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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0e4b0c4a-2a5f-4c91-bb0a-6f6b23aee9ad | automation-of-mathematical-induction-as-part | 1309.6226 | null | http://arxiv.org/abs/1309.6226v5 | http://arxiv.org/pdf/1309.6226v5.pdf | Automation of Mathematical Induction as part of the History of Logic | We review the history of the automation of mathematical induction | ['Claus-Peter Wirth', 'J Strother Moore'] | 2013-09-24 | null | null | null | null | ['mathematical-induction'] | ['reasoning'] | [ 1.81574300e-01 4.88026500e-01 -4.71003801e-01 -7.09474802e-01
4.85418350e-01 -7.73223579e-01 6.34565294e-01 6.52186871e-02
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-7.54365146e-01 7.54890978e-01 -4.01282787e-01 -3.13619167... | [8.859493255615234, 6.94291353225708] |
e972dac2-964e-41da-8fd5-628594444e41 | phrase-grounding-by-soft-label-chain | 1909.00301 | null | https://arxiv.org/abs/1909.00301v1 | https://arxiv.org/pdf/1909.00301v1.pdf | Phrase Grounding by Soft-Label Chain Conditional Random Field | The phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image. Although there are clear dependencies between how different mentions of the same caption should be grounded, previous structured prediction methods that aim to capture such dependencies n... | ['Julia Hockenmaier', 'Jiacheng Liu'] | 2019-09-01 | phrase-grounding-by-soft-label-chain-1 | https://aclanthology.org/D19-1515 | https://aclanthology.org/D19-1515.pdf | ijcnlp-2019-11 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.63139391e-01 6.40954792e-01 -4.70963269e-01 -6.98557436e-01
-1.12023139e+00 -7.91378558e-01 6.98686779e-01 1.78951919e-01
-3.12917918e-01 7.99131930e-01 4.13436949e-01 -3.02664131e-01
2.69452184e-01 -5.81028759e-01 -1.00767541e+00 -5.12978077e-01
2.75937859e-02 5.54322183e-01 1.44934654e-01 1.24923490... | [10.528800010681152, 1.3911606073379517] |
fad69a98-fd04-4d20-bfa4-b5f43aa64726 | compositional-questions-do-not-necessitate | 1906.02900 | null | https://arxiv.org/abs/1906.02900v1 | https://arxiv.org/pdf/1906.02900v1.pdf | Compositional Questions Do Not Necessitate Multi-hop Reasoning | Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions can be answered with a single hop if they target specific entit... | ['Sewon Min', 'Luke Zettlemoyer', 'Sameer Singh', 'Hannaneh Hajishirzi', 'Matt Gardner', 'Eric Wallace'] | 2019-06-07 | compositional-questions-do-not-necessitate-1 | https://aclanthology.org/P19-1416 | https://aclanthology.org/P19-1416.pdf | acl-2019-7 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 4.47226129e-02 8.69895101e-01 1.38316363e-01 -4.07185972e-01
-1.76958680e+00 -9.99233067e-01 3.33957642e-01 7.24906206e-01
-6.15278244e-01 9.31317806e-01 5.49795985e-01 -7.53655374e-01
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4.61692333e-01 1.10838068e+00 7.49148190e-01 -6.18663549... | [11.139983177185059, 7.992679595947266] |
0d1404b6-f3c7-46d4-b6b2-3b012c7110ed | explicit-graph-reasoning-fusing-knowledge-and | null | null | https://aclanthology.org/2022.dlg4nlp-1.8 | https://aclanthology.org/2022.dlg4nlp-1.8.pdf | Explicit Graph Reasoning Fusing Knowledge and Contextual Information for Multi-hop Question Answering | Current graph-neural-network-based (GNN-based) approaches to multi-hop questions integrate clues from scattered paragraphs in an entity graph, achieving implicit reasoning by synchronous update of graph node representations using information from neighbours; this is poorly suited for explaining how clues are passed thr... | ['Patricia Riddle', 'Michael Witbrock', 'Qianqian Qi', 'Yonghua Zhu', 'Zhenyun Deng'] | null | null | null | null | naacl-dlg4nlp-2022-7 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 6.88972697e-02 1.21487725e+00 -4.21999730e-02 -2.64847130e-01
-7.52530456e-01 -5.82223892e-01 4.97241050e-01 1.00412083e+00
-2.45521381e-01 8.80877495e-01 3.12984496e-01 -2.85909265e-01
-3.57306868e-01 -1.28769183e+00 -9.05656576e-01 -2.11034313e-01
-5.79204410e-02 8.31626713e-01 8.07053983e-01 -5.98276258... | [10.709518432617188, 7.910505294799805] |
6914bd1a-07a3-48e3-a6b6-679b021d6baf | a-generic-approach-for-statistical-stability | 2211.12631 | null | https://arxiv.org/abs/2211.12631v3 | https://arxiv.org/pdf/2211.12631v3.pdf | A Generic Approach for Reproducible Model Distillation | Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable "student" model to mimic the predictions made by the black box "teacher" model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the tea... | ['Giles Hooker', 'Peiru Xu', 'Yunzhe Zhou'] | 2022-11-22 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.2726478e-01 6.1358017e-01 -3.1768736e-01 -7.2339624e-01
-8.8683289e-01 -3.4405631e-01 4.3805200e-01 1.8682550e-01
-7.1025461e-02 8.7219626e-01 -3.3716938e-01 -5.1355416e-01
-2.4742958e-01 -6.5555269e-01 -8.3029008e-01 -6.3658977e-01
2.1340294e-01 1.0807685e+00 2.1651146e-01 8.9630812e-02
2.3171948e-01... | [8.795414924621582, 5.832475185394287] |
46e4a1ad-aa2f-49a8-b7de-0c21f44b2bb6 | does-unsupervised-grammar-induction-need | 2212.10564 | null | https://arxiv.org/abs/2212.10564v1 | https://arxiv.org/pdf/2212.10564v1.pdf | Does unsupervised grammar induction need pixels? | Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-... | ['Dan Klein', 'Trevor Darrell', 'Jitendra Malik', 'Kilian Q. Weinberger', 'Serge Belongie', 'Daniel Flaherty', 'Catherine Chen', 'Karttikeya Mangalam', 'Rodolfo Corona', 'Boyi Li'] | 2022-12-20 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 6.51857257e-01 4.15338427e-01 -2.70732373e-01 -5.06287694e-01
-1.82979345e+00 -9.45047617e-01 8.38939607e-01 8.92334878e-02
-6.67059660e-01 3.23553801e-01 4.49291140e-01 -5.63036799e-01
5.18812776e-01 -2.59072542e-01 -1.10424960e+00 -4.71032649e-01
1.00087509e-01 5.35284996e-01 1.15065426e-02 4.19317186... | [10.592500686645508, 1.5365495681762695] |
cdb2c6a3-6851-4166-b052-3d98e35c21a6 | matching-options-to-tasks-using-option | 2206.05750 | null | https://arxiv.org/abs/2206.05750v1 | https://arxiv.org/pdf/2206.05750v1.pdf | Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning | The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space. Ideally, these options can be reused across different higher-level goals; indeed, such reuse is necessary to realiz... | ['Pradeep Shenoy', 'Balaraman Ravindran', 'Akash Reddy', 'Soumya Chatterjee', 'Kushal Chauhan'] | 2022-06-12 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.57419100e-01 1.87491067e-02 -2.68368244e-01 -2.03550503e-01
-9.52143669e-01 -7.74280071e-01 6.66107118e-01 1.57350928e-01
-9.09413099e-01 1.04737115e+00 6.12187088e-01 -2.06836700e-01
-4.44704384e-01 -6.85794413e-01 -6.98568106e-01 -6.65086627e-01
-5.42871952e-01 7.56478548e-01 5.02572477e-01 -4.39507306... | [4.081986427307129, 1.5949956178665161] |
8f5a3d60-2144-48d0-b91e-4e0946c9184a | individual-health-disease-phase-diagrams-for | 2205.15598 | null | https://arxiv.org/abs/2205.15598v2 | https://arxiv.org/pdf/2205.15598v2.pdf | Individual health-disease phase diagrams for disease prevention based on machine learning | Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in multivariate data. Progress in precision medicine has revealed that substantial heterogeneity exists in health ... | ['Yasushi Okuno', 'Yoshinori Tamada', 'Tatsuya Mikami', 'Ken Itoh', 'Koichi Murashita', 'Ryosuke Kojima', 'Kei Terayama', 'Ayano Araki', 'Noriaki Sato', 'Eiichiro Uchino', 'Kazuki Nakamura'] | 2022-05-31 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 3.01671147e-01 -4.24059212e-01 -7.01976657e-01 -2.85230041e-01
-3.03788573e-01 -7.17138574e-02 3.48208576e-01 9.81870532e-01
1.99981734e-01 5.86176217e-01 4.42122310e-01 -3.31730187e-01
-5.71612477e-01 -7.86952734e-01 -4.63211387e-01 -4.67269570e-01
-8.54660749e-01 4.98609930e-01 -1.57391131e-01 1.25164315... | [7.843761444091797, 5.565586090087891] |
1fbf2be7-bd36-4245-b64a-e2c87df958f8 | tackling-background-distraction-in-video | 2207.06953 | null | https://arxiv.org/abs/2207.06953v3 | https://arxiv.org/pdf/2207.06953v3.pdf | Tackling Background Distraction in Video Object Segmentation | Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background distractors that appear similar to the target objects. We propose three novel strategies to suppress such distractors: 1) a spatio-temporally... | ['Sangyoun Lee', 'Minjung Kim', 'Sungjun Jang', 'Chaewon Park', 'Minhyeok Lee', 'Heansung Lee', 'Suhwan Cho'] | 2022-07-14 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 2.23917678e-01 -1.29059538e-01 -3.53636980e-01 -7.33527765e-02
-9.90311801e-01 -7.32759833e-01 5.66520274e-01 -3.73677582e-01
-3.86578172e-01 6.07457638e-01 1.80498406e-01 2.08269745e-01
1.63715035e-01 -1.42686278e-01 -8.91389906e-01 -8.11503351e-01
-2.05512509e-01 4.61090326e-01 1.00528109e+00 1.15238674... | [9.179221153259277, -0.11628702282905579] |
22336dad-6930-4ee5-afd9-acd35061db30 | exploiting-coreference-and-schema-structure | null | null | https://openreview.net/forum?id=yQvObmRwUAP | https://openreview.net/pdf?id=yQvObmRwUAP | Exploiting Coreference and Schema Structure for Document-level Event Extraction | Document-level event extraction (DEE) extracts structured information of events from a document. Previous studies focus on improving the model architecture. We argue that exploiting data characteristics is also important. We propose to utilize coreference information to obtain better document-level entity representatio... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 2.26113692e-01 3.65374357e-01 -7.53537416e-01 -6.87472463e-01
-6.13078654e-01 -5.86225927e-01 6.53240919e-01 8.33775401e-01
-5.71026146e-01 7.16029644e-01 8.42653990e-01 -4.08161841e-02
-2.72419155e-01 -9.91142035e-01 -3.41924548e-01 8.31997842e-02
-1.25853777e-01 2.08861366e-01 6.27703369e-01 -1.76923182... | [9.155290603637695, 9.128008842468262] |
47643992-fea6-4e2b-a16b-bcc5f2ef83e2 | foon-creation-and-traversal-for-recipe | 2210.07335 | null | https://arxiv.org/abs/2210.07335v2 | https://arxiv.org/pdf/2210.07335v2.pdf | FOON Creation and Traversal for Recipe Generation | Task competition by robots is still off from being completely dependable and usable. One way a robot may decipher information given to it and accomplish tasks is by utilizing FOON, which stands for functional object-oriented network. The network first needs to be created by having a human creates action nodes as well a... | ['Raj Patel'] | 2022-10-13 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 4.84088272e-01 4.88691688e-01 -2.73257494e-01 -2.84930229e-01
4.72698435e-02 -1.20542252e+00 2.17846826e-01 2.02948704e-01
-3.94833475e-01 6.30786419e-01 -1.47581711e-01 -5.91043591e-01
-6.00846350e-01 -9.41259861e-01 -6.36317670e-01 -2.84490645e-01
-1.52191862e-01 5.97047687e-01 2.76940286e-01 -2.04169631... | [4.504071235656738, 0.9724154472351074] |
01dc8ce9-ac7c-49cf-a1c0-0f94e39b9575 | 190602010 | 1906.02010 | null | https://arxiv.org/abs/1906.02010v1 | https://arxiv.org/pdf/1906.02010v1.pdf | A Hybrid Algorithm for Metaheuristic Optimization | We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess. The information produced... | ['Sujit Pramod Khanna', 'Alexander Ororbia II'] | 2019-05-26 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-5.45082875e-02 2.33510062e-02 -3.00172329e-01 -6.41245320e-02
-5.65575957e-01 -8.29062581e-01 4.94501740e-01 2.30804026e-01
-5.78457236e-01 1.21453190e+00 -4.60022867e-01 -1.55226067e-01
-6.19670093e-01 -7.46340275e-01 -6.87163949e-01 -1.07919919e+00
-4.25410807e-01 9.72524464e-01 -3.39507833e-02 -4.14515525... | [5.679586410522461, 3.565216064453125] |
edac73fe-38d8-402b-aa75-2125836947d7 | questioning-the-validity-of-summarization | 2210.17378 | null | https://arxiv.org/abs/2210.17378v1 | https://arxiv.org/pdf/2210.17378v1.pdf | Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency | The topic of summarization evaluation has recently attracted a surge of attention due to the rapid development of abstractive summarization systems. However, the formulation of the task is rather ambiguous, neither the linguistic nor the natural language processing community has succeeded in giving a mutually agreed-up... | ['Michalis Vazirgiannis', 'Moussa Kamal Eddine', 'Chloé Clavel', 'Yanzhu Guo'] | 2022-10-31 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.61446112e-01 1.67563304e-01 -2.63929248e-01 -2.68745631e-01
-1.27040339e+00 -7.41227746e-01 9.12024617e-01 8.94957006e-01
