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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cc176bf3-d762-43b6-9dc8-69c75ec72956 | sequential-attention-source-identification | 2306.15886 | null | https://arxiv.org/abs/2306.15886v1 | https://arxiv.org/pdf/2306.15886v1.pdf | Sequential Attention Source Identification Based on Feature Representation | Snapshot observation based source localization has been widely studied due to its accessibility and low cost. However, the interaction of users in existing methods does not be addressed in time-varying infection scenarios. So these methods have a decreased accuracy in heterogeneous interaction scenarios. To solve this ... | ['Xuelong Li', 'Chao GAO', 'Zhen Wang', 'Dongpeng Hou'] | 2023-06-28 | null | null | null | null | ['graph-attention'] | ['graphs'] | [ 2.31563136e-01 -2.56539881e-01 -2.78432190e-01 -1.84695777e-02
-4.91092801e-01 -1.51663259e-01 5.12571931e-01 1.99177653e-01
-1.37681857e-01 7.77836323e-01 5.42178929e-01 -6.33743256e-02
-2.00535178e-01 -6.57084405e-01 -3.43112767e-01 -8.79477978e-01
-6.54869676e-01 1.97433591e-01 5.21316528e-01 -7.31453747... | [6.624197483062744, 2.185577392578125] |
8bc4e90f-e1e5-46f6-afd3-f1e23fdf96c3 | diffustereo-high-quality-human-reconstruction | 2207.08000 | null | https://arxiv.org/abs/2207.08000v2 | https://arxiv.org/pdf/2207.08000v2.pdf | DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras | We propose DiffuStereo, a novel system using only sparse cameras (8 in this work) for high-quality 3D human reconstruction. At its core is a novel diffusion-based stereo module, which introduces diffusion models, a type of powerful generative models, into the iterative stereo matching network. To this end, we design a ... | ['Yebin Liu', 'Jingxiang Sun', 'Hongwen Zhang', 'Zerong Zheng', 'Ruizhi Shao'] | 2022-07-16 | null | null | null | null | ['3d-human-reconstruction', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 2.75793653e-02 -4.97929752e-02 4.79933232e-01 -2.00807020e-01
-5.28011084e-01 -4.33150113e-01 4.97283548e-01 -5.50351143e-01
-5.32027960e-01 3.05907816e-01 3.28432441e-01 1.84668303e-01
2.85937548e-01 -8.31862628e-01 -6.30706966e-01 -4.52566862e-01
4.46779877e-01 7.63732255e-01 6.41571581e-01 -2.30841696... | [8.760184288024902, -2.5951480865478516] |
a4a671e7-57f7-4b9d-9953-8cef1a0924b9 | contrastive-learning-of-musical | 2103.09410 | null | https://arxiv.org/abs/2103.09410v2 | https://arxiv.org/pdf/2103.09410v2.pdf | Contrastive Learning of Musical Representations | While deep learning has enabled great advances in many areas of music, labeled music datasets remain especially hard, expensive, and time-consuming to create. In this work, we introduce SimCLR to the music domain and contribute a large chain of audio data augmentations to form a simple framework for self-supervised, co... | ['John Ashley Burgoyne', 'Janne Spijkervet'] | 2021-03-17 | null | null | null | null | ['music-auto-tagging', 'music-classification'] | ['music', 'music'] | [ 3.13494533e-01 -1.45392001e-01 -3.29682171e-01 -1.43416762e-01
-1.22254241e+00 -1.05203378e+00 2.02241018e-01 -1.83994442e-01
-4.78789955e-01 5.20327210e-01 1.46328077e-01 8.74593481e-02
-4.04097527e-01 -5.70788920e-01 -7.21293807e-01 -4.01746154e-01
-2.60276765e-01 4.61940914e-01 -2.37066239e-01 -8.62419307... | [15.793618202209473, 5.262299060821533] |
22e82438-bd28-45c3-a30b-abd08a0415e4 | the-analysis-of-online-event-streams | 2203.09619 | null | https://arxiv.org/abs/2203.09619v1 | https://arxiv.org/pdf/2203.09619v1.pdf | The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection | Anomaly detection in process mining focuses on identifying anomalous cases or events in process executions. The resulting diagnostics are used to provide measures to prevent fraudulent behavior, as well as to derive recommendations for improving process compliance and security. Most existing techniques focus on detecti... | ['Hajo A. Reijers', 'Xixi Lu', 'Suhwan Lee'] | 2022-03-17 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.22494590e-01 1.86707992e-02 4.50954698e-02 -2.37838805e-01
-2.63801813e-01 -2.14140475e-01 7.85498261e-01 1.08090043e+00
-3.01172495e-01 5.87329626e-01 -1.88438799e-02 -6.47538066e-01
-3.98260981e-01 -1.11291802e+00 -3.90861362e-01 -5.30841529e-01
-4.69323397e-01 6.45716131e-01 2.93153822e-01 2.84359753... | [7.487866401672363, 2.5852413177490234] |
90cbdd03-8df0-49e9-8401-266d4f58d305 | provable-convergence-guarantees-for-black-box | 2306.03638 | null | https://arxiv.org/abs/2306.03638v1 | https://arxiv.org/pdf/2306.03638v1.pdf | Provable convergence guarantees for black-box variational inference | While black-box variational inference is widely used, there is no proof that its stochastic optimization succeeds. We suggest this is due to a theoretical gap in existing stochastic optimization proofs-namely the challenge of gradient estimators with unusual noise bounds, and a composite non-smooth objective. For dense... | ['Robert Gower', 'Guillaume Garrigos', 'Justin Domke'] | 2023-06-04 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-5.48545867e-02 -3.47934440e-02 -2.98852801e-01 -4.65860277e-01
-1.15352404e+00 -5.20735979e-01 5.39729416e-01 -2.80963242e-01
-3.38414133e-01 1.06568718e+00 6.78514838e-02 -4.66084391e-01
-1.20890439e-01 -3.82073581e-01 -9.38896179e-01 -8.52477074e-01
-1.27021849e-01 5.68816245e-01 -3.19586415e-03 1.41103178... | [6.905270099639893, 4.034294128417969] |
25b00e98-e8e3-44d9-9fe7-8cd96fb57c9b | trans-svnet-accurate-phase-recognition-from | 2103.09712 | null | https://arxiv.org/abs/2103.09712v2 | https://arxiv.org/pdf/2103.09712v2.pdf | Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer | Real-time surgical phase recognition is a fundamental task in modern operating rooms. Previous works tackle this task relying on architectures arranged in spatio-temporal order, however, the supportive benefits of intermediate spatial features are not considered. In this paper, we introduce, for the first time in surgi... | ['Pheng-Ann Heng', 'Qi Dou', 'Yonghao Long', 'Yueming Jin', 'Xiaojie Gao'] | 2021-03-17 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 6.35773391e-02 -2.52565518e-02 -4.63501066e-01 -8.59290138e-02
-8.60435307e-01 -5.63239276e-01 4.10609752e-01 6.38361633e-01
-9.65670884e-01 1.65228397e-01 3.19972515e-01 -5.29665172e-01
-6.99943125e-01 -3.74807894e-01 -3.19511533e-01 -8.64972532e-01
-5.23491085e-01 2.91540205e-01 3.51998568e-01 -7.61996862... | [14.096832275390625, -3.3288676738739014] |
85747b06-2180-4359-a975-117120f06aa4 | matching-text-with-deep-mutual-information | 2003.11521 | null | https://arxiv.org/abs/2003.11521v1 | https://arxiv.org/pdf/2003.11521v1.pdf | Matching Text with Deep Mutual Information Estimation | Text matching is a core natural language processing research problem. How to retain sufficient information on both content and structure information is one important challenge. In this paper, we present a neural approach for general-purpose text matching with deep mutual information estimation incorporated. Our approac... | ['Zhi Yu', 'Jiajun Bu', 'Zhou Yu', 'Xixi Zhou', 'Chengwei Yao', 'Keyue Shi', 'Chengxi Li'] | 2020-03-09 | null | null | null | null | ['mutual-information-estimation', 'paraphrase-identification', 'answer-selection'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 5.49258173e-01 3.18113305e-02 -2.51101077e-01 -6.66241109e-01
-7.86392868e-01 -1.30052269e-01 6.42468095e-01 6.15124524e-01
-6.40680194e-01 2.25782916e-01 5.47717333e-01 -2.68914670e-01
-1.76460490e-01 -9.38095748e-01 -5.28848112e-01 1.98757481e-02
6.63370311e-01 6.48034513e-01 -4.73193265e-02 -1.79848358... | [11.138785362243652, 8.32764720916748] |
4afd2adb-f386-44e8-b312-865b74051952 | streaming-variational-monte-carlo | 1906.01549 | null | https://arxiv.org/abs/1906.01549v4 | https://arxiv.org/pdf/1906.01549v4.pdf | Streaming Variational Monte Carlo | Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning f... | ['Mónica Bugallo', 'Josue Nassar', 'Yuan Zhao', 'Il Memming Park', 'Ian Jordan'] | 2019-06-04 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-1.01936966e-01 -3.62896204e-01 -2.60541469e-01 -9.48433951e-03
-7.09906697e-01 -5.76869130e-01 8.42584670e-01 6.92929476e-02
-8.98465514e-02 5.59904993e-01 6.00015335e-02 -2.67675847e-01
-3.98775160e-01 -7.29787171e-01 -7.17940688e-01 -7.90515244e-01
-4.51645374e-01 7.28553116e-01 2.66076207e-01 3.24291199... | [6.807008743286133, 3.7776670455932617] |
afb60c09-c66d-4a56-8f4a-15f090b720b6 | pose-estimation-and-3d-reconstruction-of | 2107.10898 | null | https://arxiv.org/abs/2107.10898v1 | https://arxiv.org/pdf/2107.10898v1.pdf | Pose Estimation and 3D Reconstruction of Vehicles from Stereo-Images Using a Subcategory-Aware Shape Prior | The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstr... | ['Franz Rottensteiner', 'Max Coenen'] | 2021-07-22 | null | null | null | null | ['3d-object-reconstruction', 'vehicle-pose-estimation', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.70064643e-01 1.18781604e-01 -7.44455904e-02 -5.63487172e-01
-5.51183403e-01 -4.50773031e-01 9.47721601e-01 -2.92174593e-02
-4.85848367e-01 3.93009543e-01 -3.72818023e-01 -2.11672127e-01
-2.23969832e-01 -6.72974408e-01 -1.04445279e+00 -6.62490487e-01
2.86094069e-01 1.17397201e+00 5.26855707e-01 -2.01746479... | [7.712561130523682, -2.6407310962677] |
80e7a680-24e3-41a5-98b8-dfae9a863b02 | predicting-clinical-trial-results-by-implicit | 2010.05639 | null | https://arxiv.org/abs/2010.05639v1 | https://arxiv.org/pdf/2010.05639v1.pdf | Predicting Clinical Trial Results by Implicit Evidence Integration | Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical tria... | ['Songfang Huang', 'Xiaozhong Liu', 'Mosha Chen', 'Chuanqi Tan', 'Qiao Jin'] | 2020-10-12 | null | https://aclanthology.org/2020.emnlp-main.114 | https://aclanthology.org/2020.emnlp-main.114.pdf | emnlp-2020-11 | ['pico'] | ['natural-language-processing'] | [ 3.97796988e-01 2.99301326e-01 -1.05943274e+00 -4.74644363e-01
-1.13098514e+00 -4.78506297e-01 5.00193357e-01 7.82179177e-01
-4.95487809e-01 1.03163016e+00 5.36499262e-01 -8.37002397e-01
-3.69547337e-01 -4.34829742e-01 -9.18824017e-01 -6.92673981e-01
-8.34157094e-02 6.15977883e-01 -3.01822335e-01 4.16168958... | [8.478293418884277, 8.654912948608398] |
a4859433-c3f3-43ab-b23d-800e68aea858 | neobility-at-semeval-2017-task-1-an-attention | 1703.05465 | null | http://arxiv.org/abs/1703.05465v1 | http://arxiv.org/pdf/1703.05465v1.pdf | Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model | This paper describes a neural-network model which performed competitively
(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
task. Our system employs an attention-based recurrent neural network model that
optimizes the sentence similarity. In this paper, we describe our participation
in the mul... | ['Wenli Zhuang', 'Ernie Chang'] | 2017-03-16 | neobility-at-semeval-2017-task-1-an-attention-1 | https://aclanthology.org/S17-2023 | https://aclanthology.org/S17-2023.pdf | semeval-2017-8 | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [-2.84147933e-02 2.87410878e-02 -7.98763707e-02 -3.86542827e-01
-1.15143609e+00 -2.15664104e-01 7.24130094e-01 5.54214835e-01
-9.31710064e-01 2.58157015e-01 7.94794977e-01 -3.11884671e-01
1.26447394e-01 -2.01503798e-01 -5.09608209e-01 1.98956411e-02
4.58013326e-01 7.91335881e-01 -1.21565960e-01 -8.83110404... | [10.890318870544434, 9.683730125427246] |
81fcdb91-2ed2-485d-835d-c0fc68a6f1b5 | language-family-relationship-preserved-in-non | null | null | https://aclanthology.org/C14-1183 | https://aclanthology.org/C14-1183.pdf | Language Family Relationship Preserved in Non-native English | null | ['Ryo Nagata'] | 2014-08-01 | language-family-relationship-preserved-in-non-1 | https://aclanthology.org/C14-1183 | https://aclanthology.org/C14-1183.pdf | coling-2014-8 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.185693264007568, 3.9062962532043457] |
13ab6dbf-8999-438a-86ea-118148dc5b17 | learning-semantic-correspondences-from-noisy | null | null | https://aclanthology.org/2020.coling-main.272 | https://aclanthology.org/2020.coling-main.272.pdf | Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments | Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in... | ['Chin-Yew Lin', 'Jinpeng Wang', 'Feng Nie'] | 2020-12-01 | null | null | null | coling-2020-8 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.03378034e-01 4.98897642e-01 -3.71801347e-01 -5.80330014e-01
-1.17015493e+00 -4.56712991e-01 6.19632125e-01 3.85758817e-01
-8.47252235e-02 9.56394076e-01 5.83427489e-01 -1.98223248e-01
-4.58713025e-02 -1.03245795e+00 -9.69134092e-01 -3.80201817e-01
7.12448657e-01 9.02297199e-01 1.64837763e-01 -3.53576064... | [11.579787254333496, 8.836142539978027] |
