148329e+00 -7.61245251e-01
-3.25792968e-01 -1.89833969e-01 1.06216526e+00 9.04656425... | [9.070127487182617, -0.3114894926548004] |
365b76d2-5d6b-414d-8e18-1ac6f745b991 | learning-to-branch-in-combinatorial | 2307.01434 | null | https://arxiv.org/abs/2307.01434v1 | https://arxiv.org/pdf/2307.01434v1.pdf | Learning to Branch in Combinatorial Optimization with Graph Pointer Networks | Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and historical features to represent the solver state. The proposed model, ... | ['Kaiwen Li', 'Xiangke Liao', 'Xin Xu', 'Ling Wang', 'Tao Zhang', 'Zhiming Zhou', 'Rui Wang'] | 2023-07-04 | null | null | null | null | ['combinatorial-optimization', 'variable-selection'] | ['methodology', 'methodology'] | [ 1.41437026e-02 5.53512126e-02 -9.00331497e-01 -3.06716621e-01
-6.10769272e-01 -5.74692965e-01 1.92250967e-01 2.30568990e-01
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-5.76833308e-01 -9.82956588e-01 -6.83267355e-01 -6.48584604e-01
-6.02943659e-01 8.89469087e-01 3.85930508e-01 -6.34854361... | [5.145573139190674, 2.988293170928955] |
7339e470-1d7d-4762-a4d4-87de7aeceb90 | cross-spectral-image-reconstruction-using-a | 2306.15237 | null | https://arxiv.org/abs/2306.15237v1 | https://arxiv.org/pdf/2306.15237v1.pdf | Cross Spectral Image Reconstruction Using a Deep Guided Neural Network | Cross spectral camera arrays, where each camera records different spectral content, are becoming increasingly popular for RGB, multispectral and hyperspectral imaging, since they are capable of a high resolution in every dimension using off-the-shelf hardware. For these, it is necessary to build an image processing pip... | ['André Kaup', 'Jürgen Seiler', 'Frank Sippel'] | 2023-06-27 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 5.77019870e-01 -4.81718659e-01 2.36683220e-01 -1.32062986e-01
-5.54062068e-01 -5.21362364e-01 2.22594082e-01 1.15171045e-01
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2.60474950e-01 -1.78682730e-01 -8.91628340e-02 -2.64402423... | [10.222865104675293, -2.083768844604492] |
49854822-9f72-4751-844e-6fc1f2dd400f | speed-reading-learning-to-read-forbackward | null | null | https://aclanthology.org/D18-1474 | https://aclanthology.org/D18-1474.pdf | Speed Reading: Learning to Read ForBackward via Shuttle | We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension. In contrast to previous work, LSTM-Shuttle not only reads shuttling forward but also goes back. Shuttling forward enables high efficiency, and going backward gives the mod... | ['Wei-Yun Ma', 'Tsu-Jui Fu'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['news-classification'] | ['natural-language-processing'] | [ 4.06910717e-01 2.00812697e-01 -1.22061238e-01 -5.67769587e-01
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3.58321071e-02 5.10642409e-01 1.94955971e-02 -3.16181928... | [11.255081176757812, 8.36172866821289] |
0c761c35-0991-4bd9-bd2f-b40da4a17294 | an-optimal-energy-management-algorithm | 2303.04503 | null | https://arxiv.org/abs/2303.04503v1 | https://arxiv.org/pdf/2303.04503v1.pdf | An Optimal Energy Management Algorithm Considering Regenerative Braking and Renewable Energy for EV Charging in Railway Stations | This paper proposes a novel optimal Energy Management System (EMS) algorithm for Electric Vehicle (EV) charging in smart electric railway stations with renewable generation. As opposed to previous railway EMS methods, the proposed EMS coordinates the combined Regenerative Braking Energy (RBE), renewable generation, ele... | ['Gabriela Hug', 'Yannick Zwirner', 'Georgia Pierrou'] | 2023-03-08 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.94129246e-01 5.89317456e-02 -3.52204405e-02 8.41318537e-03
-6.99288070e-01 -4.64204222e-01 4.86576080e-01 -1.49723858e-01
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3.83497626e-01 8.22221398e-01 -1.83367327e-01 -7.06973910... | [5.6210126876831055, 2.3012800216674805] |
e592916e-9454-47c0-b4d8-e2876d6419ac | deepface-closing-the-gap-to-human-level-1 | null | null | https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/ | https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf | DeepFace: Closing the Gap to Human-Level Performance in Face Verification | In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine... | ['Marc’ Aurelio Ranzato', 'Ming Yang', 'Yaniv Taigman', 'Lior Wolf'] | 2014-06-24 | deepface-closing-the-gap-to-human-level | http://openaccess.thecvf.com/content_cvpr_2014/html/Taigman_DeepFace_Closing_the_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf | conference-on-computer-vision-and-pattern | ['3d-face-modeling'] | ['computer-vision'] | [ 1.68584123e-01 2.63028771e-01 -8.03703889e-02 -1.02261591e+00
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-8.96472856e-02 7.02877641e-01 -3.85662585e-01 9.41204093... | [13.342659950256348, 0.7068510055541992] |
dbac8014-aa48-4881-aa92-317b6c1d6414 | spinenetv2-automated-detection-labelling-and | 2205.01683 | null | https://arxiv.org/abs/2205.01683v1 | https://arxiv.org/pdf/2205.01683v1.pdf | SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans | This technical report presents SpineNetV2, an automated tool which: (i) detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans across a range of commonly used sequences; and (ii) performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degener... | ['Andrew Zisserman', 'Timor Kadir', 'Amir Jamaludin', 'Rhydian Windsor'] | 2022-05-03 | null | null | null | null | ['body-detection'] | ['computer-vision'] | [ 1.23404004e-01 2.22449809e-01 -3.60036492e-01 -2.71616340e-01
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-2.48859346e-01 1.26473594e+00 1.21839678e+00 -3.42916578... | [14.77028751373291, -2.3500723838806152] |
73019071-1f37-421b-aa29-a2d7f2f83005 | do-not-trust-the-neighbors-adversarial-metric | 2011.07945 | null | https://arxiv.org/abs/2011.07945v1 | https://arxiv.org/pdf/2011.07945v1.pdf | Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation | Scene flow is the task of estimating 3D motion vectors to individual points of a dynamic 3D scene. Motion vectors have shown to be beneficial for downstream tasks such as action classification and collision avoidance. However, data collected via LiDAR sensors and stereo cameras are computation and labor intensive to pr... | ['Victor Zuanazzi'] | 2020-11-01 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 9.79037657e-02 -3.37239176e-01 -2.86404908e-01 -9.40499380e-02
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-1.63408920e-01 6.54119611e-01 4.66585994e-01 -2.70576403... | [8.548983573913574, -1.9944708347320557] |
448db99b-7d7f-4ff3-b126-4565649de7fc | wabert-a-low-resource-end-to-end-model-for | 2204.10461 | null | https://arxiv.org/abs/2204.10461v1 | https://arxiv.org/pdf/2204.10461v1.pdf | WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment | Historically lower-level tasks such as automatic speech recognition (ASR) and speaker identification are the main focus in the speech field. Interest has been growing in higher-level spoken language understanding (SLU) tasks recently, like sentiment analysis (SA). However, improving performances on SLU tasks remains a ... | ['Yafeng Deng', 'Zijian Chen', 'Yingfang Yang', 'Ruizhuo Xu', 'Jianfei Song', 'Lin Yao'] | 2022-04-22 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.08988620e-01 9.54642668e-02 1.29725903e-01 -5.62769592e-01
-1.03257620e+00 -3.42233002e-01 4.17623043e-01 1.00017473e-01
-5.16260743e-01 3.90220582e-01 2.90802866e-01 -3.10407341e-01
4.51917946e-01 -5.42881668e-01 -6.04951739e-01 -5.75299382e-01
4.03654486e-01 2.18493268e-01 4.05708641e-01 -4.78035867... | [14.39553165435791, 6.682505130767822] |
f76aefd7-4a5d-4cfb-8736-59fc16ea7c03 | conditional-generation-of-temporally-ordered | 2012.15786 | null | https://arxiv.org/abs/2012.15786v2 | https://arxiv.org/pdf/2012.15786v2.pdf | Conditional Generation of Temporally-ordered Event Sequences | Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new... | ['Greg Durrett', 'Nathanael Chambers', 'Shih-ting Lin'] | 2020-12-31 | null | https://aclanthology.org/2021.acl-long.555 | https://aclanthology.org/2021.acl-long.555.pdf | acl-2021-5 | ['story-completion'] | ['natural-language-processing'] | [ 4.04212534e-01 1.64006576e-01 -1.35780245e-01 -4.41044331e-01
-7.49878168e-01 -7.99655318e-01 1.07145560e+00 4.34695959e-01
-3.41336101e-01 8.04000139e-01 1.03566217e+00 -3.21710348e-01
-4.51528728e-01 -1.09221172e+00 -9.23596740e-01 -2.45079458e-01
-4.12157148e-01 9.00529087e-01 3.34618777e-01 -2.37880588... | [11.216638565063477, 8.87234115600586] |
7dd3ce3c-5943-4d5e-b1bf-7f67f710742a | cross-lingual-word-representations-induction | null | null | https://aclanthology.org/D17-3007 | https://aclanthology.org/D17-3007.pdf | Cross-Lingual Word Representations: Induction and Evaluation | In recent past, NLP as a field has seen tremendous utility of distributional word vector representations as features in downstream tasks. The fact that these word vectors can be trained on unlabeled monolingual corpora of a language makes them an inexpensive resource in NLP. With the increasing use of monolingual word ... | ["Ivan Vuli{\\'c}", 'Anders S{\\o}gaard', 'Manaal Faruqui'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['multilingual-word-embeddings'] | ['methodology'] | [-2.88672686e-01 -4.58790749e-01 -8.58831465e-01 -3.95912975e-01
-1.10226679e+00 -1.07000613e+00 7.70701170e-01 2.16229856e-01
-8.23372066e-01 6.29550695e-01 5.38805664e-01 -5.94218969e-01
3.47554058e-01 -4.47698921e-01 -4.44434106e-01 -4.58147585e-01
2.42547736e-01 5.53112209e-01 -2.65824735e-01 -4.75424290... | [10.941676139831543, 9.921299934387207] |
035e64e8-1f20-4a5d-868a-c00ceb83f1e0 | edu-level-extractive-summarization-with | 2210.04029 | null | https://arxiv.org/abs/2210.04029v2 | https://arxiv.org/pdf/2210.04029v2.pdf | EDU-level Extractive Summarization with Varying Summary Lengths | Extractive models usually formulate text summarization as extracting fixed top-$k$ salient sentences from the document as a summary. Few works exploited extracting finer-grained Elementary Discourse Unit (EDU) with little analysis and justification for the extractive unit selection. Further, the selection strategy of t... | ['Xiao-jun Zeng', 'Goran Nenadic', 'Shengzhong Mao', 'Jiayu Shang', 'Ching-Hsun Tseng', 'Yuping Wu'] | 2022-10-08 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 0.29974878 0.39896974 -0.56888777 -0.30636337 -1.3180888 -0.50043786
0.52390987 0.65909445 -0.3117039 1.0618869 1.0377425 0.01391945
-0.32654142 -0.7376737 -0.519611 -0.40102535 0.00793976 0.39165115
0.15295109 -0.11853825 0.9829436 -0.02206064 -1.490732 0.4705737
1.5159812 0.40349564 0.4... | [12.589011192321777, 9.444276809692383] |
e53fadfd-6180-48e0-aaa7-f7d8d9d8029e | narrative-modeling-with-memory-chains-and | 1805.06122 | null | http://arxiv.org/abs/1805.06122v1 | http://arxiv.org/pdf/1805.06122v1.pdf | Narrative Modeling with Memory Chains and Semantic Supervision | Story comprehension requires a deep semantic understanding of the narrative,
