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fd8fd4ca-62d4-4439-96e7-2e20fecdc855 | unsupervised-training-of-a-deep-clustering | 1904.01340 | null | http://arxiv.org/abs/1904.01340v1 | http://arxiv.org/pdf/1904.01340v1.pdf | Unsupervised training of a deep clustering model for multichannel blind source separation | We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on de... | ['Reinhold Haeb-Umbach', 'Daniel Hasenklever', 'Lukas Drude'] | 2019-04-02 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [-8.28108639e-02 -4.24749628e-02 2.78645217e-01 -4.17551130e-01
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-2.66125321e-01 -3.78598332e-01 -7.89729178e-01 -1.19672704e+00
-9.11073089e-02 5.76548636e-01 -6.87512010e-02 1.15890712... | [9.164896011352539, 3.2286343574523926] |
5b089d3f-a9e7-4203-b461-ce95700d331c | radar-de-parite-an-nlp-system-to-measure | 2304.09982 | null | https://arxiv.org/abs/2304.09982v1 | https://arxiv.org/pdf/2304.09982v1.pdf | Radar de Parité: An NLP system to measure gender representation in French news stories | We present the Radar de Parit\'e, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system's architecture and detail the challenges we overcame to address French-specific issues, in particular re... | ['Maite Taboada', 'Philipp Eibl', 'Prashanth Rao', 'Valentin-Gabriel Soumah'] | 2023-04-19 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-1.90228820e-02 8.17171514e-01 -8.86443555e-01 -5.36459446e-01
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2.91706890e-01 8.69567156e-01 -2.57143617e-01 -6.67182386... | [9.194657325744629, 9.975492477416992] |
0a3d498d-fc16-46c2-8a1a-4c7efffeed18 | scalable-deep-graph-clustering-with-random | 2112.15530 | null | https://arxiv.org/abs/2112.15530v2 | https://arxiv.org/pdf/2112.15530v2.pdf | Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning | Web-based interactions can be frequently represented by an attributed graph, and node clustering in such graphs has received much attention lately. Multiple efforts have successfully applied Graph Convolutional Networks (GCN), though with some limits on accuracy as GCNs have been shown to suffer from over-smoothing iss... | ['Rajiv Ramnath', 'Gagan Agrawal', 'Ruoming Jin', 'Dong Li', 'Xiang Li'] | 2021-12-31 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.11901045e-01 2.92937070e-01 -1.46845937e-01 -2.13875309e-01
-6.15595996e-01 -6.23232186e-01 5.86183608e-01 3.71892124e-01
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d5048d48-ad98-4c23-8f1d-605ba5445872 | investigating-inner-properties-of-multimodal | 1711.05516 | null | http://arxiv.org/abs/1711.05516v2 | http://arxiv.org/pdf/1711.05516v2.pdf | Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics | Multimodal models have been proven to outperform text-based approaches on
learning semantic representations. However, it still remains unclear what
properties are encoded in multimodal representations, in what aspects do they
outperform the single-modality representations, and what happened in the
process of semantic c... | ['Cheng-qing Zong', 'Jiajun Zhang', 'Shaonan Wang', 'Nan Lin'] | 2017-11-15 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [ 3.79280597e-01 3.82824659e-01 -2.74166703e-01 -3.85585755e-01
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1.63953409e-01 4.88645583e-01 -2.84174502e-01 -4.04167503... | [10.636983871459961, 2.03340482711792] |
965280f6-20bc-4ecd-9096-2805a7f59a84 | virtuoso-massive-multilingual-speech-text | 2210.15447 | null | https://arxiv.org/abs/2210.15447v2 | https://arxiv.org/pdf/2210.15447v2.pdf | Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech | This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models. Existing multilingual TTS typically supports tens of languages, which are a small fraction of the thousands of languages in the world. One difficulty to scale multilingu... | ['Bhuvana Ramabhadran', 'Andrew Rosenberg', 'Ankur Bapna', 'Yu Zhang', 'Gary Wang', 'Nobuyuki Morioka', 'Zhehuai Chen', 'Heiga Zen', 'Takaaki Saeki'] | 2022-10-27 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [-3.89571674e-02 2.12831378e-01 -9.29761231e-02 -4.40715581e-01
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91f90b1d-ba9e-4bfc-9e56-9b23339deb86 | neural-ordinary-differential-equation-value | null | null | https://openreview.net/forum?id=8WKd467B8H | https://openreview.net/pdf?id=8WKd467B8H | Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces | Action spaces equipped with parameter sets are a common occurrence in reinforcement learning applications. Solutions to problems of this class have been developed under different frameworks, such as parametrized action Markov decision processes (PAMDP) or hierarchical reinforcement learning (HRL). These approaches ofte... | ['Jinkyoo Park', 'Hajime Asama', 'Atsushi Yamashita', 'Sanzhar Bakhtiyarov', 'Michael Poli', 'Stefano Massaroli'] | 2020-02-26 | null | null | null | iclr-workshop-deepdiffeq-2019-12 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.20671913e-01 4.22985345e-01 -2.23162845e-01 1.02517754e-02
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-6.81089044e-01 -7.94673979e-01 -6.02951944e-01 -1.06587183e+00
-3.24446499e-01 7.04826474e-01 4.19720083e-01 -6.30122900... | [4.185216426849365, 2.1412529945373535] |
ded0511c-c520-4822-a158-de9823280144 | sample-attackability-in-natural-language | 2306.12043 | null | https://arxiv.org/abs/2306.12043v1 | https://arxiv.org/pdf/2306.12043v1.pdf | Sample Attackability in Natural Language Adversarial Attacks | Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts have sought to identify which source samples are the most attackable or robust, i.e. can we determine for an unseen target model, which samples... | ['Mark Gales', 'Vyas Raina'] | 2023-06-21 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 1.69187412e-01 1.59055628e-02 -1.64071202e-01 -1.32841662e-01
-1.33674395e+00 -1.56691051e+00 1.22989297e+00 5.54722488e-01
-1.73818961e-01 5.07445216e-01 2.38963485e-01 -5.61823785e-01
-1.23833820e-01 -8.49210322e-01 -5.75363398e-01 -6.98826313e-01
-1.07225031e-01 6.96240962e-01 8.05959031e-02 -9.34010744... | [5.974251747131348, 7.972298622131348] |
70ab6c68-2753-4d4d-9079-26bd86df66e7 | negation-detection-for-clinical-text-mining | 2004.04980 | null | https://arxiv.org/abs/2004.04980v1 | https://arxiv.org/pdf/2004.04980v1.pdf | Negation Detection for Clinical Text Mining in Russian | Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is base... | ['Ksenia Balabaeva', 'Anastasia Funkner', 'Sergey Kovalchuk'] | 2020-04-10 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 1.10804923e-01 3.05160522e-01 -6.23178005e-01 -5.92598140e-01
-3.97957355e-01 -2.11960778e-01 4.06482726e-01 9.88113642e-01
-8.34882557e-01 1.18132925e+00 2.48759866e-01 -9.59729552e-01
-2.77103454e-01 -7.62380600e-01 -8.65227133e-02 -4.02172565e-01
-6.54536560e-02 6.49451256e-01 -2.64382660e-01 -5.07873118... | [8.447580337524414, 8.766731262207031] |
785eb824-eabb-4ae9-bd8a-602d8c28a850 | semi-supervised-learning-with-constraints-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Bauml_Semi-supervised_Learning_with_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Bauml_Semi-supervised_Learning_with_2013_CVPR_paper.pdf | Semi-supervised Learning with Constraints for Person Identification in Multimedia Data | We address the problem of person identification in TV series. We propose a unified learning framework for multiclass classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for mu... | ['Rainer Stiefelhagen', 'Martin Bauml', 'Makarand Tapaswi'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['person-identification'] | ['computer-vision'] | [ 3.60092849e-01 -2.24868670e-01 -4.52810228e-01 -1.24692178e+00
-1.31499767e+00 -8.11966002e-01 5.96878588e-01 -4.28265691e-01
-3.90512437e-01 7.52276301e-01 -1.59026995e-01 1.62524804e-01
1.40769318e-01 -3.63487780e-01 -5.91655493e-01 -6.13592863e-01
-2.85341054e-01 5.63416243e-01 -2.42320940e-01 2.38263890... | [13.595293998718262, 1.2800146341323853] |
106d4f81-647c-47fc-a96f-5a6b8b7a426b | confluence-of-artificial-intelligence-and | 2012.08545 | null | https://arxiv.org/abs/2012.08545v2 | https://arxiv.org/pdf/2012.08545v2.pdf | Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection | The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Har... | ['Ian Foster', 'Ben Blaiszik', 'Dawei Mu', 'Volodymyr Kindratenko', 'Daniel S. Katz', 'Maeve Heflin', 'Wei Wei', 'Ryan Chard', 'Maksim Levental', 'Minyang Tian', 'Xiaobo Huang', 'Asad Khan', 'E. A. Huerta'] | 2020-12-15 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.99909973e-01 -2.16650605e-01 5.18789440e-02 -4.04755957e-02
-6.31158590e-01 -6.99562132e-01 7.82972038e-01 2.74755627e-01
-2.00137004e-01 5.69334030e-01 -1.15879506e-01 -8.56200278e-01
-3.97453278e-01 -6.07248604e-01 -5.59855938e-01 -8.19935560e-01
-4.65372771e-01 1.45775998e+00 2.08607584e-01 -4.39870059... | [7.8827033042907715, 3.186253547668457] |
6cc1ff24-5557-4279-af4e-221d364a017d | utilizing-technical-data-to-discover-similar | 2301.04455 | null | https://arxiv.org/abs/2301.04455v1 | https://arxiv.org/pdf/2301.04455v1.pdf | Utilizing Technical Data to Discover Similar Companies in Dhaka Stock Exchange | Stock market investment have been an ideal form of investment for many years. Investing capitals smartly in stock market yields high profit returns. But there are many companies available in a market. Currently there are more than $345$ active companies who have stocks in Dhaka Stock Exchange (DSE). Analyzing all these... | ['Mohammad Shafiul Alam', 'Tahsin Aziz', 'Tashreef Muhammad'] | 2023-01-11 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-9.20901179e-01 1.42215148e-01 -2.63701111e-01 -1.59452483e-02
-2.38472730e-01 -6.99630141e-01 4.39753443e-01 1.07179463e-01
-2.78399199e-01 8.60338032e-01 8.30678195e-02 -5.54014564e-01
1.40871610e-02 -1.28550148e+00 -3.23283374e-01 -5.22695482e-01
-7.89585859e-02 6.19045794e-01 4.11832154e-01 -6.64073527... | [4.59636116027832, 4.23836612701416] |
b0b39e2c-6cab-40f2-b86c-ba2dae1aa911 | heuristic-algorithms-for-the-approximation-of | 2307.01639 | null | https://arxiv.org/abs/2307.01639v1 | https://arxiv.org/pdf/2307.01639v1.pdf | Heuristic Algorithms for the Approximation of Mutual Coherence | Mutual coherence is a measure of similarity between two opinions. Although the notion comes from philosophy, it is essential for a wide range of technologies, e.g., the Wahl-O-Mat system. In Germany, this system helps voters to find candidates that are the closest to their political preferences. The exact computation o... | ['Tamara Mchedlidze', 'Vera Chekan', 'Gregor Betz'] | 2023-07-04 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 2.52766013e-02 3.04976881e-01 -1.59287721e-01 -4.80838209e-01
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2.81703651e-01 1.27524018e+00 1.78772777e-01 -3.04325163... | [6.55049991607666, 4.661287307739258] |
b61617ac-0d03-4c53-ab68-554db575aa65 | explicit-and-implicit-models-in-infrared-and | 2206.09581 | null | https://arxiv.org/abs/2206.09581v1 | https://arxiv.org/pdf/2206.09581v1.pdf | Explicit and implicit models in infrared and visible image fusion | Infrared and visible images, as multi-modal image pairs, show significant differences in the expression of the same scene. The image fusion task is faced with two problems: one is to maintain the unique features between different modalities, and the other is to maintain features at various levels like local and global ... | ['Bin Sun', 'Zixuan Wang'] | 2022-06-20 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.59024879e-01 -4.97174084e-01 -1.25187263e-01 -4.38250422e-01
-9.56406772e-01 -1.43383041e-01 3.58329207e-01 -9.33650210e-02
-1.98681146e-01 5.79102457e-01 1.87801912e-01 1.94731399e-01
-4.94557112e-01 -7.29032755e-01 -1.91015363e-01 -1.28690827e+00
6.59082159e-02 -2.80048072e-01 -6.15523607e-02 -3.24047148... | [10.494902610778809, -1.7759027481079102] |
