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ba628895-75f8-4d99-9778-0d7e9f7a0d35 | combining-reinforcement-learning-and-barrier | 2306.07013 | null | https://arxiv.org/abs/2306.07013v1 | https://arxiv.org/pdf/2306.07013v1.pdf | Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization | Reinforcement learning (RL) based investment strategies have been widely adopted in portfolio management (PM) in recent years. Nevertheless, most RL-based approaches may often emphasize on pursuing returns while ignoring the risks of the underlying trading strategies that may potentially lead to great losses especially... | ['Vincent Tam', 'Hejun Huang', 'Zhenglong Li'] | 2023-06-12 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.03830078e-01 2.02514693e-01 -2.89915651e-01 -8.37491080e-03
-5.44823945e-01 -5.24751306e-01 5.75595260e-01 1.95114613e-01
-3.77852768e-01 7.76407301e-01 -1.88954756e-01 -4.63613272e-01
-5.87011337e-01 -1.23401034e+00 -2.94437408e-01 -7.10834861e-01
-2.77969599e-01 2.37798363e-01 2.41479158e-01 -2.99671561... | [4.568074703216553, 3.9259541034698486] |
a0acce55-75bc-419b-a253-0cb7b01ba7a8 | language-agnostic-representation-learning-of-1 | 2103.11318 | null | https://arxiv.org/abs/2103.11318v1 | https://arxiv.org/pdf/2103.11318v1.pdf | Language-Agnostic Representation Learning of Source Code from Structure and Context | Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program. Traditionally, designers of machine learning models have relied predominantly either on Structure or Context. We propose a new model, which jointly learns on Context and Structu... | ['Stephan Günnemann', 'Jure Leskovec', 'Michele Catasta', 'Tobias Kirschstein', 'Daniel Zügner'] | 2021-03-21 | language-agnostic-representation-learning-of | https://openreview.net/forum?id=Xh5eMZVONGF | https://openreview.net/pdf?id=Xh5eMZVONGF | iclr-2021-1 | ['code-summarization'] | ['computer-code'] | [-4.89464146e-04 -4.14945707e-02 -6.25157773e-01 -3.28160822e-01
-1.12374115e+00 -8.37553918e-01 4.59320694e-01 7.82087207e-01
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-1.74212977e-01 -1.47968084e-01 1.53006017e-01 -3.77445191... | [7.665132522583008, 7.92950439453125] |
073ca3fa-b863-4499-981d-2c2a7b6c9bfe | migration-reframed-a-multilingual-analysis-on | 2302.02813 | null | https://arxiv.org/abs/2302.02813v2 | https://arxiv.org/pdf/2302.02813v2.pdf | Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis | The war in Ukraine seems to have positively changed the attitude toward the critical societal topic of migration in Europe -- at least towards refugees from Ukraine. We investigate whether this impression is substantiated by how the topic is reflected in online news and social media, thus linking the representation of ... | ['Erick Elejalde', 'Claudia Niederée', 'Sergej Wildemann'] | 2023-02-06 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-3.38207006e-01 4.31880116e-01 -1.85523286e-01 8.66095647e-02
-3.33105713e-01 -9.57304180e-01 1.24940729e+00 8.94067943e-01
-9.45924640e-01 1.00352609e+00 1.19085538e+00 -5.40061116e-01
-9.39433128e-02 -9.15166318e-01 -5.00705779e-01 -6.07013822e-01
1.30876675e-01 3.27506393e-01 -2.21622596e-03 -1.05431473... | [8.643515586853027, 9.884262084960938] |
90afc015-416a-4ae7-a3ac-eddea7f9e75a | aser-towards-large-scale-commonsense | 2104.02137 | null | https://arxiv.org/abs/2104.02137v2 | https://arxiv.org/pdf/2104.02137v2.pdf | ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities | Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference. W... | ['Yangqiu Song', 'Tianqing Fang', 'Jiefu Ou', 'Haowen Ke', 'Haojie Pan', 'Xin Liu', 'Hongming Zhang'] | 2021-04-05 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 8.82518142e-02 5.44840217e-01 -4.54120308e-01 -4.98423696e-01
-4.52773064e-01 -7.75441945e-01 7.19684064e-01 6.12324417e-01
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-3.04319918e-01 -1.31175411e+00 -7.86738873e-01 -1.43764257e-01
9.91000049e-03 5.72758377e-01 4.55534905e-01 -3.93815249... | [9.901439666748047, 8.175593376159668] |
8d05cfc9-10a9-440d-b18c-c11a12e16703 | graph-representation-learning-via-graphical | 2002.01169 | null | https://arxiv.org/abs/2002.01169v1 | https://arxiv.org/pdf/2002.01169v1.pdf | Graph Representation Learning via Graphical Mutual Information Maximization | The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and extract the abundant information from graph-s... | ['Yu Rong', 'Qinghua Zheng', 'Zhen Peng', 'Tingyang Xu', 'Junzhou Huang', 'Wenbing Huang', 'Minnan Luo'] | 2020-02-04 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.99877495e-01 6.36320114e-01 -4.99862611e-01 -3.22726607e-01
-1.03634093e-02 -3.91788334e-01 6.46804035e-01 3.50843698e-01
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-6.09970391e-01 -1.24170983e+00 -5.39985240e-01 -6.00087523e-01
-5.42567015e-01 3.12303215e-01 -1.06773362e-01 -3.19662035... | [7.1574225425720215, 6.20635461807251] |
6bc1058c-d6a8-4351-a0db-844271293b1e | probabilistic-linguistic-knowledge-and-token | 2306.16644 | null | https://arxiv.org/abs/2306.16644v2 | https://arxiv.org/pdf/2306.16644v2.pdf | Probabilistic Linguistic Knowledge and Token-level Text Augmentation | This paper investigates the effectiveness of token-level text augmentation and the role of probabilistic linguistic knowledge within a linguistically-motivated evaluation context. Two text augmentation programs, REDA and REDA$_{NG}$, were developed, both implementing five token-level text editing operations: Synonym Re... | ['Zhengxiang Wang'] | 2023-06-29 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 5.43697119e-01 1.38826549e-01 -2.04447374e-01 -6.20304227e-01
-1.06086516e+00 -4.77351010e-01 8.56272161e-01 5.77452242e-01
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3.03454787e-01 5.52073359e-01 -1.45433605e-01 -3.99340063... | [10.78712272644043, 9.611377716064453] |
60c50b80-1a40-475f-a4cf-49923340bc6c | learning-to-segment-dominant-object-motion | 2111.14160 | null | https://arxiv.org/abs/2111.14160v1 | https://arxiv.org/pdf/2111.14160v1.pdf | Learning To Segment Dominant Object Motion From Watching Videos | Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train. Unsupervised in this context only means that no annotated frames are used during inference. As obtaining ground-truth segmentation masks for real image scenes is a laborious task, we envis... | ['Nick Barnes', 'Hongdong Li', 'Mohammad Ali Armin', 'Sahir Shrestha'] | 2021-11-28 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 7.44162261e-01 3.38463187e-01 -2.85962164e-01 -5.13655543e-01
-7.00331330e-01 -7.58620560e-01 5.99728942e-01 -8.85322317e-02
-8.14488769e-01 6.05082095e-01 -3.04192960e-01 -2.65337139e-01
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1.13856360e-01 4.59518850e-01 9.80495751e-01 -2.02372484... | [9.114644050598145, -0.20419543981552124] |
0475017a-6adf-4f8c-bf07-afe3ce4e3e82 | liplearner-customizable-silent-speech | 2302.05907 | null | https://arxiv.org/abs/2302.05907v3 | https://arxiv.org/pdf/2302.05907v3.pdf | LipLearner: Customizable Silent Speech Interactions on Mobile Devices | Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-shot... | ['Jun Rekimoto', 'Shitao Fang', 'Zixiong Su'] | 2023-02-12 | null | null | null | null | ['lipreading', 'visual-keyword-spotting', 'keyword-spotting'] | ['computer-vision', 'computer-vision', 'speech'] | [-9.70448852e-02 5.26634157e-02 -5.64999580e-01 -2.51559794e-01
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1.71871036e-01 -6.41515553e-02 1.81346565e-01 -1.35636538... | [14.312551498413086, 6.182345867156982] |
fdafb9ac-fb9e-43ba-a2c1-10f6279b9868 | training-sound-event-detection-with-soft | 2302.14572 | null | https://arxiv.org/abs/2302.14572v1 | https://arxiv.org/pdf/2302.14572v1.pdf | Training sound event detection with soft labels from crowdsourced annotations | In this paper, we study the use of soft labels to train a system for sound event detection (SED). Soft labels can result from annotations which account for human uncertainty about categories, or emerge as a natural representation of multiple opinions in annotation. Converting annotations to hard labels results in unamb... | ['Annamaria Mesaros', 'Paul Ahokas', 'Manu Harju', 'Irene Martín-Morató'] | 2023-02-28 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 4.27496016e-01 4.03232902e-01 4.32737440e-01 -6.80057228e-01
-9.03101087e-01 -1.09784758e+00 6.96558297e-01 3.29546452e-01
-3.08710128e-01 6.59482002e-01 2.33151004e-01 1.79666162e-01
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5.41966856e-02 4.18945163e-01 8.10570598e-01 -1.03523821... | [15.157950401306152, 5.149257659912109] |
6c586694-1dcd-4101-b5e3-ce06cd07827a | is-ggt-iterative-scene-graph-generation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kundu_IS-GGT_Iterative_Scene_Graph_Generation_With_Generative_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kundu_IS-GGT_Iterative_Scene_Graph_Generation_With_Generative_Transformers_CVPR_2023_paper.pdf | IS-GGT: Iterative Scene Graph Generation With Generative Transformers | Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has proven useful in several tasks, such as question answering, captioning, and even object detection, to name a few. Current approaches tak... | ['Sathyanarayanan N. Aakur', 'Sanjoy Kundu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 6.17430389e-01 4.16257262e-01 7.58473203e-02 -4.63864118e-01
-6.91477180e-01 -6.50392532e-01 9.05740499e-01 5.88488340e-01
1.41470125e-02 4.84353632e-01 1.40320882e-01 -3.71919513e-01
2.68511958e-02 -1.11797690e+00 -9.93949175e-01 -4.05639142e-01
-1.80345789e-01 6.01151466e-01 6.31337702e-01 1.77129999... | [10.368812561035156, 1.5904008150100708] |
39db779f-10e1-4dbf-9186-f05295ed0865 | genetic-algorithm-based-floor-planning-system | 1704.06016 | null | http://arxiv.org/abs/1704.06016v1 | http://arxiv.org/pdf/1704.06016v1.pdf | Genetic Algorithm Based Floor Planning System | Genetic Algorithms are widely used in many different optimization problems
including layout design. The layout of the shelves play an important role in
the total sales metrics for superstores since this affects the customers'
shopping behaviour. This paper employed a genetic algorithm based approach to
design shelf lay... | ['Mehmet Serdar Guzel', 'Hamide Ozlem Dalgic', 'Erkan Bostanci'] | 2017-04-20 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-3.57764363e-01 -1.11925535e-01 -1.22793391e-01 -5.34066200e-01
-2.79268250e-02 -8.83062720e-01 -6.72936887e-02 3.86070728e-01
-3.59381378e-01 9.82659280e-01 -6.02098070e-02 -4.11946505e-01
-7.66403675e-01 -1.27824998e+00 -5.56268752e-01 -7.99458146e-01
-9.96736661e-02 7.54053116e-01 1.73877850e-01 -8.77648115... | [5.729421138763428, 3.566906690597534] |
a42bc2e6-77a9-4b35-8595-3f8ec2459f70 | a-generalised-seizure-prediction-with | 1707.01976 | null | http://arxiv.org/abs/1707.01976v2 | http://arxiv.org/pdf/1707.01976v2.pdf | A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis | Seizure prediction has attracted a growing attention as one of the most
challenging predictive data analysis efforts in order to improve the life of
patients living with drug-resistant epilepsy and tonic seizures. Many
outstanding works have been reporting great results in providing a sensible
indirect (warning systems... | ['Mohammad Reza Bonyadi', 'Jiawei Yang', 'Anh Duy Nguyen', 'Nhan Duy Truong', 'Levin Kuhlmann', 'Omid Kavehei'] | 2017-07-06 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.30895019e-01 -1.88314825e-01 2.48212993e-01 -3.29247534e-01
-6.80862784e-01 -2.53988922e-01 3.21934611e-01 1.06669575e-01
-4.85000312e-01 1.00738132e+00 8.08542371e-02 -1.60287321e-01
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-7.29977727e-01 5.48431799e-02 -1.75090525e-02 -2.43968949... | [13.22880744934082, 3.5133612155914307] |
1419e9a5-a150-4bed-8f22-2e08148cacb5 | inspro-propagating-instance-query-and | 2301.01882 | null | https://arxiv.org/abs/2301.01882v1 | https://arxiv.org/pdf/2301.01882v1.pdf | InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation | Video instance segmentation (VIS) aims at segmenting and tracking objects in videos. Prior methods typically generate frame-level or clip-level object instances first and then associate them by either additional tracking heads or complex instance matching algorithms. This explicit instance association approach increase... | ['Kaiqi Huang', 'Xin Zhao', 'Yanhu Shan', 'Jian Jia', 'Naiyu Gao', 'Haoyang Zhang', 'Fei He'] | 2023-01-05 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.60174239e-02 -3.85817960e-02 -4.93681014e-01 -2.13200957e-01
