paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8f536069-1bce-4f6f-aa79-34a2c4eb0253 | fully-connected-tensor-network-decomposition | 2110.08754 | null | https://arxiv.org/abs/2110.08754v1 | https://arxiv.org/pdf/2110.08754v1.pdf | Fully-Connected Tensor Network Decomposition for Robust Tensor Completion Problem | The robust tensor completion (RTC) problem, which aims to reconstruct a low-rank tensor from partially observed tensor contaminated by a sparse tensor, has received increasing attention. In this paper, by leveraging the superior expression of the fully-connected tensor network (FCTN) decomposition, we propose a $\textb... | ['Ting-Zhu Huang', 'Yu-Bang Zheng', 'Guang-Jing Song', 'Xi-Le Zhao', 'Yun-Yang Liu'] | 2021-10-17 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 8.51354674e-02 -2.37460762e-01 1.19456105e-01 -4.15051430e-02
-6.30761683e-01 -2.17856184e-01 3.33978571e-02 -5.21825910e-01
-2.47295693e-01 4.21409279e-01 1.67353526e-01 -4.32430357e-01
-6.64021134e-01 -2.92979896e-01 -8.23916316e-01 -9.89918590e-01
-3.44946355e-01 -4.28879708e-02 -3.96673262e-01 -2.84190029... | [7.424502849578857, 4.465641021728516] |
cc7c6acd-7dae-4f44-9755-226e162d3bf7 | 2-d-signature-of-images-and-texture | 2205.11236 | null | https://arxiv.org/abs/2205.11236v1 | https://arxiv.org/pdf/2205.11236v1.pdf | 2-d signature of images and texture classification | We introduce a proper notion of 2-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a 2-dimensional object such as an image. It thus serves as a low-dimensional feature for pattern classification. Here we implement a simple procedur... | ['Samy Tindel', 'Guang Lin', 'Sheng Zhang'] | 2022-05-10 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 8.72939453e-02 -1.35404110e-01 -3.29854846e-01 -5.88197887e-01
-3.85419965e-01 -2.23739967e-01 7.85807431e-01 1.11897267e-01
-2.55931228e-01 3.13324749e-01 -1.58542305e-01 -1.28377274e-01
-6.71775281e-01 -1.14298451e+00 -1.54469013e-01 -8.42255473e-01
-7.38922954e-01 3.91192794e-01 4.31945115e-01 -3.92594665... | [10.063292503356934, -0.483588308095932] |
136e2938-be35-4263-8b78-112ab9f7e512 | unveiling-the-link-between-logical-fallacies | 1304.3940 | null | http://arxiv.org/abs/1304.3940v2 | http://arxiv.org/pdf/1304.3940v2.pdf | Unveiling the link between logical fallacies and web persuasion | In the last decade Human-Computer Interaction (HCI) has started to focus
attention on forms of persuasive interaction where computer technologies have
the goal of changing users behavior and attitudes according to a predefined
direction. In this work, we hypothesize a strong connection between logical
fallacies (forms ... | ['Fabiana Vernero', 'Antonio Lieto'] | 2013-04-14 | null | null | null | null | ['persuasion-strategies', 'logical-fallacies'] | ['computer-vision', 'miscellaneous'] | [ 2.60222256e-01 9.27306116e-01 -1.86397019e-03 -3.61400098e-01
2.33683735e-01 -5.56124270e-01 6.70466542e-01 5.33321142e-01
-5.41735768e-01 6.20406508e-01 3.82510245e-01 -1.18065310e+00
-3.29490811e-01 -8.25828671e-01 -3.80154103e-01 1.43193528e-01
3.25179428e-01 -3.64174992e-02 4.04181540e-01 -3.39928597... | [9.101140975952148, 6.308643341064453] |
5c4b2f8f-e0ab-4edc-b2c3-115bb697742b | ccdn-checkerboard-corner-detection-network | 2302.05097 | null | https://arxiv.org/abs/2302.05097v1 | https://arxiv.org/pdf/2302.05097v1.pdf | CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration | Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern. This... | ['Qi Zhang', 'Caihua Xiong', 'Ben Chen'] | 2023-02-10 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 1.94359139e-01 -5.97936690e-01 2.28015244e-01 -1.21219307e-01
-2.09542349e-01 -4.43457842e-01 2.97448903e-01 -5.74915819e-02