9698684d-b655-4864-8ec9-cbbf6caaf1df | descriptive-analysis-of-computational-methods | 2010.03378 | null | https://arxiv.org/abs/2010.03378v1 | https://arxiv.org/pdf/2010.03378v1.pdf | Descriptive analysis of computational methods for automating mammograms with practical applications | Mammography is a vital screening technique for early revealing and identification of breast cancer in order to assist to decrease mortality rate. Practical applications of mammograms are not limited to breast cancer revealing, identification ,but include task based lens design, image compression, image classification, ... | ['Manish Joshi', 'Aparna Bhale'] | 2020-10-06 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 9.58353281e-01 2.95865148e-01 -2.01394483e-01 -5.91976523e-01
-4.81033862e-01 -1.51025951e-01 3.57912034e-01 6.08405054e-01
-3.59570354e-01 2.16396868e-01 1.20485365e-01 -7.70335376e-01
-3.42159182e-01 -8.87260675e-01 -2.69864351e-01 -6.35015130e-01
-3.26286882e-01 6.11089647e-01 4.24764901e-01 -5.49858138... | [15.198606491088867, -2.5521717071533203] |
cb7522ac-9fa8-4a64-91a6-4ea3bf29e291 | st-pinn-a-self-training-physics-informed | 2306.09389 | null | https://arxiv.org/abs/2306.09389v1 | https://arxiv.org/pdf/2306.09389v1.pdf | ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations | Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and co... | ['Jie Liu', 'Enqiang Zhoui', 'Zhichao Wang', 'Xinhai Chen', 'Junjun Yan'] | 2023-06-15 | null | null | null | null | ['pseudo-label', 'self-learning'] | ['miscellaneous', 'natural-language-processing'] | [-2.93692917e-01 -2.07664043e-01 -2.34688371e-01 -2.10613191e-01
-6.06462836e-01 -7.53195211e-02 1.73147291e-01 -2.39679530e-01
-8.33456218e-02 1.16854000e+00 -2.24423200e-01 -2.66077846e-01
-4.70193923e-01 -9.49833393e-01 -8.91929686e-01 -8.26802492e-01
-1.64125487e-01 5.29089451e-01 3.84092450e-01 -1.34056121... | [6.5172247886657715, 3.5213773250579834] |
39526e31-36de-42a1-817f-f7bf828a1bc6 | risk-aware-scene-sampling-for-dynamic | 2202.13510 | null | https://arxiv.org/abs/2202.13510v1 | https://arxiv.org/pdf/2202.13510v1.pdf | Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems | Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynamic Assurance of these systems requires designing runtime safety components like Out-of-Distribution detectors and risk estimators, which req... | ['Abhishek Dubey', 'Gabor Karsai', 'Yogesh Barve', 'Baiting Luo', 'Shreyas Ramakrishna'] | 2022-02-28 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 1.10978559e-01 3.13301325e-01 -8.12240392e-02 -1.02060549e-01
-5.80079138e-01 -5.05940735e-01 5.56893349e-01 -6.48402423e-02
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-2.80575454e-01 2.15598151e-01 6.74440920e-01 -7.61729386... | [4.937016010284424, 2.0350987911224365] |
9e8a6eff-d150-45b2-beff-6e2526e2dc4b | duosearch-a-novel-search-engine-for-bulgarian | 2305.19392 | null | https://arxiv.org/abs/2305.19392v1 | https://arxiv.org/pdf/2305.19392v1.pdf | DuoSearch: A Novel Search Engine for Bulgarian Historical Documents | Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes. We present a new search engine for historical documents, DuoSearch, which uses ElasticSearch and machine learning methods based on deep neural networks to of... | ['Milena Dobreva', 'Ivan Koychev', 'Suzan Hadzhieva', 'Angel Beshirov'] | 2023-05-30 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [-4.01851982e-01 -6.06033325e-01 -1.15306489e-01 3.95040523e-04
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-1.78673655e-01 8.27935278e-01 6.61933646e-02 -6.56449437... | [10.275880813598633, 10.282879829406738] |
4e0a5dc0-033f-401b-8dd4-e62829f4dd26 | keep-calm-and-explore-language-models-for | 2010.02903 | null | https://arxiv.org/abs/2010.02903v1 | https://arxiv.org/pdf/2010.02903v1.pdf | Keep CALM and Explore: Language Models for Action Generation in Text-based Games | Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on huma... | ['Karthik Narasimhan', 'Matthew Hausknecht', 'Rohan Rao', 'Shunyu Yao'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.704 | https://aclanthology.org/2020.emnlp-main.704.pdf | emnlp-2020-11 | ['action-generation', 'text-based-games'] | ['computer-vision', 'playing-games'] | [-1.37609601e-01 3.94152790e-01 -1.56853884e-01 4.49307896e-02
-1.10716486e+00 -7.25033343e-01 9.79215324e-01 -8.62614363e-02
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-5.52413530e-06 -9.23687160e-01 -4.81701076e-01 -1.73796028e-01
-1.17098488e-01 9.52694356e-01 3.66828710e-01 -1.00179946... | [3.740445375442505, 1.4127981662750244] |
9cd49219-56a1-4e99-b9db-359afd6081eb | efficient-blind-deblurring-under-high-noise | 1904.09154 | null | https://arxiv.org/abs/1904.09154v2 | https://arxiv.org/pdf/1904.09154v2.pdf | Efficient Blind Deblurring under High Noise Levels | The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise levels. However, the noiseless assumption is not realistic considering that low light ... | ['Jérémy Anger', 'Gabriele Facciolo', 'Mauricio Delbracio'] | 2019-04-19 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.48214415e-01 -6.33144975e-01 5.07668555e-01 -2.67472472e-02
-4.89661187e-01 -4.95799839e-01 3.65494847e-01 -5.44154346e-01
-5.83615780e-01 8.92717600e-01 1.59495905e-01 -1.26951784e-01
-2.49255314e-01 -2.92924613e-01 -5.30818224e-01 -8.55664432e-01
2.71781415e-01 -8.36117119e-02 3.17491412e-01 2.07923427... | [11.601615905761719, -2.7164061069488525] |
915920f2-6217-4883-a0bb-cfccf64d588f | pandepth-joint-panoptic-segmentation-and | 2212.14180 | null | https://arxiv.org/abs/2212.14180v1 | https://arxiv.org/pdf/2212.14180v1.pdf | PanDepth: Joint Panoptic Segmentation and Depth Completion | Understanding 3D environments semantically is pivotal in autonomous driving applications where multiple computer vision tasks are involved. Multi-task models provide different types of outputs for a given scene, yielding a more holistic representation while keeping the computational cost low. We propose a multi-task mo... | ['Esa Rahtu', 'Juan Lagos'] | 2022-12-29 | null | null | null | null | ['panoptic-segmentation', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 1.63173199e-01 -4.45862450e-02 -9.18267518e-02 -5.67183137e-01
-8.73392701e-01 -5.26457012e-01 3.83537948e-01 -1.20439939e-01
-3.89227033e-01 3.19634318e-01 -2.68855721e-01 -4.04116333e-01
2.16158882e-01 -8.99478376e-01 -7.23297596e-01 -5.55181503e-01
3.83684486e-01 6.37863696e-01 5.68692029e-01 4.33583856... | [8.381888389587402, -2.337484359741211] |
740697fd-2909-406c-99a8-1209fdd30906 | flowface-semantic-flow-guided-shape-aware | 2212.02797 | null | https://arxiv.org/abs/2212.02797v1 | https://arxiv.org/pdf/2212.02797v1.pdf | FlowFace: Semantic Flow-guided Shape-aware Face Swapping | In this work, we propose a semantic flow-guided two-stage framework for shape-aware face swapping, namely FlowFace. Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realisti... | ['Xin Yu', 'Yu Ding', 'Lincheng Li', 'Bowen Ma', 'Zhimeng Zhang', 'Suzhen Wang', 'Tangjie Lv', 'Changjie Fan', 'Wei zhang', 'Hao Zeng'] | 2022-12-06 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 1.48709610e-01 5.27800679e-01 1.19518213e-01 -5.48910499e-01
-2.21325547e-01 -4.87394869e-01 4.13251877e-01 -6.72675908e-01
2.87820678e-02 3.70491385e-01 2.98960090e-01 4.06223416e-01
3.79777223e-01 -8.61498356e-01 -7.64464200e-01 -7.57669747e-01
3.10085475e-01 7.30201527e-02 -3.62168878e-01 -1.80943504... | [12.753357887268066, -0.03902426362037659] |
05edf913-6172-4c61-a9ad-7af0c6cd624d | green-stability-assumption-unsupervised | 1802.00776 | null | http://arxiv.org/abs/1802.00776v1 | http://arxiv.org/pdf/1802.00776v1.pdf | Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation | In the image processing pipeline of almost every digital camera there is a
part dedicated to computational color constancy i.e. to removing the influence
of illumination on the colors of the image scene. Some of the best known
illumination estimation methods are the so called statistics-based methods.
They are less acc... | ['Sven Lončarić', 'Nikola Banić'] | 2018-02-02 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 1.64778173e-01 -3.15969616e-01 1.39116511e-01 -2.69661963e-01
-3.35094243e-01 -4.86585766e-01 3.53257596e-01 1.97472841e-01
-6.58959508e-01 7.70351648e-01 -5.62419772e-01 -1.27177522e-01
-3.60627472e-02 -6.33731425e-01 -6.41800106e-01 -9.84164834e-01
2.76592195e-01 2.62557685e-01 4.87529188e-01 -6.76631108... | [10.251378059387207, -2.405738115310669] |
9d3c3fa4-43cb-4ed1-8b6d-acace1680fe3 | lhdr-hdr-reconstruction-for-legacy-content | 2211.11270 | null | https://arxiv.org/abs/2211.11270v1 | https://arxiv.org/pdf/2211.11270v1.pdf | LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN | High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenar... | ['Xiuhua Jiang', 'Cheng Guo'] | 2022-11-21 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 2.57992238e-01 -5.11487424e-01 7.61748776e-02 -1.97787479e-01
-6.44628853e-02 -4.01831307e-02 3.81302446e-01 -6.65408194e-01
-2.07484916e-01 7.70420611e-01 4.07985210e-01 -2.20541731e-01
6.89241514e-02 -7.94067681e-01 -4.60450530e-01 -6.73394680e-01
2.43141532e-01 -1.07546680e-01 2.40711570e-01 -3.07656467... | [10.900629997253418, -2.2299203872680664] |
74bc396a-1909-42e0-a90b-b2b93ce47d5b | automated-detection-of-covid-19-cases-from | 2109.02428 | null | https://arxiv.org/abs/2109.02428v1 | https://arxiv.org/pdf/2109.02428v1.pdf | Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost | In late 2019 and after COVID-19 pandemic in the world, many researchers and scholars have tried to provide methods for detection of COVID-19 cases. Accordingly, this study focused on identifying COVID-19 cases from chest X-ray images. In this paper, a novel approach to diagnosing coronavirus disease from X-ray images w... | ['Sharif Hasani', 'Hamid Nasiri'] | 2021-09-03 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.57673410e-03 -7.98669159e-01 6.96669798e-03 -4.21526849e-01
-1.61895454e-01 -2.79991150e-01 2.56508440e-01 3.62265050e-01
-6.53113961e-01 7.85929263e-01 -1.11559391e-01 -5.13641298e-01
-4.63423401e-01 -7.52310157e-01 -3.11941683e-01 -6.06852591e-01
-2.45043665e-01 7.36516595e-01 -9.83380824e-02 -9.69276875... | [15.573984146118164, -1.7016030550003052] |
442d54e9-c091-45d0-b5c9-b1ccccd55d68 | volnet-estimating-human-body-part-volumes | 2107.02259 | null | https://arxiv.org/abs/2107.02259v1 | https://arxiv.org/pdf/2107.02259v1.pdf | VolNet: Estimating Human Body Part Volumes from a Single RGB Image | Human body volume estimation from a single RGB image is a challenging problem despite minimal attention from the research community. However VolNet, an architecture leveraging 2D and 3D pose estimation, body part segmentation and volume regression extracted from a single 2D RGB image combined with the subject's body he... | ['Torsten Schön', 'Vittorio Cozzolino', 'Fabian Leinen'] | 2021-07-05 | null | null | null | null | ['3d-pose-estimation', '3d-volumetric-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.36989135e-02 5.92330396e-01 1.22776791e-01 -2.50107944e-01
-5.50000727e-01 -2.18667835e-01 -3.75535265e-02 -1.68875590e-01
-4.66884404e-01 6.97175086e-01 2.14192197e-02 2.93737829e-01
4.86859798e-01 -6.78981781e-01 -6.89076662e-01 -2.43515581e-01
-2.12770075e-01 1.11329806e+00 3.97753924e-01 -2.30377600... | [6.97276496887207, -0.9977486729621887] |
14772319-a297-49a2-845d-946be32524bb | devil-s-on-the-edges-selective-quad-attention | 2304.03495 | null | https://arxiv.org/abs/2304.03495v1 | https://arxiv.org/pdf/2304.03495v1.pdf | Devil's on the Edges: Selective Quad Attention for Scene Graph Generation | Scene graph generation aims to construct a semantic graph structure from an image such that its nodes and edges respectively represent objects and their relationships. One of the major challenges for the task lies in the presence of distracting objects and relationships in images; contextual reasoning is strongly distr... | ['Minsu Cho', 'Won Hwa Kim', 'Sanghyun Kim', 'Deunsol Jung'] | 2023-04-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jung_Devils_on_the_Edges_Selective_Quad_Attention_for_Scene_Graph_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jung_Devils_on_the_Edges_Selective_Quad_Attention_for_Scene_Graph_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 2.91234225e-01 7.41377324e-02 7.16030523e-02 -2.84537315e-01