-3.71137351e-01 1.02485311e+00 8.77407491e-01 -1.27525017e-01
-2.72001952e-01 -4.52592641e-01 -3.81440133e-01 -1.94753468e-01
3.41766685e-01 6.94633782e-01 1.65391341e-01 -3.19375336... | [12.234195709228516, 9.424081802368164] |
8151dbaf-2e2f-4051-b7ba-36e4cb48809e | barycenters-of-natural-images-constrained | 1912.11545 | null | https://arxiv.org/abs/1912.11545v1 | https://arxiv.org/pdf/1912.11545v1.pdf | Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing | Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in eac... | ['Dror Simon', 'Aviad Aberdam'] | 2019-12-24 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 5.69100916e-01 3.77516121e-01 2.51802385e-01 -2.33149186e-01
-4.23576623e-01 -5.27527869e-01 6.86712205e-01 -9.50973928e-02
-4.83041778e-02 6.74735725e-01 -1.93186224e-01 -3.65783274e-01
6.78923279e-02 -8.05151463e-01 -9.54896629e-01 -5.80878615e-01
2.26726249e-01 -1.61458608e-02 3.25095475e-01 -1.78290114... | [11.620227813720703, -0.6131957769393921] |
dc690198-a84f-4c33-80ea-3607055fca6c | distributionally-robust-multi-output | 2109.12803 | null | https://arxiv.org/abs/2109.12803v1 | https://arxiv.org/pdf/2109.12803v1.pdf | Distributionally Robust Multi-Output Regression Ranking | Despite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or adversarial data perturbations. To fill this gap, we introduce a new listwise LTR model called Distributionally Robust Multi-output Regressio... | ['Ioannis Paschalidis', 'Ruidi Chen', 'Shahabeddin Sotudian'] | 2021-09-27 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 4.43157017e-01 -4.12702411e-01 -3.68933350e-01 -3.82745981e-01
-1.69630086e+00 -7.60109723e-01 5.97557783e-01 4.44365323e-01
-4.93651062e-01 9.07709360e-01 4.73516494e-01 -2.79718429e-01
-1.75479114e-01 -3.29873085e-01 -7.31249094e-01 -9.32009280e-01
-1.66033834e-01 6.60544813e-01 7.16854446e-03 -1.65263742... | [9.378268241882324, 3.991457939147949] |
89344e93-216a-472b-b761-10b6d0d093e3 | online-invariance-selection-for-local-feature | 2007.08988 | null | https://arxiv.org/abs/2007.08988v3 | https://arxiv.org/pdf/2007.08988v3.pdf | Online Invariance Selection for Local Feature Descriptors | To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors. A limitation of current feature descriptors is the trade-off between generalization and discriminative power: more invariance means less informative descriptors. We propose to overcome this limitation with a d... | ['Marc Pollefeys', 'Rémi Pautrat', 'Viktor Larsson', 'Martin R. Oswald'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4158_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470698.pdf | eccv-2020-8 | ['homography-estimation'] | ['computer-vision'] | [ 2.16346160e-01 -8.77841413e-01 -3.01446021e-01 -6.72449648e-01
-6.97047412e-01 -8.45473945e-01 7.40313470e-01 2.54170805e-01
-3.73715937e-01 2.12261826e-01 2.82045066e-01 3.33927602e-01
-5.10169983e-01 -5.94074905e-01 -3.21640462e-01 -8.29279482e-01
-1.14819646e-01 1.65040582e-01 5.89475751e-01 -4.07085866... | [8.231036186218262, -1.8007408380508423] |
89bde8e8-0286-4f5b-aae4-551e8488f8fe | skin-lesion-classification-using-deep-multi | 1703.01402 | null | http://arxiv.org/abs/1703.01402v1 | http://arxiv.org/pdf/1703.01402v1.pdf | Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks | We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images. | ['Terrance DeVries', 'Dhanesh Ramachandram'] | 2017-03-04 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 7.79246151e-01 -1.56791255e-01 -4.23917443e-01 -6.07718974e-02
-9.81869400e-01 -4.79541481e-01 4.63169158e-01 1.16153203e-01
-6.26553655e-01 2.94911861e-01 -1.29443128e-02 -5.66915214e-01
4.01368625e-02 -7.38140762e-01 -4.35987949e-01 -5.66710532e-01
5.33974636e-03 -1.07314765e-01 6.18668675e-01 -3.09853643... | [15.714091300964355, -2.9995744228363037] |
3c81d849-318f-4cd0-971b-90656ca62dcc | contextual-dialogue-act-classification-for | 2005.13804 | null | https://arxiv.org/abs/2005.13804v1 | https://arxiv.org/pdf/2005.13804v1.pdf | Contextual Dialogue Act Classification for Open-Domain Conversational Agents | Classifying the general intent of the user utterance in a conversation, also known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or request for an opinion, is a key step in Natural Language Understanding (NLU) for conversational agents. While DA classification has been extensively studied in hu... | ['Eugene Agichtein', 'Ali Ahmadvand', 'Jason Ingyu Choi'] | 2020-05-28 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 2.39491776e-01 4.62834775e-01 -6.28150553e-02 -6.74126863e-01
-6.95923209e-01 -8.42392623e-01 1.26288474e+00 -7.57452175e-02
-2.79284120e-01 7.82708108e-01 8.22366238e-01 -5.11792719e-01
5.57877600e-01 -5.23510754e-01 -5.08339107e-02 -2.81616956e-01
2.86170363e-01 9.54546630e-01 -6.26294166e-02 -7.66700029... | [12.788280487060547, 7.926846981048584] |
5de8ee27-1e65-4ad8-a73d-5939c9760c66 | filteraugment-an-acoustic-environmental-data | 2110.03282 | null | https://arxiv.org/abs/2110.03282v4 | https://arxiv.org/pdf/2110.03282v4.pdf | FilterAugment: An Acoustic Environmental Data Augmentation Method | Acoustic environments affect acoustic characteristics of sound to be recognized by physically interacting with sound wave propagation. Thus, training acoustic models for audio and speech tasks requires regularization on various acoustic environments in order to achieve robust performance in real life applications. We p... | ['Seong-Hu Kim', 'Yong-Hwa Park', 'Hyeonuk Nam'] | 2021-10-07 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 2.02981874e-01 -1.13777094e-01 5.32194197e-01 -1.40407845e-01
-1.14051402e+00 -4.48864341e-01 2.55318433e-01 -3.33669297e-02
-6.75909519e-01 3.65699351e-01 4.96493727e-01 -2.57045805e-01
1.94428936e-01 -1.94098786e-01 -6.75458014e-01 -6.78807020e-01
-3.49163741e-01 -4.18826699e-01 2.71987706e-01 -2.49851141... | [14.973343849182129, 5.914299488067627] |
9d333587-5ab2-49d2-ad4e-7608a1f5c4c9 | constrained-optimization-under-uncertainty | 1901.00942 | null | http://arxiv.org/abs/1901.00942v3 | http://arxiv.org/pdf/1901.00942v3.pdf | Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games | Decision-making problems can be modeled as combinatorial optimization
problems with Constraint Programming formalisms such as Constrained
Optimization Problems. However, few Constraint Programming formalisms can deal
with both optimization and uncertainty at the same time, and none of them are
convenient to model probl... | ['Florian Richoux', 'Valentin Antuori'] | 2019-01-03 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 1.10573478e-01 8.21595073e-01 -7.13111311e-02 -4.66454625e-01
-6.15714788e-01 -6.28618777e-01 5.08612037e-01 1.45349264e-01
-5.45526028e-01 8.24605525e-01 -1.27977058e-01 -3.57405841e-01
-7.20849216e-01 -9.20277536e-01 -5.45098364e-01 -4.88423944e-01
-8.82372633e-02 1.33034050e+00 3.70954365e-01 -3.43187898... | [8.592409133911133, 6.598361015319824] |
57e62322-6af9-42ea-9ea3-9c7bda735823 | higher-order-motif-based-time-series | 2306.13397 | null | https://arxiv.org/abs/2306.13397v1 | https://arxiv.org/pdf/2306.13397v1.pdf | Higher-order Motif-based Time Series Classification for Forced Oscillation Source Location in Power Grids | Time series motifs are used for discovering higher-order structures of time series data. Based on time series motifs, the motif embedding correlation field (MECF) is proposed to characterize higher-order temporal structures of dynamical system time series. A MECF-based unsupervised learning approach is applied in locat... | ['Xin Chen', 'Long Huo'] | 2023-06-23 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 1.26496702e-01 -3.57975662e-01 1.51033074e-01 3.56935591e-01
-3.06001782e-01 -5.62322676e-01 3.58085483e-01 3.01991969e-01
3.18452299e-01 5.87547064e-01 1.58408791e-01 -4.71091181e-01
-9.96816576e-01 -5.27903557e-01 -2.12885872e-01 -1.17855418e+00
-9.18601155e-01 -2.19799086e-01 -9.17427093e-02 -7.62924626... | [6.445959091186523, 2.6638782024383545] |
e7248bd0-c0d7-4e88-85cd-25e0c1ba7874 | unleashing-potential-of-unsupervised-pre | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.pdf | Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification | Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised repre... | ['Feng Zhao', 'Kecheng Zheng', 'Xin Jin', 'Zizheng Yang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['unsupervised-pre-training'] | ['methodology'] | [-2.14267354e-02 -2.66439080e-01 -2.03018654e-02 -6.23698592e-01
-2.24496782e-01 -3.90199691e-01 7.97206342e-01 -7.73888752e-02
-7.81082809e-01 5.48685372e-01 4.79607224e-01 2.02328205e-01
-1.98731005e-01 -6.78289115e-01 -5.80003500e-01 -6.06101751e-01
1.40568629e-01 3.48842680e-01 -1.48926765e-01 -3.21767002... | [14.74070930480957, 1.005842924118042] |
a5dc38d2-dac5-4147-8aea-b7754a199bcf | real-time-hybrid-mapping-of-populated-indoor | 2203.02453 | null | https://arxiv.org/abs/2203.02453v1 | https://arxiv.org/pdf/2203.02453v1.pdf | Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV | Unmanned aerial vehicles (UAVs) have been used for many applications in recent years, from urban search and rescue, to agricultural surveying, to autonomous underground mine exploration. However, deploying UAVs in tight, indoor spaces, especially close to humans, remains a challenge. One solution, when limited payload ... | ['Niki Trigoni', 'Andrew Markham', 'Sangyun Shin', 'Aluna Everitt', 'Madhu Vankadari', 'Stuart Golodetz'] | 2022-03-04 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.34349167e-02 -2.12770432e-01 3.63036811e-01 -1.87003195e-01
-3.21460456e-01 -6.71102822e-01 3.10298353e-01 -1.92212492e-01
-6.25430822e-01 7.73563504e-01 -5.34711719e-01 -5.23445345e-02
5.12499884e-02 -8.49019766e-01 -7.49240816e-01 -2.81256616e-01
1.47800865e-02 1.01156187e+00 3.87535214e-01 -3.73532563... | [7.297304630279541, -1.5260788202285767] |
4b799ffa-fa79-4c79-800e-882b04d555c6 | temporal-driver-action-localization-using | null | null | https://openaccess.thecvf.com/content/CVPR2022W/AICity/html/Alyahya_Temporal_Driver_Action_Localization_Using_Action_Classification_Methods_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/AICity/papers/Alyahya_Temporal_Driver_Action_Localization_Using_Action_Classification_Methods_CVPRW_2022_paper.pdf | temporal driver action Localization using action classifications method | Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization (TDAL) framework for classifying driver distraction actions, as well as identifying the start and end ... | ['Taghreed Alhussan', 'Shahad Alghannam', 'Munirah Alyahya'] | 2022-06-11 | null | null | null | cvpr-2022-6 | ['action-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.71824157e-01 -4.85216260e-01 -2.89400190e-01 -3.77796710e-01
-7.92366087e-01 -4.04233754e-01 7.50993669e-01 -2.63523608e-01
-4.20582533e-01 2.43849456e-01 4.21439677e-01 -5.40444791e-01
2.42646597e-02 -2.78207928e-01 -2.93071091e-01 -8.23615849e-01
4.08273637e-01 -7.76997134e-02 5.14246285e-01 -2.17956454... | [7.651154518127441, -0.0939338430762291] |
1bc7edf1-5f26-494c-beb9-bd286244d227 | safe-exploration-method-for-reinforcement | 2209.15452 | null | https://arxiv.org/abs/2209.15452v2 | https://arxiv.org/pdf/2209.15452v2.pdf | Safe Exploration Method for Reinforcement Learning under Existence of Disturbance | Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their exploring property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal ... | ['Toru Namerikawa', 'Hitoshi Yanami', 'Tomotake Sasaki', 'Yoshihiro Okawa'] | 2022-09-30 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.61526519e-01 5.01149833e-01 -2.13478193e-01 1.60938844e-01
-8.77564922e-02 -4.32494402e-01 4.51612890e-01 4.18240458e-01
-7.62446821e-01 1.23382521e+00 -5.34890652e-01 -2.94658214e-01
-8.58386517e-01 -8.22816133e-01 -8.99853230e-01 -9.79069531e-01
-2.28162453e-01 3.38207424e-01 9.86407846e-02 -2.61324406... | [4.790380001068115, 2.1385319232940674] |
9f65906d-225b-458c-b0a5-a6736b8aa5a9 | fast-classification-of-small-x-ray | 1811.08425 | null | http://arxiv.org/abs/1811.08425v2 | http://arxiv.org/pdf/1811.08425v2.pdf | Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks | X-ray diffraction (XRD) data acquisition and analysis is among the most
time-consuming steps in the development cycle of novel thin-film materials. We
propose a machine-learning-enabled approach to predict crystallographic
dimensionality and space group from a limited number of thin-film XRD patterns.