-9.19499636e-01 -5.87115705e-01 6.29724860e-01 1.09424189e-01
-4.15779650e-01 7.04255760e-01 -2.53930122e-01 1.64318278e-01
1.04314752e-01 -6.54638350e-01 -9.87398446e-01 -4.56104547e-01
-3.14708173e-01 5.70854187e-01 1.00876009e+00 3.35089974... | [9.163708686828613, -0.07813439518213272] |
32e2d53f-3412-4006-91bb-f782e78cf0c6 | few-shot-action-recognition-via-intra-and | 2305.06114 | null | https://arxiv.org/abs/2305.06114v1 | https://arxiv.org/pdf/2305.06114v1.pdf | Few-shot Action Recognition via Intra- and Inter-Video Information Maximization | Current few-shot action recognition involves two primary sources of information for classification:(1) intra-video information, determined by frame content within a single video clip, and (2) inter-video information, measured by relationships (e.g., feature similarity) among videos. However, existing methods inadequate... | ['John See', 'Shuyuan Li', 'Yuxi Li', 'Tieyuan Chen', 'Weiyao Lin', 'Huabin Liu'] | 2023-05-10 | null | null | null | null | ['few-shot-action-recognition', 'action-recognition-in-videos', 'video-similarity', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.28690165e-01 -4.47184622e-01 -6.20161951e-01 -2.88280606e-01
-7.22010314e-01 -3.40968430e-01 3.55051607e-01 -9.61063206e-02
-2.68747807e-01 3.95430386e-01 4.16422278e-01 2.96320647e-01
-4.43828821e-01 -4.48508650e-01 -5.22125661e-01 -8.96863222e-01
-4.15703561e-03 -2.69633681e-01 3.17922980e-01 2.75726169... | [8.618916511535645, 0.5240207314491272] |
81ec73eb-d795-4b6c-ab96-b59a434d91a7 | iqpp-a-benchmark-for-image-query-performance | 2302.10126 | null | https://arxiv.org/abs/2302.10126v3 | https://arxiv.org/pdf/2302.10126v3.pdf | iQPP: A Benchmark for Image Query Performance Prediction | To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image. To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance... | ['Josiane Mothe', 'Radu Tudor Ionescu', 'Eduard Poesina'] | 2023-02-20 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 8.31933692e-02 -7.35757291e-01 -4.79236692e-01 -3.60541940e-01
-1.43150997e+00 -6.86371207e-01 6.85819566e-01 6.16557859e-02
-3.96257132e-01 1.86031833e-01 1.24018915e-01 -2.44786181e-02
-3.72322768e-01 -4.48408604e-01 -6.48279905e-01 -5.57161033e-01
-1.54832443e-02 3.02361161e-01 3.71636659e-01 -5.78835718... | [10.801763534545898, 0.8486325740814209] |
2c08d1b1-8707-4985-b01a-cfd61b1ffb2d | graph-classification-gaussian-processes-via | 2306.03770 | null | https://arxiv.org/abs/2306.03770v1 | https://arxiv.org/pdf/2306.03770v1.pdf | Graph Classification Gaussian Processes via Spectral Features | Graph classification aims to categorise graphs based on their structure and node attributes. In this work, we propose to tackle this task using tools from graph signal processing by deriving spectral features, which we then use to design two variants of Gaussian process models for graph classification. The first varian... | ['Xiaowen Dong', 'Pietro Liò', 'Yin-Cong Zhi', 'Felix L. Opolka'] | 2023-06-06 | null | null | null | null | ['graph-classification', 'gaussian-processes'] | ['graphs', 'methodology'] | [ 3.31179678e-01 3.77577364e-01 1.52137280e-01 -4.92943600e-02
-5.20177186e-01 -5.32833636e-01 9.00466800e-01 6.65316701e-01
-2.81768609e-02 3.47575575e-01 1.84238821e-01 -2.09631383e-01
-4.45612520e-01 -1.06399071e+00 -5.04465938e-01 -9.12135005e-01
-5.70864618e-01 2.78823256e-01 2.09890857e-01 -1.89820807... | [6.992751121520996, 5.3981242179870605] |
d7600ce1-5b67-4e7b-a87e-51f193f45c2e | from-argumentation-mining-to-stance | null | null | https://aclanthology.org/W15-0509 | https://aclanthology.org/W15-0509.pdf | From Argumentation Mining to Stance Classification | null | ['Stan Matwin', 'Parinaz Sobhani', 'Diana Inkpen'] | 2015-06-01 | null | null | null | ws-2015-6 | ['subjectivity-analysis'] | ['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... | [-7.234838485717773, 3.840644359588623] |
dbff3537-cbce-415f-b1ba-97be52a31f1c | zscrgan-a-gan-based-expectation-maximization | 2007.12212 | null | https://arxiv.org/abs/2007.12212v3 | https://arxiv.org/pdf/2007.12212v3.pdf | ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions | Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.g., text) to the mode of the documents (e.g., images) from a given training set. Such a setup assumes that the training set contains an exhaustive representat... | ['Vinay Kumar Verma', 'Saptarshi Ghosh', 'Kripabandhu Ghosh', 'Anurag Roy'] | 2020-07-23 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 3.28201801e-01 -3.28572571e-01 -4.14719135e-01 -3.45828176e-01
-1.60260427e+00 -5.59498608e-01 9.81444955e-01 1.00058727e-01
-2.45536655e-01 2.17501760e-01 -5.11201248e-02 1.44702584e-01
-2.06372350e-01 -8.32154930e-01 -6.49953842e-01 -7.43997693e-01
4.69201028e-01 1.06209981e+00 3.10149372e-01 -3.45851928... | [11.299522399902344, 0.9635635614395142] |
c941071c-108c-4b09-8be0-e8c2ed7a4e8f | good-robot-now-watch-this-repurposing | null | null | https://proceedings.mlr.press/v164/hundt22a.html | https://proceedings.mlr.press/v164/hundt22a/hundt22a.pdf | "Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer | Modern Reinforcement Learning (RL) algorithms are not sample efficient to train on multi-step tasks in complex domains, impeding their wider deployment in the real world. We address this problem by leveraging the insight that RL models trained to complete one set of tasks can be repurposed to complete related tasks whe... | ['Gregory D. Hager', 'Matthew Gombolay', 'Nakul Gopalan', 'Ran Liu', 'Priyanka Hubli', 'Aditya Murali', 'Andrew Hundt'] | 2021-11-08 | null | null | null | conference-on-robot-learning-corl-2021-11 | ['robotic-grasping'] | ['robots'] | [ 4.74321038e-01 -2.83261556e-02 -1.46374553e-01 -1.92619532e-01
-1.02697492e+00 -5.86820722e-01 7.24955916e-01 -8.65189582e-02
-7.92415619e-01 8.98542583e-01 -4.69305068e-02 -2.65194237e-01
-1.36904404e-01 -1.85892537e-01 -7.89932311e-01 -5.19048393e-01
-2.22145945e-01 5.90003669e-01 3.33128065e-01 -2.11682647... | [4.49249792098999, 0.9343538284301758] |
f8bc9661-bcdc-4005-83d6-318d5dbf6fed | mgpfusion-predicting-protein-stability | 1802.02852 | null | http://arxiv.org/abs/1802.02852v2 | http://arxiv.org/pdf/1802.02852v2.pdf | mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion | Proteins are commonly used by biochemical industry for numerous processes.
Refining these proteins' properties via mutations causes stability effects as
well. Accurate computational method to predict how mutations affect protein
stability are necessary to facilitate efficient protein design. However,
accuracy of predic... | ['Harri Lähdesmäki', 'Markus Heinonen', 'Emmi Jokinen'] | 2018-02-08 | null | null | null | null | ['protein-design'] | ['medical'] | [ 1.85824677e-01 -2.12110072e-01 -1.59323186e-01 -3.08185309e-01
-5.63915312e-01 -5.86858690e-01 2.41501436e-01 7.38687038e-01
-1.46823153e-01 1.22336781e+00 -1.46258786e-01 -4.51953143e-01
7.54859205e-03 -4.98924643e-01 -1.04209387e+00 -1.23152268e+00
7.45752305e-02 8.96298289e-01 8.72861445e-01 -2.83727944... | [4.776124954223633, 5.545172214508057] |
85f2c7f4-db55-4762-bcab-285d4ddb88a5 | navigation-in-urban-environments-amongst | 2110.05205 | null | https://arxiv.org/abs/2110.05205v1 | https://arxiv.org/pdf/2110.05205v1.pdf | Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning | Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem. This work formulates navigation in urban environments as a multi objective reinforcement learning problem. A deep learning variant of thresholded lexicographic Q-learning is presen... | ['Anne Spalanzani', 'Dominique Vaufreydaz', 'Niranjan Deshpande'] | 2021-10-11 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.60732502e-01 -7.82204419e-03 -1.01345904e-01 -3.25182855e-01
-6.42338514e-01 -1.98689222e-01 7.36049354e-01 -4.22337949e-02
-1.28543460e+00 1.39798760e+00 -4.46511656e-02 -6.34450793e-01
-4.20473278e-01 -1.10681927e+00 -6.11994088e-01 -6.45716488e-01
-1.90723523e-01 6.99519813e-01 6.65150583e-01 -1.00839806... | [5.237006187438965, 1.1922353506088257] |
ed8fe2b8-23b6-4171-a9be-e24ed18698a7 | latent-video-diffusion-models-for-high | 2211.13221 | null | https://arxiv.org/abs/2211.13221v2 | https://arxiv.org/pdf/2211.13221v2.pdf | Latent Video Diffusion Models for High-Fidelity Long Video Generation | AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length of generated videos are far from satisfactory. Diffusion models have shown remar... | ['Qifeng Chen', 'Ying Shan', 'Yong Zhang', 'Tianyu Yang', 'Yingqing He'] | 2022-11-23 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 3.12035799e-01 7.08342195e-02 -2.13163897e-01 -8.15405250e-02
-9.74376559e-01 -4.54428166e-01 8.13108683e-01 -7.35984743e-01
1.85969621e-02 8.79153430e-01 7.56748736e-01 -1.29528027e-02
3.74586970e-01 -6.92503512e-01 -8.18058610e-01 -7.32345462e-01
1.99850783e-01 2.15909649e-02 2.60082424e-01 6.76856712... | [10.90688419342041, -0.5223299860954285] |
b82eeacf-e035-4d0c-a1c3-32574a8cc900 | unsupervised-augmentation-optimization-for | 2306.05107 | null | https://arxiv.org/abs/2306.05107v1 | https://arxiv.org/pdf/2306.05107v1.pdf | Unsupervised augmentation optimization for few-shot medical image segmentation | The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples. However, searching optimal augmentation parameters for few-shot segmentation models without annotations is a challenge that current methods fail ... | ['S. Kevin Zhou', 'Heqin Zhu', 'Qingsong Yao', 'Shang Zhao', 'Quan Quan'] | 2023-06-08 | null | null | null | null | ['few-shot-image-segmentation', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 3.64073545e-01 4.53504741e-01 -4.05344754e-01 -5.86678684e-01
-1.17869651e+00 -1.41056627e-01 2.59945512e-01 4.30667520e-01
-6.19674385e-01 4.85189170e-01 5.57988742e-03 2.25718752e-01
6.63439091e-03 -6.17919028e-01 -7.84573257e-01 -9.63928819e-01
2.64176968e-02 7.65232205e-01 4.38671529e-01 -2.38334119... | [14.563264846801758, -2.1341946125030518] |
2dd08c65-fba7-47a8-9316-ebbe730340cd | a-simple-method-for-predicting-covariance | 2305.19484 | null | https://arxiv.org/abs/2305.19484v1 | https://arxiv.org/pdf/2305.19484v1.pdf | A Simple Method for Predicting Covariance Matrices of Financial Returns | We consider the well-studied problem of predicting the time-varying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling window or exponentially weighted moving average (EWMA) to more sophisticated predictors such as generalized autoregressive conditional heteros... | ['Stephen Boyd', 'Thomas Schmelzer', 'Markus Pelger', 'Mehmet Giray Ogut', 'Kasper Johansson'] | 2023-05-31 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-6.06588960e-01 -1.41639531e-01 2.81863123e-01 -5.27329326e-01
-8.83779824e-01 -8.97207975e-01 7.20467210e-01 4.95361909e-02
-2.68916875e-01 7.73500443e-01 1.34909719e-01 -7.04166472e-01
-4.08861160e-01 -8.86353970e-01 -5.73575616e-01 -7.07272410e-01
-4.13247645e-01 5.25342703e-01 9.68187377e-02 1.81868137... | [4.913432598114014, 4.040506839752197] |
989ee75d-5028-428c-95f5-f9cba67d4a4e | conceptual-study-and-performance-analysis-of | 2306.10246 | null | https://arxiv.org/abs/2306.10246v1 | https://arxiv.org/pdf/2306.10246v1.pdf | Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry | Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase component... | ['YaQiu Jin', 'Chibiao Ding', 'Xiaolan Qiu', 'Feng Xu', 'Fengming Hu'] | 2023-06-17 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 3.32712024e-01 -3.28775048e-01 4.54780459e-01 -2.88084328e-01
-8.66568446e-01 -3.49671632e-01 3.88528347e-01 -4.08616185e-01
-1.84835538e-01 7.83397257e-01 -2.18402594e-01 -1.79260045e-01
-8.80369186e-01 -9.19494629e-01 -2.91399896e-01 -1.00825250e+00
-4.48101670e-01 6.18496537e-01 -2.99434885e-02 -5.56705356... | [6.790736198425293, 0.9825596809387207] |
33a97f19-7046-435d-8f90-04bb6843b61d | face-alignment-by-explicit-shape-regression | null | null | https://ieeexplore.ieee.org/document/6248015 | https://www.microsoft.com/en-us/research/wp-content/uploads/2013/01/Face-Alignment-by-Explicit-Shape-Regression.pdf | Face alignment by explicit shape regression | We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors ov... | ['Jian Sun', 'Fang Wen', 'Yichen Wei', 'Xudong Cao'] | 2013-12-13 | null | null | null | int-j-comput-vis-2013-12 | ['face-alignment'] | ['computer-vision'] | [-9.96401161e-02 -1.13019466e-01 -1.63424566e-01 -8.04936469e-01
-9.78343964e-01 -3.62249732e-01 4.01143730e-01 -7.39525557e-02
-3.65307927e-01 4.36469227e-01 -8.65191668e-02 -4.58966494e-02
1.07092731e-01 -5.34688115e-01 -8.48883212e-01 -4.75669086e-01