c4054e4f-86ea-407f-a944-200a89cecdb9 | multilingual-models-for-compositional | 1404.4641 | null | http://arxiv.org/abs/1404.4641v1 | http://arxiv.org/pdf/1404.4641v1.pdf | Multilingual Models for Compositional Distributed Semantics | We present a novel technique for learning semantic representations, which
extends the distributional hypothesis to multilingual data and joint-space
embeddings. Our models leverage parallel data and learn to strongly align the
embeddings of semantically equivalent sentences, while maintaining sufficient
distance betwee... | ['Karl Moritz Hermann', 'Phil Blunsom'] | 2014-04-17 | multilingual-models-for-compositional-1 | https://aclanthology.org/P14-1006 | https://aclanthology.org/P14-1006.pdf | acl-2014-6 | ['learning-semantic-representations', 'cross-lingual-document-classification'] | ['methodology', 'natural-language-processing'] | [-2.75373667e-01 2.45197546e-02 -5.65633059e-01 -5.31995654e-01
-8.09669733e-01 -9.64224696e-01 1.07857418e+00 6.36219740e-01
-6.26362443e-01 4.19553876e-01 9.62039530e-01 -5.16125679e-01
-8.68029296e-02 -6.99644923e-01 -4.14896697e-01 -1.28763184e-01
9.85745192e-02 5.09016156e-01 -1.10430062e-01 -4.21865284... | [10.886643409729004, 9.710125923156738] |
22a4505e-8833-4012-a296-1dbe0ec1ae57 | agent-performing-autonomous-stock-trading | 2306.03985 | null | https://arxiv.org/abs/2306.03985v1 | https://arxiv.org/pdf/2306.03985v1.pdf | Agent Performing Autonomous Stock Trading under Good and Bad Situations | Stock trading is one of the popular ways for financial management. However, the market and the environment of economy is unstable and usually not predictable. Furthermore, engaging in stock trading requires time and effort to analyze, create strategies, and make decisions. It would be convenient and effective if an age... | ['Zhangqi Duan', 'Yunfei Luo'] | 2023-06-06 | null | null | null | null | ['q-learning'] | ['methodology'] | [-8.23666632e-01 -1.53875798e-01 -2.13186190e-01 -4.91343550e-02
-3.92329663e-01 -6.16035640e-01 6.75029635e-01 6.86376616e-02
-5.58199704e-01 1.09679973e+00 1.42799588e-02 -6.83028638e-01
1.09126717e-02 -1.02780008e+00 -5.20214081e-01 -3.50326270e-01
-3.43186051e-01 5.51206529e-01 1.15103178e-01 -5.78357697... | [4.4478230476379395, 3.9400532245635986] |
ad559eed-af7b-4b00-a313-f73c24430949 | geographical-distance-is-the-new | 2205.08621 | null | https://arxiv.org/abs/2205.08621v1 | https://arxiv.org/pdf/2205.08621v1.pdf | Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation | Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation. Moreover, a lack of techniques to handle the complexities of morphological... | ['Innocent Amos Mchechesi', 'Muhammad Umair Nasir'] | 2022-05-17 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-1.10366553e-01 -2.37018883e-01 -5.83779275e-01 -1.54189020e-01
-1.34716642e+00 -8.19350302e-01 7.96626031e-01 -2.37372052e-02
-6.35204017e-01 1.08630288e+00 4.79704857e-01 -9.69027519e-01
-1.42451391e-01 -6.21177554e-01 -5.82028508e-01 -4.89732563e-01
3.22903961e-01 7.17707217e-01 -2.96119988e-01 -5.96500754... | [11.384538650512695, 10.369169235229492] |
9b492bf5-7317-40be-b0dd-42622efc1d7c | defusionnet-defocus-blur-detection-via | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Tang_DeFusionNET_Defocus_Blur_Detection_via_Recurrently_Fusing_and_Refining_Multi-Scale_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_DeFusionNET_Defocus_Blur_Detection_via_Recurrently_Fusing_and_Refining_Multi-Scale_CVPR_2019_paper.pdf | DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-Scale Deep Features | Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting more and more attention due to its widespread applications, defocus blur detection still confronts several challenges such as the interference of background clutter, sensitivity to scales and missing boundary details of defocu... | [' Albert Zomaya', ' Lizhe Wang', ' Xinwang Liu', ' Xinzhong Zhu', 'Chang Tang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['defocus-blur-detection', 'defocus-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.15739626e-02 -6.12770915e-01 1.99076608e-01 -4.57561225e-01
-2.62975663e-01 -4.83512193e-01 2.99655944e-01 -6.26525134e-02
-2.26958498e-01 7.30065703e-01 5.39068758e-01 2.11734965e-01
-1.22428454e-01 -5.20071328e-01 -5.46521902e-01 -8.90442729e-01
1.72479331e-01 -3.30088466e-01 5.17488658e-01 1.02414645... | [11.344001770019531, -2.7346229553222656] |
018ba0bb-9587-49b9-abdc-98a49c78fd1d | nilm-as-a-regression-versus-classification | 2010.16050 | null | https://arxiv.org/abs/2010.16050v1 | https://arxiv.org/pdf/2010.16050v1.pdf | NILM as a regression versus classification problem: the importance of thresholding | Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regre... | ['David Gómez-Ullate', 'Daniel Precioso'] | 2020-10-28 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.01697224e-01 -4.71007228e-02 -1.13812052e-01 -6.94802880e-01
-5.25655508e-01 -4.49519038e-01 6.96547627e-01 9.53641832e-02
-2.56137788e-01 5.25342226e-01 -3.37545127e-02 -1.52448863e-01
1.70906503e-02 -8.33021462e-01 -2.72271067e-01 -1.12457132e+00
1.31301954e-01 5.71628034e-01 -2.74041951e-01 1.26622524... | [16.058664321899414, 7.576294422149658] |
6a44a7ab-6ff3-47f7-b92b-30be126efedd | a-nonparametric-bayesian-approach-for-spoken | 1606.05967 | null | http://arxiv.org/abs/1606.05967v1 | http://arxiv.org/pdf/1606.05967v1.pdf | A Nonparametric Bayesian Approach for Spoken Term detection by Example Query | State of the art speech recognition systems use data-intensive
context-dependent phonemes as acoustic units. However, these approaches do not
translate well to low resourced languages where large amounts of training data
is not available. For such languages, automatic discovery of acoustic units is
critical. In this pa... | ['Joseph Picone', 'Amir Hossein Harati Nejad Torbati'] | 2016-06-20 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 1.46434620e-01 8.48228484e-02 7.50556961e-02 -6.38016105e-01
-1.36420059e+00 -5.55074215e-01 6.49879694e-01 2.54830390e-01
-7.16748297e-01 7.62768567e-01 2.27531388e-01 -2.41312861e-01
2.55979151e-01 -4.00637865e-01 -5.48811972e-01 -6.74766183e-01
-4.40356024e-02 5.53443491e-01 5.34373820e-01 1.63886324... | [14.461377143859863, 6.681459903717041] |
a65f1d3a-f6ad-45f3-9120-55c9e94255ba | data-efficient-visuomotor-policy-training | 2007.13134 | null | https://arxiv.org/abs/2007.13134v2 | https://arxiv.org/pdf/2007.13134v2.pdf | Data-efficient visuomotor policy training using reinforcement learning and generative models | We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models. Our framework trains deep visuomotor policies by introducing an action latent variable such that the feed-forward policy s... | ['Mårten Björkman', 'Ville Kyrki', 'Petra Poklukar', 'Ali Ghadirzadeh', 'Danica Kragic'] | 2020-07-26 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.30151579e-01 3.08742702e-01 -3.48175883e-01 -1.12585254e-01
-4.62157726e-01 -5.26452422e-01 1.11149383e+00 -6.87691346e-02
-5.97765267e-01 8.90580297e-01 2.58931369e-01 -2.90772915e-01
-4.88714367e-01 -7.94453442e-01 -8.70980322e-01 -1.17869735e+00
-1.42656922e-01 8.37142110e-01 2.95527373e-02 -1.44711575... | [4.213240623474121, 1.7313885688781738] |
33589420-876d-4844-8902-dac302788ab6 | statistical-and-machine-learning-ensemble | 1909.08573 | null | https://arxiv.org/abs/1909.08573v2 | https://arxiv.org/pdf/1909.08573v2.pdf | Statistical and machine learning ensemble modelling to forecast sea surface temperature | In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches were developed to estimate sea surface temperatures (SST). Training data consisted of satellite-deriv... | ["Fearghal O'Donncha", 'Bei Chen', 'Stefan Wolff'] | 2019-09-18 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-3.65090221e-01 -2.88545668e-01 8.96470696e-02 -4.93256241e-01
-6.02231383e-01 -7.05333889e-01 7.58337438e-01 2.37422362e-01
-1.26208425e-01 7.86771417e-01 7.51900300e-02 -1.00935578e+00
-5.03216743e-01 -1.03767121e+00 -2.73047477e-01 -9.24051762e-01
-7.50420570e-01 3.36558044e-01 -1.08688764e-01 -6.15095317... | [6.505917549133301, 3.0412042140960693] |
72a9a378-ec63-432f-afb0-3d98764f016a | lie-sensor-a-live-emotion-verifier-or-a | 2102.11318 | null | https://arxiv.org/abs/2102.11318v1 | https://arxiv.org/pdf/2102.11318v1.pdf | Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence | Veracity is an essential key in research and development of innovative products. Live Emotion analysis and verification nullify deceit made to complainers on live chat, corroborate messages of both ends in messaging apps and promote an honest conversation between users. The main concept behind this emotion artificial i... | ['Santosh Kumar Bharti', 'NirmalKumar Patel', 'Falguni Patel'] | 2021-02-11 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 1.15411632e-01 2.81526655e-01 -3.87084633e-01 -6.12388372e-01
-1.60391077e-01 -6.22800291e-01 5.32680213e-01 2.46784724e-02
4.10147272e-02 6.81007564e-01 3.04757785e-02 -3.13219279e-01
4.76532698e-01 -4.19872940e-01 -1.66038617e-01 -5.16884685e-01
2.22545400e-01 -4.01556045e-02 -6.41216934e-01 -4.09351662... | [12.19404125213623, 6.313703536987305] |
336a0d99-c932-4d8f-a4c4-04c7764e7a79 | a-new-family-of-constitutive-artificial | 2210.02202 | null | https://arxiv.org/abs/2210.02202v2 | https://arxiv.org/pdf/2210.02202v2.pdf | A new family of Constitutive Artificial Neural Networks towards automated model discovery | For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials in response to mechanical loading. Now, computer science offers a universal solution: Neural Networks. Neural Networks are powerful functio... | ['Ellen Kuhl', 'Kevin Linka'] | 2022-09-15 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.11577928e-01 6.71136156e-02 -4.14518923e-01 -2.12724403e-01
-1.23968266e-01 -3.06474805e-01 1.54669270e-01 -2.31590301e-01
-1.53539613e-01 9.76858139e-01 -2.13804860e-02 -2.17176408e-01
-6.41447365e-01 -8.60929132e-01 -9.88241076e-01 -9.79383647e-01
-1.04004636e-01 5.94857395e-01 7.72710592e-02 -3.32898796... | [6.380465030670166, 3.4253063201904297] |
e875b797-b93b-4d05-b351-c6ee7d23446f | investigation-on-combining-3d-convolution-of | 1903.04176 | null | http://arxiv.org/abs/1903.04176v2 | http://arxiv.org/pdf/1903.04176v2.pdf | Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals | In this paper, several variants of two-stream architectures for temporal
action proposal generation in long, untrimmed videos are presented. Inspired by
the recent advances in the field of human action recognition utilizing 3D
convolutions in combination with two-stream networks and based on the
Single-Stream Temporal ... | ['David Münch', 'Michael Arens', 'Patrick Schlosser'] | 2019-03-11 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 3.24535817e-01 -7.53147602e-02 -3.32802571e-02 -4.84098047e-02
7.61271045e-02 -2.29427502e-01 1.10618854e+00 -3.78974259e-01
-7.67734408e-01 6.35091841e-01 4.19238865e-01 -2.66134460e-02
-1.97825789e-01 -4.75543499e-01 -3.13930571e-01 -6.29310071e-01
-4.02532548e-01 7.29255751e-02 7.80095398e-01 -3.69748324... | [8.249382019042969, 0.14921210706233978] |
a788cbc6-7500-4d86-a916-384efeda7bf9 | discovering-latent-concepts-learned-in-bert-1 | 2205.07237 | null | https://arxiv.org/abs/2205.07237v1 | https://arxiv.org/pdf/2205.07237v1.pdf | Discovering Latent Concepts Learned in BERT | A large number of studies that analyze deep neural network models and their ability to encode various linguistic and non-linguistic concepts provide an interpretation of the inner mechanics of these models. The scope of the analyses is limited to pre-defined concepts that reinforce the traditional linguistic knowledge ... | ['Hassan Sajjad', 'Jia Xu', 'Nadir Durrani', 'Firoj Alam', 'Abdul Rafae Khan', 'Fahim Dalvi'] | 2022-05-15 | discovering-latent-concepts-learned-in-bert | https://openreview.net/forum?id=POTMtpYI1xH | https://openreview.net/pdf?id=POTMtpYI1xH | iclr-2022-4 | ['novel-concepts'] | ['reasoning'] | [ 1.59118742e-01 4.62591171e-01 -3.62610042e-01 -5.79066575e-01
5.01113236e-01 -7.54661977e-01 8.56450200e-01 7.24332511e-01
-6.94677711e-01 6.69103444e-01 3.88165534e-01 -3.04234654e-01
-4.73545343e-01 -9.60268855e-01 -8.65787506e-01 -4.58117634e-01
-5.91441572e-01 8.19473863e-01 2.31552467e-01 -4.23574954... | [10.45700454711914, 9.080110549926758] |