-2.62479395e-01 -3.55425835e-01 4.84581679e-01 3.14740181e-01
-1.67232603e-01 4.10588384e-01 3.69101137e-01 1.05120361e-01
-3.28383774e-01 -8.85003030e-01 -7.76116192e-01 -5.69836318e-01
1.13355294e-02 3.78479451e-01 5.63256919e-01 -3.40784878... | [10.250443458557129, 1.6046746969223022] |
99145e71-18d8-4694-bd48-95b830b7ee82 | multi-kernel-positional-embedding-convnext | 2301.06673 | null | https://arxiv.org/abs/2301.06673v2 | https://arxiv.org/pdf/2301.06673v2.pdf | Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation | Medical image segmentation is the technique that helps doctor view and has a precise diagnosis, particularly in Colorectal Cancer. Specifically, with the increase in cases, the diagnosis and identification need to be faster and more accurate for many patients; in endoscopic images, the segmentation task has been vital ... | ['Hai-Dang Nguyen', 'Minh-Triet Tran', 'Nhat-Tan Bui', 'Quoc-Huy Trinh', 'Trong-Hieu Nguyen Mau'] | 2023-01-17 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [-5.81318960e-02 1.76805988e-01 -1.70388788e-01 -2.41304785e-01
-5.46675146e-01 -2.89649576e-01 -1.06499411e-01 3.66534114e-01
-3.97279471e-01 1.52596742e-01 2.26952448e-01 -2.85393536e-01
-6.10828809e-02 -7.33751476e-01 -3.16420376e-01 -8.87636840e-01
6.60716668e-02 5.67274988e-02 3.68817121e-01 -3.39304134... | [14.546889305114746, -2.720065116882324] |
cf2398c6-278f-4863-aa84-41c456573cb3 | continually-learning-from-existing-models | 2212.09097 | null | https://arxiv.org/abs/2212.09097v2 | https://arxiv.org/pdf/2212.09097v2.pdf | Continual Knowledge Distillation for Neural Machine Translation | While many parallel corpora are not publicly accessible for data copyright, data privacy and competitive differentiation reasons, trained translation models are increasingly available on open platforms. In this work, we propose a method called continual knowledge distillation to take advantage of existing translation m... | ['Yang Liu', 'Maosong Sun', 'Peng Li', 'Yuanchi Zhang'] | 2022-12-18 | null | null | null | null | ['nmt'] | ['computer-code'] | [-3.92178148e-02 -3.02130561e-02 -1.03261220e+00 -1.73785076e-01
-1.43600774e+00 -8.99045587e-01 9.43281651e-01 -3.23669076e-01
-4.44756925e-01 1.12328064e+00 2.91571051e-01 -6.65678620e-01
5.21352410e-01 -3.98007900e-01 -1.02801740e+00 -4.77944940e-01
5.46746075e-01 9.45075750e-01 1.53232381e-01 -1.74062744... | [11.605687141418457, 10.30553150177002] |
9b21fe74-eb9f-4eed-b1e0-f75f8de37b66 | roof-material-classification-from-aerial | 2004.11482 | null | https://arxiv.org/abs/2004.11482v1 | https://arxiv.org/pdf/2004.11482v1.pdf | Roof material classification from aerial imagery | This paper describes an algorithm for classification of roof materials using aerial photographs. Main advantages of the algorithm are proposed methods to improve prediction accuracy. Proposed methods includes: method of converting ImageNet weights of neural networks for using multi-channel images; special set of featur... | ['Roman Solovyev'] | 2020-04-23 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 2.02686340e-01 -3.72700617e-02 2.06970394e-01 -5.36721289e-01
-3.50991115e-02 -2.65817136e-01 2.97475517e-01 -4.36295599e-01
-2.99276561e-01 8.21523309e-01 -6.85596466e-03 5.28209917e-02
-2.35408381e-01 -1.01299202e+00 -8.26800108e-01 -4.63244796e-01
-3.59352082e-01 3.05720747e-01 2.68378198e-01 -6.62545621... | [9.460518836975098, -1.1133235692977905] |
aa867ba1-e6f5-49dc-aff7-89a057cb9680 | summarizing-community-based-question-answer | 2211.09892 | null | https://arxiv.org/abs/2211.09892v1 | https://arxiv.org/pdf/2211.09892v1.pdf | Summarizing Community-based Question-Answer Pairs | Community-based Question Answering (CQA), which allows users to acquire their desired information, has increasingly become an essential component of online services in various domains such as E-commerce, travel, and dining. However, an overwhelming number of CQA pairs makes it difficult for users without particular int... | ['Xiaolan Wang', 'Yoshi Suhara', 'Ting-Yao Hsu'] | 2022-11-17 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.70130968e-01 3.08228973e-02 9.00395438e-02 -2.78989702e-01
-1.67109752e+00 -8.14833701e-01 4.94333059e-01 8.66896212e-01
-3.05917084e-01 8.02103639e-01 9.86447155e-01 -4.76405025e-02
-2.89154775e-03 -6.22544289e-01 -2.89540023e-01 -3.30060542e-01
2.41307393e-01 5.11666000e-01 1.69536144e-01 -7.12585449... | [12.3326416015625, 9.28466796875] |
08a13143-ed58-46ac-a081-45cd2f13d305 | texttt-causalassembly-generating-realistic | 2306.10816 | null | https://arxiv.org/abs/2306.10816v1 | https://arxiv.org/pdf/2306.10816v1.pdf | $\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery | Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true... | ['Mathias Drton', 'Martin Roth', 'Steffen Sonntag', 'Tim Pychynski', 'Tobias Windisch', 'Konstantin Göbler'] | 2023-06-19 | null | null | null | null | ['causal-discovery', 'benchmarking', 'benchmarking'] | ['knowledge-base', 'miscellaneous', 'robots'] | [ 4.02431935e-01 1.80346072e-01 6.52125292e-03 -4.45227742e-01
-5.94110310e-01 -6.23567224e-01 8.79496157e-01 5.27587414e-01
2.62167096e-01 1.23724115e+00 1.77071661e-01 -3.05274814e-01
-7.25860953e-01 -1.10886538e+00 -9.82784271e-01 -6.59784615e-01
-3.32035959e-01 8.21469665e-01 2.13405900e-02 1.68179289... | [7.805120944976807, 5.2033610343933105] |
e93912c7-8bd5-4487-89ba-604f091f15f6 | deep-spatial-semantic-attention-for-fine | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Song_Deep_Spatial-Semantic_Attention_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Song_Deep_Spatial-Semantic_Attention_ICCV_2017_paper.pdf | Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval | Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheles... | ['Yi-Zhe Song', 'Qian Yu', 'Jifei Song', 'Timothy M. Hospedales', 'Tao Xiang'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [-1.02816299e-02 -5.27978182e-01 -1.87596396e-01 -3.57560009e-01
-8.31816316e-01 -7.37337708e-01 1.02414858e+00 7.70231634e-02
1.49268191e-02 3.46089631e-01 4.61833388e-01 2.86607474e-01
-5.69457114e-01 -7.55657077e-01 -7.29545295e-01 -3.91874164e-01
1.78698421e-01 2.77576834e-01 2.55647391e-01 -1.95049152... | [11.648605346679688, 0.5904399156570435] |
05b592a4-5178-4342-8eeb-b40bb7a09536 | wyweb-a-nlp-evaluation-benchmark-for | 2305.14150 | null | https://arxiv.org/abs/2305.14150v1 | https://arxiv.org/pdf/2305.14150v1.pdf | WYWEB: A NLP Evaluation Benchmark For Classical Chinese | To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The fi eld of natural language understanding has traditionally focused on benchmarks for various tasks in languages such as Chinese, English, and multilingua, h... | ['Yin Zhang', 'Xiaomi Zhong', 'Tianyu Wang', 'Qianglong Chen', 'Bo Zhou'] | 2023-05-23 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [-2.12047324e-02 -2.09857509e-01 -2.80377895e-01 -2.62716055e-01
-1.13805926e+00 -8.45713079e-01 5.33146858e-01 2.92797774e-01
-5.12664318e-01 1.04587162e+00 4.96571094e-01 -6.65784299e-01
3.42579454e-01 -5.79063356e-01 -4.93962765e-01 -3.35802794e-01
3.22905898e-01 4.08744842e-01 1.22357294e-01 -3.84532332... | [10.881230354309082, 9.255338668823242] |
84dca1a5-5df0-40bf-84ec-64bd638e84ce | predicting-cardiovascular-disease-risk-using | 2305.05648 | null | https://arxiv.org/abs/2305.05648v1 | https://arxiv.org/pdf/2305.05648v1.pdf | Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning | Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such hea... | ['Diego Ardila', 'Goodarz Danaei', 'Yun Liu', 'Shruthi Prabhakara', 'Shravya Shetty', 'Greg S. Corrado', 'Yossi Matias', 'Cory Y. McLean', 'Babak Behsaz', 'Mariam Jabara', 'Sujay Kakarmath', 'Lauren Harrell', 'Christina Chen', 'Mayank Daswani', 'Sebastien Baur', 'Wei-Hung Weng'] | 2023-05-09 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 9.17729586e-02 8.39166045e-02 -4.23660666e-01 -3.08568835e-01
-8.66816223e-01 -1.85338333e-01 3.47919315e-01 5.90758741e-01
-2.06533954e-01 7.97242641e-01 4.68415499e-01 -8.14734340e-01
-8.87256116e-02 -1.23020339e+00 -2.09618762e-01 -3.00599873e-01
-4.92361546e-01 3.30488175e-01 7.45233074e-02 3.65797758... | [14.095785140991211, 3.07401442527771] |
3267484e-afc3-43b1-b39c-a84c2a66dea9 | npb-rec-non-parametric-assessment-of | 2208.03966 | null | https://arxiv.org/abs/2208.03966v1 | https://arxiv.org/pdf/2208.03966v1.pdf | NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data | Uncertainty quantification in deep-learning (DL) based image reconstruction models is critical for reliable clinical decision making based on the reconstructed images. We introduce "NPB-REC", a non-parametric fully Bayesian framework for uncertainty assessment in MRI reconstruction from undersampled "k-space" data. We ... | ['Moti Freiman', 'Samah Khawaled'] | 2022-08-08 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [-1.56549767e-01 1.34558231e-01 2.08619133e-01 -5.99481702e-01
-1.37529695e+00 -6.21606829e-03 6.18953295e-02 5.37512191e-02
-9.00937021e-01 1.10906553e+00 1.43487945e-01 -3.73130590e-01
-4.75469768e-01 -4.27936941e-01 -1.05267084e+00 -8.82248580e-01
-5.85258424e-01 5.46855330e-01 -6.31814301e-02 3.82987767... | [13.55212688446045, -2.356722593307495] |
a2b1e1c4-01f3-4c9d-a96d-ee7d64d1b4eb | generative-x-vectors-for-text-independent | 1809.06798 | null | http://arxiv.org/abs/1809.06798v1 | http://arxiv.org/pdf/1809.06798v1.pdf | Generative x-vectors for text-independent speaker verification | Speaker verification (SV) systems using deep neural network embeddings,
so-called the x-vector systems, are becoming popular due to its good
performance superior to the i-vector systems. The fusion of these systems
provides improved performance benefiting both from the discriminatively trained
x-vectors and generative ... | ['Jichen Yang', 'Emre Yilmaz', 'Longting Xu', 'Haizhou Li', 'Rohan Kumar Das'] | 2018-09-17 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.51749820e-01 -3.90316486e-01 7.94003680e-02 -7.86382675e-01
-1.17189229e+00 -4.28433627e-01 7.54762352e-01 -2.37944096e-01
-2.22427621e-01 2.86288857e-01 5.85043311e-01 -4.05193746e-01
1.47984788e-01 -3.49734634e-01 -3.04534644e-01 -1.05659425e+00
1.70421407e-01 -9.58216563e-02 -3.55848551e-01 -1.31634176... | [14.355993270874023, 6.092092037200928] |
20ffab78-a837-4056-8ba6-aa620c9a7e4d | incorporating-dynamic-semantics-into-pre | 2203.16369 | null | https://arxiv.org/abs/2203.16369v2 | https://arxiv.org/pdf/2203.16369v2.pdf | Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging. To this end, in this paper, we propose to address this pro... | ['Enhong Chen', 'Wei Wu', 'Qi Liu', 'Hongke Zhao', 'Mengdi Zhang', 'Kun Zhang', 'Kai Zhang'] | 2022-03-30 | null | https://aclanthology.org/2022.findings-acl.285 | https://aclanthology.org/2022.findings-acl.285.pdf | findings-acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.09165005e-01 2.47031853e-01 -2.44355172e-01 -1.02825224e+00
-5.19329667e-01 -6.06303811e-01 5.30094445e-01 2.33518362e-01
-2.29757458e-01 2.53058434e-01 7.38741338e-01 -3.57258022e-01
5.83530776e-02 -1.09009850e+00 -6.26454532e-01 -3.09499890e-01
4.75481004e-01 3.13568950e-01 2.03305036e-01 -7.54926503... | [11.489680290222168, 6.62973690032959] |
275f5bcd-2065-49c9-b110-732e236d2c1d | transductive-learning-with-string-kernels-for | 1811.01734 | null | http://arxiv.org/abs/1811.01734v1 | http://arxiv.org/pdf/1811.01734v1.pdf | Transductive Learning with String Kernels for Cross-Domain Text Classification | For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string ke... | ['Radu Tudor Ionescu', 'Andrei M. Butnaru'] | 2018-11-02 | null | null | null | null | ['cross-domain-text-classification', 'native-language-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.75400066e-01 -1.96629480e-01 -6.40957415e-01 -6.40207171e-01
-1.38755929e+00 -1.02248931e+00 6.79786265e-01 5.66502631e-01
-4.32134032e-01 1.02096355e+00 4.35158573e-02 -4.58671391e-01
-6.93782046e-02 -7.50009179e-01 -4.75448072e-01 -4.21674073e-01
5.25965214e-01 7.23574400e-01 4.19023633e-01 -2.22502455... | [10.426767349243164, 8.92202377319336] |