-5.49307503e-02 8.21783841e-01 4.92272619e-03 -1.63182870... | [13.430732727050781, 0.2757883071899414] |
b2b0e995-3609-43ee-b187-5845459ac784 | efficient-encoder-decoder-and-dual-path | 2306.05861 | null | https://arxiv.org/abs/2306.05861v1 | https://arxiv.org/pdf/2306.05861v1.pdf | Efficient Encoder-Decoder and Dual-Path Conformer for Comprehensive Feature Learning in Speech Enhancement | Current speech enhancement (SE) research has largely neglected channel attention and spatial attention, and encoder-decoder architecture-based networks have not adequately considered how to provide efficient inputs to the intermediate enhancement layer. To address these issues, this paper proposes a time-frequency (T-F... | ['Junyu Wang'] | 2023-06-09 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-4.77214456e-02 5.32299355e-02 -1.03791758e-01 -2.86607683e-01
-8.02510440e-01 1.65203884e-01 2.83059061e-01 -3.26932758e-01
-5.15717149e-01 6.40686333e-01 7.54142821e-01 -3.69689673e-01
1.84088841e-01 -6.22487426e-01 -6.04039609e-01 -3.97865117e-01
-5.03258780e-02 -3.85348588e-01 3.53677660e-01 -4.24934566... | [14.740074157714844, 5.923710823059082] |
31f8e96e-7208-4adf-b0e0-76e68f91e995 | fingerprint-recognition-under-missing-image | 1902.05389 | null | http://arxiv.org/abs/1902.05389v1 | http://arxiv.org/pdf/1902.05389v1.pdf | Fingerprint Recognition under Missing Image Pixels Scenario | This work observed the problem of fingerprint image recognition in the case
of missing pixels from the original image. The possibility of missing pixels
recovery is tested by applying the Compressive Sensing approach. Namely,
different percentage of missing pixels is observed and the image reconstruction
is done by app... | ['Kristina Tomovic', 'Dejan Brajovic', 'Jovan Radonjic'] | 2019-02-06 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.45655692e+00 5.15870936e-02 -1.91233993e-01 4.57186066e-02
-2.00169325e-01 -2.62855381e-01 2.34970510e-01 -5.94036460e-01
-1.70230180e-01 1.02198136e+00 1.49033636e-01 -1.10676207e-01
-2.64808893e-01 -8.39101851e-01 -8.89596581e-01 -7.64704704e-01
1.77607372e-01 3.11760902e-02 -3.93658578e-01 8.45667496... | [12.430561065673828, 0.31963491439819336] |
9812ce72-b56a-4f40-90b2-95730ca26480 | depgraph-towards-any-structural-pruning | 2301.12900 | null | https://arxiv.org/abs/2301.12900v2 | https://arxiv.org/pdf/2301.12900v2.pdf | DepGraph: Towards Any Structural Pruning | Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks. However, the parameter-grouping patterns vary widely across different models, making architecture-specific pruners, which rely on manually-designed grouping schemes, non-generalizable to new architectures. In... | ['Xinchao Wang', 'Michael Bi Mi', 'Mingli Song', 'Xinyin Ma', 'Gongfan Fang'] | 2023-01-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 6.60695955e-02 1.78069234e-01 2.91750759e-01 -2.00339973e-01
5.04371151e-03 -5.33653259e-01 4.36834604e-01 3.82122979e-03
-6.13271415e-01 3.74469995e-01 -1.47713006e-01 -6.32353902e-01
-2.68467844e-01 -5.66368759e-01 -7.19370723e-01 -6.00947678e-01
-8.35659448e-03 3.68884325e-01 3.01319808e-01 -3.35876763... | [8.602266311645508, 3.2327702045440674] |
ac29775c-3e1b-469d-9c2c-4501ac890b84 | learning-content-enhanced-mask-transformer | 2307.00371 | null | https://arxiv.org/abs/2307.00371v1 | https://arxiv.org/pdf/2307.00371v1.pdf | Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation | Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized semantic predictions across diverse urban-scene styles. Unlike domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes i... | ['Theo Gevers', 'ShaoDi You', 'Qi Bi'] | 2023-07-01 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.42086166e-01 -3.96401882e-02 -1.25549659e-01 -5.53060949e-01
-8.18733335e-01 -4.61212367e-01 3.93542916e-01 -1.02123111e-01
-1.77044600e-01 4.83138204e-01 2.08384424e-01 -5.43456115e-02
8.72336999e-02 -9.15347278e-01 -9.03015912e-01 -4.58161563e-01
4.99687731e-01 2.50928879e-01 4.40251678e-01 -4.33913797... | [9.675786018371582, 1.136521816253662] |
bc92107a-f356-47a2-99a0-8adaea07f349 | spatio-temporal-latent-graph-structure | 2202.12586 | null | https://arxiv.org/abs/2202.12586v2 | https://arxiv.org/pdf/2202.12586v2.pdf | Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting | Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing. Recently, Graph Neural Network truly outperforms the traditional methods. Nevertheless, the most conventional GNN-based model... | ['Tianrui Li', 'Jie Hu', 'Shengdong Du', 'Shijing Liu', 'Tang Qian', 'Jiabin Tang'] | 2022-02-25 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-1.89082280e-01 -9.53653306e-02 -2.67621607e-01 -1.77534357e-01
1.64629161e-01 -2.43369713e-01 7.74369061e-01 1.19015299e-01
-1.05935037e-01 6.10016406e-01 9.47793573e-02 -7.73672342e-01
-4.79930967e-01 -1.66683900e+00 -5.76073050e-01 -7.63391554e-01
-3.83828342e-01 6.32881522e-01 5.29883564e-01 -4.43956167... | [6.47738790512085, 2.0761375427246094] |
125eb805-55b9-4c48-929e-82d86f149771 | cross-modal-and-hierarchical-modeling-of | 1810.07212 | null | http://arxiv.org/abs/1810.07212v1 | http://arxiv.org/pdf/1810.07212v1.pdf | Cross-Modal and Hierarchical Modeling of Video and Text | Visual data and text data are composed of information at multiple
granularities. A video can describe a complex scene that is composed of
multiple clips or shots, where each depicts a semantically coherent event or
action. Similarly, a paragraph may contain sentences with different topics,
which collectively conveys a ... | ['Bowen Zhang', 'Hexiang Hu', 'Fei Sha'] | 2018-10-16 | cross-modal-and-hierarchical-modeling-of-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Bowen_Zhang_Cross-Modal_and_Hierarchical_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Bowen_Zhang_Cross-Modal_and_Hierarchical_ECCV_2018_paper.pdf | eccv-2018-9 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.31555814e-01 -2.68098474e-01 -4.46013063e-01 -2.92802304e-01
-8.62650454e-01 -4.52541620e-01 1.01345789e+00 3.47971141e-01
-6.25979751e-02 5.28770626e-01 1.18575680e+00 2.70291805e-01
1.30609378e-01 -4.04517084e-01 -7.34267831e-01 -5.56086779e-01
-4.64883111e-02 -3.20869498e-02 3.06355506e-01 1.45230547... | [10.230262756347656, 0.8733316659927368] |
fedff40f-c1c8-4dcd-94fd-89963d65fa56 | morphological-classification-of-extragalactic | 2304.12729 | null | https://arxiv.org/abs/2304.12729v1 | https://arxiv.org/pdf/2304.12729v1.pdf | Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods | The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio sources based on their morphologies. Most recent contributions in the field of morpho... | ['Abir Hussain', 'Marley Vellasco', 'Ilias Fernini', 'Abdollah Masoud Darya'] | 2023-04-25 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [-3.56441170e-01 -4.00338620e-01 -1.83076069e-01 -3.28585744e-01
-3.34965080e-01 -4.81469333e-01 1.03768229e+00 -2.40117833e-02
-6.20292544e-01 7.20433772e-01 -4.16729972e-02 -7.58869886e-01
-5.24669051e-01 -9.04533565e-01 -3.86493653e-01 -8.43397081e-01
-2.52117720e-02 5.26616275e-01 3.56423318e-01 -4.15314823... | [7.729050159454346, 3.0335752964019775] |
99324c05-c00f-48d7-8caa-85d7b4e8abf0 | on-learning-to-summarize-with-large-language | 2305.14239 | null | https://arxiv.org/abs/2305.14239v1 | https://arxiv.org/pdf/2305.14239v1.pdf | On Learning to Summarize with Large Language Models as References | Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. Therefore, we investigate a new learning paradigm of text summarization models that considers the LLMs as the reference or the... | ['Arman Cohan', 'Dragomir Radev', 'PengFei Liu', 'Alexander R. Fabbri', 'Yixin Liu'] | 2023-05-23 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 2.55849928e-01 3.05855483e-01 -4.52930659e-01 -3.92581165e-01
-1.30927575e+00 -5.95194101e-01 8.35377693e-01 3.83917630e-01
-5.72413504e-01 9.33411419e-01 6.84242368e-01 -1.23344041e-01
8.66063759e-02 -6.42597973e-01 -8.72798979e-01 -2.82812297e-01
2.91786492e-01 3.44429404e-01 2.94507947e-02 5.15268184... | [12.320684432983398, 9.346410751342773] |
4bc8159c-71a6-4d17-b6c1-80ab0bbbb7a1 | bert-post-training-for-review-reading | 1904.02232 | null | https://arxiv.org/abs/1904.02232v2 | https://arxiv.org/pdf/1904.02232v2.pdf | BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis | Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential o... | ['Bing Liu', 'Philip S. Yu', 'Hu Xu', 'Lei Shu'] | 2019-04-03 | bert-post-training-for-review-reading-1 | https://aclanthology.org/N19-1242 | https://aclanthology.org/N19-1242.pdf | naacl-2019-6 | ['aspect-extraction'] | ['natural-language-processing'] | [ 3.91699970e-01 2.43902683e-01 -3.84172410e-01 -7.07412124e-01
-9.66980934e-01 -5.74959755e-01 5.89591563e-01 6.03262067e-01
-3.98929715e-01 2.36502409e-01 2.86919236e-01 -9.25100327e-01
7.64626563e-02 -9.22955871e-01 -5.32028675e-01 -1.58961922e-01
3.41431081e-01 3.33153367e-01 1.69950753e-01 -8.01496863... | [11.467771530151367, 6.650811195373535] |
75f9b016-08bf-43f3-b1ed-617ca5b6012f | improving-audio-caption-fluency-with | 2306.10090 | null | https://arxiv.org/abs/2306.10090v1 | https://arxiv.org/pdf/2306.10090v1.pdf | Improving Audio Caption Fluency with Automatic Error Correction | Automated audio captioning (AAC) is an important cross-modality translation task, aiming at generating descriptions for audio clips. However, captions generated by previous AAC models have faced ``false-repetition'' errors due to the training objective. In such scenarios, we propose a new task of AAC error correction a... | ['Kai Yu', 'Mengyue Wu', 'Xuenan Xu', 'Zeyu Xie', 'Hanxue Zhang'] | 2023-06-16 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 8.92074227e-01 4.58627015e-01 4.28365409e-01 -2.09675357e-01
-1.38297904e+00 -3.24056119e-01 3.93812627e-01 6.04100302e-02
-1.29020840e-01 1.12717342e+00 6.00818992e-01 -1.66901276e-01
4.71029162e-01 -4.63990390e-01 -1.12048960e+00 -2.74069104e-02
3.03591460e-01 5.14094055e-01 -2.42383912e-01 -2.48414382... | [15.2745943069458, 4.896003246307373] |
0d2cb716-5b1e-4ef4-838d-1d7c10f42c9e | on-strengthening-and-defending-graph | 2306.09104 | null | https://arxiv.org/abs/2306.09104v1 | https://arxiv.org/pdf/2306.09104v1.pdf | On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation | Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored. To close this gap, we perform the first comprehensive study of graph reconstruction attack that aims to reconstruct the adjacency of nodes. We show that a range of factors in ... | ['Bo Han', 'Quanming Yao', 'Jiangchao Yao', 'Xuan Li', 'Chenyu Zhou', 'Zhanke Zhou'] | 2023-06-15 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 3.86933297e-01 5.49390674e-01 -4.49465066e-01 1.15122475e-01
-5.57967603e-01 -9.25346017e-01 2.67844260e-01 -1.42774852e-02
-1.87418722e-02 5.35362601e-01 2.31357008e-01 -9.79444623e-01
-2.71541864e-01 -1.06740880e+00 -9.89507139e-01 -7.35889256e-01
-4.74585801e-01 7.59593770e-02 -4.87746000e-02 -3.24583679... | [6.034623622894287, 7.1940598487854] |
6cb8cd8b-dfec-4fb0-9dde-22c2b8c67a62 | geometric-graph-filters-and-neural-networks | 2305.18467 | null | https://arxiv.org/abs/2305.18467v2 | https://arxiv.org/pdf/2305.18467v2.pdf | Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs | This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider convolutional MNNs and GNNs where the manifold and the graph convolutions are respec... | ['Alejandro Ribeiro', 'Luana Ruiz', 'Zhiyang Wang'] | 2023-05-29 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.53659225e-01 6.13921821e-01 2.41501927e-01 -2.78935977e-03
7.70320520e-02 -6.07477486e-01 4.33541387e-01 2.36092940e-01
-2.06464782e-01 2.31923386e-01 -3.85091780e-03 -2.18319580e-01
-4.19070095e-01 -9.99825656e-01 -1.32544315e+00 -6.26466393e-01
-6.22111440e-01 2.83365026e-02 -1.01229131e-01 -2.81215638... | [6.835962295532227, 6.059092998504639] |
569c20d5-ce9e-4c33-afe6-45d98bbb9826 | length-of-stay-prediction-for-hospital | 2306.16823 | null | https://arxiv.org/abs/2306.16823v1 | https://arxiv.org/pdf/2306.16823v1.pdf | Length of Stay prediction for Hospital Management using Domain Adaptation | Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for p... | ['Bart De Moor', 'Frank Rademakers', 'Elaine O. Nsoesie', 'Nyalleng Moorosi', 'Lyse Naomi Wamba Momo'] | 2023-06-29 | null | null | null | null | ['length-of-stay-prediction', 'management'] | ['medical', 'miscellaneous'] | [ 4.76797074e-02 3.18291694e-01 -4.38647389e-01 -4.43606436e-01
-4.92836982e-01 -2.10276544e-01 -2.49558672e-01 7.45523512e-01
-6.02909923e-01 1.10389042e+00 3.91468197e-01 -7.78799713e-01
