paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
87d26b6a-c49c-4121-9168-e4654f9754f0 | syntactically-informed-text-compression-with | 1608.02893 | null | http://arxiv.org/abs/1608.02893v2 | http://arxiv.org/pdf/1608.02893v2.pdf | Syntactically Informed Text Compression with Recurrent Neural Networks | We present a self-contained system for constructing natural language models
for use in text compression. Our system improves upon previous neural network
based models by utilizing recent advances in syntactic parsing -- Google's
SyntaxNet -- to augment character-level recurrent neural networks. RNNs have
proven excepti... | ['David Cox'] | 2016-08-08 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 3.21956217e-01 2.58161366e-01 -5.85965216e-01 -5.68861246e-01
-7.08648324e-01 6.96700960e-02 9.58698764e-02 2.78502464e-01
-8.15774143e-01 6.55499160e-01 1.05632329e+00 -1.03085935e+00
4.33012336e-01 -8.43173504e-01 -6.36289597e-01 6.13364689e-02
-2.49483734e-01 3.99197340e-01 -6.03051670e-02 -4.72487688... | [10.519225120544434, 8.925326347351074] |
5be8385e-ae70-47e2-8dee-6bc15a3071f8 | local-exceptionality-detection-in-time-series | 2108.11751 | null | https://arxiv.org/abs/2108.11751v1 | https://arxiv.org/pdf/2108.11751v1.pdf | Local Exceptionality Detection in Time Series Using Subgroup Discovery | In this paper, we present a novel approach for local exceptionality detection on time series data. This method provides the ability to discover interpretable patterns in the data, which can be used to understand and predict the progression of a time series. This being an exploratory approach, the results can be used to... | ['Martin Atzmueller', 'Travis J. Wiltshire', 'Dan Hudson'] | 2021-08-05 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.85890853e-01 7.47618675e-02 2.78184023e-02 -2.24188060e-01
3.32614750e-01 -4.90498006e-01 7.47849047e-01 8.06441128e-01
-1.94169283e-02 4.64277208e-01 4.94685799e-01 -4.91948009e-01
-1.08378947e+00 -6.28406763e-01 -1.67522088e-01 -3.87910038e-01
-6.21505201e-01 3.04882973e-01 -5.11261262e-02 -4.01605099... | [7.2515106201171875, 3.380784511566162] |
04d1cac0-fb8a-4a7d-b6b4-5037f886d387 | geometrically-adaptive-dictionary-attack-on | 2111.04371 | null | https://arxiv.org/abs/2111.04371v1 | https://arxiv.org/pdf/2111.04371v1.pdf | Geometrically Adaptive Dictionary Attack on Face Recognition | CNN-based face recognition models have brought remarkable performance improvement, but they are vulnerable to adversarial perturbations. Recent studies have shown that adversaries can fool the models even if they can only access the models' hard-label output. However, since many queries are needed to find imperceptible... | ['Changick Kim', 'Hyojun Go', 'Junyoung Byun'] | 2021-11-08 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 4.45367277e-01 -2.55932540e-01 1.88759461e-01 -1.28601402e-01
-9.26656961e-01 -1.06534314e+00 3.54513466e-01 -6.26173675e-01
-3.12052727e-01 2.31659248e-01 -1.59578741e-01 -4.52972561e-01
8.43374953e-02 -9.80471790e-01 -8.81721199e-01 -8.25425208e-01
1.55019224e-01 2.16087252e-01 2.94025332e-01 -5.22513032... | [5.588708877563477, 7.900088310241699] |
d4d82bbc-13d9-41cd-afd0-eaa28c4313c1 | trojanpuzzle-covertly-poisoning-code | 2301.02344 | null | https://arxiv.org/abs/2301.02344v1 | https://arxiv.org/pdf/2301.02344v1.pdf | TrojanPuzzle: Covertly Poisoning Code-Suggestion Models | With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massive corpora of code mined from unvetted public sources. As a result, these models are susceptible to data poisoning attacks where an adversary... | ['Robert Sim', 'Ben Zorn', 'David Evans', 'Giovanni Vigna', 'Christopher Kruegel', 'Anant Kharkar', 'Xavier Fernandes', 'Andre Manoel', 'Wei Dai', 'Hojjat Aghakhani'] | 2023-01-06 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.20708898e-01 -1.18331112e-01 -4.99048322e-01 -2.34198086e-02
-9.02066946e-01 -1.41622877e+00 3.57608169e-01 4.11480427e-01
1.05122263e-02 1.57294810e-01 6.89213127e-02 -1.16598618e+00
2.75803506e-01 -7.71042943e-01 -8.82814109e-01 -4.38581645e-01
-2.28357121e-01 -4.71348409e-04 5.77922344e-01 -2.59144306... | [6.186281204223633, 7.844913482666016] |
ed6f43cf-452b-42ba-85b4-befc973cba5b | shared-task-on-feedback-comment-generation | null | null | https://aclanthology.org/2021.inlg-1.35 | https://aclanthology.org/2021.inlg-1.35.pdf | Shared Task on Feedback Comment Generation for Language Learners | In this paper, we propose a generation challenge called Feedback comment generation for language learners. It is a task where given a text and a span, a system generates, for the span, an explanatory note that helps the writer (language learner) improve their writing skills. The motivations for this challenge are: (i) ... | ['Olena Nahorna', 'Artem Chernodub', 'Masato Mita', 'Kazuaki Hanawa', 'Masato Hagiwara', 'Ryo Nagata'] | null | null | null | null | inlg-acl-2021-8 | ['comment-generation'] | ['natural-language-processing'] | [ 1.67348966e-01 6.37175918e-01 -1.94251508e-01 -8.15032274e-02
-7.41140604e-01 -6.81625962e-01 9.32887673e-01 4.31474805e-01
-2.23034352e-01 8.57273817e-01 6.58895373e-01 -7.72242963e-01
2.66346514e-01 -6.90160096e-01 -3.87686044e-01 -2.55150229e-01
3.84844303e-01 3.96781832e-01 2.16988876e-01 -6.24837220... | [11.744851112365723, 9.01779556274414] |
a0f7d558-dbd8-4426-9b00-377600eaa56e | motchallenge-a-benchmark-for-single-camera | 2010.07548 | null | https://arxiv.org/abs/2010.07548v2 | https://arxiv.org/pdf/2010.07548v2.pdf | MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking | Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOT... | ['Laura Leal-Taixé', 'Stefan Roth', 'Ian Reid', 'Daniel Cremers', 'Konrad Schindler', 'Anton Milan', 'Aljoša Ošep', 'Patrick Dendorfer'] | 2020-10-15 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-1.91067576e-01 -4.78866279e-01 -2.73110151e-01 -5.63086383e-02
-6.31878376e-01 -6.20588303e-01 7.35568106e-01 1.16814904e-01
-7.74821699e-01 7.04576552e-01 -1.82559401e-01 2.59634815e-02
1.85388729e-01 -2.93023288e-01 -8.29702258e-01 -8.06841612e-01
-3.75637531e-01 6.63996220e-01 8.63502622e-01 -8.95365998... | [6.38870906829834, -2.01430344581604] |
49520261-b9e4-4f19-a5f4-11037eaf1dd8 | causal-kl-evaluating-causal-discovery | 2111.06029 | null | https://arxiv.org/abs/2111.06029v1 | https://arxiv.org/pdf/2111.06029v1.pdf | Causal KL: Evaluating Causal Discovery | The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in... | ['Lloyd Allison', 'Kevin B. Korb', "Rodney T. O'Donnell"] | 2021-11-11 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.94073975e-01 1.97592989e-01 -3.91287684e-01 -5.61949253e-01
-6.57728076e-01 -7.74209797e-01 1.06300485e+00 6.68541610e-01
-3.82948190e-01 1.04024577e+00 1.98390186e-01 -5.15564680e-01
-9.02555943e-01 -6.49149001e-01 -6.50879025e-01 -4.97959554e-01
-4.58943546e-01 4.43449676e-01 1.94225237e-01 4.08266306... | [8.013571739196777, 5.390882968902588] |
fd31b67f-acc9-4cce-8b82-682a63ded1cc | biored-a-comprehensive-biomedical-relation | 2204.04263 | null | https://arxiv.org/abs/2204.04263v2 | https://arxiv.org/pdf/2204.04263v2.pdf | BioRED: A Rich Biomedical Relation Extraction Dataset | Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e.g., protein-protein interactions) at the sentence l... | ['Zhiyong Lu', 'Cecilia N Arighi', 'Chih-Hsuan Wei', 'Po-Ting Lai', 'Ling Luo'] | 2022-04-08 | null | null | null | null | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 1.71716303e-01 1.79215074e-01 -2.62967855e-01 -5.31259067e-02
-1.00448287e+00 -4.77622598e-01 3.90230447e-01 8.71318340e-01
-4.56642300e-01 1.23933637e+00 3.48484606e-01 -5.97357869e-01
-2.00555071e-01 -7.01279044e-01 -5.14287770e-01 -6.61823153e-01
5.84917106e-02 4.82138157e-01 -6.77448437e-02 -8.10878426... | [8.453547477722168, 8.819746017456055] |
45f6776c-0218-4c96-9ded-11421a7e960f | computer-aided-tuberculosis-diagnosis-with | 2207.00251 | null | https://arxiv.org/abs/2207.00251v1 | https://arxiv.org/pdf/2207.00251v1.pdf | Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance | Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption. Weakly supervised learning (WSL), which leverages coarse-grained labels to accomplish fine-grained tasks, ... | ['Yizhou Yu', 'Jinpeng Li', 'Dingwen Zhang', 'Chaowei Fang', 'Jiaheng Liu', 'Baolian Qi', 'Junjie Fang', 'Gangming Zhao', 'Chengwei Pan'] | 2022-07-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 0.33058122 0.0322638 -0.75918823 -0.5693452 -1.4069613 -0.10310203
0.5737395 0.20679988 -0.14874996 0.7926565 0.01448628 -0.6064009
-0.36972657 -0.9117584 -0.52214 -1.0548717 0.20396906 0.928641
0.108872 0.31127572 -0.34229955 0.42478576 -0.94452804 0.6963606
0.61647046 1.2562054 0.4374... | [15.177621841430664, -2.0834617614746094] |
44cc0209-06aa-440b-95c4-6afb59bab043 | classification-of-categorical-time-series | 2102.02794 | null | https://arxiv.org/abs/2102.02794v1 | https://arxiv.org/pdf/2102.02794v1.pdf | Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings | This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the spectral envelope and its corresponding set of optimal scalings. These q... | ['Tian Cai', 'Scott A. Bruce', 'Zeda Li'] | 2021-02-04 | null | null | null | null | ['image-classification-shift-consistency'] | ['computer-vision'] | [ 2.37782046e-01 -4.27792877e-01 -4.84619141e-01 -4.76252347e-01
-2.75927573e-01 -6.15577698e-01 1.62009895e-01 4.70047176e-01
-5.35849072e-02 8.15069973e-01 1.51163056e-01 -2.25620180e-01
-8.46602559e-01 -3.75007540e-01 3.35827529e-01 -9.84975636e-01
-9.88840520e-01 -5.89042753e-02 -3.28670472e-01 -7.56814703... | [7.272459506988525, 3.364410638809204] |
fa78b614-34fc-418d-95fd-9cdc40edb38b | pico-primitive-imitation-for-control | 2006.12551 | null | https://arxiv.org/abs/2006.12551v1 | https://arxiv.org/pdf/2006.12551v1.pdf | PICO: Primitive Imitation for COntrol | In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO. The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors. Demonstrations are automatically decomposed into e... | ['Edward W. Staley', 'Kapil D. Katyal', 'Bart L. Paulhamus', 'Chace Ashcraft', 'Katie M. Popek', 'Corban G. Rivera'] | 2020-06-22 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-1.01113394e-02 1.35799110e-01 8.18133913e-03 1.18261404e-01
-1.42809212e-01 -8.95529449e-01 8.43323469e-01 -1.38290077e-01
-3.27701092e-01 1.15336585e+00 -1.92481637e-01 -3.16485241e-02
-1.17208101e-01 -4.60514612e-02 -6.41404629e-01 -5.64770937e-01
-4.02095258e-01 5.52098870e-01 7.27413654e-01 -3.15597355... | [4.501247406005859, 0.9909875392913818] |
dc2adbf5-fd97-406d-bee0-4d4d3c98b725 | chain-of-skills-a-configurable-model-for-open | 2305.03130 | null | https://arxiv.org/abs/2305.03130v2 | https://arxiv.org/pdf/2305.03130v2.pdf | Chain-of-Skills: A Configurable Model for Open-domain Question Answering | The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we prop... | ['Jianfeng Gao', 'Eric Nyberg', 'Xiaodong Liu', 'Yu Zhang', 'Hao Cheng', 'Kaixin Ma'] | 2023-05-04 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-1.31443173e-01 -5.86693101e-02 -1.13792801e-02 -2.12166533e-01
-1.30825043e+00 -8.99851084e-01 4.47212905e-01 2.70902842e-01
-6.99652433e-01 5.71534991e-01 3.61845225e-01 9.67254713e-02
-5.16903698e-01 -6.32801831e-01 -6.81220651e-01 -1.53824449e-01
2.44493499e-01 1.02380872e+00 8.69780838e-01 -8.96039486... | [11.399322509765625, 7.7950825691223145] |