0d63747c-dc02-4c91-8cf8-ae2117322133 | the-limits-of-word-level-differential-privacy | 2205.02130 | null | https://arxiv.org/abs/2205.02130v1 | https://arxiv.org/pdf/2205.02130v1.pdf | The Limits of Word Level Differential Privacy | As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence... | ['Florian Kerschbaum', 'Benjamin Weggenmann', 'Justus Mattern'] | 2022-05-02 | null | https://aclanthology.org/2022.findings-naacl.65 | https://aclanthology.org/2022.findings-naacl.65.pdf | findings-naacl-2022-7 | ['text-anonymization'] | ['natural-language-processing'] | [ 9.73592550e-02 1.22241490e-01 -3.94698381e-02 -3.26304346e-01
-7.30004013e-01 -9.36105907e-01 6.00027561e-01 4.97484177e-01
-5.35602331e-01 8.41426671e-01 5.36099970e-01 -4.17693526e-01
-2.01811016e-01 -6.79714978e-01 -4.53236401e-01 -6.22634828e-01
1.47176757e-01 8.65612328e-02 3.51381004e-02 -3.42940718... | [6.122953414916992, 6.969371795654297] |
b2e52979-1705-4d11-b729-75d85224258d | mic-masked-image-consistency-for-context | 2212.01322 | null | https://arxiv.org/abs/2212.01322v2 | https://arxiv.org/pdf/2212.01322v2.pdf | MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation | In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previous UDA methods struggle with classes that have a similar visual appearance on the target domain as no ground truth is available to learn th... | ['Luc van Gool', 'Haoran Wang', 'Dengxin Dai', 'Lukas Hoyer'] | 2022-12-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hoyer_MIC_Masked_Image_Consistency_for_Context-Enhanced_Domain_Adaptation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hoyer_MIC_Masked_Image_Consistency_for_Context-Enhanced_Domain_Adaptation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.18637213e-01 7.00483993e-02 -1.06805615e-01 -6.10642970e-01
-1.04043436e+00 -6.27869248e-01 6.65299594e-01 -1.62068591e-01
-2.60181189e-01 6.78619146e-01 -3.56593311e-01 -1.35000408e-01
3.73745143e-01 -7.05726266e-01 -9.29499626e-01 -7.19292998e-01
2.22436488e-01 4.62020516e-01 4.53105092e-01 -2.88047940... | [9.769550323486328, 1.3514660596847534] |
7a9876b9-a848-4aba-b9c4-5c35cbfb468a | nighttime-smartphone-reflective-flare-removal | 2303.15046 | null | https://arxiv.org/abs/2303.15046v1 | https://arxiv.org/pdf/2303.15046v1.pdf | Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior | Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, ... | ['Chen Change Loy', 'Chongyi Li', 'Shangchen Zhou', 'Yihang Luo', 'Yuekun Dai'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dai_Nighttime_Smartphone_Reflective_Flare_Removal_Using_Optical_Center_Symmetry_Prior_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dai_Nighttime_Smartphone_Reflective_Flare_Removal_Using_Optical_Center_Symmetry_Prior_CVPR_2023_paper.pdf | cvpr-2023-1 | ['flare-removal'] | ['computer-vision'] | [ 8.26032460e-01 -6.33490503e-01 4.02892709e-01 -2.33175918e-01
-2.79058099e-01 -8.24680090e-01 3.46278101e-01 -7.40272462e-01
3.15325797e-01 7.17003286e-01 2.15142369e-01 1.61211461e-01
7.34753683e-02 -5.57554007e-01 -8.47626567e-01 -8.09190452e-01
4.45205927e-01 -4.83387411e-01 2.87618816e-01 -1.82109207... | [10.697420120239258, -3.0583784580230713] |
f0680a0f-88f3-4c23-bb90-67c5fdd6266e | reasoning-through-memorization-nearest | 2201.05575 | null | https://arxiv.org/abs/2201.05575v3 | https://arxiv.org/pdf/2201.05575v3.pdf | Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings | Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they typically struggle to reason rare or emerging unseen entities. In this paper, we propose kNN-KGE, a new knowledge graph embedding approach with pre-trained language ... | ['Huajun Chen', 'Xu Cheng', 'Yongheng Wang', 'Xiang Chen', 'Xin Xie', 'Ningyu Zhang'] | 2022-01-14 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-5.07642210e-01 5.93935788e-01 -8.63918960e-01 -2.45536506e-01
-4.13971394e-01 -5.81212342e-01 7.40153611e-01 6.42464399e-01
-4.57421839e-01 9.83049810e-01 4.79588002e-01 -3.17367375e-01
-2.89114654e-01 -1.32568574e+00 -8.72160316e-01 -1.51113942e-01
-1.78518206e-01 7.72407055e-01 1.96295142e-01 -1.86973155... | [8.880461692810059, 8.008665084838867] |
96b1fd5b-9e51-48f7-8bc4-e31a28be2e04 | a-new-outlier-removal-strategy-based-on | 2205.07404 | null | https://arxiv.org/abs/2205.07404v1 | https://arxiv.org/pdf/2205.07404v1.pdf | A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration | Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuiti... | ['Ming Huang', 'Hao Wu', 'Jicheng Dai', 'Hong Xie', 'Pengcheng Wei', 'Li Yan'] | 2022-05-16 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-3.87971401e-01 -3.40321481e-01 1.37245417e-01 9.77697317e-03
-6.21907473e-01 -3.22660685e-01 1.34735122e-01 2.94141144e-01
-5.44834062e-02 1.83536083e-01 -2.99609601e-01 2.44626980e-02
-3.93445075e-01 -7.54239917e-01 -6.03641808e-01 -5.92447996e-01
-1.08731367e-01 6.35922968e-01 4.22960997e-01 -1.93453193... | [7.721286773681641, -2.8823838233947754] |
6d49248c-f959-4dc8-a448-2abe44846f81 | ido-vfi-identifying-dynamics-via-optical-flow | 2305.10198 | null | https://arxiv.org/abs/2305.10198v2 | https://arxiv.org/pdf/2305.10198v2.pdf | IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events | Video frame interpolation aims to generate high-quality intermediate frames from boundary frames and increase frame rate. While existing linear, symmetric and nonlinear models are used to bridge the gap from the lack of inter-frame motion, they cannot reconstruct real motions. Event cameras, however, are ideal for capt... | ['Yibo Zhang', 'Boyi Wei', 'Yuzhen Li', 'Wenzhuo Li', 'Jing Jin', 'Hanxiao Liu', 'Chenyang Shi'] | 2023-05-17 | null | null | null | null | ['event-based-optical-flow', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 1.32117663e-02 -4.24710035e-01 -2.68606633e-01 -1.92657590e-01
-4.46973979e-01 -2.76870787e-01 3.90426874e-01 -2.13548183e-01
-3.24281007e-01 9.03845489e-01 1.96724340e-01 -1.50462225e-01
1.44027919e-01 -7.64256001e-01 -6.20764434e-01 -6.28607929e-01
2.55389549e-02 -1.09844238e-01 5.96733749e-01 2.13697124... | [10.719619750976562, -1.495254635810852] |
05522907-6a3e-4b89-b6d1-7a261ae8658b | lvlm-ehub-a-comprehensive-evaluation | 2306.09265 | null | https://arxiv.org/abs/2306.09265v1 | https://arxiv.org/pdf/2306.09265v1.pdf | LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models | Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their efficacy. This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (L... | ['Ping Luo', 'Yu Qiao', 'Siyuan Huang', 'Fanqing Meng', 'Meng Lei', 'Shuo Liu', 'Peng Gao', 'Kaipeng Zhang', 'Wenqi Shao', 'Peng Xu'] | 2023-06-15 | null | null | null | null | ['visual-question-answering-1', 'image-captioning', 'instruction-following'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-1.56736761e-01 3.97472968e-03 2.78478917e-02 -1.88550174e-01
-1.08335495e+00 -7.76178896e-01 6.54225349e-01 1.39823735e-01
-6.60171270e-01 3.59324545e-01 1.92360058e-01 -5.75662732e-01
1.36046007e-01 -4.99810487e-01 -8.97903204e-01 -3.48293632e-01
3.60063851e-01 4.80871916e-01 6.65701404e-02 -4.31411088... | [10.912623405456543, 1.6095521450042725] |
76a906e6-34e4-4fb7-a1d2-ed915eb83186 | learned-3d-shape-representations-using-fused | 1904.04297 | null | http://arxiv.org/abs/1904.04297v1 | http://arxiv.org/pdf/1904.04297v1.pdf | Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection | This paper proposes an approach to learn generic multi-modal mesh surface
representations using a novel scheme for fusing texture and geometric data. Our
approach defines an inverse mapping between different geometric descriptors
computed on the mesh surface or its down-sampled version, and the corresponding
2D texture... | ['Munawar Hayat', 'Naoufel Werghi', 'Bilal Taha', 'Stefano Berretti'] | 2019-04-08 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 4.78721052e-01 -4.16409075e-02 -6.85771331e-02 -4.93754268e-01
-9.29921269e-01 -1.84937045e-01 5.60889423e-01 -5.22015914e-02
-2.42299184e-01 3.46218616e-01 -9.45243612e-02 4.92263049e-01
-1.71113864e-01 -9.85046744e-01 -7.06581235e-01 -9.04793262e-01
-1.07786402e-01 3.52147460e-01 -4.98483777e-02 -3.78218174... | [13.494872093200684, 1.2702604532241821] |
b505571c-99fb-4a45-b282-189a643f837d | on-solutions-of-the-distributional-bellman | 2202.00081 | null | https://arxiv.org/abs/2202.00081v3 | https://arxiv.org/pdf/2202.00081v3.pdf | On solutions of the distributional Bellman equation | In distributional reinforcement learning not only expected returns but the complete return distributions of a policy are taken into account. The return distribution for a fixed policy is given as the solution of an associated distributional Bellman equation. In this note we consider general distributional Bellman equat... | ['Denis Spiegel', 'Ralph Neininger', 'Julian Gerstenberg'] | 2022-01-31 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-4.96371299e-01 -7.88726099e-03 -3.90634060e-01 -3.75193805e-01
-5.51543415e-01 -8.68309855e-01 2.35776380e-01 8.04535896e-02
-8.69742215e-01 1.15737414e+00 1.80926675e-03 -6.65241361e-01
-7.66764998e-01 -8.01590621e-01 -7.80605912e-01 -1.13756025e+00
-1.95452631e-01 5.90493917e-01 -3.71484965e-01 -2.31171474... | [4.137731075286865, 2.590224266052246] |
9a444c05-f6a4-4c44-ab1c-dad7ff3206fc | visual-analogy-deep-learning-versus | 2105.07065 | null | https://arxiv.org/abs/2105.07065v1 | https://arxiv.org/pdf/2105.07065v1.pdf | Visual analogy: Deep learning versus compositional models | Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by computing similarities between structured representations of analogs? We address this question by comparing human performance on visual analogie... | ['Hongjing Lu', 'Alan Yuille', 'Keith J. Holyoak', 'Shuhao Fu', 'Qing Liu', 'Nicholas Ichien'] | 2021-05-14 | null | null | null | null | ['visual-analogies'] | ['computer-vision'] | [-7.38703385e-02 1.97150201e-01 2.19395697e-01 -3.37292969e-01
-9.84211266e-02 -7.56773889e-01 8.69248033e-01 4.55884904e-01
-5.23483515e-01 6.72581732e-01 4.02455181e-01 -5.54540694e-01
-5.70693791e-01 -9.20839906e-01 -7.10864246e-01 -7.53563941e-02
2.69593537e-01 1.15707552e+00 1.39812917e-01 -6.07889771... | [10.587660789489746, 2.308619499206543] |
8a10ff02-bd1a-480e-a3fa-afa280e78d82 | algorithms-for-learning-graphs-in-financial | 2012.15410 | null | https://arxiv.org/abs/2012.15410v1 | https://arxiv.org/pdf/2012.15410v1.pdf | Algorithms for Learning Graphs in Financial Markets | In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical asset selection and portfolio allocation are now part of practitioners' toolboxes. In this paper, we investigate the fundamental problem of... | ['Daniel Perez Palomar', 'Jiaxi Ying', 'José Vinícius de Miranda Cardoso'] | 2020-12-31 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.27213824e-01 3.29905115e-02 -1.65588409e-01 -9.08310041e-02
3.76139767e-02 -7.40271568e-01 3.82965952e-01 1.96355730e-02
1.54961497e-01 6.37113094e-01 -1.10756405e-01 -7.03594685e-01
-8.99117708e-01 -1.05912912e+00 -4.16093826e-01 -7.07159996e-01
-8.35582137e-01 4.62007821e-01 -7.55286366e-02 -1.53298289... | [5.106787204742432, 4.097522258758545] |
0533ef0e-1077-47a6-929d-4047b507a12a | a-simple-algorithm-for-the-constrained | 2103.02919 | null | https://arxiv.org/abs/2103.02919v1 | https://arxiv.org/pdf/2103.02919v1.pdf | A Simple Algorithm for the Constrained Sequence Problems | In this paper we address the constrained longest common subsequence problem. Given two sequences $X$, $Y$ and a constrained sequence $P$, a sequence $Z$ is a constrained longest common subsequence for $X$ and $Y$ with respect to $P$ if $Z$ is the longest subsequence of $X$ and $Y$ such that $P$ is a subsequence of $Z$.... | ['S. K. Kim', 'Alfredo De Santis', 'Ngai Lam Ho', 'Francis Yuk Lun Chin'] | 2021-03-04 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 5.40133417e-01 -3.44684005e-01 -9.41122696e-02 -2.53257155e-01
-5.91732442e-01 -8.46355319e-01 -4.52844113e-01 2.85009503e-01
-8.46393645e-01 6.05344296e-01 -6.63066626e-01 -6.20268643e-01
-5.05029321e-01 -8.65116894e-01 -6.26574636e-01 -7.34152496e-01
-6.06526077e-01 1.39006317e-01 1.60930708e-01 -5.63053727... | [6.430612564086914, 4.7293548583984375] |
7d3225e9-d0b9-460e-ad4c-b22ea6769e31 | exploring-spatial-temporal-variations-of | 2306.16031 | null | https://arxiv.org/abs/2306.16031v1 | https://arxiv.org/pdf/2306.16031v1.pdf | Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy | This paper proposes a methodology for exploring how linguistic behaviour on social media can be used to explore societal reactions to important events such as those that transpired during the SARS CoV2 pandemic. In particular, where spatial and temporal aspects of events are important features. Our methodology consists... | ['Galletti Martina', 'Anslow Michael'] | 2023-06-28 | null | null | null | null | ['time-series'] | ['time-series'] | [-1.69201240e-01 2.22633541e-01 -1.51320547e-01 -1.81459576e-01
-5.68571016e-02 -5.84726214e-01 8.93443167e-01 1.24302816e+00