-7.30423450e-01 -8.53957653e-01 -3.26344222e-01 -4.58443582e-01
-2.47935429e-01 8.64451110e-01 -4.16368127e-01 2.34264862... | [7.974412441253662, 6.209477424621582] |
dcb8a591-038a-4cc8-aaff-12bdbc449a58 | lsd-c-linearly-separable-deep-clusters | 2006.10039 | null | https://arxiv.org/abs/2006.10039v1 | https://arxiv.org/pdf/2006.10039v1.pdf | LSD-C: Linearly Separable Deep Clusters | We present LSD-C, a novel method to identify clusters in an unlabeled dataset. Our algorithm first establishes pairwise connections in the feature space between the samples of the minibatch based on a similarity metric. Then it regroups in clusters the connected samples and enforces a linear separation between clusters... | ['Andrew Zisserman', 'Sylvestre-Alvise Rebuffi', 'Kai Han', 'Sebastien Ehrhardt', 'Andrea Vedaldi'] | 2020-06-17 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 9.57367383e-03 2.09937170e-01 -4.01033491e-01 -6.52401090e-01
-5.59208572e-01 -9.04289424e-01 7.65835762e-01 3.11157823e-01
-4.64846969e-01 4.43945169e-01 -2.86738779e-02 -1.04626648e-01
-3.68198305e-01 -3.68301243e-01 -6.98486626e-01 -9.63660538e-01
-3.94309282e-01 8.30586433e-01 4.26475406e-02 3.97792578... | [9.30469799041748, 3.0021655559539795] |
6af4b8ea-7cd3-499e-bfad-c1fc8c9df9cf | sparse-over-complete-patch-matching | 1806.03556 | null | http://arxiv.org/abs/1806.03556v2 | http://arxiv.org/pdf/1806.03556v2.pdf | Sparse Over-complete Patch Matching | Image patch matching, which is the process of identifying corresponding
patches across images, has been used as a subroutine for many computer vision
and image processing tasks. State -of-the-art patch matching techniques take
image patches as input to a convolutional neural network to extract the patch
features and ev... | ['Clinton Fookes', 'Sridha Sridharan', 'Kien Nguyen', 'Akila Pemasiri'] | 2018-06-09 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 4.79074746e-01 -2.59662598e-01 -2.52325207e-01 -2.73631811e-01
-5.69277346e-01 -3.36999685e-01 8.72820735e-01 4.57536966e-01
-1.62686542e-01 2.38169611e-01 1.72228038e-01 1.83144093e-01
-2.92759418e-01 -1.11850178e+00 -1.01011527e+00 -6.39694691e-01
-2.79222727e-01 1.21139936e-01 2.46427059e-01 -2.31894076... | [10.258938789367676, 0.08440513908863068] |
ffa045df-02d4-46bb-8d64-2efb3b736f14 | deeplidarflow-a-deep-learning-architecture | 2008.08136 | null | https://arxiv.org/abs/2008.08136v1 | https://arxiv.org/pdf/2008.08136v1.pdf | DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR | Scene flow is the dense 3D reconstruction of motion and geometry of a scene. Most state-of-the-art methods use a pair of stereo images as input for full scene reconstruction. These methods depend a lot on the quality of the RGB images and perform poorly in regions with reflective objects, shadows, ill-conditioned light... | ['Didier Stricker', 'Oliver Wasenmüller', 'René Schuster', 'Ramy Battrawy', 'Rishav'] | 2020-08-18 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-1.86651275e-01 -6.54818177e-01 2.57436335e-01 -4.19094503e-01
-5.00782490e-01 -5.51735163e-01 5.40286720e-01 -7.26500675e-02
-5.58073163e-01 6.36407197e-01 -6.80701956e-02 -2.03662571e-02
-5.79666463e-04 -9.87029672e-01 -6.46370411e-01 -6.16115153e-01
2.25633547e-01 5.06053746e-01 5.43483555e-01 -3.00581425... | [8.526579856872559, -2.3272433280944824] |
2a45de15-9cec-4fae-8ee1-530c404cb6fe | hard-exudate-segmentation-supplemented-by | 2211.09404 | null | https://arxiv.org/abs/2211.09404v1 | https://arxiv.org/pdf/2211.09404v1.pdf | Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module | Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE segmentation is vital for disease diagnosis and treatment, but automatic segmentation is challenged by its large variation of characteristics including size, shape and position, which makes it difficult to detect tiny lesions and lesion boun... | ['Jiang Liu', 'Yan Hu', 'Mingming Yang', 'Zhongxi Qiu', 'Xiaoshan Chen', 'Jiayi Zhang'] | 2022-11-17 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.89555854e-01 -2.27841213e-01 -7.66958296e-02 -2.30897307e-01
-9.61865246e-01 -1.43245563e-01 1.05569243e-01 -6.87525654e-03
-4.90287006e-01 5.60311139e-01 2.31079906e-01 -1.64365396e-01
-1.21597148e-01 -6.28090799e-01 -3.31726134e-01 -7.13036895e-01
1.54123276e-01 -1.23393899e-02 7.48702526e-01 8.61888230... | [15.774075508117676, -3.947934150695801] |
2b7bd203-7196-40fe-bf48-bc3e9900c1b6 | cased-curriculum-adaptive-sampling-for | 1807.10819 | null | http://arxiv.org/abs/1807.10819v1 | http://arxiv.org/pdf/1807.10819v1.pdf | CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance | We introduce CASED, a novel curriculum sampling algorithm that facilitates
the optimization of deep learning segmentation or detection models on data sets
with extreme class imbalance. We evaluate the CASED learning framework on the
task of lung nodule detection in chest CT. In contrast to two-stage solutions,
wherein ... | ['Nicolas Chapados', 'Florian Soudan', 'Andrew Jesson', 'Nicolas Guizard', 'Sina Hamidi Ghalehjegh', 'Damien Goblot'] | 2018-07-27 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.79733950e-01 7.01691091e-01 -4.72699016e-01 -2.42643535e-01
-1.17753363e+00 -5.17174602e-01 4.34499830e-01 2.12822437e-01
-4.14049685e-01 3.94552290e-01 -1.77809149e-01 -6.23276651e-01
-1.93063572e-01 -6.07907116e-01 -6.22028589e-01 -8.92070174e-01
8.04602727e-02 1.17298126e+00 7.69818544e-01 1.88313931... | [15.331160545349121, -2.1710824966430664] |
3a2df2c9-13f6-4ee2-806b-4da531b05405 | taqyim-evaluating-arabic-nlp-tasks-using | 2306.16322 | null | https://arxiv.org/abs/2306.16322v1 | https://arxiv.org/pdf/2306.16322v1.pdf | Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models | Large language models (LLMs) have demonstrated impressive performance on various downstream tasks without requiring fine-tuning, including ChatGPT, a chat-based model built on top of LLMs such as GPT-3.5 and GPT-4. Despite having a lower training proportion compared to English, these models also exhibit remarkable capa... | ['Ali Fadel', 'Ebrahim Alareqi', 'Hamzah Luqman', 'Badr AlKhamissi', 'Maged S. Alshaibani', 'Zaid Alyafeai'] | 2023-06-28 | null | null | null | null | ['sentiment-analysis', 'part-of-speech-tagging', 'transliteration'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.63286045e-01 6.89790100e-02 -6.70606494e-02 -3.98473501e-01
-1.22303343e+00 -8.07384431e-01 6.42792046e-01 1.72437429e-01
-4.50359851e-01 6.99908078e-01 3.37801337e-01 -6.86797559e-01
4.78651732e-01 -3.65252107e-01 -4.89888936e-01 -2.05666721e-01
1.22771353e-01 6.57495499e-01 -2.57453680e-01 -6.55520737... | [11.151154518127441, 9.681744575500488] |
abd67cdf-0590-491f-ba8f-d35e81f2cde2 | speaker-diaphragm-excursion-prediction-deep | 2305.06640 | null | https://arxiv.org/abs/2305.06640v1 | https://arxiv.org/pdf/2305.06640v1.pdf | Speaker Diaphragm Excursion Prediction: deep attention and online adaptation | Speaker protection algorithm is to leverage the playback signal properties to prevent over excursion while maintaining maximum loudness, especially for the mobile phone with tiny loudspeakers. This paper proposes efficient DL solutions to accurately model and predict the nonlinear excursion, which is challenging for co... | ['Hao Xu', 'Chirag Patel', 'Eddie Choy', 'Yin Huang', 'Matt Zivney', 'Yuwei Ren'] | 2023-05-11 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.38144638e-02 -1.58792824e-01 7.71261603e-02 -2.30117232e-01
-6.20379090e-01 -4.50389326e-01 -1.10792555e-01 -2.15282366e-01
-2.55660787e-02 6.81367040e-01 3.76166463e-01 -3.35746288e-01
2.83246208e-02 -1.92577496e-01 -3.67402971e-01 -7.04385579e-01
-2.36172795e-01 -4.91162717e-01 -1.72539473e-01 -2.38409474... | [14.81933307647705, 5.887237071990967] |
f455f514-f579-4160-9a91-8e8019b1f0fa | adaptive-rate-sparse-signal-reconstruction | 1503.03231 | null | http://arxiv.org/abs/1503.03231v1 | http://arxiv.org/pdf/1503.03231v1.pdf | Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction | We propose and analyze an online algorithm for reconstructing a sequence of
signals from a limited number of linear measurements. The signals are assumed
sparse, with unknown support, and evolve over time according to a generic
nonlinear dynamical model. Our algorithm, based on recent theoretical results
for $\ell_1$-$... | ['Aswin C. Sankaranarayanan', 'Nikos Deligiannis', 'Volkan Cevher', 'Miguel R. D. Rodrigues', 'Joao F. C. Mota'] | 2015-03-11 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 1.03275108e+00 -3.64158452e-01 4.52089161e-01 -1.04758538e-01
-5.22932589e-01 -4.96820033e-01 5.25637448e-01 -4.06402498e-01
-3.46439779e-01 5.39675117e-01 -3.54094028e-01 -3.13824683e-01
-5.68821467e-02 -4.43315148e-01 -6.48605227e-01 -1.05117214e+00
-3.22707117e-01 2.38648325e-01 1.03628740e-01 -7.92534947... | [9.057456016540527, -0.8683642148971558] |
52b07dbb-dfcc-42d7-8157-e44425f4652d | knowda-all-in-one-knowledge-mixture-model-for | 2206.10265 | null | https://arxiv.org/abs/2206.10265v2 | https://arxiv.org/pdf/2206.10265v2.pdf | KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP | This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to prod... | ['Daxin Jiang', 'Chongyang Tao', 'Tao Shen', 'Xiubo Geng', 'Can Xu', 'Jiayi Zheng', 'YuFei Wang'] | 2022-06-21 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [ 2.10413516e-01 2.24960193e-01 -5.11780441e-01 -1.99464783e-01
-1.08516979e+00 -6.15770340e-01 6.30930066e-01 -4.65911388e-01
-4.97516125e-01 1.16519892e+00 1.79991782e-01 -3.27697515e-01
1.35107905e-01 -6.61411226e-01 -9.79450166e-01 -6.49831593e-01
5.97275078e-01 1.04972279e+00 -2.53362864e-01 -4.00780410... | [11.004700660705566, 8.420083999633789] |
3dc2a1fc-d726-4f2a-8a81-00df555bda94 | automatic-right-ventricle-segmentation-using | 2004.02317 | null | https://arxiv.org/abs/2004.02317v1 | https://arxiv.org/pdf/2004.02317v1.pdf | Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI | Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function. Yet, due to its complex anatomy and motion segmentation of the RV has not been as largely studied as the left ventricle. This paper presents a fully automatic method for the segmentation of t... | ['Sébastien Ourselin', 'Maria A. Zuluaga', 'M. Jorge Cardoso'] | 2020-04-05 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-7.09149707e-03 5.61155826e-02 2.96467572e-01 -2.32269078e-01
-4.04692948e-01 -7.07335234e-01 1.04061142e-01 2.07305685e-01
-4.56639677e-01 6.40028119e-01 -3.61577012e-02 -2.55954325e-01
3.95706072e-02 -4.15950924e-01 8.70693401e-02 -7.47308671e-01
4.70247632e-03 1.11903799e+00 7.18412399e-01 7.66018480... | [14.143900871276855, -2.521662473678589] |
c761733a-2b35-44fd-997f-534ea64e1ef2 | recurrent-models-for-situation-recognition | 1703.06233 | null | http://arxiv.org/abs/1703.06233v2 | http://arxiv.org/pdf/1703.06233v2.pdf | Recurrent Models for Situation Recognition | This work proposes Recurrent Neural Network (RNN) models to predict
structured 'image situations' -- actions and noun entities fulfilling semantic
roles related to the action. In contrast to prior work relying on Conditional
Random Fields (CRFs), we use a specialized action prediction network followed
by an RNN for nou... | ['Svetlana Lazebnik', 'Arun Mallya'] | 2017-03-18 | recurrent-models-for-situation-recognition-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Mallya_Recurrent_Models_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Mallya_Recurrent_Models_for_ICCV_2017_paper.pdf | iccv-2017-10 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.36545503e-01 6.53632581e-01 -3.12158376e-01 -8.20427358e-01
-6.10763431e-01 -2.90845424e-01 9.11207557e-01 -1.17355533e-01
-6.71345890e-01 7.04933226e-01 1.01848614e+00 -1.26922235e-01
2.16608420e-01 -4.69905287e-01 -8.80195975e-01 -3.54748517e-01
2.37320900e-01 7.28224754e-01 9.12116002e-03 5.62352547... | [10.358128547668457, 1.3294955492019653] |
7ea3240d-8c8f-4fc3-9360-10d5f1ee0e92 | a-case-study-on-profiling-of-an-eeg-based | 2110.02785 | null | https://arxiv.org/abs/2110.02785v1 | https://arxiv.org/pdf/2110.02785v1.pdf | A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers | Brain-Computer Interfaces (BCIs) enable converting the brain electrical activity of an interface user to the user commands. BCI research studies demonstrated encouraging results in different areas such as neurorehabilitation, control of artificial limbs, control of computer environments, communication and detection of ... | ['Lev Mukhanov', 'Georgios Karakonstantis', 'Barry J. Devereux', 'Alexandra Samsonova'] | 2021-10-04 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.81437805e-01 -5.92155933e-01 3.79290164e-01 -1.82759628e-01