ba799f81-0143-4f2f-bd69-bdf1383cb036 | simple-sorting-criteria-help-find-the-causal | 2303.18211 | null | https://arxiv.org/abs/2303.18211v1 | https://arxiv.org/pdf/2303.18211v1.pdf | Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models | Additive Noise Models (ANM) encode a popular functional assumption that enables learning causal structure from observational data. Due to a lack of real-world data meeting the assumptions, synthetic ANM data are often used to evaluate causal discovery algorithms. Reisach et al. (2021) show that, for common simulation p... | ['Sebastian Weichwald', 'Antoine Chambaz', 'Christof Seiler', 'Myriam Tami', 'Alexander G. Reisach'] | 2023-03-31 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.73366874e-01 2.58417670e-02 -5.56179345e-01 -3.09951305e-01
-6.75187647e-01 -6.61557257e-01 1.03223419e+00 2.51721919e-01
-3.44078720e-01 1.02003658e+00 5.60227692e-01 -5.89430153e-01
-7.06567764e-01 -9.73630071e-01 -1.00980532e+00 -5.26998818e-01
-6.69361472e-01 2.95797318e-01 1.38857201e-01 1.73133135... | [7.92240571975708, 5.321852207183838] |
2fc6e643-3a59-492b-ba12-3ab235e5c51a | unsupervised-learning-on-3d-point-clouds-by | 2202.02543 | null | https://arxiv.org/abs/2202.02543v2 | https://arxiv.org/pdf/2202.02543v2.pdf | Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting | Learning from unlabeled or partially labeled data to alleviate human labeling remains a challenging research topic in 3D modeling. Along this line, unsupervised representation learning is a promising direction to auto-extract features without human intervention. This paper proposes a general unsupervised approach, name... | ['Mohammed Bennamoun', 'Jian Zhang', 'Qiang Wu', 'Litao Yu', 'Guofeng Mei'] | 2022-02-05 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 7.06116334e-02 3.07642639e-01 -2.26041615e-01 -7.07717299e-01
-1.02335572e+00 -5.26200593e-01 5.29651463e-01 2.50206232e-01
-2.06245407e-01 3.21215004e-01 -4.76224869e-01 -2.33136520e-01
-4.00721222e-01 -7.07231104e-01 -8.05386782e-01 -9.38942730e-01
-1.11249778e-04 6.87339246e-01 1.82143912e-01 1.01106785... | [8.03272533416748, -3.1550307273864746] |
58c732ad-679f-49eb-8765-4478599e01eb | sc-ml-self-supervised-counterfactual-metric | 2304.01647 | null | https://arxiv.org/abs/2304.01647v1 | https://arxiv.org/pdf/2304.01647v1.pdf | SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering | Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the model generating answers only by associating with the questions while ignoring the visual content, resulting in ... | ['Zhenyu Lu', 'Zhongfeng Chen', 'Ziheng Wu', 'Xu Yang', 'ShiYang Yan', 'Xinyao Shu'] | 2023-04-04 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.72149986e-01 1.01136394e-01 -8.06989148e-02 -2.71887362e-01
-7.94516504e-01 -7.30764627e-01 7.95976341e-01 -1.96537152e-01
-4.81091350e-01 6.07333064e-01 5.20590901e-01 -5.26879549e-01
8.18152130e-02 -7.41223276e-01 -6.88389063e-01 -7.21883297e-01
2.68478602e-01 1.34819135e-01 1.86517656e-01 -2.09372789... | [10.787108421325684, 1.7193355560302734] |
8d881117-84a2-431a-aec2-7f8195e00230 | improving-retrieval-augmented-large-language | 2307.03027 | null | https://arxiv.org/abs/2307.03027v1 | https://arxiv.org/pdf/2307.03027v1.pdf | Improving Retrieval-Augmented Large Language Models via Data Importance Learning | Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the performance of such retrieval-augmented models is limited by the data quality of their underlying retrieval corpus. In this paper, we propose an al... | ['Ce Zhang', 'Sebastian Schelter', 'Meng Cao', 'Shaopeng Wei', 'Samantha Biegel', 'Stefan Grafberger', 'Xiaozhong Lyu'] | 2023-07-06 | null | null | null | null | ['imputation', 'retrieval', 'imputation', 'question-answering', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing', 'time-series'] | [-1.01825267e-01 1.04631081e-01 -1.93504453e-01 -1.58470735e-01
-1.45842719e+00 -5.75922072e-01 1.82420030e-01 6.05507255e-01
-9.18718636e-01 6.99197054e-01 -4.65271845e-02 -5.13292372e-01
-4.57025766e-01 -8.17095399e-01 -9.35378671e-01 -4.08845693e-01
-1.34340063e-01 8.67010236e-01 1.11843251e-01 -2.22737998... | [11.329200744628906, 7.644433975219727] |
2b02c65d-4316-46ea-8910-02584b54600a | learning-deep-mri-reconstruction-models-from | 2301.02613 | null | https://arxiv.org/abs/2301.02613v1 | https://arxiv.org/pdf/2301.02613v1.pdf | Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes | Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan times. Reconstruction methods can alleviate this limitation by recovering clinically usable images from accelerated acquisitions. In particular, learning-based methods promise performance leaps by employing deep neural net... | ['Tolga Çukur', 'Muzaffer Özbey', 'Şaban Öztürk', 'Salman UH Dar'] | 2023-01-06 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 5.89692771e-01 1.16901398e-01 -1.31034970e-01 -5.77144682e-01
-1.15784311e+00 -2.30990246e-01 5.11784613e-01 -1.26429573e-01
-6.86262190e-01 4.57590669e-01 3.00716728e-01 -5.14149308e-01
-2.86813259e-01 -3.16395849e-01 -7.66457677e-01 -8.61065924e-01
-3.36340666e-01 6.27647936e-01 3.41412485e-01 1.59877062... | [13.524965286254883, -2.384619951248169] |
23df7b94-ee91-4339-8cc1-8879e912b844 | bottom-up-higher-resolution-networks-for | 1908.10357 | null | https://arxiv.org/abs/1908.10357v3 | https://arxiv.org/pdf/1908.10357v3.pdf | HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation | Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped... | ['Bin Xiao', 'Bowen Cheng', 'Thomas S. Huang', 'Lei Zhang', 'Jingdong Wang', 'Honghui Shi'] | 2019-08-27 | higherhrnet-scale-aware-representation | null | null | cvpr-2020-6 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-4.87202644e-01 -5.82943968e-02 5.29639900e-01 -1.74618855e-01
-9.17249739e-01 -1.45139217e-01 2.33909249e-01 -1.38868559e-02
-7.69714952e-01 5.88573098e-01 3.16821396e-01 7.69958079e-01
2.82907695e-01 -6.72734201e-01 -8.47191095e-01 -2.98958033e-01
-1.46772176e-01 7.24898994e-01 5.84445357e-01 -6.21415377... | [7.22398042678833, -0.7391964793205261] |
f38d5088-52d2-4574-82a3-0562404309c4 | a-reinforcement-learning-environment-for-2 | 2107.07373 | null | https://arxiv.org/abs/2107.07373v2 | https://arxiv.org/pdf/2107.07373v2.pdf | A Reinforcement Learning Environment for Mathematical Reasoning via Program Synthesis | We convert the DeepMind Mathematics Dataset into a reinforcement learning environment by interpreting it as a program synthesis problem. Each action taken in the environment adds an operator or an input into a discrete compute graph. Graphs which compute correct answers yield positive reward, enabling the optimization ... | ['Alok Singh', 'Johnny Ye', 'Joseph Palermo'] | 2021-07-15 | a-reinforcement-learning-environment-for-3 | https://openreview.net/forum?id=-GU1sfGnM5K | https://openreview.net/pdf?id=-GU1sfGnM5K | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.70796275e-01 4.90746796e-01 -3.33204508e-01 -3.85902494e-01
-6.79957807e-01 -9.45767164e-01 5.73552549e-01 4.01782304e-01
-3.28860521e-01 6.50869370e-01 -2.26014424e-02 -9.02161241e-01
1.36147007e-01 -1.59748137e+00 -1.29334712e+00 -1.86053187e-01
-4.73642200e-01 5.75375497e-01 1.31072011e-02 -1.51087344... | [8.6090726852417, 7.248565673828125] |
fd2a3d8e-f379-4f86-a1c3-30b24b293133 | is-centralized-training-with-decentralized | 2305.17352 | null | https://arxiv.org/abs/2305.17352v1 | https://arxiv.org/pdf/2305.17352v1.pdf | Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL? | Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use additional global state information to guide training in a centralized way and make their own decisions only based on decentralized local p... | ['Mingli Song', 'Jie Song', 'Yanhao Huang', 'Tongya Zheng', 'KaiXuan Chen', 'Yunpeng Qing', 'Shunyu Liu', 'Yihe Zhou'] | 2023-05-27 | null | null | null | null | ['multi-agent-reinforcement-learning', 'starcraft-ii', 'starcraft'] | ['methodology', 'playing-games', 'playing-games'] | [-5.51219285e-01 9.25763622e-02 -6.75022066e-01 -1.41790891e-02
-4.50222075e-01 -3.95389199e-01 4.56716865e-01 1.11311719e-01
-8.34211826e-01 1.27711713e+00 -2.32812271e-01 -4.30355906e-01
-2.89757729e-01 -8.19437504e-01 -5.90634406e-01 -1.09842074e+00
-3.48414212e-01 7.44628549e-01 2.96781123e-01 -3.55290949... | [3.7710211277008057, 2.0598902702331543] |
4766f81d-0d4b-43f7-89c4-872f3feb4d8c | snp2vec-scalable-self-supervised-pre-training | 2204.06699 | null | https://arxiv.org/abs/2204.06699v1 | https://arxiv.org/pdf/2204.06699v1.pdf | SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study | Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these pre-training methods for genome understanding. However, they focus only on understanding haploid sequences, which hinders their applicability... | ['Pascale Fung', 'Nancy Y. Ip', 'Xiaopu Zhou', 'Tiffany T. W. Mak', 'Zihan Liu', 'Tiezheng Yu', 'Samuel Cahyawijaya'] | 2022-04-14 | null | https://aclanthology.org/2022.bionlp-1.14 | https://aclanthology.org/2022.bionlp-1.14.pdf | bionlp-acl-2022-5 | ['genome-understanding'] | ['medical'] | [ 2.02556670e-01 1.96388125e-01 -2.10576788e-01 -6.36114359e-01
-8.59180748e-01 -4.19547170e-01 1.42392144e-01 2.10722163e-01
-2.63135731e-01 9.08985794e-01 6.42475426e-01 -3.88691187e-01
1.64529786e-01 -6.51296198e-01 -7.76332557e-01 -5.98422825e-01
-1.36648947e-02 5.10170102e-01 -1.77819207e-01 -1.16610311... | [6.163951873779297, 5.706696510314941] |
a9130ce0-9db8-4527-893e-a84b84079f92 | autonovel-automatically-discovering-and | 2106.15252 | null | https://arxiv.org/abs/2106.15252v1 | https://arxiv.org/pdf/2106.15252v1.pdf | AutoNovel: Automatically Discovering and Learning Novel Visual Categories | We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled data only i... | ['Andrew Zisserman', 'Andrea Vedaldi', 'Sébastien Ehrhardt', 'Sylvestre-Alvise Rebuffi', 'Kai Han'] | 2021-06-29 | null | null | null | null | ['image-clustering', 'novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.13894737e-01 3.33655596e-01 -6.31721169e-02 -4.96220171e-01
-7.82213807e-01 -6.24083698e-01 7.28390574e-01 4.19553727e-01
-5.26784539e-01 8.11434209e-01 -2.97128428e-02 -4.04010080e-02
-3.04750055e-01 -5.91620922e-01 -7.84698725e-01 -9.46402669e-01
9.23832506e-02 9.80585515e-01 3.67510796e-01 2.92932749... | [9.50295639038086, 3.0110929012298584] |
68e13bd1-71db-4527-b941-c2100c5412c1 | improving-depression-estimation-from-facial | 2212.06400 | null | https://arxiv.org/abs/2212.06400v1 | https://arxiv.org/pdf/2212.06400v1.pdf | Improving Depression estimation from facial videos with face alignment, training optimization and scheduling | Deep learning models have shown promising results in recognizing depressive states using video-based facial expressions. While successful models typically leverage using 3D-CNNs or video distillation techniques, the different use of pretraining, data augmentation, preprocessing, and optimization techniques across exper... | ['Miguel Bordallo López', 'Le Nguyen', 'Constantino Álvarez Casado', 'Manuel Lage Cañellas'] | 2022-12-13 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.01560920e-01 -2.08340243e-01 -2.77649701e-01 -7.99865544e-01
-1.99473947e-01 -1.46015093e-01 8.94204497e-01 2.16356441e-02
-7.09738851e-01 3.63975853e-01 3.79280686e-01 -4.39059660e-02
-1.13500878e-02 -4.45642531e-01 -5.03129840e-01 -4.19245213e-01
-5.20493686e-01 3.45892549e-01 -1.05421074e-01 -4.27401572... | [13.516631126403809, 1.6988434791564941] |
bbefb0ff-d30c-4a0e-95b7-fac73fc4aa9e | global-distance-distributions-separation-for | 2006.00752 | null | https://arxiv.org/abs/2006.00752v3 | https://arxiv.org/pdf/2006.00752v3.pdf | Global Distance-distributions Separation for Unsupervised Person Re-identification | Supervised person re-identification (ReID) often has poor scalability and usability in real-world deployments due to domain gaps and the lack of annotations for the target domain data. Unsupervised person ReID through domain adaptation is attractive yet challenging. Existing unsupervised ReID approaches often fail in c... | ['Wen-Jun Zeng', 'Xin Jin', 'Zhibo Chen', 'Cuiling Lan'] | 2020-06-01 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/392_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520715.pdf | eccv-2020-8 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-3.62534374e-01 -1.48535743e-01 -2.97209561e-01 -7.10523427e-01