-5.80667675e-01 3.92348677e-01 1.16648448e+00 -5.60842574e-01
-5.14228106e-01 -7.25928128e-01 -3.33669364e-01 -4.93282795e-01
-5.87264299e-01 1.56335726e-01 -2.52557188e-01 -4.86319244... | [8.567516326904297, 9.780698776245117] |
b9eb5e17-cc1a-4040-86f1-379babb4a1f4 | learning-object-bounding-boxes-for-3d | 1906.01140 | null | https://arxiv.org/abs/1906.01140v2 | https://arxiv.org/pdf/1906.01140v2.pdf | Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds | We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Our method, called 3D-BoNet, follows the simple design philosophy of per-point multilayer perceptrons (MLPs). The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultane... | ['Bo Yang', 'Sen Wang', 'Ronald Clark', 'Andrew Markham', 'Niki Trigoni', 'Qingyong Hu', 'Jianan Wang'] | 2019-06-04 | learning-object-bounding-boxes-for-3d-1 | http://papers.nips.cc/paper/8899-learning-object-bounding-boxes-for-3d-instance-segmentation-on-point-clouds | http://papers.nips.cc/paper/8899-learning-object-bounding-boxes-for-3d-instance-segmentation-on-point-clouds.pdf | neurips-2019-12 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 2.64250338e-01 2.40036771e-01 -1.65428579e-01 -6.08581185e-01
-7.11801648e-01 -3.29100728e-01 6.81229770e-01 2.95039177e-01
-4.12702948e-01 1.39141336e-01 -3.70069265e-01 -7.27434635e-01
1.68813542e-02 -8.56168687e-01 -1.20362687e+00 -2.50223547e-01
-3.02162290e-01 9.06640053e-01 5.75445473e-01 1.15122609... | [7.941258907318115, -3.4245734214782715] |
4712df51-768c-4c25-b5a6-6b9bd21bc747 | interpretable-multimodal-emotion-recognition | 2208.11868 | null | https://arxiv.org/abs/2208.11868v2 | https://arxiv.org/pdf/2208.11868v2.pdf | Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data | This paper proposes a multimodal emotion recognition system based on hybrid fusion that classifies the emotions depicted by speech utterances and corresponding images into discrete classes. A new interpretability technique has been developed to identify the important speech & image features leading to the prediction of... | ['Balasubramanian Raman', 'Sarthak Malik', 'Puneet Kumar'] | 2022-08-25 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.93885756e-01 1.86528698e-01 2.61093318e-01 -7.86021292e-01
-9.41580415e-01 -3.14998418e-01 7.48269737e-01 1.36192232e-01
-2.75423348e-01 5.35310447e-01 4.61937219e-01 4.91401851e-02
-1.40693039e-01 6.20405525e-02 1.30207941e-01 -8.60561371e-01
4.66726273e-02 1.03306167e-01 -4.72408503e-01 -2.59444922... | [13.25274658203125, 5.2092413902282715] |
87a0062b-8940-425c-b2bd-8cc517c9345d | reclor-a-reading-comprehension-dataset-1 | 2002.04326 | null | https://arxiv.org/abs/2002.04326v3 | https://arxiv.org/pdf/2002.04326v3.pdf | ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning | Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comp... | ['Zi-Hang Jiang', 'Yanfei Dong', 'Weihao Yu', 'Jiashi Feng'] | 2020-02-11 | null | https://openreview.net/forum?id=HJgJtT4tvB | https://openreview.net/pdf?id=HJgJtT4tvB | iclr-2020-1 | ['logical-reasoning-question-ansering', 'logical-reasoning-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.84790868e-02 5.14203548e-01 -3.44583809e-01 -6.73176944e-01
-5.60281754e-01 -3.53935242e-01 3.37660074e-01 7.18390465e-01
-3.45196635e-01 6.59999490e-01 3.64070714e-01 -8.15878212e-01
-3.31342518e-01 -9.43227530e-01 -5.28415978e-01 -1.10256724e-01
6.35733604e-01 4.26919490e-01 1.80827811e-01 -5.86637795... | [10.076277732849121, 7.659755229949951] |
8c8c9ce6-85a6-404d-b160-f8993a5e8c4e | reconstructing-vehicles-from-orthographic | 2206.08789 | null | https://arxiv.org/abs/2206.08789v1 | https://arxiv.org/pdf/2206.08789v1.pdf | Reconstructing vehicles from orthographic drawings using deep neural networks | This paper explores the current state-of-the-art of object reconstruction from multiple orthographic drawings using deep neural networks. It proposes two algorithms to extract multiple views from a single image. The paper proposes a system based on pixel-aligned implicit functions (PIFu) and develops an advanced sampli... | ['Robin Klippert'] | 2022-06-14 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 1.23319559e-01 2.87156254e-01 -8.96882149e-04 -4.41365629e-01
-4.24779147e-01 -2.10939810e-01 6.66377008e-01 -5.42648077e-01
-2.49360114e-01 8.19705427e-01 7.01184273e-02 -6.10886253e-02
-2.44158342e-01 -1.20284200e+00 -1.22275662e+00 -1.67363420e-01
3.65276635e-01 9.63460743e-01 3.34197819e-01 -3.99611980... | [8.938570022583008, -3.119022846221924] |
e208114d-5412-42d5-a446-1a22067f7c5d | pre-training-image-language-transformers-for | 2209.04372 | null | https://arxiv.org/abs/2209.04372v1 | https://arxiv.org/pdf/2209.04372v1.pdf | Pre-training image-language transformers for open-vocabulary tasks | We present a pre-training approach for vision and language transformer models, which is based on a mixture of diverse tasks. We explore both the use of image-text captioning data in pre-training, which does not need additional supervision, as well as object-aware strategies to pre-train the model. We evaluate the metho... | ['Anelia Angelova', 'Weicheng Kuo', 'AJ Piergiovanni'] | 2022-09-09 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 4.57764775e-01 3.81565571e-01 8.88826475e-02 -5.49986660e-01
-8.48034501e-01 -5.60400188e-01 1.12669241e+00 -5.22771925e-02
-5.57578981e-01 4.33218598e-01 4.10505831e-01 -7.27407098e-01
6.18215144e-01 -4.88006651e-01 -1.15857947e+00 -1.44658938e-01
6.30026758e-01 8.12110662e-01 3.89487058e-01 -1.26199424... | [10.859192848205566, 1.724151611328125] |
61d98e75-5d25-4188-91b4-dcd74271b805 | bn-htrd-a-benchmark-dataset-for-document | 2206.08977 | null | https://arxiv.org/abs/2206.08977v1 | https://arxiv.org/pdf/2206.08977v1.pdf | BN-HTRd: A Benchmark Dataset for Document Level Offline Bangla Handwritten Text Recognition (HTR) and Line Segmentation | We introduce a new dataset for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant to act as ground truth texts. These texts were subsequently used to generate the annotations ... | ['Mohammad Khairul Islam', 'Riya Pal', 'Mitu Paul', 'Nazifa Tabassum', 'Md. Ataur Rahman'] | 2022-05-29 | null | null | null | null | ['handwritten-line-segmentation'] | ['computer-vision'] | [ 1.42766386e-01 -3.76812488e-01 -2.86918897e-02 -5.32085359e-01
-5.69907844e-01 -1.02585316e+00 7.66145170e-01 2.59357374e-02
-4.67581987e-01 4.80785310e-01 -7.09700808e-02 -4.50665474e-01
-9.99542177e-02 -7.22806811e-01 -4.16338265e-01 -7.60423839e-01
5.26548862e-01 8.28516483e-01 2.15408370e-01 -9.62684825... | [11.830108642578125, 2.6056110858917236] |
50163adc-b3a4-4623-b941-dbac4a38d518 | 190506229 | 1905.06229 | null | https://arxiv.org/abs/1905.06229v2 | https://arxiv.org/pdf/1905.06229v2.pdf | Toward Standardized Classification of Foveated Displays | Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acui... | ['David Luebke', 'Rachel Albert', 'Michael Stengel', 'Kaan Aksit', 'Josef Spjut', 'Trey Greer', 'Jonghyun Kim', 'Ben Boudaoud'] | 2019-05-03 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 2.12880388e-01 -1.37487993e-01 3.55480969e-01 -2.29684561e-01
-1.56555831e-01 -7.98788726e-01 3.08557719e-01 -1.31200746e-01
-3.71859848e-01 3.88508767e-01 3.03169370e-01 -7.14891791e-01
-1.19533524e-01 -3.11293781e-01 -1.29122213e-01 -3.28733623e-01
2.91180342e-01 -4.84927922e-01 3.88913721e-01 -5.32541648... | [14.070602416992188, 0.10945285111665726] |
01c25cb7-1bd4-4c97-956a-f4ba9a0c7698 | hawk-an-industrial-strength-multi-label | 2301.06057 | null | https://arxiv.org/abs/2301.06057v1 | https://arxiv.org/pdf/2301.06057v1.pdf | Hawk: An Industrial-strength Multi-label Document Classifier | There are a plethora of methods and algorithms that solve the classical multi-label document classification. However, when it comes to deployment and usage in an industry setting, most, if not all the contemporary approaches fail to address some of the vital aspects or requirements of an ideal solution: i. ability to o... | ['Arshad Javeed'] | 2023-01-15 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 3.12115222e-01 -1.49368510e-01 -1.08414508e-01 -4.87856328e-01
-6.86560988e-01 -2.60208905e-01 6.20460093e-01 3.35397273e-01
-5.64571083e-01 4.66598302e-01 1.79793030e-01 -4.33789760e-01
-3.67100179e-01 -4.10055012e-01 -6.17462754e-01 -7.96341300e-01
3.82979959e-02 5.52966356e-01 1.23668991e-01 -2.19466627... | [10.398131370544434, 7.849839687347412] |
d9633e26-f479-43e0-b7a7-8cba562d87e6 | satellite-image-search-in-agoraeo | 2208.10830 | null | https://arxiv.org/abs/2208.10830v1 | https://arxiv.org/pdf/2208.10830v1.pdf | Satellite Image Search in AgoraEO | The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To ad... | ['Volker Markl', 'Begüm Demir', 'Jorge-Arnulfo Quiané-Ruiz', 'Marcela Charfuelan', 'Holmer Hemsen', 'Eleni Tzirita Zacharatou', 'Pavel Dushev', 'Ahmet Kerem Aksoy'] | 2022-08-23 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-5.40670276e-01 -5.40889323e-01 -6.30545150e-03 -2.74756700e-01
-8.43882442e-01 -8.44829142e-01 5.88835299e-01 4.54054207e-01
-8.06138039e-01 3.26384515e-01 2.59757251e-01 -4.17863339e-01
-3.11803609e-01 -1.26423955e+00 -1.60446897e-01 -6.13211274e-01
-7.80665755e-01 5.42926788e-01 4.66734141e-01 -4.79843259... | [7.763664245605469, -1.9286174774169922] |
6ad47fa9-a1b9-4102-aad5-7cb6b29d653c | adaptive-temporal-encoding-network-for-video | 1808.00661 | null | http://arxiv.org/abs/1808.00661v2 | http://arxiv.org/pdf/1808.00661v2.pdf | Adaptive Temporal Encoding Network for Video Instance-level Human Parsing | Beyond the existing single-person and multiple-person human parsing tasks in
static images, this paper makes the first attempt to investigate a more
realistic video instance-level human parsing that simultaneously segments out
each person instance and parses each instance into more fine-grained parts
(e.g., head, leg, ... | ['Liang Lin', 'Qixian Zhou', 'Ke Gong', 'Xiaodan Liang'] | 2018-08-02 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 4.63901907e-01 -8.46436247e-02 -2.30406567e-01 -3.89262646e-01
-7.98941493e-01 -4.99422550e-01 4.80010390e-01 -7.53976852e-02
-5.32707810e-01 4.34713840e-01 9.93865207e-02 -1.45783544e-01
2.76038289e-01 -8.32058191e-01 -8.68590772e-01 -5.14539838e-01
-1.90555602e-01 -1.18382432e-01 5.69057107e-01 1.16885282... | [9.194022178649902, -0.0267354566603899] |
2e3c9def-7c66-4510-a562-02c8260b029c | multi-scale-interaction-for-real-time-lidar | 2008.09162 | null | https://arxiv.org/abs/2008.09162v2 | https://arxiv.org/pdf/2008.09162v2.pdf | Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform | Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which are very expensive and difficul... | ['Dengxin Dai', 'Yun Liu', 'Xieyuanli Chen', 'Shijie Li', 'Juergen Gall', 'Cyrill Stachniss'] | 2020-08-20 | null | null | null | null | ['real-time-3d-semantic-segmentation'] | ['computer-vision'] | [-1.85082421e-01 -1.64792314e-01 -9.80565883e-03 -4.70213234e-01
-1.54797733e-01 -1.41927645e-01 3.57739091e-01 1.35178104e-01
-9.06667113e-01 4.39338237e-01 -1.04207575e+00 -6.07543707e-01
-8.74618143e-02 -1.46277344e+00 -7.92100191e-01 -4.81501341e-01
2.42207721e-01 9.65138197e-01 1.25788522e+00 -1.93010181... | [8.097898483276367, -2.642282485961914] |
b7e1ac4f-5066-475c-9d07-33fdbcb89764 | automated-audio-captioning-an-overview-of | 2205.05949 | null | https://arxiv.org/abs/2205.05949v2 | https://arxiv.org/pdf/2205.05949v2.pdf | Automated Audio Captioning: An Overview of Recent Progress and New Challenges | Automated audio captioning is a cross-modal translation task that aims to generate natural language descriptions for given audio clips. This task has received increasing attention with the release of freely available datasets in recent years. The problem has been addressed predominantly with deep learning techniques. N... | ['Wenwu Wang', 'Mark D. Plumbley', 'Xubo Liu', 'Xinhao Mei'] | 2022-05-12 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.48716748e-01 3.12899381e-01 -1.06354743e-01 -2.85069168e-01
-1.54332185e+00 -5.05224109e-01 7.49755561e-01 -1.26353034e-03
-2.18373790e-01 1.10627043e+00 7.84617126e-01 1.69854328e-01
1.55149683e-01 -3.91515672e-01 -7.98825026e-01 -3.31918329e-01
-1.02514185e-01 5.30904233e-01 -1.68436840e-01 -1.55626789... | [15.274275779724121, 4.844076156616211] |
6e95c9b4-b127-49c8-a3bf-3ee251265231 | tractable-epistemic-reasoning-with-functional | 1403.0034 | null | https://arxiv.org/abs/1403.0034v4 | https://arxiv.org/pdf/1403.0034v4.pdf | Tractable Epistemic Reasoning with Functional Fluents, Static Causal Laws and Postdiction | We present an epistemic action theory for tractable epistemic reasoning as an extension to the h-approximation (HPX) theory. In contrast to existing tractable approaches, the theory supports functional fluents and postdictive reasoning with static causal laws. We argue that this combination is particularly synergistic ... | ['Manfred Eppe'] | 2014-03-01 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [ 9.81011391e-02 1.21327817e+00 -3.18154126e-01 7.36028794e-03