We overcome the s... | ['Noor Titan Putri Hartono', 'Zhe Liu', 'Charlie Settens', 'Siyu I. P. Tian', 'Aaron Gilad Kusne', 'Tonio Buonassisi', 'Felipe Oviedo', 'Brian L. DeCost', 'Ramasamy Savitha', 'Zekun Ren', 'Shijing Sun', 'Giuseppe Romano'] | 2018-11-20 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 4.60212678e-01 1.55269980e-01 -1.41939491e-01 -5.17777085e-01
-6.12785399e-01 -1.95337802e-01 6.00208223e-01 2.54178792e-01
-3.86588722e-01 7.83519089e-01 -6.04258537e-01 -4.72126275e-01
-3.39392573e-01 -7.71176457e-01 -7.54949391e-01 -9.25057173e-01
-1.59800183e-02 8.33371520e-01 3.31881940e-02 2.63700783... | [5.221860885620117, 5.304994106292725] |
aec931ca-cb08-4c1c-93ef-df75e0dcf149 | federated-learning-over-harmonized-data-silos | 2305.08985 | null | https://arxiv.org/abs/2305.08985v1 | https://arxiv.org/pdf/2305.08985v1.pdf | Federated Learning over Harmonized Data Silos | Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existing work operates on unstructured data, such as images or text, or on structured data assumed to be consistent a... | ['Jose Luis Ambite', 'Dimitris Stripelis'] | 2023-05-15 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-4.10796642e-01 9.73389894e-02 -6.78174794e-01 -8.42852235e-01
-8.93256664e-01 -5.99405408e-01 5.58487356e-01 7.67921448e-01
-2.48638481e-01 7.87963271e-01 2.85921454e-01 4.59229872e-02
-5.63762486e-01 -1.03446078e+00 -6.08122349e-01 -1.36864930e-01
-4.56628725e-02 8.73443007e-01 -1.35339409e-01 7.16809323... | [6.062503814697266, 6.556394577026367] |
f32efef0-df17-4077-a9d0-92d392fab672 | e2timt-efficient-and-effective-modal-adapter | 2305.05166 | null | https://arxiv.org/abs/2305.05166v2 | https://arxiv.org/pdf/2305.05166v2.pdf | E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation | Text image machine translation (TIMT) aims to translate texts embedded in images from one source language to another target language. Existing methods, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recog... | ['Chengqing Zong', 'Yu Zhou', 'Yang Zhao', 'Mei Tu', 'Yaping Zhang', 'Cong Ma'] | 2023-05-09 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.69036555e-01 -3.54284197e-01 -1.56019241e-01 -4.10533994e-01
-1.04892182e+00 -5.73912621e-01 6.49406791e-01 -6.48195922e-01
-5.26492238e-01 4.51651216e-01 5.45910262e-02 -3.24160129e-01
4.26220208e-01 -2.23005340e-01 -7.66617477e-01 -5.15480876e-01
8.15731466e-01 2.99707323e-01 1.32775292e-01 -3.93114164... | [11.562731742858887, 1.5477029085159302] |
8f93bf55-38d9-48cf-9179-298d6c34cfab | explainable-predictive-process-monitoring-a | 2202.07760 | null | https://arxiv.org/abs/2202.07760v1 | https://arxiv.org/pdf/2202.07760v1.pdf | Explainable Predictive Process Monitoring: A User Evaluation | Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the Predictive Process Monitoring field, where predictions, obtained by applying Machine Learning techniques, need to be explain... | ['Alexander Nolte', 'Fabrizio Maria Maggi', 'Suhwan Lee', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Marco Comuzzi', 'Williams Rizzi'] | 2022-02-15 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 1.18969046e-01 9.46109235e-01 4.83009703e-02 -4.90486294e-01
1.98381603e-01 -5.51262498e-01 5.26560783e-01 1.14416718e+00
7.94101786e-03 2.49663711e-01 2.89441407e-01 -8.96642447e-01
-4.01256919e-01 -5.14602721e-01 -3.46439958e-01 -2.30595142e-01
2.71262884e-01 6.36853576e-01 -1.87204063e-01 1.14402935... | [8.726777076721191, 5.944135665893555] |
91499e99-83b9-4a9d-ab0e-10a647b3b49a | improving-online-continual-learning | 2306.16817 | null | https://arxiv.org/abs/2306.16817v2 | https://arxiv.org/pdf/2306.16817v2.pdf | Improving Online Continual Learning Performance and Stability with Temporal Ensembles | Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting b... | ['Joost Van de Weijer', 'Antonio Carta', 'Albin Soutif--Cormerais'] | 2023-06-29 | null | null | null | null | ['continual-learning'] | ['methodology'] | [ 4.41406332e-02 -5.70455529e-02 2.05827475e-01 -8.43129903e-02
-3.30322415e-01 -4.76654232e-01 7.61413813e-01 3.96773309e-01
-8.32047045e-01 7.33303785e-01 -3.06289196e-01 -2.29790196e-01
-3.12075198e-01 -5.09972692e-01 -9.38423038e-01 -6.48051679e-01
-3.01376551e-01 4.61243719e-01 5.56707561e-01 -2.28163183... | [9.696063041687012, 3.311431884765625] |
3608828d-2669-410d-b7f8-8e3ce11738ff | joint-learning-of-multiple-image-restoration | 1907.04508 | null | https://arxiv.org/abs/1907.04508v2 | https://arxiv.org/pdf/1907.04508v2.pdf | Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network | The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors. However, high-performance networks have been specifically designed for a single degradation factor. In this paper, we tackle a harder problem, restoring a clean i... | ['Takayuki Okatani', 'Masanori Suganuma', 'Xiyang Luo', 'Xing Liu'] | 2019-07-10 | null | null | null | null | ['jpeg-artifact-removal'] | ['computer-vision'] | [ 4.33201998e-01 -1.90035462e-01 1.35198891e-01 -8.98604617e-02
-6.16269290e-01 -2.05704361e-01 4.92569655e-01 -3.07875365e-01
-3.21086138e-01 6.56652629e-01 1.88028455e-01 -3.60687941e-01
-1.83731452e-01 -3.69485527e-01 -7.06900001e-01 -1.12336421e+00
-7.24083651e-03 1.71842158e-01 3.02055955e-01 -4.67463017... | [11.527647972106934, -2.313626527786255] |
2e2e3e51-9a38-4fe9-aa86-747d571c95b7 | dropmae-masked-autoencoders-with-spatial | 2304.00571 | null | https://arxiv.org/abs/2304.00571v2 | https://arxiv.org/pdf/2304.00571v2.pdf | DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks | In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-based downstream tasks, including visual object tracking (VOT) and video object segmentation (VOS). A simple extension of MAE is to randomly mask out frame patches in videos and reconstruct the frame pixels. However, we find that this s... | ['Antoni B. Chan', 'Ying Shan', 'Baoyuan Wu', 'Ziquan Liu', 'Tianyu Yang', 'Qiangqiang Wu'] | 2023-04-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_DropMAE_Masked_Autoencoders_With_Spatial-Attention_Dropout_for_Tracking_Tasks_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-object-segmentation', 'video-semantic-segmentation', 'visual-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.56283259e-01 -3.33050489e-01 -4.95275050e-01 -1.88263401e-01
-7.07782269e-01 -5.98100185e-01 4.02505904e-01 -5.10441840e-01
-5.43427587e-01 3.72558177e-01 1.21773608e-01 -2.17308864e-01
3.23186845e-01 -3.71047199e-01 -1.22330844e+00 -5.70986748e-01
1.00452388e-02 9.08824503e-02 7.57995784e-01 -3.50786857... | [9.056668281555176, 0.0467764288187027] |
0d4e7cfa-cb03-41c9-affe-d1dae0a2bf4c | active-teacher-for-semi-supervised-object-1 | 2303.08348 | null | https://arxiv.org/abs/2303.08348v1 | https://arxiv.org/pdf/2303.08348v1.pdf | Active Teacher for Semi-Supervised Object Detection | In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{https://github.com/HunterJ-Lin/ActiveTeacher}) for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student frame... | ['Rongrong Ji', 'Qiang Xu', 'Rongrong Fu', 'Liujuan Cao', 'Xiaoshuai Sun', 'Gen Luo', 'Yunhang Shen', 'Yiyi Zhou', 'Jianghang Lin', 'Peng Mi'] | 2023-03-15 | active-teacher-for-semi-supervised-object | http://openaccess.thecvf.com//content/CVPR2022/html/Mi_Active_Teacher_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mi_Active_Teacher_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.36036086e-02 2.73214936e-01 -6.25593483e-01 -4.69869047e-01
-9.34864998e-01 -6.14322901e-01 4.48309034e-01 2.35772386e-01
-6.29059911e-01 5.40691137e-01 -1.45602658e-01 -1.94763497e-01
8.22451618e-03 -3.73554558e-01 -3.98039848e-01 -8.52644742e-01
2.16396481e-01 4.83656704e-01 3.76800209e-01 1.68404609... | [9.442809104919434, 3.476712703704834] |
83bc8f37-16d8-40a8-837c-457aa449a080 | a-group-symmetric-stochastic-differential | 2305.18407 | null | https://arxiv.org/abs/2305.18407v1 | https://arxiv.org/pdf/2305.18407v1.pdf | A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining | Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maxim... | ['Jian Tang', 'Hongyu Guo', 'ZhiMing Ma', 'Weitao Du', 'Shengchao Liu'] | 2023-05-28 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.94546568e-01 1.29056843e-02 -5.38888574e-01 -3.36537182e-01
-7.20542073e-01 -8.10302734e-01 7.36364484e-01 2.06814423e-01
-3.57957499e-04 9.67730582e-01 3.28479439e-01 -4.43196774e-01
-4.15451378e-02 -9.81031477e-01 -1.13340259e+00 -8.67732346e-01
-1.05448529e-01 6.01378679e-01 -3.31688464e-01 -2.97538698... | [5.089460372924805, 5.818934440612793] |
49b8b1e1-27e5-4367-99b0-2ac5005443a8 | medical-imaging-with-deep-learning-for-covid | 2107.09602 | null | https://arxiv.org/abs/2107.09602v1 | https://arxiv.org/pdf/2107.09602v1.pdf | Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review | The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based... | ['V. B. Surya Prasath', 'M. Rubaiyat Hossain Mondal', 'Prajoy Podder', 'Subrato Bharati'] | 2021-07-13 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 8.14126879e-02 -4.00550604e-01 -1.52639896e-01 9.63297859e-02
-6.42014563e-01 -5.00758588e-01 2.85385579e-01 3.72837961e-01
-5.49700499e-01 6.67678118e-01 -1.81396440e-01 -5.90846419e-01
-2.41632406e-02 -5.24790585e-01 -3.33541989e-01 -9.65480268e-01
-3.57446313e-01 1.09967113e+00 -1.42371997e-01 4.68522489... | [15.557677268981934, -1.7171481847763062] |
acb58f68-257d-4abc-b828-75d2fa9f35d7 | an-empirical-analysis-of-topic-models | null | null | https://aclanthology.org/2021.ranlp-main.157 | https://aclanthology.org/2021.ranlp-main.157.pdf | An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures | Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed. In this paper, we present an empirical analysis and comparison of Neural Topic Models by finding the optimal h... | ['Elisabetta Fersini', 'Silvia Terragni'] | null | null | https://aclanthology.org/2021.ranlp-1.157 | https://aclanthology.org/2021.ranlp-1.157.pdf | ranlp-2021-9 | ['topic-models'] | ['natural-language-processing'] | [-1.86248302e-01 1.72744721e-01 -4.19095665e-01 -5.64530671e-01
-9.56769884e-01 -4.27902281e-01 1.00724971e+00 3.57371747e-01
-5.46303988e-01 6.55156672e-01 4.51635510e-01 -3.78125310e-02
-7.56434679e-01 -6.36432409e-01 -4.21027124e-01 -8.07813168e-01
-4.25410569e-01 7.10487068e-01 3.34595501e-01 9.10568535... | [10.42877197265625, 7.002638816833496] |
aaa2227f-6969-4e66-95bc-dbad65b981cd | neural-based-context-representation-learning | 1708.02561 | null | http://arxiv.org/abs/1708.02561v1 | http://arxiv.org/pdf/1708.02561v1.pdf | Neural-based Context Representation Learning for Dialog Act Classification | We explore context representation learning methods in neural-based models for
dialog act classification. We propose and compare extensively different methods
which combine recurrent neural network architectures and attention mechanisms
(AMs) at different context levels. Our experimental results on two benchmark
dataset... | ['Ngoc Thang Vu', 'Daniel Ortega'] | 2017-08-08 | neural-based-context-representation-learning-1 | https://aclanthology.org/W17-5530 | https://aclanthology.org/W17-5530.pdf | ws-2017-8 | ['dialog-act-classification'] | ['natural-language-processing'] | [ 1.73297837e-01 3.80813912e-03 -5.76782711e-02 -5.80352366e-01
-2.00819165e-01 -2.29826227e-01 1.05474842e+00 8.69540572e-02
-7.28558540e-01 8.20317388e-01 8.72887731e-01 -5.08195996e-01
1.21644616e-01 -4.25318629e-01 3.16876739e-01 -4.60733771e-01
3.51448119e-01 7.09311128e-01 1.76192224e-01 -9.34062898... | [12.821436882019043, 7.698126792907715] |
00f1e21c-1f59-4c1a-84c1-e25985abef36 | deep-image-harmonization | 1703.00069 | null | http://arxiv.org/abs/1703.00069v1 | http://arxiv.org/pdf/1703.00069v1.pdf | Deep Image Harmonization | Compositing is one of the most common operations in photo editing. To
generate realistic composites, the appearances of foreground and background
need to be adjusted to make them compatible. Previous approaches to harmonize
composites have focused on learning statistical relationships between