3.53062041e-02 -4.92487758e-01 1.14345081e-01 -2.35287733e-02
-4.92165625e-01 7.65640855e-01 -2.17886195e-01 -4.46868241e-01
-2.62516886e-01 -5.97609520e-01 -3.27765614e-01 -6.69894040e-01
-1.95525214e-01 2.44751886e-01 1.39640421e-01 1.63865566... | [13.200429916381836, 3.3742973804473877] |
1921e259-d3fb-4309-baea-61fe97ea70a4 | sinc-spatial-composition-of-3d-human-motions | 2304.10417 | null | https://arxiv.org/abs/2304.10417v1 | https://arxiv.org/pdf/2304.10417v1.pdf | SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation | Our goal is to synthesize 3D human motions given textual inputs describing simultaneous actions, for example 'waving hand' while 'walking' at the same time. We refer to generating such simultaneous movements as performing 'spatial compositions'. In contrast to temporal compositions that seek to transition from one acti... | ['Gül Varol', 'Michael J. Black', 'Mathis Petrovich', 'Nikos Athanasiou'] | 2023-04-20 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 4.30994362e-01 3.45376551e-01 -1.74875900e-01 1.47918269e-01
-4.73327100e-01 -7.10109830e-01 1.13084674e+00 -3.48337084e-01
6.48832843e-02 8.13786864e-01 8.08444083e-01 -2.05424994e-01
1.34428423e-02 -9.95718479e-01 -7.70858705e-01 -6.02087796e-01
8.35178792e-02 7.57456958e-01 3.31217557e-01 -5.71515381... | [7.344239234924316, -0.15215519070625305] |
418cc1f0-a7c1-4d88-a716-972a43772d87 | macroeconomic-forecasting-and-sovereign-risk | 2301.09856 | null | https://arxiv.org/abs/2301.09856v1 | https://arxiv.org/pdf/2301.09856v1.pdf | Macroeconomic forecasting and sovereign risk assessment using deep learning techniques | In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques. We focus particularly on the US economy but the methodology can be applied also to other economies. Specifically US economy has suffered a severe recession from 2008 to 2010 whi... | ['Sotirios Chatzis', 'Loukas Papadoulas', 'Konstantinos P. Panousis', 'Vassilis Siakoulis', 'Anastasios Petropoulos'] | 2023-01-24 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-7.25516677e-01 -2.02542379e-01 -1.30707979e-01 -2.73038924e-01
-6.73037231e-01 -3.09034556e-01 1.10740125e+00 -1.02750331e-01
-2.93844312e-01 9.20694411e-01 4.51632529e-01 -8.87971044e-01
-2.83507228e-01 -9.71405923e-01 -3.81533086e-01 -1.04614627e+00
-1.02973208e-01 3.98761451e-01 -4.90003943e-01 -5.14204055... | [5.8377485275268555, 3.798964500427246] |
12fb8ab7-4ccb-4789-b183-6f2f36e5dd94 | icdar-2023-competition-on-reading-the-seal | 2304.11966 | null | https://arxiv.org/abs/2304.11966v2 | https://arxiv.org/pdf/2304.11966v2.pdf | ICDAR 2023 Competition on Reading the Seal Title | Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research... | ['Xiang Bai', 'Dimosthenis Karatzas', 'Yuliang Liu', 'Yinlong Wen', 'Ning Lu', 'Mingrui Chen', 'MingYu Liu', 'Wenwen Yu'] | 2023-04-24 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 5.25399208e-01 1.67754088e-02 1.06551521e-01 -1.43753991e-01
-1.11522770e+00 -7.39536464e-01 5.30009568e-01 4.36363667e-01
-4.81597066e-01 5.52283704e-01 3.20925832e-01 -2.66084522e-01
3.87409568e-01 -2.89219648e-01 -8.59972537e-01 -2.74886757e-01
-5.76567426e-02 1.65637657e-01 2.75140196e-01 -6.03675991... | [11.838737487792969, 2.53881573677063] |
05bc2ba3-4c77-4644-9778-136fd0ce78d3 | effective-hierarchical-information-threading | null | null | https://link.springer.com/chapter/10.1007/978-3-031-28244-7_44 | https://doi.org/10.1007/978-3-031-28244-7_44 | Effective Hierarchical Information Threading Using Network Community Detection | With the tremendous growth in the volume of information produced online every day (e.g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i.e., information threads). In this work, we propose a novel unsupervised approach, called HINT, whic... | ['Iadh Ounis', 'Graham McDonald', 'Hitarth Narvala'] | 2023-03-17 | null | null | null | european-conference-on-information-retrieval-3 | ['community-detection'] | ['graphs'] | [-3.73575121e-01 -1.13585591e-01 -1.85716152e-01 1.98605144e-03
-1.77293837e-01 -8.29174817e-01 8.12720120e-01 1.08207345e+00
-1.98868886e-01 2.57897288e-01 9.60260332e-01 -3.51220280e-01
-3.87587249e-01 -7.46685207e-01 -1.07046038e-01 -2.50278115e-01
-4.41188127e-01 4.20357704e-01 8.05331171e-01 -3.31724465... | [10.361712455749512, 7.42232608795166] |
f6310b2e-a90e-4f04-81f9-164c1f0a9e40 | efficient-gender-debiasing-of-pre-trained | 2209.03661 | null | https://arxiv.org/abs/2209.03661v1 | https://arxiv.org/pdf/2209.03661v1.pdf | Efficient Gender Debiasing of Pre-trained Indic Language Models | The gender bias present in the data on which language models are pre-trained gets reflected in the systems that use these models. The model's intrinsic gender bias shows an outdated and unequal view of women in our culture and encourages discrimination. Therefore, in order to establish more equitable systems and increa... | ['Aditya Kane', 'V Manushree', 'Neeraja Kirtane'] | 2022-09-08 | null | null | null | null | ['culture'] | ['speech'] | [-2.14717150e-01 2.74317056e-01 -4.39283758e-01 -8.42825711e-01
-2.95626409e-02 -4.29521650e-01 8.62456441e-01 1.13816358e-01
-9.64186072e-01 9.40543354e-01 6.36402667e-01 -4.73906994e-01
1.15070399e-02 -8.33994508e-01 -2.51965165e-01 -3.98173660e-01
5.11389732e-01 7.76031017e-01 -5.16748354e-02 -8.44824195... | [9.390625, 10.220355987548828] |
d1f3bae6-2f89-40fb-b5ad-fdbbb9e8d0e2 | title-guided-encoding-for-keyphrase | 1808.08575 | null | http://arxiv.org/abs/1808.08575v5 | http://arxiv.org/pdf/1808.08575v5.pdf | Title-Guided Encoding for Keyphrase Generation | Keyphrase generation (KG) aims to generate a set of keyphrases given a
document, which is a fundamental task in natural language processing (NLP).
Most previous methods solve this problem in an extractive manner, while
recently, several attempts are made under the generative setting using deep
neural networks. However,... | ['Yifan Gao', 'Michael R. Lyu', 'Irwin King', 'Wang Chen', 'Jiani Zhang'] | 2018-08-26 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.18242818e-01 1.03335008e-01 -4.13372576e-01 3.97991985e-02
-9.50038850e-01 -6.43163741e-01 1.15964234e+00 2.51355678e-01
-3.40321660e-01 8.17534029e-01 8.24148059e-01 -3.25407475e-01
1.24507695e-01 -1.09775746e+00 -8.70889723e-01 -6.70473278e-01
2.93774188e-01 5.45325935e-01 8.57175067e-02 -2.99156904... | [12.312376976013184, 8.927029609680176] |
1738cff4-7976-4eb1-9da8-18f2f7c6f98f | bayesian-knowledge-driven-critiquing-with | 2306.05636 | null | https://arxiv.org/abs/2306.05636v1 | https://arxiv.org/pdf/2306.05636v1.pdf | Bayesian Knowledge-driven Critiquing with Indirect Evidence | Conversational recommender systems (CRS) enhance the expressivity and personalization of recommendations through multiple turns of user-system interaction. Critiquing is a well-known paradigm for CRS that allows users to iteratively refine recommendations by providing feedback about attributes of recommended items. Whi... | ['Scott Sanner', 'Zhenwei Tang', 'Griffin Floto', 'Armin Toroghi'] | 2023-06-09 | null | null | null | null | ['knowledge-graphs', 'bayesian-inference'] | ['knowledge-base', 'methodology'] | [-1.76301911e-01 4.45992768e-01 -3.07422876e-01 -4.24540043e-01
-3.00877512e-01 -7.73151457e-01 6.96416497e-01 3.88037384e-01
-3.66066754e-01 7.73440301e-01 9.05971587e-01 -4.59432751e-01
-9.51134384e-01 -8.35806787e-01 -5.56816876e-01 -3.73435915e-01
3.29735912e-02 6.15752518e-01 1.83791265e-01 -5.69890320... | [10.020988464355469, 5.846883296966553] |
7a686ff3-8975-45fe-a325-12a23879ba50 | efficient-stereo-depth-estimation-for-pseudo | 2205.08089 | null | https://arxiv.org/abs/2205.08089v1 | https://arxiv.org/pdf/2205.08089v1.pdf | Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder | Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to ... | ['Xianke Lin', 'Sabir Hossain'] | 2022-05-17 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-9.46470946e-02 -1.30166292e-01 8.91230032e-02 -7.11861134e-01
-8.15562367e-01 -5.00668883e-01 6.30562186e-01 3.88509519e-02
-5.35197914e-01 9.14055347e-01 -5.93926907e-01 -3.06633234e-01
2.13379636e-01 -1.26556742e+00 -9.46573734e-01 -4.96244162e-01
8.56501758e-02 1.17724681e+00 5.59391260e-01 -1.16098173... | [7.76619815826416, -2.546610116958618] |
14c89f3c-81da-4534-bf96-bbc0b7d93cf2 | learning-3d-photography-videos-via-self | 2302.10781 | null | https://arxiv.org/abs/2302.10781v1 | https://arxiv.org/pdf/2302.10781v1.pdf | Learning 3D Photography Videos via Self-supervised Diffusion on Single Images | 3D photography renders a static image into a video with appealing 3D visual effects. Existing approaches typically first conduct monocular depth estimation, then render the input frame to subsequent frames with various viewpoints, and finally use an inpainting model to fill those missing/occluded regions. The inpaintin... | ['Nan Duan', 'Yuejian Fang', 'Zicheng Liu', 'Lijuan Wang', 'Fan Yang', 'Zhengyuan Yang', 'Linjie Li', 'JianFeng Wang', 'Minheng Ni', 'Shengming Yin', 'Chenfei Wu', 'Xiaodong Wang'] | 2023-02-21 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 3.48725259e-01 7.66770169e-02 -1.96752876e-01 -3.44271839e-01
-5.83303571e-01 -3.06110173e-01 4.06328440e-01 -6.92906380e-01
-6.54701665e-02 5.69583833e-01 1.08520880e-01 -1.31474286e-01
6.05536401e-01 -8.24603975e-01 -7.62494981e-01 -5.78903258e-01
2.68126398e-01 2.57831573e-01 4.67978179e-01 1.64302826... | [10.556764602661133, -1.5521032810211182] |
e9bcc47a-5350-487e-a230-d6e6494465c9 | interpreting-wide-band-neural-activity-using | null | null | https://elifesciences.org/articles/66551 | https://elifesciences.org/articles/66551#downloads | Interpreting wide-band neural activity using convolutional neural networks | Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a m... | ['Caswell Barry', 'Christian F Doeller', 'Julie Lefort', 'Daniel Bendor', 'Andrea Banino', 'Jack Kelly', 'Matthias Nau', "Alice O'Leary", 'Catherine Perrodin', 'Sander Tanni', 'Markus Frey'] | 2021-08-02 | null | null | null | elife-2021-8 | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.36509836e-01 -2.23471373e-01 2.32333943e-01 -5.28653979e-01
-6.85852289e-01 -8.21422398e-01 2.52175629e-01 6.58059657e-01
-7.27240860e-01 8.37225556e-01 5.41606322e-02 -2.05192119e-01
7.36336783e-02 -4.71589863e-01 -9.71184254e-01 -9.68793035e-01
-3.09232390e-03 3.42445672e-01 2.67197430e-01 1.48829045... | [9.666563034057617, 2.5298197269439697] |
ee935c73-545c-41c3-b337-eb88713a078a | polygames-improved-zero-learning | 2001.09832 | null | https://arxiv.org/abs/2001.09832v1 | https://arxiv.org/pdf/2001.09832v1.pdf | Polygames: Improved Zero Learning | Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more ... | ['Shi-Jim Yen', 'Yi-Jun Ye', 'Shi-Cheng Ye', 'Yu-Jin Lin', 'Hsin-I Lin', 'Cheng-Ling Li', 'Vasil Khalidov', 'Qucheng Gong', 'Maria Elsa', 'Xian-Dong Chiu', 'Shi-Yu Chen', 'Guan-Wei Chen', 'Yen-Chi Chen', 'Xavier Martinet', 'Sergey Zagoruyko', 'Olivier Teytaud', 'Jeremy Rapin', 'Hengyuan Hu', 'Gabriel Synnaeve', 'Fabien... | 2020-01-27 | null | null | null | null | ['board-games'] | ['playing-games'] | [-5.81454873e-01 1.78110525e-01 -3.14893693e-01 2.32836440e-01
-5.27665019e-01 -7.35003948e-01 5.10618806e-01 -2.94080317e-01
-8.87020111e-01 9.62156892e-01 -1.65006638e-01 -5.68843126e-01
-2.29517981e-01 -1.14287722e+00 -8.12017262e-01 -4.47265774e-01
-4.41202819e-01 5.31636536e-01 9.53672230e-01 -1.03681207... | [3.4343791007995605, 1.3905863761901855] |
319d5b97-e71f-40c0-9c48-2ec456541677 | towards-single-camera-human-3d-kinematics | 2301.05435 | null | https://arxiv.org/abs/2301.05435v1 | https://arxiv.org/pdf/2301.05435v1.pdf | Towards Single Camera Human 3D-Kinematics | Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techni... | ['Frans C. T. van der Helm', 'Jan van Gemert', 'Ajay Seth', 'Xucong Zhang', 'Wei-Tse Yang', 'Marian Bittner'] | 2023-01-13 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [-1.44604012e-01 -9.03183743e-02 -2.04415441e-01 -5.03636673e-02
-1.00496471e+00 -3.92570704e-01 -1.83545314e-02 -1.01993144e-01
-8.28851461e-01 3.87105644e-01 1.18729010e-01 -1.74197927e-01
-7.00789737e-03 -5.44716455e-02 -7.03181148e-01 -2.10999310e-01
-3.13275129e-01 7.62870610e-01 2.20557243e-01 -8.29404071... | [7.071767807006836, -0.5534766316413879] |
