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1f212f5e-6300-443c-9477-23511fb2f55f | classification-by-sparse-additive-models | 2212.01792 | null | https://arxiv.org/abs/2212.01792v1 | https://arxiv.org/pdf/2212.01792v1.pdf | Classification by sparse additive models | We consider (nonparametric) sparse additive models (SpAM) for classification. The design of a SpAM classifier is based on minimizing the logistic loss with a sparse group Lasso/Slope-type penalties on the coefficients of univariate components' expansions in orthonormal series (e.g., Fourier or wavelets). The resulting ... | ['Felix Abramovich'] | 2022-12-04 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 9.70059782e-02 -9.20612589e-02 -4.04682487e-01 -5.19896567e-01
-9.30000901e-01 -4.08266515e-01 3.70365679e-01 -2.27969736e-01
-1.13593236e-01 9.75155771e-01 1.12383761e-01 -1.80956542e-01
-2.37741083e-01 -5.01837373e-01 -7.09660232e-01 -8.94949317e-01
-4.34306353e-01 2.15991452e-01 -5.44244908e-02 -2.44674861... | [7.061201095581055, 4.421534061431885] |
d561b7e6-faa8-45f3-a8e6-9b0f31269bdb | texture-cnn-for-histopathological-image | 1905.12005 | null | https://arxiv.org/abs/1905.12005v1 | https://arxiv.org/pdf/1905.12005v1.pdf | Texture CNN for Histopathological Image Classification | Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, dataset... | ['Luiz E. S. de Oliveira', 'Alceu de S. Britto Jr.', 'Jonathan de Matos', 'Alessandro L. Koerich'] | 2019-05-28 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [-3.78623977e-02 2.21969858e-01 -1.74575135e-01 -4.75544155e-01
-5.96951246e-01 8.97184107e-03 3.45589548e-01 2.04539135e-01
-3.38181227e-01 4.45028126e-01 -1.24940611e-01 -4.34234530e-01
-1.02588553e-02 -9.68196988e-01 -1.17336847e-01 -1.10536551e+00
6.60248846e-03 6.72049344e-01 2.99675524e-01 7.88245574... | [15.181212425231934, -2.8858642578125] |
c7e948d6-508a-4285-84ae-a48ddab9e882 | fitvid-overfitting-in-pixel-level-video | 2106.13195 | null | https://arxiv.org/abs/2106.13195v1 | https://arxiv.org/pdf/2106.13195v1.pdf | FitVid: Overfitting in Pixel-Level Video Prediction | An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. Thi... | ['Dumitru Erhan', 'Chelsea Finn', 'Sergey Levine', 'Suraj Nair', 'Mohammad Taghi Saffar', 'Mohammad Babaeizadeh'] | 2021-06-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.34126920e-01 1.65623263e-01 -3.23524833e-01 -2.07057983e-01
-8.85930732e-02 -2.11277455e-01 1.02177370e+00 -2.32885361e-01
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-1.91242546e-01 4.21729207e-01 6.11128092e-01 -3.36205631... | [8.303807258605957, 0.41535335779190063] |
372ab0a0-fa6b-4441-a968-7694807da9dd | scaling-densities-for-improved-density-ratio | null | null | https://openreview.net/forum?id=vdbidlOkeF0 | https://openreview.net/pdf?id=vdbidlOkeF0 | Scaling Densities For Improved Density Ratio Estimation | Estimating the discrepancy between two densities ($p$ and $q$) is central to machine learning. Most frequently used methods for the quantification of this discrepancy capture it as a function of the ratio of the densities $p/q$. In practice, closed-form expressions for these densities or their ratio are rarely availabl... | ['Michael U. Gutmann', 'Kai Xu', 'Benjamin Rhodes', 'Seungwook Han', 'Akash Srivastava'] | 2021-09-29 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-1.63690582e-01 -1.47855714e-01 -2.74548620e-01 -2.63604879e-01
-1.11997581e+00 -4.38756138e-01 2.74473071e-01 2.16456667e-01
-4.88817215e-01 1.08630192e+00 -6.47722363e-01 -3.41148823e-01
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-1.96454108e-01 6.97115600e-01 1.56698659e-01 -9.75502189... | [7.306686878204346, 4.109114170074463] |
106029b3-a517-44fd-a12e-a6995492191f | starmap-for-category-agnostic-keypoint-and | 1803.09331 | null | http://arxiv.org/abs/1803.09331v2 | http://arxiv.org/pdf/1803.09331v2.pdf | StarMap for Category-Agnostic Keypoint and Viewpoint Estimation | Semantic keypoints provide concise abstractions for a variety of visual
understanding tasks. Existing methods define semantic keypoints separately for
each category with a fixed number of semantic labels in fixed indices. As a
result, this keypoint representation is in-feasible when objects have a varying
number of par... | ['Qi-Xing Huang', 'Linjie Luo', 'Xingyi Zhou', 'Arjun Karpur'] | 2018-03-25 | starmap-for-category-agnostic-keypoint-and-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xingyi_Zhou_Category-Agnostic_Semantic_Keypoint_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xingyi_Zhou_Category-Agnostic_Semantic_Keypoint_ECCV_2018_paper.pdf | eccv-2018-9 | ['viewpoint-estimation'] | ['computer-vision'] | [-2.19837710e-01 -1.26746282e-01 -2.81217903e-01 -3.23037386e-01
-7.22447872e-01 -1.04002798e+00 8.11644495e-01 2.31137201e-01
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-5.36720678e-02 4.34662700e-01 5.57266831e-01 -1.56123757... | [7.701097011566162, -2.6687965393066406] |
2d43c595-2048-4090-a9c7-16219b9e075e | minif2f-a-cross-system-benchmark-for-formal | 2109.00110 | null | https://arxiv.org/abs/2109.00110v2 | https://arxiv.org/pdf/2109.00110v2.pdf | MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics | We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and consists of 488 problem statements drawn from... | ['Stanislas Polu', 'Jesse Michael Han', 'Kunhao Zheng'] | 2021-08-31 | minif2f-a-cross-system-benchmark-for-formal-1 | https://openreview.net/forum?id=9ZPegFuFTFv | https://openreview.net/pdf?id=9ZPegFuFTFv | iclr-2022-4 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-7.59041831e-02 4.72753674e-01 -2.07540050e-01 -2.71692216e-01
-5.41835129e-01 -5.97888350e-01 5.96342623e-01 4.55624610e-01
9.40960497e-02 9.00540471e-01 -3.25573862e-01 -1.28419828e+00
-4.74306583e-01 -1.25412178e+00 -1.42996395e+00 4.29006636e-01
-6.51974976e-01 5.07670462e-01 2.99187303e-01 -6.77852869... | [8.933149337768555, 7.0489959716796875] |
ecee4ff3-dcd4-4273-9cfd-7cf9f07c9191 | learning-alignment-for-multimodal-emotion | 1909.05645 | null | https://arxiv.org/abs/1909.05645v2 | https://arxiv.org/pdf/1909.05645v2.pdf | Learning Alignment for Multimodal Emotion Recognition from Speech | Speech emotion recognition is a challenging problem because human convey emotions in subtle and complex ways. For emotion recognition on human speech, one can either extract emotion related features from audio signals or employ speech recognition techniques to generate text from speech and then apply natural language p... | ['HUI ZHANG', 'Haiyang Xu', 'Yun Wang', 'Xiangang Li', 'Kun Han', 'Yiping Peng'] | 2019-09-06 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.59272808e-01 1.71820801e-02 1.32734433e-01 -6.31046295e-01
-8.17979395e-01 -3.81940097e-01 6.52675331e-01 -9.48174521e-02
-4.13323164e-01 4.33344990e-01 3.48878115e-01 6.33998811e-02
2.17557594e-01 -2.52914637e-01 -3.23965162e-01 -7.44522393e-01
7.03256354e-02 1.24488346e-01 -1.35000095e-01 -2.25264370... | [13.334254264831543, 5.425734043121338] |
323d1b72-26ce-4ada-befa-719654806f7a | learnable-behavior-control-breaking-atari | 2305.05239 | null | https://arxiv.org/abs/2305.05239v1 | https://arxiv.org/pdf/2305.05239v1.pdf | Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection | The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which collect samples with diverse behaviors derived from a population of different exploratory policies. Adaptive policy selection has been adopte... | ['Shu-Tao Xia', 'Hao Wang', 'Jiangcheng Zhu', 'Bin Wang', 'Jianye Hao', 'Yuecheng Liu', 'Yuzheng Zhuang', 'Jiajun Fan'] | 2023-05-09 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-3.32957953e-01 -9.47522745e-02 -5.76187670e-01 6.72911257e-02
-7.47821689e-01 -1.80948481e-01 4.57443774e-01 -3.42883766e-01
-8.43057454e-01 1.21255481e+00 1.36550367e-01 5.68538643e-02
-3.52611691e-01 -5.52640080e-01 -7.28595078e-01 -1.12615716e+00
-1.18800148e-01 5.71306169e-01 3.49719040e-02 -4.38703120... | [4.049007892608643, 2.098214864730835] |
6c9d0202-bed4-4815-8b44-057070247425 | ninjadesc-content-concealing-visual | 2112.12785 | null | https://arxiv.org/abs/2112.12785v2 | https://arxiv.org/pdf/2112.12785v2.pdf | NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning | In the light of recent analyses on privacy-concerning scene revelation from visual descriptors, we develop descriptors that conceal the input image content. In particular, we propose an adversarial learning framework for training visual descriptors that prevent image reconstruction, while maintaining the matching accur... | ['Daniel DeTone', 'Chris Sweeney', 'Krystian Mikolajczyk', 'Vassileios Balntas', 'Eddy Ilg', 'Tianwei Shen', 'Tsun-Yi Yang', 'Vincent Lee', 'Hyo Jin Kim', 'Tony Ng'] | 2021-12-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['camera-localization'] | ['computer-vision'] | [ 7.01435030e-01 2.43123472e-01 -1.66624516e-01 -1.32390320e-01
-7.78269947e-01 -9.25183594e-01 4.76996154e-01 -1.88219219e-01
-3.87478769e-01 3.88616890e-01 1.20749623e-01 -1.85121864e-01
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3.07453126e-02 -3.33982140e-01 -2.75218666e-01 1.85332760... | [12.646944999694824, 0.7204179763793945] |
84151bee-4e0e-493f-92fb-13e9c3854826 | accelerating-self-imitation-learning-from | 2212.03562 | null | https://arxiv.org/abs/2212.03562v1 | https://arxiv.org/pdf/2212.03562v1.pdf | Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble | Deep reinforcement learning (DRL) provides a new way to generate robot control policy. However, the process of training control policy requires lengthy exploration, resulting in a low sample efficiency of reinforcement learning (RL) in real-world tasks. Both imitation learning (IL) and learning from demonstrations (LfD... | ['Chao Li'] | 2022-12-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.52911031e-01 5.60640506e-02 -3.98719877e-01 1.85319281e-03
-7.49981582e-01 -5.49896717e-01 5.38750410e-01 -1.83264479e-01
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4.06271778e-02 -4.62982357e-01 -1.10236967e+00 -6.83390558e-01
-2.55067199e-01 4.29836810e-01 2.63047189e-01 -3.19696635... | [4.1515679359436035, 1.7615957260131836] |
c6fc838a-ee2d-4f93-9e85-8d216022fecf | unsupervised-image-classification-for-deep | 2006.11480 | null | https://arxiv.org/abs/2006.11480v2 | https://arxiv.org/pdf/2006.11480v2.pdf | Unsupervised Image Classification for Deep Representation Learning | Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowledge to design pretext tasks. However, the key component, embedding clustering, limits its extension to the extremely large-scale dataset due ... | ['Wei-Jie Chen', 'Shicai Yang', 'ShiLiang Pu', 'Luojun Lin', 'Yilu Guo', 'Di Xie'] | 2020-06-20 | null | null | null | null | ['image-clustering', 'multi-label-image-classification', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.71496317e-01 1.31864682e-01 -3.90639395e-01 -4.67609555e-01
-1.94014877e-01 -3.23686928e-01 4.79547203e-01 1.50419533e-01
-6.80333138e-01 2.24878430e-01 -1.32290959e-01 -1.98267817e-01
-1.61795944e-01 -6.30919993e-01 -4.09498483e-01 -9.20654476e-01
9.12428573e-02 3.90829384e-01 2.52587348e-01 2.54354179... | [9.391097068786621, 2.7514028549194336] |
9921c870-5095-4d87-8e8e-099610456677 | emergence-of-implicit-filter-sparsity-in | null | null | https://openreview.net/forum?id=rylVvNS3hE | https://openreview.net/pdf?id=rylVvNS3hE | Emergence of Implicit Filter Sparsity in Convolutional Neural Networks | We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay. Through an extensive empirical study (Anonymous, 2019) we hypothesize the mech... | ['Christian Theobalt', 'Kwang In Kim', 'Dushyant Mehta'] | 2019-05-17 | null | null | null | icml-workshop-deep-phenomen-2019-6 | ['l2-regularization'] | ['methodology'] | [-6.27222657e-02 4.97449994e-01 -4.01793480e-01 -6.67739809e-01
4.11076427e-01 -3.87114465e-01 5.86325169e-01 -2.72480011e-01
-1.00867176e+00 7.98706889e-01 7.39786625e-01 -6.02801800e-01
-1.40241720e-03 -7.68988013e-01 -8.35066199e-01 -4.03674901e-01
-2.16845259e-01 -4.28721607e-01 5.83392158e-02 -4.90346223... | [8.529695510864258, 3.2628157138824463] |