making it a challenging task. Inspired by previous studies on ROC Story Cloze
Test, we propose a novel method, tracking various semantic aspects with
external neural memory chains while encouraging each to focus on a particular
semantic aspect... | ['Timothy Baldwin', 'Fei Liu', 'Trevor Cohn'] | 2018-05-16 | narrative-modeling-with-memory-chains-and-1 | https://aclanthology.org/P18-2045 | https://aclanthology.org/P18-2045.pdf | acl-2018-7 | ['cloze-test'] | ['natural-language-processing'] | [ 3.28187317e-01 2.18995541e-01 -5.50787210e-01 -3.98050010e-01
-6.03360534e-01 -7.08946764e-01 1.02325547e+00 2.30648667e-01
-2.69719064e-01 5.88116050e-01 1.34772980e+00 1.57455638e-01
1.61410511e-01 -8.89320433e-01 -6.71630025e-01 -5.16416356e-02
2.77433187e-01 6.49011016e-01 1.96586668e-01 -5.33552110... | [11.210871696472168, 8.853647232055664] |
3bf98cde-fcb9-47a9-8107-77f41e51615a | generative-category-level-shape-and-pose | 2210.01112 | null | https://arxiv.org/abs/2210.01112v2 | https://arxiv.org/pdf/2210.01112v2.pdf | Generative Category-Level Shape and Pose Estimation with Semantic Primitives | Empowering autonomous agents with 3D understanding for daily objects is a grand challenge in robotics applications. When exploring in an unknown environment, existing methods for object pose estimation are still not satisfactory due to the diversity of object shapes. In this paper, we propose a novel framework for cate... | ['Guofeng Zhang', 'Zhaopeng Cui', 'Tao Kong', 'Qihang Zhang', 'Zhichao Ye', 'Yifeng Li', 'Guanglin Li'] | 2022-10-03 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-2.51980931e-01 -2.17692077e-01 -7.76625723e-02 -4.52116072e-01
-5.15439332e-01 -7.96832025e-01 6.13840401e-01 -1.39687464e-01
-1.39763728e-01 1.79923669e-01 -8.07559874e-04 2.08153650e-01
-1.85099095e-01 -6.21384501e-01 -6.53301835e-01 -7.83152580e-01
3.00825536e-01 9.92420256e-01 3.08315367e-01 -8.30323994... | [7.353254318237305, -2.510502815246582] |
7be1c37b-4790-49c1-bb03-5426cc13e292 | improving-graph-representation-for-point | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.pdf | Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering | Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies. However, compared to self-attention mechanism, we find graph convolutions show a stronger ability in capturing local geometry information with less computational... | ['Ge Li', 'Wei Gao', 'Thomas H. Li', 'Zhiyi Pan', 'Nan Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [-3.06788385e-01 1.37804687e-01 1.71065435e-01 -6.13537610e-01
-1.37738794e-01 -1.35799035e-01 1.90036729e-01 9.59453732e-03
-5.98961823e-02 2.01879278e-01 1.21075593e-01 -4.63963002e-01
-4.04546736e-03 -1.36995316e+00 -1.07270980e+00 -3.62134963e-01
-2.35799044e-01 2.29894802e-01 4.02297556e-01 -1.36249036... | [7.982937335968018, -3.596414089202881] |
73b5f8d8-6b03-41ff-8ff4-8ecd3c495607 | centralised-rehearsal-of-decentralised | 2305.18875 | null | https://arxiv.org/abs/2305.18875v2 | https://arxiv.org/pdf/2305.18875v2.pdf | Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility | This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable e... | ['Malcolm McCulloch', 'Thomas Morstyn', 'Bei Peng', 'Flora Charbonnier'] | 2023-05-30 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-6.54405594e-01 4.07559961e-01 -1.45743743e-01 -2.46326357e-01
-6.32374406e-01 -7.66378641e-01 3.91873538e-01 2.12469697e-01
-5.63149810e-01 1.43740129e+00 -4.16462608e-02 -2.24129125e-01
-2.02135652e-01 -1.21226442e+00 -3.62943739e-01 -1.06653297e+00
-1.45351514e-01 5.11735141e-01 -3.82832140e-01 -1.06921107... | [5.517105579376221, 2.552633285522461] |
d017d610-8938-4ba8-aa94-da62b8115f70 | pubchemqc-b3lyp-6-31g-pm6-dataset-the | 2305.18454 | null | https://arxiv.org/abs/2305.18454v1 | https://arxiv.org/pdf/2305.18454v1.pdf | PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations | This article presents the "PubChemQC B3LYP/6-31G*//PM6" dataset, containing electronic properties of 85,938,443 molecules. It includes orbitals, orbital energies, total energies, dipole moments, and other relevant properties. The dataset encompasses a wide range of molecules, from essential compounds to biomolecules up... | ['Toshiyuki Maeda', 'Maho Nakata'] | 2023-05-29 | null | null | null | null | ['drug-discovery'] | ['medical'] | [-3.82272489e-02 -3.93858761e-01 -6.12946272e-01 2.71900445e-01
-5.73231101e-01 -4.24258620e-01 2.08477050e-01 8.54802132e-01
-1.31595537e-01 1.49875522e+00 1.45113289e-01 -4.43638206e-01
2.00667642e-02 -9.56843257e-01 -4.27248120e-01 -1.27402604e+00
-2.73394346e-01 8.12582001e-02 1.54355407e-01 -1.59552053... | [5.070320129394531, 5.486292839050293] |
16fe0955-7556-4082-979c-d13c2ffd7227 | few-shot-video-object-detection | 2104.14805 | null | https://arxiv.org/abs/2104.14805v3 | https://arxiv.org/pdf/2104.14805v3.pdf | Few-Shot Video Object Detection | We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Ne... | ['Yu-Wing Tai', 'Chi-Keung Tang', 'Qi Fan'] | 2021-04-30 | null | null | null | null | ['few-shot-video-object-detection'] | ['computer-vision'] | [-1.12482414e-01 -5.44572234e-01 -5.32003939e-01 -1.53210074e-01
-1.00760412e+00 -4.22390312e-01 4.07685429e-01 -5.75218618e-01
-2.66271263e-01 2.79930055e-01 4.54966843e-01 4.18132395e-02
2.45969161e-01 -3.27252358e-01 -9.10802722e-01 -3.01849574e-01
-4.84598905e-01 3.02430749e-01 9.60773647e-01 1.24108367... | [8.790526390075684, 0.8481943011283875] |
0c01620f-7aa0-441e-8755-ea86b942ced6 | stitch-it-in-time-gan-based-facial-editing-of | 2201.08361 | null | https://arxiv.org/abs/2201.08361v2 | https://arxiv.org/pdf/2201.08361v2.pdf | Stitch it in Time: GAN-Based Facial Editing of Real Videos | The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their success with videos has proven challenging. Sets of high-quality facial videos are lacking, and working with videos introduces a fundamental barr... | ['Daniel Cohen-Or', 'Amit H. Bermano', 'Rinon Gal', 'Ron Mokady', 'Rotem Tzaban'] | 2022-01-20 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 6.24168336e-01 2.74416387e-01 7.26189315e-02 -4.39829767e-01
-4.91195828e-01 -6.78663909e-01 8.45047593e-01 -8.59578013e-01
-4.79617640e-02 6.68156862e-01 6.47175252e-01 2.61772245e-01
-3.60400341e-02 -4.08688724e-01 -9.27201569e-01 -5.62298656e-01
-2.03953639e-01 7.80621469e-02 -2.63368428e-01 -2.22193778... | [12.585700988769531, -0.3156823515892029] |
804d321e-5d24-4211-90cf-e0d42b2a18bb | real-time-multi-object-tracking-based-on-bi | 2303.08444 | null | https://arxiv.org/abs/2303.08444v1 | https://arxiv.org/pdf/2303.08444v1.pdf | Real-time Multi-Object Tracking Based on Bi-directional Matching | In recent years, anchor-free object detection models combined with matching algorithms are used to achieve real-time muti-object tracking and also ensure high tracking accuracy. However, there are still great challenges in multi-object tracking. For example, when most part of a target is occluded or the target just dis... | ['Zehua Zeng', 'Huilan Luo'] | 2023-03-15 | null | null | null | null | ['motion-prediction', 'occlusion-handling', 'real-time-multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.28768045e-02 -7.01674998e-01 -3.06396514e-01 1.09024987e-01
-3.30447704e-01 -4.28172737e-01 1.22237787e-01 3.73795666e-02
-4.43299323e-01 6.76302850e-01 -3.54646474e-01 -1.21685781e-01
2.06931204e-01 -7.64294744e-01 -8.59163463e-01 -7.46490836e-01
-3.57243605e-02 5.12367666e-01 1.23651218e+00 2.31129289... | [6.508765697479248, -2.04012131690979] |
97d41055-ba38-4778-9bbd-a88caabd086c | action-attending-graphic-neural-network | 1711.06427 | null | http://arxiv.org/abs/1711.06427v1 | http://arxiv.org/pdf/1711.06427v1.pdf | Action-Attending Graphic Neural Network | The motion analysis of human skeletons is crucial for human action
recognition, which is one of the most active topics in computer vision. In this
paper, we propose a fully end-to-end action-attending graphic neural network
(A$^2$GNN) for skeleton-based action recognition, in which each irregular
skeleton is structured... | ['Jian Yang', 'Zhen Cui', 'Wenming Zheng', 'Rongrong Ji', 'Chunyan Xu', 'Chaolong Li'] | 2017-11-17 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 6.15968525e-01 1.35527298e-01 -1.54267490e-01 -2.89413452e-01
-5.09949088e-01 1.39253572e-01 2.93922991e-01 -5.16237676e-01
-3.52668315e-01 2.16657028e-01 6.60399735e-01 2.34235480e-01
-1.38031960e-01 -5.93663275e-01 -6.68528140e-01 -8.44508708e-01
-1.15129486e-01 -6.17629960e-02 5.56470811e-01 -1.15017660... | [7.886346340179443, 0.4079086184501648] |
79a140aa-7dff-4857-b571-4178ace25c4d | grace-generation-using-associated-code-edits | 2305.14129 | null | https://arxiv.org/abs/2305.14129v2 | https://arxiv.org/pdf/2305.14129v2.pdf | GrACE: Generation using Associated Code Edits | Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the difficulty of capturing the developer intent... | ['Ashish Tiwari', 'Gustavo Soares', 'Arjun Radhakrishna', 'Aditya Kanade', 'Sumit Gulwani', 'Saikat Chakraborty', 'Yasharth Bajpai', 'Avishree Khare', 'Priyanshu Gupta'] | 2023-05-23 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 2.34392419e-01 2.06327051e-01 -1.76281929e-01 -5.06458580e-01
-6.58903420e-01 -5.38075566e-01 4.68191087e-01 3.97803158e-01
1.77582100e-01 2.68199623e-01 2.33985886e-01 -2.15360820e-01
-3.43000442e-02 -4.28925574e-01 -8.76842082e-01 2.02532277e-01
-4.70052548e-02 7.78723657e-02 1.36414468e-01 -2.74441183... | [7.714548587799072, 7.846083641052246] |
d1c73a1b-1e6b-4d1a-b7ea-1fb18f945c80 | sentiment-predictability-for-stocks | 1712.05785 | null | http://arxiv.org/abs/1712.05785v2 | http://arxiv.org/pdf/1712.05785v2.pdf | Sentiment Predictability for Stocks | In this work, we present our findings and experiments for stock-market
prediction using various textual sentiment analysis tools, such as mood
analysis and event extraction, as well as prediction models, such as LSTMs and
specific convolutional architectures. | ['Xingyou Song', 'Jordan Prosky', 'Michael Zhao', 'Andrew Tan'] | 2017-12-15 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.33682406e-01 -1.65437222e-01 -2.96776444e-01 -6.55681789e-01
-7.77437761e-02 -5.84465206e-01 6.37225688e-01 4.56510514e-01
-4.53489184e-01 9.20250177e-01 6.33985579e-01 -6.15067720e-01
4.01741594e-01 -1.06560838e+00 -1.70538977e-01 -8.97031948e-02
-3.04512411e-01 -4.31490950e-02 3.16331498e-02 -6.12935483... | [4.4321794509887695, 4.28432035446167] |
45f98af3-2753-4bb1-ab1c-cc8d91d5dcbe | a-survey-on-deep-learning-and-explainability | 2010.10563 | null | https://arxiv.org/abs/2010.10563v2 | https://arxiv.org/pdf/2010.10563v2.pdf | A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images | Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with ... | ['Daniel Capurro', 'Claudia Prieto', 'Cristian Tejos', 'Marcelo andía', 'Sergio Uribe', 'Cecilia Besa', 'Alvaro Soto', 'Denis Parra', 'Pablo Pino', 'Pablo Messina'] | 2020-10-20 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.80596572e-01 5.53383708e-01 -1.82931855e-01 -3.74630094e-01