8d211213-4ea6-429a-882a-9520b8c27610 | what-and-when-to-look-temporal-span-proposal | 2107.07154 | null | https://arxiv.org/abs/2107.07154v2 | https://arxiv.org/pdf/2107.07154v2.pdf | What and When to Look?: Temporal Span Proposal Network for Video Relation Detection | Identifying relations between objects is central to understanding the scene. While several works have been proposed for relation modeling in the image domain, there have been many constraints in the video domain due to challenging dynamics of spatio-temporal interactions (e.g., between which objects are there an intera... | ['Kangil Kim', 'Junhyug Noh', 'Sangmin Woo'] | 2021-07-15 | null | null | null | null | ['video-visual-relation-detection'] | ['computer-vision'] | [ 2.41565574e-02 -2.17892990e-01 -5.88305593e-01 -1.93158492e-01
-2.17042089e-01 -4.61514145e-01 6.20281518e-01 1.52291328e-01
-3.06398302e-01 4.47385758e-01 1.68184295e-01 -4.71997589e-01
-4.71940726e-01 -6.76690519e-01 -6.38820589e-01 -4.33316946e-01
-5.03706396e-01 3.66139561e-01 6.98978364e-01 -1.51554376... | [9.316313743591309, 0.7595458626747131] |
44813055-8958-4a78-887a-27e3fa09dca1 | roi-tanh-polar-transformer-network-for-face | 2102.02717 | null | https://arxiv.org/abs/2102.02717v3 | https://arxiv.org/pdf/2102.02717v3.pdf | RoI Tanh-polar Transformer Network for Face Parsing in the Wild | Face parsing aims to predict pixel-wise labels for facial components of a target face in an image. Existing approaches usually crop the target face from the input image with respect to a bounding box calculated during pre-processing, and thus can only parse inner facial Regions of Interest~(RoIs). Peripheral regions li... | ['Maja Pantic', 'Yujiang Wang', 'Jie Shen', 'Yiming Lin'] | 2021-02-04 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.26824397e-01 5.17767012e-01 -7.75781870e-02 -9.24602687e-01
-5.23753941e-01 -6.14126146e-01 1.89038575e-01 -8.11724603e-01
-3.68905097e-01 3.37305814e-01 -2.70664394e-01 4.99845706e-02
3.32954139e-01 -7.36235797e-01 -9.59921479e-01 -6.69192731e-01
5.66075705e-02 3.38468283e-01 5.35911173e-02 -1.29881263... | [13.451590538024902, 0.5809422135353088] |
58379462-e2e3-40a3-911e-0cc00d1ca145 | recycling-an-anechoic-pre-trained-speech | 2208.04626 | null | https://arxiv.org/abs/2208.04626v1 | https://arxiv.org/pdf/2208.04626v1.pdf | Recycling an anechoic pre-trained speech separation deep neural network for binaural dereverberation of a single source | Reverberation results in reduced intelligibility for both normal and hearing-impaired listeners. This paper presents a novel psychoacoustic approach of dereverberation of a single speech source by recycling a pre-trained binaural anechoic speech separation neural network. As training the deep neural network (DNN) is a ... | ['Ata Ur-Rehman', 'Syed Waqar Shah', 'Muhammad Salman Khan', 'Sania Gul'] | 2022-08-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.71600902e-01 -2.93048322e-01 1.12884951e+00 -2.62147933e-02
-9.26151037e-01 -3.66563171e-01 -5.13077788e-02 -5.15613370e-02
-5.01660109e-01 7.02361941e-01 4.39814508e-01 -4.69114035e-01
-1.67050168e-01 -3.34525913e-01 -7.17746913e-01 -1.01358843e+00
-1.10336527e-01 -3.66858155e-01 2.08969600e-02 -4.15629357... | [15.07512092590332, 5.832651138305664] |
96f8c01f-1e7e-4df8-aa68-7618bad480a1 | backdoor-attacks-with-input-unique-triggers | 2303.14325 | null | https://arxiv.org/abs/2303.14325v1 | https://arxiv.org/pdf/2303.14325v1.pdf | Backdoor Attacks with Input-unique Triggers in NLP | Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems. Existing backdoor attacking systems face two severe issues:firstly, most backdo... | ['Jun He', 'Muqiao Yang', 'Lingjuan Lyu', 'Tianwei Zhang', 'Jiwei Li', 'Xukun Zhou'] | 2023-03-25 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 2.23267660e-01 -2.14229345e-01 -8.50515664e-02 -1.05991758e-01
-5.21516621e-01 -1.34401572e+00 6.51765049e-01 3.26252162e-01
-3.23303759e-01 4.27228749e-01 -1.71961430e-02 -6.88544214e-01
3.64155531e-01 -1.00234640e+00 -7.72313476e-01 -5.22712708e-01
2.06238657e-01 1.55481920e-01 4.18648481e-01 -5.01290619... | [6.0407609939575195, 7.98039436340332] |
83540eb5-0f76-47ec-a6d4-37bd98b3db87 | a-bayesian-encourages-dropout | 1412.7003 | null | http://arxiv.org/abs/1412.7003v3 | http://arxiv.org/pdf/1412.7003v3.pdf | A Bayesian encourages dropout | Dropout is one of the key techniques to prevent the learning from
overfitting. It is explained that dropout works as a kind of modified L2
regularization. Here, we shed light on the dropout from Bayesian standpoint.
Bayesian interpretation enables us to optimize the dropout rate, which is
beneficial for learning of wei... | ['Shin-ichi Maeda'] | 2014-12-22 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-3.25777531e-01 2.51626074e-01 -6.51764691e-01 -5.19559979e-01
-2.75212914e-01 2.49247681e-02 2.63580028e-02 -1.63565084e-01
-6.51116312e-01 9.43217337e-01 2.46076822e-01 -2.02466682e-01
-5.60803004e-02 -5.37304223e-01 -7.47099876e-01 -1.02767491e+00
4.31973279e-01 -1.63473636e-01 2.90113002e-01 1.78334430... | [8.882065773010254, 3.6489007472991943] |
cd3628f2-9b9a-4d99-9f2c-04d403578b0e | bayesian-hyperparameter-optimization-for-deep | 2207.09902 | null | https://arxiv.org/abs/2207.09902v1 | https://arxiv.org/pdf/2207.09902v1.pdf | Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection | Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tu... | ['Alfredo Cuzzocrea', 'Muhaiminul I. Adnan', 'Mohammad A. Rahman', 'Md Abdullah Khan', 'Maria Valero', 'Md Jobair Hossain Faruk', 'Hisham Haddad', 'Hossain Shahriar', 'Mohammad Masum'] | 2022-07-07 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.33639672e-01 -6.72933102e-01 -6.82826787e-02 -5.01004755e-01
-5.62369451e-03 -3.14112693e-01 4.16893721e-01 1.68674424e-01
-8.39601040e-01 7.35230982e-01 -4.05422777e-01 -5.39116561e-01
-6.82751119e-01 -9.95536983e-01 1.09919570e-01 -7.81710923e-01
-1.88287541e-01 8.40544939e-01 4.59386736e-01 -2.03878224... | [5.251033306121826, 7.18540620803833] |
b62af475-a5ef-48e9-a256-997bba2b6646 | faster-and-better-grammar-based-text-to-sql | 2204.12186 | null | https://arxiv.org/abs/2204.12186v1 | https://arxiv.org/pdf/2204.12186v1.pdf | Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss | Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries. Meanwhile, how to better align SQL clauses and question segments has been a key ch... | ['Xinyan Xiao', 'Zhenghua Li', 'Lijie Wang', 'Kun Wu'] | 2022-04-26 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-5.66361146e-03 -4.75750044e-02 -1.28935620e-01 -8.00078869e-01
-1.53929651e+00 -7.01924443e-01 -1.16946556e-01 3.75183761e-01
-2.70001739e-01 4.90585357e-01 1.50905937e-01 -6.77417338e-01
3.20695221e-01 -1.17156243e+00 -6.74465001e-01 4.85677645e-02
2.04875827e-01 6.53484702e-01 6.11072719e-01 -2.11257070... | [9.94326114654541, 7.834648132324219] |
6618d9d6-323a-468e-bb68-d7a95c36fd90 | rec4ad-a-free-lunch-to-mitigate-sample | 2306.03527 | null | https://arxiv.org/abs/2306.03527v1 | https://arxiv.org/pdf/2306.03527v1.pdf | Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao | Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thu... | ['Bo Zheng', 'Jian Xu', 'Yuning Jiang', 'Siran Yang', 'Han Zhu', 'Shuguang Han', 'Jingyue Gao'] | 2023-06-06 | null | null | null | null | ['click-through-rate-prediction', 'selection-bias'] | ['miscellaneous', 'natural-language-processing'] | [ 9.46385562e-02 7.41545111e-02 -6.79149449e-01 -7.56228507e-01
-7.47494161e-01 -4.70245481e-01 6.16920114e-01 -2.16314822e-01
-6.44763112e-02 3.35778624e-01 3.20070952e-01 -6.57595694e-01
-2.03045413e-01 -8.87697875e-01 -7.72941768e-01 -4.51298326e-01
1.71222463e-02 3.94554496e-01 -1.86482698e-01 -6.01031303... | [9.877665519714355, 5.534348964691162] |
d134d447-084c-4718-9a4b-b2d19b4452aa | provably-sample-efficient-rl-with-side | 2205.14237 | null | https://arxiv.org/abs/2205.14237v1 | https://arxiv.org/pdf/2205.14237v1.pdf | Provably Sample-Efficient RL with Side Information about Latent Dynamics | We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is tasked to go to a specific room in a building using observations from its own camera, whil... | ['Robert E. Schapire', 'Miro Dudík', 'Dipendra Misra', 'Yao Liu'] | 2022-05-27 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 2.05907047e-01 6.10315382e-01 -1.39453024e-01 2.38567188e-01
-7.00361073e-01 -7.72103190e-01 6.50206149e-01 1.69425800e-01
-6.40858233e-01 8.85736465e-01 1.76743522e-01 -4.28597957e-01
-3.09967296e-03 -8.22579682e-01 -1.34479463e+00 -9.28427100e-01
-4.76859599e-01 7.83996224e-01 5.32118487e-04 -3.92638028... | [4.237235069274902, 2.0245206356048584] |
5769fa1f-ee71-473f-b8ca-0a84d9f1b58d | the-emotions-that-we-perceive-in-music-the | 1909.05882 | null | https://arxiv.org/abs/1909.05882v2 | https://arxiv.org/pdf/1909.05882v2.pdf | The emotions that we perceive in music: the influence of language and lyrics comprehension on agreement | In the present study, we address the relationship between the emotions perceived in pop and rock music (mainly in Euro-American styles with English lyrics) and the language spoken by the listener. Our goal is to understand the influence of lyrics comprehension on the perception of emotions and use this information to i... | ['Estefanía Cano', 'Emilia Gómez', 'Perfecto Herrera', 'Juan Sebastián Gómez Cañón'] | 2019-09-12 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-2.54668087e-01 -2.47647032e-01 -2.58674294e-01 -2.94602960e-01
-4.74443167e-01 -6.73232377e-01 -7.36891329e-02 2.45048106e-01
-6.31032944e-01 -1.79971501e-01 9.18187439e-01 2.43531987e-01
-3.34667772e-01 -3.76174241e-01 -2.81237401e-02 -3.57924312e-01
3.58392537e-01 5.47593683e-02 -4.20543313e-01 -2.47603849... | [15.720149040222168, 5.342926025390625] |
939c8707-73c6-450f-9ea1-60e4bfa7ee7b | site-assessment-and-layout-optimization-for | 2212.03516 | null | https://arxiv.org/abs/2212.03516v2 | https://arxiv.org/pdf/2212.03516v2.pdf | Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery | With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability.... | ['Olivier L. de Weck', 'Abdulelah H. Habib', 'Abdulaziz Alharbi', 'Zeyad Awwad'] | 2022-12-07 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 1.31686971e-01 -9.98777151e-02 1.49555624e-01 -2.60776550e-01
-5.45551956e-01 -1.18481219e+00 2.62291312e-01 3.75542462e-01
3.77152771e-01 1.06502008e+00 2.70413488e-01 -8.24624240e-01
-4.02275354e-01 -1.02985668e+00 -5.21721423e-01 -6.27241671e-01
2.83966344e-02 2.59959698e-01 -1.54634863e-01 -8.71502757... | [5.839636325836182, 3.3880679607391357] |
87053943-4417-49b4-8988-0ee2a841ba7d | personal-comfort-estimation-in-partial | 2112.00971 | null | https://arxiv.org/abs/2112.00971v4 | https://arxiv.org/pdf/2112.00971v4.pdf | Towards Personalization of User Preferences in Partially Observable Smart Home Environments | The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes peop... | ['Francois Rivest', 'Ali Etemad', 'Shashi Suman'] | 2021-12-02 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.35091701e-01 -1.00264266e-01 1.55796260e-02 -4.33347106e-01
-3.69920313e-01 -1.57662600e-01 6.91395551e-02 1.29368201e-01
-4.48588669e-01 9.68027234e-01 3.64462227e-01 7.54287317e-02
1.44471945e-02 -9.12170649e-01 -4.63470459e-01 -1.07192135e+00
-2.37688851e-02 4.72444654e-01 5.71741536e-02 7.23809451... | [5.794196128845215, 2.4516139030456543] |
2c54bc42-0017-4301-9d61-59b5b1c60ab9 | revisiting-modulated-convolutions-for-visual | 2004.11883 | null | https://arxiv.org/abs/2004.11883v3 | https://arxiv.org/pdf/2004.11883v3.pdf | MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond | This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category). Unlike most prior works that use explicit, symbolic models which can be computationally expensive and limited in generalization, we propose a simple and effective alt... | ['Xinlei Chen', 'Duy-Kien Nguyen', 'Vedanuj Goswami'] | 2020-04-24 | movie-revisiting-modulated-convolutions-for | https://openreview.net/forum?id=8e6BrwU6AjQ | https://openreview.net/pdf?id=8e6BrwU6AjQ | iclr-2021-1 | ['object-counting'] | ['computer-vision'] | [-4.97079268e-02 -1.72676116e-01 1.08453035e-01 -4.76470798e-01