hand-crafted appearance fe... | ['Ming-Hsuan Yang', 'Kalyan Sunkavalli', 'Yi-Hsuan Tsai', 'Xin Lu', 'Xiaohui Shen', 'Zhe Lin'] | 2017-02-28 | deep-image-harmonization-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Tsai_Deep_Image_Harmonization_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tsai_Deep_Image_Harmonization_CVPR_2017_paper.pdf | cvpr-2017-7 | ['image-harmonization'] | ['computer-vision'] | [ 3.37967932e-01 -4.05529171e-01 3.39058071e-01 -2.84409225e-01
-5.02659440e-01 -3.10230702e-01 4.24323797e-01 -1.52110502e-01
1.26830870e-02 3.93740118e-01 2.14943260e-01 1.31996155e-01
2.79246986e-01 -9.78336751e-01 -8.86157393e-01 -7.14272439e-01
3.20515871e-01 -1.62845422e-02 3.24814111e-01 -3.80020648... | [11.279677391052246, -1.2153047323226929] |
063d8839-f34d-4a02-855c-c1ac9cca8e08 | query-specific-knowledge-graphs-for-complex | 2211.04142 | null | https://arxiv.org/abs/2211.04142v1 | https://arxiv.org/pdf/2211.04142v1.pdf | Query-Specific Knowledge Graphs for Complex Finance Topics | Across the financial domain, researchers answer complex questions by extensively "searching" for relevant information to generate long-form reports. This workshop paper discusses automating the construction of query-specific document and entity knowledge graphs (KGs) for complex research topics. We focus on the CODEC d... | ['Jeffrey Dalton', 'Iain Mackie'] | 2022-11-08 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-1.55261323e-01 1.85826004e-01 -3.64419878e-01 -9.58365425e-02
-1.51824200e+00 -1.00384533e+00 7.70252168e-01 7.54895449e-01
-2.92534858e-01 6.21131539e-01 5.95780075e-01 -2.98091292e-01
-9.03500617e-01 -8.82807195e-01 -5.18567920e-01 2.90886164e-02
-2.81241834e-01 9.05610919e-01 4.59708035e-01 -2.58673072... | [10.207563400268555, 8.01783275604248] |
7202600e-c439-4e2f-8242-1a58dc9d37ac | a-mood-based-genre-classification-of | 1508.01571 | null | http://arxiv.org/abs/1508.01571v1 | http://arxiv.org/pdf/1508.01571v1.pdf | A Mood-based Genre Classification of Television Content | The classification of television content helps users organise and navigate
through the large list of channels and programs now available. In this paper,
we address the problem of television content classification by exploiting text
information extracted from program transcriptions. We present an analysis which
adapts a... | ["Michael P. O'Mahony", 'Humberto Corona'] | 2015-08-06 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.31227243e-01 -3.51480454e-01 -4.47191209e-01 -7.51923144e-01
-7.26429701e-01 -7.82541037e-01 7.52415240e-01 8.23059618e-01
-1.84253484e-01 1.43761814e-01 6.17794037e-01 3.18235867e-02
-2.97571898e-01 -9.02688682e-01 -3.57140660e-01 -5.22756338e-01
-4.30628508e-02 1.49976090e-01 -4.80959155e-02 -6.01396322... | [15.647870063781738, 5.081452369689941] |
4edc8c16-f51a-4610-9e4b-b6c57b7adcea | sustainable-palm-tree-farming-leveraging-iot | 2306.16862 | null | https://arxiv.org/abs/2306.16862v1 | https://arxiv.org/pdf/2306.16862v1.pdf | Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil | The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for the early detection and management of RPW. Our approach combines computer vision... | ['Anis Koubaa', 'Imed Riadh Farah', 'Wadii Boulila', 'Ayyub Alzahem', 'Yosra Hajjaji'] | 2023-06-29 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 2.40227699e-01 -6.46885335e-01 -3.61841559e-01 2.06422508e-01
6.28431141e-03 -8.89924765e-01 2.11690634e-01 6.43347681e-01
-1.41112715e-01 1.25692636e-01 4.66144159e-02 -9.20145571e-01
-6.69309735e-01 -1.57204676e+00 -5.87909967e-02 -9.93288159e-01
-5.06883562e-01 1.29565537e-01 3.93589467e-01 -3.50660115... | [9.292314529418945, -1.5624351501464844] |
21b163ce-2897-4864-adf1-56fdd01c4a09 | shape-driven-kernel-adaptation-in | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Li_Shape_Driven_Kernel_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Shape_Driven_Kernel_2015_CVPR_paper.pdf | Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Traits Recognition | One key challenge of facial traits recognition is the large non-rigid appearance variations due to irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neura... | ['Zhiheng Niu', 'Junliang Xing', 'Shiguang Shan', 'Shaoxin Li', 'Shuicheng Yan'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['age-and-gender-classification'] | ['computer-vision'] | [-9.23721939e-02 -4.61139670e-03 -1.70945838e-01 -9.23984885e-01
-1.07107088e-01 -4.92506713e-01 2.78656244e-01 -3.71303409e-01
-2.50251800e-01 2.45527416e-01 1.01181231e-01 1.58927858e-01
-2.51311183e-01 -3.97056192e-01 -5.51903963e-01 -8.45563948e-01
-1.61527458e-03 -3.06175016e-02 -1.98380455e-01 -1.46460846... | [13.3283052444458, 0.7081615328788757] |
7cd015eb-cc01-4c5e-b9fa-01d0f3907da3 | mrl-learning-to-mix-with-attention-and | 2208.13975 | null | https://arxiv.org/abs/2208.13975v1 | https://arxiv.org/pdf/2208.13975v1.pdf | MRL: Learning to Mix with Attention and Convolutions | In this paper, we present a new neural architectural block for the vision domain, named Mixing Regionally and Locally (MRL), developed with the aim of effectively and efficiently mixing the provided input features. We bifurcate the input feature mixing task as mixing at a regional and local scale. To achieve an efficie... | ['Yoshiki Tanaka', 'Hisahiro Suganuma', 'Shlok Mohta'] | 2022-08-30 | null | null | null | null | ['histopathological-segmentation', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical'] | [ 1.28433868e-01 -6.42270073e-02 -6.86420500e-02 -4.76888269e-01
-8.22544098e-01 -5.43669701e-01 7.08512425e-01 2.36649409e-01
-8.41954827e-01 4.74310637e-01 1.19523183e-01 -9.17131826e-02
-2.86425501e-01 -6.50187314e-01 -6.48340762e-01 -9.90851521e-01
-6.40157685e-02 -2.37139910e-01 1.99054122e-01 5.99971414... | [14.679224014282227, -2.5623767375946045] |
d0a1b9c6-dd09-4731-b1ee-883973b14251 | 3d-part-assembly-generation-with-instance | 2207.01779 | null | https://arxiv.org/abs/2207.01779v1 | https://arxiv.org/pdf/2207.01779v1.pdf | 3D Part Assembly Generation with Instance Encoded Transformer | It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this paper, we focus on the setting of furniture assembly from a complete set of part geometries, which is essentially a 6-DoF part pose estima... | ['Mingyu You', 'Xuan Han', 'WeiHao Wang', 'Tao Kong', 'Rufeng Zhang'] | 2022-07-05 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.43459454e-01 1.14666387e-01 6.82996362e-02 -3.28597426e-01
-4.70754474e-01 -8.89807522e-01 1.59672111e-01 -2.57814545e-02
1.86803877e-01 2.59636402e-01 -1.81948140e-01 4.00055908e-02
-5.08342028e-01 -6.22809410e-01 -1.35916328e+00 -3.09022158e-01
-1.31211122e-02 1.06086659e+00 2.16720864e-01 -5.30178308... | [6.18159818649292, -1.1937042474746704] |
6f1b7011-abba-4036-a295-56989c6e0565 | select-supplement-and-focus-for-rgb-d | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.pdf | Select, Supplement and Focus for RGB-D Saliency Detection | Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction. However, RGB-D saliency detection methods are also negatively influenced by randomly distributed erroneous or missing regions on the depth map or along the object boundaries. This offe... | [' Huchuan Lu', ' Zhengkun Rong', ' Yongri Piao', ' Weisong Ren', 'Miao Zhang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.32997203e-01 1.96267128e-01 -3.09787214e-01 -4.98731226e-01
-6.32587790e-01 -4.14419435e-02 4.16993797e-01 9.74530429e-02
-3.62560600e-01 7.37880290e-01 2.57937610e-01 1.50302291e-01
-1.81872919e-01 -6.96095765e-01 -7.88991988e-01 -7.78558671e-01
1.47464558e-01 1.57244373e-02 8.17451298e-01 -1.29968151... | [9.677518844604492, -0.7880049347877502] |
b9f32dca-7e2a-4795-a8d3-9ee52c7fc0d8 | point-transformer-1 | 2012.09164 | null | https://arxiv.org/abs/2012.09164v2 | https://arxiv.org/pdf/2012.09164v2.pdf | Point Transformer | Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we investigate the application of self-attention networks to 3D point cloud processing. We design self-attention ... | ['Vladlen Koltun', 'Philip Torr', 'Jiaya Jia', 'Li Jiang', 'Hengshuang Zhao'] | 2020-12-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhao_Point_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhao_Point_Transformer_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-part-segmentation'] | ['computer-vision'] | [ 3.19053173e-01 3.79290015e-01 -1.11376181e-01 -4.56127763e-01
-4.80939567e-01 -5.60667515e-01 4.08241987e-01 2.59478778e-01
-5.41258991e-01 -6.96970001e-02 -1.99149013e-01 -3.90751809e-01
1.29445598e-01 -6.78613722e-01 -1.18262160e+00 -1.72916986e-02
-5.14630266e-02 6.85663342e-01 5.46031177e-01 -1.38007343... | [7.9514665603637695, -3.419536590576172] |
1be35c85-0de1-45ea-b0c5-8f054cdee725 | towards-accurate-localization-by-instance | 2107.05005 | null | https://arxiv.org/abs/2107.05005v2 | https://arxiv.org/pdf/2107.05005v2.pdf | Towards Accurate Localization by Instance Search | Visual object localization is the key step in a series of object detection tasks. In the literature, high localization accuracy is achieved with the mainstream strongly supervised frameworks. However, such methods require object-level annotations and are unable to detect objects of unknown categories. Weakly supervised... | ['Wan-Lei Zhao', 'Hui-Chu Xiao', 'Yi-Geng Hong'] | 2021-07-11 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 1.72729045e-01 -2.06243500e-01 -5.78458846e-01 -1.05672210e-01
-1.08111227e+00 -5.65816641e-01 5.81609666e-01 5.44425070e-01
-4.97792155e-01 6.18084490e-01 -2.73283690e-01 4.46630508e-01
-1.18366972e-01 -4.18428779e-01 -6.89629078e-01 -7.85478592e-01
5.18413587e-03 5.07747412e-01 7.90746868e-01 3.50483656... | [9.424718856811523, 1.436740756034851] |
f0ad03e0-ba07-44ad-a6ad-0822d2ac6a1b | enriching-epidemiological-thematic-features | null | null | https://aclanthology.org/2022.lrec-1.399 | https://aclanthology.org/2022.lrec-1.399.pdf | Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification | We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted from the web. We train a classifier to discriminate between relevant and irrelevant document... | ['Dickson Owuor', 'Roberto Interdonato', 'Mathieu Roche', 'Edmond Menya'] | null | null | null | null | lrec-2022-6 | ['epidemiology', 'document-classification'] | ['medical', 'natural-language-processing'] | [ 2.76550919e-01 2.70278513e-01 -1.75150812e-01 -1.78381264e-01
-6.05019212e-01 -5.00117600e-01 1.02970123e+00 9.94640410e-01
-9.00694966e-01 5.83434403e-01 6.66974723e-01 -3.59863460e-01
-3.83055091e-01 -9.93622541e-01 -8.05485487e-01 -6.54552341e-01
-7.00625598e-01 5.03474593e-01 2.78039068e-01 -2.68812776... | [8.449490547180176, 8.996840476989746] |
54252fb4-a8bc-4f7f-9360-a5f63665bfe5 | compact-low-profile-wearable-antennas-for | 1809.07475 | null | http://arxiv.org/abs/1809.07475v1 | http://arxiv.org/pdf/1809.07475v1.pdf | Compact Low-Profile Wearable Antennas For Breast Cancer Detection | Many lives can be saved if tumors are detected in early stages, which can
result in a bigger chance for recovery. Many patients find it irritating to get
regular checkups due to the fact that the majority of the monitoring systems
are complicated, not available everywhere and not mobile. Furthermore, for
medical field ... | [] | 2018-09-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-1.63155809e-01 2.04595640e-01 -5.09779334e-01 1.02676474e-01
8.17811638e-02 -3.96788679e-02 -1.43131763e-01 5.10075867e-01
-2.99662024e-01 7.06858099e-01 -2.03165472e-01 -3.99829477e-01
-2.86691755e-01 -1.15353572e+00 -4.31676582e-02 -8.89957309e-01
3.45984772e-02 2.87937641e-01 4.82320607e-01 -3.54198515... | [15.037590980529785, -2.7978227138519287] |
d8998105-707b-4dc0-91bf-241b6db570ca | palm-pre-training-an-autoencoding-1 | null | null | https://aclanthology.org/2020.emnlp-main.700 | https://aclanthology.org/2020.emnlp-main.700.pdf | PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation | Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text... | ['Luo Si', 'Fei Huang', 'Songfang Huang', 'Wei Wang', 'Ming Yan', 'Chen Wu', 'Chenliang Li', 'Bin Bi'] | null | null | null | null | emnlp-2020-11 | ['generative-question-answering', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.56415737e-01 6.47843003e-01 2.52120495e-01 -5.15659332e-01