-5.44692218e-01 -8.41858685e-01 9.69889045e-01 5.02138697e-02
-7.87955523e-02 9.08346713e-01 7.88500249e-01 -8.49036098e-01
-7.32013583e-01 -1.14292824e+00 -7.05500424e-01 -4.06172216e-01
-4.38563615e-01 5.07476211e-01 7.04782128e-01 -3.56201679... | [8.486250877380371, 6.540452003479004] |
7d4e160e-ffe7-4ef5-9767-c148f2f211d0 | open-set-likelihood-maximization-for-few-shot | 2301.08390 | null | https://arxiv.org/abs/2301.08390v2 | https://arxiv.org/pdf/2301.08390v2.pdf | Open-Set Likelihood Maximization for Few-Shot Learning | We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore the popular transductive setting, which leverages the unlabelled query i... | ['Ismail Ben Ayed', 'Céline Hudelot', 'Pablo Piantanida', 'Antoine Toubhans', 'Myriam Tami', 'Etienne Bennequin', 'Malik Boudiaf'] | 2023-01-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Boudiaf_Open-Set_Likelihood_Maximization_for_Few-Shot_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Boudiaf_Open-Set_Likelihood_Maximization_for_Few-Shot_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-image-classification', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 3.49328876e-01 3.02980930e-01 -2.39953920e-01 -4.73718494e-01
-1.34387696e+00 -5.78073561e-01 4.28931773e-01 3.52918804e-01
-1.86619535e-01 6.47150040e-01 -6.99765831e-02 7.62529895e-02
-2.89541423e-01 -5.16640723e-01 -1.07370710e+00 -7.58206069e-01
1.21014483e-01 8.79970014e-01 1.06769226e-01 8.68538991... | [9.592618942260742, 3.1508677005767822] |
d5894520-fc74-41fe-96b2-62b03ea94c4c | the-effectiveness-of-mae-pre-pretraining-for | 2303.13496 | null | https://arxiv.org/abs/2303.13496v1 | https://arxiv.org/pdf/2303.13496v1.pdf | The effectiveness of MAE pre-pretraining for billion-scale pretraining | This paper revisits the standard pretrain-then-finetune paradigm used in computer vision for visual recognition tasks. Typically, state-of-the-art foundation models are pretrained using large scale (weakly) supervised datasets with billions of images. We introduce an additional pre-pretraining stage that is simple and ... | ['Ishan Misra', 'Rohit Girdhar', 'Ross Girshick', 'Christoph Feichtenhofer', 'Piotr Dollár', 'Armand Joulin', 'Aaron Adcock', 'Vaibhav Aggarwal', 'Haoqi Fan', 'Kalyan Vasudev Alwala', 'Quentin Duval', 'Mannat Singh'] | 2023-03-23 | null | null | null | null | ['video-classification', 'zero-shot-transfer-image-classification', 'video-recognition', 'action-classification', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.50868762e-01 -2.44537815e-01 -4.46338147e-01 -3.71029198e-01
-5.55838883e-01 -3.43053222e-01 7.26639330e-01 -2.64722496e-01
-8.44019234e-01 2.33016133e-01 -1.98257029e-01 -3.48439991e-01
2.69516885e-01 -5.71146667e-01 -1.22251582e+00 -3.67181152e-01
-4.53328937e-02 4.56881106e-01 4.56620336e-01 -2.54452646... | [9.837603569030762, 1.9590505361557007] |
668ebbf8-2701-4cab-9d46-1f72681b6aa9 | analyzing-the-vulnerabilities-in-splitfed | 2307.03197 | null | https://arxiv.org/abs/2307.03197v1 | https://arxiv.org/pdf/2307.03197v1.pdf | Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks | Distributed Collaborative Machine Learning (DCML) is a potential alternative to address the privacy concerns associated with centralized machine learning. The Split learning (SL) and Federated Learning (FL) are the two effective learning approaches in DCML. Recently there have been an increased interest on the hybrid o... | ['Raj Mani Shukla', 'Aysha Thahsin Zahir Ismail'] | 2023-07-04 | null | null | null | null | ['data-poisoning', 'handwritten-digit-recognition', 'federated-learning'] | ['adversarial', 'computer-vision', 'methodology'] | [-2.67948121e-01 -2.44186148e-01 3.25684279e-01 -2.80929148e-01
-6.35964751e-01 -1.02684760e+00 6.95372283e-01 2.62411088e-01
-4.06026900e-01 6.75825775e-01 -4.34271805e-02 -5.38571179e-01
-5.15331388e-01 -4.51777548e-01 -2.39228144e-01 -1.06551433e+00
-3.13008815e-01 2.18644306e-01 1.89180031e-01 2.80013740... | [5.604349613189697, 7.054699897766113] |
56a487bb-3092-4466-8e6b-5a0feb93117e | anatomically-aware-dual-hop-learning-for | 2303.17593 | null | https://arxiv.org/abs/2303.17593v1 | https://arxiv.org/pdf/2303.17593v1.pdf | Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms | Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold standard for PE diagnosis, it is still susceptible to misdiagnosis or significant diagnosis delays, which may be fatal for critical cases. Des... | ['Marius Leordeanu', 'A Mohamed Ali', 'Jonathan Sperl', 'Puneet Sharma', 'Lucian Itu', 'Saikiran Rapaka', 'Florin Condrea'] | 2023-03-30 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [ 1.07299440e-01 -5.62098995e-02 -2.22373217e-01 1.46966696e-01
-1.19270837e+00 -4.70778346e-01 2.25654811e-01 4.18488979e-01
-6.23641610e-01 6.61163747e-01 1.56689644e-01 -8.76343429e-01
-2.41857708e-01 -6.88546658e-01 -3.53250802e-01 -6.18190467e-01
-2.80004352e-01 1.20306981e+00 5.34070134e-01 4.53761667... | [15.17671012878418, -2.00669527053833] |
26fdd151-b8de-4567-b1e5-7041b73040a0 | learning-progressive-point-embeddings-for-3d | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.pdf | Learning Progressive Point Embeddings for 3D Point Cloud Generation | Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point... | ['DaCheng Tao', 'Baosheng Yu', 'Cheng Wen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['point-cloud-generation', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.02008409e-02 1.31393030e-01 2.75610209e-01 -7.70073608e-02
-8.90492439e-01 -5.11291564e-01 9.42793965e-01 5.01728393e-02
-1.02855898e-01 6.68598473e-01 -3.84454697e-01 -2.83502638e-01
4.32429463e-03 -1.28588808e+00 -1.18104780e+00 -6.08119369e-01
1.74125105e-01 1.00928366e+00 4.57009710e-02 -4.23365057... | [8.733473777770996, -3.6424381732940674] |
7f9989c0-1816-4d83-982e-58645900da04 | exploring-deep-models-for-practical-gait | 2303.03301 | null | https://arxiv.org/abs/2303.03301v2 | https://arxiv.org/pdf/2303.03301v2.pdf | Exploring Deep Models for Practical Gait Recognition | Gait recognition is a rapidly advancing vision technique for person identification from a distance. Prior studies predominantly employed relatively small and shallow neural networks to extract subtle gait features, achieving impressive successes in indoor settings. Nevertheless, experiments revealed that these existing... | ['Shiqi Yu', 'Yongzhen Huang', 'Saihui Hou', 'Chao Fan'] | 2023-03-06 | null | null | null | null | ['gait-recognition', 'person-identification'] | ['computer-vision', 'computer-vision'] | [-1.73626587e-01 -8.94815445e-01 -1.51871189e-01 -3.06929141e-01
-5.66152215e-01 -2.35174432e-01 2.56154239e-01 -2.37414047e-01
-3.81734550e-01 6.36178792e-01 1.35884389e-01 9.99441221e-02
-5.99272549e-03 -7.54843593e-01 -2.25471467e-01 -7.34859467e-01
-4.39055800e-01 4.61168945e-01 8.71620979e-03 -4.89249438... | [14.29710578918457, 1.4279143810272217] |
e9454fa3-3a4b-401c-9645-0bfead3d1d84 | visualizing-relation-between-de-motivating | 2306.12118 | null | https://arxiv.org/abs/2306.12118v2 | https://arxiv.org/pdf/2306.12118v2.pdf | Visualizing Relation Between (De)Motivating Topics and Public Stance toward COVID-19 Vaccine | While social media plays a vital role in communication nowadays, misinformation and trolls can easily take over the conversation and steer public opinion on these platforms. We saw the effect of misinformation during the COVID-19 pandemic when public health officials faced significant push-back while trying to motivate... | ['Hamed Alhoori', 'Ashiqur Rahman'] | 2023-06-21 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 2.90199444e-02 3.52019936e-01 -1.18897790e-02 1.00233763e-01
1.59360049e-03 -7.15333283e-01 5.76710343e-01 1.00918794e+00
-5.22105217e-01 8.01404417e-01 7.43798971e-01 -8.53844225e-01
1.56130612e-01 -9.36953604e-01 -3.29913467e-01 -7.87899077e-01
-2.33516678e-01 3.06390822e-01 4.63452265e-02 -7.79138088... | [8.491903305053711, 9.75222110748291] |
522b5360-150b-4914-8b8f-c79c793dec7b | mind-the-gap-polishing-pseudo-labels-for | 2207.08185 | null | https://arxiv.org/abs/2207.08185v2 | https://arxiv.org/pdf/2207.08185v2.pdf | Mind the Gap: Polishing Pseudo labels for Accurate Semi-supervised Object Detection | Exploiting pseudo labels (e.g., categories and bounding boxes) of unannotated objects produced by a teacher detector have underpinned much of recent progress in semi-supervised object detection (SSOD). However, due to the limited generalization capacity of the teacher detector caused by the scarce annotations, the prod... | ['Wei Wei', 'Yuxuan Sun', 'Lei Zhang'] | 2022-07-17 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.24411869e-01 4.81349438e-01 -3.04219306e-01 -4.51106280e-01
-7.33155131e-01 -7.99246311e-01 5.92536569e-01 1.45133123e-01
-5.10186493e-01 5.72825730e-01 -1.54864684e-01 9.36326906e-02
9.93663371e-02 -6.91869259e-01 -9.32324708e-01 -9.65815961e-01
3.30412865e-01 5.68109989e-01 6.30858183e-01 2.94833213... | [9.218660354614258, 1.3630975484848022] |
96aebd50-ad1c-4a6b-b2ab-17ee024467f5 | learning-from-adjective-noun-pairs-a | null | null | https://aclanthology.org/2022.coling-1.590 | https://aclanthology.org/2022.coling-1.590.pdf | Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification | Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair. Recently, dominant works employ the attention mechanism to capture the corresponding visual represen... | ['Jiajun Chen', 'ShuJian Huang', 'Xinyu Dai', 'Siyu Long', 'Zhen Wu', 'Fei Zhao'] | null | null | null | null | coling-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.30555600e-01 -3.43884736e-01 -1.64005294e-01 -3.51187497e-01
-4.80651081e-01 -5.26643097e-01 5.35685539e-01 2.39456128e-02
-1.41984239e-01 3.26253086e-01 5.89689314e-01 -1.17799386e-01
3.70762616e-01 -4.69204813e-01 -5.24823546e-01 -9.65007603e-01
5.69261789e-01 -8.35067704e-02 1.89583197e-01 -4.08988237... | [10.86031723022461, 1.7689217329025269] |
b821611a-b943-42e6-b944-7ced3625bf45 | reasoning-based-software-testing | 2303.01302 | null | https://arxiv.org/abs/2303.01302v1 | https://arxiv.org/pdf/2303.01302v1.pdf | Reasoning-Based Software Testing | With software systems becoming increasingly pervasive and autonomous, our ability to test for their quality is severely challenged. Many systems are called to operate in uncertain and highly-changing environment, not rarely required to make intelligent decisions by themselves. This easily results in an intractable stat... | ['Stefano Russo', 'Roberto Pietrantuono', 'Luca Giamattei'] | 2023-03-02 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.13245749e-01 3.98144513e-01 -3.99211556e-01 -2.66786486e-01
-2.82667458e-01 -5.68916202e-01 7.62589753e-01 2.26825476e-01
-5.65620838e-03 7.77567923e-01 -2.95883685e-01 -7.80328929e-01
-5.38357019e-01 -1.12877464e+00 -8.32563698e-01 -5.77524662e-01
-2.54000604e-01 7.41214633e-01 4.68368024e-01 -2.97101110... | [5.455946445465088, 2.7785487174987793] |
edd0033c-c978-4f86-a296-8709f5ac6e9d | learning-discrete-structures-for-graph-neural | 1903.11960 | null | https://arxiv.org/abs/1903.11960v4 | https://arxiv.org/pdf/1903.11960v4.pdf | Learning Discrete Structures for Graph Neural Networks | Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are oft... | ['Massimiliano Pontil', 'Luca Franceschi', 'Xiao He', 'Mathias Niepert'] | 2019-03-28 | null | null | null | null | ['music-genre-recognition'] | ['music'] | [-1.04027696e-01 1.84257090e-01 -1.98262542e-01 -1.98361561e-01
-6.71892911e-02 -6.43500865e-01 4.29770201e-01 4.42435563e-01
-2.79890209e-01 7.25660801e-01 -2.75235951e-01 -5.21786273e-01
-3.63379061e-01 -1.03343666e+00 -1.05372560e+00 -5.29030323e-01
-4.34177727e-01 7.58173406e-01 -2.31144149e-02 -3.58754247... | [6.910409450531006, 6.160907745361328] |
1096b07f-59cc-4ee8-b704-8986ecb60dc5 | mdssd-multi-scale-deconvolutional-single-shot | 1805.07009 | null | https://arxiv.org/abs/1805.07009v3 | https://arxiv.org/pdf/1805.07009v3.pdf | MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects | For most of the object detectors based on multi-scale feature maps, the shallow layers are rich in fine spatial information and thus mainly responsible for small object detection. The performance of small object detection, however, is still less than satisfactory because of the deficiency of semantic information on sha... | ['Zhimin Gao', 'Pei Lv', 'Rui Ma', 'Xiaoheng Jiang', 'Mingliang Xu', 'Bing Zhou', 'Lisha Cui'] | 2018-05-18 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-4.36130539e-02 -1.85021713e-01 1.81999415e-01 -2.19771296e-01