-7.30172753e-01 -2.69046694e-01 4.66274321e-01 7.60496378e-01
-2.71868438e-01 9.88269746e-01 2.96943188e-01 -2.92917937e-01
-2.79723287e-01 -8.08455944e-01 -4.04399097e-01 -3.23866159e-01
-5.54945767e-02 6.32433534e-01 -3.29766601e-01 1.09426342... | [15.04098129272461, -1.4057430028915405] |
4d413151-3357-4edc-8277-e92f2603fb12 | deep-evolution-for-facial-emotion-recognition | 2009.14194 | null | https://arxiv.org/abs/2009.14194v2 | https://arxiv.org/pdf/2009.14194v2.pdf | Deep Evolution for Facial Emotion Recognition | Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability. We propose a novel method based on evolutionary algorithms, that deals with both challenges by massively reducing the number of trainable parameters, ... | ['Emmanuel Dufourq', 'Bruce A. Bassett'] | 2020-09-29 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 4.52788830e-01 1.72789231e-01 1.30399242e-01 -6.74205542e-01
-6.41383290e-01 -5.36477983e-01 2.32159525e-01 -2.26014689e-01
-6.77395165e-01 6.69122279e-01 -2.69183189e-01 3.61495353e-02
-4.00030881e-01 -4.54271734e-01 -5.71965158e-01 -9.22605991e-01
-1.06444836e-01 5.45401275e-01 -4.31826681e-01 -3.70890886... | [13.488551139831543, 1.6357522010803223] |
25e06e33-c68d-468d-8acb-bbaeec94872b | generating-fast-and-slow-scene-decomposition | 2203.11194 | null | https://arxiv.org/abs/2203.11194v3 | https://arxiv.org/pdf/2203.11194v3.pdf | Test-time Adaptation with Slot-Centric Models | Current visual detectors, though impressive within their training distribution, often fail to parse out-of-distribution scenes into their constituent entities. Recent test-time adaptation methods use auxiliary self-supervised losses to adapt the network parameters to each test example independently and have shown promi... | ['Katerina Fragkiadaki', 'Thomas Kipf', 'Gaurav Aggarwal', 'Mehdi S. M. Sajjadi', 'Sjoerd van Steenkiste', 'Sujoy Paul', 'Deepak Pathak', 'Anirudh Goyal', 'Mihir Prabhudesai'] | 2022-03-21 | null | null | null | null | ['scene-segmentation', 'semi-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.26651365e-01 1.97175145e-01 -7.24878237e-02 -7.57393241e-01
-8.70787442e-01 -6.22769356e-01 8.63268971e-01 -2.01081783e-01
-2.25424901e-01 3.79188210e-01 9.32134595e-03 -2.56305784e-01
1.14120618e-01 -7.58960545e-01 -1.15955877e+00 -5.04939914e-01
2.50817299e-01 1.02834749e+00 4.10164207e-01 2.74607092... | [8.526506423950195, -3.0636515617370605] |
a214b09d-a438-469e-86fc-95ce0a4411b3 | image-reconstruction-algorithms-in-radio | 2202.12959 | null | https://arxiv.org/abs/2202.12959v2 | https://arxiv.org/pdf/2202.12959v2.pdf | Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers | We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it f... | ['Yves Wiaux', 'Chao Tang', 'Arwa Dabbech', 'Matthieu Terris'] | 2022-02-25 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 4.68710631e-01 -8.01118165e-02 5.94570577e-01 -4.74890441e-01
-9.35867012e-01 -2.72660047e-01 3.92650843e-01 -7.87552714e-01
-7.26220846e-01 7.07916439e-01 2.08794370e-01 -3.07520598e-01
-6.64543033e-01 -6.47884369e-01 -9.58946347e-01 -1.19965363e+00
-2.24959806e-01 4.32757616e-01 -4.26639497e-01 -3.48358303... | [11.533658027648926, -2.40824818611145] |
b52c7d4a-3abd-4a78-a340-7617d842b526 | deep-exhaustive-model-for-nested-named-entity | null | null | https://aclanthology.org/D18-1309 | https://aclanthology.org/D18-1309.pdf | Deep Exhaustive Model for Nested Named Entity Recognition | We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity menti... | ['Mohammad Golam Sohrab', 'Makoto Miwa'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.88845235e-01 4.90220010e-01 -1.84725076e-01 -3.43066216e-01
-6.98118329e-01 -5.69422007e-01 3.15782130e-01 4.99421924e-01
-1.01961923e+00 9.67349172e-01 5.50512910e-01 -2.88267285e-01
-5.09048346e-03 -1.09278226e+00 -7.60036945e-01 -3.11518192e-01
-2.22984955e-01 6.11070991e-01 1.33639783e-01 -1.53573513... | [9.046955108642578, 9.0829439163208] |
623c0500-f68b-4791-a383-1580d7f6cb20 | task-space-control-of-robot-manipulators | 2302.04163 | null | https://arxiv.org/abs/2302.04163v1 | https://arxiv.org/pdf/2302.04163v1.pdf | Task Space Control of Robot Manipulators based on Visual SLAM | This paper aims to address the open problem of designing a globally stable vision-based controller for robot manipulators. Accordingly, based on a hybrid mechanism, this paper proposes a novel task-space control law attained by taking the gradient of a potential function in SE(3). The key idea is to employ the Visual S... | ['Jouni Mattila', 'Seyed Hamed Hashemi'] | 2023-02-08 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 3.75587530e-02 1.73294380e-01 -2.08034426e-01 4.25286204e-01
-1.10843100e-01 -5.82285345e-01 3.80922109e-01 -2.55028993e-01
-3.31790775e-01 6.51145637e-01 -4.97834474e-01 -3.99203569e-01
-5.82831621e-01 -1.71366587e-01 -5.10181546e-01 -8.77102137e-01
3.02946776e-01 -1.41167983e-01 -5.90943620e-02 -3.58922362... | [5.202285289764404, 2.3103692531585693] |
9a4346cd-8834-4aa6-864b-bca733cfc284 | aqua-a-benchmarking-tool-for-label-quality | 2306.09467 | null | https://arxiv.org/abs/2306.09467v1 | https://arxiv.org/pdf/2306.09467v1.pdf | AQuA: A Benchmarking Tool for Label Quality Assessment | Machine learning (ML) models are only as good as the data they are trained on. But recent studies have found datasets widely used to train and evaluate ML models, e.g. ImageNet, to have pervasive labeling errors. Erroneous labels on the train set hurt ML models' ability to generalize, and they impact evaluation and mod... | ['Artur Dubrawski', 'Chalisa Udompanyawit', 'Arvind Srinivasan', 'Arjun Choudhry', 'Vedant Sanil', 'Mononito Goswami'] | 2023-06-15 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 2.82480359e-01 -1.61073133e-02 -5.42636693e-01 -7.67297387e-01
-9.35509980e-01 -8.40097129e-01 3.88354093e-01 7.01121315e-02
-5.30483961e-01 6.34282470e-01 -5.69975257e-01 -5.18183172e-01
9.21495035e-02 -3.89145344e-01 -7.05182850e-01 -3.38837862e-01
4.57650900e-01 5.69132388e-01 7.66952187e-02 3.88914764... | [9.39620590209961, 4.035164833068848] |
8a11704a-d527-4012-9a31-05ff75e70233 | 05-petabyte-simulation-of-a-45-qubit-quantum | 1704.01127 | null | https://arxiv.org/abs/1704.01127v2 | https://arxiv.org/pdf/1704.01127v2.pdf | 0.5 Petabyte Simulation of a 45-Qubit Quantum Circuit | Near-term quantum computers will soon reach sizes that are challenging to directly simulate, even when employing the most powerful supercomputers. Yet, the ability to simulate these early devices using classical computers is crucial for calibration, validation, and benchmarking. In order to make use of the full potenti... | ['Thomas Häner', 'Damian S. Steiger'] | 2017-04-04 | null | null | null | null | ['image-outpainting', 'image-relighting'] | ['computer-vision', 'computer-vision'] | [ 1.09856658e-01 -2.68754750e-01 4.44856614e-01 -9.05737206e-02
-7.65473187e-01 -7.81581283e-01 4.59001839e-01 3.44711930e-01
-6.32941604e-01 9.33086276e-01 -5.22028983e-01 -9.51150179e-01
-5.07377796e-02 -1.09642851e+00 -4.05970335e-01 -7.61952400e-01
-1.96243554e-01 8.28902245e-01 2.66858518e-01 -5.36720932... | [5.57776403427124, 4.927721977233887] |
d254d099-caf9-4a45-8a22-6c521f001b71 | self-supervised-deformation-modeling-for | 1911.00735 | null | https://arxiv.org/abs/1911.00735v2 | https://arxiv.org/pdf/1911.00735v2.pdf | Self-supervised Deformation Modeling for Facial Expression Editing | Recent advances in deep generative models have demonstrated impressive results in photo-realistic facial image synthesis and editing. Facial expressions are inherently the result of muscle movement. However, existing neural network-based approaches usually only rely on texture generation to edit expressions and largely... | ['Dimitris Samaras', 'Zhixin Shu', 'ShahRukh Athar'] | 2019-11-02 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 3.02447021e-01 2.71543771e-01 7.95044154e-02 -6.91341579e-01
-5.78928709e-01 -3.26205820e-01 6.41444266e-01 -7.11556792e-01
-1.36285588e-01 6.09296679e-01 2.60554463e-01 1.46761805e-01
5.47768354e-01 -7.78714001e-01 -9.24984097e-01 -7.66882420e-01
4.38275605e-01 2.65868008e-01 -3.21876526e-01 -3.48288596... | [12.74233341217041, -0.42162564396858215] |
5dc7e187-b0a2-48c3-a55f-4bd75bd6346f | lightts-lightweight-time-series | 2302.12721 | null | https://arxiv.org/abs/2302.12721v1 | https://arxiv.org/pdf/2302.12721v1.pdf | LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version | Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy is often achieved by ensemble learning where results are synthesized fr... | ['Christian S. Jensen', 'Chenjuan Guo', 'Tung Kieu', 'Bin Yang', 'Miao Zhang', 'David Campos'] | 2023-02-24 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 3.53386849e-01 -5.96291125e-01 -1.15183592e-01 -4.06467915e-01
-6.14545286e-01 -5.93324959e-01 6.04995191e-01 3.11843395e-01
-3.43734175e-01 6.58771813e-01 -2.29146689e-01 -4.96420443e-01
-6.38977945e-01 -6.56899095e-01 -2.16436356e-01 -7.16941953e-01
-3.57431471e-01 6.30318224e-01 -2.08915338e-01 -1.69418320... | [7.269883632659912, 3.0792715549468994] |
6399a3c1-98e2-448a-86bb-95381979334b | bronchoscopic-video-synchronization-for | 2303.11258 | null | https://arxiv.org/abs/2303.11258v1 | https://arxiv.org/pdf/2303.11258v1.pdf | Bronchoscopic video synchronization for interactive multimodal inspection of bronchial lesions | With lung cancer being the most fatal cancer worldwide, it is important to detect the disease early. A potentially effective way of detecting early cancer lesions developing along the airway walls (epithelium) is bronchoscopy. To this end, developments in bronchoscopy offer three promising noninvasive modalities for im... | ['William E. Higgins', 'Rebecca Bascom', 'Jennifer Toth', 'Danish Ahmad', 'Patrick D. Byrnes', 'Qi Chang'] | 2023-03-20 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 3.94335926e-01 -2.39211395e-01 -4.41920668e-01 2.30426550e-01
-8.79249454e-01 -9.28599358e-01 4.59255099e-01 3.13190550e-01
-2.46523291e-01 3.86110216e-01 -3.83877382e-02 -7.54049659e-01
4.90993224e-02 -6.91471517e-01 -2.03587219e-01 -8.41039240e-01
1.49745777e-01 3.01564515e-01 7.67941773e-01 2.73009956... | [15.268305778503418, -2.1613807678222656] |
e2024bf1-86ff-48e8-a5aa-f8aa94641e3f | visual-attention-consistency-under-image | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Guo_Visual_Attention_Consistency_Under_Image_Transforms_for_Multi-Label_Image_Classification_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Guo_Visual_Attention_Consistency_Under_Image_Transforms_for_Multi-Label_Image_Classification_CVPR_2019_paper.pdf | Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification | Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are included for training b... | [' Song Wang', ' Hongkai Yu', ' Xiaochuan Fan', ' Kang Zheng', 'Hao Guo'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.33910871e-01 4.82426994e-02 -2.49011397e-01 -6.93898559e-01