-7.46649802e-01 -8.29729557e-01 9.20820177e-01 5.91463208e-01
-9.27736282e-01 4.59630430e-01 1.98431179e-01 -4.32903558e-01
1.59158528e-01 -1.02071381e+00 -9.64706242e-01 -1.16266273e-01
1.91303045e-01 6.95452988e-01 2.06594288e-01 -5.09639923... | [10.530765533447266, 1.683207392692566] |
06f87327-dcda-45a1-a32b-35e759154257 | discrete-predictor-corrector-diffusion-models | null | null | https://openreview.net/forum?id=VM8batVBWvg | https://openreview.net/pdf?id=VM8batVBWvg | Discrete Predictor-Corrector Diffusion Models for Image Synthesis | We introduce Discrete Predictor-Corrector diffusion models (DPC), extending predictor-corrector samplers in Gaussian diffusion models to the discrete case. Predictor-corrector samplers are a class of samplers for diffusion models, which improve on ancestral samplers by correcting the sampling distribution of intermedia... | ['Anonymous'] | 2022-09-29 | null | null | null | iclr-anonymous-submission-2022-9 | ['conditional-image-generation'] | ['computer-vision'] | [ 4.29439694e-01 3.38723421e-01 -2.75482684e-01 -2.40774542e-01
-8.91687870e-01 -5.17576873e-01 1.41742945e+00 -3.95618290e-01
-1.39063716e-01 6.37953758e-01 5.70587218e-01 -3.01016271e-01
2.60921299e-01 -8.82871032e-01 -9.39260006e-01 -9.27638233e-01
4.32008356e-01 9.75745320e-01 1.81796059e-01 1.87868580... | [11.358354568481445, -0.1622379720211029] |
133bdf85-f9e5-4b74-b66c-813aedaed3e0 | automatic-product-categorization-for-official | null | null | https://aclanthology.org/W19-3623 | https://aclanthology.org/W19-3623.pdf | Automatic Product Categorization for Official Statistics | The North American Product Classification System (NAPCS) is a comprehensive, hierarchical classification system for products (goods and services) that is consistent across the three North American countries. Beginning in 2017, the Economic Census will use NAPCS to produce economy-wide product tabulations. Respondents a... | ['Andrea Roberson'] | 2019-08-01 | null | null | null | ws-2019-8 | ['product-categorization'] | ['miscellaneous'] | [-2.05686435e-01 -2.28811860e-01 -9.65888739e-01 -4.60785300e-01
-6.51542306e-01 -1.03261364e+00 6.29412293e-01 6.48757160e-01
-1.99646071e-01 4.19646412e-01 4.56718981e-01 -1.10598552e+00
-2.01200083e-01 -9.50157046e-01 -1.21083103e-01 -2.14219600e-01
2.58736819e-01 6.23738766e-01 -2.97442347e-01 -1.12954095... | [9.626871109008789, 6.09825325012207] |
2a0f1074-b009-4105-98e6-919d1e251245 | putting-the-con-in-context-identifying-1 | 2207.02253 | null | https://arxiv.org/abs/2207.02253v1 | https://arxiv.org/pdf/2207.02253v1.pdf | Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia | While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker's conversational role is an open area of research. In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned e... | ['John DeNero', 'Gaoyue Zhou', 'Samee Ibraheem'] | 2022-07-05 | null | https://aclanthology.org/2022.naacl-main.11 | https://aclanthology.org/2022.naacl-main.11.pdf | naacl-2022-7 | ['deception-detection', 'dialogue-understanding'] | ['miscellaneous', 'natural-language-processing'] | [-1.09566011e-01 1.66917130e-01 -1.09139038e-02 -5.41383326e-01
-4.95083004e-01 -9.67143178e-01 9.60766852e-01 2.33869016e-01
-7.88355649e-01 3.46583128e-01 4.37793404e-01 -5.67939103e-01
7.58768097e-02 -7.27376282e-01 -4.91853431e-02 -3.15047652e-01
1.55330747e-01 5.40220559e-01 -5.85673861e-02 -6.67680621... | [8.473453521728516, 10.320534706115723] |
7a8a0bbd-6950-4067-99ed-9eb60df7046b | hippo-recurrent-memory-with-optimal | 2008.07669 | null | https://arxiv.org/abs/2008.07669v2 | https://arxiv.org/pdf/2008.07669v2.pdf | HiPPO: Recurrent Memory with Optimal Polynomial Projections | A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the online compression of continuous signals and discrete time series by projection onto polynomial bases. Given a measure that specifie... | ['Albert Gu', 'Christopher Re', 'Stefano Ermon', 'Atri Rudra', 'Tri Dao'] | 2020-08-17 | null | http://proceedings.neurips.cc/paper/2020/hash/102f0bb6efb3a6128a3c750dd16729be-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/102f0bb6efb3a6128a3c750dd16729be-Paper.pdf | neurips-2020-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 7.81859756e-02 -1.39255524e-01 -2.88649529e-01 -3.03119332e-01
-6.03884459e-01 -3.81354183e-01 6.41590536e-01 3.89169604e-02
-5.90993047e-01 7.30303943e-01 4.88133818e-01 -1.84292540e-01
-2.89095640e-01 -8.34661603e-01 -1.07883346e+00 -6.08418703e-01
-6.40597641e-01 2.72999257e-01 1.21859513e-01 -1.26173839... | [7.491255760192871, 3.400200605392456] |
e68643c1-1603-4e1b-8547-3e8e677cf620 | classical-shadows-with-noise | 2011.11580 | null | https://arxiv.org/abs/2011.11580v2 | https://arxiv.org/pdf/2011.11580v2.pdf | Classical Shadows With Noise | The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the protocol can be implemented on near-term quantum hardware and requires few quantum... | ['Sabee Grewal', 'Dax Enshan Koh'] | 2020-11-23 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 6.19513690e-01 1.30724296e-01 3.35468501e-01 -1.29922837e-01
-1.15585434e+00 -5.69582224e-01 3.13232630e-01 8.57131109e-02
-6.10017836e-01 9.98262405e-01 -2.39219770e-01 -5.99602759e-01
-2.26829406e-02 -1.10915124e+00 -7.02708304e-01 -1.17453361e+00
-3.99903618e-02 3.19828302e-01 1.26793057e-01 -3.04115534... | [5.637444019317627, 4.925233364105225] |
bd97bd54-9bd9-4902-812e-65844617a0d8 | run-procrustes-run-on-the-convergence-of | 1806.00534 | null | https://arxiv.org/abs/1806.00534v5 | https://arxiv.org/pdf/1806.00534v5.pdf | Provably convergent acceleration in factored gradient descent with applications in matrix sensing | We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss. The purpose of this work is to study the effects of acceleration in non-convex settings, where provable convergence with acceleration should not be considered a \em... | ['Tayo Ajayi', 'Kristofer Bouchard', 'David Mildebrath', 'Georgios Kollias', 'Shashanka Ubaru', 'Anastasios Kyrillidis'] | 2018-06-01 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.28679979e-01 7.48266652e-02 1.14584565e-01 -1.10357910e-01
-7.85332382e-01 -3.87977034e-01 -1.01036116e-01 -1.01871036e-01
-1.01658893e+00 1.00117743e+00 -2.20200405e-01 -5.79911888e-01
-4.85369742e-01 -4.95679498e-01 -8.50654781e-01 -1.14992547e+00
-5.76747775e-01 1.36757806e-01 -5.42610884e-01 -5.10790527... | [6.526159286499023, 4.639347553253174] |
2a37af5b-2cf0-42de-80d2-31701a7292a8 | estimating-the-pose-of-a-euro-pallet-with-an | 2210.06001 | null | https://arxiv.org/abs/2210.06001v1 | https://arxiv.org/pdf/2210.06001v1.pdf | Estimating the Pose of a Euro Pallet with an RGB Camera based on Synthetic Training Data | Estimating the pose of a pallet and other logistics objects is crucial for various use cases, such as automatized material handling or tracking. Innovations in computer vision, computing power, and machine learning open up new opportunities for device-free localization based on cameras and neural networks. Large image ... | ['Jochen Kreutzfeldt', 'Johannes Hinckeldeyn', 'Asan Adamanov', 'Jakob Schyga', 'Markus Knitt'] | 2022-10-12 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 2.98088174e-02 -3.35735306e-02 2.93946952e-01 -4.54221338e-01
-5.14188826e-01 -7.53854811e-01 3.56900573e-01 2.98828334e-01
-7.16612577e-01 4.39390451e-01 -4.45841432e-01 1.63561553e-01
-1.51613608e-01 -5.23788333e-01 -9.82889116e-01 -7.49613583e-01
-6.23427667e-02 8.35377634e-01 -5.25094829e-02 -1.49138525... | [7.668614387512207, -1.988966941833496] |
52c19bdd-7299-4a16-92b8-b3928c776155 | cnn-rnn-a-unified-framework-for-multi-label | 1604.04573 | null | http://arxiv.org/abs/1604.04573v1 | http://arxiv.org/pdf/1604.04573v1.pdf | CNN-RNN: A Unified Framework for Multi-label Image Classification | While deep convolutional neural networks (CNNs) have shown a great success in
single-label image classification, it is important to note that real world
images generally contain multiple labels, which could correspond to different
objects, scenes, actions and attributes in an image. Traditional approaches to
multi-labe... | ['Wei Xu', 'Chang Huang', 'Zhiheng Huang', 'Yi Yang', 'Junhua Mao', 'Jiang Wang'] | 2016-04-15 | cnn-rnn-a-unified-framework-for-multi-label-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.62499166e-01 -3.89730871e-01 -4.73236203e-01 -8.58892918e-01
-9.21596944e-01 -4.82862204e-01 3.48838598e-01 3.23166370e-01
-5.01889527e-01 3.60261261e-01 -1.20098358e-02 -2.11200975e-02
8.01465195e-03 -4.78236914e-01 -4.46811676e-01 -8.02069366e-01
5.10899067e-01 4.26272720e-01 1.69038773e-04 3.00006777... | [9.726533889770508, 4.111126899719238] |
16f43e34-0f0b-452e-9df8-6872112064ab | embedding-neurophysiological-signals | null | null | https://doi.org/10.1109/MetroXRAINE54828.2022.9967496 | https://hal.inria.fr/hal-03878615v1/file/MetroXRAINE_2022_GuePapTan.pdf | Embedding neurophysiological signals | Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e.g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the su... | ['Michael Tangermann', 'Théodore Papadopoulo', 'Pierre Guetschel'] | 2022-10-25 | null | null | null | ieee-international-conference-on-metrology | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.95590490e-01 3.82511392e-02 3.47624213e-01 -5.05717278e-01
-6.16871655e-01 -5.33204257e-01 5.05081892e-01 2.59124368e-01
-7.77556777e-01 9.10337746e-01 1.08579151e-01 -2.67008930e-01
-3.30315232e-01 -5.32216549e-01 -8.36296737e-01 -7.41594017e-01
-3.05092603e-01 5.92291176e-01 2.16832101e-01 -1.37485206... | [13.07866096496582, 3.458726406097412] |
1dd48dfd-4dfd-4c42-9bd8-e5e2d31fe29e | visual-commonsense-graphs-reasoning-about-the | 2004.10796 | null | https://arxiv.org/abs/2004.10796v3 | https://arxiv.org/pdf/2004.10796v3.pdf | VisualCOMET: Reasoning about the Dynamic Context of a Still Image | Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man at the moment is to ... | ['Roozbeh Mottaghi', 'Yejin Choi', 'Jae Sung Park', 'Chandra Bhagavatula', 'Ali Farhadi'] | 2020-04-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3684_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500494.pdf | eccv-2020-8 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 3.34405184e-01 2.61870772e-01 -3.33393253e-02 -3.23592365e-01
-2.65886188e-01 -8.07773709e-01 9.77673471e-01 5.75259745e-01
-3.60794902e-01 6.48610890e-01 9.24446762e-01 -2.96497792e-01
1.36275932e-01 -7.05118299e-01 -7.40566552e-01 -2.86368459e-01
2.80928344e-01 5.02487302e-01 2.30351865e-01 -3.97808522... | [10.667474746704102, 1.363749384880066] |
ca80df60-cc83-4dbb-a348-c667f83aa72f | image-stitching-and-rectification-for-hand | 2008.09229 | null | https://arxiv.org/abs/2008.09229v1 | https://arxiv.org/pdf/2008.09229v1.pdf | Image Stitching and Rectification for Hand-Held Cameras | In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitching and rectification at one stroke. Despite the high complexity of RS geometry, we focus in this paper on a sp... | ['Quoc-Huy Tran', 'Bingbing Zhuang'] | 2020-08-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/184_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520239.pdf | eccv-2020-8 | ['image-stitching'] | ['computer-vision'] | [ 6.39512539e-01 -1.39184624e-01 9.85724777e-02 1.02731936e-01