-4.44746912e-01 -3.44470412e-01 6.27139032e-01 3.25348049e-01
-6.45978153e-01 9.46628213e-01 1.43897265e-01 7.90094286e-02
-3.97793621e-01 -6.56384408e-01 -4.74429429e-01 -8.76035988e-01
-1.11570610e-02 9.34443951e-01 2.08143845e-01 -7.81453960... | [14.741352081298828, 1.0656429529190063] |
5c1cf30f-f7f5-498d-b549-b03d363a1f5d | paraphrasing-textual-entailment-and-semantic | 2208.05387 | null | https://arxiv.org/abs/2208.05387v1 | https://arxiv.org/pdf/2208.05387v1.pdf | Paraphrasing, textual entailment, and semantic similarity above word level | This dissertation explores the linguistic and computational aspects of the meaning relations that can hold between two or more complex linguistic expressions (phrases, clauses, sentences, paragraphs). In particular, it focuses on Paraphrasing, Textual Entailment, Contradiction, and Semantic Similarity. In Part I: "Simi... | ['Venelin Kovatchev'] | 2022-08-10 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.82577842e-01 -1.19331278e-01 -4.52848405e-01 -5.22206604e-01
-3.61879230e-01 -9.37517762e-01 6.37590230e-01 7.90985584e-01
3.89892026e-03 3.58958453e-01 9.63248372e-01 -5.12248516e-01
-5.84050834e-01 -5.63765764e-01 -9.36810151e-02 -1.30275384e-01
5.82957745e-01 5.98967671e-02 -3.31072360e-01 -4.70299035... | [10.832481384277344, 9.142715454101562] |
0650f689-f9cc-43b6-9e65-7a708aed67f6 | the-stable-entropy-hypothesis-and-entropy | 2302.06784 | null | https://arxiv.org/abs/2302.06784v1 | https://arxiv.org/pdf/2302.06784v1.pdf | The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation | State-of-the-art language generation models can degenerate when applied to open-ended generation problems such as text completion, story generation, or dialog modeling. This degeneration usually shows up in the form of incoherence, lack of vocabulary diversity, and self-repetition or copying from the context. In this p... | ['Jackie C. K. Cheung', 'Jason Weston', 'Doina Precup', "Timothy J. O'Donnell", 'Kushal Arora'] | 2023-02-14 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 2.52056897e-01 5.63694179e-01 -1.76416710e-01 -1.92644119e-01
-4.34193820e-01 -7.00252056e-01 9.36632037e-01 9.18862745e-02
-1.92512944e-02 1.13073635e+00 7.33905733e-01 -2.41570100e-01
-7.81093165e-03 -6.38813198e-01 -5.07589936e-01 -3.71069282e-01
3.12458307e-01 7.02566206e-01 -1.13994896e-01 -4.73450333... | [11.586286544799805, 9.20119857788086] |
8562f0ca-6513-4b47-ba85-e3d0fc017bb1 | a-graph-based-method-for-soccer-action | 2211.12334 | null | https://arxiv.org/abs/2211.12334v1 | https://arxiv.org/pdf/2211.12334v1.pdf | A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification | Action spotting in soccer videos is the task of identifying the specific time when a certain key action of the game occurs. Lately, it has received a large amount of attention and powerful methods have been introduced. Action spotting involves understanding the dynamics of the game, the complexity of events, and the va... | ['Gloria Haro', 'Coloma Ballester', 'Alejandro Cartas'] | 2022-11-22 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 3.43790166e-02 -4.05187130e-01 -2.56736994e-01 2.15632051e-01
-5.93859076e-01 -6.46176994e-01 6.01255655e-01 2.21344680e-01
-3.04345250e-01 2.31755957e-01 3.09714437e-01 2.24847257e-01
4.27177213e-02 -5.42872250e-01 -4.53729689e-01 -6.08640909e-01
-2.51928359e-01 2.78219402e-01 8.54691446e-01 -2.83753365... | [7.906529903411865, 0.14123082160949707] |
72d14a7f-8ffe-49ed-b941-e6137cf64a84 | resset-a-recurrent-model-for-sequence-of-sets | 1802.00948 | null | http://arxiv.org/abs/1802.00948v1 | http://arxiv.org/pdf/1802.00948v1.pdf | Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records | Modern healthcare is ripe for disruption by AI. A game changer would be
automatic understanding the latent processes from electronic medical records,
which are being collected for billions of people worldwide. However, these
healthcare processes are complicated by the interaction between at least three
dynamic componen... | ['Truyen Tran', 'Svetha Venkatesh', 'Phuoc Nguyen'] | 2018-02-03 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 3.82866889e-01 3.81009221e-01 -2.10740834e-01 -1.71694994e-01
-6.19064510e-01 -3.18070918e-01 3.70489478e-01 3.94018233e-01
-3.29259336e-02 7.32825637e-01 1.07605386e+00 -1.91221565e-01
-5.76960444e-01 -7.87503183e-01 -7.97772229e-01 -9.45097446e-01
-2.00611547e-01 1.20692778e+00 -6.12421989e-01 -7.79403746... | [7.831291198730469, 6.132724761962891] |
b4289162-02b4-4866-b600-fdc873438408 | learning-local-descriptors-by-optimizing-the | 1603.09095 | null | https://arxiv.org/abs/1603.09095v6 | https://arxiv.org/pdf/1603.09095v6.pdf | Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval | Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint ... | ['Jörgen Ahlberg', 'Igor S. Pandžić', 'Nenad Markuš'] | 2016-03-30 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-3.20129007e-01 -2.02465042e-01 -3.49843413e-01 -4.81654584e-01
-1.18731892e+00 -6.71471238e-01 3.16519648e-01 3.05444986e-01
-2.51121819e-01 5.54099798e-01 5.59587665e-02 2.57876337e-01
-1.29936382e-01 -6.94641113e-01 -1.20620775e+00 -5.99432826e-01
-1.95435718e-01 3.62756848e-01 2.83253998e-01 2.56550252... | [7.9529643058776855, -2.4755403995513916] |
ad0ecee0-76a1-4321-b577-d296a90e549f | practical-fast-and-robust-point-cloud | 2111.04228 | null | https://arxiv.org/abs/2111.04228v1 | https://arxiv.org/pdf/2111.04228v1.pdf | Practical, Fast and Robust Point Cloud Registration for 3D Scene Stitching and Object Localization | 3D point cloud registration ranks among the most fundamental problems in remote sensing, photogrammetry, robotics and geometric computer vision. Due to the limited accuracy of 3D feature matching techniques, outliers may exist, sometimes even in very large numbers, among the correspondences. Since existing robust solve... | ['Lei Sun'] | 2021-11-08 | null | null | null | null | ['3d-feature-matching'] | ['computer-vision'] | [-5.40453494e-02 -4.36365962e-01 2.93214172e-01 -2.93030869e-02
-1.09834599e+00 -4.90086228e-01 5.74168086e-01 1.55030444e-01
-2.45889217e-01 4.94610429e-01 -2.55857110e-01 -1.17701344e-01
-3.69295806e-01 -5.49238384e-01 -8.53113592e-01 -7.32454658e-01
-1.91032782e-01 7.96851635e-01 -5.41831693e-03 -2.03446358... | [7.732126235961914, -2.8319382667541504] |
9a521c3f-2d5e-4fa9-9852-f09417feaf45 | neural-pipeline-for-zero-shot-data-to-text | null | null | https://openreview.net/forum?id=Lz2fD-uQyeh | https://openreview.net/pdf?id=Lz2fD-uQyeh | Neural Pipeline for Zero-Shot Data-to-Text Generation | In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise. We examine how to avoid finetuning the pretrained language models (PLMs) on D2T generation datasets while still taking advantage of surface realization capabilities of PLMs. In... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.41475070e-01 1.03904772e+00 -6.27874658e-02 -2.88843870e-01
-1.03124559e+00 -3.80893379e-01 1.07586300e+00 3.01264614e-01
-7.38066658e-02 9.44446921e-01 6.55564666e-01 -3.15227777e-01
1.57310903e-01 -1.37414432e+00 -1.27967918e+00 9.99135673e-02
1.94813550e-01 1.01662457e+00 1.19729765e-01 -5.48935175... | [11.394323348999023, 8.7838773727417] |
a884bfb3-0d59-4651-b517-47a19e9cdcc7 | fitvid-high-capacity-pixel-level-video | null | null | https://openreview.net/forum?id=iim-R8xu0TG | https://openreview.net/pdf?id=iim-R8xu0TG | FitVid: High-Capacity Pixel-Level Video Prediction | An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. Thi... | ['Dumitru Erhan', 'Chelsea Finn', 'Sergey Levine', 'Suraj Nair', 'Mohammad Taghi Saffar', 'Mohammad Babaeizadeh'] | 2021-09-29 | null | null | null | null | ['image-augmentation', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [ 3.20720553e-01 2.00340763e-01 -2.95075297e-01 -2.33153880e-01
-1.27407327e-01 -2.36010209e-01 1.03754878e+00 -2.31251314e-01
-3.44310254e-01 7.05232084e-01 3.59450310e-01 -2.93511122e-01
2.33945549e-01 -6.04981422e-01 -1.05795944e+00 -4.33702916e-01
-1.99707553e-01 4.67374444e-01 6.28012717e-01 -3.55337381... | [8.411170959472656, 0.3922211229801178] |
5f8e58eb-392b-4d3f-979d-dc0bbbebe1e6 | power-system-anomaly-detection-and | 2209.12629 | null | https://arxiv.org/abs/2209.12629v2 | https://arxiv.org/pdf/2209.12629v2.pdf | Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine Learning | Power system state estimation is being faced with different types of anomalies. These might include bad data caused by gross measurement errors or communication system failures. Sudden changes in load or generation can be considered as anomaly depending on the implemented state estimation method. Additionally, consider... | ['Vladimir Terzija', 'Elena Gryazina', 'Victor Levi', 'Dragan Ćetenović', 'Mile Mitrovic', 'Sajjad Asefi'] | 2022-09-26 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 6.21017069e-02 -3.34486783e-01 1.57280490e-02 1.20686352e-01
-1.15068004e-01 -7.77667046e-01 5.68503976e-01 8.43181193e-01
6.08689710e-02 9.78025854e-01 -4.62111562e-01 -6.84603870e-01
-3.51280391e-01 -9.50812399e-01 -2.17420042e-01 -8.96668553e-01
-5.05779326e-01 5.73344350e-01 3.35604459e-01 -6.10321797... | [6.226116180419922, 2.5562891960144043] |
69121545-3e78-4671-8797-48a940089cc2 | universal-adversarial-attack-on-deep-learning | 2109.07142 | null | https://arxiv.org/abs/2109.07142v1 | https://arxiv.org/pdf/2109.07142v1.pdf | Universal Adversarial Attack on Deep Learning Based Prognostics | Deep learning-based time series models are being extensively utilized in engineering and manufacturing industries for process control and optimization, asset monitoring, diagnostic and predictive maintenance. These models have shown great improvement in the prediction of the remaining useful life (RUL) of industrial eq... | ['Venkataramana Runkana', 'Sagar Srinivas', 'Sri Harsha Nistala', 'Pradeep Rathore', 'Arghya Basak'] | 2021-09-15 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 3.22290361e-01 -2.64419109e-01 4.24185008e-01 2.17095509e-01
-3.97876799e-01 -9.33943689e-01 2.37922221e-01 2.90132344e-01
9.70451534e-02 5.57273746e-01 -7.12698698e-01 -6.20513737e-01
-4.02114660e-01 -1.03126597e+00 -1.04850972e+00 -8.45660806e-01
-3.55587065e-01 3.08190495e-01 7.43695721e-02 -3.46766680... | [5.444273948669434, 7.475541591644287] |
7662ad04-5cbf-475e-a1ca-e87386c7c6ee | image-based-automatic-dial-meter-reading-in | 2201.02850 | null | https://arxiv.org/abs/2201.02850v2 | https://arxiv.org/pdf/2201.02850v2.pdf | Image-based Automatic Dial Meter Reading in Unconstrained Scenarios | The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries. The Energy Company of Parana (Copel) (Brazil) performs more than 4 million meter readings (almost entirely of non-smart devices) per month, and we estimate that 850 thousand of them are from dial mete... | ['David Menotti', 'Rayson Laroca', 'Gabriel Salomon'] | 2022-01-08 | null | null | null | null | ['dial-meter-reading', 'image-based-automatic-meter-reading'] | ['computer-vision', 'computer-vision'] | [-4.29954045e-02 -2.06836179e-01 2.47884676e-01 -5.20704627e-01
-1.12169409e+00 -5.95116138e-01 7.13592649e-01 -6.11206926e-02
-1.77922294e-01 9.15387213e-01 8.47721398e-02 -6.16668165e-01
1.21929586e-01 -1.22268581e+00 -2.08915994e-01 -7.30618000e-01
4.64845270e-01 5.95710516e-01 -2.38335878e-01 -5.24763949... | [11.385509490966797, 2.668581247329712] |
5e5c6b81-b8cf-483a-bb7f-793584c89f77 | compact-representation-for-image | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_Compact_Representation_for_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_Compact_Representation_for_2014_CVPR_paper.pdf | Compact Representation for Image Classification: To Choose or to Compress? | In large scale image classification, features such as Fisher vector or VLAD have achieved state-of-the-art results. However, the combination of large number of examples and high dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the... | ['Jianfei Cai', 'Jianxin Wu', 'Yu Zhang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['feature-compression'] | ['computer-vision'] | [ 2.80345529e-01 -6.13858342e-01 -3.59800369e-01 -5.69449902e-01