-4.94507998e-01 4.77170706e-01 -2.87894636e-01 -3.35391372e-01
-1.17089689e-01 -6.94070816e-01 -7.74719715e-01 -7.27317095e-01
1.68230549e-01 -2.13463590e-01 8.27713251e-01 7.75138661... | [8.637683868408203, -0.8387932181358337] |
d3e9db0e-9e33-4c46-bc72-e9ae0d008c2b | cross-domain-few-shot-learning-with-meta-fine | 2005.10544 | null | https://arxiv.org/abs/2005.10544v4 | https://arxiv.org/pdf/2005.10544v4.pdf | Cross-Domain Few-Shot Learning with Meta Fine-Tuning | In this paper, we tackle the new Cross-Domain Few-Shot Learning benchmark proposed by the CVPR 2020 Challenge. To this end, we build upon state-of-the-art methods in domain adaptation and few-shot learning to create a system that can be trained to perform both tasks. Inspired by the need to create models designed to be... | ['Sheng Mei Shen', 'John Cai'] | 2020-05-21 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.63048851e-01 2.23741785e-01 -1.55666456e-01 -4.92172956e-01
-6.89089119e-01 -1.10439315e-01 8.83488119e-01 1.04649045e-01
-6.01095498e-01 6.36403561e-01 2.92156547e-01 1.94869593e-01
7.78111145e-02 -8.77845585e-01 -7.11034775e-01 -4.24603701e-01
8.40099156e-02 5.14483154e-01 8.23208988e-01 -7.24876881... | [9.938042640686035, 2.9019992351531982] |
b0282133-cb6b-4451-b92f-c1f731d3c395 | on-pitfalls-and-advantages-of-sophisticated | 2303.17511 | null | https://arxiv.org/abs/2303.17511v1 | https://arxiv.org/pdf/2303.17511v1.pdf | On pitfalls (and advantages) of sophisticated large language models | Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of hum... | ['Anna Strasser'] | 2023-02-25 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 1.96469530e-01 4.64307725e-01 1.65857166e-01 1.16923593e-01
-3.39138448e-01 -6.88721120e-01 7.42811382e-01 4.94064569e-01
-9.68845725e-01 9.82600391e-01 5.61667002e-05 -4.87834662e-01
2.82055456e-02 -9.53778625e-01 -3.17204088e-01 -2.33223170e-01
-4.59577590e-02 2.52099633e-01 4.72793318e-02 -1.01616092... | [8.935675621032715, 6.59321928024292] |
880b4212-9baa-49b0-a712-c5918c0cd483 | icface-interpretable-and-controllable-face | 1904.01909 | null | https://arxiv.org/abs/1904.01909v2 | https://arxiv.org/pdf/1904.01909v2.pdf | ICface: Interpretable and Controllable Face Reenactment Using GANs | This paper presents a generic face animator that is able to control the pose and expressions of a given face image. The animation is driven by human interpretable control signals consisting of head pose angles and the Action Unit (AU) values. The control information can be obtained from multiple sources including exter... | ['Esa Rahtu', 'Soumya Tripathy', 'Juho Kannala'] | 2019-04-03 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 3.69394451e-01 2.05248863e-01 7.26185217e-02 -5.60289323e-01
6.89043989e-03 -4.38208491e-01 7.68723249e-01 -4.47121918e-01
-3.38837564e-01 5.66852391e-01 -2.55745530e-01 2.06262410e-01
1.00521453e-01 -3.02489221e-01 -7.40203142e-01 -6.90099418e-01
-4.65960940e-03 6.16995096e-01 -1.14750803e-01 -4.10928279... | [13.046059608459473, -0.32509931921958923] |
35640eba-00c5-49c9-a1ec-c7ab2686cd9c | dd-cisenet-dual-domain-cross-iteration | 2305.00088 | null | https://arxiv.org/abs/2305.00088v1 | https://arxiv.org/pdf/2305.00088v1.pdf | DD-CISENet: Dual-Domain Cross-Iteration Squeeze and Excitation Network for Accelerated MRI Reconstruction | Magnetic resonance imaging (MRI) is widely employed for diagnostic tests in neurology. However, the utility of MRI is largely limited by its long acquisition time. Acquiring fewer k-space data in a sparse manner is a potential solution to reducing the acquisition time, but it can lead to severe aliasing reconstruction ... | ['Gerardo Hermosillo Valadez', 'Zhigang Peng', 'Xiongchao Chen'] | 2023-04-28 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 2.19688267e-01 4.97357324e-02 2.15795636e-01 -2.67939299e-01