22f014f7-274d-4706-bc04-a0a6e1216b36 | natural-language-generation-and-understanding | 2307.02503 | null | https://arxiv.org/abs/2307.02503v1 | https://arxiv.org/pdf/2307.02503v1.pdf | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review | This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software nat... | ['Chee Wei Tan', 'Siu Wai Ho', 'Ching Nam Hang', 'Shangxin Guo', 'Man Fai Wong'] | 2023-07-04 | null | null | null | null | ['code-generation', 'code-translation', 'defect-detection', 'text-generation'] | ['computer-code', 'computer-code', 'computer-vision', 'natural-language-processing'] | [ 1.36946931e-01 3.71959567e-01 -1.90331236e-01 -2.46227086e-01
-6.80704415e-01 -5.53221822e-01 1.86588347e-01 4.95905370e-01
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1.52003244e-01 -6.25332773e-01 -6.37191832e-01 1.68542102e-01
-1.56035349e-01 1.48824722e-01 -3.17572027e-01 -3.52627516... | [7.757561683654785, 7.77773380279541] |
1b4a9705-ab19-442b-b863-f7ed8c3f459e | retrospective-analysis-of-sars-cov-2-omicron | null | null | https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-022-07821-5 | https://bmcinfectdis.biomedcentral.com/counter/pdf/10.1186/s12879-022-07821-5.pdf | Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model | Background: SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS... | ['Lulla Opatowski', 'Chiara Poletto', 'Benjamin Roche', 'Elisabeta Vergu', 'Thomas Haschka'] | 2022-11-03 | null | null | null | bmc-infectious-diseases-2022-11 | ['epidemiology'] | ['medical'] | [ 1.30228102e-01 -3.06619167e-01 1.98000968e-01 3.03618819e-01
-2.76728779e-01 -6.84773266e-01 8.48386526e-01 5.54683089e-01
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-2.29200557e-01 -6.65356040e-01 -5.44991612e-01 -9.09767091e-01
-6.13509774e-01 8.21473718e-01 1.49154007e-01 -3.09492111... | [5.805393218994141, 4.426083087921143] |
465b795e-ccee-43eb-a448-41266d9d046d | easyportrait-face-parsing-and-portrait | 2304.13509 | null | https://arxiv.org/abs/2304.13509v2 | https://arxiv.org/pdf/2304.13509v2.pdf | EasyPortrait - Face Parsing and Portrait Segmentation Dataset | Recently, due to COVID-19 and the growing demand for remote work, video conferencing apps have become especially widespread. The most valuable features of video chats are real-time background removal and face beautification. While solving these tasks, computer vision researchers face the problem of having relevant data... | ['Sofia Kirillova', 'Karina Kvanchiani', 'Alexander Kapitanov'] | 2023-04-26 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.72633666e-01 1.35423213e-01 2.68478878e-02 -6.74508691e-01
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5.71795285e-01 4.64897752e-01 4.12524015e-01 -7.92314187... | [13.41102409362793, 0.5166155695915222] |
132e51da-0b02-4e84-bfb2-5674037f3db8 | surface-defect-detection-and-evaluation-for | 2203.09580 | null | https://arxiv.org/abs/2203.09580v1 | https://arxiv.org/pdf/2203.09580v1.pdf | Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning | Detecting and evaluating surface coating defects is important for marine vessel maintenance. Currently, the assessment is carried out manually by qualified inspectors using international standards and their own experience. Automating the processes is highly challenging because of the high level of variation in vessel t... | ['Vishal Monga', 'James Z. Wang', 'Kareem Metwaly', 'Li Yu'] | 2022-03-17 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-2.80345622e-02 -3.33997369e-01 8.37058008e-01 -3.29285949e-01
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-4.04211469e-02 -1.03347123e+00 -4.49869186e-01 -7.13078439e-01
6.47886842e-02 3.80334705e-01 2.92535990e-01 -2.29790926... | [7.402544021606445, 1.681443214416504] |
13321129-4df3-4602-8cb0-fc6872ced3c2 | knowledge-and-keywords-augmented-abstractive | null | null | https://aclanthology.org/2021.newsum-1.3 | https://aclanthology.org/2021.newsum-1.3.pdf | Knowledge and Keywords Augmented Abstractive Sentence Summarization | In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowle... | ['Shuo Guan'] | null | null | null | null | emnlp-newsum-2021-11 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 1.79771945e-01 1.25432551e-01 -6.48792982e-01 -1.39105365e-01
-7.10078597e-01 -1.67801961e-01 4.63326246e-01 3.97927880e-01
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3.04272640e-02 -5.05656838e-01 -7.79898345e-01 -2.20121160e-01
2.60852873e-01 5.32340333e-02 4.24346179e-01 -2.33919501... | [12.575650215148926, 9.516080856323242] |
b5fd2443-1b6d-4cc2-8900-c510ce81d803 | when-color-constancy-goes-wrong-correcting | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Afifi_When_Color_Constancy_Goes_Wrong_Correcting_Improperly_White-Balanced_Images_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Afifi_When_Color_Constancy_Goes_Wrong_Correcting_Improperly_White-Balanced_Images_CVPR_2019_paper.pdf | When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images | This paper focuses on correcting a camera image that has been improperly white-balanced. This situation occurs when a camera's auto white balance fails or when the wrong manual white-balance setting is used. Even after decades of computational color constancy research, there are no effective solutions to this problem. ... | [' Michael S. Brown', ' Scott Cohen', ' Brian Price', 'Mahmoud Afifi'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['color-constancy'] | ['computer-vision'] | [ 5.34362555e-01 -4.79872167e-01 1.14376336e-01 -3.23698163e-01
-5.28442264e-01 -8.73771429e-01 3.30410808e-01 -2.65308768e-01
-3.92132670e-01 6.19809508e-01 -2.03925803e-01 -5.20012259e-01
1.80086046e-01 -4.46970344e-01 -5.43013036e-01 -6.14468813e-01
6.41147673e-01 2.47994855e-01 3.49624515e-01 -1.80801138... | [10.488207817077637, -2.53421688079834] |
c6fe1157-e15a-4dde-9ae5-a78b77741db1 | cv4code-sourcecode-understanding-via-visual | 2205.08585 | null | https://arxiv.org/abs/2205.08585v1 | https://arxiv.org/pdf/2205.08585v1.pdf | CV4Code: Sourcecode Understanding via Visual Code Representations | We present CV4Code, a compact and effective computer vision method for sourcecode understanding. Our method leverages the contextual and the structural information available from the code snippet by treating each snippet as a two-dimensional image, which naturally encodes the context and retains the underlying structur... | ['Sean J. Moran', 'Fran Silavong', 'Rohan Saphal', 'Lili Tao', 'Ruibo Shi'] | 2022-05-11 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 3.30788225e-01 -1.69201680e-02 -8.01217183e-02 -3.44652623e-01
-7.20381379e-01 -1.04293704e+00 5.99407911e-01 4.32518095e-01
-1.68292165e-01 -2.63083249e-01 7.17533827e-02 -5.64030647e-01
2.07892451e-02 -8.69124174e-01 -9.63709891e-01 -3.45198065e-01
1.94021419e-01 -8.04870874e-02 1.70832530e-01 9.23642144... | [7.474151134490967, 7.915016174316406] |
d38ffa14-92f6-4412-a0df-4f153789288f | using-morphological-knowledge-in-open | null | null | https://aclanthology.org/N18-1130 | https://aclanthology.org/N18-1130.pdf | Using Morphological Knowledge in Open-Vocabulary Neural Language Models | Languages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically na{\"\i}ve: they must discover that words exist and are delimited by spaces{---}basic linguistic facts... | ['Chris Dyer', 'Austin Matthews', 'Graham Neubig'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.95592242e-01 4.02206868e-01 -2.30598435e-01 -3.24546874e-01
-8.56297076e-01 -1.18060553e+00 7.07156658e-01 3.18663001e-01
-4.86221254e-01 8.31910849e-01 3.97164166e-01 -7.74547875e-01
4.43091840e-01 -1.16191375e+00 -7.39114404e-01 -3.88981849e-01
2.89345026e-01 7.83211648e-01 -6.30330667e-02 -5.46053231... | [10.670655250549316, 9.717960357666016] |
0c5217bd-2ca8-48fe-8c42-90eb543ea94e | tabfact-a-large-scale-dataset-for-table-based | 1909.02164 | null | https://arxiv.org/abs/1909.02164v5 | https://arxiv.org/pdf/1909.02164v5.pdf | TabFact: A Large-scale Dataset for Table-based Fact Verification | The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natur... | ['Yunkai Zhang', 'William Yang Wang', 'Wenhu Chen', 'Hongmin Wang', 'Shiyang Li', 'Jianshu Chen', 'Hong Wang', 'Xiyou Zhou'] | 2019-09-05 | null | https://openreview.net/forum?id=rkeJRhNYDH | https://openreview.net/pdf?id=rkeJRhNYDH | iclr-2020-1 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 8.28939155e-02 3.96142721e-01 -8.75721991e-01 -5.73578894e-01
-1.01894724e+00 -9.48068857e-01 8.08460772e-01 7.95429766e-01
7.07416609e-02 8.05267036e-01 3.87578398e-01 -9.38894331e-01
2.20107019e-01 -1.03675640e+00 -1.45959508e+00 1.13797128e-01
4.67146859e-02 2.48867020e-01 3.04099768e-01 6.06281012... | [9.49670124053955, 7.693531036376953] |
4b5c3693-ebdf-469e-8e97-4aa554766114 | self-similarity-driven-scale-invariant | 2302.12986 | null | https://arxiv.org/abs/2302.12986v1 | https://arxiv.org/pdf/2302.12986v1.pdf | Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search | Weakly supervised person search aims to jointly detect and match persons with only bounding box annotations. Existing approaches typically focus on improving the features by exploring relations of persons. However, scale variation problem is a more severe obstacle and under-studied that a person often owns images with ... | ['Zhen Lei', 'Guo-Jun Qi', 'Jinlin Wu', 'Yang Yang', 'Benzhi Wang'] | 2023-02-25 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.12033650e-01 -3.58196765e-01 -5.99857271e-02 -6.08116984e-01
-3.50167751e-01 -4.71322209e-01 4.29160655e-01 6.18936941e-02
-4.31579590e-01 7.20994055e-01 9.27508771e-02 4.98034269e-01
-4.12683189e-01 -8.51047695e-01 -3.89429182e-01 -7.41477609e-01
2.07404360e-01 6.25307143e-01 4.89137053e-01 -1.99061602... | [14.777445793151855, 1.0623859167099] |
e7716ea0-ad23-46a1-a7e1-87c8ef39e9cb | distributed-mpc-with-data-driven-estimation | 2202.14014 | null | https://arxiv.org/abs/2202.14014v2 | https://arxiv.org/pdf/2202.14014v2.pdf | Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control | This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance matrix $\mathbf{Y}$ to let it be time-varying and unknown a priori. The prevalent control designs are either (a) centralized, providing opti... | ['Ratnesh Kumar', 'Ramij R. Hossain'] | 2022-02-28 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.76991373e-01 4.04918678e-02 -9.21453014e-02 1.62522599e-01
-6.23679757e-01 -8.30967546e-01 9.39058214e-02 5.23544192e-01
2.87192911e-01 1.06644464e+00 -5.64160764e-01 -4.11463350e-01
-8.83120120e-01 -8.94086242e-01 -5.21388948e-01 -1.10412371e+00
-7.47887969e-01 3.04491967e-01 -5.23688155e-04 -4.62425023... | [5.676575183868408, 2.591651678085327] |
8b77e062-9a08-4291-b0d2-c8ce2dd4f937 | roarnet-a-robust-3d-object-detection-based-on | 1811.03818 | null | http://arxiv.org/abs/1811.03818v1 | http://arxiv.org/pdf/1811.03818v1.pdf | RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement | We present RoarNet, a new approach for 3D object detection from a 2D image
and 3D Lidar point clouds. Based on two-stage object detection framework with
PointNet as our backbone network, we suggest several novel ideas to improve 3D
object detection performance. The first part of our method, RoarNet_2D,
estimates the 3D... | ['Kiwoo Shin', 'Youngwook Paul Kwon', 'Masayoshi Tomizuka'] | 2018-11-09 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [-2.52544492e-01 -4.48062479e-01 -1.29624099e-01 -4.59482186e-02
-5.21705747e-01 -6.76884115e-01 4.99772817e-01 1.88381597e-02
-6.19902551e-01 6.54525161e-02 -4.05599654e-01 -5.39885581e-01
1.47298768e-01 -8.45281541e-01 -8.29418778e-01 -1.60241678e-01
-6.97103292e-02 8.50554526e-01 8.82099032e-01 -7.45817125... | [7.686828136444092, -2.746798038482666] |
68dcb85f-487f-42ec-9375-f413056060e1 | optmsm-optimizing-multi-scenario-modeling-for | 2306.13382 | null | https://arxiv.org/abs/2306.13382v1 | https://arxiv.org/pdf/2306.13382v1.pdf | OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction | A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them simultaneously. Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning modu... | ['Xiuqiang He', 'Dugang Liu', 'Fuyuan Lyu', 'Yuwen Fu', 'Yang Qiao', 'Xing Tang'] | 2023-06-23 | null | null | null | null | ['disentanglement', 'click-through-rate-prediction'] | ['methodology', 'miscellaneous'] | [ 1.08698994e-01 -4.54040945e-01 -4.00907725e-01 -3.65499854e-01