-1.49661040e+00 -5.44911504e-01 1.05116868e+00 2.02303808e-02
-2.64263839e-01 1.11311555e+00 9.73568320e-01 -2.69633561e-01
2.20734000e-01 -8.90752792e-01 -7.62750268e-01 -4.63263661e-01
4.08544511e-01 1.01132941e+00 -3.13283086e-01 -8.11268687... | [11.946982383728027, 8.957592010498047] |
1820da84-d786-47f4-ae01-dbaebb69e05f | query-focused-extractive-summarisation-for-1 | 2209.01815 | null | https://arxiv.org/abs/2209.01815v1 | https://arxiv.org/pdf/2209.01815v1.pdf | Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering | This paper presents Macquarie University's participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B (BioASQ10b), Phase~B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one se... | ['Diego Mollá'] | 2022-09-05 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 3.32284778e-01 1.53636888e-01 6.68304786e-02 -2.61436164e-01
-1.24273407e+00 -5.57408869e-01 7.12580502e-01 8.69614124e-01
-8.13347995e-01 9.52409208e-01 5.30870736e-01 -3.17607194e-01
-6.38039291e-01 -6.43172026e-01 -3.67033839e-01 -3.98298562e-01
-5.51083088e-02 7.69481063e-01 4.20880258e-01 -4.06377017... | [12.298164367675781, 9.503435134887695] |
6fd8b654-ac17-4ca6-8298-6b5dc8a36860 | ecrecer-enzyme-commission-number | 2202.03632 | null | https://arxiv.org/abs/2202.03632v1 | https://arxiv.org/pdf/2202.03632v1.pdf | ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning | Enzyme Commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab-initio computational approaches were proposed to predict EC numbers for given input sequences directly. Howeve... | ['Hongwu Ma', 'Xiaoping Liao', 'Hoaran Li', 'Ruoyu Wang', 'Qianqian Yuan', 'Zhenkun Shi'] | 2022-02-08 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 5.25317006e-02 -2.36175671e-01 -1.16494469e-01 -1.34102941e-01
-5.91094136e-01 -5.78188717e-01 2.58286804e-01 3.82261932e-01
-3.88136983e-01 1.11122561e+00 -2.63399649e-02 -3.99247229e-01
3.98935415e-02 -6.11062169e-01 -8.25538218e-01 -9.59544122e-01
2.53325459e-02 3.01561594e-01 -9.36440304e-02 -1.30082816... | [4.711822986602783, 5.629776477813721] |
ce8d7846-90f1-4ed8-a049-6a9907152371 | an-empirical-evaluation-of-federated | 2303.10218 | null | https://arxiv.org/abs/2303.10218v1 | https://arxiv.org/pdf/2303.10218v1.pdf | An Empirical Evaluation of Federated Contextual Bandit Algorithms | As the adoption of federated learning increases for learning from sensitive data local to user devices, it is natural to ask if the learning can be done using implicit signals generated as users interact with the applications of interest, rather than requiring access to explicit labels which can be difficult to acquire... | ['Zheng Xu', 'H. Brendan McMahan', 'Alekh Agarwal'] | 2023-03-17 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.66562724e-01 7.51569197e-02 -8.41875374e-01 -5.75558603e-01
-1.10813713e+00 -7.84690261e-01 5.52376926e-01 -3.74278814e-01
-3.99973631e-01 9.34043944e-01 4.36197519e-01 -6.27257288e-01
-5.24230361e-01 -1.99541822e-01 -8.94129932e-01 -7.51938283e-01
-1.44397721e-01 5.71581423e-01 -3.15381616e-01 1.48081586... | [4.504220962524414, 3.2272610664367676] |
166c62ba-d61b-415a-a5e1-09d904716d02 | fair-representation-learning-using | 2108.00295 | null | https://arxiv.org/abs/2108.00295v2 | https://arxiv.org/pdf/2108.00295v2.pdf | Fair Representation Learning using Interpolation Enabled Disentanglement | With the growing interest in the machine learning community to solve real-world problems, it has become crucial to uncover the hidden reasoning behind their decisions by focusing on the fairness and auditing the predictions made by these black-box models. In this paper, we propose a novel method to address two key issu... | ['Chandan K. Reddy', 'Bhanukiran Vinzamuri', 'Akshita Jha'] | 2021-07-31 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.53932267e-01 5.62822342e-01 -6.58567309e-01 -4.09289867e-01
-8.42287481e-01 -4.40788537e-01 5.01418412e-01 2.17325300e-01
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9.07689519e-03 1.47337094e-01 -6.33404851e-01 -8.44932124... | [8.857060432434082, 5.196700572967529] |
91bfb578-59cd-410a-a1f5-3d198e9bc7bb | exclusivity-consistency-regularized-multi | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Exclusivity-Consistency_Regularized_Multi-View_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Exclusivity-Consistency_Regularized_Multi-View_CVPR_2017_paper.pdf | Exclusivity-Consistency Regularized Multi-View Subspace Clustering | Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views... | ['Xiaojie Guo', 'Zhen Lei', 'Stan Z. Li', 'Xiaobo Wang', 'Changqing Zhang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.38543442e-01 -3.71685445e-01 -3.83863151e-01 -1.23232163e-01
-5.12394488e-01 -6.50839210e-01 6.75548196e-01 -1.80966988e-01
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3.15513819e-01 6.30580932e-02 7.65717179e-02 -5.17587317... | [8.262083053588867, 4.596033573150635] |
a46bce26-024b-4318-b666-10f4ccdc6ef2 | k-diag-knowledge-enhanced-disease-diagnosis | 2302.11557 | null | https://arxiv.org/abs/2302.11557v2 | https://arxiv.org/pdf/2302.11557v2.pdf | K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging | In this paper, we consider the problem of disease diagnosis. Unlike the conventional learning paradigm that treats labels independently, we propose a knowledge-enhanced framework, that enables training visual representation with the guidance of medical domain knowledge. In particular, we make the following contribution... | ['Weidi Xie', 'Ya zhang', 'Yanfeng Wang', 'Xiaoman Zhang', 'Chaoyi Wu'] | 2023-02-22 | null | null | null | null | ['anatomy', 'implicit-relations'] | ['miscellaneous', 'natural-language-processing'] | [ 3.78487289e-01 5.44743240e-01 -3.35789979e-01 -3.17010611e-01
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-4.32332456e-02 3.79281402e-01 -8.84503871e-03 -3.25915962... | [14.627459526062012, -2.1691229343414307] |
308de818-2def-4ed4-ba48-ed9c94ee713a | graph-contrastive-topic-model | 2307.02078 | null | https://arxiv.org/abs/2307.02078v1 | https://arxiv.org/pdf/2307.02078v1.pdf | Graph Contrastive Topic Model | Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with similar semantics to the prototypes. In this paper, we aim to explore the efficient sampling strategy and contrastive learning in NTMs to addre... | ['Sophia Ananiadou', 'Qianqian Xie', 'Lei Liu', 'Zheheng Luo'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'representation-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 7.98905492e-02 4.40514266e-01 -6.20050609e-01 -1.26010746e-01
-4.39418584e-01 -2.15698063e-01 8.43225360e-01 -8.26751962e-02
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-1.91925272e-01 6.35905206e-01 1.12630114e-01 -1.29018631... | [10.322704315185547, 6.877812385559082] |
e2a9fc06-3c60-4185-9b28-e931be55ec36 | deep-polarization-cues-for-transparent-object | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Kalra_Deep_Polarization_Cues_for_Transparent_Object_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kalra_Deep_Polarization_Cues_for_Transparent_Object_Segmentation_CVPR_2020_paper.pdf | Deep Polarization Cues for Transparent Object Segmentation | Segmentation of transparent objects is a hard, open problem in computer vision. Transparent objects lack texture of their own, adopting instead the texture of scene background. This paper reframes the problem of transparent object segmentation into the realm of light polarization, i.e., the rotation of light waves. We ... | [' Achuta Kadambi', ' Ramesh Raskar', ' Kartik Venkataraman', ' Supreeth Krishna Rao', ' Vage Taamazyan', 'Agastya Kalra'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['transparent-objects'] | ['computer-vision'] | [ 6.81461871e-01 4.78965491e-02 2.05710277e-01 -2.61648923e-01
-3.37136030e-01 -1.06651056e+00 1.90555170e-01 -7.79795051e-01
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4.29959059e-01 8.96854103e-01 6.08607888e-01 -9.65770334... | [9.547492980957031, -2.484870672225952] |
e3c32f9d-272a-4ca5-8e0b-634f3b4d9567 | signal-quality-assessment-of | 2202.00606 | null | https://arxiv.org/abs/2202.00606v1 | https://arxiv.org/pdf/2202.00606v1.pdf | Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition and lightweight CNN Architecture | Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, while recording, these PPG signals are easily corrupted by motion artifacts and body movements, leading to noise enriched, poor quality signals. Therefore ensuring high-quality signals is necessary to ext... | ['Sayan Sarkar', 'Aayushman Ghosh', 'Tamaghno Chatterjee'] | 2022-02-01 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.88306260e-01 -2.33242065e-01 2.73139756e-02 -2.56024092e-01
-5.12351632e-01 -3.04702222e-01 -2.75475055e-01 -6.08651526e-02
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-1.82480603e-01 -6.50199592e-01 -3.17860216e-01 -8.71406436e-01
-1.41513273e-01 -3.76650035e-01 -1.29120231e-01 1.15232587... | [14.046624183654785, 2.9726247787475586] |
b181c733-1b5e-4003-b030-28b55d876e2a | openqa-hybrid-qa-system-relying-on-structured | 2112.15356 | null | https://arxiv.org/abs/2112.15356v1 | https://arxiv.org/pdf/2112.15356v1.pdf | OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data | Search engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages. How to quickly, accurately and effectively obtain the information needed by users from massive Internet data has become on... | ['Ziwei Wang', 'Lingyu Liu', 'Yang Liu', 'Yuxin Qin', 'Bin Xu', 'Gaochen Wu'] | 2021-12-31 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-3.26770395e-01 1.28573358e-01 1.40912920e-01 -4.55989093e-01
-1.00260282e+00 -7.00692832e-01 1.83515921e-02 -4.80294302e-02
-4.18351859e-01 5.85616529e-01 2.46204883e-01 -5.67395091e-01
-6.85333431e-01 -1.40178990e+00 -4.04988021e-01 -5.21321557e-02
4.84928817e-01 9.03978765e-01 7.55494237e-01 -8.78214121... | [11.01896858215332, 7.866026401519775] |
b157e902-f2ae-4323-a0fb-598e2ceeac72 | an-accurate-texture-complexity-estimation-for | null | null | https://ieeexplore.ieee.org/document/8963890 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8963890 | An Accurate Texture Complexity Estimation for Quality-Enhanced and Secure Image Steganography | Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central ... | ['YASAR AMIN', 'MANSOOR SHAUKAT KHAN', 'MUHAMMAD ALI RIAZ', 'SYEDA IFFAT NAQVI', 'HUMAYUN SHAHID', 'MUHAMMAD JAMIL KHAN', 'FAWAD', 'AYESHA SAEED'] | 2020-01-20 | null | null | null | journal-ieee-2020-1 | ['image-steganography'] | ['computer-vision'] | [ 9.33772981e-01 -6.98374733e-02 1.77426543e-02 1.35467932e-01
-2.05042839e-01 -1.17904790e-01 4.64711547e-01 -1.41354918e-01
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-1.53536886e-01 -9.39053893e-01 -2.85231441e-01 -1.26854193e+00
-2.31637731e-01 -1.92860588e-01 4.40585405e-01 -2.06081077... | [4.299746513366699, 8.048328399658203] |
8143943b-72f0-4da3-aa68-c242ad6925f8 | topodiff-a-performance-and-constraint-guided | 2208.09591 | null | https://arxiv.org/abs/2208.09591v2 | https://arxiv.org/pdf/2208.09591v2.pdf | Diffusion Models Beat GANs on Topology Optimization | Structural topology optimization, which aims to find the optimal physical structure that maximizes mechanical performance, is vital in engineering design applications in aerospace, mechanical, and civil engineering. Generative adversarial networks (GANs) have recently emerged as a popular alternative to traditional ite... | ['Faez Ahmed', 'François Mazé'] | 2022-08-20 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-6.10430352e-02 2.70109892e-01 -1.79986537e-01 7.43400082e-02
-5.53445160e-01 -4.71491694e-01 1.78398937e-01 -3.91030788e-01
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-4.14237112e-01 -1.06946921e+00 -8.28765869e-01 -6.75644815e-01
1.12899147e-01 6.02340579e-01 -1.31409496e-01 -5.56052327... | [5.818150043487549, 3.2865328788757324] |