-8.09694052e-01 -2.83225417e-01 4.28378314e-01 2.01088563e-01
-4.54155415e-01 7.10443377e-01 1.92634165e-01 -2.23411560e-01
-8.02339315e-01 -9.40814018e-01 3.31687368e-02 -8.55965257e-01
-3.24594468e-01 1.96407095e-01 5.79072610e-02 -8.03638101... | [8.384576797485352, 3.9275076389312744] |
0926c326-b71c-4747-9533-bdab0aecf1c8 | adversarial-training-for-satire-detection | 1902.11145 | null | http://arxiv.org/abs/1902.11145v2 | http://arxiv.org/pdf/1902.11145v2.pdf | Adversarial Training for Satire Detection: Controlling for Confounding Variables | The automatic detection of satire vs. regular news is relevant for downstream
applications (for instance, knowledge base population) and to improve the
understanding of linguistic characteristics of satire. Recent approaches build
upon corpora which have been labeled automatically based on article sources. We
hypothesi... | ['Roman Klinger', 'Robert McHardy', 'Heike Adel'] | 2019-02-28 | adversarial-training-for-satire-detection-1 | https://aclanthology.org/N19-1069 | https://aclanthology.org/N19-1069.pdf | naacl-2019-6 | ['knowledge-base-population', 'satire-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.04352556e-01 7.96566606e-02 -6.51653707e-01 -2.77940899e-01
-9.33375657e-01 -1.09329379e+00 9.49111462e-01 4.58295405e-01
-4.39136118e-01 4.53966379e-01 7.72602260e-01 -4.87848461e-01
8.99024904e-02 -7.66215026e-01 -8.62683773e-01 -5.58050931e-01
3.47567767e-01 4.30283546e-01 -2.34241530e-01 -5.40801644... | [8.980667114257812, 10.242315292358398] |
c2c58f14-d1ed-4565-85bc-d59932450d8d | fusion-of-simple-models-for-native-language | null | null | https://aclanthology.org/W17-5048 | https://aclanthology.org/W17-5048.pdf | Fusion of Simple Models for Native Language Identification | In this paper we describe the approaches we explored for the 2017 Native Language Identification shared task. We focused on simple word and sub-word units avoiding heavy use of hand-crafted features. Following recent trends, we explored linear and neural networks models to attempt to compensate for the lack of rich fea... | ['Ramon Astudillo', 'Fabio Kepler', 'Alberto Abad'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 4.11557779e-02 -2.18446003e-04 -3.28746103e-02 -3.80854726e-01
-1.21790469e+00 -7.45466411e-01 9.69247103e-01 1.01908699e-01
-1.04288602e+00 7.00314224e-01 2.19482392e-01 -4.83576745e-01
6.63767532e-02 -1.47912055e-01 -4.62381750e-01 -4.90524232e-01
2.16176003e-01 3.64075691e-01 1.93514507e-02 -2.14917988... | [10.40861988067627, 10.534111022949219] |
27b83f5d-83c8-44fc-95a6-aa3f1b0c132e | inter-slice-context-residual-learning-for-3d | 2011.14155 | null | https://arxiv.org/abs/2011.14155v1 | https://arxiv.org/pdf/2011.14155v1.pdf | Inter-slice Context Residual Learning for 3D Medical Image Segmentation | Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further ... | ['Yong Xia', 'Yan Wang', 'Yutong Xie', 'Jianpeng Zhang'] | 2020-11-28 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 2.64229834e-01 3.62358004e-01 -2.92568207e-01 -4.57040399e-01
-9.82543707e-01 -1.73820183e-01 4.76734132e-01 2.21658468e-01
-4.67596650e-01 3.26896310e-01 4.68641907e-01 -4.99644548e-01
1.63142178e-02 -4.28782344e-01 -6.07408464e-01 -6.50675118e-01
6.00934029e-02 5.92455029e-01 4.20130938e-01 -1.44290389... | [14.618496894836426, -2.5244925022125244] |
18621ccf-c2d7-4318-96e7-954f4b558b17 | electronic-structure-properties-from-atom | 2206.14087 | null | https://arxiv.org/abs/2206.14087v1 | https://arxiv.org/pdf/2206.14087v1.pdf | Electronic-structure properties from atom-centered predictions of the electron density | The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the scalar field using a multi-centered atomic basis analogous to that routi... | ['Michele Ceriotti', 'Mariana Rossi', 'Alan M. Lewis', 'Andrea Grisafi'] | 2022-06-28 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 2.58760244e-01 -3.34045216e-02 -2.03434631e-01 -2.05786094e-01
-9.83435631e-01 -6.39192984e-02 5.45510113e-01 3.79082769e-01
-6.84341669e-01 1.43327117e+00 -1.08838402e-01 -2.12858409e-01
9.83065516e-02 -8.66556227e-01 -6.68775737e-01 -1.25359643e+00
-4.56881747e-02 6.72120154e-01 -1.22939460e-02 -1.08504191... | [5.303282260894775, 5.219146251678467] |
7552c22a-5f4e-4db8-b1b4-1971a5e5358d | evaluation-of-the-potential-of-near-infrared | 2301.08252 | null | https://arxiv.org/abs/2301.08252v1 | https://arxiv.org/pdf/2301.08252v1.pdf | Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug | The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops, compromising agri-food production. Field monitoring procedures are fundamental to perform risk assessment operations, in order to promptly face crop infestations and avoid economical los... | ['Alessandro Ulrici', 'Peter Offermans', 'Lara Maistrello', 'Aravind Krishnaswamy Rangarajan', 'Camilla Menozzi', 'Bas Boom', 'Rosalba Calvini', 'Veronica Ferrari'] | 2023-01-19 | null | null | null | null | ['scene-classification', 'variable-selection'] | ['computer-vision', 'methodology'] | [ 6.75262034e-01 -5.35322666e-01 -8.67492706e-02 1.74213365e-01
8.51752535e-02 -8.19985271e-01 9.98385549e-02 2.16973305e-01
-2.83171058e-01 7.56893873e-01 -5.53354681e-01 -4.22186404e-01
-5.60222983e-01 -1.12475204e+00 -3.02848965e-01 -1.01406634e+00
-2.09884733e-01 2.09376514e-01 2.28825007e-02 -5.42919040... | [9.509947776794434, -1.665880799293518] |
f99db249-1b79-4094-bc3a-56145deed006 | fedora-flying-event-dataset-for-reactive | 2305.14392 | null | https://arxiv.org/abs/2305.14392v1 | https://arxiv.org/pdf/2305.14392v1.pdf | FEDORA: Flying Event Dataset fOr Reactive behAvior | The ability of living organisms to perform complex high speed manoeuvers in flight with a very small number of neurons and an incredibly low failure rate highlights the efficacy of these resource-constrained biological systems. Event-driven hardware has emerged, in recent years, as a promising avenue for implementing c... | ['Kaushik Roy', 'Manish Nagaraj', 'Wachirawit Ponghiran', 'Adarsh Kosta', 'Amogh Joshi'] | 2023-05-22 | null | null | null | null | ['simultaneous-localization-and-mapping', 'autonomous-navigation'] | ['computer-vision', 'computer-vision'] | [ 1.67983919e-02 -4.94668752e-01 1.99569285e-01 -1.46761134e-01
4.96526770e-02 -6.24519527e-01 6.87998056e-01 -3.06015998e-01
-7.31143534e-01 8.57097387e-01 -1.42203376e-01 9.46991146e-02
4.75861598e-03 -4.03445840e-01 -4.81560141e-01 -4.07746524e-01
-3.53798509e-01 1.64268494e-01 5.68814218e-01 -2.19985411... | [8.593239784240723, -1.3269559144973755] |
ddb303e6-cddc-492f-834a-8f52a62bd5a7 | nonparallel-high-quality-audio-super | 2210.15887 | null | https://arxiv.org/abs/2210.15887v2 | https://arxiv.org/pdf/2210.15887v2.pdf | Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs | Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs. Although these methods achieve highly accurate super-resolution if the acoustic characteristics of the input data are similar to those of the training data, challenges remain: the models suffer from quality degrad... | ['Kentaro Tachibana', 'Ryuichi Yamamoto', 'Reo Yoneyama'] | 2022-10-28 | null | null | null | null | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 3.22139680e-01 -1.35063946e-01 7.53383785e-02 -7.38353878e-02
-1.65616667e+00 -5.24150968e-01 2.96193182e-01 -8.13044250e-01
1.73062950e-01 7.53050268e-01 5.64196050e-01 2.85169303e-01
1.78435475e-01 -6.91720068e-01 -7.90235937e-01 -6.67890906e-01
7.41557330e-02 2.02779584e-02 6.97767511e-02 -3.15848798... | [15.364683151245117, 6.014230728149414] |
6b112748-4225-494c-ad6a-6549c6dfcb34 | brain-intelligence-go-beyond-artificial | 1706.01040 | null | http://arxiv.org/abs/1706.01040v1 | http://arxiv.org/pdf/1706.01040v1.pdf | Brain Intelligence: Go Beyond Artificial Intelligence | Artificial intelligence (AI) is an important technology that supports daily
social life and economic activities. It contributes greatly to the sustainable
growth of Japan's economy and solves various social problems. In recent years,
AI has attracted attention as a key for growth in developed countries such as
Europe a... | ['Min Chen', 'Seiichi Serikawa', 'Yujie Li', 'Hyoungseop Kim', 'Huimin Lu'] | 2017-06-04 | null | null | null | null | ['artificial-life', 'industrial-robots'] | ['miscellaneous', 'robots'] | [-1.52444541e-01 5.16189396e-01 -4.93556075e-02 -1.90123275e-01
4.39498097e-01 -7.61064813e-02 6.87988400e-01 -1.97908700e-01
-2.08774582e-01 8.44430149e-01 5.02580479e-02 -4.13138002e-01
-2.16810659e-01 -1.17872965e+00 -3.44156802e-01 -3.41332614e-01
1.65755526e-04 5.71218789e-01 6.97237104e-02 -5.10570347... | [9.159613609313965, 6.386629104614258] |
dcff279d-4e38-40a8-9bc1-d4f06a8236e6 | adversarial-transfer-learning-for-punctuation | 2004.00248 | null | https://arxiv.org/abs/2004.00248v1 | https://arxiv.org/pdf/2004.00248v1.pdf | Adversarial Transfer Learning for Punctuation Restoration | Previous studies demonstrate that word embeddings and part-of-speech (POS) tags are helpful for punctuation restoration tasks. However, two drawbacks still exist. One is that word embeddings are pre-trained by unidirectional language modeling objectives. Thus the word embeddings only contain left-to-right context infor... | ['Jian-Hua Tao', 'Zhengkun Tian', 'Jiangyan Yi', 'Cunhang Fan', 'Ye Bai'] | 2020-04-01 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 7.46799335e-02 8.74551162e-02 -2.19023138e-01 -3.00662488e-01
-9.67917800e-01 -5.28541982e-01 2.27241665e-01 9.12222043e-02
-7.15731621e-01 6.53309703e-01 3.45213145e-01 -3.56649846e-01
4.98451144e-01 -6.59367681e-01 -8.87333393e-01 -6.54208243e-01
6.50061443e-02 6.42328784e-02 3.16797316e-01 -3.40228647... | [14.115266799926758, 7.292847156524658] |
a489e46a-b91b-450f-8fd0-676bdf5e8e18 | complete-solution-for-vehicle-re-id-in | 2212.04126 | null | https://arxiv.org/abs/2212.04126v1 | https://arxiv.org/pdf/2212.04126v1.pdf | Complete Solution for Vehicle Re-ID in Surround-view Camera System | Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car's surround-view camera system. Our analysis identifie... | ['Jing Song', 'Xiaoquan Wang', 'Fan Wang', 'Tianhao Xu', 'Zizhang Wu'] | 2022-12-08 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [-2.48508677e-01 -2.98815995e-01 3.36414874e-02 -3.92957449e-01
-5.02225637e-01 -6.22520626e-01 5.62343776e-01 -2.12227702e-01
-5.76454461e-01 3.15197021e-01 -2.80486345e-01 -2.70454466e-01
1.94356769e-01 -2.59613723e-01 -7.33354092e-01 -6.59461260e-01
4.23215777e-01 1.69528723e-01 8.63376617e-01 -2.21554130... | [7.968259334564209, -1.2705531120300293] |
66f4bdeb-e931-48a3-be67-a409b214e4e1 | towards-controlled-and-diverse-generation-of | 2107.11781 | null | https://arxiv.org/abs/2107.11781v1 | https://arxiv.org/pdf/2107.11781v1.pdf | Towards Controlled and Diverse Generation of Article Comments | Much research in recent years has focused on automatic article commenting. However, few of previous studies focus on the controllable generation of comments. Besides, they tend to generate dull and commonplace comments, which further limits their practical application. In this paper, we make the first step towards cont... | ['Houfeng Wang', 'Linhao Zhang'] | 2021-07-25 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 1.52099982e-01 1.54564112e-01 -1.42520115e-01 -5.39876699e-01
-6.54523611e-01 -3.68692160e-01 4.85633165e-01 9.99362767e-02
-1.42782718e-01 6.85493290e-01 7.52729237e-01 -8.16319361e-02
5.60197473e-01 -7.87197351e-01 -2.65346974e-01 -6.06077373e-01
6.70292556e-01 -2.80823056e-02 -1.11810938e-01 -5.07819533... | [12.069561958312988, 8.999163627624512] |
e945c55b-a7d9-4a09-b111-b60f3933c172 | predicting-individual-responses-to-vasoactive | 1901.10400 | null | http://arxiv.org/abs/1901.10400v1 | http://arxiv.org/pdf/1901.10400v1.pdf | Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock | Objective: Predict individual septic children's personalized physiologic
responses to vasoactive titrations by training a Recurrent Neural Network (RNN)
using EMR data.