-7.80315816e-01 -2.07963467e-01 4.18998003e-01 1.20628826e-01
-7.82450795e-01 4.29502130e-01 -6.75512478e-02 3.71874034e-01
1.65589601e-01 -8.54626358e-01 -7.55429804e-01 -1.01042295e+00
9.75663289e-02 -4.80121374e-02 1.18067443e+00 -7.53888041... | [8.806475639343262, -0.48596686124801636] |
0f193f1d-be47-4b6e-a896-51a59b252df5 | end-to-end-conversational-search-for-online | 2109.05460 | null | https://arxiv.org/abs/2109.05460v1 | https://arxiv.org/pdf/2109.05460v1.pdf | End-to-End Conversational Search for Online Shopping with Utterance Transfer | Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data.In this work we first propose Con... | ['Yaohui Jin', 'Hao He', 'Tong Zhao', 'Wei Chen', 'Nasser Zalmout', 'Pascual Martinez-Gomez', 'Xin Luna Dong', 'Jun Ma2', 'Liqiang Xiao'] | 2021-09-12 | null | https://aclanthology.org/2021.emnlp-main.280 | https://aclanthology.org/2021.emnlp-main.280.pdf | emnlp-2021-11 | ['conversational-search'] | ['natural-language-processing'] | [-1.04988776e-01 6.32768571e-01 -1.43330336e-01 -7.55950451e-01
-9.77801979e-01 -7.55007982e-01 8.57713521e-01 -8.65619779e-02
-2.33274817e-01 4.10610825e-01 6.16646469e-01 -2.33191773e-01
8.57607424e-02 -5.29816091e-01 -2.87249953e-01 -8.13707933e-02
4.45407838e-01 1.33508873e+00 9.28363428e-02 -1.03860760... | [12.441850662231445, 7.90454626083374] |
107c8d32-89bb-426d-984f-05437e81467a | smaller3d-smaller-models-for-3d-semantic | 2305.03188 | null | https://arxiv.org/abs/2305.03188v1 | https://arxiv.org/pdf/2305.03188v1.pdf | Smaller3d: Smaller Models for 3D Semantic Segmentation Using Minkowski Engine and Knowledge Distillation Methods | There are various optimization techniques in the realm of 3D, including point cloud-based approaches that use mesh, texture, and voxels which optimize how you store, and how do calculate in 3D. These techniques employ methods such as feed-forward networks, 3D convolutions, graph neural networks, transformers, and spars... | ['Erik Harutyunyan', 'Alen Adamyan'] | 2023-05-04 | null | null | null | null | ['3d-semantic-segmentation'] | ['computer-vision'] | [-5.53435683e-01 -1.01631820e-01 2.16470689e-01 -3.30916107e-01
-2.41695136e-01 -1.44363597e-01 4.59944367e-01 -2.65704319e-02
-4.89180237e-01 3.52754831e-01 -1.16045969e-02 -4.61044639e-01
-2.61924773e-01 -1.10782957e+00 -1.00520253e+00 -3.43350142e-01
-6.21756554e-01 8.76762092e-01 5.01322627e-01 -2.16328233... | [7.937671184539795, -3.6251511573791504] |
981cfe80-2a84-494f-87b1-9665d073ead9 | teaching-the-pre-trained-model-to-generate | 2305.12463 | null | https://arxiv.org/abs/2305.12463v1 | https://arxiv.org/pdf/2305.12463v1.pdf | Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification | Randomly masking text spans in ordinary texts in the pre-training stage hardly allows models to acquire the ability to generate simple texts. It can hurt the performance of pre-trained models on text simplification tasks. In this paper, we propose a new continued pre-training strategy to teach the pre-trained model to ... | ['Xiaojun Wan', 'Wei Xu', 'Renliang Sun'] | 2023-05-21 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 4.26546931e-01 4.29577023e-01 6.31404668e-02 -4.44579095e-01
-7.35589802e-01 -2.82930583e-01 5.87819099e-01 1.61721319e-01
-5.69833040e-01 8.35442662e-01 4.01005626e-01 -6.84589624e-01
2.13162512e-01 -5.25764525e-01 -7.29066670e-01 -1.41091123e-01
5.44424653e-01 6.65087402e-01 -7.94673860e-02 -5.09944499... | [11.14171028137207, 10.307579040527344] |
a54ef58a-fd0b-4c6f-bdd2-682faee3c21e | envgan-adversarial-synthesis-of-environmental | 2104.07326 | null | https://arxiv.org/abs/2104.07326v1 | https://arxiv.org/pdf/2104.07326v1.pdf | EnvGAN: Adversarial Synthesis of Environmental Sounds for Data Augmentation | The research in Environmental Sound Classification (ESC) has been progressively growing with the emergence of deep learning algorithms. However, data scarcity poses a major hurdle for any huge advance in this domain. Data augmentation offers an excellent solution to this problem. While Generative Adversarial Networks (... | ['Suresh K', 'Aswathy Madhu'] | 2021-04-15 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 4.50451195e-01 1.69716805e-01 5.91753483e-01 1.86967269e-01
-7.79609919e-01 -5.40073276e-01 5.97260535e-01 -6.16349816e-01
-1.02487184e-01 9.99414742e-01 2.58538902e-01 -2.98943460e-01
2.39895210e-01 -1.08802772e+00 -5.85303187e-01 -1.00326419e+00
1.47042572e-01 1.63025588e-01 -1.06286794e-01 -5.93643665... | [15.547972679138184, 5.962237358093262] |
45222772-c394-4651-a474-6df7718c5235 | correcting-semantic-parses-with-natural | 2305.19974 | null | https://arxiv.org/abs/2305.19974v1 | https://arxiv.org/pdf/2305.19974v1.pdf | Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding | In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges. It becomes increasingly important to understand and remedy the points of failure as the performance of semantic parsing systems improve. We explore semantic parse correction with natural language f... | ['Preethi Raghavan', 'Parag Pravin Dakle', 'Parker Glenn'] | 2023-05-31 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 2.69374937e-01 6.18447423e-01 3.22427712e-02 -7.96481550e-01
-1.49911094e+00 -5.60268223e-01 2.91024148e-01 4.87739652e-01
-3.20532650e-01 3.38335127e-01 6.46894455e-01 -6.24871016e-01
3.18849653e-01 -7.26119578e-01 -9.86967981e-01 4.15853232e-01
4.60047811e-01 7.17859745e-01 4.24653411e-01 -5.05066931... | [9.947724342346191, 7.954697608947754] |
4f4c6f01-9728-4c75-92db-a110a616e32a | quark-a-gradient-free-quantum-learning | 2210.01311 | null | https://arxiv.org/abs/2210.01311v1 | https://arxiv.org/pdf/2210.01311v1.pdf | Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks | As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1) embed a classical model into a target Hamiltonian to enable quantum optimization or (2) represent a quantum model using variational quantum ... | ['Zhihao Jia', 'Heyang Huang', 'Zhuoming Chen', 'Zhihao Zhang'] | 2022-10-02 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 1.10948339e-01 -9.70929712e-02 -2.77243644e-01 -1.71267271e-01
-1.14997625e+00 -4.12526339e-01 3.70471835e-01 1.63284287e-01
-6.67715907e-01 6.33869886e-01 -2.87030041e-01 -6.24506056e-01
1.45704998e-02 -1.02473736e+00 -7.83316851e-01 -8.18676293e-01
5.79903722e-02 6.06070995e-01 1.82654709e-01 -2.78320044... | [5.643320560455322, 4.881498336791992] |
f8e81403-1ef3-41c5-ac4e-e11c7a91026b | decoding-urban-health-nexus-interpretable | 2306.11847 | null | https://arxiv.org/abs/2306.11847v2 | https://arxiv.org/pdf/2306.11847v2.pdf | Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features | This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence. Utilizing data from five Metropolitan Statistical Areas in the United States: Chicago, Dallas, Houston, Los Angeles, and New Yo... | ['Ali Mostafavi', 'Chenyue Liu'] | 2023-06-20 | null | null | null | null | ['causal-inference', 'interpretable-machine-learning', 'causal-inference'] | ['knowledge-base', 'methodology', 'miscellaneous'] | [ 1.88215151e-02 5.07188216e-02 -8.70427191e-01 1.48362210e-02
-6.41196430e-01 3.03304493e-01 4.38875318e-01 9.99408007e-01
-3.48001122e-01 5.25175452e-01 1.29352498e+00 -1.00307858e+00
-3.17474365e-01 -1.43692720e+00 -5.20950258e-01 -5.04560649e-01
3.74876037e-02 -3.38523120e-01 -3.95692229e-01 -1.94330946... | [6.505660057067871, 1.9556492567062378] |
bcd29956-3084-46ea-85a4-920e90606d1e | pmp-net-point-cloud-completion-by-transformer | 2202.09507 | null | https://arxiv.org/abs/2202.09507v3 | https://arxiv.org/pdf/2202.09507v3.pdf | PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths | Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered nature of point clouds will degrade generation of high-quality 3D shapes, as detailed topology and structure of unordered points are hard to... | ['Zhizhong Han', 'Yu-Shen Liu', 'Wen Zheng', 'Pengfei Wan', 'Yan-Pei Cao', 'Peng Xiang', 'Xin Wen'] | 2022-02-19 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.16198938e-02 -6.43905550e-02 1.29925936e-01 1.70931611e-02
-6.16848648e-01 -6.06865704e-01 4.80450571e-01 -2.74052560e-01
3.27299327e-01 4.00965005e-01 -8.28481764e-02 -2.06039146e-01
-6.13097101e-02 -1.26108098e+00 -1.15404558e+00 -4.04131740e-01
7.45495141e-04 1.13924098e+00 2.92351041e-02 -3.70630115... | [8.426887512207031, -3.594987392425537] |
d1f658a8-7ce7-4e6f-92fa-f7db57efa4df | masked-path-modeling-for-vision-and-language | 2305.14268 | null | https://arxiv.org/abs/2305.14268v1 | https://arxiv.org/pdf/2305.14268v1.pdf | Masked Path Modeling for Vision-and-Language Navigation | Vision-and-language navigation (VLN) agents are trained to navigate in real-world environments by following natural language instructions. A major challenge in VLN is the limited availability of training data, which hinders the models' ability to generalize effectively. Previous approaches have attempted to address thi... | ['Nanyun Peng', 'Feng Gao', 'Zi-Yi Dou'] | 2023-05-23 | null | null | null | null | ['action-generation', 'navigate', 'vision-and-language-navigation'] | ['computer-vision', 'reasoning', 'robots'] | [ 3.40532094e-01 1.09507732e-01 2.76701227e-02 -5.10874927e-01
-7.64777184e-01 -5.93301713e-01 6.25479758e-01 -2.36170664e-02
-8.36891353e-01 8.88557196e-01 7.33435825e-02 -7.49031961e-01
2.11645141e-01 -8.20156217e-01 -1.09409463e+00 -6.32227719e-01
-8.10026675e-02 3.79738390e-01 3.24842155e-01 -4.43804711... | [4.495048522949219, 0.6228921413421631] |
957763f2-6be8-4655-8165-ae5a8752d424 | learning-to-see-moving-objects-in-the-dark | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Jiang_Learning_to_See_Moving_Objects_in_the_Dark_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Jiang_Learning_to_See_Moving_Objects_in_the_Dark_ICCV_2019_paper.pdf | Learning to See Moving Objects in the Dark | Video surveillance systems have wide range of utilities, yet easily suffer from great quality degeneration under dim light circumstances. Industrial solutions mainly use extra near-infrared illuminations, even though it doesn't preserve color and texture information. A variety of researches enhanced low-light videos sh... | [' Yinqiang Zheng', 'Haiyang Jiang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['video-enhancement', 'miscellaneous'] | ['computer-vision', 'miscellaneous'] | [ 4.44398284e-01 -7.06339598e-01 9.21165943e-02 -3.43564540e-01
-1.89108655e-01 -4.94077921e-01 4.31039304e-01 -9.68353450e-01
-3.55956316e-01 7.72842646e-01 -9.55245495e-02 -5.99587373e-02
3.01621705e-01 -8.25061858e-01 -7.68518984e-01 -1.04514194e+00
3.93495858e-01 -6.53640032e-01 3.93707007e-01 -4.11481887... | [10.698983192443848, -2.367225170135498] |
2a80d19b-a69c-4413-8a7e-a1ff689a92a5 | automatic-punctuation-restoration-with-bert | 2101.07343 | null | https://arxiv.org/abs/2101.07343v1 | https://arxiv.org/pdf/2101.07343v1.pdf | Automatic punctuation restoration with BERT models | We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used benchmark for punctuation restoration, while for Hungarian we evaluate our models on the Szeged Treebank dataset. Our best models achieve a macro... | ['Judit Ács', 'Bence Bial', 'Attila Nagy'] | 2021-01-18 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [-3.60715181e-01 2.95842320e-01 1.27315745e-01 -4.15345967e-01
-1.49757528e+00 -4.48873132e-01 2.79821996e-02 2.24926829e-01
-8.10055494e-01 9.92311239e-01 7.12089181e-01 -4.25990373e-01
3.65363330e-01 -4.61663157e-01 -6.60443962e-01 -3.72387052e-01
-6.68387637e-02 2.72768438e-01 1.80469394e-01 -6.66158557... | [14.25218391418457, 7.130068302154541] |
1c859a47-427f-455d-9342-43df9e5dab3c | deep-joint-face-hallucination-and-recognition | 1611.08091 | null | http://arxiv.org/abs/1611.08091v1 | http://arxiv.org/pdf/1611.08091v1.pdf | Deep Joint Face Hallucination and Recognition | Deep models have achieved impressive performance for face hallucination
tasks. However, we observe that directly feeding the hallucinated facial images
into recog- nition models can even degrade the recognition performance despite
the much better visualization quality. In this paper, we address this problem
by jointly ... | ['Shengyong Ding', 'Wei Xu', 'Junyu Wu', 'Hongyang Chao'] | 2016-11-24 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 3.53122413e-01 4.78744447e-01 6.95549995e-02 -5.40858924e-01
-6.32286072e-01 9.60212275e-02 4.40995932e-01 -9.50735211e-01
-1.40344277e-01 5.68994045e-01 1.25496119e-01 1.70214012e-01
1.35551870e-01 -7.53196180e-01 -8.39146435e-01 -7.02570856e-01
2.08686963e-01 7.60456026e-02 -6.74945295e-01 2.85265684... | [12.784052848815918, 0.0031386511400341988] |