-3.21652949e-01 -4.06635553e-01 3.83365482e-01 1.45504355e-01
-5.37589788e-01 5.25264621e-01 -2.18036294e-01 -1.24962211e-01
5.24736680e-02 -5.46841502e-01 -8.87655616e-01 -8.20677221e-01
6.10355794e-01 -1.92560442e-02 3.37189883e-01 1.19134583... | [9.847466468811035, 3.9186007976531982] |
294bbfdc-22da-4da1-83da-2f3d21ff6b9c | qasr-qcri-aljazeera-speech-resource-a-large-1 | null | null | https://aclanthology.org/2021.acl-long.177 | https://aclanthology.org/2021.acl-long.177.pdf | QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus | We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. The dataset is released with lightly supervised transcriptions, aligned with the audio segments. Un... | ['Ahmed Ali', 'Shammur Absar Chowdhury', 'Amir Hussein', 'Hamdy Mubarak'] | 2021-08-01 | null | null | null | acl-2021-5 | ['dialect-identification', 'punctuation-restoration', 'speaker-identification'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.51301488e-01 2.76677907e-01 1.35009795e-01 -7.74584293e-01
-1.69526029e+00 -6.91843510e-01 3.03057432e-01 6.69192374e-02
-3.79202753e-01 3.39824855e-01 7.23991990e-01 -5.85101128e-01
2.33218700e-01 -2.15697512e-01 -5.46874106e-01 -6.29608691e-01
-1.18805356e-01 7.36952603e-01 9.59389210e-02 -6.50598526... | [14.420191764831543, 6.782005786895752] |
3ff837ac-1d63-48c3-9396-3e60d6db1212 | photon-field-networks-for-dynamic-real-time | 2304.07338 | null | https://arxiv.org/abs/2304.07338v1 | https://arxiv.org/pdf/2304.07338v1.pdf | Photon Field Networks for Dynamic Real-Time Volumetric Global Illumination | Volume data is commonly found in many scientific disciplines, like medicine, physics, and biology. Experts rely on robust scientific visualization techniques to extract valuable insights from the data. Recent years have shown path tracing to be the preferred approach for volumetric rendering, given its high levels of r... | ['Kwan-Liu Ma', 'Qi Wu', 'David Bauer'] | 2023-04-14 | null | null | null | null | ['data-visualization', 'data-visualization'] | ['methodology', 'miscellaneous'] | [ 1.30257383e-01 -4.16645557e-01 4.41571563e-01 -3.79252076e-01
-6.52426600e-01 -5.89582741e-01 4.96995091e-01 3.17709655e-01
-3.70422184e-01 7.18463719e-01 -1.72541335e-01 -7.65402973e-01
1.40098721e-01 -1.08452356e+00 -6.72278702e-01 -4.60182488e-01
-4.08360362e-01 3.95617545e-01 4.79006261e-01 -1.23107294... | [9.430990219116211, -3.1617398262023926] |
49084ef2-a50f-4b43-bb06-fc5ec231fe85 | being-right-for-whose-right-reasons | 2306.00639 | null | https://arxiv.org/abs/2306.00639v1 | https://arxiv.org/pdf/2306.00639v1.pdf | Being Right for Whose Right Reasons? | Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are 'right for the right reasons'. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a c... | ['Anders Søgaard', 'Laura Cabello', 'Terne Sasha Thorn Jakobsen'] | 2023-06-01 | null | null | null | null | ['sentiment-analysis', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [-1.66459121e-02 8.71536851e-01 -4.75363165e-01 -7.18609035e-01
-3.57822180e-01 -5.86747468e-01 6.56214654e-01 5.60056925e-01
-4.24781352e-01 7.33954370e-01 1.27233911e+00 -4.24903959e-01
-1.20355397e-01 -1.82627410e-01 8.19548368e-02 -2.62913764e-01
8.80415618e-01 5.84082544e-01 -3.27524811e-01 -2.24583209... | [9.265551567077637, 9.853100776672363] |
8e06ab4f-3288-4730-89f9-553388a7585f | learning-generative-structure-prior-for-blind | 2303.14726 | null | https://arxiv.org/abs/2303.14726v1 | https://arxiv.org/pdf/2303.14726v1.pdf | Learning Generative Structure Prior for Blind Text Image Super-resolution | Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the ... | ['Chen Change Loy', 'WangMeng Zuo', 'Xiaoming Li'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 1.01766670e+00 -1.89931870e-01 2.97678821e-02 -2.18933627e-01
-5.56673646e-01 -6.79750562e-01 5.92757463e-01 -5.58638871e-01
1.03555247e-01 7.23519623e-01 5.79805017e-01 -6.56203367e-03
1.96633279e-01 -6.13609552e-01 -5.96976757e-01 -9.50855672e-01
6.36056066e-01 5.56326434e-02 9.97444391e-02 -3.29621792... | [11.3684720993042, -1.7171335220336914] |
eda7b0da-993c-4b74-b30d-ca03729bc9e9 | hey-human-if-your-facial-emotions-are | 2008.07426 | null | https://arxiv.org/abs/2008.07426v1 | https://arxiv.org/pdf/2008.07426v1.pdf | Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks! | Facial emotion recognition is the task to classify human emotions in face images. It is a difficult task due to high aleatoric uncertainty and visual ambiguity. A large part of the literature aims to show progress by increasing accuracy on this task, but this ignores the inherent uncertainty and ambiguity in the task. ... | ['Matias Valdenegro-Toro', 'Maryam Matin'] | 2020-08-17 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 1.09568425e-01 2.69867957e-01 1.80848598e-01 -1.14925742e+00
-5.93398750e-01 -2.85485983e-01 4.50979263e-01 -4.88355011e-01
-4.18220878e-01 8.55812609e-01 -1.12168752e-01 -1.58808772e-02
-3.95482183e-02 -2.54631639e-01 -6.41186118e-01 -7.72683561e-01
8.27706605e-02 4.59738582e-01 -2.09429041e-01 2.69392729... | [8.6558837890625, 4.5988383293151855] |
557fb0f2-0f27-4c23-8493-9b9519cb840e | weakly-supervised-anomaly-detection-in-the | 2305.03761 | null | https://arxiv.org/abs/2305.03761v1 | https://arxiv.org/pdf/2305.03761v1.pdf | Weakly-Supervised Anomaly Detection in the Milky Way | Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels (CWoLa), a weakly-supervised anomaly detection method, to identify cold stellar strea... | ['Jack H. Collins', 'Matthew R. Buckley', 'David Shih', 'Benjamin Nachman', 'Sowmya Thanvantri', 'Mariel Pettee'] | 2023-05-05 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.10047296e-01 -3.68508130e-01 6.86674118e-02 -1.67397156e-01
-3.05404752e-01 -6.27206504e-01 1.35575879e+00 6.04483902e-01
-3.28110635e-01 5.62284350e-01 -5.49947202e-01 -7.80477464e-01
1.16131552e-01 -7.57302046e-01 -4.72197652e-01 -1.14101589e+00
-1.83433712e-01 8.74955535e-01 5.39557934e-01 1.24333590... | [7.624627590179443, 2.7111496925354004] |
4180d9d8-3ed9-45d3-b405-fa6f34d428e1 | a-multimodal-sensor-fusion-framework-robust | 2210.10972 | null | https://arxiv.org/abs/2210.10972v2 | https://arxiv.org/pdf/2210.10972v2.pdf | A Multimodal Sensor Fusion Framework Robust to Missing Modalities for Person Recognition | Utilizing the sensor characteristics of the audio, visible camera, and thermal camera, the robustness of person recognition can be enhanced. Existing multimodal person recognition frameworks are primarily formulated assuming that multimodal data is always available. In this paper, we propose a novel trimodal sensor fus... | ['Yasutomo Kawanishi', 'Vijay John'] | 2022-10-20 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 3.01834166e-01 -2.35324010e-01 1.14743806e-01 -5.75675130e-01
-9.02201533e-01 -6.70270994e-02 6.48550451e-01 -2.81640142e-01
-2.73928523e-01 4.63416874e-01 5.27350247e-01 4.45795625e-01
-5.16561838e-03 -4.11125064e-01 -5.11882126e-01 -9.95226085e-01
5.33845544e-01 -9.89039913e-02 -5.38253427e-01 2.70802945... | [13.225430488586426, 4.921758651733398] |
16bf4ac8-0800-4d87-980f-ba5672a2c67b | cochlscene-acquisition-of-acoustic-scene-data | 2211.02289 | null | https://arxiv.org/abs/2211.02289v1 | https://arxiv.org/pdf/2211.02289v1.pdf | CochlScene: Acquisition of acoustic scene data using crowdsourcing | This paper describes a pipeline for collecting acoustic scene data by using crowdsourcing. The detailed process of crowdsourcing is explained, including planning, validation criteria, and actual user interfaces. As a result of data collection, we present CochlScene, a novel dataset for acoustic scene classification. Ou... | ['Jeongsoo Park', 'Il-Young Jeong'] | 2022-11-04 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 3.73401940e-02 -1.38177291e-01 7.90474117e-01 -1.00401413e+00
-1.01118767e+00 -7.78093100e-01 3.79778206e-01 1.98092356e-01
-7.85634339e-01 2.80637890e-01 4.40953732e-01 1.99043900e-01
4.64695156e-01 -2.89622962e-01 -5.20902872e-01 -4.42096889e-01
-9.43809003e-02 3.64601672e-01 8.40308785e-01 -2.59202659... | [14.929625511169434, 5.246989727020264] |
8b7e387d-6b4f-4508-9e41-da3d1a1316d8 | emowoz-a-large-scale-corpus-and-labelling | 2109.04919 | null | https://arxiv.org/abs/2109.04919v2 | https://arxiv.org/pdf/2109.04919v2.pdf | EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems | The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a nat... | ['Milica Gašić', 'Carel van Niekerk', 'Michael Heck', 'Hsien-Chin Lin', 'Christian Geishauser', 'Nurul Lubis', 'Shutong Feng'] | 2021-09-10 | null | https://aclanthology.org/2022.lrec-1.436 | https://aclanthology.org/2022.lrec-1.436.pdf | lrec-2022-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-6.25991002e-02 5.39509356e-01 1.20173305e-01 -6.93981826e-01
-4.64695752e-01 -8.45685542e-01 6.14486992e-01 2.35277295e-01
-3.79102588e-01 7.74267614e-01 4.97175455e-01 -1.74436316e-01
3.25437039e-01 -2.77434975e-01 2.56312102e-01 -2.79819638e-01
-1.09139688e-01 7.16199577e-01 8.07117950e-03 -8.67353201... | [12.97579574584961, 6.399018287658691] |
be87cd25-6237-482f-a356-85ebaaafd788 | selective-structured-state-spaces-for-long | 2303.14526 | null | https://arxiv.org/abs/2303.14526v1 | https://arxiv.org/pdf/2303.14526v1.pdf | Selective Structured State-Spaces for Long-Form Video Understanding | Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all image-tokens equally as done by S4 mode... | ['Raffay Hamid', 'Mohamed Omar', 'Linda Liu', 'Xiang Yu', 'Pichao Wang', 'Wentao Zhu', 'Jue Wang'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 4.54865575e-01 -1.73725933e-01 -2.51771182e-01 -1.90893546e-01
-6.92167103e-01 -4.19872403e-01 6.21565640e-01 -3.38954240e-01
-5.57332754e-01 6.08543217e-01 4.08688523e-02 -2.36746535e-01
1.39031082e-01 -2.09298879e-01 -9.74217176e-01 -6.75115347e-01
-4.53970246e-02 3.72426659e-02 5.24073422e-01 5.30569255... | [9.231781959533691, 0.2945002019405365] |
3ca3885a-15a1-4bd7-9503-6f13ff212070 | concept-identification-of-directly-and | 2107.00955 | null | https://arxiv.org/abs/2107.00955v1 | https://arxiv.org/pdf/2107.00955v1.pdf | Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons | Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization... | ['Bela Gipp', 'Karsten Donnay', 'Felix Hamborg', 'Anastasia Zhukova'] | 2021-07-02 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-2.58196235e-01 3.72587413e-01 -1.10035583e-01 -1.01966068e-01
-4.59635466e-01 -7.91291773e-01 8.43774259e-01 8.85687888e-01
-7.27139950e-01 7.90452600e-01 1.08634508e+00 -1.48973376e-01
-1.22429915e-01 -7.87353337e-01 -3.01264450e-02 -9.10045445e-01
1.76491529e-01 7.06883788e-01 -5.28832555e-01 -2.54255742... | [9.575508117675781, 9.22384262084961] |