-3.85807157e-01 -7.84604430e-01 4.46035028e-01 -8.25236976e-01
-2.99903870e-01 3.40718627e-01 -7.26578832e-02 -1.86260998e-01
-1.37871653e-01 -1.41671330e-01 -7.86334038e-01 -7.47741699e-01
3.18839401e-01 1.28226653e-01 2.96080381e-01 -3.54326099... | [9.137530326843262, -2.367255210876465] |
95225abf-f6e4-4416-b7ae-b951dd5a83c5 | adversarial-domain-adaptation-with-self | 2107.04470 | null | https://arxiv.org/abs/2107.04470v4 | https://arxiv.org/pdf/2107.04470v4.pdf | ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training | Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects of interest. However,... | ['Cuntai Guan', 'XiaoLi Li', 'Chee-Keong Kwoh', 'Min Wu', 'Zhenghua Chen', 'Mohamed Ragab', 'Emadeldeen Eldele'] | 2021-07-09 | null | null | null | null | ['sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification', 'eeg-based-sleep-staging'] | ['medical', 'medical', 'medical', 'time-series'] | [ 7.60055631e-02 -2.05044836e-01 -3.09716761e-01 -5.86973548e-01
-4.79614645e-01 -4.10931826e-01 2.22333968e-01 -9.25698578e-02
-5.25452137e-01 1.03439510e+00 1.93455629e-02 4.87897098e-02
-7.41436379e-03 -5.34622908e-01 -3.04788858e-01 -9.20009375e-01
5.59244633e-01 5.47745168e-01 2.86530823e-01 -1.54712439... | [10.389780044555664, 3.0788214206695557] |
6abf3595-974a-4cee-99bc-3ffafc256074 | learning-set-functions-under-the-optimal | 2203.01693 | null | https://arxiv.org/abs/2203.01693v4 | https://arxiv.org/pdf/2203.01693v4.pdf | Learning Neural Set Functions Under the Optimal Subset Oracle | Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervisio... | ['Yatao Bian', 'Peilin Zhao', 'Yingzhen Li', 'Qinliang Su', 'Tingyang Xu', 'Zijing Ou'] | 2022-03-03 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 6.17073238e-01 -1.43008351e-01 -5.10948122e-01 -3.55079681e-01
-8.94507825e-01 -8.34243953e-01 3.74819845e-01 1.46690041e-01
-2.52535313e-01 8.95092487e-01 -3.29573303e-01 -4.88028526e-01
-6.12489343e-01 -4.97522920e-01 -1.18926048e+00 -9.58806872e-01
-2.13662922e-01 9.06432569e-01 -1.15141332e-01 -3.36764991... | [5.300300121307373, 5.241058826446533] |
299597c1-98ac-4aa5-8196-b98fcba36763 | feedback-graph-attention-convolutional | 2006.13863 | null | https://arxiv.org/abs/2006.13863v2 | https://arxiv.org/pdf/2006.13863v2.pdf | Feedback Graph Attention Convolutional Network for Medical Image Enhancement | Artifacts, blur and noise are the common distortions degrading MRI images during the acquisition process, and deep neural networks have been demonstrated to help in improving image quality. To well exploit global structural information and texture details, we propose a novel biomedical image enhancement network, named ... | ['Amirhossein Bayat', 'Yu Zhao', 'Yanyang Yan', 'Bjoern Menze', 'Xiaobin Hu', 'Wenqi Ren', 'Hongwei Li'] | 2020-06-24 | null | null | null | null | ['medical-image-enhancement', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 5.46470225e-01 1.54818058e-01 1.42415181e-01 -2.72708446e-01
-5.41711867e-01 8.97161365e-02 2.73451209e-01 -2.80586332e-02
-2.94668227e-01 5.09524405e-01 6.34082496e-01 2.37954512e-01
-5.42525887e-01 -6.48533225e-01 -6.37276530e-01 -9.75041568e-01
-4.75896865e-01 -9.56881717e-02 4.59942132e-01 -3.71102631... | [13.617679595947266, -2.394343137741089] |
1cac1dda-1a19-4a50-b20c-a3bafd34fb14 | its-all-relative-monocular-3d-human-pose | 1805.06880 | null | http://arxiv.org/abs/1805.06880v2 | http://arxiv.org/pdf/1805.06880v2.pdf | It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data | We address the problem of 3D human pose estimation from 2D input images using
only weakly supervised training data. Despite showing considerable success for
2D pose estimation, the application of supervised machine learning to 3D pose
estimation in real world images is currently hampered by the lack of varied
training ... | ['Pietro Perona', 'Robert Eng', 'Oisin Mac Aodha', 'Matteo Ruggero Ronchi'] | 2018-05-17 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 2.02643782e-01 2.78507441e-01 -2.02800825e-01 -5.11296511e-01
-9.20470059e-01 -5.30622423e-01 4.91977751e-01 5.74672653e-04
-7.55100310e-01 6.26186490e-01 1.22533418e-01 7.70805329e-02
2.12590322e-01 -3.63443762e-01 -7.22837150e-01 -4.36512113e-01
-3.61271948e-02 1.19190586e+00 2.68131286e-01 -2.28190988... | [6.971345901489258, -1.0279070138931274] |
4834e4e5-ff87-45a5-b243-7df85295901b | retrieval-augmented-multilingual-keyphrase | 2205.10471 | null | https://arxiv.org/abs/2205.10471v2 | https://arxiv.org/pdf/2205.10471v2.pdf | Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training | Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute ... | ['Michael R. Lyu', 'Irwin King', 'Bing Yin', 'Tong Zhao', 'Rui Meng', 'Zheng Li', 'Qingyu Yin', 'Yifan Gao'] | 2022-05-21 | null | https://aclanthology.org/2022.findings-naacl.92 | https://aclanthology.org/2022.findings-naacl.92.pdf | findings-naacl-2022-7 | ['keyphrase-generation', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [-2.22326130e-01 -2.09978789e-01 -3.99406016e-01 3.97545010e-01
-1.84914994e+00 -9.63827491e-01 1.05199754e+00 4.45131302e-01
-8.70781898e-01 1.33943594e+00 8.43362689e-01 -4.54661459e-01
-4.70903292e-02 -8.38170052e-01 -9.89467919e-01 -3.12102824e-01
4.08745170e-01 4.59476739e-01 1.44801393e-01 -7.06966519... | [12.314011573791504, 8.941458702087402] |
91144e94-75f1-4211-88dc-6fe08be36fb5 | the-role-of-output-vocabulary-in-t2t-lms-for | 2305.15108 | null | https://arxiv.org/abs/2305.15108v1 | https://arxiv.org/pdf/2305.15108v1.pdf | The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing | In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe tha... | ['Chris Biemann', 'Ricardo Usbeck', 'Pranav Ajit Nair', 'Debayan Banerjee'] | 2023-05-24 | null | null | null | null | ['graph-question-answering', 'semantic-parsing'] | ['graphs', 'natural-language-processing'] | [ 2.30087250e-01 9.55339015e-01 -2.22493634e-01 -4.14313525e-01
-9.31840301e-01 -7.50034630e-01 6.10075891e-01 5.00457048e-01
-5.81122577e-01 4.56137657e-01 4.69045281e-01 -8.04073930e-01
2.42181420e-02 -1.05689430e+00 -9.03528452e-01 8.01817030e-02
1.96474969e-01 8.13239753e-01 7.13733375e-01 -5.42409837... | [10.329704284667969, 7.945582389831543] |
6161d62f-3c50-4058-942b-e8d0e2e33421 | scalable-primal-dual-actor-critic-method-for | 2305.17568 | null | https://arxiv.org/abs/2305.17568v1 | https://arxiv.org/pdf/2305.17568v1.pdf | Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities | We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraints. The objective and constraints are described by {\it general utilities}, i.e., nonlinear functions of the long-term state-action occupancy... | ['Javad Lavaei', 'Alec Koppel', 'Yuhao Ding', 'Yunkai Zhang', 'Donghao Ying'] | 2023-05-27 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.09283157e-01 3.62629324e-01 -2.22414359e-01 3.36432517e-01
-9.32757139e-01 -5.65269232e-01 -1.72058828e-02 3.72033745e-01
-9.12824571e-01 1.17893887e+00 -4.32865351e-01 -5.04239857e-01
-8.43905091e-01 -8.44484508e-01 -7.13114560e-01 -9.65832353e-01
-9.28026438e-01 3.19985032e-01 3.71031687e-02 -2.30570734... | [4.346160888671875, 2.8514249324798584] |
28d5a515-9afb-4733-90b2-d36f29cba9f7 | fine-grained-population-mapping-from-coarse | 2211.04039 | null | https://arxiv.org/abs/2211.04039v1 | https://arxiv.org/pdf/2211.04039v1.pdf | Fine-grained Population Mapping from Coarse Census Counts and Open Geodata | Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a dee... | ['Devis Tuia', 'Konrad Schindler', 'Muhammad Imran', 'Ferda Ofli', 'Thao Ton-That Whelan', 'Benjamin Kellenberger', 'Rodrigo C. Daudt', 'John E. Vargas-Muñoz', 'Nando Metzger'] | 2022-11-08 | null | null | null | null | ['population-mapping'] | ['computer-vision'] | [-4.95028019e-01 -5.61974128e-04 -2.12159321e-01 -8.84324163e-02
-6.97708905e-01 -4.00125057e-01 9.09312546e-01 5.76929569e-01
-7.40154028e-01 1.49107850e+00 8.95959437e-01 -3.59394103e-01
3.57479905e-03 -1.62110722e+00 -5.64164937e-01 -5.05827546e-01
-2.62412220e-01 8.55316401e-01 -1.48668215e-01 -2.64212310... | [9.357978820800781, -1.257755994796753] |
2a1c987e-3f75-4096-b9d7-bf5abbf5d99e | objects-in-semantic-topology | 2110.02687 | null | https://arxiv.org/abs/2110.02687v2 | https://arxiv.org/pdf/2110.02687v2.pdf | Objects in Semantic Topology | A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their ... | ['Min Xu', 'Ping Luo', 'Changhu Wang', 'Zehuan Yuan', 'Ruiheng Zhang', 'Xiaobo Xia', 'Yi Jiang', 'Peize Sun', 'Shuo Yang'] | 2021-10-06 | objects-in-semantic-topology-1 | https://openreview.net/forum?id=d5SCUJ5t1k | https://openreview.net/pdf?id=d5SCUJ5t1k | iclr-2022-4 | ['open-world-object-detection'] | ['computer-vision'] | [-2.03961641e-01 1.66484892e-01 -2.31021062e-01 -4.48476285e-01
-4.56760377e-01 -7.45247781e-01 4.32918906e-01 4.32677031e-01
-4.20287371e-01 4.72679853e-01 -2.66845226e-01 1.97016478e-01
-7.73603171e-02 -9.38202500e-01 -7.54774690e-01 -4.93381143e-01
-1.91156030e-01 7.76854873e-01 1.01416004e+00 2.59942770... | [9.476349830627441, 1.5069189071655273] |
ee337aa2-e16d-4c92-8ae4-0d258b04ec40 | quiz-style-question-generation-for-news | 2102.09094 | null | https://arxiv.org/abs/2102.09094v1 | https://arxiv.org/pdf/2102.09094v1.pdf | Quiz-Style Question Generation for News Stories | A large majority of American adults get at least some of their news from the Internet. Even though many online news products have the goal of informing their users about the news, they lack scalable and reliable tools for measuring how well they are achieving this goal, and therefore have to resort to noisy proxy metri... | ['Cong Yu', 'Vinh Q. Tran', 'Adam D. Lelkes'] | 2021-02-18 | null | null | null | null | ['distractor-generation', 'question-answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.05683243e-02 2.73672223e-01 -8.95409830e-05 -5.16576529e-01
-1.79350114e+00 -8.31583738e-01 7.15491235e-01 2.15814933e-01
-3.50965708e-01 1.01107037e+00 6.81818306e-01 -4.67855901e-01
2.46099338e-01 -8.23147774e-01 -8.27994764e-01 2.10639477e-01
7.17161417e-01 5.89598596e-01 3.17605525e-01 -5.78785598... | [11.554691314697266, 8.116254806518555] |
0b9ae832-b552-486c-9a66-baca56e0fa16 | crowdsourced-collective-entity-resolution | 2002.09361 | null | https://arxiv.org/abs/2002.09361v1 | https://arxiv.org/pdf/2002.09361v1.pdf | Crowdsourced Collective Entity Resolution with Relational Match Propagation | Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the same real-world object. Recent studies have shown significant benefits of involving humans in the loop of ER. They often resolve entities with pairwise similarity measures ov... | ['Jiacheng Huang', 'Zhifeng Bao', 'Wei Hu', 'Yuzhong Qu'] | 2020-02-21 | null | null | null | null | ['question-selection'] | ['natural-language-processing'] | [-3.13358128e-01 4.69590753e-01 -3.67465287e-01 -3.23307723e-01
-1.29194593e+00 -7.06086159e-01 2.40934238e-01 8.05198133e-01
-5.94432771e-01 1.28722107e+00 -2.65858676e-02 2.00041890e-01
-1.31595030e-01 -1.02363467e+00 -7.89194524e-01 -2.25723416e-01
1.05215959e-01 1.06347430e+00 7.59980738e-01 -3.74331586... | [9.445014953613281, 8.84496021270752] |
e9754689-1d55-4d02-9047-3fcc4bc933e0 | improved-nl2sql-based-on-multi-layer-expert | 2306.17727 | null | https://arxiv.org/abs/2306.17727v1 | https://arxiv.org/pdf/2306.17727v1.pdf | Improved NL2SQL based on Multi-layer Expert Network | The Natural Language to SQL (NL2SQL) technique is used to convert natural language queries into executable SQL statements. Typically, slot-filling is employed as a classification method for multi-task cases to achieve this goal. However, slot-filling can result in inaccurate SQL statement generation due to negative mig... | ['Xu Zhang', 'Chenduo Hao'] | 2023-06-30 | null | null | null | null | ['classification-1', 'slot-filling'] | ['methodology', 'natural-language-processing'] | [ 2.58356392e-01 3.82047355e-01 1.99936442e-02 -6.82784915e-01