-7.21364617e-01 -6.49947762e-01 2.90537059e-01 2.60568094e-02
-1.77464634e-02 3.09861839e-01 1.53127640e-01 -5.11377811e-01
-5.63595772e-01 -8.18133473e-01 -6.40965343e-01 -6.34857833e-01
1.00885563e-01 8.86451304e-02 1.41603470e-01 -3.26855659... | [10.078617095947266, 5.420742988586426] |
c059db7f-e50f-4af3-9f7b-94cd91e1432c | kgs-causal-discovery-using-knowledge-guided | 2304.05493 | null | https://arxiv.org/abs/2304.05493v1 | https://arxiv.org/pdf/2304.05493v1.pdf | KGS: Causal Discovery Using Knowledge-guided Greedy Equivalence Search | Learning causal relationships solely from observational data provides insufficient information about the underlying causal mechanism and the search space of possible causal graphs. As a result, often the search space can grow exponentially for approaches such as Greedy Equivalence Search (GES) that uses a score-based a... | ['Md Osman Gani', 'Uzma Hasan'] | 2023-04-11 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.79912078e-01 2.21496001e-01 -7.45654106e-01 -4.16672707e-01
-3.02092791e-01 -6.49080276e-01 6.61708891e-01 5.17258942e-01
1.19813457e-02 9.18055952e-01 3.30050677e-01 -7.05822945e-01
-8.26974332e-01 -1.03752506e+00 -8.10329378e-01 -4.03733462e-01
-4.56158608e-01 4.36786801e-01 4.41725582e-01 1.43208608... | [7.804656982421875, 5.360896587371826] |
e09a1d8f-8a47-427f-babb-0faebebaef02 | linguistically-enriched-and-context-aware | 2101.06514 | null | https://arxiv.org/abs/2101.06514v1 | https://arxiv.org/pdf/2101.06514v1.pdf | Linguistically-Enriched and Context-Aware Zero-shot Slot Filling | Slot filling is identifying contiguous spans of words in an utterance that correspond to certain parameters (i.e., slots) of a user request/query. Slot filling is one of the most important challenges in modern task-oriented dialog systems. Supervised learning approaches have proven effective at tackling this challenge,... | ['Vagelis Hristidis', 'Fuad Jamour', 'A. B. Siddique'] | 2021-01-16 | null | null | null | null | ['zero-shot-slot-filling'] | ['natural-language-processing'] | [ 1.74382627e-01 1.10434569e-01 -4.87394720e-01 -5.88740468e-01
-8.20025921e-01 -5.30841470e-01 5.12182176e-01 2.97020197e-01
-5.76320648e-01 7.66453207e-01 3.60697508e-01 -5.23463726e-01
2.90855438e-01 -6.84002697e-01 -2.02052519e-01 -3.31155479e-01
2.59874225e-01 9.58411217e-01 5.47002017e-01 -6.68293655... | [12.591020584106445, 7.409951686859131] |
9dcde38e-4a0d-4ceb-8730-4263ca769a9c | artificial-intelligence-for-breast-cancer | 2110.00942 | null | https://arxiv.org/abs/2110.00942v1 | https://arxiv.org/pdf/2110.00942v1.pdf | Artificial Intelligence For Breast Cancer Detection: Trends & Directions | In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly d... | ['Unaiza Sajid', 'Sheeraz Arif', 'Rizwan Ahmed Khan', 'Shahid Munir Shah'] | 2021-10-03 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.82733011e-01 3.05776060e-01 -2.95328140e-01 -5.82860887e-01
-3.17194790e-01 -1.83784336e-01 3.95510316e-01 3.19019586e-01
-3.57582539e-01 5.17902017e-01 -3.77120264e-02 -3.86684477e-01
-3.74678522e-01 -7.14051664e-01 -3.74611408e-01 -6.09239221e-01
-3.34999785e-02 7.47285843e-01 2.95645352e-02 -9.43194106... | [15.165694236755371, -2.636277437210083] |
b8913610-2c78-4f39-8ef7-56ac09a8ed9d | causal-and-counterfactual-views-of-missing | 2210.05558 | null | https://arxiv.org/abs/2210.05558v1 | https://arxiv.org/pdf/2210.05558v1.pdf | Causal and counterfactual views of missing data models | It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this paper, we consider the implications of the converse ... | ['James Robins', 'Ilya Shpitser', 'Rohit Bhattacharya', 'Razieh Nabi'] | 2022-10-11 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 8.40378404e-01 5.84545374e-01 -9.14705098e-01 -6.90592051e-01
-3.24079901e-01 -5.12029767e-01 6.50172055e-01 1.75131083e-01
-3.42995405e-01 1.46122324e+00 7.89711356e-01 -9.38743889e-01
-7.73644209e-01 -8.56391370e-01 -8.61764729e-01 -7.40446210e-01
-1.30327940e-01 4.90422457e-01 -5.64437687e-01 2.49953419... | [8.08875846862793, 5.406367301940918] |
2fac6fef-c639-41fa-96fa-cd52320f729a | vital-vision-transformer-neural-networks-for | 2302.09443 | null | https://arxiv.org/abs/2302.09443v1 | https://arxiv.org/pdf/2302.09443v1.pdf | VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization | Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones. Unfortunately, the heterogeneity of wireless transceivers across diverse smartphones carried by users has been shown to reduce the ... | ['Sudeep Pasricha', 'Saideep Tiku', 'Danish Gufran'] | 2023-02-18 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.71033904e-01 -6.48138821e-02 -3.81954134e-01 -3.39626938e-01
-1.04975021e+00 -5.10219336e-01 1.67223543e-01 -5.85522294e-01
-1.85423970e-01 9.11492944e-01 2.00533614e-01 -7.07686782e-01
-3.70418251e-01 -5.74670434e-01 -8.34838748e-01 -4.65993434e-01
-6.84167445e-02 -1.24599911e-01 8.24151263e-02 2.77278125... | [6.430196285247803, 0.8821361064910889] |
01636e6e-b0e5-4fac-8ef5-1cf54693ddb4 | federated-multi-target-domain-adaptation | 2108.07792 | null | https://arxiv.org/abs/2108.07792v1 | https://arxiv.org/pdf/2108.07792v1.pdf | Federated Multi-Target Domain Adaptation | Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a mo... | ['Ming-Hsuan Yang', 'Yukun Zhu', 'Hang Qi', 'Yin Cui', 'Boqing Gong', 'Chun-Han Yao'] | 2021-08-17 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.33059481e-01 4.85014059e-02 -3.33685696e-01 -8.60991001e-01
-1.14509439e+00 -7.85101533e-01 3.26116055e-01 -3.64450693e-01
-4.68179703e-01 8.73202324e-01 -1.94374084e-01 -3.10060143e-01
2.55274624e-01 -4.74175602e-01 -6.52366221e-01 -1.00122130e+00
2.28103235e-01 8.03498268e-01 9.07759592e-02 4.70360041... | [5.872074127197266, 6.352395534515381] |
c0095344-358e-47d5-8ae9-2985a51884be | geometric-moment-invariants-to-motion-blur | 2101.08647 | null | https://arxiv.org/abs/2101.08647v2 | https://arxiv.org/pdf/2101.08647v2.pdf | Geometric Moment Invariants to Motion Blur | In this paper, we focus on removing interference of motion blur by the derivation of motion blur invariants.Unlike earlier work, we don't restore any blurred image. Based on geometric moment and mathematical model of motion blur, we prove that geometric moments of blurred image and original image are linearly related. ... | ['Hanlin Mo.', 'Hongxiang Hao.', 'Hua Li'] | 2021-01-21 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 4.23045978e-02 -6.76146507e-01 1.69785231e-01 -1.14884578e-01
-2.57383380e-02 -7.19188631e-01 5.96383572e-01 -3.83369237e-01
-2.92094052e-01 7.26089239e-01 3.69426996e-01 -6.25648573e-02
-8.11382353e-01 -3.01982075e-01 -3.59880418e-01 -6.47167683e-01
-2.23211005e-01 -3.29767197e-01 3.18049073e-01 1.39721427... | [11.612308502197266, -2.775665283203125] |
ef8e17ef-5e42-42ee-b2b2-37e958bbc3ce | incorporating-deep-syntactic-and-semantic | 2306.02078 | null | https://arxiv.org/abs/2306.02078v1 | https://arxiv.org/pdf/2306.02078v1.pdf | Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN | Recently, it is quite common to integrate Chinese sequence labeling results to enhance syntactic and semantic parsing. However, little attention has been paid to the utility of hierarchy and structure information encoded in syntactic and semantic features for Chinese sequence labeling tasks. In this paper, we propose a... | ['Qi Su', 'Jun Wang', 'Xuemei Tang'] | 2023-06-03 | null | null | null | null | ['part-of-speech-tagging', 'semantic-parsing', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.38307303e-01 -3.56546529e-02 -1.59852713e-01 -7.38243818e-01
-4.31514233e-01 -7.47799993e-01 6.06000647e-02 1.07621118e-01
-5.95499814e-01 6.58968925e-01 4.84973311e-01 -5.04190743e-01
6.05001509e-01 -9.10094023e-01 -2.83010811e-01 -4.51656252e-01
1.62802011e-01 3.46394666e-02 3.06589961e-01 -3.35136205... | [9.969940185546875, 10.067935943603516] |
dc518d94-8625-4d9f-8c47-0e6d0cd71830 | ji-yu-zi-ci-ji-bie-ci-xiang-liang-he-zhi-zhen | null | null | https://aclanthology.org/2020.ccl-1.43 | https://aclanthology.org/2020.ccl-1.43.pdf | 基于子词级别词向量和指针网络的朝鲜语句子排序(Korean Sentence Ordering Based on Sub Word Level Word Vector and Pointer Network) | 句子排序是多文档摘要系统和机器阅读理解中重要的任务之一,排序的质量将直接 影响摘要和答案的连贯性与可读性。因此,本文采用在中英文上大规模使用的深度 学习方法,同时结合朝鲜语词语形态变化丰富的特点,提出了一种基于子词级别词 向量和指针网络的朝鲜语句子排序模型,其目的是解决传统方法无法挖掘深层语义 信息问题。 本文提出基于形态素拆分的词向量训练方法(MorV),同时对比子词n元 词向量训练方法(SG),得到朝鲜语词向量;采用了两种句向量方法:基于卷积神经网 络(CNN)、基于长短时记忆网络(LSTM),结合指针网络分别进行实验。结果表明本文 采用MorV和LSTM的句向量结合方法可以更好地捕获句子间的语义逻辑关系,提升句 子排序的效果。... | ['Xiaoqing Xie', 'Xiaodong Yan'] | null | null | null | null | ccl-2020-10 | ['sentence-ordering'] | ['natural-language-processing'] | [-9.05410528e-01 -9.62505817e-01 3.39770824e-01 5.83569884e-01
2.79510111e-01 -1.03939438e+00 2.56444424e-01 8.07662427e-01
-4.36569124e-01 1.05178094e+00 5.24322867e-01 -2.90019631e-01
1.32387936e-01 -9.88840878e-01 -3.48404422e-02 -1.18467462e+00
-4.38034564e-01 1.09863472e+00 1.08641334e-01 -8.12239170... | [-3.3160603046417236, 6.907695770263672] |
32a6442e-a826-4c6c-bf77-d545f2d75307 | real-time-anomaly-detection-and-feature | null | null | https://ieeexplore.ieee.org/abstract/document/9426191 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9426191 | Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video | The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve
the extremely uneven human supervision resource and digital cameras. Besides, the number of anomaly types that real-time
machine monitoring could recognize has not met the need. This paper presents a fast ... | ['Yajun Fang', 'Jingyuan Chen', 'Ruoyu Xue'] | 2021-05-11 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 9.18863267e-02 -5.25840819e-01 2.77182221e-01 -4.42454547e-01
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-4.22380239e-01 -2.04000011e-01 5.25660813e-01 -1.85421303... | [7.816904544830322, 1.6095694303512573] |
b969256a-4b4d-4492-ae23-4e40765e4fc2 | unsupervised-neural-aspect-extraction-with | null | null | https://www.ijcai.org/proceedings/2019/712 | https://www.ijcai.org/proceedings/2019/0712.pdf | Unsupervised Neural Aspect Extraction with Sememes | Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we ... | ['Dong Yu', 'Qing He', 'Xiaopeng Yang', 'Jinyao Li', 'Yan Song', 'Xiang Ao', 'Ling Luo'] | 2019-08-01 | null | null | null | ijcai-2019-8 | ['aspect-term-extraction-and-sentiment', 'aspect-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23152751e-01 4.20265406e-01 -6.05388582e-01 -5.63458383e-01
-6.69768214e-01 -4.68234837e-01 8.43532562e-01 1.93153083e-01
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1.88483775e-01 -1.02753556e+00 -4.27859753e-01 -3.46428424e-01
4.09545511e-01 1.85482979e-01 -2.49446005e-01 -3.16872418... | [11.386713981628418, 6.744948863983154] |
6719336a-9d49-457f-abe8-9dd8ebe64efe | multimodal-machine-translation-with-embedding | 1904.00639 | null | http://arxiv.org/abs/1904.00639v1 | http://arxiv.org/pdf/1904.00639v1.pdf | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine
translation (NMT). It helps computers to deeply understand visual objects and
their relations with natural languages. However, multimodal NMT systems suffer
from a shortage of available training data, resulting in poor performance for
transla... | ['Hayahide Yamagishi', 'Mamoru Komachi', 'Yukio Matsumura', 'Tosho Hirasawa'] | 2019-04-01 | multimodal-machine-translation-with-embedding-1 | https://aclanthology.org/N19-3012 | https://aclanthology.org/N19-3012.pdf | naacl-2019-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.34959888e-01 -1.63766757e-01 -4.46459174e-01 -7.92412460e-02
-1.16901350e+00 -5.69688618e-01 7.63702810e-01 -8.43027234e-02
-6.29094422e-01 7.40552843e-01 2.90863067e-01 -4.92973834e-01
3.88166994e-01 -5.53067029e-01 -8.88254464e-01 -5.16351163e-01
4.83978570e-01 5.28896868e-01 -3.06161493e-01 -3.76719117... | [11.458178520202637, 1.5291695594787598] |
09f75008-3c21-48cd-bf3b-c00662da0ebc | cot-unsupervised-domain-adaptation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.pdf | COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge transfer, aligning the distribution between source and target domain from a global perspective is widely adopted in UDA. Recent researchers furt... | ['Baigui Sun', 'Zhipeng Zhou', 'Yang Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 1.19796246e-01 -1.71781391e-01 -5.62225103e-01 -5.63544393e-01