8add0f82-9b40-4f3a-81fa-a609377d4e0a | uni-fedrec-a-unified-privacy-preserving-news | 2109.05236 | null | https://arxiv.org/abs/2109.05236v1 | https://arxiv.org/pdf/2109.05236v1.pdf | Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving | News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models offline and provide online recommendation services. However, user data is usually highly privacy-sensitive, and centrally storing them may ra... | ['Xing Xie', 'Yongfeng Huang', 'Chuhan Wu', 'Fangzhao Wu', 'Tao Qi'] | 2021-09-11 | null | https://aclanthology.org/2021.findings-emnlp.124 | https://aclanthology.org/2021.findings-emnlp.124.pdf | findings-emnlp-2021-11 | ['news-generation'] | ['natural-language-processing'] | [-0.17897792 -0.07581278 -0.6179729 -0.6684934 -0.9235289 -0.66471857
0.41909876 0.04734858 -0.16199298 0.6233099 0.86244285 -0.01878562
0.1297399 -1.1967126 -0.71191037 -0.7522254 0.485189 0.3391466
0.21453911 -0.24393813 0.20857628 0.19259854 -1.5213549 0.8135222
0.6970169 1.2588408 -0.22... | [5.919621467590332, 6.4227142333984375] |
85b345dd-dc50-4c53-9c73-4198a6e87015 | subword-mapping-and-anchoring-across | 2109.04556 | null | https://arxiv.org/abs/2109.04556v1 | https://arxiv.org/pdf/2109.04556v1.pdf | Subword Mapping and Anchoring across Languages | State-of-the-art multilingual systems rely on shared vocabularies that sufficiently cover all considered languages. To this end, a simple and frequently used approach makes use of subword vocabularies constructed jointly over several languages. We hypothesize that such vocabularies are suboptimal due to false positives... | ['Andrei Popescu-Belis', 'Giorgos Vernikos'] | 2021-09-09 | null | https://aclanthology.org/2021.findings-emnlp.224 | https://aclanthology.org/2021.findings-emnlp.224.pdf | findings-emnlp-2021-11 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-1.00270741e-01 -1.17979825e-01 -5.52616239e-01 -4.68886465e-01
-1.20681489e+00 -9.56275225e-01 6.94758058e-01 3.32792312e-01
-8.02329242e-01 1.14058423e+00 3.56363714e-01 -3.52111250e-01
1.76391467e-01 -6.32999837e-01 -9.55660462e-01 -4.22913164e-01
2.79240668e-01 6.34024382e-01 3.64769921e-02 -6.11742318... | [11.055376052856445, 10.0125150680542] |
45f6db7f-3b39-488c-8f9c-3f219be9db0e | optimality-of-variational-inference-for-1 | null | null | http://proceedings.neurips.cc/paper/2021/hash/a5e308070bd6dd3cc56283f2313522de-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a5e308070bd6dd3cc56283f2313522de-Paper.pdf | Optimality of variational inference for stochasticblock model with missing links | Variational methods are extremely popular in the analysis of network data. Statistical guarantees obtained for these methods typically provide asymptotic normality for the problem of estimation of global model parameters under the stochastic block model. In the present work, we consider the case of networks with missin... | ['Olga Klopp', 'Solenne Gaucher'] | 2021-12-01 | null | https://openreview.net/forum?id=iYzsR0JNaa2 | https://openreview.net/pdf?id=iYzsR0JNaa2 | neurips-2021-12 | ['stochastic-block-model'] | ['graphs'] | [ 3.72470655e-02 3.04924428e-01 -5.93142688e-01 -4.58405949e-02
-5.58695614e-01 -2.65169173e-01 3.12484801e-01 -9.22902822e-02
-2.50021279e-01 1.23748064e+00 -2.42812574e-01 -6.32264555e-01
-7.78720617e-01 -6.02907538e-01 -7.74888694e-01 -7.33238399e-01
-6.14691556e-01 5.32936394e-01 2.18144562e-02 6.78159818... | [6.891709804534912, 5.160587310791016] |
bebcf91a-89f8-4003-8a56-57cb10c26be4 | dependency-aware-prototype-learning-for-few | null | null | https://aclanthology.org/2022.coling-1.205 | https://aclanthology.org/2022.coling-1.205.pdf | Dependency-aware Prototype Learning for Few-shot Relation Classification | Few-shot relation classification aims to classify the relation type between two given entities in a sentence by training with a few labeled instances for each relation. However, most of existing models fail to distinguish multiple relations that co-exist in one sentence. This paper presents a novel dependency-aware pro... | ['Xiaoyan Zhao', 'Min Yang', 'Tianshu Yu'] | null | null | null | null | coling-2022-10 | ['few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.77161505e-02 4.48400885e-01 -4.76311207e-01 -7.76102960e-01
-6.88302517e-01 -4.56936359e-01 6.30080879e-01 8.10004950e-01
-3.35626990e-01 7.67514884e-01 4.88035321e-01 -4.06263858e-01
6.51606778e-03 -9.37559485e-01 -4.48864281e-01 -3.18268180e-01
-1.44633606e-01 4.19864029e-01 1.84677228e-01 -4.43180203... | [9.355977058410645, 8.668853759765625] |
5c8bb8e6-07ee-42f3-beaf-f7291510dd5c | spatio-temporal-encoding-improves | 2010.14184 | null | https://arxiv.org/abs/2010.14184v2 | https://arxiv.org/pdf/2010.14184v2.pdf | Spatio-temporal encoding improves neuromorphic tactile texture classification | With the increase in interest in deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important. In this work, we implement a multi-channel neuromorphic tactile system that encodes contact events as discrete spike events that mimic t... | ['Nitish V. Thakor', 'Nathan F. Lepora', 'Andrei Nakagawa', 'Anupam K. Gupta'] | 2020-10-27 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 8.76894653e-01 -4.97921467e-01 5.52325487e-01 1.13882385e-01
-2.47188255e-01 -5.55691421e-01 5.30729592e-01 2.27224872e-01
-8.69458377e-01 9.59107041e-01 -1.42556489e-01 2.99132824e-01
-3.39110076e-01 -7.33592391e-01 -7.97144711e-01 -1.16334152e+00
-3.34834129e-01 7.25130644e-03 7.04058588e-01 -2.58494645... | [8.285067558288574, 2.4269487857818604] |
fe2d27f4-e2fd-4c61-aa10-a1140e29e236 | adaptive-pd-control-using-deep-reinforcement | 2305.16979 | null | https://arxiv.org/abs/2305.16979v1 | https://arxiv.org/pdf/2305.16979v1.pdf | Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays | Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping between local and remote devices can be difficult due to time delays that can compromise system performance and stability. Enhancing the synchr... | ['Saber Fallah', 'Luc McCutcheon'] | 2023-05-26 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-1.02461748e-01 2.93259621e-01 -1.08547360e-01 2.72233337e-01
-5.59473097e-01 -4.90580678e-01 3.38931888e-01 2.21979752e-01
-4.54550654e-01 8.96075308e-01 -6.20689511e-01 -5.01730621e-01
-6.73122108e-01 -5.99379241e-01 -6.54625535e-01 -9.86430109e-01
-1.72179386e-01 5.10169268e-01 1.99774384e-01 -5.05310416... | [4.785290241241455, 1.7817612886428833] |
883a565f-1452-4cc4-aae7-9f9e8f5459e4 | learning-environment-aware-control-barrier | 2303.04313 | null | https://arxiv.org/abs/2303.04313v1 | https://arxiv.org/pdf/2303.04313v1.pdf | Learning Environment-Aware Control Barrier Functions for Safe and Feasible Multi-Robot Navigation | Control Barrier Functions (CBFs) have been applied to provide safety guarantees for robot navigation. Traditional approaches consider fixed CBFs during navigation and hand-tune the underlying parameters apriori. Such approaches are inefficient and vulnerable to changes in the environment. The goal of this paper is to l... | ['Amanda Prorok', 'Guang Yang', 'Zhan Gao'] | 2023-03-08 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-2.71196775e-02 1.04241572e-01 -2.92885602e-01 -1.31772414e-01
-5.28079808e-01 -7.94521153e-01 4.10715789e-01 1.47224665e-01
-7.80869484e-01 7.47274697e-01 -1.63572311e-01 -5.21876097e-01
-6.46274030e-01 -8.54327321e-01 -9.30267513e-01 -8.84188235e-01
-6.52645767e-01 1.64318159e-01 3.72868448e-01 -8.49586189... | [4.785665512084961, 1.6936392784118652] |
450ebb2d-5454-45db-b2fb-156699e2b99e | towards-robust-and-semantically-organised | 2205.02309 | null | https://arxiv.org/abs/2205.02309v1 | https://arxiv.org/pdf/2205.02309v1.pdf | Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer | Recent studies show that auto-encoder based approaches successfully perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. The latent space geometry of such models is organised well enough to perform on datasets where the st... | ['Maunendra Sankar Desarkar', 'Suvodip Dey', 'Sharan Narasimhan'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.34 | https://aclanthology.org/2022.naacl-main.34.pdf | naacl-2022-7 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.17691791e-01 1.98654443e-01 2.86282122e-01 -5.46810985e-01
-8.57890069e-01 -8.66649032e-01 1.13120103e+00 -2.47386340e-02
-4.80653971e-01 7.66144156e-01 6.77738845e-01 -1.74718842e-01
6.06717989e-02 -9.06500638e-01 -7.04318047e-01 -7.97740102e-01
2.56905377e-01 6.39093041e-01 -1.03974715e-01 -5.27453959... | [11.45560073852539, 9.701375961303711] |
7bc923d7-d609-42fb-bce9-fe1023d7999d | copilot-human-collision-prediction-and | 2210.01781 | null | https://arxiv.org/abs/2210.01781v2 | https://arxiv.org/pdf/2210.01781v2.pdf | COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos | The ability to forecast human-environment collisions from egocentric observations is vital to enable collision avoidance in applications such as VR, AR, and wearable assistive robotics. In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos ... | ['Leonidas J. Guibas', 'Yanchao Yang', 'Kaichun Mo', 'Despoina Paschalidou', 'Davis Rempe', 'Bokui Shen', 'Boxiao Pan'] | 2022-10-04 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-1.17695756e-01 -1.11264825e-01 2.36288354e-01 -3.66844654e-01
-4.22627032e-01 -4.66548473e-01 3.51738006e-01 -4.51280206e-01
-3.25370997e-01 4.85597134e-01 3.37161928e-01 7.44539201e-02
1.84489340e-01 -5.33586681e-01 -9.51407731e-01 -1.98286891e-01
-7.20947459e-02 7.26201594e-01 3.15257758e-01 -5.16394138... | [6.996034622192383, -0.8411203026771545] |
76414b65-a929-4d3b-ba9a-d46ee455d1fc | function-prediction | 2307.02173 | null | https://arxiv.org/abs/2307.02173v2 | https://arxiv.org/pdf/2307.02173v2.pdf | Function Prediction | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['K. Anton Feenstra', 'Sanne Abeln', 'Jose Gavaldá-Garciá', 'Katharina Waury', 'Olga Ivanova', 'Hans de Ferrante', 'Qingzhen Hou', 'Annika Jacobsen', 'Bas Stringer'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 3.94529462e-01 -1.37012014e-02 -1.39527142e-01 -6.21602312e-02
-3.21062952e-01 -6.86111152e-01 1.13686889e-01 4.29581910e-01
-2.06554770e-01 1.31669831e+00 -1.46745712e-01 -6.51798606e-01
-9.40288380e-02 -3.61698985e-01 -7.54769146e-01 -1.22828329e+00
-9.57064703e-02 4.07259315e-01 3.93499523e-01 -6.06064022... | [4.7344512939453125, 5.279197692871094] |
e90df107-201b-4904-ace9-b3e1bc69e6f8 | bridging-the-gap-point-clouds-for-merging | 2112.02039 | null | https://arxiv.org/abs/2112.02039v2 | https://arxiv.org/pdf/2112.02039v2.pdf | Bridging the Gap: Point Clouds for Merging Neurons in Connectomics | In the field of Connectomics, a primary problem is that of 3D neuron segmentation. Although deep learning-based methods have achieved remarkable accuracy, errors still exist, especially in regions with image defects. One common type of defect is that of consecutive missing image sections. Here, data is lost along some ... | ['Jingpeng Wu', 'Dmitri B. Chklovskii', 'Jules Berman'] | 2021-12-03 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 2.18764409e-01 1.29797742e-01 1.88302591e-01 -1.24057807e-01
-7.52498686e-01 -6.69037104e-01 3.17699641e-01 4.70737576e-01
-4.48828369e-01 5.53658724e-01 -3.33184570e-01 -4.81354356e-01
5.19352779e-02 -6.24638557e-01 -1.12367058e+00 -5.32405496e-01
2.12293491e-01 6.66821897e-01 3.75464231e-01 -4.88544144... | [14.259784698486328, -3.1247987747192383] |
94507a7f-a204-4a7e-9f4b-ca600b3e8dd8 | twitter-sentiment-analysis-lexicon-method | 1507.00955 | null | http://arxiv.org/abs/1507.00955v3 | http://arxiv.org/pdf/1507.00955v3.pdf | Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination | This paper covers the two approaches for sentiment analysis: i) lexicon based
method; ii) machine learning method. We describe several techniques to
implement these approaches and discuss how they can be adopted for sentiment
classification of Twitter messages. We present a comparative study of different
lexicon combin... | ['Tomaso Aste', 'Tharsis T. P. Souza', 'Olga Kolchyna', 'Philip Treleaven'] | 2015-07-03 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.72599480e-02 -1.16740294e-01 -7.60861412e-02 -7.30579674e-01
-5.43026865e-01 -6.68399990e-01 8.37550104e-01 8.01785231e-01
-6.76358819e-01 7.66229272e-01 5.01146495e-01 -2.75201619e-01
7.46645108e-02 -8.96190763e-01 1.11553133e-01 -4.98731166e-01
3.99340749e-01 4.38837200e-01 1.10541321e-01 -9.87628162... | [11.084571838378906, 6.819433212280273] |