1711ab78-f2fa-4081-b5d4-e30caf26710e | incorporating-external-pos-tagger-for | 2106.06731 | null | https://arxiv.org/abs/2106.06731v1 | https://arxiv.org/pdf/2106.06731v1.pdf | Incorporating External POS Tagger for Punctuation Restoration | Punctuation restoration is an important post-processing step in automatic speech recognition. Among other kinds of external information, part-of-speech (POS) taggers provide informative tags, suggesting each input token's syntactic role, which has been shown to be beneficial for the punctuation restoration task. In thi... | ['Zhouhan Lin', 'Xiangyu Liu', 'Jinfeng Li', 'Boxin Wang', 'Wei Wang', 'Ning Shi'] | 2021-06-12 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.79727453e-01 1.27926454e-01 -5.18705368e-01 -4.55947280e-01
-1.19757605e+00 -5.46805859e-01 3.66119295e-01 9.78159308e-02
-6.14833772e-01 6.42236412e-01 5.85545063e-01 -6.52281165e-01
6.71253800e-01 -1.59849927e-01 -6.63711727e-01 -5.14922023e-01
6.20133244e-02 6.45143837e-02 5.29218674e-01 -1.11378863... | [14.214620590209961, 7.137498378753662] |
bb21f4b2-ae64-4115-9601-3bbea6116f13 | scalable-method-for-bayesian-experimental | 2306.17615 | null | https://arxiv.org/abs/2306.17615v1 | https://arxiv.org/pdf/2306.17615v1.pdf | Scalable method for Bayesian experimental design without integrating over posterior distribution | We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational model is based on partial differential equations and, consequently, is computationally expensive to evaluate. A-optimality is a widely used and easy-to-interpret criterion for the Bayesia... | ['Raúl Tempone', 'Sebastian Krumscheid', 'Luis Espath', 'Vinh Hoang'] | 2023-06-30 | null | null | null | null | ['experimental-design', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [ 1.76957786e-01 -1.12417117e-01 1.17882095e-01 -9.24743712e-02
-5.58187068e-01 -2.55101115e-01 1.57034755e-01 -1.11971036e-01
-7.07223654e-01 8.84918451e-01 -4.30213124e-01 -6.81099772e-01
-4.82962012e-01 -5.85123658e-01 -9.82559204e-01 -9.70673680e-01
1.97758228e-01 2.79815346e-01 -8.94837454e-03 2.80385464... | [6.657273292541504, 3.8574371337890625] |
069245f6-167a-4d04-9c9e-0a10f3f7fcde | adversarial-attacks-on-deep-algorithmic | 2010.11388 | null | https://arxiv.org/abs/2010.11388v1 | https://arxiv.org/pdf/2010.11388v1.pdf | Adversarial Attacks on Deep Algorithmic Trading Policies | Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown to be susceptible to adversarial attacks. It follows that algorithmic trading DRL agents may also be compromised by such adversarial techni... | ['Ali Fathi', 'Vahid Behzadan', 'Nancirose Piazza', 'Yaser Faghan'] | 2020-10-22 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.52305102e-01 -1.02131203e-01 -9.59945023e-02 -4.68876623e-02
-4.44660217e-01 -1.35383451e+00 7.29621410e-01 -2.82335095e-02
-3.37413847e-01 1.05037999e+00 -2.72959858e-01 -7.67730176e-01
6.64917678e-02 -1.12329555e+00 -8.54405224e-01 -6.17300272e-01
-6.15810573e-01 5.12121975e-01 4.11475956e-01 -2.47506440... | [5.6820807456970215, 7.576443672180176] |
be649c1b-66cd-403c-9497-c5084e8d5099 | estimation-of-standard-auction-models | 2205.02060 | null | https://arxiv.org/abs/2205.02060v1 | https://arxiv.org/pdf/2205.02060v1.pdf | Estimation of Standard Auction Models | We provide efficient estimation methods for first- and second-price auctions under independent (asymmetric) private values and partial observability. Given a finite set of observations, each comprising the identity of the winner and the price they paid in a sequence of identical auctions, we provide algorithms for non-... | ['Manolis Zampetakis', 'Andrew Ilyas', 'Constantinos Daskalakis', 'Yeshwanth Cherapanamjeri'] | 2022-05-04 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-3.77141237e-01 -9.09520760e-02 -7.24568903e-01 -2.51584709e-01
-9.05756295e-01 -9.17703927e-01 2.62415707e-01 -2.02059612e-01
-6.75562203e-01 1.16312444e+00 1.81444243e-01 -1.91729262e-01
-5.94431221e-01 -4.57902163e-01 -7.26648927e-01 -5.65767407e-01
-5.27858377e-01 5.56926310e-01 -4.64502543e-01 -3.90809998... | [4.399096488952637, 3.1439857482910156] |
4711bb06-6125-4216-9105-847b2f005526 | vulberta-simplified-source-code-pre-training | 2205.12424 | null | https://arxiv.org/abs/2205.12424v1 | https://arxiv.org/pdf/2205.12424v1.pdf | VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection | This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects. The model learns a deep knowledge representation of the code syntax and semantics, whi... | ['Sergio Maffeis', 'Hazim Hanif'] | 2022-05-25 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-7.06227183e-01 -1.41617954e-01 -4.57618713e-01 -2.27860630e-01
-8.93261075e-01 -1.13675964e+00 4.60036874e-01 4.52779412e-01
-1.70574039e-01 1.11063913e-01 -4.33871262e-02 -1.01231897e+00
8.21922049e-02 -8.42440367e-01 -6.33122921e-01 1.22154749e-03
-5.72667599e-01 -3.68171096e-01 5.69523752e-01 -2.39742190... | [7.083368301391602, 7.7761688232421875] |
86b14ae0-1297-49d8-b432-738d0131965c | multilevel-sentiment-analysis-in-arabic | 2205.12328 | null | https://arxiv.org/abs/2205.12328v1 | https://arxiv.org/pdf/2205.12328v1.pdf | Multilevel sentiment analysis in arabic | In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, spec... | ['Ebru Sezer', 'Ahmed Nassar'] | 2022-05-24 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.22730409e-02 3.27115059e-02 -2.38860279e-01 -4.66671914e-01
1.30670607e-01 -7.68665373e-01 5.37095428e-01 8.05566430e-01
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-3.75046551e-01 -1.01201046e+00 -1.90618008e-01 -7.97212422e-01
1.92134991e-01 2.51505017e-01 -1.25158250e-01 -9.08801377... | [11.058826446533203, 6.92095947265625] |
f14422d9-e290-4beb-9f77-79445d40ef1b | how-state-of-the-art-models-can-deal-with | null | null | https://aclanthology.org/2020.paclic-1.43 | https://aclanthology.org/2020.paclic-1.43.pdf | How State-Of-The-Art Models Can Deal With Long-Form Question Answering | null | ['Le-Minh Nguyen', 'Ha-Thanh Nguyen', 'Vu Tran', 'Minh-Quan Bui'] | null | null | null | null | paclic-2020-10 | ['long-form-question-answering'] | ['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... | [-7.214654922485352, 3.8068766593933105] |
37a5b9ac-e947-4294-8443-8056abd76a73 | an-efficient-general-purpose-modular-vision | 2306.17165 | null | https://arxiv.org/abs/2306.17165v1 | https://arxiv.org/pdf/2306.17165v1.pdf | An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training | We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Despite considerable progress in multi-task learning, most efforts focus on learning from multi-label data: a single image set with multiple task labels. Such multi-label data sets are rare, small, and ex... | ['Chuang Gan', 'Erik Learned-Miller', 'Masayoshi Tomizuka', 'Wei Zhan', 'Yikang Shen', 'Mingyu Ding', 'Zitian Chen'] | 2023-06-29 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 2.24740461e-01 -3.01872671e-01 -3.55432779e-02 -2.74439692e-01
-7.56944537e-01 -7.59344757e-01 4.79053915e-01 -2.88185149e-01
-5.98606527e-01 3.92621279e-01 -8.89190584e-02 -2.57794827e-01
1.69517193e-02 -2.97626734e-01 -5.72050691e-01 -7.22144544e-01
3.54831785e-01 6.00126028e-01 5.75597644e-01 2.07538083... | [9.7359037399292, 1.714705228805542] |
e7750914-a756-433a-b477-bb16da6178f4 | real-time-high-quality-stereo-matching-system | 2212.00488 | null | https://arxiv.org/abs/2212.00488v1 | https://arxiv.org/pdf/2212.00488v1.pdf | Real-Time High-Quality Stereo Matching System on a GPU | In this paper, we propose a low error rate and real-time stereo vision system on GPU. Many stereo vision systems on GPU have been proposed to date. In those systems, the error rates and the processing speed are in trade-off relationship. We propose a real-time stereo vision system on GPU for the high resolution images.... | ['Tsutomu Maruyama', 'Qiong Chang'] | 2022-12-01 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-2.72444449e-02 -5.18065453e-01 5.20108700e-01 -3.06186616e-01
-7.59329647e-02 -1.00273296e-01 4.31395203e-01 3.23807970e-02
-8.71589184e-01 4.56346005e-01 -1.30081058e-01 -3.69174987e-01
2.07770333e-01 -1.04428363e+00 -3.38666260e-01 -5.48949718e-01
5.91994643e-01 2.17637375e-01 1.08310747e+00 -4.13452499... | [9.00308609008789, -2.180448293685913] |
297040e4-5c66-49b3-acdf-b61fabc17eb1 | ask-me-anything-in-your-native-language-1 | null | null | https://aclanthology.org/2022.naacl-main.30 | https://aclanthology.org/2022.naacl-main.30.pdf | Ask Me Anything in Your Native Language | Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach based on single encoder for q... | ['Valentin Malykh', 'Irina Piontkovskaya', 'Dmitry Abulkhanov', 'Nikita Sorokin'] | null | null | null | null | naacl-2022-7 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-3.46493781e-01 -2.96133220e-01 -3.14406566e-02 -1.92900464e-01
-2.00705719e+00 -9.66911554e-01 6.17714882e-01 3.10139716e-01
-6.42319918e-01 8.18727970e-01 4.64948624e-01 -3.14535975e-01
-2.48843893e-01 -7.52483070e-01 -6.00927055e-01 -5.85561395e-02
3.06115568e-01 1.14059246e+00 7.13184714e-01 -7.97046244... | [11.41561222076416, 7.940675258636475] |
a3ab882f-f419-4588-9b7c-971194af50ed | bertic-the-transformer-language-model-for | 2104.09243 | null | https://arxiv.org/abs/2104.09243v1 | https://arxiv.org/pdf/2104.09243v1.pdf | BERTić -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian | In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-speech tagging, named-entity-recognition, geo-location prediction and commonsense causal reasoning, sho... | ['Davor Lauc', 'Nikola Ljubešić'] | 2021-04-19 | null | null | null | null | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [-2.07453266e-01 4.96600240e-01 -3.56328815e-01 -4.85482991e-01
-9.26318228e-01 -5.81102848e-01 1.14796853e+00 3.22540492e-01
-4.84628975e-01 1.19401491e+00 8.11445951e-01 -6.67482853e-01
-3.03859144e-01 -8.87037098e-01 -6.09380364e-01 -2.10777566e-01
-5.58605865e-02 9.86831248e-01 2.72588521e-01 -5.75883687... | [9.889949798583984, 8.23092269897461] |
742a47a8-2ee0-4d32-a6eb-27786627548d | multi-attribute-relation-extraction-mare | 2111.09035 | null | https://arxiv.org/abs/2111.09035v1 | https://arxiv.org/pdf/2111.09035v1.pdf | Multi-Attribute Relation Extraction (MARE) -- Simplifying the Application of Relation Extraction | Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amo... | ['Albert Zündorf', 'Bodo Kraft', 'Philipp Kohl', 'Lars Klöser'] | 2021-11-17 | null | null | null | null | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 6.65130839e-02 9.42416966e-01 -7.56913662e-01 -4.84337240e-01
-3.27503681e-01 -5.30867636e-01 1.10285211e+00 8.80258679e-01
-3.45657796e-01 1.09237707e+00 1.42923757e-01 -7.28693247e-01
-3.73636931e-01 -1.28513968e+00 -2.68821418e-01 1.46112457e-01
-1.96022421e-01 1.04397440e+00 5.38033724e-01 -3.98490310... | [9.330089569091797, 8.714823722839355] |
9384796d-9198-40f2-b93b-779bb56bfafb | towards-compact-cnns-via-collaborative | 2105.11228 | null | https://arxiv.org/abs/2105.11228v1 | https://arxiv.org/pdf/2105.11228v1.pdf | Towards Compact CNNs via Collaborative Compression | Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in an isolated manner, leading to significant accuracy drop when pursuing high compression rates. In this paper, we propose a Collaborative Com... | ['Rongrong Ji', 'Qi Tian', 'Jincheng Ma', 'Fan Yang', 'Fei Chao', 'Mengdi Wang', 'Qixiang Ye', 'Jianzhuang Liu', 'Shaohui Lin', 'Yuchao Li'] | 2021-05-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Towards_Compact_CNNs_via_Collaborative_Compression_CVPR_2021_paper.pdf | cvpr-2021-1 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.11068207e-01 -2.37496216e-02 -2.91334778e-01 -3.74619275e-01
-5.39306879e-01 -1.14762165e-01 2.33868882e-01 1.60529390e-01
-8.16415429e-01 4.59840149e-01 2.24227518e-01 -5.73516011e-01
-2.09870934e-01 -8.30596149e-01 -9.73861217e-01 -5.14781415e-01
-1.21793551e-02 9.11269188e-02 4.70733978e-02 3.69083881... | [8.51242733001709, 3.0899951457977295] |