687187a8-99f2-4a69-8766-0ddeaaf8c643 | facial-action-units-detection-aided-by-global | 2210.13718 | null | https://arxiv.org/abs/2210.13718v1 | https://arxiv.org/pdf/2210.13718v1.pdf | Facial Action Units Detection Aided by Global-Local Expression Embedding | Since Facial Action Unit (AU) annotations require domain expertise, common AU datasets only contain a limited number of subjects. As a result, a crucial challenge for AU detection is addressing identity overfitting. We find that AUs and facial expressions are highly associated, and existing facial expression datasets o... | ['Xin Yu', 'Zhigang Deng', 'Wei Chen', 'Yu Ding', 'Lincheng Li', 'Wei zhang', 'Zhipeng Hu'] | 2022-10-25 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.43446892e-01 -2.37222388e-01 -9.61817801e-02 -5.51079750e-01
-8.00017059e-01 -4.62279677e-01 3.94599259e-01 -5.76706946e-01
3.09690982e-02 3.80179375e-01 6.86980709e-02 4.09241199e-01
4.09758985e-01 -6.51700199e-01 -3.83421361e-01 -8.67152035e-01
3.19652706e-01 -1.28523976e-01 -4.21447903e-01 -3.95483762... | [13.62267017364502, 1.5776262283325195] |
474ae113-3db5-4a50-8341-bd4046d709e6 | qrrt-quality-biased-incremental-rrt-for | 2101.02635 | null | https://arxiv.org/abs/2101.02635v1 | https://arxiv.org/pdf/2101.02635v1.pdf | qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems | This paper presents a sampling-based method for optimal motion planning in non-holonomic systems in the absence of known cost functions. It uses the principle of learning through experience to deduce the cost-to-go of regions within the workspace. This cost information is used to bias an incremental graph-based search ... | ['Suril V. Shah', 'Balaraman Ravindran', 'Francis James', 'Nahas Pareekutty'] | 2021-01-07 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [ 6.34669587e-02 2.87485331e-01 -4.55703348e-01 3.28857116e-02
-4.69411135e-01 -3.84037256e-01 4.18806762e-01 9.44451522e-03
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-7.18628466e-01 -5.61898887e-01 -5.76945186e-01 -6.05935931e-01
-5.81979692e-01 4.27206010e-01 1.31664053e-01 -4.29353386... | [4.769009113311768, 1.5417054891586304] |
94c52a51-2358-40ad-8167-c666f96d53f8 | extending-the-adverbial-coverage-of-a-french | null | null | https://aclanthology.org/L12-1564 | https://aclanthology.org/L12-1564.pdf | Extending the adverbial coverage of a French morphological lexicon | We present an extension of the adverbial entries of the French morphological lexicon DELA (Dictionnaires Electroniques du LADL / LADL electronic dictionaries). Adverbs were extracted from LGLex, a NLP-oriented syntactic resource for French, which in its turn contains all adverbs extracted from the Lexicon-Grammar table... | ['Matthieu Constant', 'Claude Martineau', 'Elsa Tolone', 'Stavroula Voyatzi'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-3.43962610e-01 3.02506000e-01 -5.72085619e-01 -2.45190397e-01
-7.30438828e-01 -1.25365853e+00 1.55747667e-01 9.77357149e-01
-6.45530641e-01 1.19954073e+00 5.40130019e-01 -4.31104600e-01
-3.91254500e-02 -8.07840288e-01 -6.71903729e-01 -1.68900281e-01
3.07944596e-01 3.90387267e-01 3.68980646e-01 -7.01046348... | [10.330694198608398, 9.951729774475098] |
173afcc8-d767-4ba8-9c6d-e908d7c654de | instance-adaptive-self-training-for | 2008.12197 | null | https://arxiv.org/abs/2008.12197v1 | https://arxiv.org/pdf/2008.12197v1.pdf | Instance Adaptive Self-Training for Unsupervised Domain Adaptation | The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in bala... | ['Jiaqi Zou', 'Shanghang Zhang', 'Chuang Zhu', 'Ke Mei'] | 2020-08-27 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5406_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710409.pdf | eccv-2020-8 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.33960286e-01 1.45690441e-01 -3.37807536e-01 -7.73240685e-01
-8.77068818e-01 -5.65643609e-01 4.38295096e-01 -1.31869316e-01
-5.03448248e-01 7.29389131e-01 -1.96250334e-01 -1.44607082e-01
-5.81020750e-02 -6.63014293e-01 -5.75785220e-01 -8.02519083e-01
4.63771969e-01 7.94316411e-01 5.16525269e-01 -4.16396819... | [9.703028678894043, 1.311428189277649] |
1fa70813-47e4-42f5-a03b-e590095de452 | graph-convolutional-network-with-sequential | null | null | https://openreview.net/forum?id=Skz-3j05tm | https://openreview.net/pdf?id=Skz-3j05tm | Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems | Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz., (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances and (iii) the current utterance for which the response needs to be generated. While modeling t... | ['Suman Banerjee', 'Mitesh M. Khapra'] | 2019-05-01 | graph-convolutional-network-with-sequential-1 | https://aclanthology.org/Q19-1034 | https://aclanthology.org/Q19-1034.pdf | iclr-2019-5 | ['document-dating', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.61019528e-01 5.15927434e-01 -8.37097466e-02 -4.16930497e-01
-3.56968701e-01 -7.53882766e-01 7.85269558e-01 5.46843648e-01
-4.46462601e-01 7.68209994e-01 8.74011695e-01 -5.28988957e-01
1.13758720e-01 -8.62114072e-01 -4.83622581e-01 -9.82168019e-02
-2.44319409e-01 7.07372546e-01 2.35484898e-01 -8.93603623... | [12.359253883361816, 8.108255386352539] |
d4f235ac-8c22-4aca-920c-3a85965d1a00 | diverse-sequential-subset-selection-for | null | null | http://papers.nips.cc/paper/5413-diverse-sequential-subset-selection-for-supervised-video-summarization | http://papers.nips.cc/paper/5413-diverse-sequential-subset-selection-for-supervised-video-summarization.pdf | Diverse Sequential Subset Selection for Supervised Video Summarization | Video summarization is a challenging problem with great application potential. Whereas prior approaches, largely unsupervised in nature, focus on sampling useful frames and assembling them as summaries, we consider video summarization as a supervised subset selection problem. Our idea is to teach the system to learn fr... | ['Wei-Lun Chao', 'Boqing Gong', 'Kristen Grauman', 'Fei Sha'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['supervised-video-summarization'] | ['computer-vision'] | [ 5.53770006e-01 -2.88154110e-02 -5.29314101e-01 -3.09152603e-01
-9.01830256e-01 -6.14107668e-01 5.43522537e-01 1.23175852e-01
-4.64541279e-02 8.37515354e-01 9.40883160e-01 1.98285282e-01
-1.07858635e-01 -4.40768898e-01 -6.84684038e-01 -7.83163249e-01
4.21322882e-02 1.53883323e-01 3.59488308e-01 1.14942335... | [10.501274108886719, 0.4015130400657654] |
138783d1-4b4f-4bb8-829e-a532c67543e2 | slotformer-unsupervised-visual-dynamics | 2210.05861 | null | https://arxiv.org/abs/2210.05861v2 | https://arxiv.org/pdf/2210.05861v2.pdf | SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models | Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge. We ad... | ['Animesh Garg', 'Thomas Kipf', 'Klaus Greff', 'Nikita Dvornik', 'Ziyi Wu'] | 2022-10-12 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.92614231e-02 2.58592755e-01 -4.35993612e-01 -2.79429555e-01
-5.86577952e-01 -6.22594357e-01 9.46380615e-01 -4.75925878e-02
8.71664360e-02 2.02365085e-01 6.28351867e-01 -1.55420065e-01
-2.67799236e-02 -6.64955318e-01 -9.53913987e-01 -5.41216612e-01
-8.76825899e-02 1.01966381e+00 3.60368192e-01 -9.92719010... | [9.05795669555664, 0.43565845489501953] |
a6a78fa2-ea75-4cea-80e2-388b5dc60d10 | fedbone-towards-large-scale-federated-multi | 2306.17465 | null | https://arxiv.org/abs/2306.17465v1 | https://arxiv.org/pdf/2306.17465v1.pdf | FedBone: Towards Large-Scale Federated Multi-Task Learning | Heterogeneous federated multi-task learning (HFMTL) is a federated learning technique that combines heterogeneous tasks of different clients to achieve more accurate, comprehensive predictions. In real-world applications, visual and natural language tasks typically require large-scale models to extract high-level abstr... | ['Wuliang Huang', 'Chenlong Gao', 'Qian Chen', 'Xinlong Jiang', 'Teng Zhang', 'Yiqiang Chen'] | 2023-06-30 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [-1.42425790e-01 -5.32503903e-01 -1.45219713e-01 -4.16373670e-01
-8.41451108e-01 -1.56270742e-01 3.07936370e-01 -3.54064971e-01
-6.36139289e-02 6.61860168e-01 -1.09125584e-01 -1.12481110e-01
-2.66811609e-01 -5.44154823e-01 -5.21874428e-01 -9.22824562e-01
1.91056579e-01 6.70435309e-01 3.46783131e-01 1.44362822... | [5.8280415534973145, 6.284786701202393] |
1299f1a6-3f58-4da9-b8d3-6db0040d8d9a | towards-complete-view-and-high-level-pose | 2209.11577 | null | https://arxiv.org/abs/2209.11577v1 | https://arxiv.org/pdf/2209.11577v1.pdf | Towards Complete-View and High-Level Pose-based Gait Recognition | The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intra-class variance of human pose due to camera views changing. In this paper, we propose to generate multi-view pose sequences for each single... | ['Zhenyu He', 'Yunqi He', 'Tingyang Xu', 'Yongyong Chen', 'Honghu Pan'] | 2022-09-23 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 1.03010952e-01 -1.82054833e-01 1.58000216e-01 -8.20026472e-02
-4.38125342e-01 -4.63768095e-01 3.68589014e-01 -1.06511676e+00
-4.15923670e-02 6.79010749e-01 2.52549052e-01 2.14956641e-01
3.51777285e-01 -9.94511902e-01 -9.24319327e-01 -8.46356690e-01
-1.35653401e-02 3.55182916e-01 1.23652264e-01 -3.34109128... | [11.996466636657715, -0.8574856519699097] |
499a0040-58ed-4123-8b9f-62c36ab836f9 | mdgcf-multi-dependency-graph-collaborative | null | null | https://doi.org/10.1145/3511808.3557390 | https://dl.acm.org/doi/pdf/10.1145/3511808.3557390 | MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies | Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and it... | ['Chaoyang Wang', 'Jianjun Li', 'Zhiqiang Guo', 'GuoHui Li'] | 2022-10-17 | null | null | null | cikm-2022-10 | ['graph-representation-learning', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-2.46444300e-01 -3.30899149e-01 -3.56883913e-01 -4.56709325e-01
2.37310156e-01 -4.08162475e-01 3.38236064e-01 6.17970347e-01
-1.02508157e-01 3.18616986e-01 4.86565828e-01 -3.44816595e-01
-6.58703864e-01 -1.20456135e+00 -4.23878103e-01 -5.08800030e-01
-4.65173662e-01 -4.86119166e-02 2.03773737e-01 -2.81385541... | [10.210638999938965, 5.631474018096924] |
41dc646b-00cd-4ac1-be8e-48835ada54cb | more-synergy-less-redundancy-exploiting-joint | 2307.00651 | null | https://arxiv.org/abs/2307.00651v1 | https://arxiv.org/pdf/2307.00651v1.pdf | More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning | Self-supervised learning (SSL) is now a serious competitor for supervised learning, even though it does not require data annotation. Several baselines have attempted to make SSL models exploit information about data distribution, and less dependent on the augmentation effect. However, there is no clear consensus on whe... | ['Donald A. Adjeroh', 'Gianfranco Doretto', 'Salman Mohamadi'] | 2023-07-02 | null | null | null | null | ['self-supervised-learning'] | ['computer-vision'] | [ 4.06920880e-01 3.71032685e-01 -4.16070938e-01 -6.07789099e-01
-7.44300485e-01 -4.73221809e-01 7.24938571e-01 1.78938225e-01
-4.86040823e-02 6.17155254e-01 5.09958565e-01 5.27794808e-02
-5.49237728e-01 -5.07046282e-01 -5.35226822e-01 -7.72014499e-01
3.14509541e-01 2.21766442e-01 -1.91930249e-01 -5.17618544... | [8.523531913757324, 4.313082218170166] |