-1.07128143e+00 -6.51798487e-01 5.62567711e-01 3.10715437e-01
-3.57023478e-01 6.65264130e-01 -9.81999859e-02 -2.36027136e-01
-2.96143681e-01 -7.87752271e-01 -7.51545370e-01 -7.96530664e-01
5.21882534e-01 8.57556462e-01 1.75864413e-01 7.47808367... | [10.35218620300293, 3.0926883220672607] |
34ab5a68-5837-4fbe-b85f-8d1c26b7222f | dense-color-constancy-with-effective-edge | 1911.07163 | null | https://arxiv.org/abs/1911.07163v2 | https://arxiv.org/pdf/1911.07163v2.pdf | ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities | As a novel method eliminating chromatic aberration on objects, computational color constancy has becoming a fundamental prerequisite for many computer vision applications. Among algorithms performing this task, the learning-based ones have achieved great success in recent years. However, they fail to fully consider the... | ['Neal N. Xiong', 'Yilang Zhang', 'Jian Wang', 'Zheng Wei', 'Xin Yuan'] | 2019-11-17 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.27251612e-02 -8.74951124e-01 1.07026540e-01 -2.57399321e-01
-1.39913157e-01 -2.93886483e-01 3.01605284e-01 -6.75783157e-02
-3.72576386e-01 5.88636756e-01 -7.37948995e-03 -1.97732508e-01
-6.64230585e-02 -7.15785503e-01 -3.31989139e-01 -1.34422684e+00
2.57769734e-01 -3.49476427e-01 6.80607632e-02 -1.53252035... | [10.660491943359375, -2.5218803882598877] |
6a05201e-4fb3-4581-87d3-d42a3753071f | performance-preserving-event-log-sampling-for | 2301.07624 | null | https://arxiv.org/abs/2301.07624v1 | https://arxiv.org/pdf/2301.07624v1.pdf | Performance-Preserving Event Log Sampling for Predictive Monitoring | Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex mach... | ['Wil M. P. van der Aalst', 'Sebastiaan J. van Zelst', 'Marco Pegoraro', 'Gyunam Park', 'Mozhgan Vazifehdoostirani', 'Mohammadreza Fani Sani'] | 2023-01-18 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 6.98513150e-01 2.74759233e-01 -2.60163963e-01 -1.94304392e-01
-4.67040092e-01 -7.85068795e-02 6.32895470e-01 8.79758894e-01
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-4.96701002e-01 -1.28908372e+00 -2.97303796e-01 -3.29521537e-01
-2.83735275e-01 7.40269303e-01 3.43434095e-01 5.29135048... | [8.59038257598877, 5.984050273895264] |
b730e53f-f81a-414d-82dc-622fb02d2638 | sead-end-to-end-text-to-sql-generation-with-1 | null | null | https://openreview.net/forum?id=Dgx157I8729 | https://openreview.net/pdf?id=Dgx157I8729 | SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising | On the WikiSQL benchmark, most methods tackle the challenge of text-to-SQL with predefined sketch slots and build sophisticated sub-tasks to fill these slots. Though achieving promising results, these methods suffer from over-complex model structure. In this paper, we present a simple yet effective approach that enable... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['text-to-sql', 'slot-filling'] | ['computer-code', 'natural-language-processing'] | [ 6.93133712e-01 2.68584400e-01 9.09253284e-02 -5.86080194e-01
-1.30718637e+00 -5.60018659e-01 5.67984998e-01 -5.15335687e-02
-1.46635681e-01 6.77565992e-01 1.17365003e-01 -5.83866835e-01
1.42544180e-01 -1.04118896e+00 -1.23259890e+00 -2.12312967e-01
4.50520903e-01 8.30898225e-01 1.50495172e-01 -4.86778855... | [9.997257232666016, 7.873898983001709] |
ee31096f-4940-4ea7-bff4-eab10b04bc4b | de-gan-a-conditional-generative-adversarial-1 | 2010.08764 | null | https://arxiv.org/abs/2010.08764v1 | https://arxiv.org/pdf/2010.08764v1.pdf | DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement | Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to re... | ['Yousri Kessentini', 'Mohamed Ali Souibgui'] | 2020-10-17 | de-gan-a-conditional-generative-adversarial | https://ieeexplore.ieee.org/document/9187695 | https://ieeexplore.ieee.org/document/9187695 | null | ['document-enhancement'] | ['computer-vision'] | [ 7.34324157e-01 -2.46895716e-01 4.28990811e-01 -6.52366430e-02
-8.05356920e-01 -7.63295591e-01 9.14475620e-01 -3.02496225e-01
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-8.55311230e-02 -6.11642182e-01 -6.98132455e-01 -1.16013348e+00
1.71656266e-01 -6.56004995e-02 -3.09474528e-01 -3.79291564... | [11.32332992553711, -2.0719759464263916] |
43c43002-0042-4716-9409-622b833720aa | accurate-prediction-using-triangular-type-2 | 2109.05461 | null | https://arxiv.org/abs/2109.05461v1 | https://arxiv.org/pdf/2109.05461v1.pdf | Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression | Many works have been done to handle the uncertainties in the data using type 1 fuzzy regression. Few type 2 fuzzy regression works used interval type 2 for indeterminate modeling using type 1 fuzzy membership. The current survey proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the efficiency of... | ['Abbas Khosravi', 'Majid Halaji', 'Roohallah Alizadehsani', 'Parisa Moridian', 'Narges Shafaei', 'Afshin Shoeibi', 'Assef Zare'] | 2021-09-12 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.12281656e-01 -1.00116111e-01 -1.15801260e-01 -6.52753890e-01
-4.64742742e-02 -4.77883488e-01 3.59132767e-01 9.84436572e-02
-3.07156682e-01 1.18983102e+00 -5.60020685e-01 -3.81015658e-01
-7.18352020e-01 -1.21699357e+00 -5.03655076e-01 -5.03666162e-01
-1.77783534e-01 9.48937476e-01 4.72786158e-01 -7.04614997... | [5.924811840057373, 3.624744176864624] |
59239fb7-1c46-4b32-a8f9-6ac16df0b3c0 | self-corrective-perturbations-for-semantic | 1703.07928 | null | http://arxiv.org/abs/1703.07928v2 | http://arxiv.org/pdf/1703.07928v2.pdf | Self corrective Perturbations for Semantic Segmentation and Classification | Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of re... | ['Arpit Jain', 'Ser Nam Lim', 'Swami Sankaranarayanan'] | 2017-03-23 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 7.15428054e-01 3.21056157e-01 -9.59093962e-03 -7.84448326e-01
-1.93520039e-01 -7.25768209e-01 5.48743069e-01 -6.29458949e-02
-6.34077430e-01 6.47401273e-01 -7.02614263e-02 -3.13393444e-01
2.41495803e-01 -6.60766602e-01 -1.11581433e+00 -7.36348093e-01
1.77966210e-03 2.48361886e-01 6.37092054e-01 -2.99877673... | [9.449833869934082, 1.8705687522888184] |
af592dcc-570c-42b4-a534-119cc48e4d0c | two-stream-transformer-architecture-for-long | 2208.01753 | null | https://arxiv.org/abs/2208.01753v1 | https://arxiv.org/pdf/2208.01753v1.pdf | Two-Stream Transformer Architecture for Long Video Understanding | Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention and lack of inductive bias, transformers are resource intensive and suffer from data inefficiencies. Long form video understanding tasks ampl... | ['Andrew Gilbert', 'Jon Weinbren', 'Edward Fish'] | 2022-08-02 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 1.96360365e-01 -4.27464217e-01 -2.98757553e-01 -2.23855406e-01
-5.50109744e-01 -3.66386145e-01 5.59867382e-01 -1.81057751e-01
-4.43131000e-01 3.91327292e-01 2.74556428e-01 -3.90208274e-01
2.98731588e-02 -7.67880142e-01 -8.56398523e-01 -5.28299987e-01
-4.23496701e-02 4.01558578e-01 5.13173342e-01 9.73623693... | [8.89328670501709, 0.5241658687591553] |
c80f734b-9b9c-496d-819f-52fc07188f07 | local-interpretability-of-random-forests-for | 2303.16506 | null | https://arxiv.org/abs/2303.16506v1 | https://arxiv.org/pdf/2303.16506v1.pdf | Local Interpretability of Random Forests for Multi-Target Regression | Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in machine learning, especially when it can directly impact human well-being. Although model-agnostic techniques exist for multi-... | ['Grigorios Tsoumakas', 'Ioannis Mollas', 'Nikolaos Mylonas', 'Avraam Bardos'] | 2023-03-29 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 7.61152685e-01 6.25790954e-01 -9.27473187e-01 -6.84988379e-01
-6.20658636e-01 -7.95152709e-02 6.42181098e-01 2.99289137e-01
3.52903828e-02 1.25437701e+00 2.80021913e-02 -5.36659300e-01
-4.65726674e-01 -7.12268829e-01 -3.94828975e-01 -5.26275098e-01
1.32463366e-01 9.94871557e-01 -9.11324993e-02 -1.27638891... | [8.659852027893066, 5.567319393157959] |
74ae388a-b093-43a8-a6d8-c7203322eb3f | style-a-video-agile-diffusion-for-arbitrary | 2305.05464 | null | https://arxiv.org/abs/2305.05464v1 | https://arxiv.org/pdf/2305.05464v1.pdf | Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer | Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and the necessary computational resources for training, directly applying these models for video stylization remains difficult. Also, given that ... | ['WeiMing Dong', 'Yuxin Zhang', 'Nisha Huang'] | 2023-05-09 | null | null | null | null | ['style-transfer', 'video-style-transfer'] | ['computer-vision', 'computer-vision'] | [ 2.63503551e-01 -3.07994455e-01 -6.66443929e-02 -1.01481855e-01
-5.12358189e-01 -5.55640101e-01 4.64171976e-01 -5.21127999e-01
3.99110019e-02 6.64407253e-01 2.59873331e-01 -5.54702580e-02
1.61647782e-01 -6.54651821e-01 -7.48069584e-01 -8.71775866e-01
4.96416658e-01 -9.14830156e-03 7.45834336e-02 1.43596372... | [11.136306762695312, -0.7296223044395447] |
c3810c31-de96-4190-a386-d7029b5ca88f | star-a-session-based-time-aware-recommender | 2211.06394 | null | https://arxiv.org/abs/2211.06394v1 | https://arxiv.org/pdf/2211.06394v1.pdf | STAR: A Session-Based Time-Aware Recommender System | Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Modern SBRs utilize deep neural networks to map users' current interest(s) during an ongoing session to a latent space so that their next prefer... | ['Saman Haratizadeh', 'Reza Yeganegi'] | 2022-11-11 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-9.46805254e-02 -1.55008465e-01 -7.19604611e-01 -7.89160728e-01
-1.61834896e-01 -5.33381462e-01 7.38280773e-01 1.82780981e-01
-2.40884259e-01 6.44575059e-01 8.62322628e-01 -4.29921113e-02
-4.87638801e-01 -9.97264385e-01 -4.40474004e-01 -3.75911951e-01
-5.87288320e-01 3.38365167e-01 -1.10547684e-01 -2.61640728... | [10.140125274658203, 5.63767147064209] |
bcae5771-6e28-4055-9704-325ee4b42180 | hats-a-hierarchical-graph-attention-network | 1908.07999 | null | https://arxiv.org/abs/1908.07999v3 | https://arxiv.org/pdf/1908.07999v3.pdf | HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction | Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use ... | ['Sang-Hoon Lee', 'Minbyul Jeong', 'Raehyun Kim', 'Jaewoo Kang', 'Chan Ho So', 'Jinkyu Kim'] | 2019-08-07 | null | null | null | null | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-4.64780986e-01 6.75263926e-02 -7.24393308e-01 -2.72429168e-01
2.29521990e-02 -2.66646445e-01 5.29982865e-01 3.35224807e-01
-7.21412003e-02 4.45773959e-01 2.07631037e-01 -2.30100334e-01
-3.28553468e-02 -1.56971395e+00 -5.21181524e-01 -2.23979890e-01
-1.31759882e-01 3.77096981e-01 7.06541002e-01 -6.70689166... | [4.318922996520996, 4.340164661407471] |
3f7fcecc-9e07-45aa-b225-507e1e3c878a | hierarchical-control-in-islanded-dc | 1910.05107 | null | https://arxiv.org/abs/1910.05107v2 | https://arxiv.org/pdf/1910.05107v2.pdf | Hierarchical Control in Islanded DC Microgrids with Flexible Structures | Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines. However, a comprehensive analysis of all the layers put together is often missing. In... | ['Giancarlo Ferrari-Trecate', 'Riccardo Scattolini', 'Alessio La Bella', 'Pulkit Nahata'] | 2019-10-11 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.89115632e-01 2.22010940e-01 -2.54737109e-01 2.49998108e-01
7.83948984e-04 -1.17619479e+00 3.92478496e-01 1.78719893e-01
4.01679605e-01 1.31816113e+00 -2.88106352e-01 -1.92625701e-01
-2.78859794e-01 -1.04623580e+00 -3.40372801e-01 -1.22859776e+00
-3.44075322e-01 1.98233232e-01 -7.43600875e-02 -4.43109453... | [5.655068397521973, 2.540534019470215] |
5b6461c5-cf2c-44c6-850e-93cdacdf0011 | graph-neural-networks-for-text-classification | 2304.11534 | null | https://arxiv.org/abs/2304.11534v2 | https://arxiv.org/pdf/2304.11534v2.pdf | Graph Neural Networks for Text Classification: A Survey | Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many re... | ['Soyeon Caren Han', 'Yihao Ding', 'Kunze Wang'] | 2023-04-23 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 1.47937357e-01 1.37697950e-01 -5.41048706e-01 -4.32286739e-01