-8.65191460e-01 -4.82195497e-01 3.34814757e-01 3.66431057e-01
-1.13715813e-01 1.08906174e+00 1.01044085e-02 -4.64118510e-01
-1.72376633e-02 -9.28353429e-01 -4.80870724e-01 -6.70593157e-02
4.86064583e-01 5.38311124e-01 5.49232960e-01 -1.12237334... | [9.918843269348145, 7.859039783477783] |
038eb8c4-6c86-43fb-b166-9034c4c8d4ce | 190600679 | 1906.00679 | null | https://arxiv.org/abs/1906.00679v1 | https://arxiv.org/pdf/1906.00679v1.pdf | The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks? | The holy grail of networking is to create \textit{cognitive networks} that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the progress in the field of machine learning (ML), which has now already disrupted a number of industries and revolutionized practically all fiel... | ['Ala Al-Fuqaha', 'Mounir Hamdi', 'Junaid Qadir', 'Muhammad Usama'] | 2019-06-03 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.45149308e-01 4.21561480e-01 2.06258863e-01 -1.52571782e-01
6.17308430e-02 -1.27395391e+00 6.64713264e-01 -2.24124938e-01
-2.96233535e-01 7.26796448e-01 -4.21372056e-01 -1.17795718e+00
-4.17479962e-01 -1.06553757e+00 -5.25153637e-01 -5.54912150e-01
-5.60307980e-01 2.94207722e-01 3.03352475e-01 -7.99391389... | [5.517579078674316, 7.584959030151367] |
af46327f-032e-4624-a1eb-0398ca7e95dc | bidirectional-representations-for-low | 2211.14320 | null | https://arxiv.org/abs/2211.14320v1 | https://arxiv.org/pdf/2211.14320v1.pdf | Bidirectional Representations for Low Resource Spoken Language Understanding | Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach forces hard decisions when converting continuous inputs into discrete language symbols. Instead, we propose a representation model to encode... | ['Hugo Van hamme', 'Marie-Francine Moens', 'Quentin Meeus'] | 2022-11-24 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 4.01047528e-01 6.51484668e-01 -5.45325801e-02 -8.45830619e-01
-6.38265014e-01 -6.24419749e-01 9.73265409e-01 1.60511121e-01
-5.09664834e-01 3.93137813e-01 6.97843909e-01 -7.21937537e-01
3.52942854e-01 -7.17104197e-01 -7.89499462e-01 -2.29447111e-01
1.50187641e-01 7.87014604e-01 2.14756265e-01 -5.13576448... | [13.824756622314453, 7.035109519958496] |
82a3b52d-8ac8-42df-90bb-aec25bca2fe4 | magicvideo-efficient-video-generation-with | 2211.11018 | null | https://arxiv.org/abs/2211.11018v2 | https://arxiv.org/pdf/2211.11018v2.pdf | MagicVideo: Efficient Video Generation With Latent Diffusion Models | We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. MagicVideo can generate smooth video clips that are concordant with the given text descriptions. Due to a novel and efficient 3D U-Net design and modeling video distributions in a low-dimensional space, Magic... | ['Jiashi Feng', 'Yizhe Zhu', 'Weiwei Lv', 'Hanshu Yan', 'Weimin WANG', 'Daquan Zhou'] | 2022-11-20 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [-6.58430681e-02 -1.22876570e-01 -1.42607391e-01 -5.06033190e-02
-6.51369393e-01 -4.83581781e-01 3.45243871e-01 -8.95371377e-01
-1.25321701e-01 5.13460219e-01 4.56198096e-01 -1.23674303e-01
4.33731169e-01 -8.11090589e-01 -1.17932820e+00 -7.68474042e-01
1.63431659e-01 -6.79316893e-02 6.48559108e-02 1.37314409... | [10.856450080871582, -0.6950474381446838] |
428076f0-97a8-4259-90e4-74b819fa43ce | protsi-prototypical-siamese-network-with-data | 2211.09855 | null | https://arxiv.org/abs/2211.09855v1 | https://arxiv.org/pdf/2211.09855v1.pdf | ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation | Subjective answer evaluation is a time-consuming and tedious task, and the quality of the evaluation is heavily influenced by a variety of subjective personal characteristics. Instead, machine evaluation can effectively assist educators in saving time while also ensuring that evaluations are fair and realistic. However... | ['Gaurav Gupta', 'Jingxi Qiu', 'Yining Lu'] | 2022-11-17 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.04720942e-02 -2.23951176e-01 -3.12104374e-01 -6.56601250e-01
-7.67075300e-01 -4.10979390e-01 2.41629392e-01 4.70460683e-01
-6.42204106e-01 7.71010399e-01 2.94351161e-01 -1.41679704e-01
-3.32430780e-01 -9.26151991e-01 -2.68109024e-01 -3.13566893e-01
8.36380601e-01 4.76168901e-01 4.42195773e-01 -5.92729807... | [11.348148345947266, 9.282574653625488] |
2f627de3-4ee4-47bb-a0ff-59a419e2c244 | non-flat-aba-is-an-instance-of-bipolar | 2305.12453 | null | https://arxiv.org/abs/2305.12453v1 | https://arxiv.org/pdf/2305.12453v1.pdf | Non-flat ABA is an Instance of Bipolar Argumentation | Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and their contraries. A common restriction imposed on ABA frameworks (ABAFs) is that they are flat, i.e., each of the defeasible assumptions ca... | ['Francesca Toni', 'Nico Potyka', 'Markus Ulbricht'] | 2023-05-21 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [ 2.32349426e-01 9.74098563e-01 -2.07553044e-01 -5.42201519e-01
1.65225076e-03 -9.24699008e-01 8.03825855e-01 4.91139233e-01
3.55174206e-02 9.55959439e-01 1.44086912e-01 -8.43299925e-01
-6.02217793e-01 -1.31104088e+00 -7.34800816e-01 -2.91949302e-01
1.96575150e-01 5.42916059e-01 6.13685369e-01 -8.14038575... | [8.852167129516602, 6.806921482086182] |
e21545dd-06df-472b-892e-c80b6a72dc1a | the-study-of-highway-for-lifelong-multi-agent | 2304.04217 | null | https://arxiv.org/abs/2304.04217v1 | https://arxiv.org/pdf/2304.04217v1.pdf | The Study of Highway for Lifelong Multi-Agent Path Finding | In modern fulfillment warehouses, agents traverse the map to complete endless tasks that arrive on the fly, which is formulated as a lifelong Multi-Agent Path Finding (lifelong MAPF) problem. The goal of tackling this challenging problem is to find the path for each agent in a finite runtime while maximizing the throug... | ['Min Sun', 'Ming-Feng Li'] | 2023-04-09 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.45179051e-01 1.38163149e-01 -8.75368491e-02 -1.19703777e-01
-2.34490737e-01 -7.20367670e-01 4.45525646e-01 4.60176885e-01
-6.13093257e-01 9.94871378e-01 -2.18837380e-01 -1.91691846e-01
-6.92825615e-01 -1.18881977e+00 -6.23057246e-01 -6.70889556e-01
-6.80554807e-01 8.29801381e-01 8.33835900e-01 -4.91286546... | [4.93590784072876, 1.7940245866775513] |
eaf6df04-11b2-4865-ad72-0145baf646df | multi-task-pre-finetuning-for-zero-shot-cross | null | null | https://aclanthology.org/2021.icon-main.57 | https://aclanthology.org/2021.icon-main.57.pdf | Multi-task pre-finetuning for zero-shot cross lingual transfer | Building machine learning models for low resource languages is extremely challenging due to the lack of available training data (either un-annotated or annotated). To support such scenarios, zero-shot cross lingual transfer is used where the machine learning model is trained on a resource rich language and is directly ... | ['Anurag Dwarakanath', 'Pritam Varma', 'Moukthika Yerramilli'] | null | null | null | null | icon-2021-12 | ['intent-classification'] | ['natural-language-processing'] | [-2.05161050e-01 -1.97188705e-01 -3.54831368e-01 -3.69813740e-01
-1.15326560e+00 -5.76526999e-01 7.29704857e-01 9.32720676e-02
-1.03405166e+00 9.66152132e-01 2.89029598e-01 -3.38628083e-01
4.23740327e-01 -8.40717077e-01 -6.79690957e-01 -2.16011450e-01
2.21293062e-01 8.25431466e-01 2.58752435e-01 -4.56619203... | [10.713899612426758, 9.896488189697266] |
071f4901-5af4-4422-89ec-b220ad8501a6 | gaitgl-learning-discriminative-global-local | 2208.01380 | null | https://arxiv.org/abs/2208.01380v1 | https://arxiv.org/pdf/2208.01380v1.pdf | GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition | Existing gait recognition methods either directly establish Global Feature Representation (GFR) from original gait sequences or generate Local Feature Representation (LFR) from several local parts. However, GFR tends to neglect local details of human postures as the receptive fields become larger in the deeper network ... | ['Xin Yu', 'Lincheng Li', 'Ming Wang', 'Shunli Zhang', 'Beibei Lin'] | 2022-08-02 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.73241466e-01 -5.05868673e-01 -2.10545823e-01 -3.12187344e-01
-4.54066038e-01 -8.33694190e-02 9.45514515e-02 -3.88339490e-01
-4.10203159e-01 7.26753652e-01 3.04550052e-01 3.77104700e-01
1.20660685e-01 -9.60549712e-01 -4.69883442e-01 -7.81472266e-01
-4.14767385e-01 5.17683923e-02 4.01162624e-01 -2.58945018... | [14.295938491821289, 1.4172828197479248] |
ab683b01-dbf6-465c-8a4d-e94325596ab5 | multiplicative-fourier-level-of-detail | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.pdf | Multiplicative Fourier Level of Detail | We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated b... | ['Bingbing Ni', 'Qiaoqiao Jin', 'Zhong Zheng', 'Yishun Dou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-representation'] | ['computer-vision'] | [ 4.87625748e-01 -1.82877481e-02 1.21457025e-01 -5.24935365e-01
-6.48444295e-01 5.99504635e-02 7.08526373e-01 2.31414378e-01
-6.94689304e-02 8.04453731e-01 2.78952390e-01 2.60241190e-03
-3.87307167e-01 -1.01756346e+00 -7.47948885e-01 -1.01805687e+00
-5.49219251e-01 -7.24613369e-02 -1.13902278e-02 -2.08881721... | [11.057183265686035, -1.9581899642944336] |
e250c23b-11d9-496e-bf4e-052173a4da9c | value-based-ctde-methods-in-symmetric-two | 2211.11886 | null | https://arxiv.org/abs/2211.11886v2 | https://arxiv.org/pdf/2211.11886v2.pdf | Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition | In this paper, we identify the best learning scenario to train a team of agents to compete against multiple possible strategies of opposing teams. We evaluate cooperative value-based methods in a mixed cooperative-competitive environment. We restrict ourselves to the case of a symmetric, partially observable, two-team ... | ['Damien Ernst', 'Jonathan Pisane', 'Pascal Leroy'] | 2022-11-21 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-8.86379480e-02 1.35418847e-01 1.42716557e-01 2.35718280e-01
-6.18885696e-01 -8.36627364e-01 8.37047160e-01 -5.59749492e-02
-8.89342010e-01 1.24489939e+00 -1.89225107e-01 -2.81340063e-01
-4.56921697e-01 -4.64342266e-01 -2.69977927e-01 -9.80346501e-01
-3.14093769e-01 1.03552175e+00 3.94677997e-01 -6.02068305... | [3.7275750637054443, 1.9696604013442993] |
3b0bd44a-4d53-490b-a398-04ae9baccf9a | soft-activation-mapping-of-lung-nodules-in | 1810.12494 | null | https://arxiv.org/abs/1810.12494v2 | https://arxiv.org/pdf/1810.12494v2.pdf | Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping | A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored for interpreting localization and classification tasks while they ign... | ['Hongming Shan', 'Mannudeep Kalra', 'Junping Zhang', 'Yukun Tian', 'Yiming Lei', 'Ge Wang'] | 2018-10-30 | null | null | null | null | ['mapping-of-lung-nodules-in-low-dose-ct-images', 'lung-nodule-classification'] | ['medical', 'medical'] | [ 1.37769058e-01 2.94394284e-01 -4.82623339e-01 -2.28796616e-01
-5.71463704e-01 -3.36599052e-01 5.78177154e-01 -1.02969989e-01
-2.86047339e-01 4.38049853e-01 3.70410293e-01 -5.91821373e-01
-3.99522334e-01 -8.24124038e-01 -2.52938151e-01 -6.86321974e-01
-8.53649452e-02 2.85911322e-01 6.61172211e-01 5.35246618... | [15.347450256347656, -2.174316167831421] |