bfbba482-1709-43d1-8a6d-79447a159752 | cont-contrastive-neural-text-generation | 2205.14690 | null | https://arxiv.org/abs/2205.14690v4 | https://arxiv.org/pdf/2205.14690v4.pdf | CoNT: Contrastive Neural Text Generation | Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive l... | ['Xuanjing Huang', 'Xipeng Qiu', 'Lingpeng Kong', 'Kai Lv', 'Jiangtao Feng', 'Chenxin An'] | 2022-05-29 | null | null | null | null | ['code-comment-generation', 'comment-generation', 'data-to-text-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 6.05091393e-01 1.76770046e-01 -1.59543872e-01 -4.15003076e-02
-1.27584553e+00 -4.08193707e-01 1.07064426e+00 -2.94383280e-02
-4.12947565e-01 1.41283941e+00 6.23942554e-01 -5.68392515e-01
3.81639421e-01 -7.62476861e-01 -8.47579062e-01 -6.58962727e-01
6.01403058e-01 5.79308867e-01 -2.44381025e-01 -5.74441016... | [11.891698837280273, 9.162307739257812] |
fb23f412-8ad3-4e49-b4ca-59a760074568 | unsupervised-domain-adaptation-on-person-re | 2301.12439 | null | https://arxiv.org/abs/2301.12439v1 | https://arxiv.org/pdf/2301.12439v1.pdf | Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning | Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generate... | ['Rongrong Ji', 'Yongjian Wu', 'Liujuan Cao', 'Qixiang Ye', 'Pingyang Dai', 'Jiahan Li', 'Qiong Wu'] | 2023-01-29 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 3.72415222e-02 -1.30553972e-02 -2.56102890e-01 -5.49984872e-01
-3.46278787e-01 -4.87962306e-01 4.39460307e-01 -3.03749889e-01
-7.38574684e-01 1.08693743e+00 6.47680238e-02 2.75796086e-01
1.40737757e-01 -9.04664397e-01 -5.47987103e-01 -8.62208068e-01
5.45528650e-01 6.63912952e-01 7.04031661e-02 1.31235182... | [14.799805641174316, 1.100234866142273] |
69f46333-05a5-491b-ae0d-3bc160aa5c7f | dataset-and-baseline-for-automatic-student | null | null | https://aclanthology.org/2022.lrec-1.219 | https://aclanthology.org/2022.lrec-1.219.pdf | Dataset and Baseline for Automatic Student Feedback Analysis | In this paper, we present a student feedback corpus, which contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, document-level opinion polarities and sentence separations. We develop... | ['Surangika Ranathunga', 'Hashan Maduwantha', 'Kushan Chamindu', 'Missaka Herath'] | null | null | null | null | lrec-2022-6 | ['aspect-extraction'] | ['natural-language-processing'] | [-4.58995700e-02 6.54402018e-01 -4.39914972e-01 -6.59465551e-01
-5.16958535e-01 -1.15418482e+00 6.97591007e-01 1.12171543e+00
-3.74669135e-01 5.96228480e-01 7.32387602e-01 -7.37028897e-01
-1.16835438e-01 -7.93556869e-01 -4.71182540e-02 -6.01107538e-01
2.69752651e-01 4.46944773e-01 1.71329334e-01 -5.90284824... | [11.304617881774902, 6.802974700927734] |
17d1e310-156a-44eb-a947-6b512f23a17b | co-teaching-for-unsupervised-domain | 2204.01210 | null | https://arxiv.org/abs/2204.01210v2 | https://arxiv.org/pdf/2204.01210v2.pdf | Co-Teaching for Unsupervised Domain Adaptation and Expansion | Unsupervised Domain Adaptation (UDA) is known to trade a model's performance on a source domain for improving its performance on a target domain. To resolve the issue, Unsupervised Domain Expansion (UDE) has been proposed recently to adapt the model for the target domain as UDA does, and in the meantime maintain its pe... | ['Xirong Li', 'Qijie Wei', 'Kaibin Tian'] | 2022-04-04 | null | null | null | null | ['scene-segmentation', 'unsupervised-domain-expansion'] | ['computer-vision', 'methodology'] | [ 2.01276451e-01 3.68727565e-01 -1.35527849e-01 -3.81261826e-01
-3.81163478e-01 -6.27729893e-01 4.21054989e-01 2.00096384e-01
-2.70970851e-01 7.67284870e-01 -3.22602987e-01 -3.49718809e-01
-2.52075523e-01 -8.76241088e-01 -6.76260948e-01 -9.06751454e-01
5.21192133e-01 6.98132098e-01 7.89165020e-01 -1.82957232... | [10.227041244506836, 2.824460029602051] |
3292874f-9b6f-4ee4-833d-dccf52d17ac2 | continuous-sign-language-recognition-through-1 | null | null | https://www.mdpi.com/1424-8220/21/7/2437 | https://www.mdpi.com/1424-8220/21/7/2437 | Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network | Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs. Most of the existing methods focus mainly on the extraction of spatio-temporal visual fe... | ['Petros Daras', 'Kosmas Dimitropoulos', 'Ilias Papastratis'] | 2021-04-01 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 4.98224854e-01 -2.08080038e-01 1.55186981e-01 -4.36380625e-01
-7.78739512e-01 -4.34220731e-01 8.61113012e-01 -1.01338995e+00
-5.99875033e-01 6.05889916e-01 4.82277155e-01 -2.22437367e-01
6.91633150e-02 -6.56603336e-01 -5.74918985e-01 -9.75864232e-01
1.28559530e-01 3.22480559e-01 1.26873667e-04 -1.21019170... | [9.194379806518555, -6.510060787200928] |
ec5fa778-752b-4ab0-805e-de57008f949e | analytical-engines-with-context-rich | 2212.07517 | null | https://arxiv.org/abs/2212.07517v1 | https://arxiv.org/pdf/2212.07517v1.pdf | Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics | As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such formats are unsuitable for RDBMS. To tap into the dark data, domain exper... | ['Anastasia Ailamaki', 'Viktor Sanca'] | 2022-12-14 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-1.79250482e-02 -1.51851878e-01 -6.50647134e-02 -3.48960221e-01
-8.49723339e-01 -6.73250139e-01 1.90760374e-01 6.99200571e-01
-2.14082494e-01 1.72430947e-01 -4.63538663e-03 -6.23148561e-01
-2.32398942e-01 -9.95354831e-01 -5.51827908e-01 9.47300047e-02
-2.60682218e-02 9.07508552e-01 4.78889376e-01 -2.38955989... | [9.187161445617676, 7.493495464324951] |
9a8643c7-a3e2-4c3e-820a-b19773ed3ff4 | a-generative-deep-learning-approach-to | 2204.02028 | null | https://arxiv.org/abs/2204.02028v2 | https://arxiv.org/pdf/2204.02028v2.pdf | A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts | Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Genera... | ['Tim N. Palmer', 'Peter D. Dueben', 'Matthew Chantry', 'Andrew T. T. McRae', 'Lucy Harris'] | 2022-04-05 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 4.14854348e-01 -3.11584938e-02 4.18387204e-01 -4.00207222e-01
-9.63287055e-01 -7.77269781e-01 1.04533160e+00 -2.01302767e-01
-1.54964566e-01 1.53394997e+00 3.02790761e-01 -3.54886919e-01
-6.09366223e-02 -1.28651667e+00 -5.41818261e-01 -1.03911316e+00
-3.99179995e-01 5.36965668e-01 -3.70338589e-01 -6.50911391... | [6.601593494415283, 2.9043047428131104] |
311e7e0e-6dd7-44e6-aa96-3d1146d0b4ff | a-survey-of-natural-language-generation | 2112.11739 | null | https://arxiv.org/abs/2112.11739v2 | https://arxiv.org/pdf/2112.11739v2.pdf | A Survey of Natural Language Generation | This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis o... | ['Min Yang', 'Ying Shen', 'Junxin Li', 'Miaoxin Chen', 'Haifan Gong', 'Yinghui Li', 'Chenhe Dong'] | 2021-12-22 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.16359216e-01 5.84029794e-01 -1.25486106e-01 1.23918742e-01
-5.20885527e-01 -5.60815454e-01 1.13675642e+00 -1.77863970e-01
1.46830101e-02 1.10495448e+00 8.19040477e-01 -1.68576062e-01
1.62366658e-01 -1.12111139e+00 -2.42551133e-01 -5.83266854e-01
1.02780700e-01 7.64103293e-01 -1.09111035e+00 -5.60222328... | [11.963883399963379, 9.184344291687012] |
0ed66d7f-3693-49cb-bd6f-accb9f4d2634 | a-multi-perspective-architecture-for-semantic | 2005.06980 | null | https://arxiv.org/abs/2005.06980v1 | https://arxiv.org/pdf/2005.06980v1.pdf | A Multi-Perspective Architecture for Semantic Code Search | The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previo... | ['JinJun Xiong', 'Lingfei Wu', 'Julia Hockenmaier', 'Rajarshi Haldar'] | 2020-05-06 | a-multi-perspective-architecture-for-semantic-1 | https://aclanthology.org/2020.acl-main.758 | https://aclanthology.org/2020.acl-main.758.pdf | acl-2020-6 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.26196006e-01 -2.57103831e-01 -3.82348567e-01 -5.00390053e-01
-1.32361901e+00 -6.31819189e-01 6.37171507e-01 3.85106117e-01
-1.55585304e-01 -2.38710687e-01 4.25339311e-01 -2.65177220e-01
-1.12232931e-01 -4.18525457e-01 -5.65909564e-01 1.73494726e-01
1.92453012e-01 4.01321620e-01 -1.25607818e-01 -1.60759017... | [7.542616844177246, 8.013815879821777] |
9fd256e0-2c2f-45c2-b0f0-0cf0e63a458d | accurate-brain-extraction-using-active-shape | 1802.01268 | null | https://arxiv.org/abs/1802.01268v3 | https://arxiv.org/pdf/1802.01268v3.pdf | ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks | Brain extraction (skull stripping) is a challenging problem in neuroimaging. It is due to the variability in conditions from data acquisition or abnormalities in images, making brain morphology and intensity characteristics changeable and complicated. In this paper, we propose an algorithm for skull stripping in Magnet... | ['Pham T. Bao', 'Thu Nguyen', 'Mai T. N. Truong', 'Nguyen A. Triet', 'Khanh T. Tran', 'Duy M. Nguyen', 'Duy H. M. Nguyen', 'Binh T. Nguyen'] | 2018-02-05 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 4.68424618e-01 8.17197859e-02 2.44280428e-01 -5.26924074e-01
-1.88908145e-01 -1.56377196e-01 2.72944301e-01 2.63283923e-02
-7.46529222e-01 6.12476707e-01 -6.91170618e-02 3.56911346e-02
-9.17693898e-02 -5.52041471e-01 -3.27116191e-01 -8.31889629e-01
-1.90395698e-01 4.79378134e-01 6.41328573e-01 2.60802120... | [14.250799179077148, -2.3229470252990723] |
ffc79d9c-cef0-4528-b2bb-988d0b51d7a8 | m2fpa-a-multi-yaw-multi-pitch-high-quality | 1904.00168 | null | https://arxiv.org/abs/1904.00168v2 | https://arxiv.org/pdf/1904.00168v2.pdf | M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis | Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi... | ['Pei-Pei Li', 'Yibo Hu', 'Xiang Wu', 'Zhenan Sun', 'Ran He'] | 2019-03-30 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-1.88035429e-01 -5.29001094e-02 -1.54125169e-02 -6.98585451e-01
-8.82647455e-01 -4.83425319e-01 3.96640271e-01 -1.30098832e+00
3.38412791e-01 3.68994325e-01 1.82859063e-01 3.65127504e-01
2.06352845e-01 -5.37770689e-01 -6.18938327e-01 -1.17618775e+00
1.75464094e-01 3.47047031e-01 -5.82198739e-01 -2.26075262... | [13.172333717346191, 0.34851035475730896] |
d62004b7-406f-4748-9735-0bb9a98d3143 | dtp-net-a-convolutional-neural-network-model | null | null | https://www.sciencedirect.com/science/article/pii/S0010482522006047 | https://doi.org/10.1016/j.compbiomed.2022.105852 | DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images | Highly focused images of skin captured with ordinary cameras, called macro-images, are extensively used in dermatology. Being highly focused views, the macro-images contain only lesions and background regions. Hence, the localization of lesions on the macro-images is a simple thresholding problem. However, algorithms t... | ['Malaya Kumar Nath', 'M Vipin Das', 'Justin Joseph', 'Vipin Venugopal'] | 2022-07-12 | null | null | null | computers-in-biology-and-medicine-2022-7 | ['skin-lesion-segmentation'] | ['medical'] | [ 4.48472142e-01 5.51320538e-02 -1.90890536e-01 -4.31912839e-01
-5.42635262e-01 -3.42061967e-01 -2.00792849e-01 3.70903850e-01
-6.10101819e-01 5.29241383e-01 -4.57369208e-01 9.36562642e-02