a23a8b50-aafa-4b61-a838-e660f7ae125a | aggretriever-a-simple-approach-to-aggregate | 2208.00511 | null | https://arxiv.org/abs/2208.00511v2 | https://arxiv.org/pdf/2208.00511v2.pdf | Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval | Pre-trained language models have been successful in many knowledge-intensive NLP tasks. However, recent work has shown that models such as BERT are not ``structurally ready'' to aggregate textual information into a [CLS] vector for dense passage retrieval (DPR). This ``lack of readiness'' results from the gap between l... | ['Jimmy Lin', 'Minghan Li', 'Sheng-Chieh Lin'] | 2022-07-31 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-6.33611158e-02 -1.44595414e-01 -3.09348524e-01 -2.99509555e-01
-1.59407175e+00 -5.64747930e-01 7.40158677e-01 5.19396007e-01
-7.21050680e-01 6.28707945e-01 7.74689317e-01 -2.90300906e-01
1.12653218e-01 -6.88533247e-01 -7.44388640e-01 -2.40518674e-01
5.20495847e-02 5.34264982e-01 -4.51097675e-02 -3.98098111... | [11.425405502319336, 7.737371444702148] |
c10d634e-64e4-4030-b5f4-4bc82343eb8a | difffacto-controllable-part-based-3d-point | 2305.01921 | null | https://arxiv.org/abs/2305.01921v2 | https://arxiv.org/pdf/2305.01921v2.pdf | DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion | While the community of 3D point cloud generation has witnessed a big growth in recent years, there still lacks an effective way to enable intuitive user control in the generation process, hence limiting the general utility of such methods. Since an intuitive way of decomposing a shape is through its parts, we propose t... | ['Leonidas J Guibas', 'Ke Li', 'Shi-Min Hu', 'Jiahui Huang', 'Mikaela Angelina Uy', 'Kiyohiro Nakayama'] | 2023-05-03 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [-1.63313687e-01 1.83808982e-01 2.27348894e-01 1.74481124e-02
-4.68003303e-01 -9.69237208e-01 9.65224862e-01 -1.02021270e-01
4.15725619e-01 5.25354564e-01 2.12363780e-01 -1.63696647e-01
-7.71652013e-02 -1.15333807e+00 -8.89535546e-01 -4.86649990e-01
2.16246650e-01 8.50521445e-01 -3.83465886e-02 -3.87751311... | [8.96389389038086, -3.6081926822662354] |
8be18371-d69a-4340-a3ca-b4a7e3966ead | data-augmentation-for-intent-classification-3 | null | null | https://aclanthology.org/2022.konvens-1.1 | https://aclanthology.org/2022.konvens-1.1.pdf | Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain | null | ['Martin Rückert', 'Christian Stab', 'Martin Riedl', 'Sophie Rentschler'] | null | null | null | null | konvens-ws-2022-9 | ['intent-classification'] | ['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... | [-7.260087490081787, 3.7425098419189453] |
450bc44a-5054-48ab-8932-93ea0afb00e7 | latent-cognizance-what-machine-really-learns | 2110.15548 | null | https://arxiv.org/abs/2110.15548v1 | https://arxiv.org/pdf/2110.15548v1.pdf | Latent Cognizance: What Machine Really Learns | Despite overwhelming achievements in recognition accuracy, extending an open-set capability -- ability to identify when the question is out of scope -- remains greatly challenging in a scalable machine learning inference. A recent research has discovered Latent Cognizance (LC) -- an insight on a recognition mechanism b... | ['Tatpong Katanyukul', 'Jiradej Ponsawat', 'Pisit Nakjai'] | 2021-10-29 | null | null | null | null | ['sign-language-recognition', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 8.24258327e-01 5.12722254e-01 -5.27920783e-01 -5.63914716e-01
-5.29537022e-01 -4.54325765e-01 7.55872965e-01 -3.68695199e-01
-3.57447052e-03 6.21163785e-01 3.71747702e-01 -7.08077550e-01
-8.17225635e-01 -6.23002052e-01 -6.81362748e-01 -7.90864348e-01
2.34096840e-01 5.13521850e-01 1.47571966e-01 2.70090938... | [8.838802337646484, 6.435564994812012] |
78d9fcc1-0bd5-4715-9654-785be8aea563 | cclap-controllable-chinese-landscape-painting | 2304.04156 | null | https://arxiv.org/abs/2304.04156v2 | https://arxiv.org/pdf/2304.04156v2.pdf | CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model | With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese landscape painting generation due to the lack of data and limited modeling capabilities. In this work, we propose a controllable Chinese landsca... | ['Shiguang Shan', 'Jinfeng Bai', 'Zhilong Ji', 'Jie Zhang', 'Zhongqi Wang'] | 2023-04-09 | null | null | null | null | ['chinese-landscape-painting-generation'] | ['computer-vision'] | [ 2.93297946e-01 -2.13057995e-01 1.64916769e-01 -2.37787277e-01
-6.84150696e-01 -6.15073323e-01 7.64449239e-01 -5.72956622e-01
2.73667455e-01 8.06637406e-01 3.85177344e-01 1.51217490e-01
9.34052765e-02 -1.30328572e+00 -3.01971853e-01 -8.87694299e-01
5.76769054e-01 1.47256911e-01 -1.87478568e-02 -2.90632933... | [11.610400199890137, -0.38211458921432495] |
067feb5f-b020-4e39-b91b-58fdc4982934 | sentence-pair-embeddings-based-evaluation | null | null | https://aclanthology.org/2022.lrec-1.646 | https://aclanthology.org/2022.lrec-1.646.pdf | Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization | The development of an automatic evaluation metric remains an open problem in text generation. Widely used evaluation metrics, like ROUGE and BLEU, are based on exact word matching and fail to capture semantic similarity. Recent works, such as BERTScore, MoverScore and, Sentence Mover’s Similarity, are an improvement ov... | ['Ivan Garibay', 'Ramya Akula'] | null | null | null | null | lrec-2022-6 | ['sentence-embeddings', 'sentence-embeddings', 'extractive-summarization'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.90749067e-01 -5.16683348e-02 -5.38838878e-02 -3.81788433e-01
-1.12397540e+00 -5.08554518e-01 1.02638471e+00 8.48266125e-01
-4.94806081e-01 6.65583909e-01 9.54758406e-01 -1.06974676e-01
-1.61670640e-01 -8.46420825e-01 -3.64232212e-01 -1.54677957e-01
3.63730073e-01 4.58167374e-01 1.49413124e-01 -6.89466894... | [11.601219177246094, 9.178308486938477] |
9fdf994a-5e0e-439a-97c3-be99b3862bd2 | quantitative-day-trading-from-natural | null | null | https://aclanthology.org/2021.naacl-main.316 | https://aclanthology.org/2021.naacl-main.316.pdf | Quantitative Day Trading from Natural Language using Reinforcement Learning | It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media. Existing NLP approaches largely treat stock prediction as a classification or regression problem and ar... | ['Rajiv Ratn Shah', 'Shivam Agarwal', 'Arnav Wadhwa', 'Ramit Sawhney'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['stock-prediction'] | ['time-series'] | [-8.69389474e-01 -3.26728761e-01 -6.21600032e-01 -5.02084531e-02
-6.99180245e-01 -8.79467070e-01 8.98737907e-01 2.08003953e-01
-4.96677279e-01 9.07498658e-01 4.13265646e-01 -4.73321229e-01
3.17491516e-02 -1.33821642e+00 -6.25664413e-01 -3.11620802e-01
-4.95927602e-01 8.55920434e-01 2.78655082e-01 -4.10302103... | [4.467689037322998, 4.129909992218018] |
167089ec-aa4f-4aa8-8d0b-e03568873a1f | adaptive-navigation-scheme-for-optimal-deep | 1906.04888 | null | https://arxiv.org/abs/1906.04888v1 | https://arxiv.org/pdf/1906.04888v1.pdf | Adaptive Navigation Scheme for Optimal Deep-Sea Localization Using Multimodal Perception Cues | Underwater robot interventions require a high level of safety and reliability. A major challenge to address is a robust and accurate acquisition of localization estimates, as it is a prerequisite to enable more complex tasks, e.g. floating manipulation and mapping. State-of-the-art navigation in commercial operations, ... | ['Andreas Birk', 'Sören Schwertfeger', 'Christian A. Mueller', 'Arturo Gomez Chavez', 'Qingwen Xu'] | 2019-06-12 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 1.94395036e-01 -1.23181321e-01 6.29903018e-01 -3.90037924e-01
-5.37420094e-01 -6.10716343e-01 2.16090664e-01 6.55281663e-01
-9.78310227e-01 7.69151509e-01 -2.77573556e-01 -1.36502311e-01
-6.42672837e-01 -1.00859082e+00 -7.64648199e-01 -8.02899778e-01
-3.95868719e-01 6.87018394e-01 5.21970093e-01 -6.82837367... | [7.4569091796875, -1.8099533319473267] |
1d5dd07f-5217-43c1-8e7b-225849a3f569 | introducing-two-vietnamese-datasets-for | 1804.05388 | null | http://arxiv.org/abs/1804.05388v2 | http://arxiv.org/pdf/1804.05388v2.pdf | Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness | We present two novel datasets for the low-resource language Vietnamese to
assess models of semantic similarity: ViCon comprises pairs of synonyms and
antonyms across word classes, thus offering data to distinguish between
similarity and dissimilarity. ViSim-400 provides degrees of similarity across
five semantic relati... | ['Sabine Schulte im Walde', 'Ngoc Thang Vu', 'Kim Anh Nguyen'] | 2018-04-15 | introducing-two-vietnamese-datasets-for-1 | https://aclanthology.org/N18-2032 | https://aclanthology.org/N18-2032.pdf | naacl-2018-6 | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 3.84186320e-02 -9.21030492e-02 -7.13240921e-01 -5.24561822e-01
-1.38049632e-01 -7.20012486e-01 9.90123987e-01 8.10487390e-01
-9.76542830e-01 5.99221230e-01 7.50746489e-01 -1.14476256e-01
-5.57990789e-01 -6.06754243e-01 3.00743401e-01 8.95113777e-03
1.25145987e-01 8.05358291e-01 -2.06024632e-01 -9.23418343... | [10.583477973937988, 9.176840782165527] |
3a6836a5-9162-418b-b5c2-4734952a0ec7 | trwp-text-relatedness-using-word-and-phrase | null | null | https://aclanthology.org/S15-2016 | https://aclanthology.org/S15-2016.pdf | TrWP: Text Relatedness using Word and Phrase Relatedness | null | ['Evangelos Milios', 'Aminul Islam', 'Md Rashadul Hasan Rakib'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['phrase-relatedness'] | ['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... | [-7.068718910217285, 3.949030637741089] |
faa132e4-03cb-4bd7-ad9c-4b29f65bbc95 | team-ainlpml-mup-in-sdp-2021-scientific | null | null | https://aclanthology.org/2022.sdp-1.36 | https://aclanthology.org/2022.sdp-1.36.pdf | Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach | This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022. We present a method to produce abstractive summaries of scientific documents. First, we perform an extractive summarization step to identify the essen... | ['Asif Ekbal', 'Kartik Shinde', 'Guneet Singh Kohli', 'Sandeep Kumar'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.12512636e-01 4.01096433e-01 -1.32649273e-01 -2.87311655e-02
-1.61483443e+00 -7.10250556e-01 5.82337260e-01 6.03660285e-01
-3.36985946e-01 1.08760452e+00 8.95755827e-01 -6.29539937e-02
1.14737429e-01 -3.22656423e-01 -7.16493249e-01 -3.66033345e-01
3.07714999e-01 4.48587328e-01 4.03360166e-02 -3.27782519... | [12.5426607131958, 9.534754753112793] |
5fd269d7-0607-4176-b7aa-396e39835716 | illumination-estimation-based-on-bilayer | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Li_Illumination_Estimation_Based_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Illumination_Estimation_Based_2013_CVPR_paper.pdf | Illumination Estimation Based on Bilayer Sparse Coding | Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of using high level visual content cues for improving illumination estimation. However, nearly all the existing methods are essentially combinational... | ['Houwen Peng', 'Weiming Hu', 'Weihua Xiong', 'Bing Li'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['color-constancy'] | ['computer-vision'] | [ 3.44581515e-01 -1.01234794e+00 -5.75355776e-02 -3.04337859e-01
-2.43504852e-01 -3.10052454e-01 3.88604075e-01 -2.29067251e-01
-1.29549265e-01 7.25038886e-01 1.92952082e-01 1.47146851e-01
3.28604355e-02 -5.07315814e-01 -2.55112261e-01 -1.14595532e+00
5.35052061e-01 -2.56507009e-01 2.69863099e-01 -5.17883524... | [10.647780418395996, -2.552541732788086] |
982dc34b-e682-42ed-9a0f-1ce7e79eb3ac | ensemble-prosody-prediction-for-expressive | 2304.00714 | null | https://arxiv.org/abs/2304.00714v1 | https://arxiv.org/pdf/2304.00714v1.pdf | Ensemble prosody prediction for expressive speech synthesis | Generating expressive speech with rich and varied prosody continues to be a challenge for Text-to-Speech. Most efforts have focused on sophisticated neural architectures intended to better model the data distribution. Yet, in evaluations it is generally found that no single model is preferred for all input texts. This ... | ['Simon King', 'Mark Gales', 'James Leoni', 'Alexandra Torresquintero', 'Tomás Gomez Ibarrondo', 'Christopher G. R. Wallis', 'Zack Hodari', 'Devang S Ram Mohan', 'Vivian Hu', 'Tian Huey Teh'] | 2023-04-03 | null | null | null | null | ['prosody-prediction', 'expressive-speech-synthesis', 'speech-synthesis'] | ['natural-language-processing', 'speech', 'speech'] | [ 3.42117012e-01 -7.94981942e-02 -1.11122690e-01 -4.09173936e-01
-1.07132840e+00 -6.29439592e-01 4.34355080e-01 -1.12192757e-01
-2.12151870e-01 8.38294685e-01 4.92433339e-01 -2.68598765e-01
-2.64194105e-02 -3.01594824e-01 -2.22020611e-01 -7.15962052e-01
2.08101705e-01 5.96955955e-01 8.61981958e-02 -5.38941443... | [14.90961742401123, 6.645076274871826] |
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