Materials and Methods: This study retrospectively analyzed EMR of patients
admitted to a pediatric ICU from 2009 to 2017. Data included charted time
... | ['Randall Wetzel', 'Jessica Asencio', 'David Ledbetter', 'Cameron Carlin', 'Nicole Fronda', 'Melissa Aczon', 'Barry Markovitz'] | 2019-01-15 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [-1.27502397e-01 -3.90166044e-02 5.72511852e-02 -3.36655796e-01
-4.98496294e-01 -4.29465503e-01 -4.61721539e-01 8.13611269e-01
-4.61672306e-01 8.22507381e-01 6.00418270e-01 -9.06493604e-01
-3.24449390e-01 -5.03309309e-01 -4.20895875e-01 -4.89711076e-01
-3.55675370e-01 7.88933635e-01 -3.77374381e-01 -2.76485160... | [8.030557632446289, 6.18095588684082] |
1803d502-3d3e-484b-aab7-58f5d3c65b09 | exploring-edge-tpu-for-network-intrusion | 2103.16295 | null | https://arxiv.org/abs/2103.16295v1 | https://arxiv.org/pdf/2103.16295v1.pdf | Exploring Edge TPU for Network Intrusion Detection in IoT | This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While there are a significant number of related works that explore machine learning based NIDS for the IoT edge, they generally do not consider the issue of... | ['Marius Portmann', 'Raja Jurdak', 'Mohanad Sarhan', 'Siamak Layeghy', 'Seyedehfaezeh Hosseininoorbin'] | 2021-03-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.45838076e-01 -2.12196916e-01 -4.91897374e-01 4.37220000e-02
4.45663154e-01 -2.02487215e-01 2.84069270e-01 -2.55352855e-01
-4.81050074e-01 2.07964972e-01 -6.88278675e-01 -1.17660975e+00
-1.25628427e-01 -1.14322841e+00 -4.24785525e-01 -5.38532555e-01
-1.14697069e-01 3.90318602e-01 2.51949102e-01 1.26922309... | [8.293167114257812, 2.744737148284912] |
41c19346-782b-4ae4-b55f-c56c3b403724 | cnn-based-real-time-2d-3d-deformable | 2212.07692 | null | https://arxiv.org/abs/2212.07692v2 | https://arxiv.org/pdf/2212.07692v2.pdf | CNN-based real-time 2D-3D deformable registration from a single X-ray projection | Purpose: The purpose of this paper is to present a method for real-time 2D-3D non-rigid registration using a single fluoroscopic image. Such a method can find applications in surgery, interventional radiology and radiotherapy. By estimating a three-dimensional displacement field from a 2D X-ray image, anatomical struct... | ['Stéphane Cotin', 'Jean-Louis Dillenseger', 'François Lecomte'] | 2022-12-15 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 1.97261542e-01 6.31333649e-01 1.34431822e-02 -2.83288568e-01
-7.64303684e-01 -5.76684952e-01 4.52104658e-01 1.09977357e-01
-6.44744217e-01 3.66764486e-01 8.02749097e-02 -2.59261221e-01
-2.72903174e-01 -6.37377620e-01 -8.86353970e-01 -7.28090942e-01
-7.90365860e-02 1.17902637e+00 2.06491947e-01 -2.36997291... | [13.82205867767334, -2.767116069793701] |
8ba3b5a5-1fd1-4bf7-a217-996519743ef8 | c-saw-a-framework-for-graph-sampling-and | 2009.09103 | null | https://arxiv.org/abs/2009.09103v1 | https://arxiv.org/pdf/2009.09103v1.pdf | C-SAW: A Framework for Graph Sampling and Random Walk on GPUs | Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies. Many applications have requirements to analyze, transform, visualize and learn large scale graphs. These graphs are often too... | ['Hang Liu', 'Xiaoye S. Li', 'Adolfy Hoisie', 'Lingda Li', 'Santosh Pandey'] | 2020-09-18 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-1.23801455e-01 -2.49502674e-01 -1.24622829e-01 -1.26713822e-02
-4.92713094e-01 -6.39429331e-01 3.55950594e-01 5.27904332e-01
-2.72567540e-01 2.97553092e-01 -1.80235237e-01 -8.00533712e-01
-1.33700535e-01 -1.50583065e+00 -5.05464911e-01 -3.71101469e-01
-5.39039016e-01 7.24560261e-01 8.90456378e-01 -1.46906763... | [7.042637825012207, 5.507956027984619] |
3b848623-18de-4949-a873-62090e7ebed7 | rs-gv-at-semeval-2021-task-1-sense-relative | null | null | https://aclanthology.org/2021.semeval-1.82 | https://aclanthology.org/2021.semeval-1.82.pdf | RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction | We present the technical report of the system called RS{\_}GV at SemEval-2021 Task 1 on lexical complexity prediction of English words. RS{\_}GV is a neural network using hand-crafted linguistic features in combination with character and word embeddings to predict target words{'} complexity. For the generation of the h... | ['Gayatri Venugopal', 'Regina Stodden'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 9.93417948e-03 2.63999641e-01 -2.91205883e-01 -5.24505734e-01
-3.20220619e-01 -1.76593721e-01 5.75978160e-01 6.83836997e-01
-9.32183266e-01 5.47461808e-01 5.76627612e-01 -8.54168713e-01
-2.32906967e-01 -8.16355705e-01 -2.67159611e-01 -3.66530381e-02
-2.65131623e-01 5.71403682e-01 -1.33967176e-01 -6.74073994... | [10.612018585205078, 10.40668773651123] |
3824c70f-29ac-4b06-9ff0-ae745b49f039 | cacti-a-framework-for-scalable-multi-task | 2212.05711 | null | https://arxiv.org/abs/2212.05711v2 | https://arxiv.org/pdf/2212.05711v2.pdf | CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning | Large-scale training have propelled significant progress in various sub-fields of AI such as computer vision and natural language processing. However, building robot learning systems at a comparable scale remains challenging. To develop robots that can perform a wide range of skills and adapt to new scenarios, efficien... | ['Vikash Kumar', 'Aravind Rajeswaran', 'Shuran Song', 'Vincent Moens', 'Homanga Bharadhwaj', 'Zhao Mandi'] | 2022-12-12 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.59832239e-01 -2.05069721e-01 -1.95515957e-02 -3.09188068e-01
-3.54988039e-01 -6.09940052e-01 5.05439341e-01 -1.21867642e-01
-5.67272007e-01 5.37809014e-01 -3.78244668e-01 -2.74782836e-01
-5.22852801e-02 -6.34426296e-01 -1.15836251e+00 -5.83905041e-01
-2.38709807e-01 9.24086392e-01 3.64184141e-01 -2.60998696... | [4.541669845581055, 0.8522148132324219] |
a25a5e9f-d86d-4c80-84df-e26e72e82094 | prospects-and-applications-of-incoherent | 2304.09922 | null | https://arxiv.org/abs/2304.09922v1 | https://arxiv.org/pdf/2304.09922v1.pdf | Prospects and Applications of Incoherent Light in Non-contact Wireless Sensing Systems | The increasing demand for wireless sensing systems has led to the exploration of alternative technologies to overcome the spectrum scarcity of traditional approaches based on radio frequency (RF) waves or microwaves. Incoherent light sources such as light-emitting diodes (LED), paired with light sensors, have the poten... | ["John F. O'Hara", 'Sabit Ekin', 'Md Zobaer Islam'] | 2023-04-19 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 6.61979437e-01 -4.11972314e-01 -2.23451704e-01 -1.11421920e-01
-2.82823369e-02 -4.38982755e-01 1.92614079e-01 -1.89521983e-01
-6.46003187e-01 1.20683694e+00 -1.00669697e-01 -7.40263388e-02
-1.10830128e-01 -8.66002679e-01 6.58991784e-02 -1.20869946e+00
2.01763794e-01 -5.71525753e-01 -1.09043038e-02 1.50301069... | [6.611507415771484, 0.6265912055969238] |
5f9cdb86-28e9-4a46-9713-d0c8e848e850 | adversarially-regularized-policy-learning | 2109.07627 | null | https://arxiv.org/abs/2109.07627v3 | https://arxiv.org/pdf/2109.07627v3.pdf | Adversarially Regularized Policy Learning Guided by Trajectory Optimization | Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse tasks in robot systems. Despite their great flexibility, the large neural networks for parameterizing control policies impose significant challe... | ['Ye Zhao', 'Tuo Zhao', 'Simiao Zuo', 'Zhigen Zhao'] | 2021-09-16 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 6.55458495e-02 2.03625575e-01 -5.53982437e-01 9.93151292e-02
-5.00631094e-01 -5.77608705e-01 3.69164705e-01 -1.63695395e-01
-4.42744434e-01 1.13346529e+00 -1.02816895e-01 -4.84809905e-01
-2.67888099e-01 -5.29721260e-01 -1.25777078e+00 -1.09188926e+00
-1.44929975e-01 -4.98362854e-02 -7.75921866e-02 -1.95155278... | [4.762088298797607, 2.1828415393829346] |
8241561e-1916-4e8d-821e-1ff904ff8e97 | multilingual-multimodal-machine-translation | null | null | https://aclanthology.org/W19-6809 | https://aclanthology.org/W19-6809.pdf | Multilingual Multimodal Machine Translation for Dravidian Languages utilizing Phonetic Transcription | null | ['John P. McCrae', 'Manel Zarrouk', 'Sridevy S', 'Bernardo Stearns', 'Ruba Priyadharshini', 'Arun Jayapal', 'Mihael Arcan', 'Bharathi Raja Chakravarthi'] | 2019-08-01 | null | null | null | ws-2019-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.376296520233154, 3.715768575668335] |
89986952-0524-43ce-a50d-4f9a93cdcb19 | task-wise-split-gradient-boosting-trees-for | 2108.07107 | null | https://arxiv.org/abs/2108.07107v1 | https://arxiv.org/pdf/2108.07107v1.pdf | Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction | Diabetes prediction is an important data science application in the social healthcare domain. There exist two main challenges in the diabetes prediction task: data heterogeneity since demographic and metabolic data are of different types, data insufficiency since the number of diabetes cases in a single medical center ... | ['Guang Ning', 'Weiqing Wang', 'Yufang Bi', 'Yong Yu', 'Wei-Wei Tu', 'Mian Li', 'Tiange Wang', 'Yu Xu', 'Min Xu', 'Jieli Lu', 'Yanru Qu', 'Jian Shen', 'Xiawei Guo', 'Weinan Zhang', 'Zhiyun Zhao', 'Zhenghui Wang', 'Mingcheng Chen'] | 2021-08-16 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 8.54650587e-02 -2.69615203e-01 -5.01325727e-01 -6.35773063e-01
-8.16376925e-01 3.96234661e-01 1.28482282e-01 5.59381723e-01
-1.56173736e-01 8.26814353e-01 1.98802173e-01 -4.37957168e-01
-4.51467991e-01 -6.40465319e-01 -4.14522648e-01 -8.88930619e-01
-1.71866551e-01 7.06964731e-01 -7.28072599e-02 -7.19749406... | [14.255770683288574, 3.118441581726074] |
2365bf93-b926-4c6d-92e5-ef7872dad425 | fastwave-accelerating-autoregressive | 2002.04971 | null | https://arxiv.org/abs/2002.04971v1 | https://arxiv.org/pdf/2002.04971v1.pdf | FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA | Autoregressive convolutional neural networks (CNNs) have been widely exploited for sequence generation tasks such as audio synthesis, language modeling and neural machine translation. WaveNet is a deep autoregressive CNN composed of several stacked layers of dilated convolution that is used for sequence generation. Whi... | ['Farinaz Koushanfar', 'Shehzeen Hussain', 'Mojan Javaheripi', 'Ryan Kastner', 'Paarth Neekhara'] | 2020-02-09 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.78361917e-01 -1.71746954e-01 4.23743986e-02 -1.63683996e-01
-4.69462961e-01 -2.81068146e-01 3.90714467e-01 -2.85839260e-01
-2.64042646e-01 3.50029171e-01 4.69987988e-01 -7.60001004e-01
4.27793086e-01 -9.10702765e-01 -6.76548779e-01 -5.36543906e-01
-9.78830382e-02 1.27973789e-02 -3.73160839e-02 -3.17178458... | [15.236981391906738, 5.765880584716797] |
92880aef-a1dd-457e-90a8-8b1872f062ff | top-k-extreme-contextual-bandits-with-arm | 2102.07800 | null | https://arxiv.org/abs/2102.07800v1 | https://arxiv.org/pdf/2102.07800v1.pdf | Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy | Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed to select $k$ arms and observe all or some of the rewards for the chosen arms. We first propose an a... | ['Inderjit Dhillon', 'Daniel Hill', 'Dean Foster', 'Rahul Kidambi', 'Lexing Ying', 'Alexander Rakhlin', 'Rajat Sen'] | 2021-02-15 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.14651182e-01 2.24707037e-01 -6.46868110e-01 -4.73737359e-01
-1.54432535e+00 -9.90462601e-01 -1.48464993e-01 2.00988278e-01
-6.91138685e-01 8.80647242e-01 -2.97343642e-01 -8.77084553e-01
-7.50109076e-01 -9.27768886e-01 -1.34509122e+00 -7.45593369e-01
-3.93404216e-01 8.49398553e-01 -2.53928632e-01 -2.79570818... | [4.758172035217285, 3.5068204402923584] |
ab5181f2-ce40-4b8b-bf76-80b799cb8564 | communication-robust-multi-agent-learning-by | 2305.05116 | null | https://arxiv.org/abs/2305.05116v1 | https://arxiv.org/pdf/2305.05116v1.pdf | Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation | Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning (MARL). Nowadays, existing works mainly focus on improving the communication efficiency of agents, neglecting that real-world communication is much more challenging as there may exist noise or potential attackers. Thus the robustne... | ['Yang Yu', 'Zhongzhang Zhang', 'Feng Chen', 'Lei Yuan'] | 2023-05-09 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.96765342e-02 1.60752043e-01 1.41430482e-01 6.41951799e-01
-4.38387156e-01 -7.45959401e-01 6.59741998e-01 2.54197586e-02
-3.55922729e-01 8.54840517e-01 -1.25231862e-01 -2.84565210e-01
-1.76169500e-01 -1.02445161e+00 -7.54949152e-01 -1.28206146e+00
-4.39338595e-01 1.36428699e-01 1.56352788e-01 -7.63738751... | [3.8058900833129883, 2.336461305618286] |
c8bbdf48-85b2-446e-be12-d2d6467edc18 | dumb-a-benchmark-for-smart-evaluation-of | 2305.13026 | null | https://arxiv.org/abs/2305.13026v1 | https://arxiv.org/pdf/2305.13026v1.pdf | DUMB: A Benchmark for Smart Evaluation of Dutch Models | We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of eight tasks include three tasks that were previously not available in Dutch. Instead of relying on a mean score across tasks, we propose Relative Error Reduction (RE... | ['Malvina Nissim', 'Martijn Wieling', 'Wietse de Vries'] | 2023-05-22 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-2.28841364e-01 1.08050473e-01 -1.30511343e-01 -4.81034428e-01
-1.25422657e+00 -6.51597023e-01 1.08481538e+00 -4.12364528e-02
-9.43109870e-01 8.37636650e-01 6.77988946e-01 -3.45736623e-01
1.05732551e-03 -2.05510259e-01 -7.77891636e-01 -1.71824589e-01
2.37409949e-01 8.01622689e-01 9.07411128e-02 -3.24989945... | [10.935775756835938, 10.027222633361816] |