-1.56989187e-01 -9.04680908e-01 -2.72473991e-01 -1.05948317e+00
2.15853781e-01 1.69521406e-01 6.00764036e-01 2.52570331... | [15.606273651123047, -3.0160486698150635] |
163a1a88-76ad-4640-a114-1aa072a8d814 | hd-bind-encoding-of-molecular-structure-with | 2303.15604 | null | https://arxiv.org/abs/2303.15604v1 | https://arxiv.org/pdf/2303.15604v1.pdf | HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations | Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying ``hit'' molecules from a large collection of potential drug-like candidates have relied on biophysical theory to comp... | ['Tajana S. Rosing', 'Niema Moshiri', 'Weihong Xu', 'Jaeyoung Kang', 'Behnam Khaleghi', 'Xiaohua Zhang', 'Jonathan E. Allen', 'Derek Jones'] | 2023-03-27 | null | null | null | null | ['molecular-docking', 'molecular-property-prediction'] | ['medical', 'miscellaneous'] | [ 4.69701231e-01 -4.49184030e-01 -6.37516856e-01 -3.36709052e-01
-1.08750629e+00 -6.23599529e-01 5.78476429e-01 8.74159217e-01
-3.56295317e-01 1.22735071e+00 -3.57311219e-01 -8.36088181e-01
-3.40544999e-01 -8.13238382e-01 -7.86630094e-01 -8.84908557e-01
-5.02924800e-01 6.36619329e-01 1.55505642e-01 1.65908728... | [5.108695983886719, 5.6297407150268555] |
00efcb8a-c83f-402b-ab25-b6beb35e8bfd | post-training-model-quantization-using-gans | 2305.06052 | null | https://arxiv.org/abs/2305.06052v1 | https://arxiv.org/pdf/2305.06052v1.pdf | Post-training Model Quantization Using GANs for Synthetic Data Generation | Quantization is a widely adopted technique for deep neural networks to reduce the memory and computational resources required. However, when quantized, most models would need a suitable calibration process to keep their performance intact, which requires data from the target domain, such as a fraction of the dataset us... | ['Raymond Lo', 'Zhuo Wu', 'Alexander Kozlov', 'Adrian Boguszewski', 'Mansi Sharma', 'Athanasios Masouris'] | 2023-05-10 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.63045073e-01 3.01698565e-01 6.33196309e-02 -2.39695713e-01
-7.47666597e-01 -5.64934850e-01 7.41026282e-01 -9.55176875e-02
-7.03111410e-01 8.43812883e-01 -2.18505070e-01 -3.75562608e-01
4.46292818e-01 -1.05297136e+00 -9.98142779e-01 -6.28078759e-01
2.26962775e-01 4.06602055e-01 1.13491505e-01 -1.23556815... | [8.782267570495605, 2.9094738960266113] |
91cd5b67-acbe-4f27-99bb-61c13930ed0c | prokaryotic-genome-editing-based-on-the | 2305.05093 | null | https://arxiv.org/abs/2305.05093v1 | https://arxiv.org/pdf/2305.05093v1.pdf | Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system | Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas systems, however, non-self-targeting genome editing based on a single Cas3 of type I CRISPR-Cas systems has not been reported. Here, we present the subtype I-B-Svi CRISPR-Cas system (with three confirmed CRISPRs and a cas gene cluster) and geno... | ['Xue Li', 'Yan Sun', 'Su-Li Cao', 'Qing-Yang Liu', 'Ting-Ting Xia', 'Xing-Wang Yang', 'Yan Zhang', 'Cai-Hua Qiu', 'Xin Xu', 'De-Xiang Yong', 'Wang-Yu Tong'] | 2023-05-08 | null | null | null | null | ['type'] | ['speech'] | [ 1.08717310e+00 -2.43343581e-02 8.49313214e-02 3.54010224e-01
-3.75775278e-01 -1.64748490e+00 4.55698192e-01 6.64083600e-01
-1.57412440e-01 8.82690012e-01 -2.55621105e-01 -6.92467451e-01
-1.52628243e-01 -6.34277701e-01 -1.28488266e+00 -8.66682291e-01
2.15968322e-02 -2.44588912e-01 5.17404795e-01 -5.28676987... | [4.865823745727539, 5.062830448150635] |
5706794f-be75-4ccd-aece-d59388c9a725 | interpretable-deep-learning-based-forensic | 2112.00849 | null | https://arxiv.org/abs/2112.00849v2 | https://arxiv.org/pdf/2112.00849v2.pdf | Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition | Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged... | ['Eric Benjamin', 'Dennis Chute', 'Patrick Flynn', 'Kevin Bowyer', 'Adam Czajka', 'Aidan Boyd', 'Andrey Kuehlkamp'] | 2021-12-01 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 1.46216610e-02 1.16022199e-01 7.28532001e-02 -9.56130251e-02
-3.75407040e-01 -3.67274433e-01 3.65315914e-01 1.73079699e-01
-5.10257542e-01 5.29228032e-01 1.25780672e-01 -3.68306190e-01
-2.77029127e-01 -2.33439058e-01 -1.50536686e-01 -7.26421833e-01
-6.21065795e-02 5.68660438e-01 -3.70068252e-01 3.19142729... | [3.7450945377349854, -3.630293369293213] |
80ecccbc-0940-47be-9899-c9761ea9c098 | towards-a-common-understanding-of | 2305.16768 | null | https://arxiv.org/abs/2305.16768v1 | https://arxiv.org/pdf/2305.16768v1.pdf | Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review | In recent years, pre-trained Multilingual Language Models (MLLMs) have shown a strong ability to transfer knowledge across different languages. However, given that the aspiration for such an ability has not been explicitly incorporated in the design of the majority of MLLMs, it is challenging to obtain a unique and str... | ['Shohreh Haddadan', 'Siwen Guo', 'Fred Philippy'] | 2023-05-26 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-8.82262066e-02 6.78481981e-02 -8.55870008e-01 -4.19540018e-01
-1.13733101e+00 -9.15680289e-01 7.02928841e-01 1.82287797e-01
-6.78328812e-01 7.40531266e-01 3.04988950e-01 -9.68744278e-01
-1.97880380e-02 -2.68715411e-01 -7.66973913e-01 -2.38847002e-01
1.49859428e-01 1.63701773e-01 -1.99791491e-01 -1.00444727... | [10.880279541015625, 9.859192848205566] |
67d69db5-7081-4b81-a1da-7881ca571051 | high-frequency-stereo-matching-network | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.pdf | High-Frequency Stereo Matching Network | In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency inf... | ['Yong Zhao', 'Yitong Yang', 'Jie Chen', 'Yongjun Zhang', 'Huizhou Zhou', 'Haoliang Zhao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['stereo-matching-1'] | ['computer-vision'] | [-4.83657643e-02 -1.55301765e-01 1.16051726e-01 -3.66390616e-01
-6.79802835e-01 -4.19723690e-01 7.16565967e-01 -2.12747931e-01
-5.07551968e-01 5.75088143e-01 5.92450202e-01 -5.18578663e-02
1.39121786e-01 -6.92886829e-01 -6.48633659e-01 -5.45286298e-01
2.68723935e-01 -8.21498558e-02 5.07051885e-01 -5.26980102... | [8.813682556152344, -2.2191052436828613] |
848da995-7eec-45de-855d-2ecf650b61e3 | surgical-phase-and-instrument-recognition-how | 2306.16879 | null | https://arxiv.org/abs/2306.16879v1 | https://arxiv.org/pdf/2306.16879v1.pdf | Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits | Purpose: The development of machine learning models for surgical workflow and instrument recognition from temporal data represents a challenging task due to the complex nature of surgical workflows. In particular, the imbalanced distribution of data is one of the major challenges in the domain of surgical workflow reco... | ['Sandy Engelhardt', 'Bernhard Preim', 'Ivo Wolf', 'Benedikt Mayer', 'Lalith Sharan', 'Georgii Kostiuchik'] | 2023-06-29 | null | null | null | null | ['instrument-recognition', 'data-visualization', 'data-visualization'] | ['audio', 'methodology', 'miscellaneous'] | [ 3.37429121e-02 -2.11915374e-01 -2.55617857e-01 -1.76613584e-01
-4.23391312e-01 -6.86329126e-01 1.09246731e-01 1.03310955e+00
-4.49393988e-01 5.46833217e-01 4.83836792e-02 -6.74498320e-01
-6.73232019e-01 -4.53243434e-01 -8.47211033e-02 -7.95109272e-01
-1.65312603e-01 7.99596131e-01 -7.62552768e-02 4.38058861... | [14.039698600769043, -3.1536083221435547] |
54cf08de-de0e-46c7-8fd0-7ef6c64197c2 | context-dependent-domain-adversarial-neural | null | null | https://www.isca-speech.org/archive/interspeech_2020/lian20b_interspeech.html | https://www.isca-speech.org/archive/pdfs/interspeech_2020/lian20b_interspeech.pdf | Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition | Emotion recognition remains a complex task due to speaker variations and low-resource training samples. To address these difficulties, we focus on the domain adversarial neural networks (DANN) for emotion recognition. The primary task is to predict emotion labels. The secondary task is to learn a common representation ... | ['Rongjun Li', 'Zhanlei Yang', 'Jian Huang', 'Bin Liu', 'JianHua Tao', 'Zheng Lian'] | 2020-10-28 | null | null | null | interspeech-2020-10 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.23906547e-01 -2.94316024e-01 9.05084312e-02 -5.65513968e-01
-7.16059446e-01 -5.69790602e-01 3.92473400e-01 -3.87156576e-01
-3.72302175e-01 7.41708279e-01 2.43203178e-01 6.79118261e-02
3.97167981e-01 -3.28974962e-01 -3.78134221e-01 -8.95515025e-01
3.33583266e-01 2.31573619e-02 -2.84956187e-01 -1.75235093... | [13.47658634185791, 5.739260673522949] |
83e24dad-bf68-425c-89ad-6b2721b1cd74 | using-integrated-gradients-to-explain | 2106.07349 | null | https://arxiv.org/abs/2106.07349v2 | https://arxiv.org/pdf/2106.07349v2.pdf | Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT | Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical. It has applications in several use cases like Question-Answering, Natural Language Generation, Neural Machine Translation, where grammatical correctness is crucial. In this paper we aim to understand the decision-mak... | ['Hari Prasad Timmapathini', 'Anmol Nayak'] | 2021-06-01 | null | https://aclanthology.org/2021.icon-main.11 | https://aclanthology.org/2021.icon-main.11.pdf | icon-2021-12 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 9.09828469e-02 6.51303411e-01 1.31352693e-01 -8.50712061e-01
-8.62314105e-01 -7.11847007e-01 5.48193872e-01 4.55241770e-01
-2.76581347e-01 6.83163643e-01 3.46775681e-01 -7.60175943e-01
8.89701098e-02 -6.98715448e-01 -8.81063700e-01 -2.88779110e-01
-3.40216048e-02 2.61029392e-01 -3.81196737e-02 -3.24650198... | [10.67893123626709, 9.510418891906738] |
58d9c0be-9808-4002-a3b1-97d89829c63c | why-a-naive-way-to-combine-symbolic-and | null | null | https://openreview.net/forum?id=JQHqeGx6qFw | https://openreview.net/pdf?id=JQHqeGx6qFw | Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well | We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essential differences between symbolic and latent approaches. Based on our insights, we construct a simpl... | ['Heiner Stuckenschmidt', 'Patrick Betz', 'Christian Meilicke'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 1.46928802e-01 4.44351226e-01 -4.32579070e-01 -1.86347976e-01
-3.88758540e-01 -6.16403818e-01 1.08323514e+00 4.56576943e-01
-1.61296979e-01 5.92316210e-01 2.31934413e-01 -4.11590904e-01
-7.57371485e-01 -9.51447785e-01 -4.94743675e-01 -2.92194970e-02
-2.94499815e-01 5.29186904e-01 5.31929493e-01 -2.25864023... | [8.94846248626709, 7.609666347503662] |
25426063-8675-471f-9213-c2add18336d0 | one-class-slab-support-vector-machine | 1608.01026 | null | http://arxiv.org/abs/1608.01026v1 | http://arxiv.org/pdf/1608.01026v1.pdf | One-Class Slab Support Vector Machine | This work introduces the one-class slab SVM (OCSSVM), a one-class classifier
that aims at improving the performance of the one-class SVM. The proposed
strategy reduces the false positive rate and increases the accuracy of
detecting instances from novel classes. To this end, it uses two parallel