774ae18b-5f9b-4325-88a3-be00b548cbb4 | adaptive-graph-convolutional-network | 2205.04885 | null | https://arxiv.org/abs/2205.04885v1 | https://arxiv.org/pdf/2205.04885v1.pdf | Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction | In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence betwe... | ['Ning Wang'] | 2022-05-08 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.80959177e-01 -5.17340899e-01 3.28629054e-02 -1.72235966e-01
-2.83405986e-02 -3.62937272e-01 4.46421891e-01 -1.63537562e-01
3.14758152e-01 2.13865831e-01 5.03193676e-01 -5.71016610e-01
-3.64753395e-01 -1.08150232e+00 -3.69688869e-01 -4.14139152e-01
-4.21882331e-01 3.50765169e-01 4.60344613e-01 -5.29361665... | [6.796027183532715, 2.7396631240844727] |
7dd25caa-2df8-4426-8dad-3a4cc385c550 | uniform-in-time-wasserstein-stability-bounds | 2305.12056 | null | https://arxiv.org/abs/2305.12056v1 | https://arxiv.org/pdf/2305.12056v1.pdf | Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent | Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various p... | ['Umut Simsekli', 'Anant Raj', 'Mert Gurbuzbalaban', 'Lingjiong Zhu'] | 2023-05-20 | null | null | null | null | ['stochastic-optimization', 'generalization-bounds'] | ['methodology', 'methodology'] | [-9.35909450e-02 -1.15029849e-01 -1.34128138e-01 -1.10038973e-01
-8.62157643e-01 -6.39892638e-01 -4.59241010e-02 1.88819990e-01
-5.18147826e-01 9.50645268e-01 -1.86550140e-01 -3.29559475e-01
-4.97578174e-01 -4.46374297e-01 -7.40306914e-01 -1.01293695e+00
-3.57731551e-01 1.10721789e-01 3.02086234e-01 -1.77956983... | [7.059230804443359, 4.182153701782227] |
06e3cd23-1b8d-46fc-acde-41b75af39a61 | a-system-for-multilingual-dependency-parsing | null | null | https://aclanthology.org/K17-3006 | https://aclanthology.org/K17-3006.pdf | A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations | In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with {``}Multilingual Parsing from Raw Text to Universal Dependencies{''}. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddin... | ['KyungTae Lim', 'Thierry Poibeau'] | 2017-08-01 | null | null | null | conll-2017-8 | ['multilingual-word-embeddings'] | ['methodology'] | [-5.87682247e-01 4.22258116e-02 -4.96457547e-01 -4.63911384e-01
-1.66099226e+00 -1.08078575e+00 4.32268113e-01 1.08150445e-01
-8.27527046e-01 1.26078320e+00 4.32311326e-01 -7.91276038e-01
4.34204787e-01 -4.99398321e-01 -8.86831939e-01 -3.59367907e-01
-2.16551032e-02 6.71721160e-01 2.42293641e-01 -5.56205213... | [10.458579063415527, 9.977383613586426] |
dc182b00-ce5d-438c-8b8f-0c71db91cd69 | one-shot-model-for-mixed-precision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.pdf | One-Shot Model for Mixed-Precision Quantization | Neural network quantization is a popular approach for model compression. Modern hardware supports quantization in mixed-precision mode, which allows for greater compression rates but adds the challenging task of searching for the optimal bit width. The majority of existing searchers find a single mixed-precision ar... | ['Gleb Odinokikh', 'Temur Isaev', 'Valentin Buchnev', 'Alexandra Yakovleva', 'Ivan Koryakovskiy'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [ 1.97822496e-01 -4.97720271e-01 -5.60493410e-01 -1.66634366e-01
-9.07598197e-01 -2.90708512e-01 1.87517375e-01 2.91321069e-01
-6.24499261e-01 6.43691599e-01 -2.49114066e-01 -3.93269300e-01
-3.14976037e-01 -8.56176317e-01 -5.82176745e-01 -5.31979442e-01
-1.80835336e-01 5.00666201e-01 4.91547316e-01 -3.90683204... | [8.468318939208984, 3.253345489501953] |
40c5070b-7737-4127-ac69-56da2b8b6957 | deep-reinforcement-learning-based-dynamic-3 | null | null | https://ieeexplore.ieee.org/document/9214878 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9214878 | Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things | Nowadays, driven by the rapid development ofsmart mobile equipments and 5G network technologies, theapplication scenarios of Internet of Things (IoT) technologyare becoming increasingly widespread. The integration ofIoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of ... | ['Lian Zhao', 'Xin Chen', 'Yuan Wu', 'Yongchao Zhang', 'Zhiyong Liu', 'Ying Chen'] | 2020-10-06 | null | null | null | ieee-2020-10 | ['edge-computing'] | ['time-series'] | [-3.34661007e-01 -2.32143015e-01 -4.01676357e-01 3.56262118e-01
5.27629666e-02 -1.78278089e-01 5.02548553e-02 -6.31567657e-01
-3.03960294e-01 1.01162708e+00 -5.43244481e-01 -4.76502120e-01
-5.69872022e-01 -5.42905986e-01 -4.34559703e-01 -1.09628451e+00
1.54166207e-01 2.45341584e-01 -1.41341435e-02 2.35135183... | [5.8313069343566895, 1.7708593606948853] |
68d9aca2-c711-44e3-8670-ecb9c74c7cb9 | pixel-wise-deep-learning-for-contour | 1504.01989 | null | http://arxiv.org/abs/1504.01989v1 | http://arxiv.org/pdf/1504.01989v1.pdf | Pixel-wise Deep Learning for Contour Detection | We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM class... | ['Tyng-Luh Liu', 'Jyh-Jing Hwang'] | 2015-04-08 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 8.54322836e-02 -1.98013499e-01 -3.07679445e-01 -1.77406240e-02
-5.50403059e-01 -3.65181446e-01 2.94937670e-01 1.78830698e-01
-3.03987473e-01 3.98290366e-01 1.22613236e-01 -4.84567255e-01
5.33933878e-01 -1.17024076e+00 -4.51802939e-01 -5.21884084e-01
-4.09139603e-01 -4.29022104e-01 5.50836325e-01 -9.61019993... | [9.529023170471191, 0.19842150807380676] |
ecae67f2-88e3-4b28-b992-14a5c6d0a1f3 | real-time-distributed-model-predictive | 2208.12531 | null | https://arxiv.org/abs/2208.12531v1 | https://arxiv.org/pdf/2208.12531v1.pdf | Real-Time Distributed Model Predictive Control with Limited Communication Data Rates | The application of distributed model predictive controllers (DMPC) for multi-agent systems (MASs) necessitates communication between agents, yet the consequence of communication data rates is typically overlooked. This work focuses on developing stability-guaranteed control methods for MASs with limited data rates. Ini... | ['Ye Pu', 'Chris Manzie', 'Ye Wang', 'Yujia Yang'] | 2022-08-26 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 2.29534358e-02 3.69533181e-01 -1.73860416e-01 5.26182532e-01
-6.16300106e-01 -3.86041939e-01 3.61983359e-01 3.54421198e-01
-2.81915188e-01 9.84445333e-01 -4.74455893e-01 -1.67012125e-01
-5.00553668e-01 -5.69870591e-01 -6.48794830e-01 -1.22171593e+00
-2.39908934e-01 5.40581822e-01 1.32079097e-02 -1.63345098... | [5.160030841827393, 2.5098578929901123] |
9d4b8779-98fa-4f85-8258-92117545a567 | centralized-adversarial-learning-for-robust | 2204.10779 | null | https://arxiv.org/abs/2204.10779v6 | https://arxiv.org/pdf/2204.10779v6.pdf | CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval | Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adversarial defense methods for image retrieval. Existing solutions achieved limited defense performance ... | ['Yiqun Lin', 'Xiaomeng Li', 'Xunguang Wang'] | 2022-04-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-2.79253155e-01 -4.42225784e-01 1.89857800e-02 -1.30791634e-01
-1.21799529e+00 -9.67710853e-01 3.72595012e-01 9.53362882e-02
-3.96165073e-01 3.62872094e-01 9.33427140e-02 -8.97376388e-02
6.80043623e-02 -1.06497443e+00 -7.36699224e-01 -1.11749971e+00
-2.71120012e-01 1.50856763e-01 -2.80046389e-02 -4.34793174... | [5.567792892456055, 7.940651893615723] |
1a35042e-a327-4504-a51e-cdab324a2340 | rethinking-of-radar-s-role-a-camera-radar | 2105.05207 | null | https://arxiv.org/abs/2105.05207v1 | https://arxiv.org/pdf/2105.05207v1.pdf | Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment | Radar has long been a common sensor on autonomous vehicles for obstacle ranging and speed estimation. However, as a robust sensor to all-weather conditions, radar's capability has not been well-exploited, compared with camera or LiDAR. Instead of just serving as a supplementary sensor, radar's rich information hidden i... | ['Jenq-Neng Hwang', 'Hui Liu', 'Hung-Min Hsu', 'Gaoang Wang', 'Yizhou Wang'] | 2021-05-11 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 1.73814848e-01 -2.68665642e-01 -2.43065044e-01 -7.29310513e-01
-7.87330031e-01 -5.69184959e-01 5.97406387e-01 -2.92574883e-01
-5.94310820e-01 6.61543071e-01 -2.56266147e-01 -2.89235175e-01
-2.41746292e-01 -8.34135532e-01 -2.36998901e-01 -7.19637275e-01
1.68731183e-01 4.39429402e-01 6.30591869e-01 -4.06334251... | [7.860320091247559, -1.4828795194625854] |
296713ab-199b-4975-8b77-ee178820b512 | unite-a-unified-benchmark-for-text-to-sql | 2305.16265 | null | https://arxiv.org/abs/2305.16265v2 | https://arxiv.org/pdf/2305.16265v2.pdf | UNITE: A Unified Benchmark for Text-to-SQL Evaluation | A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures. To comprehensively evaluate text-to-SQL systems, we introduce a \textbf{UNI}fied benchmark for \textbf{T}ext-to-SQL \textbf{E}valuation (UNITE). It is composed ... | ['Bing Xiang', 'Patrick Ng', 'Vittorio Castelli', 'Stephen Ash', 'Jiarong Jiang', 'Jun Wang', 'Mingwen Dong', 'Lin Pan', 'Yiqun Hu', 'Joseph Lilien', 'Chung-Wei Hang', 'Sheng Zhang', 'Jiang Guo', 'Alexander Li', 'Henghui Zhu', 'Anuj Chauhan', 'Zhiguo Wang', 'Wuwei Lan'] | 2023-05-25 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-3.92346876e-03 -6.97871149e-02 -1.14574902e-01 -1.00678611e+00
-1.39552200e+00 -1.19916058e+00 3.28151762e-01 1.37719393e-01
-3.34015548e-01 6.55232489e-01 2.43112504e-01 -7.41359770e-01
-2.31909811e-01 -9.31351840e-01 -1.20691907e+00 8.03540573e-02
2.27257803e-01 8.06284070e-01 3.63539457e-01 -7.81077027... | [9.896393775939941, 7.858328342437744] |
f7b5d088-0db0-4ef9-a0d0-b3a2779c4e7f | g2t-a-simple-but-versatile-framework-for | 2304.06653 | null | https://arxiv.org/abs/2304.06653v3 | https://arxiv.org/pdf/2304.06653v3.pdf | Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection | It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete mo... | ['Qiang Yan', 'Jiapeng Liu', 'Leihang Zhang'] | 2023-04-13 | null | null | null | null | ['community-detection', 'sentence-embeddings', 'sentence-embeddings', 'topic-models'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.97819263e-01 1.58579081e-01 -4.27288622e-01 -2.57739604e-01
-7.09879756e-01 -2.70358592e-01 1.10769904e+00 4.82081681e-01
-2.07453117e-01 2.91432977e-01 6.45810843e-01 -9.85879377e-02
-4.64174300e-01 -1.11887240e+00 -1.63032278e-01 -7.05429852e-01
8.90780520e-03 6.92870617e-01 2.97748327e-01 -4.92932908... | [10.39574146270752, 6.962278366088867] |
d1a24e99-26e0-472f-a020-ff10519e661d | the-contextual-lasso-sparse-linear-models-via | 2302.00878 | null | https://arxiv.org/abs/2302.00878v2 | https://arxiv.org/pdf/2302.00878v2.pdf | The contextual lasso: Sparse linear models via deep neural networks | Sparse linear models are a gold standard tool for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains. Unfortunately, sparse linear models are far less flexible as functions of their input features than black-box models like deep neural networks. ... | ['Robert Kohn', 'Amir Dezfouli', 'Ryan Thompson'] | 2023-02-02 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.51290888e-01 3.08446944e-01 -5.96735120e-01 -7.33464599e-01
-3.04906726e-01 -3.35908502e-01 4.41938907e-01 -2.98355550e-01
1.43928364e-01 9.02630866e-01 3.96098524e-01 -2.16360778e-01
-2.71844983e-01 -7.29056954e-01 -1.03655457e+00 -8.37728679e-01
-2.84452438e-01 6.09258294e-01 -5.12016177e-01 1.14521034... | [8.233607292175293, 4.456297874450684] |
3197684f-5a2c-46ba-bdca-7ebf62aaefe1 | automatic-image-stylization-using-deep-fully | 1811.10872 | null | http://arxiv.org/abs/1811.10872v1 | http://arxiv.org/pdf/1811.10872v1.pdf | Automatic Image Stylization Using Deep Fully Convolutional Networks | Color and tone stylization strives to enhance unique themes with artistic
color and tone adjustments. It has a broad range of applications from
professional image postprocessing to photo sharing over social networks.