-2.11223625e-02 -5.19740164e-01 6.57390118e-01 1.09206402e+00
-4.11498904e-01 4.25514936e-01 2.94184744e-01 -6.08233988e-01
-2.13878036e-01 -1.20906425e+00 -9.22687575e-02 -3.64045054e-01
-3.68744761e-01 7.65231550e-01 -7.74272755e-02 -2.42002711... | [9.944765090942383, 6.745317459106445] |
c84cc6b2-b448-4d0b-823e-ae8e6301801a | adaboost-neural-network-and-cyclopean-view | null | null | https://www.researchgate.net/publication/338455423_AdaBoost_neural_network_and_cyclopean_view_for_no-reference_stereoscopic_image_quality_assessment | https://www.researchgate.net/publication/338455423_AdaBoost_neural_network_and_cyclopean_view_for_no-reference_stereoscopic_image_quality_assessment | Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment | Stereoscopic imaging has been widely used in many fields. In many scenarios, stereo images quality could be affected by various degradations, such as asymmetric distortion. Accordingly, to guarantee the best quality of experience, robust and accurate reference-less metrics are required for quality assessment of stereos... | ['Zianou Ahmed seghir', 'Fella Hachouf', 'Oussama Messai'] | 2020-03-13 | null | null | null | signal-processing-image-communication-2020-3 | ['image-quality-estimation', 'blind-image-quality-assessment', 'stereoscopic-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.67995363e-01 -7.18564987e-01 -5.47326496e-03 -2.34592125e-01
-7.02364922e-01 -2.89288491e-01 5.41736782e-01 5.37868291e-02
-3.97576720e-01 8.08571815e-01 4.31990743e-01 -1.54653043e-01
-3.08520138e-01 -4.70154017e-01 -2.43820086e-01 -8.98090601e-01
2.02766091e-01 -8.07754397e-02 3.30784947e-01 -3.36218983... | [11.805723190307617, -1.950113296508789] |
87a7de6a-2f09-4149-b20a-bc86c92a558e | training-free-neural-active-learning-with | 2306.04454 | null | https://arxiv.org/abs/2306.04454v1 | https://arxiv.org/pdf/2306.04454v1.pdf | Training-Free Neural Active Learning with Initialization-Robustness Guarantees | Existing neural active learning algorithms have aimed to optimize the predictive performance of neural networks (NNs) by selecting data for labelling. However, other than a good predictive performance, being robust against random parameter initializations is also a crucial requirement in safety-critical applications. T... | ['Bryan Kian Hsiang Low', 'See-Kiong Ng', 'Jasraj Singh', 'Zhongxiang Dai', 'Apivich Hemachandra'] | 2023-06-07 | null | null | null | null | ['active-learning', 'gaussian-processes', 'active-learning'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 3.08838218e-01 1.75371334e-01 -3.52278054e-01 -3.80055726e-01
-7.95065403e-01 -5.86356640e-01 7.47955978e-01 3.66525739e-01
-7.95547664e-01 8.88084471e-01 -3.42847288e-01 -3.06962937e-01
-3.84285212e-01 -7.11877465e-01 -7.63151169e-01 -1.14770222e+00
-4.38827015e-02 4.14978355e-01 4.03623492e-01 4.07555640... | [9.082642555236816, 3.8419253826141357] |
b490b887-cb0a-419c-a5e7-0560f74c4972 | looking-for-change-roll-the-dice-and-demand | 2009.02062 | null | https://arxiv.org/abs/2009.02062v2 | https://arxiv.org/pdf/2009.02062v2.pdf | Looking for change? Roll the Dice and demand Attention | Change detection, i.e. identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a reliabl... | ['François Waldner', 'Foivos I. Diakogiannis', 'Peter Caccetta'] | 2020-09-04 | null | null | null | null | ['change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing'] | ['miscellaneous', 'miscellaneous'] | [ 4.94719177e-01 -4.64499235e-01 5.81149638e-01 -3.73278886e-01
-3.40042144e-01 -6.27366126e-01 7.29399979e-01 1.26519978e-01
-7.34900475e-01 5.86904347e-01 -5.62759005e-02 -8.88935253e-02
-3.46354187e-01 -1.10176337e+00 -8.50524902e-01 -8.29634607e-01
-3.09092999e-01 -2.32807711e-01 4.41121936e-01 -4.64240730... | [9.68443775177002, -1.3409119844436646] |
7521c42d-1c97-415c-a29c-ed3c2479c3b1 | cgintrinsics-better-intrinsic-image | 1808.08601 | null | http://arxiv.org/abs/1808.08601v3 | http://arxiv.org/pdf/1808.08601v3.pdf | CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering | Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we prese... | ['Zhengqi Li', 'Noah Snavely'] | 2018-08-26 | cgintrinsics-better-intrinsic-image-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.pdf | eccv-2018-9 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 3.93253744e-01 2.63537824e-01 1.62281871e-01 -1.55099392e-01
-1.05876660e+00 -3.65946949e-01 6.18554950e-01 -5.92564166e-01
3.75517234e-02 4.43510950e-01 5.20350158e-01 -3.31119746e-02
-1.59689840e-02 -5.73368549e-01 -1.00938702e+00 -6.78902090e-01
-4.88191238e-03 5.24227560e-01 5.16691767e-02 -3.90098661... | [9.538070678710938, -2.7977311611175537] |
736a49e0-f59d-4e3a-ac9c-cd50ac2e2ae7 | dialbert-a-hierarchical-pre-trained-model-for | 2004.03760 | null | https://arxiv.org/abs/2004.03760v2 | https://arxiv.org/pdf/2004.03760v2.pdf | DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement | Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to. We propose a new model, named Dialogue BERT (DialBERT), which integrates local and global semantics in a single stream of ... | ['Zhen-Hua Ling', 'Jia-Chen Gu', 'Xiaodan Zhu', 'Quan Liu', 'Tianda Li', 'Zhiming Su', 'Si Wei'] | 2020-04-08 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 2.47705385e-01 3.44866484e-01 -1.71986237e-01 -6.33638859e-01
-1.20174348e+00 -6.64606929e-01 9.06935573e-01 1.24208942e-01
-4.19873357e-01 7.03228831e-01 8.30466926e-01 -3.82328600e-01
3.35945666e-01 -4.32268053e-01 -3.46268177e-01 -4.80072290e-01
-1.21743873e-01 7.81487584e-01 2.35551506e-01 -5.84255397... | [12.601679801940918, 7.8182854652404785] |
24a1704a-b840-43e3-a812-6d92228b07b3 | vision-matters-when-it-should-sanity-checking | 2109.03415 | null | https://arxiv.org/abs/2109.03415v1 | https://arxiv.org/pdf/2109.03415v1.pdf | Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models | Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an ... | ['Rico Sennrich', 'Duygu Ataman', 'Jiaoda Li'] | 2021-09-08 | null | https://aclanthology.org/2021.emnlp-main.673 | https://aclanthology.org/2021.emnlp-main.673.pdf | emnlp-2021-11 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.68620741e-01 1.90509439e-01 -7.79426470e-02 -2.27791041e-01
-7.35723257e-01 -6.97857738e-01 1.01161170e+00 -1.78165346e-01
-4.50244218e-01 6.42910421e-01 4.35343623e-01 -6.79547191e-01
2.53973424e-01 -1.39981464e-01 -1.02482438e+00 -4.66851890e-01
5.52680194e-01 3.49833399e-01 -2.33248189e-01 -1.09881781... | [11.412618637084961, 1.4000407457351685] |
b04e3ab2-0b24-4db4-8191-20b4fc28c75c | complementary-classifier-induced-partial | 2305.09897 | null | https://arxiv.org/abs/2305.09897v1 | https://arxiv.org/pdf/2305.09897v1.pdf | Complementary Classifier Induced Partial Label Learning | In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-cand... | ['Min-Ling Zhang', 'Chongjie Si', 'Yuheng Jia'] | 2023-05-17 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.69506505e-01 6.54818863e-02 -4.70336735e-01 -1.41640037e-01
-5.38651526e-01 -7.96225786e-01 5.09668469e-01 2.34206632e-01
-1.97651222e-01 9.10961032e-01 -1.12181343e-01 -6.73531443e-02
-3.39378327e-01 -9.07678545e-01 -5.16818583e-01 -9.16892946e-01
-2.16344520e-02 3.96830976e-01 2.88498253e-01 -1.24812752... | [9.5403470993042, 3.9707934856414795] |
7d233b70-2849-4e74-8c05-11de143f1803 | ita-an-energy-efficient-attention-and-softmax | 2307.03493 | null | https://arxiv.org/abs/2307.03493v2 | https://arxiv.org/pdf/2307.03493v2.pdf | ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers | Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and audio processing. However, the efficient hardware acceleration of transformer models poses new challenges due to their high arithmetic intensit... | ['Luca Benini', 'Angelo Garofalo', 'Victor J. B. Jung', 'Tim Fischer', 'Gianna Paulin', 'Moritz Scherer', 'Gamze İslamoğlu'] | 2023-07-07 | null | null | null | null | ['quantization'] | ['methodology'] | [ 2.68545866e-01 1.84498563e-01 -6.39423072e-01 -5.09256124e-01
-4.67771918e-01 -9.37994942e-02 3.10529709e-01 5.39129794e-01
-8.06147695e-01 4.45504248e-01 1.09423641e-02 -5.46080351e-01
9.85175818e-02 -1.13069677e+00 -4.79862601e-01 -6.71925247e-01
5.62884584e-02 2.69817024e-01 5.12205720e-01 -2.49795690... | [8.404119491577148, 2.838770866394043] |
a25ea0ae-9a02-4a7b-8128-7b30d0bb2199 | instructvid2vid-controllable-video-editing | 2305.12328 | null | https://arxiv.org/abs/2305.12328v1 | https://arxiv.org/pdf/2305.12328v1.pdf | InstructVid2Vid: Controllable Video Editing with Natural Language Instructions | We present an end-to-end diffusion-based method for editing videos with human language instructions, namely $\textbf{InstructVid2Vid}$. Our approach enables the editing of input videos based on natural language instructions without any per-example fine-tuning or inversion. The proposed InstructVid2Vid model combines a ... | ['Yueting Zhuang', 'Tat-Seng Chua', 'Siliang Tang', 'Juncheng Li', 'Bosheng Qin'] | 2023-05-21 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 2.32820749e-01 -2.67200708e-01 -1.95525438e-02 -3.59274834e-01
-4.91324246e-01 -6.18996203e-01 5.39696395e-01 -3.89744192e-01
-3.63816917e-01 7.48296380e-01 9.46934223e-02 -3.25389415e-01
1.24077141e-01 -6.98945701e-01 -1.16220617e+00 -5.51689386e-01
2.42500201e-01 7.63687938e-02 2.45359883e-01 -1.37551397... | [10.89493179321289, -0.605720579624176] |
8cb0e1e5-71ee-42a4-a8af-161c25320f0b | neural-insights-for-digital-marketing-content | 2302.01416 | null | https://arxiv.org/abs/2302.01416v3 | https://arxiv.org/pdf/2302.01416v3.pdf | Neural Insights for Digital Marketing Content Design | In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement. However, creating successful marketing content is a manual and time-consuming process that lacks clear guiding principles. This paper seeks to close the loop between content creation and online experime... | ['Houssam Nassif', 'Ricardo Henao', 'Shreya Chakrabarti', 'Tanner Fiez', 'Yuan Li', 'Fanjie Kong'] | 2023-02-02 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.37852693e-01 2.88854569e-01 -6.66296661e-01 -6.14445329e-01
-6.17820919e-01 -7.20405102e-01 3.96182537e-01 3.87273103e-01
-2.53637940e-01 1.46240547e-01 5.40605307e-01 -3.91225517e-01
-3.96948367e-01 -6.79892421e-01 -6.38768971e-01 -2.88912952e-02
-1.31970169e-02 4.41938370e-01 -4.78597879e-01 -5.94036520... | [9.874629020690918, 5.969808578491211] |
2d2b8e06-1727-419d-b1f5-e6971f35a830 | multi-dimensional-frequency-dynamic | 2302.09256 | null | https://arxiv.org/abs/2302.09256v2 | https://arxiv.org/pdf/2302.09256v2.pdf | Multi-dimensional frequency dynamic convolution with confident mean teacher for sound event detection | Recently, convolutional neural networks (CNNs) have been widely used in sound event detection (SED). However, traditional convolution is deficient in learning time-frequency domain representation of different sound events. To address this issue, we propose multi-dimensional frequency dynamic convolution (MFDConv), a ne... | ['Pengyuan Zhang', 'Xueshuai Zhang', 'Shengchang Xiao'] | 2023-02-18 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [-2.23319352e-01 -4.23795134e-01 2.56977916e-01 -3.18008691e-01
-5.54980934e-01 -4.32414860e-01 1.10697329e-01 -3.58561091e-02
-3.51716727e-01 5.18534303e-01 6.06675260e-02 -8.64126980e-02
-4.58817363e-01 -7.02371299e-01 -3.90158564e-01 -8.06696653e-01
-7.20058382e-02 -5.38791418e-01 5.01021683e-01 9.07590836... | [15.152099609375, 5.197956085205078] |
8f0a173f-5b68-4af4-9866-dda5ef5f143c | relative-volume-constraints-for-single-view | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Toppe_Relative_Volume_Constraints_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Toppe_Relative_Volume_Constraints_2013_CVPR_paper.pdf | Relative Volume Constraints for Single View 3D Reconstruction | We introduce the concept of relative volume constraints in order to account for insufficient information in the reconstruction of 3D objects from a single image. The key idea is to formulate a variational reconstruction approach with shape priors in form of relative depth profiles or volume ratios relating object parts... | ['Claudia Nieuwenhuis', 'Eno Toppe', 'Daniel Cremers'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 4.54452515e-01 3.06345135e-01 1.70250744e-01 -2.29268774e-01