7883e298-0cb2-4123-b383-6e8c5a129638 | sar-based-landslide-classification | 2211.09927 | null | https://arxiv.org/abs/2211.09927v1 | https://arxiv.org/pdf/2211.09927v1.pdf | SAR-based landslide classification pretraining leads to better segmentation | Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides. Synthetic Aperture Radar (SAR) is an active remote sensing technique that ... | ['Raul Ramos-Pollan', 'Siddha Ganju', 'Freddie Kalaitzis', 'Edoardo Nemni', 'Ioannis Prapas', 'Ragini Bal Mahesh', 'Wei Ji Leong', 'Vanessa Böhm'] | 2022-11-17 | null | null | null | null | ['landslide-segmentation'] | ['computer-vision'] | [ 4.81610060e-01 -2.71919761e-02 -7.76940119e-03 -3.56149167e-01
-9.46696579e-01 -6.93348289e-01 3.59973490e-01 3.95646334e-01
-6.06417358e-01 5.95270395e-01 2.39182308e-01 -7.88254380e-01
-2.12289274e-01 -1.31283903e+00 -5.55042744e-01 -1.00906336e+00
-2.37417370e-01 6.16407514e-01 5.62359355e-02 -2.00243905... | [9.442658424377441, -1.4403406381607056] |
c190a901-37d9-4fdd-944b-853915c24b7f | multi-modal-multi-label-facial-action-unit | 2203.13301 | null | https://arxiv.org/abs/2203.13301v2 | https://arxiv.org/pdf/2203.13301v2.pdf | Multi-modal Multi-label Facial Action Unit Detection with Transformer | Facial Action Coding System is an important approach of facial expression analysis.This paper describes our submission to the third Affective Behavior Analysis (ABAW) 2022 competition. We proposed a transfomer based model to detect facial action unit (FAU) in video. To be specific, we firstly trained a multi-modal mode... | ['Jin Qi', 'Shisen Wang', 'Lingfeng Wang'] | 2022-03-24 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.38685250e-01 1.04561299e-01 -1.73000887e-01 -6.40110552e-01
-5.55648446e-01 -1.92230001e-01 4.66591656e-01 -5.03748953e-01
-2.97250450e-01 5.73201239e-01 6.62808120e-01 6.39874637e-01
4.04553175e-01 -1.88461974e-01 -2.44881794e-01 -6.36978745e-01
-4.11637664e-01 -3.70821297e-01 -4.84636910e-02 -2.57456690... | [13.579011917114258, 1.9531787633895874] |
ef382686-b047-4534-b862-cd99f93da81e | a-provably-improved-algorithm-for | 2302.07393 | null | https://arxiv.org/abs/2302.07393v1 | https://arxiv.org/pdf/2302.07393v1.pdf | A Provably Improved Algorithm for Crowdsourcing with Hard and Easy Tasks | Crowdsourcing is a popular method used to estimate ground-truth labels by collecting noisy labels from workers. In this work, we are motivated by crowdsourcing applications where each worker can exhibit two levels of accuracy depending on a task's type. Applying algorithms designed for the traditional Dawid-Skene model... | ['R. Srikant', 'Dimitrios Katselis', 'Saptarshi Mandal', 'Seo Taek Kong'] | 2023-02-14 | null | null | null | null | ['type'] | ['speech'] | [-6.08626753e-02 1.20577000e-01 1.43195353e-02 -1.94234625e-01
-1.11766708e+00 -8.80358398e-01 2.78467208e-01 1.67730063e-01
-6.11854732e-01 1.00921142e+00 -2.76698440e-01 1.33254944e-04
9.67531204e-02 -5.52763760e-01 -7.96115041e-01 -6.82874322e-01
4.78840560e-01 9.49506581e-01 5.02222121e-01 -1.62408561... | [9.619168281555176, 4.635506629943848] |
6dd58e5c-877d-4aae-9064-1f1815bbade1 | 3d-feature-pyramid-attention-module-for | 1810.06178 | null | http://arxiv.org/abs/1810.06178v4 | http://arxiv.org/pdf/1810.06178v4.pdf | 3D Feature Pyramid Attention Module for Robust Visual Speech Recognition | Visual speech recognition is the task to decode the speech content from a
video based on visual information, especially the movements of lips. It is also
referenced as lipreading. Motivated by two problems existing in lipreading,
words with similar pronunciation and the variation of word duration, we propose
a novel 3D... | ['Jing-Yun Xiao'] | 2018-10-15 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 6.00243732e-02 -4.80865359e-01 -3.10915172e-01 -2.35674288e-02
-6.36808574e-01 -4.16311435e-02 4.60572511e-01 -2.36844093e-01
-5.22311449e-01 4.98663306e-01 6.77678406e-01 -1.28822736e-02
2.40977034e-01 -2.97913432e-01 -5.81020057e-01 -7.20739841e-01
2.23202109e-01 -7.37609982e-01 5.92209101e-01 1.39648631... | [14.35925006866455, 5.009498596191406] |
74c27754-3077-4ee2-a232-1ca3a0a19c93 | measuring-sentiment-bias-in-machine | 2306.07152 | null | https://arxiv.org/abs/2306.07152v1 | https://arxiv.org/pdf/2306.07152v1.pdf | Measuring Sentiment Bias in Machine Translation | Biases induced to text by generative models have become an increasingly large topic in recent years. In this paper we explore how machine translation might introduce a bias in sentiments as classified by sentiment analysis models. For this, we compare three open access machine translation models for five different lang... | ['Munir Georges', 'Sören Gröttrup', 'Taylan Volkan', 'Khabbab Zakaria', 'Shubham Kurlekar', 'Aaricia Herygers', 'Kai Hartung'] | 2023-06-12 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 1.69749022e-01 2.51948535e-01 -4.97103065e-01 -8.23486745e-01
-6.52266502e-01 -8.82861435e-01 1.28370202e+00 1.27828484e-02
-3.90396595e-01 1.07035875e+00 7.37687588e-01 -6.53681934e-01
4.26163793e-01 -7.69627869e-01 -9.23478901e-01 -7.15665400e-01
7.02483416e-01 8.36326599e-01 -1.47056744e-01 -4.35157567... | [9.85326099395752, 10.140080451965332] |
bfdacfd1-e077-47c2-8c6b-091a8db56506 | end-to-end-lung-nodule-detection-in-computed | 1711.02074 | null | http://arxiv.org/abs/1711.02074v2 | http://arxiv.org/pdf/1711.02074v2.pdf | End-to-end Lung Nodule Detection in Computed Tomography | Computer aided diagnostic (CAD) system is crucial for modern med-ical
imaging. But almost all CAD systems operate on reconstructed images, which were
optimized for radiologists. Computer vision can capture features that is subtle
to human observers, so it is desirable to design a CAD system op-erating on the
raw data. ... | ['Kyungsang Kim', 'Dufan Wu', 'Bin Dong', 'Quanzheng Li', 'Georges El Fakhri'] | 2017-11-06 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.54998767e-01 2.00028181e-01 -5.67644415e-03 -2.61894494e-01
-9.85345006e-01 -9.70269293e-02 2.02478305e-01 -1.78264990e-01
-7.12019980e-01 -3.47493179e-02 -1.71148881e-01 -7.61652052e-01
6.61789253e-02 -6.10343397e-01 -3.17482799e-01 -7.42039382e-01
-1.28736362e-01 7.75523543e-01 6.41748369e-01 3.62531483... | [15.365561485290527, -2.1189513206481934] |
dd00f8cb-488a-4c32-9138-27e4de44a196 | benchmarking-robot-manipulation-with-the | 2202.07074 | null | https://arxiv.org/abs/2202.07074v1 | https://arxiv.org/pdf/2202.07074v1.pdf | Benchmarking Robot Manipulation with the Rubik's Cube | Benchmarks for robot manipulation are crucial to measuring progress in the field, yet there are few benchmarks that demonstrate critical manipulation skills, possess standardized metrics, and can be attempted by a wide array of robot platforms. To address a lack of such benchmarks, we propose Rubik's cube manipulation ... | ['Joshua R. Smith', 'Siddhartha S. Srinivasa', 'Patrick E. Lancaster', 'Boling Yang'] | 2022-02-14 | null | null | null | null | ['rubik-s-cube', 'robot-manipulation'] | ['graphs', 'robots'] | [ 6.24257326e-02 1.82704687e-01 -5.61682656e-02 1.14832625e-01
-6.08299911e-01 -1.02746904e+00 2.10194990e-01 -3.64268601e-01
-2.08666220e-01 6.01166964e-01 -4.04970706e-01 -2.19669685e-01
-7.13114738e-01 -6.41596019e-01 -1.01713836e+00 -4.66161847e-01
-4.41994011e-01 8.73079777e-01 4.71206248e-01 -8.59915197... | [4.7920002937316895, 0.632317841053009] |
e296c4a6-a615-481c-8815-787ba63a8fbc | bioblp-a-modular-framework-for-learning-on | 2306.03606 | null | https://arxiv.org/abs/2306.03606v1 | https://arxiv.org/pdf/2306.03606v1.pdf | BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs | Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedic... | ['Paul Groth', 'Michael Cochez', 'Thom Pijnenburg', 'Payal Mitra', 'Dimitrios Alivanistos', 'Daniel Daza'] | 2023-06-06 | null | null | null | null | ['link-prediction', 'knowledge-graphs', 'entity-embeddings'] | ['graphs', 'knowledge-base', 'methodology'] | [-1.47783440e-02 5.54877460e-01 -5.26652098e-01 -3.56747985e-01
-3.54223311e-01 -3.82306576e-01 3.47482324e-01 1.07294381e+00
-5.15863538e-01 9.84529376e-01 2.95601130e-01 -4.01264936e-01
-1.78936958e-01 -1.10529363e+00 -9.46854830e-01 -5.10947287e-01
-2.55776614e-01 8.21450412e-01 2.23857969e-01 -1.44937396... | [8.493001937866211, 7.8457770347595215] |
f1f160b6-d37f-413d-a233-e6ea1bb2ac55 | effectiveness-of-data-augmentation-and | null | null | https://aclanthology.org/2022.lrec-1.381 | https://aclanthology.org/2022.lrec-1.381.pdf | Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings | We tackle the problem of neural headline generation in a low-resource setting, where only limited amount of data is available to train a model. We compare the ideal high-resource scenario on English with results obtained on a smaller subset of the same data and also run experiments on two small news corpora covering lo... | ['Elaine Zosa', 'Lidia Pivovarova', 'Syrielle Montariol', 'Matej Martinc'] | null | null | null | null | lrec-2022-6 | ['headline-generation'] | ['natural-language-processing'] | [ 6.52783811e-02 4.79559273e-01 1.09771766e-01 -2.90577263e-01
-1.15128803e+00 -4.02836561e-01 1.03494394e+00 3.95809531e-01
-1.06272268e+00 1.16692114e+00 5.88893712e-01 -3.90409857e-01
3.81328523e-01 -6.88705921e-01 -9.11624730e-01 -5.74138701e-01
3.69911015e-01 8.59953225e-01 3.88709843e-01 -5.56487918... | [11.487679481506348, 9.947848320007324] |
0d336622-6929-49d2-bb42-0a6fa5fca761 | span-based-joint-entity-and-relation | 1909.07755 | null | https://arxiv.org/abs/1909.07755v4 | https://arxiv.org/pdf/1909.07755v4.pdf | Span-based Joint Entity and Relation Extraction with Transformer Pre-training | We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our key contribution is a light-weight reasoning on BERT embeddings, which features entity recognition and filtering, as well as relation classification with a localized, marker-free context representation. The model is trained ... | ['Markus Eberts', 'Adrian Ulges'] | 2019-09-17 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.39350444e-01 4.30464268e-01 -5.78965127e-01 -4.27954942e-01
-1.18443549e+00 -5.24608254e-01 5.07141590e-01 9.37249362e-01
-8.69031012e-01 8.73877227e-01 4.96417373e-01 -1.60538256e-01
7.15804175e-02 -8.53467107e-01 -5.82412601e-01 1.61419008e-02
-4.37635630e-01 5.01775622e-01 2.55877435e-01 -3.69591117... | [9.34901237487793, 8.800207138061523] |
839de106-1e71-4324-abfc-e7ab8934e11f | approximate-information-for-efficient | 2307.01563 | null | https://arxiv.org/abs/2307.01563v1 | https://arxiv.org/pdf/2307.01563v1.pdf | Approximate information for efficient exploration-exploitation strategies | This paper addresses the exploration-exploitation dilemma inherent in decision-making, focusing on multi-armed bandit problems. The problems involve an agent deciding whether to exploit current knowledge for immediate gains or explore new avenues for potential long-term rewards. We here introduce a novel algorithm, app... | ['Jean-Baptiste Masson', 'Christian L. Vestergaard', 'Alex Barbier-Chebbah'] | 2023-07-04 | null | null | null | null | ['thompson-sampling', 'efficient-exploration', 'decision-making'] | ['methodology', 'methodology', 'reasoning'] | [ 8.15532580e-02 2.08126724e-01 -1.28362942e+00 -2.59360671e-01
-1.00864995e+00 -8.06792438e-01 7.36465096e-01 5.54990396e-02
-8.78259301e-01 1.31243610e+00 1.68102235e-01 -8.30156446e-01
-8.87850165e-01 -5.03011048e-01 -3.52858782e-01 -6.82440102e-01
-1.05631940e-01 6.48605943e-01 -3.25182289e-01 2.30076492... | [4.516361713409424, 3.2281558513641357] |
79744be6-e2c3-40db-9291-834230b23703 | vision-transformer-based-feature-extraction | 2302.00875 | null | https://arxiv.org/abs/2302.00875v1 | https://arxiv.org/pdf/2302.00875v1.pdf | Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning | Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the image attribute. In this paper, we put forth a new GZSL approach exploiting Vision Transformer (ViT) to maximize the attribute-related information contained in the image feature. In ViT, the entire i... | ['Byonghyo Shim', 'Junhan Kim', 'Kyuhong Shim', 'Jiseob Kim'] | 2023-02-02 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.64916420e-01 4.60659526e-02 -2.13095769e-02 -5.21404624e-01
-8.42548251e-01 3.09892539e-02 5.49224913e-01 2.00411171e-01
-2.42070511e-01 4.93599206e-01 2.49961153e-01 2.75296539e-01
-1.65479660e-01 -9.15985703e-01 -9.42201793e-01 -9.34480250e-01
5.00652432e-01 -7.52053708e-02 2.62212187e-01 -9.78916660... | [9.764315605163574, 2.1391022205352783] |