53375e70-6554-4b77-a560-b5318c5cc635 | explicit-knowledge-transfer-for-weakly | 2211.16740 | null | https://arxiv.org/abs/2211.16740v3 | https://arxiv.org/pdf/2211.16740v3.pdf | Explicit Knowledge Transfer for Weakly-Supervised Code Generation | Large language models (LLMs) can acquire strong code-generation capabilities through few-shot learning. In contrast, supervised fine-tuning is still needed for smaller models to achieve good performance. Such fine-tuning demands a large number of task-specific NL-code pairs, which are expensive to obtain. In this paper... | ['Dragomir Radev', 'Hailey Schoelkopf', 'Ansong Ni', 'Zhangir Azerbayev'] | 2022-11-30 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 2.12588325e-01 3.65476757e-01 -3.71737666e-02 -1.69416308e-01
-1.16820323e+00 -7.24245012e-01 3.33873957e-01 1.97584778e-01
-2.85516083e-01 6.39497042e-01 -6.46985322e-02 -7.86618173e-01
6.51000291e-02 -1.03073883e+00 -1.03285098e+00 -2.47688696e-01
1.72963485e-01 5.29314399e-01 2.33927935e-01 -4.16451633... | [10.63713264465332, 8.343557357788086] |
406ed225-57c4-4fa3-a944-ac864b407a6a | unsupervised-statistical-machine-translation | 1809.01272 | null | http://arxiv.org/abs/1809.01272v1 | http://arxiv.org/pdf/1809.01272v1.pdf | Unsupervised Statistical Machine Translation | While modern machine translation has relied on large parallel corpora, a
recent line of work has managed to train Neural Machine Translation (NMT)
systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al.,
2018). Despite the potential of this approach for low-resource settings,
existing systems are fa... | ['Mikel Artetxe', 'Gorka Labaka', 'Eneko Agirre'] | 2018-09-04 | unsupervised-statistical-machine-translation-1 | https://aclanthology.org/D18-1399 | https://aclanthology.org/D18-1399.pdf | emnlp-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.83381543e-01 1.07913740e-01 -4.23557639e-01 -2.35208645e-01
-1.39651203e+00 -8.66154730e-01 9.79263127e-01 -4.06684168e-02
-6.38891160e-01 1.16164148e+00 1.98431492e-01 -9.04730022e-01
1.96681306e-01 -4.99388725e-01 -9.74704027e-01 -3.96130890e-01
4.07287717e-01 8.07385147e-01 -2.38466516e-01 -5.05112767... | [11.578709602355957, 10.305046081542969] |
ecef1396-145d-44d3-bb45-fc30326577e9 | class-incremental-learning-using-diffusion | 2306.17560 | null | https://arxiv.org/abs/2306.17560v1 | https://arxiv.org/pdf/2306.17560v1.pdf | Class-Incremental Learning using Diffusion Model for Distillation and Replay | Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data can be used by incremental models to help mitigate catastrophic forgetting. In this work, following the recent breakthrough in text-to-imag... | ['Tsuyoshi Murata', 'Yin Jun Phua', 'Xin Liu', 'Quentin Jodelet'] | 2023-06-30 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.40849817e-01 3.46223265e-01 5.50035946e-03 -2.95770288e-01
-5.85919917e-01 -2.06825390e-01 8.22941422e-01 1.36837780e-01
-5.42638302e-01 1.05893087e+00 -1.07251398e-01 -1.88620687e-02
1.34315431e-01 -9.87219751e-01 -1.07379627e+00 -7.98918605e-01
1.06160454e-01 8.08738708e-01 6.15228891e-01 -2.43914023... | [9.841252326965332, 3.4052574634552] |
4dbea16b-58e5-470f-beff-8a0b119a9d2f | principles-and-examples-of-plausible | 1703.01697 | null | http://arxiv.org/abs/1703.01697v2 | http://arxiv.org/pdf/1703.01697v2.pdf | Principles and Examples of Plausible Reasoning and Propositional Plausible Logic | Plausible reasoning concerns situations whose inherent lack of precision is
not quantified; that is, there are no degrees or levels of precision, and hence
no use of numbers like probabilities. A hopefully comprehensive set of
principles that clarifies what it means for a formal logic to do plausible
reasoning is prese... | ['David Billington'] | 2017-03-06 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 5.17773479e-02 9.95053053e-01 -5.26158869e-01 -5.96351027e-01
-4.30438429e-01 -4.77259725e-01 9.92151976e-01 1.87627852e-01
3.40032615e-02 1.52564108e+00 1.22009709e-01 -7.49267638e-01
-8.96182656e-01 -1.09379911e+00 -5.64363360e-01 -3.92263263e-01
-2.39292771e-01 5.22536814e-01 3.03883940e-01 -4.02428597... | [8.760821342468262, 6.664388656616211] |
7b2a0357-7f36-47a1-bb8e-3b1ddc737149 | violence-detection-in-videos | 2109.08941 | null | https://arxiv.org/abs/2109.08941v1 | https://arxiv.org/pdf/2109.08941v1.pdf | Violence Detection in Videos | In the recent years, there has been a tremendous increase in the amount of video content uploaded to social networking and video sharing websites like Facebook and Youtube. As of result of this, the risk of children getting exposed to adult and violent content on the web also increased. To address this issue, an approa... | ['Andreas Dengel', 'Christian Schulze', 'Praveen Tirupattur'] | 2021-09-18 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.00353235e-01 -1.48988590e-01 -2.21542865e-01 -2.94726014e-01
-4.18934315e-01 -3.98597300e-01 5.41097641e-01 7.26928055e-01
-4.59722161e-01 4.52300936e-01 3.35253090e-01 2.97329128e-01
-8.68002176e-02 -9.74069715e-01 -1.75777093e-01 -5.60175717e-01
4.19763178e-02 -2.67355621e-01 4.68913287e-01 -1.07731588... | [15.194305419921875, 4.765729904174805] |
d3af907f-93c8-408c-aeae-f0c9cd49cae1 | on-the-robustness-of-generative-retrieval | 2306.12756 | null | https://arxiv.org/abs/2306.12756v1 | https://arxiv.org/pdf/2306.12756v1.pdf | On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective | Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their identifiers. So far, much effort has been devoted to developing effective generative retrieval models. There has been less attention paid to the... | ['Xueqi Cheng', 'Wei Chen', 'Jiafeng Guo', 'Ruqing Zhang', 'Yu-An Liu'] | 2023-06-22 | null | null | null | null | ['retrieval', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [ 5.45977913e-02 -2.26177588e-01 2.72554369e-03 -7.58184567e-02
-1.10319841e+00 -8.49855900e-01 9.32190955e-01 -3.56009193e-02
6.63613807e-03 4.13862437e-01 3.39077860e-01 -1.98076829e-01
-4.94140267e-01 -8.55439842e-01 -3.26294452e-01 -6.46331787e-01
-8.57466925e-03 5.77599287e-01 1.14034146e-01 -4.37679708... | [11.425088882446289, 7.556295394897461] |
b0aa0429-ac3a-424e-9462-af23d6794f5f | multistage-model-for-robust-face-alignment | 2002.01075 | null | https://arxiv.org/abs/2002.01075v1 | https://arxiv.org/pdf/2002.01075v1.pdf | Multistage Model for Robust Face Alignment Using Deep Neural Networks | An ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exempla... | ['Jian Zhou', 'Rui Cheng', 'Huabin Wang', 'Hon Keung Kwan', 'Liang Tao'] | 2020-02-04 | null | null | null | null | ['robust-face-alignment'] | ['computer-vision'] | [-1.11454226e-01 -1.12023495e-01 1.30549803e-01 -6.99408174e-01
-4.97431457e-01 -3.63284707e-01 3.92600149e-01 -5.02971649e-01
-1.09258771e-01 5.81000805e-01 -6.62165787e-03 1.17743090e-01
5.80820628e-02 -5.21299064e-01 -8.10819685e-01 -7.98280716e-01
2.04410851e-01 5.09311497e-01 -1.84703857e-01 -2.49638140... | [13.292784690856934, 0.36028075218200684] |
081a56ee-c1ae-47bb-98f9-49f81fe58851 | innovative-drug-like-molecule-generation-from | 2211.06566 | null | https://arxiv.org/abs/2211.06566v1 | https://arxiv.org/pdf/2211.06566v1.pdf | Innovative Drug-like Molecule Generation from Flow-based Generative Model | To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural ne... | ['Linxiaoyi Wan', 'Haotian Zhang'] | 2022-11-12 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-3.07098269e-01 7.82846957e-02 -9.75805894e-02 -2.23123595e-01
2.64772177e-02 -4.55485940e-01 4.93438095e-01 -6.43578172e-02
-7.77134150e-02 1.54072011e+00 5.02990410e-02 -4.35209274e-01
1.20535761e-01 -1.13027704e+00 -1.22680211e+00 -9.78709996e-01
-5.33359721e-02 7.07998395e-01 -3.19618396e-02 -5.44905543... | [4.960721969604492, 5.699745178222656] |
d3524eb7-5594-4d42-93c2-a534f7016b61 | 1st-place-solution-to-the-epic-kitchens | 2207.05730 | null | https://arxiv.org/abs/2207.05730v1 | https://arxiv.org/pdf/2207.05730v1.pdf | 1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022 | In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student netw... | ['Changxing Ding', 'Zeyu Jiang'] | 2022-07-10 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.10195573e-02 5.48707128e-01 -3.79101127e-01 -7.63943434e-01
-7.34810233e-01 -6.11717403e-01 6.18806064e-01 -5.55729046e-02
-8.81313264e-01 6.64344311e-01 5.11745095e-01 -1.64851636e-01
9.25019532e-02 -5.61859548e-01 -7.38855302e-01 -4.89566267e-01
-2.58680373e-01 7.22671628e-01 2.87627667e-01 -3.52220774... | [9.152824401855469, 9.14661979675293] |
b2c16508-385c-47a4-8275-3977d19cb48c | end-to-end-learning-for-stochastic | 2306.04174 | null | https://arxiv.org/abs/2306.04174v2 | https://arxiv.org/pdf/2306.04174v2.pdf | End-to-End Learning for Stochastic Optimization: A Bayesian Perspective | We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learning algorithms for t... | ['Tobias Sutter', 'Daniel Kuhn', 'Yves Rychener'] | 2023-06-07 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.90956229e-01 5.07762790e-01 -2.39757627e-01 -7.69414485e-01
-1.31799591e+00 -6.79155707e-01 6.34500206e-01 9.30561870e-02
-6.46469891e-01 1.02231646e+00 3.01823854e-01 -6.10319316e-01
-8.10233712e-01 -4.85744774e-01 -7.48220623e-01 -6.64668560e-01
-2.76717901e-01 6.79391205e-01 -4.44674075e-01 9.61837322... | [4.376704216003418, 2.894803047180176] |
5375edfa-b5a2-428d-a9c3-5ad147ac499e | implicit-riemannian-concave-potential-maps | 2110.01288 | null | https://arxiv.org/abs/2110.01288v1 | https://arxiv.org/pdf/2110.01288v1.pdf | Implicit Riemannian Concave Potential Maps | We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows. This has many potential applications in physical sciences such as molecular dynamics and quantum simulations. In this work we combine ideas from implicit neural layers and opt... | ['Sébastien Racanière', 'Danilo J. Rezende'] | 2021-10-04 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-3.10980678e-01 4.16515201e-01 4.75952178e-02 -5.29653169e-02
1.02254622e-01 -3.54039937e-01 7.95455933e-01 -3.45795929e-01
-4.23597008e-01 9.23110604e-01 2.21736565e-01 -5.50643504e-01
-5.81243575e-01 -5.15546679e-01 -6.49953365e-01 -7.85973847e-01
-7.73120165e-01 6.11511052e-01 -7.70035759e-02 -3.62273991... | [7.542596340179443, 3.869220495223999] |
78056ce2-83b8-44a3-b4bb-6be923e3e192 | recent-trends-in-deep-learning-based | 1908.03628 | null | https://arxiv.org/abs/1908.03628v2 | https://arxiv.org/pdf/1908.03628v2.pdf | Recent Trends in Deep Learning Based Personality Detection | Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personal... | ['Yash Mehta', 'Navonil Majumder', 'Erik Cambria', 'Alexander Gelbukh'] | 2019-08-07 | null | null | null | null | ['personality-trait-recognition'] | ['computer-vision'] | [-2.28987038e-01 4.67463881e-02 -8.06611497e-03 -5.70194781e-01
-1.78884655e-01 -2.10157052e-01 4.33562189e-01 2.78119564e-01
-1.59664258e-01 5.47550082e-01 5.54896332e-02 8.37748826e-01
-1.04440346e-01 -4.86391097e-01 4.23996031e-01 -7.71132708e-01
-3.18724513e-01 5.69396615e-01 -5.45889318e-01 -2.37791374... | [13.03377914428711, 5.753726005554199] |
860e5a79-f419-4368-933f-c712e89a45ec | non-negative-networks-against-adversarial | 1806.06108 | null | http://arxiv.org/abs/1806.06108v2 | http://arxiv.org/pdf/1806.06108v2.pdf | Non-Negative Networks Against Adversarial Attacks | Adversarial attacks against neural networks are a problem of considerable
importance, for which effective defenses are not yet readily available. We make
progress toward this problem by showing that non-negative weight constraints
can be used to improve resistance in specific scenarios. In particular, we show
that they... | ['Jared Sylvester', 'William Fleshman', 'Mark McLean', 'Edward Raff', 'Steven Forsyth'] | 2018-06-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 6.69808507e-01 1.04402214e-01 -2.94839472e-01 -4.88541067e-01
-3.24042141e-01 -8.81291568e-01 4.78160471e-01 -1.90291941e-01
-6.48230612e-01 9.59792793e-01 -3.64117801e-01 -1.02327728e+00
-2.72108823e-01 -8.04610610e-01 -7.62388051e-01 -7.57666349e-01
-3.02976996e-01 5.71380779e-02 4.55843836e-01 -5.55291295... | [5.71511697769165, 7.728724479675293] |