-3.04425508e-01 -5.34443974e-01 5.54126561e-01 -1.76803932e-01
-1.36437461e-01 5.50055623e-01 -1.21210635e-01 -5.04291430e-03
5.61037734e-02 -7.28150308e-01 -5.49843192e-01 -5.50923944e-01
5.22325337e-01 8.56267214e-01 6.40342951e-01 -2.54052542... | [9.374128341674805, -3.011418581008911] |
db2b52f6-cdc4-4e74-b291-5ebb3bfe0683 | an-artifcial-life-approach-to-studying-niche | 1907.12812 | null | https://arxiv.org/abs/1907.12812v1 | https://arxiv.org/pdf/1907.12812v1.pdf | An artifcial life approach to studying niche differentiation in soundscape ecology | Artificial life simulations are an important tool in the study of ecological phenomena that can be difficult to examine directly in natural environments. Recent work has established the soundscape as an ecologically important resource and it has been proposed that the differentiation of animal vocalizations within a so... | ['Laura Beloff', 'David Kadish', 'Sebastian Risi'] | 2019-07-30 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.11012238e-01 -5.78942180e-01 8.18453372e-01 1.32384717e-01
6.06171429e-01 -7.30318427e-01 5.45293272e-01 2.11320132e-01
-8.39414001e-01 5.59476912e-01 2.78737605e-01 -2.97194660e-01
-2.00424597e-01 -6.79763973e-01 -6.15145981e-01 -6.33087754e-01
-6.07982993e-01 3.70672159e-03 6.64273620e-01 -4.95662898... | [5.608019828796387, 4.120912551879883] |
23af0c6d-432c-4c3b-a090-2d06ca0eab60 | hierarchical-filtering-with-online-learned | 2210.12807 | null | https://arxiv.org/abs/2210.12807v1 | https://arxiv.org/pdf/2210.12807v1.pdf | Hierarchical Filtering with Online Learned Priors for ECG Denoising | Electrocardiographic signals (ECG) are used in many healthcare applications, including at-home monitoring of vital signs. These applications often rely on wearable technology and provide low quality ECG signals. Although many methods have been proposed for denoising the ECG to boost its quality and enable clinical inte... | ['Rik Vullings', 'Ruud J. G. van Sloun', 'Nir Shlezinger', 'Guy Revach', 'Timur Locher'] | 2022-10-23 | null | null | null | null | ['ecg-denoising'] | ['medical'] | [ 4.69702214e-01 -1.31184801e-01 2.94300824e-01 -4.34973985e-01
-7.13836610e-01 -3.44152451e-01 -6.10088930e-02 2.16606095e-01
-3.26065898e-01 6.02121532e-01 3.98014039e-01 -2.46033728e-01
-4.91708100e-01 -3.51598531e-01 -1.84917271e-01 -7.84724295e-01
-2.92675823e-01 -2.85662077e-02 -2.74805278e-01 2.46462543... | [14.239282608032227, 3.2094080448150635] |
22f34bf5-f68c-4e76-804a-e6e2123065e0 | dbms-ku-at-semeval-2019-task-9-exploring | null | null | https://aclanthology.org/S19-2208 | https://aclanthology.org/S19-2208.pdf | DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion | This paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums. To deal with this task, we explore several machine learning approaches, i.e., Random Forest (RF), Logistic Regression (LR), Multinomial Naive Bayes (MNB), Linear Support Vector C... | ['Tirana Fatyanosa', 'Masayoshi Aritsugi', 'Al Hafiz Akbar Maulana Siagian'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-1.74205169e-01 2.76635885e-01 -5.77899396e-01 -6.35740399e-01
-6.09314561e-01 -5.62612593e-01 8.29208136e-01 3.52040738e-01
-5.64201295e-01 1.21126163e+00 8.18808302e-02 -1.00417352e+00
-9.85717028e-02 -7.09543884e-01 -7.49961793e-01 -3.52748513e-01
-2.99462564e-02 5.52481472e-01 1.32547706e-01 -4.02179331... | [10.915858268737793, 7.496704578399658] |
6c31c5b5-b2ff-4574-9cef-3a18d578d3f0 | cross-camera-convolutional-color-constancy | 2011.11890 | null | https://arxiv.org/abs/2011.11890v6 | https://arxiv.org/pdf/2011.11890v6.pdf | Cross-Camera Convolutional Color Constancy | We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camera previously unseen during training. C5 is a hypernetwork-like extension of the convolutional color ... | ['Francois Bleibel', 'Yun-Ta Tsai', 'Chloe LeGendre', 'Jonathan T. Barron', 'Mahmoud Afifi'] | 2020-11-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Afifi_Cross-Camera_Convolutional_Color_Constancy_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Afifi_Cross-Camera_Convolutional_Color_Constancy_ICCV_2021_paper.pdf | iccv-2021-1 | ['color-constancy'] | ['computer-vision'] | [ 4.26493913e-01 -2.55587637e-01 -2.89469417e-02 -4.46164221e-01
-6.19099200e-01 -9.29019451e-01 2.95170665e-01 -4.35419738e-01
-4.36051756e-01 5.03283083e-01 -2.28451476e-01 -5.32135725e-01
3.03635627e-01 -4.84317988e-01 -1.11480737e+00 -8.23061883e-01
3.28579932e-01 1.15264527e-01 1.45821169e-01 6.39004335... | [10.410616874694824, -2.5551319122314453] |
34bce104-32c1-4778-aca5-2e5df560fa98 | aspectual-type-and-temporal-relation | null | null | https://aclanthology.org/E12-1027 | https://aclanthology.org/E12-1027.pdf | Aspectual Type and Temporal Relation Classification | null | ["Ant{\\'o}nio Branco", 'Francisco Costa'] | 2012-04-01 | null | null | null | eacl-2012-4 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.348424434661865, 3.759068250656128] |
52deefeb-a5d4-4bf9-9e0c-8505437edf7c | inference-via-sparse-coding-in-a-hierarchical | 2108.01548 | null | https://arxiv.org/abs/2108.01548v3 | https://arxiv.org/pdf/2108.01548v3.pdf | Inference via Sparse Coding in a Hierarchical Vision Model | Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology. But how the level of sparsity contributes to performance on visual tasks is not well understood. In this work, sparse coding has been integrated into an existing hierarchical V2 model (Hosoya a... | ['Odelia Schwartz', 'Luis Sanchez-Giraldo', 'Joshua Bowren'] | 2021-08-03 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.14127957e-03 1.93898290e-01 -1.34726807e-01 -2.85407573e-01
-1.69845030e-01 -6.28924847e-01 5.37930489e-01 -9.20399949e-02
-9.81448367e-02 3.81213576e-01 3.94738138e-01 1.28878048e-02
-1.45737305e-01 -6.11149490e-01 -8.60767365e-01 -7.02777863e-01
-2.01216131e-01 1.17441356e-01 2.73554236e-01 -8.05561151... | [9.92117691040039, 2.38441801071167] |
9b219dea-09f9-4dc9-a379-d69ddcbb8d72 | cross-lingual-transfer-learning-for-semantic | null | null | https://aclanthology.org/2020.clib-1.8 | https://aclanthology.org/2020.clib-1.8.pdf | Cross-lingual Transfer Learning for Semantic Role Labeling in Russian | This work is devoted to semantic role labeling (SRL) task in Russian. We investigate the role of transfer learning strategies between English FrameNet and Russian FrameBank corpora. We perform experiments with embeddings obtained from various types of multilingual language models, including BERT, XLM-R, MUSE, and LASER... | ['Alexander Kirillovich', 'Elena Tutubalina', 'Ilseyar Alimova'] | null | null | null | null | clib-2020-9 | ['semantic-role-labeling', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-1.53489426e-01 5.79967320e-01 -3.29091519e-01 -4.59488094e-01
-8.91585290e-01 -6.39748037e-01 6.45141840e-01 8.66926387e-02
-1.07101130e+00 1.44125295e+00 8.02185714e-01 -2.14007035e-01
3.39473218e-01 -6.88358128e-01 -6.07496083e-01 -4.36388075e-01
2.19193071e-01 6.55147791e-01 4.18991178e-01 -8.59970391... | [10.433545112609863, 9.508172035217285] |
5cfce382-1dd2-448d-8a79-909db710351b | an-effective-unconstrained-correlation-filter | 1603.07800 | null | http://arxiv.org/abs/1603.07800v1 | http://arxiv.org/pdf/1603.07800v1.pdf | An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition | In this paper, an effective unconstrained correlation filter called Uncon-
strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to
robust face recognition. Compared with the conventional correlation filters in
Class-dependence Feature Analysis (CFA), UOOTF improves the overall performance
for unseen... | ['Cuihua Li', 'Chenhui Yang', 'Bineng Zhong', 'Yan Yan', 'Hanzi Wang'] | 2016-03-25 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-1.82132367e-02 -4.55359936e-01 1.43788485e-02 -4.41993535e-01
-8.34569782e-02 -3.47225815e-01 2.65999079e-01 -4.70991939e-01
-2.15226650e-01 6.08350277e-01 -1.69504374e-01 -2.91454643e-01
-8.06283951e-01 -4.62554932e-01 -1.33389279e-01 -8.98687363e-01
-2.49062344e-01 -2.21686408e-01 1.86348274e-01 1.18011080... | [12.846494674682617, 0.5563627481460571] |
49e7bb06-acff-4525-95df-785bd5b10955 | alejandro-mosquera-at-semeval-2021-task-1 | null | null | https://aclanthology.org/2021.semeval-1.68 | https://aclanthology.org/2021.semeval-1.68.pdf | Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction | This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out of 48 systems for sub-task 1 and achieved Pearson correlation coeffic... | ['Alejandro Mosquera'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-2.85125017e-01 -2.09534630e-01 -2.82566220e-01 -5.60593903e-01
-9.91269290e-01 -5.70208848e-01 7.16609120e-01 7.85753310e-01
-9.57965612e-01 6.44559145e-01 4.27732915e-01 -2.13059053e-01
7.86266029e-02 -2.95101732e-01 2.12891567e-02 -2.36810464e-02
-3.31656262e-02 2.57245243e-01 1.70761049e-01 -5.35192788... | [10.617260932922363, 10.489319801330566] |
107ae901-de93-4c8a-8bb0-2eadd8f7d832 | wikides-a-wikipedia-based-dataset-for | 2209.13101 | null | https://arxiv.org/abs/2209.13101v1 | https://arxiv.org/pdf/2209.13101v1.pdf | WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs | As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text summarization. In this paper, we introduce WikiDes, a novel dataset to ... | ['Alexander Gelbukh', 'Soujanya Poria', 'Newton Howard', 'Lotfollah Najjar', 'Amir Hussain', 'Navonil Majumder', 'Abu Bakar Siddiqur Rahman', 'Hoang Thang Ta'] | 2022-09-27 | null | null | null | null | ['extreme-summarization'] | ['natural-language-processing'] | [ 6.32549301e-02 5.20714521e-01 -5.24951518e-01 -3.40244733e-02
-1.52131510e+00 -5.74288130e-01 8.66502166e-01 5.27659237e-01
-2.88804412e-01 1.37889063e+00 9.66034651e-01 3.02915245e-01
-1.11757442e-01 -8.30696106e-01 -5.88073969e-01 -4.40196961e-01
1.75067991e-01 7.84050167e-01 5.84754720e-02 -6.12065613... | [12.345932960510254, 9.476905822753906] |
c9d3bcc3-dacd-43ac-b863-9e28e8a30bd9 | multi-step-prediction-of-occupancy-grid-maps | 1812.09395 | null | http://arxiv.org/abs/1812.09395v3 | http://arxiv.org/pdf/1812.09395v3.pdf | Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks | We investigate the multi-step prediction of the drivable space, represented
by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that
accurate multi-step prediction of the drivable space can efficiently improve
path planning and navigation resulting in safe, comfortable and optimum paths
in autonom... | ['Nima Mohajerin', 'Mohsen Rohani'] | 2018-12-21 | multi-step-prediction-of-occupancy-grid-maps-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mohajerin_Multi-Step_Prediction_of_Occupancy_Grid_Maps_With_Recurrent_Neural_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mohajerin_Multi-Step_Prediction_of_Occupancy_Grid_Maps_With_Recurrent_Neural_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['prediction-of-occupancy-grid-maps'] | ['computer-vision'] | [ 5.40646613e-02 9.55454037e-02 -1.62804067e-01 -8.58274043e-01
-5.57069361e-01 -2.77295470e-01 7.03889787e-01 -8.19026530e-01
-4.18668956e-01 6.40322149e-01 2.82388210e-01 -5.23670793e-01
-1.42891616e-01 -8.32379341e-01 -9.09065068e-01 -6.65006757e-01
-1.73431858e-01 5.90536714e-01 4.64073747e-01 -7.03135431... | [5.875116348266602, 0.7975465655326843] |
c0b89d0f-df25-4d9c-a5b8-4aef3a9a3767 | table-search-using-a-deep-contextualized | 2005.09207 | null | https://arxiv.org/abs/2005.09207v2 | https://arxiv.org/pdf/2005.09207v2.pdf | Table Search Using a Deep Contextualized Language Model | Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex syntactic word relations. In this paper, we use the deep contextua... | ['Zhiyu Chen', 'Jeff Heflin', 'Yinan Xu', 'Mohamed Trabelsi', 'Brian D. Davison'] | 2020-05-19 | null | null | null | null | ['table-retrieval', 'table-search'] | ['natural-language-processing', 'natural-language-processing'] | [-4.58467044e-02 5.59528582e-02 -6.58690035e-01 -5.25845170e-01
-1.41904342e+00 -6.78210855e-01 5.54300606e-01 7.14384377e-01
-5.97288251e-01 8.36821377e-01 5.32001615e-01 -3.50393027e-01
-2.21414521e-01 -8.99036527e-01 -9.65631664e-01 -1.62713847e-03