-1.08417165e+00 1.83867048e-02 1.74347639e-01 -2.36610830e-01
-3.65875661e-01 8.40180099e-01 3.42620879e-01 -6.66014031e-02
-5.91549337e-01 -3.16314697e-01 -5.36730945e-01 -9.85498071e-01
-5.27713954e-01 3.20299327e-01 -8.99279192e-02 1.02685332... | [13.598021507263184, -2.4191553592681885] |
53b520a0-beec-426a-a62c-141cc12997b0 | is-the-elephant-flying-resolving-ambiguities | 2211.12503 | null | https://arxiv.org/abs/2211.12503v1 | https://arxiv.org/pdf/2211.12503v1.pdf | Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models | Natural language often contains ambiguities that can lead to misinterpretation and miscommunication. While humans can handle ambiguities effectively by asking clarifying questions and/or relying on contextual cues and common-sense knowledge, resolving ambiguities can be notoriously hard for machines. In this work, we s... | ['Rahul Gupta', 'Aram Galstyan', 'Richard Zemel', 'Kai-Wei Chang', 'Qian Hu', 'Varun Kumar', 'Jwala Dhamala', 'Apurv Verma', 'Palash Goyal', 'Ninareh Mehrabi'] | 2022-11-17 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 7.33807385e-01 5.04802227e-01 3.45024437e-01 -6.29119277e-01
-9.77830470e-01 -1.01471448e+00 9.02903378e-01 2.08451197e-01
-3.24004710e-01 8.89301240e-01 3.90700549e-01 -4.63640124e-01
-1.09822661e-01 -4.92437810e-01 -6.11437976e-01 -1.13436036e-01
6.99404359e-01 6.95492268e-01 1.67125478e-01 -3.53418678... | [11.074695587158203, 1.7350010871887207] |
67791eb1-b662-4631-941d-1a2b8b2aca36 | weakly-supervised-online-action-detection-for | 2208.03648 | null | https://arxiv.org/abs/2208.03648v1 | https://arxiv.org/pdf/2208.03648v1.pdf | Weakly Supervised Online Action Detection for Infant General Movements | To make the earlier medical intervention of infants' cerebral palsy (CP), early diagnosis of brain damage is critical. Although general movements assessment(GMA) has shown promising results in early CP detection, it is laborious. Most existing works take videos as input to make fidgety movements(FMs) classification for... | ['Xiaowei Ding', 'Kang Dang', 'Guangjun Yu', 'Yuan Tian', 'Siheng Chen', 'Chuncao Zhang', 'Jia Xiao', 'Tongyi Luo'] | 2022-08-07 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 5.94323575e-02 5.46157435e-02 -5.00171661e-01 -2.15422332e-01
-8.94003153e-01 -2.81508714e-01 1.72284320e-01 9.66218635e-02
-3.96112055e-01 1.91752777e-01 1.92033127e-01 6.06353693e-02
-2.32640188e-02 -4.10679132e-01 -1.05381382e+00 -7.55802929e-01
-4.78256404e-01 1.46554098e-01 7.10630238e-01 2.19704032... | [8.548392295837402, 0.5182920098304749] |
bab166c3-3548-419e-a312-6cfb4f82bc90 | pu-gcn-point-cloud-upsampling-using-graph | 1912.03264 | null | https://arxiv.org/abs/1912.03264v3 | https://arxiv.org/pdf/1912.03264v3.pdf | PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks | The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from po... | ['Guocheng Qian', 'Abdulellah Abualshour', 'Guohao Li', 'Bernard Ghanem', 'Ali Thabet'] | 2019-11-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Qian_PU-GCN_Point_Cloud_Upsampling_Using_Graph_Convolutional_Networks_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Qian_PU-GCN_Point_Cloud_Upsampling_Using_Graph_Convolutional_Networks_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-super-resolution'] | ['computer-vision'] | [-3.58194947e-01 -1.80289581e-01 -1.99419454e-01 -2.98646897e-01
-6.88172221e-01 -3.02455157e-01 8.37454438e-01 1.41314253e-01
6.44098148e-02 4.39847261e-01 9.64405853e-03 -1.28218561e-01
-2.41058301e-02 -1.73190141e+00 -1.05534244e+00 -2.08623439e-01
-2.50139713e-01 4.88143682e-01 4.98069167e-01 -2.54154414... | [8.101410865783691, -3.5731799602508545] |