ce759618-a1e0-4478-8ce8-f564c0a1a5e1 | learning-geometric-aware-properties-in-2d | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Arsomngern_Learning_Geometric-Aware_Properties_in_2D_Representation_Using_Lightweight_CAD_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Arsomngern_Learning_Geometric-Aware_Properties_in_2D_Representation_Using_Lightweight_CAD_Models_CVPR_2023_paper.pdf | Learning Geometric-Aware Properties in 2D Representation Using Lightweight CAD Models, or Zero Real 3D Pairs | Cross-modal training using 2D-3D paired datasets, such as those containing multi-view images and 3D scene scans, presents an effective way to enhance 2D scene understanding by introducing geometric and view-invariance priors into 2D features. However, the need for large-scale scene datasets can impede scalability a... | ['Supasorn Suwajanakorn', 'Sarana Nutanong', 'Pattaramanee Arsomngern'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['scene-understanding'] | ['computer-vision'] | [ 1.83388308e-01 -6.55969977e-02 4.29231673e-02 -8.14988852e-01
-8.61056149e-01 -4.93908972e-01 7.84737229e-01 -2.38141894e-01
-3.45557302e-01 1.42424569e-01 3.16660523e-01 -1.75717488e-01
-1.98836252e-01 -1.07744622e+00 -9.90877807e-01 -1.95274070e-01
2.70147234e-01 7.52540290e-01 2.97041237e-01 -5.22675693... | [8.290914535522461, -3.043325901031494] |
de44c27d-0e6a-45c2-bd6c-bdbc1aeab044 | tempered-sigmoid-activations-for-deep | 2007.14191 | null | https://arxiv.org/abs/2007.14191v1 | https://arxiv.org/pdf/2007.14191v1.pdf | Tempered Sigmoid Activations for Deep Learning with Differential Privacy | Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using the model architectures that alre... | ['Úlfar Erlingsson', 'Shuang Song', 'Steve Chien', 'Abhradeep Thakurta', 'Nicolas Papernot'] | 2020-07-28 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 1.89665630e-01 2.96671212e-01 -8.79511237e-03 -7.69639969e-01
-9.29512382e-01 -8.02388310e-01 4.97060001e-01 2.04821959e-01
-9.68295395e-01 9.96252596e-01 -1.45563632e-01 -4.60582763e-01
-1.74995750e-01 -5.80935717e-01 -9.41734731e-01 -1.06872535e+00
-1.57134905e-01 2.47527987e-01 -2.65097082e-01 7.95413405... | [5.945583343505859, 6.865639686584473] |
6ba6dc14-8c6a-4722-a9ae-a4d9c49dadfe | unsupervised-machine-translation-on-dravidian | 2103.15877 | null | https://arxiv.org/abs/2103.15877v1 | https://arxiv.org/pdf/2103.15877v1.pdf | Unsupervised Machine Translation On Dravidian Languages | Unsupervised neural machine translation (UNMT) is beneficial especially for low resource languages such as those from the Dravidian family. However, UNMT systems tend to fail in realistic scenarios involving actual low resource languages. Recent works propose to utilize auxiliary parallel data and have achieved state-o... | ['Jan Niehues', 'Danni Liu', 'Sai Koneru'] | 2021-03-29 | null | https://aclanthology.org/2021.dravidianlangtech-1.7 | https://aclanthology.org/2021.dravidianlangtech-1.7.pdf | eacl-dravidianlangtech-2021-4 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-2.16175675e-01 -2.43237570e-01 -4.93387014e-01 -2.15593159e-01
-8.47953916e-01 -7.25957036e-01 7.82316864e-01 -1.10497460e-01
-6.89193249e-01 8.49981546e-01 4.88731027e-01 -5.70856333e-01
1.92911565e-01 -6.77323103e-01 -5.39107740e-01 -4.20131773e-01
3.88021231e-01 8.63420010e-01 -2.82752037e-01 -6.60576344... | [11.509965896606445, 10.267807006835938] |
d19b624c-e608-4217-9136-b929d65e4b28 | progressive-class-semantic-matching-for-semi | null | null | https://openreview.net/forum?id=FitLLp-Jwa | https://openreview.net/pdf?id=FitLLp-Jwa | Progressive Class Semantic Matching for Semi-supervised Text Classification | Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In this work, we further investigate the marriage between semi-supervised learning an... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 2.98654288e-01 3.80927682e-01 -6.51776373e-01 -1.02027690e+00
-1.09193301e+00 -3.60690624e-01 8.68672132e-01 3.33938122e-01
-5.30183494e-01 5.15273929e-01 1.32643014e-01 -3.06847245e-01
3.49046856e-01 -5.96221030e-01 -4.97339517e-01 -3.80370915e-01
3.73021662e-01 8.83751631e-01 2.92238295e-01 4.56012562... | [10.597535133361816, 7.3415303230285645] |
caf2b564-cc87-498f-b868-ab3345069d9c | augmenting-data-driven-models-for-energy | 2301.01720 | null | https://arxiv.org/abs/2301.01720v1 | https://arxiv.org/pdf/2301.01720v1.pdf | Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering | Data-driven modeling is an approach in energy systems modeling that has been gaining popularity. In data-driven modeling, machine learning methods such as linear regression, neural networks or decision-tree based methods are being applied. While these methods do not require domain knowledge, they are sensitive to data ... | ['Sandra Wilfling'] | 2023-01-04 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-1.10598609e-01 -1.90527484e-01 -3.00439090e-01 -7.88346052e-01
-1.71185583e-01 -2.13665888e-01 6.17775500e-01 6.59326613e-01
-8.69814213e-03 4.03563470e-01 1.71161070e-01 -1.25819102e-01
-4.65665817e-01 -1.45153892e+00 -3.53041172e-01 -4.77288991e-01
2.43019447e-01 1.91283360e-01 5.47307059e-02 -2.46589422... | [8.394818305969238, 4.756916522979736] |
288f834b-7b2b-4c54-9b01-c7c4a49bdbe2 | todynet-temporal-dynamic-graph-neural-network | 2304.05078 | null | https://arxiv.org/abs/2304.05078v1 | https://arxiv.org/pdf/2304.05078v1.pdf | TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification | Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of ti... | ['Jun Gu', 'Yong Cui', 'Hongzhi Wang', 'Zhiyu Liang', 'Donghua Yang', 'Xianzhang Liu', 'Huaiyuan Liu'] | 2023-04-11 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [-3.90004605e-01 -4.58206773e-01 -1.17495313e-01 -1.80451468e-01
3.83060202e-02 -2.50869364e-01 2.51831859e-01 1.78268492e-01
-1.40545890e-01 3.73573899e-01 -5.97910769e-02 -6.10521913e-01
-6.29049420e-01 -8.82916987e-01 -3.84591877e-01 -8.95085096e-01
-7.10730672e-01 7.99579695e-02 3.46178591e-01 -2.74226040... | [6.805309295654297, 2.8297643661499023] |
e2cdc78e-e1dc-4e45-9d3b-f3f55f0d9572 | openbox-a-python-toolkit-for-generalized | 2304.13339 | null | https://arxiv.org/abs/2304.13339v1 | https://arxiv.org/pdf/2304.13339v1.pdf | OpenBox: A Python Toolkit for Generalized Black-box Optimization | Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning. However, users still face challenges when applying BBO methods to their problems at hand with existing software packages in terms of applicability, performance, and effici... | ['Bin Cui', 'Ce Zhang', 'Wentao Zhang', 'Yang Li', 'Yu Shen', 'Huaijun Jiang'] | 2023-04-26 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-8.40581179e-01 -5.19346714e-01 -3.50671262e-01 -3.60084176e-01
-4.70748514e-01 -5.75838327e-01 -6.72748461e-02 2.47598030e-02
-8.68758932e-02 4.72410709e-01 6.36487603e-02 -5.95967829e-01
-9.13840681e-02 -3.15150380e-01 -2.61711031e-01 -5.11749864e-01
-2.77819131e-02 2.69863963e-01 5.81155494e-02 -3.84063125... | [7.336239814758301, 4.401630878448486] |
592f7280-0229-44f8-962c-7b679b35536a | learning-contextually-fused-audio-visual | 2202.07428 | null | https://arxiv.org/abs/2202.07428v2 | https://arxiv.org/pdf/2202.07428v2.pdf | Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition | With the advance in self-supervised learning for audio and visual modalities, it has become possible to learn a robust audio-visual speech representation. This would be beneficial for improving the audio-visual speech recognition (AVSR) performance, as the multi-modal inputs contain more fruitful information in princip... | ['Li-Rong Dai', 'Xin Fang', 'Ming-Hui Wu', 'Jian-Shu Zhang', 'Jie Zhang', 'Zi-Qiang Zhang'] | 2022-02-15 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 6.51561916e-01 2.17536047e-01 -2.63428658e-01 -1.33452371e-01
-1.07730985e+00 -3.19841206e-01 9.61461306e-01 2.21670583e-01
-3.25163335e-01 6.84595823e-01 2.63978988e-01 -3.50876004e-01
-2.38375440e-01 -3.89026552e-01 -3.97656411e-01 -1.05366743e+00
1.98985711e-01 6.74766526e-02 1.06056154e-01 -1.37685224... | [14.346601486206055, 5.110989570617676] |
c4890d8c-62cb-4982-bceb-b6ab4b345f97 | simple-and-effective-semi-supervised-question | 1804.00720 | null | http://arxiv.org/abs/1804.00720v1 | http://arxiv.org/pdf/1804.00720v1.pdf | Simple and Effective Semi-Supervised Question Answering | Recent success of deep learning models for the task of extractive Question
Answering (QA) is hinged on the availability of large annotated corpora.
However, large domain specific annotated corpora are limited and expensive to
construct. In this work, we envision a system where the end user specifies a
set of base docum... | ['Dheeraj Rajagopal', 'Danish Pruthi', 'Bhuwan Dhingra'] | 2018-04-02 | simple-and-effective-semi-supervised-question-1 | https://aclanthology.org/N18-2092 | https://aclanthology.org/N18-2092.pdf | naacl-2018-6 | ['triviaqa'] | ['miscellaneous'] | [ 4.19462845e-02 2.60312915e-01 1.67315885e-01 -7.39862025e-01
-1.54378307e+00 -1.03987980e+00 5.10149896e-01 -2.28250697e-02
-4.90146339e-01 7.69654453e-01 1.33465677e-01 -5.41198254e-01
-4.44718935e-02 -6.45209849e-01 -6.69416130e-01 -1.87581256e-01
1.98966458e-01 1.06786680e+00 3.09306711e-01 -5.89812100... | [11.226177215576172, 8.205024719238281] |
2e6fc977-d116-4d91-8faa-6e20149f33b6 | fast-reinforcement-learning-with-generalized | null | null | https://www.pnas.org/doi/full/10.1073/pnas.1907370117 | https://www.pnas.org/doi/epdf/10.1073/pnas.1907370117 | Fast reinforcement learning with generalized policy updates | The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decision-making problems that are currently intractable. One obstacle to overcome is the amount of data needed by learning systems of this type. In this article, we propose to address this issue through a... | ['and Doina Precup.', 'David Silver', 'Diana Borsa', 'Shaobo Hou', 'André Barreto'] | 2020-07-09 | null | null | null | proceedings-of-the-national-academy-of-2 | ['problem-decomposition'] | ['miscellaneous'] | [ 2.91406363e-01 2.18768239e-01 -1.44659355e-01 -7.83195123e-02
-4.26825464e-01 -7.80480623e-01 5.42289019e-01 4.02425736e-01
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-2.66082317e-01 -7.82150090e-01 -5.90154648e-01 -9.38617349e-01
4.44817264e-03 5.09138763e-01 2.26575240e-01 -4.82627690... | [4.1761088371276855, 1.782096266746521] |
3a6dce11-1b81-4da6-a0b3-4e3665d19eb5 | incorporating-multi-target-in-multi-stage | 2107.04232 | null | https://arxiv.org/abs/2107.04232v1 | https://arxiv.org/pdf/2107.04232v1.pdf | Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization | Recent single-channel speech enhancement methods based on deep neural networks (DNNs) have achieved remarkable results, but there are still generalization problems in real scenes. Like other data-driven methods, DNN-based speech enhancement models produce significant performance degradation on untrained data. In this s... | ['Xuyi Zhuang', 'Zehua Zhang', 'Andong Li', 'Mingjiang Wang', 'Lu Zhang'] | 2021-07-09 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 2.22514898e-01 -4.84020293e-01 2.99488306e-01 -4.94345129e-01
-1.01515782e+00 1.43567011e-01 2.80082822e-01 -4.24272090e-01
-6.25364244e-01 3.96178454e-01 5.39538026e-01 -2.41716281e-01
3.13769560e-03 -3.22263628e-01 -3.66197050e-01 -9.22341049e-01
1.38094246e-01 -5.70797861e-01 2.45603904e-01 -5.36624134... | [14.948225021362305, 5.942246437072754] |
3b7ddef0-d418-4ebc-809f-6fc98853c00a | peer-to-peer-energy-trading-in-smart-grid | null | null | https://ieeexplore.ieee.org/abstract/document/9386079 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9386079 | Peer-to-peer energy trading in smart grid through blockchain: A double auction-based game theoretic approach | In a smart grid, each residential unit with renewable energy sources can trade energy with others for profit. Buyers with insufficient energy meet their demand by buying the required energy from other houses with surplus energy. However, they will not be willing to engage in the trade if it is not beneficial. With the ... | ['D Kim', 'J Cho', 'HT Doan'] | 2021-04-05 | null | null | null | journal-2021-4 | ['energy-management'] | ['time-series'] | [-5.70179462e-01 4.48493332e-01 -8.81648362e-02 5.65843247e-02
-1.87442288e-01 -1.18419373e+00 -4.90061156e-02 1.46670252e-01
-3.61826897e-01 1.10117710e+00 -6.99985325e-02 -2.45588750e-01
-5.49144447e-02 -1.33220744e+00 -2.16969639e-01 -1.36715400e+00
-2.06048280e-01 3.92999262e-01 3.21086147e-03 -4.39863466... | [5.596586227416992, 2.556356906890869] |
4927e23d-48c1-4c8a-afd0-b2e2086e94f2 | reversible-vision-transformers-1 | 2302.04869 | null | https://arxiv.org/abs/2302.04869v1 | https://arxiv.org/pdf/2302.04869v1.pdf | Reversible Vision Transformers | We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reversible Vision Transformers enable scaling up architectures with efficient memory usage. We adapt two popular models, namely Vision Transform... | ['Jitendra Malik', 'Christoph Feichtenhofer', 'Bo Xiong', 'Chao-yuan Wu', 'Yanghao Li', 'Haoqi Fan', 'Karttikeya Mangalam'] | 2023-02-09 | reversible-vision-transformers | http://openaccess.thecvf.com//content/CVPR2022/html/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-classification'] | ['computer-vision'] | [ 2.14321554e-01 -2.11148053e-01 -1.66398168e-01 -1.28270537e-01