-3.18417519e-01 8.94377589e-01 3.83449674e-01 -6.11718893... | [9.700984001159668, 7.911064624786377] |
a01dea65-cd5b-476e-a36d-06ec926ac454 | memory-augmented-online-video-anomaly | 2302.10719 | null | https://arxiv.org/abs/2302.10719v1 | https://arxiv.org/pdf/2302.10719v1.pdf | Memory-augmented Online Video Anomaly Detection | The ability to understand the surrounding scene is of paramount importance for Autonomous Vehicles (AVs). This paper presents a system capable to work in a real time guaranteed response times and online fashion, giving an immediate response to the arise of anomalies surrounding the AV, exploiting only the videos captur... | ['Andrea Prati', 'Massimo Bertozzi', 'Tomaso Fontanini', 'Vittorio Bernuzzi', 'Leonardo Rossi'] | 2023-02-21 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 2.42943168e-02 5.00754081e-02 1.93157017e-01 -3.71842951e-01
-9.24308717e-01 -3.38644266e-01 7.18609452e-01 6.96949363e-02
-7.17684507e-01 2.59942323e-01 -6.52211383e-02 -3.72171670e-01
-7.90557265e-02 -7.24394441e-01 -9.19774890e-01 -3.50280255e-01
-2.26724297e-01 3.08465123e-01 8.27437639e-01 -2.65978187... | [7.308967590332031, 0.30565401911735535] |
13e89272-5db3-4afa-9d4b-3a9d6c17ecce | task-related-self-supervised-learning-for | 2105.04951 | null | https://arxiv.org/abs/2105.04951v2 | https://arxiv.org/pdf/2105.04951v2.pdf | Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection | Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an... | ['Yuan Yuan', 'Zhiyu Jiang', 'Zhinan Cai'] | 2021-05-11 | null | null | null | null | ['change-detection-for-remote-sensing-images'] | ['miscellaneous'] | [ 2.72914529e-01 -5.43358803e-01 -3.78584713e-02 -4.18131083e-01
-4.01711375e-01 1.77913196e-02 5.64146280e-01 1.73040628e-01
-5.52280009e-01 6.32442832e-01 8.32763016e-02 -2.09586456e-01
-1.23603344e-01 -1.18121207e+00 -4.35502410e-01 -7.57237971e-01
-1.89418435e-01 -3.50169420e-01 5.90812743e-01 -5.79869032... | [9.72714900970459, -1.345125436782837] |
107e67f3-89dc-4467-bc7d-b90d600f6cb4 | additive-poisson-process-learning-intensity-1 | null | null | https://openreview.net/forum?id=voEpzgY8gsT | https://openreview.net/pdf?id=voEpzgY8gsT | Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Poisson Processes | We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in Poisson processes using projections into lower-dimensional space. Our model combines the techniques in information geometry to model higher-order interactions on a statistic... | ['Mahito Sugiyama', 'Lamiae Azizi', 'Feng Zhou', 'Simon Luo'] | 2021-09-29 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.08767949e-01 1.17557257e-01 3.01093727e-01 -1.03220463e-01
-4.86537606e-01 -2.36612052e-01 8.51383448e-01 -6.19147867e-02
-4.95277822e-01 6.37028575e-01 4.56376433e-01 5.58025092e-02
-2.82401621e-01 -8.57314527e-01 -9.37427640e-01 -8.97278249e-01
-2.46039182e-01 9.97539282e-01 -1.53921163e-02 3.50584596... | [6.902986526489258, 3.840087413787842] |
0d7b5c26-96c8-40b5-a632-513cbee94246 | training-a-fully-convolutional-neural-network | 1706.08948 | null | http://arxiv.org/abs/1706.08948v2 | http://arxiv.org/pdf/1706.08948v2.pdf | Training a Fully Convolutional Neural Network to Route Integrated Circuits | We present a deep, fully convolutional neural network that learns to route a
circuit layout net with appropriate choice of metal tracks and wire class
combinations. Inputs to the network are the encoded layouts containing spatial
location of pins to be routed. After 15 fully convolutional stages followed by
a score com... | ['Sambhav R. Jain', 'Kye Okabe'] | 2017-06-27 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 2.32525066e-01 1.44278094e-01 -3.74035567e-01 -7.44700134e-01
-7.47024536e-01 -9.12098467e-01 -1.97800651e-01 7.14430586e-02
-3.19086313e-01 7.44025409e-01 -5.93547821e-01 -7.50264704e-01
-1.82499439e-01 -1.05599892e+00 -1.20214593e+00 -2.17282563e-01
-7.10729063e-02 3.09084356e-01 2.50391543e-01 3.19252014... | [5.863379955291748, 3.083012104034424] |
1cfedc34-9b4c-4557-8d9b-1876cf10bf4f | unlocking-high-accuracy-differentially | 2204.13650 | null | https://arxiv.org/abs/2204.13650v2 | https://arxiv.org/pdf/2204.13650v2.pdf | Unlocking High-Accuracy Differentially Private Image Classification through Scale | Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method for deep learning, realizes this pro... | ['Borja Balle', 'Samuel L. Smith', 'Jamie Hayes', 'Leonard Berrada', 'Soham De'] | 2022-04-28 | null | null | null | null | ['image-classification-with-dp'] | ['computer-vision'] | [ 1.61967129e-01 1.93916366e-01 2.53074281e-02 -4.82602984e-01
-1.10013878e+00 -6.43845439e-01 2.22703293e-01 -2.61971653e-01
-8.65997612e-01 7.59675622e-01 -6.75588250e-02 -7.43600547e-01
4.16089594e-01 -6.90637708e-01 -9.66161251e-01 -9.96505439e-01
-2.10378304e-01 -2.46563509e-01 -4.85645756e-02 1.44556928... | [5.87821626663208, 6.931325435638428] |
ba8ad2ee-eba7-4b58-89c5-8a281fbf25ef | clustering-evolving-data-using-kernel-based | 1411.5988 | null | http://arxiv.org/abs/1411.5988v1 | http://arxiv.org/pdf/1411.5988v1.pdf | Clustering evolving data using kernel-based methods | In this thesis, we propose several modelling strategies to tackle evolving
data in different contexts. In the framework of static clustering, we start by
introducing a soft kernel spectral clustering (SKSC) algorithm, which can
better deal with overlapping clusters with respect to kernel spectral
clustering (KSC) and p... | ['Rocco Langone'] | 2014-11-20 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 2.89768904e-01 -2.21417814e-01 2.20623687e-01 5.23405932e-02
-1.12049289e-01 -4.12596613e-01 4.34619933e-01 5.45882344e-01
-3.79418612e-01 4.64228153e-01 -6.39393806e-01 -1.87551394e-01
-8.96204710e-01 -6.24543965e-01 -3.40814143e-01 -1.23691106e+00
-6.23647690e-01 5.81796885e-01 4.16041821e-01 -1.87739637... | [7.235946178436279, 3.3500030040740967] |
318eb2be-d9ef-4c04-88b5-ec20d4bc38bf | grounding-consistency-distilling-spatial | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Diomataris_Grounding_Consistency_Distilling_Spatial_Common_Sense_for_Precise_Visual_Relationship_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Diomataris_Grounding_Consistency_Distilling_Spatial_Common_Sense_for_Precise_Visual_Relationship_ICCV_2021_paper.pdf | Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection | Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet. Most SGGs silently exploit datasets' bias on relationships' context, i.e. its subject and object, to improve recall and neglect spatial and visual evidence, ... | ['Petros Maragos', 'Vassilis Pitsikalis', 'Nikolaos Gkanatsios', 'Markos Diomataris'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['visual-relationship-detection'] | ['computer-vision'] | [ 6.20365739e-01 6.99168861e-01 -1.22211732e-01 -5.04114270e-01
-4.89294380e-01 -8.15816522e-01 8.64280760e-01 2.74413884e-01
-8.18156302e-02 7.41297364e-01 8.64448100e-02 -4.38029945e-01
-2.47209892e-01 -9.53618407e-01 -1.21845663e+00 -4.20014322e-01
5.01805265e-03 4.02112305e-01 3.51023078e-01 -1.60694614... | [10.40292739868164, 1.6679400205612183] |
d25d32ae-5468-4abf-9708-fed492488781 | team-noconflict-at-case-2021-task-1 | null | null | https://aclanthology.org/2021.case-1.20 | https://aclanthology.org/2021.case-1.20.pdf | Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection | An ever-increasing amount of text, in the form of social media posts and news articles, gives rise to new challenges and opportunities for the automatic extraction of socio-political events. In this paper, we present our submission to the Shared Tasks on Socio-Political and Crisis Events Detection, Task 1, Multilingual... | ['Niklas Stoehr', 'Tiancheng Hu'] | null | null | null | null | acl-case-2021-8 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.09928258e-01 2.00707912e-01 1.18865639e-01 -3.42691869e-01
-1.39728177e+00 -7.77269363e-01 1.03723919e+00 6.72670543e-01
-9.19482291e-01 9.43201005e-01 1.02569914e+00 -4.57532287e-01
2.45485261e-01 -6.06122553e-01 -6.00168526e-01 -1.56113908e-01
-1.68953225e-01 2.91382939e-01 9.63565856e-02 -4.14033502... | [9.015725135803223, 9.733196258544922] |
a8ed3c17-782a-4070-a13f-779d3711baf7 | cooperative-self-training-for-multi-target | 2210.01578 | null | https://arxiv.org/abs/2210.01578v1 | https://arxiv.org/pdf/2210.01578v1.pdf | Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation | In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions. To address MTDA, we propose a self-training strategy that employs... | ['Stéphane Lathuilière', 'Elisa Ricci', 'Hongtao Lu', 'Subhankar Roy', 'Yangsong Zhang'] | 2022-10-04 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.58963394e-01 4.19042349e-01 -3.25640261e-01 -6.03599131e-01
-1.10365164e+00 -9.21776891e-01 6.04063034e-01 -3.12453657e-01
-2.46121615e-01 7.53084242e-01 -4.77438532e-02 -9.96276066e-02
1.72844335e-01 -6.39557004e-01 -1.00083876e+00 -6.49014652e-01
5.66097319e-01 6.99679136e-01 2.55194634e-01 6.95560798... | [9.754273414611816, 1.3355951309204102] |
aea33a45-72ad-4e66-a920-20d45ea00507 | l3cube-hindbert-and-devbert-pre-trained-bert | 2211.11418 | null | https://arxiv.org/abs/2211.11418v4 | https://arxiv.org/pdf/2211.11418v4.pdf | L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages | The monolingual Hindi BERT models currently available on the model hub do not perform better than the multi-lingual models on downstream tasks. We present L3Cube-HindBERT, a Hindi BERT model pre-trained on Hindi monolingual corpus. Further, since Indic languages, Hindi and Marathi share the Devanagari script, we train ... | ['Raviraj Joshi'] | 2022-11-21 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-8.10430825e-01 7.33333156e-02 -2.10876800e-02 -6.95462227e-01
-1.33483994e+00 -1.06492591e+00 9.74931419e-01 -1.00943439e-01
-6.63175821e-01 1.21840084e+00 2.97491133e-01 -7.60227621e-01
2.35686660e-01 -5.16201794e-01 -7.63803244e-01 -3.68376732e-01
-3.76834944e-02 1.19610572e+00 2.07400322e-02 -8.41314673... | [10.863940238952637, 10.02342700958252] |
f6b80120-82e5-47ac-ae70-9811dc4ff5bb | a-study-on-bias-and-fairness-in-deep-speaker | 2303.08026 | null | https://arxiv.org/abs/2303.08026v1 | https://arxiv.org/pdf/2303.08026v1.pdf | A Study on Bias and Fairness In Deep Speaker Recognition | With the ubiquity of smart devices that use speaker recognition (SR) systems as a means of authenticating individuals and personalizing their services, fairness of SR systems has becomes an important point of focus. In this paper we study the notion of fairness in recent SR systems based on 3 popular and relevant defin... | ['Ali Etemad', 'Amirhossein Hajavi'] | 2023-03-14 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 8.33974220e-03 1.14133418e-01 -7.23838329e-01 -1.00541198e+00
-3.55736315e-01 -3.95193607e-01 7.62999296e-01 7.36168548e-02
-7.07358420e-01 8.54828238e-01 5.58415353e-01 -4.66207683e-01
6.43931180e-02 -5.97889006e-01 -1.85047820e-01 -1.83460325e-01
4.18027267e-02 6.89973012e-02 -4.41701531e-01 -3.11458498... | [8.94985294342041, 5.294302463531494] |
c86d2326-16f9-4419-828e-7490868a2315 | learning-cross-lingual-mappings-for-data | 2306.08577 | null | https://arxiv.org/abs/2306.08577v1 | https://arxiv.org/pdf/2306.08577v1.pdf | Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition | Exploiting cross-lingual resources is an effective way to compensate for data scarcity of low resource languages. Recently, a novel multilingual model fusion technique has been proposed where a model is trained to learn cross-lingual acoustic-phonetic similarities as a mapping function. However, handcrafted lexicons ha... | ['Thomas Hain', 'Muhammad Umar Farooq'] | 2023-06-14 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.48698241e-01 9.99316014e-03 -2.47180521e-01 -4.99877691e-01
-1.51667631e+00 -6.42451465e-01 6.62850559e-01 -2.66511202e-01
-7.26281762e-01 6.54559672e-01 2.16780111e-01 -4.33114767e-01
5.22971690e-01 -3.49262148e-01 -7.82689333e-01 -4.75127786e-01
5.24114788e-01 6.03048086e-01 -1.87512919e-01 -2.41091236... | [14.421005249023438, 6.98206090927124] |