Mainstream photo enhancement softwares provide users with predefined styles,
which are often hand-craft... | ['Feida Zhu', 'Yizhou Yu'] | 2018-11-27 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 5.51628113e-01 -1.07207417e-01 -1.35231256e-01 -6.39352202e-01
-2.88577646e-01 -5.60834765e-01 5.50343990e-01 -1.47445634e-01
-3.17798913e-01 2.54291236e-01 1.49151608e-01 -2.80016541e-01
3.04303735e-01 -8.18992615e-01 -8.00574839e-01 -2.73972124e-01
6.13806307e-01 7.12281987e-02 -6.33344380e-03 -4.59484488... | [11.444751739501953, -0.8964853286743164] |
ab351ee1-a3d5-4693-95ff-26bba0cb84b7 | a-novel-multi-task-learning-method-for | 2201.05782 | null | https://arxiv.org/abs/2201.05782v1 | https://arxiv.org/pdf/2201.05782v1.pdf | A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition | Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. Previous work mainly focused on learning better representation via (mask) language model pre-training but ignored the intrinsic structure of the music, which is extremely important to the emotional expres... | ['Tong Zhang', 'C. L. Philip Chen', 'Jibao Qiu'] | 2022-01-15 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 7.65275117e-03 -3.72429281e-01 4.96604592e-02 -3.32932174e-01
-8.67230892e-01 -4.46461886e-01 4.47758995e-02 -4.84339535e-01
-3.56207758e-01 5.20272732e-01 4.11164341e-03 5.30104876e-01
-1.34622693e-01 -3.16314757e-01 -4.35936242e-01 -5.34739792e-01
8.34272951e-02 1.58579469e-01 -6.02974176e-01 -3.28112036... | [15.798157691955566, 5.170168876647949] |
643d4885-4e4b-437a-af6e-9dc817a16115 | s-3-hqa-a-three-stage-approach-for-multi-hop | 2305.11725 | null | https://arxiv.org/abs/2305.11725v1 | https://arxiv.org/pdf/2305.11725v1.pdf | S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering | Answering multi-hop questions over hybrid factual knowledge from the given text and table (TextTableQA) is a challenging task. Existing models mainly adopt a retriever-reader framework, which have several deficiencies, such as noisy labeling in training retriever, insufficient utilization of heterogeneous information o... | ['Kang Liu', 'Jun Zhao', 'Yiming Huang', 'Shizhu He', 'Yifan Wei', 'Xiang Li', 'Fangyu Lei'] | 2023-05-19 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 2.16815189e-01 5.03113866e-01 -1.23925865e-01 -3.41891348e-01
-1.87383771e+00 -5.39240122e-01 4.13571060e-01 2.06003189e-01
-3.54213506e-01 9.37235534e-01 6.46844983e-01 -2.36241654e-01
-2.35930264e-01 -1.08972192e+00 -7.35080481e-01 -3.48454416e-01
7.14298964e-01 1.08731401e+00 7.93739140e-01 -7.71187484... | [10.919255256652832, 7.883440971374512] |
5fe631a6-1b07-4479-9136-5b93981ddbb2 | cavl-learning-contrastive-and-adaptive | 2304.04399 | null | https://arxiv.org/abs/2304.04399v1 | https://arxiv.org/pdf/2304.04399v1.pdf | CAVL: Learning Contrastive and Adaptive Representations of Vision and Language | Visual and linguistic pre-training aims to learn vision and language representations together, which can be transferred to visual-linguistic downstream tasks. However, there exists semantic confusion between language and vision during the pre-training stage. Moreover, current pre-trained models tend to take lots of com... | ['Ihor Markevych', 'Jingfei Xia', 'Shentong Mo'] | 2023-04-10 | null | null | null | null | ['visual-reasoning', 'phrase-grounding', 'visual-commonsense-reasoning', 'visual-reasoning'] | ['computer-vision', 'natural-language-processing', 'reasoning', 'reasoning'] | [ 1.97053865e-01 -4.43621203e-02 8.50123614e-02 -5.52458942e-01
-9.35607135e-01 -6.09668612e-01 7.01291680e-01 8.98638368e-02
-8.09480131e-01 2.22918868e-01 2.12420672e-01 -5.74281454e-01
3.35708678e-01 -6.10508323e-01 -8.52441967e-01 -3.36046487e-01
6.15841389e-01 1.82407692e-01 2.73940951e-01 -2.73913413... | [10.757047653198242, 1.6204419136047363] |
604970d8-dc81-40d9-95e2-2e84fd0451cc | antisemitic-messages-a-guide-to-high-quality | 2304.14599 | null | https://arxiv.org/abs/2304.14599v1 | https://arxiv.org/pdf/2304.14599v1.pdf | Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets | One of the major challenges in automatic hate speech detection is the lack of datasets that cover a wide range of biased and unbiased messages and that are consistently labeled. We propose a labeling procedure that addresses some of the common weaknesses of labeled datasets. We focus on antisemitic speech on Twitter an... | ['Katharina Soemer', 'Daniel Miehling', 'Sameer Karali', 'Gunther Jikeli'] | 2023-04-28 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 2.92233169e-01 1.89408898e-01 -4.28200185e-01 -4.04729217e-01
-5.26701450e-01 -8.95667791e-01 1.07387602e+00 4.73012388e-01
-4.62168038e-01 7.33388484e-01 6.30730629e-01 -3.69535059e-01
-9.73783657e-02 -7.64211655e-01 -9.75622535e-02 -5.25073647e-01
1.62940636e-01 6.89724982e-01 -2.54986674e-01 -5.36647439... | [8.704375267028809, 10.433486938476562] |
d23f0fd8-d412-4703-a74e-f5df90f3aab5 | combining-multiple-clusterings-via-crowd | 1405.1297 | null | http://arxiv.org/abs/1405.1297v2 | http://arxiv.org/pdf/1405.1297v2.pdf | Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis | The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability t... | ['Chang-Dong Wang', 'Jian-Huang Lai', 'Dong Huang'] | 2014-05-06 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-2.89460748e-01 -3.50355595e-01 3.94351780e-02 -1.77021399e-01
-3.63745838e-01 -3.33224684e-01 5.81517160e-01 7.22490728e-01
-8.77048299e-02 7.09181130e-01 3.37773263e-01 2.97987133e-01
-8.59245062e-01 -9.47711587e-01 -1.20397396e-02 -1.13159907e+00
-2.60291427e-01 4.05113369e-01 5.57410896e-01 7.40723917... | [7.679210186004639, 4.568835258483887] |
f90b0ae2-a49b-4ad2-bd49-0e0158a7120c | joint-event-extraction-via-structural | 2306.03469 | null | https://arxiv.org/abs/2306.03469v1 | https://arxiv.org/pdf/2306.03469v1.pdf | Joint Event Extraction via Structural Semantic Matching | Event Extraction (EE) is one of the essential tasks in information extraction, which aims to detect event mentions from text and find the corresponding argument roles. The EE task can be abstracted as a process of matching the semantic definitions and argument structures of event types with the target text. This paper ... | ['Weiping Li', 'Jingkun Wang', 'Tianhao Gao', 'Haochen Li'] | 2023-06-06 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.55000705e-01 5.85077286e-01 -4.30400014e-01 -6.32131219e-01
-5.72529137e-01 -2.88825691e-01 8.32376003e-01 5.83999515e-01
-7.45422602e-01 6.26607835e-01 7.42674947e-01 -2.85780668e-01
4.93860133e-02 -1.07241333e+00 -8.07444394e-01 -1.92625105e-01
-1.98509041e-02 2.53285080e-01 3.30557078e-01 -7.20710754... | [9.032313346862793, 9.189579010009766] |
27903ae9-3cfa-4f26-ad25-f0746950ab00 | adafit-rethinking-learning-based-normal | 2108.05836 | null | https://arxiv.org/abs/2108.05836v1 | https://arxiv.org/pdf/2108.05836v1.pdf | AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds | This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding... | ['Bisheng Yang', 'Wenping Wang', 'YuAn Wang', 'Tengping Jiang', 'Zhen Dong', 'YuAn Liu', 'Runsong Zhu'] | 2021-08-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_AdaFit_Rethinking_Learning-Based_Normal_Estimation_on_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_AdaFit_Rethinking_Learning-Based_Normal_Estimation_on_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['surface-normals-estimation'] | ['computer-vision'] | [-1.07149228e-01 -4.06795204e-01 1.15829870e-01 -6.33751214e-01
-7.51873195e-01 -1.70177504e-01 5.34163266e-02 7.20251724e-02
-3.48941356e-01 1.28394827e-01 -5.28972507e-01 -1.17049694e-01
-5.22047803e-02 -8.74345481e-01 -9.67443109e-01 -5.48970997e-01
5.32217743e-03 6.97479486e-01 5.22121966e-01 -1.65877327... | [8.059854507446289, -3.3988029956817627] |
e6458860-33c0-46f0-bd35-c0ac79b4b7d2 | fast-context-annotated-classification-of | 1806.02374 | null | http://arxiv.org/abs/1806.02374v1 | http://arxiv.org/pdf/1806.02374v1.pdf | Fast Context-Annotated Classification of Different Types of Web Service Descriptions | In the recent rapid growth of web services, IoT, and cloud computing, many
web services and APIs appeared on the web. With the failure of global UDDI
registries, different service repositories started to appear, trying to list
and categorize various types of web services for client applications' discover
and use. In or... | ['Arash Khodadadi', 'Serguei A. Mokhov', 'Joey Paquet'] | 2018-05-31 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-2.72971243e-02 -3.70077342e-01 2.37205904e-02 -5.54268241e-01
-3.61496925e-01 -5.78070760e-01 7.91314125e-01 2.12621152e-01
-1.73282810e-02 1.48074374e-01 3.28391224e-01 -3.20133358e-01
-4.41568196e-01 -7.85363853e-01 2.44745553e-01 -4.85991925e-01
-2.10162163e-01 5.41577458e-01 6.65485203e-01 -2.00503588... | [8.613908767700195, 6.947588920593262] |
9ef9abf4-72dc-458f-9c08-5c6850fd8538 | alvisae-a-collaborative-web-text-annotation | null | null | https://aclanthology.org/W12-3621 | https://aclanthology.org/W12-3621.pdf | AlvisAE: a collaborative Web text annotation editor for knowledge acquisition | null | ["Claire N{\\'e}dellec", 'Robert Bossy', "Fr{\\'e}d{\\'e}ric Papazian"] | 2012-07-01 | alvisae-a-collaborative-web-text-annotation-1 | https://aclanthology.org/W12-3621 | https://aclanthology.org/W12-3621.pdf | ws-2012-7 | ['text-annotation'] | ['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... | [-7.4116973876953125, 3.60520601272583] |
c1c3d21f-8181-49fe-9c1b-dec964fb2f40 | neural-prototype-trees-for-interpretable-fine | 2012.02046 | null | https://arxiv.org/abs/2012.02046v2 | https://arxiv.org/pdf/2012.02046v2.pdf | Neural Prototype Trees for Interpretable Fine-grained Image Recognition | Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image r... | ['Christin Seifert', 'Ron van Bree', 'Meike Nauta'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.pdf | cvpr-2021-1 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.80096738e-03 6.64234698e-01 -4.00827080e-01 -8.20669711e-01
2.96289474e-02 -6.90835834e-01 6.30771041e-01 2.86395788e-01
9.16732326e-02 3.61651808e-01 1.93110377e-01 -7.53780425e-01
-3.16334516e-01 -7.70107567e-01 -7.58423030e-01 -3.81614119e-01
-1.59146115e-01 9.01977777e-01 3.80706377e-02 -1.27696291... | [8.92143440246582, 5.6588311195373535] |
0b02b4dd-3f6c-47c2-a773-139c018188a9 | forecasting-solar-irradiance-without-direct | 2303.06010 | null | https://arxiv.org/abs/2303.06010v2 | https://arxiv.org/pdf/2303.06010v2.pdf | Local-Global Methods for Generalised Solar Irradiance Forecasting | As the use of solar power increases, having accurate and timely forecasts will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradiance / solar power production. However, many of these methods formulate the problem as a time-series, relying on near real-time access to obs... | ['Isaac Triguero', 'Dario Landa-Silva', 'Timothy Cargan'] | 2023-03-10 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-1.76678710e-02 -2.91750163e-01 2.43437067e-01 -4.17286396e-01
-6.35255337e-01 -1.01947832e+00 7.76430666e-01 3.01456004e-01
1.41655251e-01 1.19880009e+00 -4.29642871e-02 -5.62139750e-01
-1.62150681e-01 -1.19431090e+00 -3.84574860e-01 -8.80322516e-01
-2.42620051e-01 2.94589847e-01 1.47882365e-02 -3.73371154... | [6.250667572021484, 2.795602798461914] |
31ad1002-7eb5-44fc-968f-314fa99f7071 | flow-based-svdd-for-anomaly-detection | 2108.04907 | null | https://arxiv.org/abs/2108.04907v1 | https://arxiv.org/pdf/2108.04907v1.pdf | Flow-based SVDD for anomaly detection | We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-based models, which naturally prevents from collapsing of bounding hypersp... | ['Jacek Tabor', 'Przemysław Spurek', 'Łukasz Struski', 'Łukasz Maziarka', 'Marek Śmieja', 'Marcin Sendera'] | 2021-08-10 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-8.87538671e-01 -2.93443114e-01 5.86476959e-02 -2.86923852e-02
2.54344404e-01 -2.42832392e-01 7.65182614e-01 2.86109775e-01
-6.29596934e-02 4.05997306e-01 3.36314648e-01 -7.25151181e-01
-2.27920532e-01 -8.92507315e-01 -1.96219206e-01 -3.56851757e-01
-8.21613669e-01 5.28979838e-01 4.26480323e-01 -2.87202388... | [7.607238292694092, 2.389014720916748] |
12bbfaed-fafb-484b-b98d-79a1bad4be86 | speechformer-reducing-information-loss-in | 2109.04574 | null | https://arxiv.org/abs/2109.04574v1 | https://arxiv.org/pdf/2109.04574v1.pdf | Speechformer: Reducing Information Loss in Direct Speech Translation | Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer's quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by... | ['Marco Turchi', 'Matteo Negri', 'Marco Gaido', 'Sara Papi'] | 2021-09-09 | null | https://aclanthology.org/2021.emnlp-main.127 | https://aclanthology.org/2021.emnlp-main.127.pdf | emnlp-2021-11 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 4.65883940e-01 2.87735760e-01 -9.29959789e-02 -2.88629889e-01
-1.14337122e+00 -3.90111923e-01 5.01726508e-01 4.70298767e-01
-6.58250690e-01 6.00104332e-01 3.72003883e-01 -4.69692826e-01
7.00206831e-02 -4.59798366e-01 -6.64197505e-01 -4.27558005e-01
5.17633883e-03 4.92455930e-01 1.00180246e-01 -3.41881007... | [14.390167236328125, 6.946511745452881] |
3ed7458d-608c-4106-ab73-dd65f2064a92 | unsupervised-visual-representation-learning-3 | 2105.11527 | null | https://arxiv.org/abs/2105.11527v3 | https://arxiv.org/pdf/2105.11527v3.pdf | Unsupervised Visual Representation Learning by Online Constrained K-Means | Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering sin... | ['Rong Jin', 'Hao Li', 'Juhua Hu', 'Yuanhong Xu', 'Qi Qian'] | 2021-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification', 'online-clustering'] | ['computer-vision', 'computer-vision'] | [ 1.91017538e-01 1.02663592e-01 -3.87140751e-01 -4.95846599e-01
-8.18661809e-01 -6.23129427e-01 2.46315882e-01 1.76929981e-01