9bbc94d5-ac91-4602-926d-2ecb7c504cb3 | unsupervised-cross-dataset-person-re | 1803.07293 | null | http://arxiv.org/abs/1803.07293v1 | http://arxiv.org/pdf/1803.07293v1.pdf | Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns | Most of the proposed person re-identification algorithms conduct supervised
training and testing on single labeled datasets with small size, so directly
deploying these trained models to a large-scale real-world camera network may
lead to poor performance due to underfitting. It is challenging to
incrementally optimize... | ['Qing Li', 'Jianming Lv', 'Weihang Chen', 'Can Yang'] | 2018-03-20 | unsupervised-cross-dataset-person-re-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Lv_Unsupervised_Cross-Dataset_Person_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Lv_Unsupervised_Cross-Dataset_Person_CVPR_2018_paper.pdf | cvpr-2018-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.74134091e-01 -4.73593533e-01 -2.94230998e-01 -5.99915862e-01
-4.36419070e-01 -2.87451744e-01 5.59222639e-01 -1.19116299e-01
-5.24042785e-01 7.09589839e-01 2.74142116e-01 1.28338039e-01
-9.31400508e-02 -5.21678686e-01 -4.74518955e-01 -7.16320395e-01
-4.34468267e-03 5.14546692e-01 3.06071788e-01 2.54991323... | [14.775957107543945, 1.0399638414382935] |
f2ba7e88-8505-42ae-8798-4628f70c5eca | the-disrpt-2021-shared-task-on-elementary | null | null | https://aclanthology.org/2021.disrpt-1.1 | https://aclanthology.org/2021.disrpt-1.1.pdf | The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification | In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task (Discourse Relation Parsing and Treebanking). Adding to the 2019 tasks on Elementary Discourse Unit Segmentation and Connective Detection, this iteration of t... | ['Sonia Badene', 'Chloé Braud', 'Philippe Muller', 'Mikel Iruskieta', 'Yang Janet Liu', 'Amir Zeldes'] | null | null | null | null | emnlp-disrpt-2021-11 | ['discourse-parsing', 'connective-detection', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.28970724e-01 1.12109971e+00 -3.97425234e-01 -3.45124513e-01
-1.36305308e+00 -1.00272036e+00 9.14322734e-01 4.86650795e-01
-4.53505307e-01 1.32622766e+00 8.28275919e-01 -7.03306615e-01
1.09674893e-01 -6.84370279e-01 -4.08454508e-01 -2.47478038e-01
-1.15668893e-01 8.09972048e-01 7.06341982e-01 -5.35340726... | [10.821043968200684, 9.458447456359863] |
c62f5052-a09d-4092-bb90-46ef7ba42922 | distributionally-robust-end-to-end-portfolio | 2206.05134 | null | https://arxiv.org/abs/2206.05134v1 | https://arxiv.org/pdf/2206.05134v1.pdf | Distributionally Robust End-to-End Portfolio Construction | We propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parameter and the degree of robustness directly from data. End-to-end systems hav... | ['Garud N. Iyengar', 'Giorgio Costa'] | 2022-06-10 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.89605737e-01 2.00985685e-01 -1.38138101e-01 -7.04067111e-01
-1.30232513e+00 -1.00643110e+00 3.42352957e-01 5.54221161e-02
-3.74470890e-01 4.96904641e-01 2.35611781e-01 -5.71055949e-01
-8.08778226e-01 -8.72220397e-01 -6.25133514e-01 -5.59287667e-01
-8.73360857e-02 7.51065910e-01 -4.53871101e-01 1.72793612... | [5.058665752410889, 3.8072609901428223] |
5dc311fc-5ed3-4ef4-91da-8a23df3f561d | a-deep-variational-bayesian-framework-for | 2106.02884 | null | https://arxiv.org/abs/2106.02884v1 | https://arxiv.org/pdf/2106.02884v1.pdf | A Deep Variational Bayesian Framework for Blind Image Deblurring | Blind image deblurring is an important yet very challenging problem in low-level vision. Traditional optimization based methods generally formulate this task as a maximum-a-posteriori estimation or variational inference problem, whose performance highly relies on the handcraft priors for both the latent image and the b... | ['Deyu Meng', 'Qian Zhao', 'Zongsheng Yue', 'Hui Wang'] | 2021-06-05 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.58644497e-01 -3.10606778e-01 2.29996309e-01 -3.01362455e-01