6c261f2e-1fae-4b32-a907-489f309ed9d0 | towards-a-robust-framework-for-nerf | 2305.18079 | null | https://arxiv.org/abs/2305.18079v3 | https://arxiv.org/pdf/2305.18079v3.pdf | Towards a Robust Framework for NeRF Evaluation | Neural Radiance Field (NeRF) research has attracted significant attention recently, with 3D modelling, virtual/augmented reality, and visual effects driving its application. While current NeRF implementations can produce high quality visual results, there is a conspicuous lack of reliable methods for evaluating them. C... | ['David R Bull', 'Nantheera Anantrasirichai', 'Adrian Azzarelli'] | 2023-05-29 | null | null | null | null | ['neural-rendering', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.38566333e-01 -2.66900361e-01 4.52014536e-01 -3.91418040e-01
-6.80640817e-01 -5.33749342e-01 8.37933421e-01 1.80765659e-01
-3.31328183e-01 5.21806896e-01 9.67336297e-02 -3.98586303e-01
-2.97236979e-01 -9.84355569e-01 -6.75588727e-01 -4.90094423e-01
-1.46318719e-01 -7.74520338e-02 3.12941968e-01 -3.79091173... | [10.972745895385742, -2.2146472930908203] |
e803e257-4bbd-4ade-bd5e-633f95d29269 | improving-translation-of-out-of-vocabulary | null | null | https://aclanthology.org/2022.amta-research.11 | https://aclanthology.org/2022.amta-research.11.pdf | Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation | Dictionary-based data augmentation techniques have been used in the field of domain adaptation to learn words that do not appear in the parallel training data of a machine translation model. These techniques strive to learn correct translations of these words by generating a synthetic corpus from in-domain monolingual ... | ['Antonio Valerio Micele Barone', 'Barry Hadow', 'Alexandra Birch', 'Jonas Waldendorf'] | null | null | null | null | amta-2022-9 | ['word-translation'] | ['natural-language-processing'] | [ 2.22441778e-01 -1.09310605e-01 -7.23290145e-01 -3.70391369e-01
-9.39314365e-01 -8.16293299e-01 9.90286589e-01 3.28490883e-01
-8.03087652e-01 1.06246400e+00 5.47204256e-01 -8.60735118e-01
4.58714157e-01 -6.58846140e-01 -8.10861886e-01 -3.46937299e-01
5.43689430e-01 1.20732820e+00 -1.93422914e-01 -8.01698864... | [11.267314910888672, 10.226995468139648] |
d3be8e16-4786-4b8d-83c5-e617c4655907 | a-domain-knowledge-inspired-music-embedding | 2212.00973 | null | https://arxiv.org/abs/2212.00973v1 | https://arxiv.org/pdf/2212.00973v1.pdf | A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling | Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e... | ['D. Herremans', 'J. Kang', 'Z. Guo'] | 2022-12-02 | null | null | null | null | ['music-generation', 'music-modeling', 'music-generation'] | ['audio', 'music', 'music'] | [ 1.49786100e-01 -2.68259436e-01 -8.17323625e-02 -4.02622558e-02
-5.49298644e-01 -7.44893014e-01 5.04880011e-01 -4.42380421e-02
-1.53204992e-01 3.41910303e-01 2.70508915e-01 1.98253989e-01
-5.83672464e-01 -6.04431868e-01 -6.10455751e-01 -8.33280206e-01
3.71933840e-02 2.66496778e-01 -6.03489019e-02 -4.10160482... | [15.96082592010498, 5.491605758666992] |
712f199e-c39f-491f-9571-dc8e8807b054 | global-sensitivity-analysis-in-probabilistic | 2110.03749 | null | https://arxiv.org/abs/2110.03749v1 | https://arxiv.org/pdf/2110.03749v1.pdf | Global sensitivity analysis in probabilistic graphical models | We show how to apply Sobol's method of global sensitivity analysis to measure the influence exerted by a set of nodes' evidence on a quantity of interest expressed by a Bayesian network. Our method exploits the network structure so as to transform the problem of Sobol index estimation into that of marginalization infer... | ['Manuele Leonelli', 'Rafael Ballester-Ripoll'] | 2021-10-07 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 1.19893730e-01 7.90769607e-03 -1.11229599e-01 -3.51036608e-01
-1.59173831e-01 -5.15045166e-01 3.27844858e-01 -3.74724492e-02
-2.17668876e-01 7.63175607e-01 1.50587246e-01 -5.81422269e-01
-9.58482563e-01 -1.22823012e+00 -7.29488492e-01 -6.27155304e-01
-6.66440904e-01 5.05180478e-01 4.50880677e-01 -1.80655539... | [7.420628070831299, 4.90974760055542] |
ff7ef679-d34c-43a6-beaf-61e9649e22a4 | explanation-based-weakly-supervised-learning | 2006.09562 | null | https://arxiv.org/abs/2006.09562v1 | https://arxiv.org/pdf/2006.09562v1.pdf | Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks | Visual relationship detection is fundamental for holistic image understanding. However, localizing and classifying (subject, predicate, object) triplets constitutes a hard learning objective due to the combinatorial explosion of possible relationships, their long-tail distribution in natural images, and an expensive an... | ['Hossein Azizpour', 'Federico Baldassarre', 'Josephine Sullivan', 'Kevin Smith'] | 2020-06-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6336_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730613.pdf | eccv-2020-8 | ['visual-relationship-detection'] | ['computer-vision'] | [ 6.86330318e-01 5.25560915e-01 -4.87172961e-01 -5.39243877e-01
-3.27418536e-01 -7.33815134e-01 6.52481139e-01 6.42904341e-01
-2.30729692e-02 5.39718330e-01 1.09544098e-01 -1.75859213e-01
-6.25928715e-02 -3.64428192e-01 -1.02447808e+00 -5.26070416e-01
-3.21765721e-01 9.98884141e-01 4.10405636e-01 8.24728757... | [10.27157211303711, 1.6368355751037598] |
a4d8fb8b-6f7a-4e2b-beb4-91cedb03067a | hifa-high-fidelity-text-to-3d-with-advanced | 2305.18766 | null | https://arxiv.org/abs/2305.18766v2 | https://arxiv.org/pdf/2305.18766v2.pdf | HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance | Automatic text-to-3D synthesis has achieved remarkable advancements through the optimization of 3D models. Existing methods commonly rely on pre-trained text-to-image generative models, such as diffusion models, providing scores for 2D renderings of Neural Radiance Fields (NeRFs) and being utilized for optimizing NeRFs... | ['Junzhe Zhu', 'Peiye Zhuang'] | 2023-05-30 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 2.85361707e-01 9.45819169e-02 4.82874960e-02 -4.05937374e-01
-6.36062801e-01 -4.90947247e-01 9.14659619e-01 -3.36257637e-01
3.11549827e-02 3.82894307e-01 6.20570719e-01 -1.59965053e-01
1.43777877e-01 -9.38407362e-01 -7.60841727e-01 -5.97324610e-01
3.79262060e-01 1.10643998e-01 5.19776717e-03 -1.29384831... | [9.278450965881348, -3.142446756362915] |
bb4a4ce8-3f59-4931-b6ec-6f85ac9eb6f8 | hybrid-local-global-transformer-for-image | 2109.07100 | null | https://arxiv.org/abs/2109.07100v3 | https://arxiv.org/pdf/2109.07100v3.pdf | Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing | Conventional CNNs-based dehazing models suffer from two essential issues: the dehazing framework (limited in interpretability) and the convolution layers (content-independent and ineffective to learn long-range dependency information). In this paper, firstly, we propose a new complementary feature enhanced framework, i... | ['Long Xu', 'Hongyu Li', 'Jia Li', 'Dong Zhao'] | 2021-09-15 | null | null | null | null | ['image-dehazing', 'intrinsic-image-decomposition'] | ['computer-vision', 'computer-vision'] | [ 1.44198254e-01 -3.68138701e-01 2.72423208e-01 -3.02431613e-01
-3.22331071e-01 -1.16544656e-01 7.11979568e-01 -3.73053700e-01
-3.06306750e-01 3.47962022e-01 1.79730654e-01 1.42300099e-01
-3.86208653e-01 -1.09392262e+00 -6.79178834e-01 -1.22289932e+00
2.74198234e-01 -4.16534662e-01 6.04153275e-01 -5.82072794... | [10.958629608154297, -2.937124729156494] |
45a454da-ab21-40ce-8691-a985abbb8c80 | semi-supervised-vector-quantization-in-visual | 2207.06738 | null | https://arxiv.org/abs/2207.06738v1 | https://arxiv.org/pdf/2207.06738v1.pdf | Semi-supervised Vector-Quantization in Visual SLAM using HGCN | In this paper, two semi-supervised appearance based loop closure detection technique, HGCN-FABMAP and HGCN-BoW are introduced. Furthermore an extension to the current state of the art localization SLAM algorithm, ORB-SLAM, is presented. The proposed HGCN-FABMAP method is implemented in an off-line manner incorporating ... | ['Ali Mohades Khorasani', 'Saeed Shiry Ghidary', 'Amir Zarringhalam'] | 2022-07-14 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.24706902e-01 2.38922283e-01 6.62273169e-02 -1.84115693e-01
-4.96661752e-01 -2.84892827e-01 7.46555924e-01 4.40161765e-01
-4.39318955e-01 6.07873857e-01 1.20156091e-02 -3.55355114e-01
-3.36860687e-01 -7.79176533e-01 -8.12282145e-01 -4.73335624e-01
-3.88589472e-01 8.13042879e-01 4.52692509e-01 -1.57560706... | [7.368973731994629, -2.028442621231079] |
fe8588a3-10f8-4121-89fe-3e9ee48c998b | logical-reasoning-for-natural-language | 2305.13214 | null | https://arxiv.org/abs/2305.13214v1 | https://arxiv.org/pdf/2305.13214v1.pdf | Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms | State-of-the-art neural models can now reach human performance levels across various natural language understanding tasks. However, despite this impressive performance, models are known to learn from annotation artefacts at the expense of the underlying task. While interpretability methods can identify influential feat... | ['Marek Rei', 'Oana-Maria Camburu', 'Haim Dubossarsky', 'Pasquale Minervini', 'Joe Stacey'] | 2023-05-22 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 3.88933599e-01 8.98703516e-01 -5.32872796e-01 -6.77365601e-01
-7.73284137e-01 -4.81778562e-01 9.85525191e-01 1.52242765e-01
-1.03808083e-01 7.68480062e-01 5.94393671e-01 -4.40839410e-01
-2.41120070e-01 -7.03087330e-01 -1.05376422e+00 -2.45866328e-01
2.09020212e-01 1.17201793e+00 1.47710089e-03 4.91133444... | [9.529972076416016, 6.950017929077148] |
5532790a-93fb-4ebd-a340-a8998a76053e | a-simple-multi-modality-transfer-learning | 2203.04287 | null | https://arxiv.org/abs/2203.04287v2 | https://arxiv.org/pdf/2203.04287v2.pdf | A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation | This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pairs of sign videos, gloss annotations and texts, which are an order of magnitude smaller than typical parallel data for training spoken lang... | ['Stephen Lin', 'Zhirong Wu', 'Xiao Sun', 'Fangyun Wei', 'Yutong Chen'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 1.08136535e-02 -8.95656124e-02 -4.84498411e-01 -6.33747458e-01
-1.17504764e+00 -6.38440430e-01 9.13808703e-01 -9.04269457e-01
-7.30929315e-01 6.07726932e-01 5.17328262e-01 -3.05562496e-01
4.53957081e-01 -3.54979187e-01 -9.14850771e-01 -5.75730979e-01
3.00183952e-01 7.55178094e-01 2.15520963e-01 -2.39367604... | [9.315308570861816, -6.6402268409729] |
0448985d-6d22-4ff1-9fdf-ce313cf2affa | evaluation-of-speech-representations-for-mos | 2306.09979 | null | https://arxiv.org/abs/2306.09979v1 | https://arxiv.org/pdf/2306.09979v1.pdf | Evaluation of Speech Representations for MOS prediction | In this paper, we evaluate feature extraction models for predicting speech quality. We also propose a model architecture to compare embeddings of supervised learning and self-supervised learning models with embeddings of speaker verification models to predict the metric MOS. Our experiments were performed on the VCC201... | ['Arlindo R. Galvão Filho', 'Anderson S. Soares', 'Lucas R. S. Gris', 'Arnaldo Cândido Júnior', 'Edresson Casanova', 'Frederico S. Oliveira'] | 2023-06-16 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-5.18385053e-01 1.09207623e-01 -1.99544504e-02 -5.05123675e-01
-8.63034010e-01 -3.66803467e-01 5.52623332e-01 1.77633747e-01
-4.06022757e-01 4.13554013e-01 4.59990591e-01 -3.27469438e-01
-7.36679882e-02 -4.27377909e-01 -2.81071782e-01 -4.90317196e-01
-3.22268575e-01 2.50192702e-01 5.24798892e-02 -3.67204130... | [14.270849227905273, 6.166194438934326] |
f36af5c3-9359-4523-aa5d-35c6a86849f2 | provable-burer-monteiro-factorization-for-a | 1606.01316 | null | http://arxiv.org/abs/1606.01316v3 | http://arxiv.org/pdf/1606.01316v3.pdf | Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems | We study the projected gradient descent method on low-rank matrix problems
with a strongly convex objective. We use the Burer-Monteiro factorization
approach to implicitly enforce low-rankness; such factorization introduces
non-convexity in the objective. We focus on constraint sets that include both
positive semi-defi... | ['Sujay Sanghavi', 'Dohyung Park', 'Srinadh Bhojanapalli', 'Constantine Caramanis', 'Anastasios Kyrillidis'] | 2016-06-04 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.36713940e-01 1.00778401e-01 -1.96338370e-01 -2.30645284e-01
-1.08819973e+00 -6.52772307e-01 5.41597426e-01 -5.04021466e-01
-6.65352345e-01 9.62606728e-01 4.46379095e-01 -6.11713350e-01
-3.95786136e-01 -5.96849203e-01 -6.26204669e-01 -9.25261140e-01
-1.61139026e-01 6.26108050e-01 -1.87191829e-01 -5.09632647... | [6.472716808319092, 4.655330181121826] |
eeb45128-0897-412f-b74b-5460fdb8f071 | incorporating-bert-into-neural-machine-1 | 2002.06823 | null | https://arxiv.org/abs/2002.06823v1 | https://arxiv.org/pdf/2002.06823v1.pdf | Incorporating BERT into Neural Machine Translation | The recently proposed BERT has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc. However, how to effectively apply BERT to neural machine translation (NMT) lacks enough exploration. While BERT is more commonly used as fine-tuning instead of ... | ['Tie-Yan Liu', 'Di He', 'Wengang Zhou', 'Jinhua Zhu', 'Houqiang Li', 'Tao Qin', 'Lijun Wu', 'Yingce Xia'] | 2020-02-17 | null | https://openreview.net/forum?id=Hyl7ygStwB | https://openreview.net/pdf?id=Hyl7ygStwB | iclr-2020-1 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.44751704e-01 3.23198974e-01 -4.27662015e-01 -6.26680076e-01
-1.02974367e+00 -4.50217634e-01 7.48853207e-01 -6.07478246e-03
-4.00909036e-01 7.98720956e-01 5.05663097e-01 -9.98245060e-01
2.51719505e-01 -6.38789833e-01 -9.66699064e-01 -2.58690566e-01
4.67316210e-01 5.98347425e-01 -1.99151352e-01 -3.62717837... | [11.64008617401123, 9.924751281738281] |
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