c6386015-3c66-4a5b-829a-c3bf0882daf5 | robust-backdoor-attack-with-visible-semantic | 2306.00816 | null | https://arxiv.org/abs/2306.00816v1 | https://arxiv.org/pdf/2306.00816v1.pdf | Robust Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers | Deep neural networks (DNNs) can be manipulated to exhibit specific behaviors when exposed to specific trigger patterns, without affecting their performance on normal samples. This type of attack is known as a backdoor attack. Recent research has focused on designing invisible triggers for backdoor attacks to ensure vis... | ['Baoyuan Wu', 'Yanbo Fan', 'Yong Zhang', 'Li Liu', 'Zihao Zhu', 'Hongrui Chen', 'Ruotong Wang'] | 2023-06-01 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 5.38792968e-01 -1.73536927e-01 8.65521058e-02 9.90269259e-02
5.31810196e-03 -9.25178170e-01 8.49986136e-01 -3.04250836e-01
-3.92533988e-01 5.10125101e-01 -1.62670851e-01 -3.55673909e-01
4.61990088e-02 -6.51399612e-01 -8.36526036e-01 -1.08840525e+00
2.18063235e-01 -5.55854797e-01 4.22349423e-01 1.51616372... | [5.472035884857178, 7.921000957489014] |
661f54a0-57c4-4abe-b130-8df683a5c2e3 | the-twins-corpus-of-museum-visitor-questions | null | null | https://aclanthology.org/L12-1339 | https://aclanthology.org/L12-1339.pdf | The Twins Corpus of Museum Visitor Questions | The Twins corpus is a collection of utterances spoken in interactions with two virtual characters who serve as guides at the Museum of Science in Boston. The corpus contains about 200,000 spoken utterances from museum visitors (primarily children) as well as from trained handlers who work at the museum. In addition to ... | ['David Traum', 'Athanasios Katsamanis', 'Ron artstein', 'Priti Aggarwal', 'Jillian Gerten', 'Shrikanth Narayanan', 'Angela Nazarian'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [ 2.88431078e-01 6.49373114e-01 2.08083928e-01 -9.01220143e-01
-8.65563869e-01 -5.79365253e-01 6.82561696e-01 4.59252298e-01
-5.08127391e-01 2.94502050e-01 8.81830394e-01 -4.46396679e-01
4.73948985e-01 -4.41474706e-01 -3.15408617e-01 -1.66554838e-01
6.50441498e-02 9.44170833e-01 3.70194912e-01 -4.30690110... | [13.004342079162598, 7.812511920928955] |
2ad7e5ae-625c-449d-95ef-e11ce77297e3 | cognitive-semantic-communication-systems-1 | 2303.08546 | null | https://arxiv.org/abs/2303.08546v1 | https://arxiv.org/pdf/2303.08546v1.pdf | Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation | Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods of existing semantic communication frameworks are inexplicable and inflexible, which limits the achie... | ['Naofal Al-Dhahir', 'Rose Qingyang Hu', 'Qihui Wu', 'Lu Yuan', 'Ming Xu', 'Yihao Li', 'Fuhui Zhou'] | 2023-03-15 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.54762125e-01 3.05049419e-01 -1.32588685e-01 -3.97362143e-01
-4.72485751e-01 -6.70945570e-02 5.28606176e-01 1.56770721e-01
-2.69538611e-01 7.70570099e-01 1.16119981e-01 -3.12260956e-01
-7.18573749e-01 -1.24597681e+00 -3.75401199e-01 -5.06238163e-01
-4.03061183e-03 3.19293857e-01 3.48904282e-01 -4.86739337... | [6.388746738433838, 1.6552575826644897] |
a182c91e-54f9-4b24-a766-2621b2b8e4a1 | singing-voice-synthesis-based-on | 1904.06868 | null | https://arxiv.org/abs/1904.06868v2 | https://arxiv.org/pdf/1904.06868v2.pdf | Singing voice synthesis based on convolutional neural networks | The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. In these systems, the relationship between musical score... | ['Keiichi Tokuda', 'Keiichiro Oura', 'Kei Hashimoto', 'Yoshihiko Nankaku', 'Kazuhiro Nakamura'] | 2019-04-15 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-6.08453304e-02 -2.92285025e-01 2.68817842e-01 -2.08895415e-01
-3.54611367e-01 -5.11725366e-01 8.87412280e-02 -9.72992241e-01
-5.17640859e-02 3.48208606e-01 2.47396305e-01 2.99408078e-01
1.71123803e-01 -6.83050811e-01 -5.61042547e-01 -7.84159780e-01
-8.25700313e-02 -2.67892028e-03 3.30056697e-02 -3.53774995... | [15.52531909942627, 6.177762508392334] |
946e19e2-792a-4b7c-892e-dd6377b86315 | high-quality-automatic-voice-over-with | 2306.17005 | null | https://arxiv.org/abs/2306.17005v1 | https://arxiv.org/pdf/2306.17005v1.pdf | High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units | The goal of Automatic Voice Over (AVO) is to generate speech in sync with a silent video given its text script. Recent AVO frameworks built upon text-to-speech synthesis (TTS) have shown impressive results. However, the current AVO learning objective of acoustic feature reconstruction brings in indirect supervision for... | ['Haizhou Li', 'Mingyang Zhang', 'Berrak Sisman', 'Junchen Lu'] | 2023-06-29 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 2.27034017e-01 1.66141659e-01 -4.10270244e-01 -3.70975286e-01
-1.38472867e+00 -2.40090281e-01 6.40422106e-01 -3.94684583e-01
1.07999317e-01 4.45686787e-01 6.62175059e-01 -9.41498429e-02
5.50799489e-01 -5.39092794e-02 -6.61711097e-01 -7.39237070e-01
4.59005713e-01 1.35440916e-01 -3.80437262e-02 3.50486413... | [14.525988578796387, 5.414377212524414] |
ddab2ff1-adc2-4ce9-aa30-76b26db2857b | representational-efficiency-outweighs-action | 1807.07134 | null | http://arxiv.org/abs/1807.07134v1 | http://arxiv.org/pdf/1807.07134v1.pdf | Representational efficiency outweighs action efficiency in human program induction | The importance of hierarchically structured representations for tractable
planning has long been acknowledged. However, the questions of how people
discover such abstractions and how to define a set of optimal abstractions
remain open. This problem has been explored in cognitive science in the problem
solving literatur... | ['Michael Chang', 'David D. Bourgin', 'Thomas L. Griffiths', 'Sophia Sanborn'] | 2018-07-18 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.41391587e-01 6.61982119e-01 -1.81696191e-01 -5.30602299e-02
-4.96299207e-01 -6.45105720e-01 4.41722423e-01 5.66659093e-01
-3.14614296e-01 5.20479381e-01 4.16204900e-01 -5.66654265e-01
-6.18190765e-01 -9.43178177e-01 -6.31514847e-01 -5.58675766e-01
-2.98746020e-01 5.87079167e-01 -9.97448340e-02 8.07948858... | [4.068371772766113, 1.3390419483184814] |
4b55b3dc-888c-45b5-8181-9586c3ab3acd | svitt-temporal-learning-of-sparse-video-text | 2304.08809 | null | https://arxiv.org/abs/2304.08809v1 | https://arxiv.org/pdf/2304.08809v1.pdf | SViTT: Temporal Learning of Sparse Video-Text Transformers | Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. ... | ['Nuno Vasconcelos', 'Subarna Tripathi', 'Kyle Min', 'Yi Li'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-text-retrieval'] | ['computer-vision'] | [-3.99441384e-02 -2.32069679e-02 -5.96318126e-01 -1.73942044e-01
-7.93456137e-01 -5.63575745e-01 7.35693634e-01 1.68469734e-02
-3.90869737e-01 2.39476576e-01 7.86102712e-01 -4.24717933e-01
-1.43219277e-01 -5.26689827e-01 -9.25172687e-01 -3.53847921e-01
-2.18532175e-01 2.56081134e-01 4.67156231e-01 6.32929790... | [10.155277252197266, 0.911842405796051] |
77c6660c-4c22-4520-9ee4-740f3b12986c | cuts-high-dimensional-causal-discovery-from | 2305.05890 | null | https://arxiv.org/abs/2305.05890v1 | https://arxiv.org/pdf/2305.05890v1.pdf | CUTS+: High-dimensional Causal Discovery from Irregular Time-series | Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encounter... | ['Qionghai Dai', 'Kunlun He', 'Jinli Suo', 'Zongren Li', 'Tingxiong Xiao', 'Lianglong Li', 'Yuxiao Cheng'] | 2023-05-10 | null | null | null | null | ['causal-discovery', 'irregular-time-series'] | ['knowledge-base', 'time-series'] | [ 6.22085966e-02 1.24945059e-01 -6.63558006e-01 6.63108900e-02
-6.87257648e-02 -2.16697693e-01 6.22317433e-01 2.73945957e-01
1.31407827e-01 1.16508245e+00 5.75520694e-01 -8.45867515e-01
-1.00430810e+00 -1.36592770e+00 -6.48235559e-01 -4.89743680e-01
-1.24098814e+00 4.55852419e-01 3.91443878e-01 2.69125879... | [7.796012878417969, 5.3075761795043945] |
5d4b48c6-70f1-49f2-ba12-b50db49476f3 | urban-geobim-construction-by-integrating | 2304.11719 | null | https://arxiv.org/abs/2304.11719v2 | https://arxiv.org/pdf/2304.11719v2.pdf | Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models | Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructi... | ['Lei Luo', 'Zhiyi He', 'Puzuo Wang', 'Wei Yao', 'Jie Shao'] | 2023-04-23 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-3.77343386e-01 -2.40484491e-01 1.21433109e-01 -4.02160585e-01
-1.05902183e+00 6.27952218e-02 4.26098555e-01 -1.61964949e-02
-5.86763546e-02 7.22427666e-01 -2.84571648e-01 -3.69204402e-01
-3.35420191e-01 -1.77558136e+00 -7.91020215e-01 -3.40111792e-01
7.53991725e-03 9.98725235e-01 3.32558542e-01 -4.63149577... | [8.090211868286133, -2.7369837760925293] |
b9b77be6-e686-49b8-9ddc-5e5fb38e5e9b | exploring-sentiment-analysis-techniques-in | 2305.14842 | null | https://arxiv.org/abs/2305.14842v1 | https://arxiv.org/pdf/2305.14842v1.pdf | Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review | Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. Over the past decade, SA has gained significant popularity in the field of Natural Language Processing (NLP). With the widespread use of social media and online platforms, SA has become crucial fo... | ['Karthick Prasad Gunasekaran'] | 2023-05-24 | null | null | null | null | ['marketing', 'sentiment-analysis'] | ['miscellaneous', 'natural-language-processing'] | [-4.20203470e-02 -4.47127484e-02 -4.57021862e-01 -3.27259213e-01
-1.71360895e-01 -5.70274651e-01 2.90651411e-01 1.00430977e+00
-3.17689538e-01 4.36056793e-01 3.95422846e-01 -2.92240411e-01
3.54030021e-02 -8.71618211e-01 4.65331459e-03 -3.53475302e-01
1.00326657e-01 1.04116492e-01 -5.27597666e-01 -8.15990508... | [10.971287727355957, 6.890620231628418] |
2aa61a86-a9cc-4993-ad56-8a8ca8889c19 | on-the-stability-of-fine-tuning-bert | 2006.04884 | null | https://arxiv.org/abs/2006.04884v3 | https://arxiv.org/pdf/2006.04884v3.pdf | On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a... | ['Maksym Andriushchenko', 'Marius Mosbach', 'Dietrich Klakow'] | 2020-06-08 | null | https://openreview.net/forum?id=nzpLWnVAyah | https://openreview.net/pdf?id=nzpLWnVAyah | iclr-2021-1 | ['misconceptions'] | ['miscellaneous'] | [-3.25270563e-01 2.01107487e-02 -1.14018463e-01 -2.36429229e-01
-1.15769660e+00 -9.63441908e-01 7.74989963e-01 9.16958600e-02
-3.75199616e-01 1.09326732e+00 1.73635021e-01 -3.76527101e-01
-3.40789825e-01 -4.81768787e-01 -8.78673911e-01 -5.99256694e-01
1.65755600e-01 7.88405418e-01 4.78930414e-01 -4.25874978... | [10.67326545715332, 8.36410140991211] |
a4bba25a-fb4d-482d-9e6d-d6b6f27a8279 | revisiting-the-roles-of-text-in-text-games | null | null | https://openreview.net/forum?id=F_9GY8mIRSw | https://openreview.net/pdf?id=F_9GY8mIRSw | Revisiting the Roles of “Text” in Text Games | Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges. However, recent work has questioned the necessity of NLU by showing random text hashes could perform decently. In this paper, we pursue a fine-grained investigation into the roles of text ... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.40335724e-01 6.64339483e-01 -1.50708109e-01 2.04265624e-01
-1.09612596e+00 -8.51990283e-01 9.79229510e-01 1.66781858e-01
-8.11351001e-01 8.17272544e-01 5.53254306e-01 -5.77590287e-01
-1.03379376e-01 -7.91105032e-01 -8.22831690e-01 -5.95607221e-01
1.09658107e-01 8.98881257e-01 1.38035208e-01 -5.66333473... | [3.8273918628692627, 1.3934931755065918] |
44c7a03f-5a8d-4d0a-a971-c1d88ccf848c | hyperpocket-generative-point-cloud-completion | 2102.05973 | null | https://arxiv.org/abs/2102.05973v1 | https://arxiv.org/pdf/2102.05973v1.pdf | HyperPocket: Generative Point Cloud Completion | Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlusions. Therefore, completing such partial representations remains a fundamental challenge of many computer vision applications. Most of the ... | ['Tomasz Trzciński', 'Jacek Tabor', 'Łukasz Struski', 'Sławomir Tadeja', 'Diana Janik', 'Marcin Mazur', 'Artur Kasymov', 'Przemysław Spurek'] | 2021-02-11 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-3.60308290e-02 1.39693007e-01 1.64082244e-01 -3.27425063e-01
-4.56714749e-01 -3.45126688e-01 7.01455653e-01 -2.64068872e-01
6.85525462e-02 3.06547731e-01 -2.43954416e-02 -7.80851319e-02
-7.06189126e-02 -8.44143450e-01 -1.05050778e+00 -5.75298607e-01
2.50327647e-01 1.01528585e+00 1.18245155e-01 -1.24973014... | [8.385064125061035, -3.502208948135376] |
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