hyperplanes to learn the... | ['Joao Hespanha', 'Walter Scheirer', 'Victor Fragoso', 'Matthew Turk'] | 2016-08-02 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 7.35575482e-02 -1.86911765e-02 -7.67751575e-01 -5.46645224e-01
-4.56867307e-01 -3.52805734e-01 4.79887187e-01 1.22593239e-01
-2.23885536e-01 1.01658106e+00 -7.82595813e-01 -5.95311999e-01
-7.38846837e-03 -6.10976934e-01 -4.65604514e-01 -9.85587716e-01
5.44748222e-03 3.78284216e-01 1.10589075e+00 9.26988199... | [8.286130905151367, 3.9888510704040527] |
ec98155b-e904-44bc-b82c-004cc0066af9 | computing-with-subjectivity-lexicons | null | null | https://aclanthology.org/2020.lrec-1.400 | https://aclanthology.org/2020.lrec-1.400.pdf | Computing with Subjectivity Lexicons | In this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese. Besides the non-English idiom, in contrast to other subjectivity lexicons available, these lexicons represent different subjectivity dimensions (other than sentiment) and are more compact in ... | ['ro', 'Le Balby Marinho', 'Claudio E. C. Campelo', 'Allan Sales', 'Roberta Viola', 'Caio L. M. Jeronimo', 'Adriano Veloso'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['automated-essay-scoring', 'news-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-3.57232302e-01 3.91276240e-01 -6.02716386e-01 -1.78222045e-01
-2.71618754e-01 -9.21456218e-01 1.12862039e+00 6.34011626e-01
-4.98660654e-01 7.05359876e-01 9.10907209e-01 -2.03677833e-01
-8.82877037e-02 -9.01145101e-01 7.44548813e-02 -3.31284016e-01
5.27405560e-01 4.45270330e-01 3.82794277e-03 -9.10631418... | [10.40462589263916, 9.01523494720459] |
dd4c2eb1-1b01-4ab5-88ae-41c84d2f551f | fibinet-improving-fibinet-by-greatly-reducing | 2209.05016 | null | https://arxiv.org/abs/2209.05016v1 | https://arxiv.org/pdf/2209.05016v1.pdf | FiBiNet++:Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction | Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed to resolve this problem. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset.However, the l... | ['Junlin Zhang', 'PengTao Zhang'] | 2022-09-12 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-3.69038403e-01 -4.82030034e-01 -2.76253641e-01 -4.18947376e-02
-5.81979752e-01 -2.68506467e-01 6.63768470e-01 -3.09669793e-01
-6.43704891e-01 5.11268616e-01 4.24033105e-01 -4.19330269e-01
9.42392722e-02 -6.98939264e-01 -2.45639727e-01 -5.76325655e-01
3.43052119e-01 3.33172977e-01 5.38987041e-01 -3.07441473... | [10.158974647521973, 5.568612575531006] |
b7be5475-e882-45dd-b03b-820fc52c977c | reconfigurable-and-intelligent-ultra-wideband | 2008.06085 | null | https://arxiv.org/abs/2008.06085v1 | https://arxiv.org/pdf/2008.06085v1.pdf | Reconfigurable and Intelligent Ultra-Wideband Angular Sensing: Prototype Design and Validation | The emergence of beyond-licensed spectrum sharing in FR1 (0.45-6 GHz) and FR2 (24 - 52 GHz) along with the multi-antenna narrow-beam based directional transmissions demand a wideband spectrum sensing in temporal as well as spatial domains. We referred to it as ultra-wideband angular spectrum sensing (UWAS), and it cons... | ['Bhavani Shankar Mysore Rama Rao', 'Mohammad Alaee-Kerahroodi', 'Sumit J. Darak', 'Himani Joshi'] | 2020-08-13 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.44132066e-01 -1.04242742e-01 -2.98149556e-01 8.82793739e-02
-9.11662877e-01 -7.80031025e-01 2.44146362e-02 -6.74486339e-01
5.77188432e-02 1.08312762e+00 9.96305346e-02 -6.53976202e-01
-1.13262057e+00 -8.78284812e-01 -3.36892396e-01 -8.14759076e-01
-4.05832082e-01 2.45007992e-01 -3.40308666e-01 -2.84401923... | [6.394676685333252, 1.2147794961929321] |
9d308940-55d5-49f3-8dc8-681ce08be245 | unifying-cardiovascular-modelling-with-deep | 2101.08477 | null | https://arxiv.org/abs/2101.08477v3 | https://arxiv.org/pdf/2101.08477v3.pdf | Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment | Sepsis is a potentially life threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, ther... | ['David Swigon', 'Christopher James Langmead', 'Gilles Clermont', 'Thesath Nanayakkara'] | 2021-01-21 | null | null | null | null | ['distributional-reinforcement-learning', 'clinical-knowledge'] | ['methodology', 'miscellaneous'] | [-4.63400222e-02 -1.92114115e-01 -9.39146504e-02 -6.10829815e-02
-1.84975177e-01 -4.46798444e-01 3.10422853e-02 7.95910537e-01
-4.15065259e-01 1.01665032e+00 5.31050980e-01 -5.41994750e-01
-3.94656539e-01 -6.14295781e-01 -5.43919742e-01 -9.27162051e-01
-3.22020859e-01 6.96192026e-01 -4.81889784e-01 -9.08059031... | [4.011142253875732, 2.7374656200408936] |
fee78bbd-1f2b-48aa-987c-df963a371ab8 | debiased-learning-from-naturally-imbalanced | 2201.01490 | null | https://arxiv.org/abs/2201.01490v2 | https://arxiv.org/pdf/2201.01490v2.pdf | Debiased Learning from Naturally Imbalanced Pseudo-Labels | Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e.g., in a semi-supervised learning setting. Our key insight is that pseudo-labels are naturally imbalanced due to intrinsic data similarity,... | ['Stella X. Yu', 'Long Lian', 'Zhirong Wu', 'Xudong Wang'] | 2022-01-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 4.20355469e-01 6.80236220e-01 -9.62696731e-01 -8.29918325e-01
-1.03658926e+00 -5.15333593e-01 4.88041729e-01 2.35132948e-01
-2.32551366e-01 1.02986383e+00 2.31162965e-01 -1.52906477e-01
2.89565772e-01 -4.45300788e-01 -9.37841356e-01 -7.33672082e-01
4.29634333e-01 7.26803184e-01 1.56837478e-02 8.19192603... | [9.41135311126709, 3.819347620010376] |
6f5fd993-d877-4205-a235-b8d51764ebab | may-the-force-be-with-your-copy-mechanism-1 | 2112.10360 | null | https://arxiv.org/abs/2112.10360v1 | https://arxiv.org/pdf/2112.10360v1.pdf | May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation | Recent neural sequence-to-sequence models with a copy mechanism have achieved remarkable progress in various text generation tasks. These models addressed out-of-vocabulary problems and facilitated the generation of rare words. However, the identification of the word which needs to be copied is difficult, as observed b... | ['Yeonsoo Lee', 'Hyungjong Noh', 'Jeong-in Hwang', 'Sanghyuk Choi'] | 2021-12-20 | may-the-force-be-with-your-copy-mechanism | https://arxiv.org/abs/2112.10360 | https://arxiv.org/pdf/2112.10360.pdf | arxiv-2021-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 7.07693994e-01 2.39743322e-01 -3.65085602e-01 -2.11625323e-01
-6.15747809e-01 -5.09889960e-01 8.56546462e-01 1.80029646e-01
-2.40120023e-01 1.07763708e+00 8.21169257e-01 -9.22871009e-02
8.87894779e-02 -8.34888816e-01 -7.73647189e-01 -5.35359740e-01
5.49995899e-01 5.68925619e-01 -1.11308672e-01 -5.66128790... | [11.980965614318848, 9.117383003234863] |
c2e94760-9a1e-4e0b-af5e-32d15d3b9005 | deception-detection-for-the-russian-language | null | null | https://aclanthology.org/W17-7701 | https://aclanthology.org/W17-7701.pdf | Deception Detection for the Russian Language: Lexical and Syntactic Parameters | The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks... | ['Olga Litvinova', 'Dina Pisarevskaya', 'Tatiana Litvinova'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['deception-detection'] | ['miscellaneous'] | [-1.24173567e-01 -1.47700921e-01 -1.00922897e-01 -6.20715916e-01
-3.11936855e-01 -8.75366986e-01 9.64465678e-01 7.54911304e-01
-8.31175804e-01 7.45318770e-01 5.70883930e-01 -7.39659011e-01
-1.40686601e-01 -4.07943815e-01 -1.78709358e-01 -5.35727561e-01
1.24094471e-01 2.93099046e-01 4.49031740e-02 -3.22664976... | [8.273853302001953, 10.413647651672363] |
107b3508-27b9-4e63-a87b-edf72b73105f | towards-fully-automated-deep-learning-based | 2212.07497 | null | https://arxiv.org/abs/2212.07497v1 | https://arxiv.org/pdf/2212.07497v1.pdf | Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary? | State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also known as skull-stripping) and manual correction by an expert. However, f... | ['Danilo Silva', 'Guilherme de Souza e Cassia', 'Bruno Machado Pacheco'] | 2022-12-14 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation', 'skull-stripping'] | ['computer-vision', 'medical', 'medical'] | [ 3.15623969e-01 2.74233311e-01 2.58535475e-01 -2.38422960e-01
-9.75581229e-01 -3.23219806e-01 5.15828788e-01 5.14665365e-01
-8.89169872e-01 6.04227185e-01 -1.96756229e-01 -5.11816204e-01
5.07506020e-02 -6.19108558e-01 -5.96606255e-01 -8.39473486e-01
2.09761679e-01 1.00212264e+00 6.92735195e-01 -4.24383953... | [14.445904731750488, -2.479663848876953] |
8e6d1063-228f-45b5-8c6c-9a750e2982a8 | unbiased-heterogeneous-scene-graph-generation | 2212.00443 | null | https://arxiv.org/abs/2212.00443v4 | https://arxiv.org/pdf/2212.00443v4.pdf | Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural Network | Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning... | ['Chanyoung Park', 'Jinyoung Moon', 'Kibum Kim', 'Kanghoon Yoon'] | 2022-12-01 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 4.11666423e-01 2.36783594e-01 -3.02498806e-02 -4.32375371e-01
-1.45203024e-01 -8.11238308e-03 8.40404153e-01 4.36295569e-01
-6.70690686e-02 3.60875815e-01 2.22491547e-01 -1.59116462e-01
-2.04911739e-01 -1.40471244e+00 -9.70679164e-01 -5.66313028e-01
-2.79157132e-01 2.61105835e-01 5.35980165e-01 -2.02524304... | [10.297904014587402, 1.6214174032211304] |
65a61b2e-4c84-401b-b1cf-8249a743eda8 | pointclimb-an-exemplar-free-point-cloud-class | 2304.06775 | null | https://arxiv.org/abs/2304.06775v1 | https://arxiv.org/pdf/2304.06775v1.pdf | PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark | Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class incremental learning can aid endless application in 3D-computer vision. Well known 3D-point cloud class incremental learning methods for addre... | ['Uma Mudenagudi', 'Ramesh Ashok Tabib', 'Tejas Anvekar', 'Shivanand Kundargi'] | 2023-04-13 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-6.22804761e-02 -1.30080134e-01 -1.89372495e-01 -2.33686835e-01
-4.80876744e-01 -8.20908129e-01 9.07493055e-01 6.35206163e-01
-3.91627580e-01 5.62449396e-01 -6.43483341e-01 -5.30979097e-01
-3.62220734e-01 -6.70568168e-01 -1.21017301e+00 -3.81309122e-01
-6.17450953e-01 9.84636903e-01 6.80469453e-01 -1.00193426... | [8.00820541381836, -3.1840646266937256] |
e7735b9e-8136-4893-968c-f92d727dc182 | conversational-search-with-mixed-initiative | 2112.07308 | null | https://arxiv.org/abs/2112.07308v2 | https://arxiv.org/pdf/2112.07308v2.pdf | Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval | We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs. We focus on the task of... | ['David Konopnicki', 'Asaf Yehudai', 'Doron Cohen', 'Yosi Mass'] | 2021-12-14 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.86104202e-01 2.45348126e-01 -7.65507594e-02 -4.38275337e-01
-1.52952170e+00 -7.98799753e-01 9.25914168e-01 4.17055130e-01
-5.42740166e-01 7.24385619e-01 8.00774574e-01 -6.00188851e-01
-2.37633273e-01 -3.72107267e-01 -7.42561966e-02 5.38908057e-02
4.07324791e-01 1.16097438e+00 2.06448004e-01 -6.36813283... | [12.13261604309082, 7.817619323730469] |
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