cbf689c1-382f-44a8-bd05-b11fa8bfeb11 | a-higher-order-semantic-dependency-parser | 2201.11312 | null | https://arxiv.org/abs/2201.11312v1 | https://arxiv.org/pdf/2201.11312v1.pdf | A Higher-Order Semantic Dependency Parser | Higher-order features bring significant accuracy gains in semantic dependency parsing. However, modeling higher-order features with exact inference is NP-hard. Graph neural networks (GNNs) have been demonstrated to be an effective tool for solving NP-hard problems with approximate inference in many graph learning tasks... | ['Zhiqiang Gao', 'Yikemaiti Sataer', 'Yunlong Fan', 'Bin Li'] | 2022-01-27 | null | null | null | null | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-1.72619432e-01 8.24889600e-01 -3.07222694e-01 -6.99184716e-01
-5.18392384e-01 -4.87272203e-01 1.63332686e-01 5.30932844e-01
-4.63086694e-01 7.26763248e-01 1.80565909e-01 -7.20077276e-01
-7.82044306e-02 -1.22184670e+00 -1.16361654e+00 -6.18690439e-03
-5.63036799e-01 8.99909437e-01 1.67979568e-01 -1.39267787... | [10.377847671508789, 9.54267692565918] |
cd0761cd-9a06-488f-b0af-c414fc67a1eb | mum-mix-image-tiles-and-unmix-feature-tiles | 2111.10958 | null | https://arxiv.org/abs/2111.10958v2 | https://arxiv.org/pdf/2111.10958v2.pdf | MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection | Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student network by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing ... | ['Nojun Kwak', 'Jongkeun Na', 'Jisoo Jeong', 'Seunghyeon Seo', 'Jooyoung Jang', 'Jongmok Kim'] | 2021-11-22 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.13603574e-01 2.29597047e-01 -2.17216522e-01 -4.78047490e-01
-9.37080145e-01 -2.98699349e-01 5.87123096e-01 -4.97387126e-02
-4.47164237e-01 6.46552145e-01 -8.05067047e-02 -1.03279464e-01
1.67201653e-01 -6.99553132e-01 -1.08051956e+00 -1.05256474e+00
5.09140909e-01 3.07024539e-01 4.36770976e-01 -8.42308402... | [9.24660587310791, 1.4466636180877686] |
ab4a241d-31a1-446c-87ab-b78db15ce6a5 | rescuespeech-a-german-corpus-for-speech | 2306.04054 | null | https://arxiv.org/abs/2306.04054v1 | https://arxiv.org/pdf/2306.04054v1.pdf | RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain | Despite recent advancements in speech recognition, there are still difficulties in accurately transcribing conversational and emotional speech in noisy and reverberant acoustic environments. This poses a particular challenge in the search and rescue (SAR) domain, where transcribing conversations among rescue team membe... | ['Josef van Genabith', 'Ivana Kruijff Korbayova', 'Bernd Kiefer', 'Mirco Ravanelli', 'Sangeet Sagar'] | 2023-06-06 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.79551795e-01 -5.01073487e-02 6.33593798e-01 -4.45066035e-01
-1.39598441e+00 -5.34208596e-01 4.51783359e-01 -1.36490883e-02
-6.00538373e-01 7.70352066e-01 7.51803041e-01 -4.06249225e-01
5.44073470e-02 3.91526520e-02 -4.21102438e-03 -6.71387672e-01
-1.22374497e-01 5.79191804e-01 -2.51784623e-01 -6.71999812... | [14.502269744873047, 6.344523906707764] |
4b41f4b9-7090-421c-ac31-26bfd2dc358e | a-general-approach-for-traffic-classification | null | null | https://ieeexplore.ieee.org/document/9626148/authors#authors | https://ieeexplore.ieee.org/document/9626148/authors#authors | A General Approach for Traffic Classification in Wireless Networks Using Deep Learning | Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted. Most of the works on TC assume the tr... | ['Miguel Camelo'] | 2022-03-01 | null | null | null | 2022-03-2022-3 | ['traffic-classification'] | ['miscellaneous'] | [ 5.67767560e-01 -1.62750632e-01 -4.97308016e-01 -2.07057223e-01
-4.36539829e-01 -4.21377033e-01 2.59866148e-01 -5.68596125e-01
-1.14072464e-01 8.98472309e-01 -4.48839843e-01 -1.20458782e+00
-1.33927569e-01 -1.11116731e+00 -6.13876641e-01 -5.99472642e-01
-1.99232861e-01 4.23709810e-01 5.96155167e-01 -3.62124801... | [5.073483467102051, 7.22132682800293] |
eb6ee306-b5a2-4d65-b1db-a9a7c99e8fcb | on-the-validity-of-conformal-prediction-for | 2306.07252 | null | https://arxiv.org/abs/2306.07252v3 | https://arxiv.org/pdf/2306.07252v3.pdf | On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling | We study the properties of conformal prediction for network data under various sampling mechanisms that commonly arise in practice but often result in a non-representative sample of nodes. We interpret these sampling mechanisms as selection rules applied to a superpopulation and study the validity of conformal predicti... | ['Robert Lunde'] | 2023-06-12 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [ 6.65483832e-01 6.37444735e-01 -3.89013559e-01 -3.07779789e-01
-3.08196634e-01 -3.84390175e-01 8.01920533e-01 9.53747034e-02
-1.36046097e-01 1.10377836e+00 7.22059831e-02 -2.15993151e-01
-7.73178756e-01 -1.64003241e+00 -8.00538361e-01 -8.49003136e-01
-5.48120379e-01 1.08729517e+00 5.92404068e-01 1.02576196... | [7.0052289962768555, 5.187817573547363] |
deef7177-1812-45da-b783-9efccc014580 | frame-fusion-with-vehicle-motion-prediction | 2306.10699 | null | https://arxiv.org/abs/2306.10699v1 | https://arxiv.org/pdf/2306.10699v1.pdf | Frame Fusion with Vehicle Motion Prediction for 3D Object Detection | In LiDAR-based 3D detection, history point clouds contain rich temporal information helpful for future prediction. In the same way, history detections should contribute to future detections. In this paper, we propose a detection enhancement method, namely FrameFusion, which improves 3D object detection results by fusin... | ['Chao Ma', 'Naiyan Wang', 'Feng Wang', 'Xirui Li'] | 2023-06-19 | null | null | null | null | ['motion-prediction', '3d-object-detection', 'future-prediction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.31267709e-01 -3.75434160e-01 -3.97918403e-01 -4.84058917e-01
-6.72160029e-01 -3.89873236e-01 5.85084975e-01 3.26201059e-02
-5.51014841e-01 3.67899835e-01 -1.73350573e-01 -4.26357001e-01
6.74596608e-01 -8.51801753e-01 -8.11005712e-01 -4.28084940e-01
-3.07409465e-01 2.06496626e-01 1.25358927e+00 -6.57749549... | [6.674850940704346, -2.227431535720825] |
3bdb31e2-8008-4c27-98e7-7f89fb966c1b | enhancing-covid-19-severity-analysis-through | 2303.07130 | null | https://arxiv.org/abs/2303.07130v3 | https://arxiv.org/pdf/2303.07130v3.pdf | Enhancing COVID-19 Severity Analysis through Ensemble Methods | Computed Tomography (CT) scans provide a detailed image of the lungs, allowing clinicians to observe the extent of damage caused by COVID-19. The CT severity score (CTSS) based scoring method is used to identify the extent of lung involvement observed on a CT scan. This paper presents a domain knowledge-based pipeline ... | ['Hema A Murthy', 'Anand Thyagachandran'] | 2023-03-13 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 7.97127113e-02 -2.73397207e-01 -1.05330378e-01 -2.71757782e-01
-9.90091681e-01 -5.87924838e-01 2.31832176e-01 3.54390740e-01
-3.20600092e-01 2.91446686e-01 2.47039452e-01 -5.42710364e-01
-3.39267612e-01 -6.02018952e-01 -1.06719255e-01 -7.57780731e-01
-3.21730338e-02 1.15568745e+00 3.87374759e-01 2.58284301... | [15.38739013671875, -1.8842588663101196] |
619c12e6-2ab1-4526-84d7-84ff1db3f591 | estimation-and-hac-based-inference-for | 1912.06307 | null | https://arxiv.org/abs/1912.06307v4 | https://arxiv.org/pdf/1912.06307v4.pdf | High-Dimensional Granger Causality Tests with an Application to VIX and News | We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the ti... | ['Eric Ghysels', 'Jonas Striaukas', 'Andrii Babii'] | 2019-12-13 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-2.67035097e-01 -2.47284159e-01 -3.40127289e-01 -3.80633235e-01
-7.76178837e-01 -4.55057770e-01 4.91841644e-01 -1.36644751e-01
-1.28980234e-01 9.83889043e-01 1.89685389e-01 -6.59068108e-01
-7.44195580e-01 -7.32525885e-01 -6.97580159e-01 -1.00055110e+00
-7.64424026e-01 5.65604150e-01 -4.65328515e-01 2.51265585... | [6.469241619110107, 4.207021236419678] |
e79a9966-aa9d-404a-9e59-342a89275e7b | a-character-level-ngram-based-mt-approach-for | null | null | https://openreview.net/forum?id=U_cqFnIXla | https://openreview.net/pdf?id=U_cqFnIXla | A Character-level Ngram-based MT Approach for Lexical Normalization in Social Media | This paper presents an ngram-based MT approach that operates at character-level to generate possible canonical forms for lexical variants in social media text. It utilizes a joint n-gram model to learn edit sequences of word pairs, thus overcomes the shortage of phrase-based approach that is unable to capture dependenc... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.31545293e-01 -2.96364762e-02 -4.44092959e-01 -2.49787256e-01
-9.67763901e-01 -6.14580750e-01 8.24466825e-01 5.39007246e-01
-8.49912822e-01 1.06548834e+00 3.93596113e-01 -5.10196984e-01
2.42325604e-01 -1.05710793e+00 -4.41122234e-01 -2.32321650e-01
3.74097884e-01 6.85363710e-01 6.05234683e-01 -9.57824290... | [10.914097785949707, 10.062788963317871] |
d4dca77f-a385-4ab7-8248-9ab2e60d5133 | darer-dual-task-temporal-relational-recurrent | 2203.03856 | null | https://arxiv.org/abs/2203.03856v1 | https://arxiv.org/pdf/2203.03856v1.pdf | DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition | The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. In this paper, we put forward a new framework which models the explicit dependencies via integrating \textit{prediction-level interactions} o... | ['Ivor W. Tsang', 'Bowen Xing'] | 2022-03-08 | null | https://aclanthology.org/2022.findings-acl.286 | https://aclanthology.org/2022.findings-acl.286.pdf | findings-acl-2022-5 | ['relational-reasoning', 'dialog-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.07792367e-02 4.67911482e-01 -3.17707956e-01 -1.03590977e+00
-8.39690208e-01 -5.47182262e-01 8.25693846e-01 3.29029225e-02
-9.72161144e-02 5.32854736e-01 6.53969228e-01 -3.31203878e-01
1.98442563e-01 -3.77600342e-01 -2.21116602e-01 -4.67454404e-01
2.45566621e-01 7.22021222e-01 2.66101778e-01 -5.52150905... | [12.582934379577637, 7.617554187774658] |
d820315d-c275-497c-8092-2c9bce05bb26 | unique-geometry-and-texture-from | 2003.08885 | null | https://arxiv.org/abs/2003.08885v3 | https://arxiv.org/pdf/2003.08885v3.pdf | Unique Geometry and Texture from Corresponding Image Patches | We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process. We show that four observations are sufficient in general, and we characterize the ambiguous cases. The results are applicable to shape from texture and texture-based structure ... | ['Todd Zickler', 'Dor Verbin', 'Steven J. Gortler'] | 2020-03-19 | null | null | null | null | ['shape-from-texture'] | ['computer-vision'] | [ 4.82936472e-01 4.51661125e-02 3.81503962e-02 -3.45627487e-01
-5.69515347e-01 -8.30654144e-01 4.45157021e-01 -1.07844579e+00
3.04877579e-01 7.38452494e-01 2.28522822e-01 -1.99026495e-01
-3.09891701e-01 -3.55507553e-01 -5.25784612e-01 -9.33664143e-01
3.83839667e-01 9.95281756e-01 3.38769794e-01 -1.92572862... | [8.388871192932129, -2.440370798110962] |
e380342c-3d07-4bd2-8b73-d538a05ded07 | object-centric-multi-task-learning-for-human | 2303.06800 | null | https://arxiv.org/abs/2303.06800v1 | https://arxiv.org/pdf/2303.06800v1.pdf | Object-Centric Multi-Task Learning for Human Instances | Human is one of the most essential classes in visual recognition tasks such as detection, segmentation, and pose estimation. Although much effort has been put into individual tasks, multi-task learning for these three tasks has been rarely studied. In this paper, we explore a compact multi-task network architecture tha... | ['ByungIn Yoo', 'Seung-In Park', 'Seongeun Kim', 'Solae Lee', 'Sangil Jung', 'Hyeongseok Son'] | 2023-03-13 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-5.24643734e-02 -6.05850853e-02 -1.66885227e-01 -4.35619444e-01
-9.46404457e-01 -1.59324810e-01 2.77384549e-01 -3.44099998e-01
-6.76807702e-01 3.69692802e-01 1.19364798e-01 1.88238278e-01
2.48156175e-01 -3.08657646e-01 -9.61107671e-01 -4.89473820e-01
3.86895865e-01 5.22703707e-01 7.21481085e-01 7.11369142... | [7.528437614440918, -0.4846934676170349] |
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