-4.27929878e-01 -1.93289965e-01 6.11536980e-01 -6.69353306e-01
-3.29400629e-01 7.50881910e-01 4.89838064e-01 2.31637321e-02
-2.02646479e-01 -2.31663212e-01 -8.24008107e-01 -1.14204848e+00
4.64929610e-01 -1.15325442e-02 -5.16933091e-02 2.70212650... | [11.55589771270752, -2.6762313842773438] |
df52a148-0b84-4790-baab-8839bfc66088 | stock-market-prediction-from-wsj-text-mining | 1406.7330 | null | http://arxiv.org/abs/1406.7330v1 | http://arxiv.org/pdf/1406.7330v1.pdf | Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization | We revisit the problem of predicting directional movements of stock prices
based on news articles: here our algorithm uses daily articles from The Wall
Street Journal to predict the closing stock prices on the same day. We propose
a unified latent space model to characterize the "co-movements" between stock
prices and ... | ['Zhenming Liu', 'Mung Chiang', 'Felix Ming Fai Wong'] | 2014-06-27 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-7.96215951e-01 -2.41550535e-01 -6.25689328e-01 -1.42850816e-01
-7.11838007e-01 -1.01926541e+00 1.19768322e+00 -7.18567595e-02
-1.86417177e-01 8.65701020e-01 5.81171036e-01 -5.78273058e-01
-7.15500563e-02 -1.09665442e+00 -8.08800638e-01 -3.16486537e-01
-2.12827638e-01 5.12913406e-01 4.69281226e-01 -3.15172344... | [4.460839748382568, 4.259009838104248] |
7b1bada3-deff-4cc4-98d8-a612e1c6221c | sfe-ai-at-semeval-2022-task-11-low-resource | 2205.14660 | null | https://arxiv.org/abs/2205.14660v1 | https://arxiv.org/pdf/2205.14660v1.pdf | SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models | Large scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play to the differentiation advantages of different models, especially in the open domain. This paper describes our NER system in the SemEval 202... | ['Qifeng Xiao', 'Benqi Wang', 'Xiandi Jiang', 'Xiaopeng Wang', 'Qizhi Lin', 'Guotong Xie', 'Peng Gao', 'Peng Jiang', 'Yixuan Qiao', 'Jun Wang', 'Changyu Hou'] | 2022-05-29 | null | https://aclanthology.org/2022.semeval-1.219 | https://aclanthology.org/2022.semeval-1.219.pdf | semeval-naacl-2022-7 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-3.29328835e-01 -2.39165664e-01 5.01430556e-02 -6.39756441e-01
-8.62485409e-01 -4.69173402e-01 5.63529611e-01 -2.02817217e-01
-1.18912756e+00 1.01499641e+00 3.51618737e-01 -9.90844220e-02
1.21928021e-01 -5.73361337e-01 -3.09349537e-01 -3.94073218e-01
2.26695046e-01 6.80564523e-01 1.61661550e-01 -4.09933686... | [9.80215835571289, 9.624140739440918] |
088722e9-afea-4f08-9373-a598196edb59 | faceswapnet-landmark-guided-many-to-many-face | 1905.11805 | null | https://arxiv.org/abs/1905.11805v2 | https://arxiv.org/pdf/1905.11805v2.pdf | FReeNet: Multi-Identity Face Reenactment | This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model. The proposed FReeNet consists of two parts: Unified Landmark Converter (ULC) and Geometry-aware Generator (GAG). The ULC adopts an enco... | ['Yong liu', 'Liang Liu', 'Yusu Pan', 'Yu Ding', 'Xianfang Zeng', 'Mengmeng Wang', 'Changjie Fan', 'Jiangning Zhang'] | 2019-05-28 | freenet-multi-identity-face-reenactment | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_FReeNet_Multi-Identity_Face_Reenactment_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_FReeNet_Multi-Identity_Face_Reenactment_CVPR_2020_paper.pdf | cvpr-2020-6 | ['face-reenactment'] | ['computer-vision'] | [ 2.82273799e-01 3.51305217e-01 2.68691927e-01 -6.76673472e-01
-8.08489323e-01 -7.07384050e-01 6.26414895e-01 -8.57425809e-01