-7.34657645e-01 -7.21702099e-01 6.10010862e-01 -1.84307665e-01
-4.31580871e-01 1.08148195e-01 1.67069554e-01 -6.28402591e-01
3.76937538e-01 -6.10431731e-01 -7.43052542e-01 -6.78754389e-01
3.77967298e-01 -3.02333366e-02 2.35803410e-01 4.31903452... | [9.378416061401367, 1.6451135873794556] |
1dae593d-20c6-40fc-bf86-b5760cb1ad4a | yimmon-at-semeval-2019-task-9-suggestion | null | null | https://aclanthology.org/S19-2222 | https://aclanthology.org/S19-2222.pdf | Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches | Suggestion mining task aims to extract tips, advice, and recommendations from unstructured text. The task includes many challenges, such as class imbalance, figurative expressions, context dependency, and long and complex sentences. This paper gives a detailed system description of our submission in SemEval 2019 Task 9... | ['Yimeng Zhuang'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 3.19227368e-01 3.55418503e-01 -2.67230779e-01 -6.20810509e-01
-8.13170791e-01 -4.32743609e-01 4.11791921e-01 2.85533369e-01
-5.39141178e-01 7.21780837e-01 7.55279899e-01 -7.20560849e-01
4.40390594e-02 -4.48069453e-01 -5.27823210e-01 -1.90076515e-01
5.88384159e-02 3.19426417e-01 -4.77470234e-02 -6.24041736... | [11.128344535827637, 8.372991561889648] |
4d00265a-2dd4-4727-ab3b-63dcdfed5917 | cist-cl-scisumm-2020-longsumm-2020-automatic | null | null | https://aclanthology.org/2020.sdp-1.25 | https://aclanthology.org/2020.sdp-1.25.pdf | CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization | Our system participates in two shared tasks, CL-SciSumm 2020 and LongSumm 2020. In the CL-SciSumm shared task, based on our previous work, we apply more machine learning methods on position features and content features for facet classification in Task1B. And GCN is introduced in Task2 to perform extractive summarizati... | ['Xingyuan Li', 'Siya Qi', 'Yafei Jiang', 'Yinan Liu', 'Wei Liu', 'Yang Xie', 'Lei LI'] | null | null | null | null | emnlp-sdp-2020-11 | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 3.71229947e-01 5.49021661e-02 -4.74084646e-01 -4.20124270e-03
-1.54507983e+00 -6.98250115e-01 9.68909562e-01 3.71747792e-01
-6.60747409e-01 1.54817080e+00 1.12922537e+00 -1.41151488e-01
-4.35842425e-01 -4.60453510e-01 -4.11190897e-01 -4.48813260e-01
7.11646751e-02 4.38272834e-01 3.52326453e-01 -1.59141183... | [12.464003562927246, 9.533185005187988] |
79ff4e77-90c1-4a3b-8a70-594223a88dcf | leveraging-auxiliary-text-for-deep-1 | 1910.12324 | null | https://arxiv.org/abs/1910.12324v1 | https://arxiv.org/pdf/1910.12324v1.pdf | Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships | One of the most difficult tasks in scene understanding is recognizing interactions between objects in an image. This task is often called visual relationship detection (VRD). We consider the question of whether, given auxiliary textual data in addition to the standard visual data used for training VRD models, VRD perfo... | ['Ran El-Yaniv', 'Gal Sadeh Kenigsfield'] | 2019-10-27 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.37596822e-01 1.21626124e-01 1.21116765e-01 -4.06966239e-01
-3.05356205e-01 -5.96329153e-01 1.26150882e+00 2.22489968e-01
-1.81583375e-01 1.64783522e-01 2.70986468e-01 -3.46136719e-01
-8.37242156e-02 -5.91785610e-01 -1.04650426e+00 -1.87123924e-01
2.21884102e-01 8.87660325e-01 4.50279981e-01 -4.46975499... | [10.430617332458496, 1.659075379371643] |
e8e8c3a1-3a4d-44cf-8e77-fac1c1c43fb6 | deep-image-harmonization-by-bridging-the | 2103.17104 | null | https://arxiv.org/abs/2103.17104v3 | https://arxiv.org/pdf/2103.17104v3.pdf | Deep Image Harmonization by Bridging the Reality Gap | Image harmonization has been significantly advanced with large-scale harmonization dataset. However, the current way to build dataset is still labor-intensive, which adversely affects the extendability of dataset. To address this problem, we propose to construct rendered harmonization dataset with fewer human efforts t... | ['Junyan Cao', 'Liqing Zhang', 'Jianfu Zhang', 'Li Niu', 'Wenyan Cong'] | 2021-03-31 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.61614025e-02 -2.74154305e-01 -9.06386748e-02 -2.30614617e-01
-7.68887997e-01 -4.18674409e-01 2.19176427e-01 -2.94270009e-01
-3.24269116e-01 8.02662075e-01 6.79129288e-02 2.68456519e-01
-3.54610048e-02 -9.47617948e-01 -7.08273232e-01 -4.48361844e-01
5.97762108e-01 1.58958033e-01 1.13147192e-01 -5.92236161... | [11.241673469543457, -1.1667207479476929] |
ea5947b0-ff19-40ea-82c6-38813717188a | test-time-adaptation-with-calibration-of | 2207.00769 | null | https://arxiv.org/abs/2207.00769v2 | https://arxiv.org/pdf/2207.00769v2.pdf | Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift | Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary o... | ['Qi Dou', 'Huimao Zhang', 'Jing Qin', 'Shuang Zheng', 'Cheng Chen', 'Wenao Ma'] | 2022-07-02 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 3.26095730e-01 -3.70901465e-01 -4.02893424e-01 -8.23742449e-01
-6.48445725e-01 -6.51189268e-01 1.30222946e-01 3.02602321e-01
-3.55695963e-01 9.01038587e-01 -2.77128100e-01 -1.25063866e-01
-3.52549344e-01 -6.31331146e-01 -3.89257580e-01 -1.12968254e+00
1.66838601e-01 1.13995254e+00 1.43976286e-01 3.76373589... | [14.889549255371094, -2.29597544670105] |
822d0b0d-ca48-404b-9616-71e62d6ba03c | brain-diffusion-for-visual-exploration | 2306.03089 | null | https://arxiv.org/abs/2306.03089v1 | https://arxiv.org/pdf/2306.03089v1.pdf | Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models | A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coarse, focusing on regions of interest (ROIs) and taking the form of selectivity for broad categories such as faces, places, bodies, food, or wo... | ['Michael J. Tarr', 'Leila Wehbe', 'Margaret M. Henderson', 'Andrew F. Luo'] | 2023-06-05 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 4.84944463e-01 8.27424005e-02 5.58868311e-02 -4.85777795e-01
-2.56831884e-01 -7.78518438e-01 1.02924967e+00 -6.21522591e-03
-5.49864352e-01 5.60875177e-01 5.30501366e-01 -1.43329367e-01
-3.46683502e-01 -5.60564578e-01 -6.01729929e-01 -6.35755897e-01
1.73645020e-02 3.76259625e-01 1.98165476e-01 6.13999739... | [10.503198623657227, 2.4492762088775635] |
9f476856-5fd6-47eb-8f2e-273b5d560467 | scaling-cross-domain-content-based-image | 2204.11593 | null | https://arxiv.org/abs/2204.11593v1 | https://arxiv.org/pdf/2204.11593v1.pdf | Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application | In this industry talk at ECIR 2022, we illustrate how we approach the main challenges from large scale cross-domain content-based image retrieval using a cascade method and a combination of our visual search and classification capabilities. Specifically, we present a system that is able to handle the scale of the data ... | ['Eran Nussinovitch', 'Minh Tran', 'Isaac Kwan Yin Chung'] | 2022-04-13 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-3.99872780e-01 -6.58450782e-01 -2.03225147e-02 -1.98015898e-01
-1.30842721e+00 -1.40439749e+00 9.18641090e-01 2.05507293e-01
-5.04640877e-01 1.27675667e-01 1.67203158e-01 -2.62764394e-01
-4.74547565e-01 -3.89167219e-01 -2.12745726e-01 -4.97277714e-02
-8.67363811e-02 7.70758390e-01 6.53668821e-01 -7.26831377... | [10.866503715515137, 1.0210316181182861] |
119d1546-8b8c-4857-87f1-2b63f43a9664 | a-survey-of-contextual-optimization-methods | 2306.10374 | null | https://arxiv.org/abs/2306.10374v1 | https://arxiv.org/pdf/2306.10374v1.pdf | A Survey of Contextual Optimization Methods for Decision Making under Uncertainty | Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty. This gave rise to the field of contextual optimization, under which data-driven pro... | ['Thibaut Vidal', 'Emma Frejinger', 'Alexandre Forel', 'Erick Delage', 'Abhilash Chenreddy', 'Utsav Sadana'] | 2023-06-17 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.12808624e-01 -2.18257122e-02 -8.56841385e-01 -6.71629429e-01
-7.13580728e-01 -2.35482872e-01 3.80797207e-01 3.68917108e-01
-4.21237618e-01 9.73340392e-01 4.47508305e-01 -6.92613065e-01
-9.86970723e-01 -4.93437320e-01 -3.14944476e-01 -7.44890451e-01
7.72449225e-02 8.14335108e-01 -5.25815606e-01 1.79224506... | [4.521487712860107, 3.1688876152038574] |
d8fd2ac8-ed0f-4ddb-8f4f-69de6754ab05 | a-multi-label-multi-hop-relation-detection | null | null | https://aclanthology.org/2021.findings-emnlp.404 | https://aclanthology.org/2021.findings-emnlp.404.pdf | A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation | Multi-hop relation detection in Knowledge Base Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may comprise multiple relations. Most of the existing methods treat it as a single-label learning problem whi... | ['Yulan He', 'Chao Lin', 'Deyu Zhou', 'Linhai Zhang'] | null | null | null | null | findings-emnlp-2021-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 3.60082954e-01 4.14615393e-01 -2.68972278e-01 -1.64899021e-01
-1.12907982e+00 -7.43657947e-01 3.51431549e-01 5.93387306e-01
-1.11551419e-01 1.01084530e+00 -7.00948909e-02 -5.06878316e-01
-4.51883197e-01 -1.02378893e+00 -6.11794829e-01 -3.00464988e-01
1.11201331e-01 9.36769783e-01 7.72543013e-01 -3.76940519... | [9.503011703491211, 8.462082862854004] |
d143364e-e400-4dc2-b588-1653caa32672 | adversarial-clean-label-backdoor-attacks-and | 2305.19607 | null | https://arxiv.org/abs/2305.19607v1 | https://arxiv.org/pdf/2305.19607v1.pdf | Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems | Clean-label (CL) attack is a form of data poisoning attack where an adversary modifies only the textual input of the training data, without requiring access to the labeling function. CL attacks are relatively unexplored in NLP, as compared to label flipping (LF) attacks, where the latter additionally requires access to... | ['Amrith Krishna', 'Ashim Gupta'] | 2023-05-31 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.94007337e-01 2.08297864e-01 -8.79059487e-04 -1.95747688e-01
-1.11786973e+00 -1.74504328e+00 8.32069516e-01 6.84687614e-01
-6.29659772e-01 7.27231264e-01 -1.97598279e-01 -6.56269014e-01
1.55119047e-01 -7.80148268e-01 -7.72683203e-01 -9.76586282e-01
4.47605252e-02 4.78086710e-01 1.09506406e-01 -1.33442700... | [5.823422431945801, 7.702909469604492] |
06eaa8a4-8fb4-4c34-8781-349fc60cfdc7 | scene-domain-active-part-models-for-object | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Ren_Scene-Domain_Active_Part_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Ren_Scene-Domain_Active_Part_ICCV_2015_paper.pdf | Scene-Domain Active Part Models for Object Representation | In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images. To this end, we propose scene-domain active part models (SDAPM), which reconstruct and characterize the 3D geometric statis... | ['Chaohui Wang', 'Zhou Ren', 'Alan L. Yuille'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['viewpoint-estimation'] | ['computer-vision'] | [ 9.57089514e-02 6.84327334e-02 -2.30491444e-01 -3.24114680e-01
-6.10615313e-01 -4.19604719e-01 7.61609256e-01 4.16689515e-02
1.41282097e-01 2.03847930e-01 -4.75689657e-02 2.26319566e-01
-3.32090497e-01 -5.30840695e-01 -8.54265094e-01 -7.52342403e-01
3.91929448e-02 9.52510178e-01 4.61172968e-01 2.76277840... | [7.6238861083984375, -2.7591769695281982] |
6da9c1c5-f4bf-4e62-a131-417b4a9981bf | utilization-of-domain-knowledge-to-improve | 2302.08748 | null | https://arxiv.org/abs/2302.08748v1 | https://arxiv.org/pdf/2302.08748v1.pdf | Utilization of domain knowledge to improve POMDP belief estimation | The partially observable Markov decision process (POMDP) framework is a common approach for decision making under uncertainty. Recently, multiple studies have shown that by integrating relevant domain knowledge into POMDP belief estimation, we can improve the learned policy's performance. In this study, we propose a no... | ['Johane Takeuchi', 'Tung Nguyen'] | 2023-02-17 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-2.15743527e-01 2.69610852e-01 -6.02686942e-01 -5.22323966e-01
-1.02662241e+00 -5.01095474e-01 6.53378189e-01 1.94401965e-01
-7.73202360e-01 1.38804638e+00 6.58795059e-01 -2.73354888e-01
-1.91832021e-01 -8.82882357e-01 -3.37665915e-01 -7.22094119e-01
8.46510753e-03 6.27570331e-01 6.28405690e-01 -8.04735720... | [4.264586925506592, 2.1645302772521973] |
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