-4.65976864e-01 2.43612438e-01 -8.59001726e-02 -2.26005375e-01
-5.18984675e-01 -6.62345171e-01 -5.13156533e-01 -1.08345246e+00
-9.68555827e-03 8.22243869e-01 -5.70844635e-02 2.11721987... | [9.251070022583008, 3.221681594848633] |
b9c163d3-5415-44b8-a33a-5740edae5c74 | adaptive-pyramid-context-network-for-semantic | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.pdf | Adaptive Pyramid Context Network for Semantic Segmentation | Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable prope... | [' Yu Qiao', ' Yali Wang', ' Lei Zhou', ' Zhongying Deng', 'Junjun He'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.93286490e-01 -2.92873472e-01 -2.14899927e-01 -8.03508699e-01
-5.28476954e-01 -4.42190260e-01 3.48101020e-01 1.44981503e-01
-6.62836075e-01 3.42873752e-01 2.21866861e-01 -2.14820251e-01
-3.78889851e-02 -7.50276029e-01 -6.14160717e-01 -7.68140078e-01
1.63838938e-01 7.11574480e-02 8.67413700e-01 -2.22007632... | [9.54420280456543, 0.3091047704219818] |
1913ec24-1d6d-43d7-a32f-fe101298c7a2 | principled-hyperedge-prediction-with | 2106.04292 | null | https://arxiv.org/abs/2106.04292v4 | https://arxiv.org/pdf/2106.04292v4.pdf | Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks | Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental problem for the full understanding of complicated interactions. The development of graph neural network (GNN) has greatly advanced the analysis o... | ['Chi Zhang', 'Pan Li', 'Sha Cao', 'Wei Hao', 'Muhan Zhang', 'Changlin Wan'] | 2021-06-08 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 2.37737939e-01 2.59321123e-01 -1.31630942e-01 -1.25853464e-01
3.92964900e-01 -4.74159092e-01 1.50333866e-01 2.35643566e-01
2.39577711e-01 8.37437212e-01 8.34602863e-02 -4.29236799e-01
-9.37022626e-01 -1.16693914e+00 -7.08902121e-01 -7.05633521e-01
-7.59585440e-01 7.51237452e-01 5.72273359e-02 -6.14878953... | [7.18673038482666, 6.2579426765441895] |
fc54bf97-b1ba-4cb3-9b3d-127077745e0e | federated-learning-based-hierarchical-3d | 2303.00450 | null | https://arxiv.org/abs/2303.00450v1 | https://arxiv.org/pdf/2303.00450v1.pdf | Federated Learning based Hierarchical 3D Indoor Localization | The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To o... | ['El Mehdi Amhoud', 'Wafa Njima', 'Yaya Etiabi'] | 2023-03-01 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-7.75846094e-02 7.07212463e-02 4.82135937e-02 -4.26745206e-01
-8.11227977e-01 -4.30588990e-01 2.54521161e-01 1.38548732e-01
-2.66498983e-01 1.10295248e+00 1.23258285e-01 -4.26110744e-01
-5.05934536e-01 -9.57594037e-01 -7.91650891e-01 -8.36816728e-01
-2.81717420e-01 -1.75458312e-01 -1.86694235e-01 2.59227246... | [6.42002534866333, 0.9269611239433289] |
9e65f6f7-2b78-4296-b72c-49787a3fb704 | temporal-alignment-prediction-for-supervised | null | null | https://openreview.net/forum?id=p3DKPQ7uaAi | https://openreview.net/pdf?id=p3DKPQ7uaAi | Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification | Explainable distances for sequence data depend on temporal alignment to tackle sequences with different lengths and local variances. Most sequence alignment methods infer the optimal alignment by solving an optimization problem under pre-defined feasible alignment constraints, which not only is time-consuming, but also... | ['Ji-Rong Wen', 'Bing Su'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 6.51183367e-01 -1.52227476e-01 -4.09979343e-01 -4.49906856e-01
-1.05893552e+00 -3.37280720e-01 3.05923134e-01 2.63816059e-01
-7.02733815e-01 6.35939240e-01 1.86374336e-01 -1.53941721e-01
-7.97638744e-02 -5.58068812e-01 -7.12456763e-01 -7.45802939e-01
-8.81306157e-02 6.36683762e-01 2.66987711e-01 -1.00002080... | [8.405948638916016, 0.7110449075698853] |
acf75cb4-9476-468e-a1cd-5e5c4cc88ad7 | ideal-gas-behavior-of-rotamerically-defined | 1408.5803 | null | http://arxiv.org/abs/1408.5803v2 | http://arxiv.org/pdf/1408.5803v2.pdf | Ideal gas behavior of rotamerically defined conformers in native globular proteins | Protein conformational transitions, which are essential for function, may be
driven either by entropy or enthalpy when molecular systems comprising solute
and solvent molecules are the focus. Revealing thermodynamic origin of a given
molecular process is an important but difficult task, and general principles
governing... | [] | 2015-04-30 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.86850750e-01 -3.48424196e-01 -4.71319348e-01 -4.06871617e-01
-4.72889334e-01 -7.26339102e-01 3.52559179e-01 4.70512062e-01
-3.08597356e-01 1.36146152e+00 -2.67690897e-01 -5.55979073e-01
3.00725073e-01 -4.61277187e-01 -7.07829237e-01 -1.61137187e+00
-2.68827856e-01 4.31183428e-01 2.80059837e-02 -2.81051069... | [4.801164150238037, 5.270985126495361] |
e9112455-c405-421e-8253-f80ca3a59948 | defect-detection-using-weakly-supervised | 2303.15092 | null | https://arxiv.org/abs/2303.15092v1 | https://arxiv.org/pdf/2303.15092v1.pdf | Defect detection using weakly supervised learning | In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this ... | ['Antonios Gasteratos', 'Spyridon Mouroutsos', 'Athanasios Psomoulis', 'Vasiliki Balaska', 'George Pavlidis', 'Vasileios Sevetlidis'] | 2023-03-27 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.58108258e-01 3.20813924e-01 -7.02891052e-01 -6.64758623e-01
-8.78080249e-01 -3.57948512e-01 4.15872991e-01 6.74980581e-01
-4.57948565e-01 6.88244879e-01 -2.89943032e-02 -1.84957787e-01
1.68153867e-01 -6.82582080e-01 -4.73858953e-01 -5.75401247e-01
2.38480084e-02 5.30160427e-01 4.01123166e-01 1.35733932... | [9.485894203186035, 3.763240337371826] |
d08f5353-92bb-4441-8c34-8bba08abdcaa | towards-training-bilingual-and-code-switched | 2306.08753 | null | https://arxiv.org/abs/2306.08753v1 | https://arxiv.org/pdf/2306.08753v1.pdf | Towards training Bilingual and Code-Switched Speech Recognition models from Monolingual data sources | Multilingual Automatic Speech Recognition (ASR) models are capable of transcribing audios across multiple languages, eliminating the need for separate models. In addition, they can perform Language Identification (LID) and handle code-switched speech. However, training these models requires special code-switch and mult... | ['Boris Ginsburg', 'Dima Rekesh', 'Kunal Dhawan'] | 2023-06-14 | null | null | null | null | ['language-identification', 'language-identification', 'automatic-speech-recognition', 'spoken-language-identification'] | ['audio', 'natural-language-processing', 'speech', 'speech'] | [ 6.01631589e-02 -1.30992085e-01 4.01186310e-02 -3.07028294e-01
-1.37816179e+00 -1.00992560e+00 6.66565120e-01 -9.80559587e-02
-3.43871772e-01 6.52972519e-01 2.46313229e-01 -9.38628316e-01
5.13636470e-01 -9.04590997e-04 -6.39236689e-01 -4.31385398e-01
2.93477058e-01 6.70416534e-01 -2.41290867e-01 -2.91506737... | [14.329095840454102, 6.8826494216918945] |
3b323152-50cd-4520-afc8-bd799a8538b4 | handling-group-fairness-in-federated-learning | 2307.04417 | null | https://arxiv.org/abs/2307.04417v1 | https://arxiv.org/pdf/2307.04417v1.pdf | Handling Group Fairness in Federated Learning Using Augmented Lagrangian Approach | Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models might be biased towards sensitive factors such as race or gender, even if they are trained using a legally compliant p... | ['Shenghui Song', 'Gerry Windiarto Mohamad Dunda'] | 2023-07-10 | null | null | null | null | ['fairness', 'federated-learning', 'fairness'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-1.12781018e-01 2.25588843e-01 -3.39374036e-01 -6.60479546e-01
-6.70078158e-01 -4.90206182e-01 4.95214671e-01 2.86668748e-01
-6.70226455e-01 1.01313806e+00 5.95384166e-02 -4.86123949e-01
-4.02923197e-01 -7.11669922e-01 -2.59791672e-01 -8.26734245e-01
-1.12716131e-01 1.27014339e-01 -2.82435566e-01 2.49490753... | [5.93626594543457, 6.536376476287842] |
1b62bf76-073f-4b6e-ba75-66154fc47782 | robust-motion-in-betweening | 2102.04942 | null | https://arxiv.org/abs/2102.04942v1 | https://arxiv.org/pdf/2102.04942v1.pdf | Robust Motion In-betweening | In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system synthesizes high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweenin... | ['Christopher Pal', 'Derek Nowrouzezahrai', 'Mike Yurick', 'Félix G. Harvey'] | 2021-02-09 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 2.08772108e-01 1.73570603e-01 -1.19394414e-01 6.00836650e-02
-8.28996897e-01 -5.86836278e-01 1.00609231e+00 -2.38313049e-01
-4.00109559e-01 5.03402114e-01 3.53724152e-01 -3.57014209e-01
4.04108435e-01 -6.90923870e-01 -1.03690445e+00 -4.76432264e-01
-4.28260356e-01 4.48314011e-01 5.93178809e-01 -3.42469156... | [7.323520660400391, -0.18706007301807404] |
26521f46-8a07-42fd-b27a-8b17ea31a3b5 | vizseq-a-visual-analysis-toolkit-for-text | 1909.05424 | null | https://arxiv.org/abs/1909.05424v1 | https://arxiv.org/pdf/1909.05424v1.pdf | VizSeq: A Visual Analysis Toolkit for Text Generation Tasks | Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE. They, however, are abstract numbers and are not perfectly aligned with human assessment. This suggests inspecting ... | ['Jiatao Gu', 'Anirudh Jain', 'Danlu Chen', 'Changhan Wang'] | 2019-09-12 | vizseq-a-visual-analysis-toolkit-for-text-1 | https://aclanthology.org/D19-3043 | https://aclanthology.org/D19-3043.pdf | ijcnlp-2019-11 | ['video-description'] | ['computer-vision'] | [ 3.26742440e-01 -9.79911722e-03 -5.82054257e-02 -3.80672552e-02
-1.34521854e+00 -9.62503433e-01 1.05062735e+00 6.11538053e-01
-4.17809486e-01 7.93244302e-01 6.84279859e-01 -6.34895742e-01
1.89633936e-01 -2.64936328e-01 -2.30399311e-01 -3.90828222e-01
2.92236477e-01 6.57211840e-01 3.23292948e-02 -2.57525235... | [11.771662712097168, 8.884381294250488] |
36f5982d-e4d4-4a2d-b479-6545918e7927 | decoding-and-interpreting-cortical-signals | null | null | https://iopscience.iop.org/article/10.1088/1741-2552/abe20e | https://iopscience.iop.org/article/10.1088/1741-2552/abe20e | Decoding and interpreting cortical signals with a compact convolutional neural network | Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the... | ['Alexei Ossadtchi', 'Mikhail Lebedev', 'Mikhail Sinkin', 'Artur Petrosyan'] | 2021-03-02 | null | null | null | journal-of-neural-engineering-2021-3 | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.37469757e-01 -8.71554092e-02 4.85883027e-01 -2.99737174e-02
-1.11167364e-01 -4.94370937e-01 7.56588161e-01 -1.11166693e-01
-8.40770006e-01 9.28992450e-01 3.42090353e-02 -2.29246363e-01
-8.69937658e-01 -2.58938193e-01 -9.70213830e-01 -1.01374042e+00
-4.68530566e-01 2.78769493e-01 2.34169483e-01 -3.33243340... | [12.992305755615234, 3.4204037189483643] |
1b815f2e-5e8f-47cc-a6c6-a5e959021209 | causal-interventions-based-few-shot-named | 2305.01914 | null | https://arxiv.org/abs/2305.01914v1 | https://arxiv.org/pdf/2305.01914v1.pdf | Causal Interventions-based Few-Shot Named Entity Recognition | Few-shot named entity recognition (NER) systems aims at recognizing new classes of entities based on a few labeled samples. A significant challenge in the few-shot regime is prone to overfitting than the tasks with abundant samples. The heavy overfitting in few-shot learning is mainly led by spurious correlation caused... | ['Chunping Ouyang', 'Yongbin Liu', 'Zhen Yang'] | 2023-05-03 | null | null | null | null | ['incremental-learning', 'few-shot-ner', 'named-entity-recognition-ner', 'selection-bias'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.23032773e-01 4.63659078e-01 -2.67841548e-01 -1.01975463e-01
-5.54890454e-01 -1.54698817e-02 6.10726595e-01 7.15104491e-02
-6.99767768e-01 7.46341348e-01 3.72592777e-01 5.22927083e-02
-1.53917938e-01 -9.97688174e-01 -8.15442741e-01 -5.17535329e-01
4.87985387e-02 4.23062772e-01 4.61720467e-01 -2.79070348... | [9.705668449401855, 9.28198528289795] |
7d5ecb65-0047-4e97-a9cd-a31c8729bfc1 | seq3-differentiable-sequence-to-sequence-to | 1904.03651 | null | https://arxiv.org/abs/1904.03651v2 | https://arxiv.org/pdf/1904.03651v2.pdf | SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression | Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent va... | ['Ioannis Konstas', 'Christos Baziotis', 'Ion Androutsopoulos', 'Alexandros Potamianos'] | 2019-04-07 | null | null | null | null | ['sentence-compression', 'unsupervised-abstractive-sentence-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.06054091e-01 4.34965491e-01 -2.83215970e-01 -4.44623649e-01
-6.68434858e-01 -2.46357113e-01 6.97893679e-01 4.49921489e-01
-9.15579200e-01 8.79095554e-01 6.11625910e-01 -1.95562765e-01
2.84986079e-01 -8.49088192e-01 -1.03076541e+00 -7.23372221e-01
1.34297282e-01 7.10661471e-01 -2.61546671e-01 -8.86401236... | [12.163424491882324, 9.308469772338867] |
27674571-0e48-46cd-974b-19869ba2cf57 | ensemble-knowledge-distillation-of-self | 2302.12757 | null | https://arxiv.org/abs/2302.12757v1 | https://arxiv.org/pdf/2302.12757v1.pdf | Ensemble knowledge distillation of self-supervised speech models | Distilled self-supervised models have shown competitive performance and efficiency in recent years. However, there is a lack of experience in jointly distilling multiple self-supervised speech models. In our work, we performed Ensemble Knowledge Distillation (EKD) on various self-supervised speech models such as HuBERT... | ['Hung-Yi Lee', 'Kai-Wei Chang', 'Wei-Cheng Tseng', 'Po-Chieh Yen', 'Tsu-Yuan Hsu', 'Yu-Kuan Fu', 'Tzu-hsun Feng', 'Kuan-Po Huang'] | 2023-02-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.08941033e-01 3.23687941e-01 -3.31304632e-02 -8.04708004e-01
-7.85422564e-01 -4.49284852e-01 5.12758434e-01 9.49221011e-03
-3.22325945e-01 7.74289787e-01 3.44584018e-01 -4.39567745e-01
1.45100355e-01 -3.09497148e-01 -4.80450839e-01 -7.25254536e-01
1.81832835e-01 3.91321242e-01 1.81868196e-01 -2.34640643... | [14.508342742919922, 6.462913990020752] |
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