-3.80936414e-02 7.08875179e-01 9.77897868e-02 3.69047374e-01
4.72381622e-01 -7.86594093e-01 -7.99207270e-01 -7.86296189e-01
4.43018228e-01 7.34464452e-02 -4.35730457e-01 -2.52858818... | [12.696833610534668, -0.11263283342123032] |
c836843a-31df-48d6-83a4-a4f6a1d2ae2d | high-dimensional-causal-discovery-learning | 2211.14221 | null | https://arxiv.org/abs/2211.14221v2 | https://arxiv.org/pdf/2211.14221v2.pdf | Learning Large Causal Structures from Inverse Covariance Matrix via Matrix Decomposition | Learning causal structures from observational data is a fundamental yet highly complex problem when the number of variables is large. In this paper, we start from linear structural equation models (SEMs) and investigate ways of learning causal structures from the inverse covariance matrix. The proposed method, called $... | ['Michèle Sebag', 'Koji Maruhashi', 'Yusuke Koyanagi', 'Shuang Chang', 'Akito Fujii', 'Kento Uemura', 'Shuyu Dong'] | 2022-11-25 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.46818054e-01 4.01936799e-01 -3.11320990e-01 -3.77776116e-01
-3.82485896e-01 -5.48697650e-01 3.00644308e-01 1.90433651e-01
-2.05662027e-01 7.79909194e-01 2.24214435e-01 -9.41819310e-01
-1.05967164e+00 -9.49482501e-01 -1.05088949e+00 -8.70385230e-01
-5.88765323e-01 6.35769010e-01 -7.57572427e-02 1.23139553... | [7.749428749084473, 5.290071487426758] |
eac7a9e5-bd5d-42e1-91d0-f5774061c62d | multi-oriented-scene-text-detection-via | 1802.08948 | null | http://arxiv.org/abs/1802.08948v2 | http://arxiv.org/pdf/1802.08948v2.pdf | Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation | Previous deep learning based state-of-the-art scene text detection methods
can be roughly classified into two categories. The first category treats scene
text as a type of general objects and follows general object detection paradigm
to localize scene text by regressing the text box locations, but troubled by
the arbit... | ['Xiang Bai', 'Wenhao Wu', 'Cong Yao', 'Pengyuan Lyu', 'Shuicheng Yan'] | 2018-02-25 | multi-oriented-scene-text-detection-via-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Lyu_Multi-Oriented_Scene_Text_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Lyu_Multi-Oriented_Scene_Text_CVPR_2018_paper.pdf | cvpr-2018-6 | ['multi-oriented-scene-text-detection'] | ['computer-vision'] | [ 0.14486589 -0.28210196 0.02535762 -0.16150261 -0.4880689 -0.501841
0.803701 0.12259955 -0.5311591 0.14245866 0.07277016 -0.1563022
0.29845828 -0.7831416 -0.460797 -0.630985 0.538759 0.6312291
0.8236843 0.02771872 0.5622947 0.3541291 -1.2464927 0.57859224
0.90514606 0.93079805 0.5445... | [12.070450782775879, 2.3170721530914307] |
a880d7a4-acdd-4192-83c2-cde49e21dd61 | response-conditioned-turn-taking-prediction | 2305.02036 | null | https://arxiv.org/abs/2305.02036v1 | https://arxiv.org/pdf/2305.02036v1.pdf | Response-conditioned Turn-taking Prediction | Previous approaches to turn-taking and response generation in conversational systems have treated it as a two-stage process: First, the end of a turn is detected (based on conversation history), then the system generates an appropriate response. Humans, however, do not take the turn just because it is likely, but also ... | ['Gabriel Skantze', 'Erik Ekstedt', "Bing'er Jiang"] | 2023-05-03 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 6.06312871e-01 5.71805775e-01 -2.97932059e-01 -9.22838330e-01
-1.14383495e+00 -9.04620707e-01 9.35497701e-01 2.42929161e-01
-1.99841917e-01 6.06837869e-01 9.24084246e-01 -6.83517992e-01
9.25163552e-02 -6.23995841e-01 -1.29131898e-01 -1.03992999e-01
4.46332306e-01 7.57819235e-01 2.27810875e-01 -6.44817770... | [12.755139350891113, 7.976809024810791] |
b27b9b10-5127-41f0-b538-5e5b78261fdb | are-multimodal-models-robust-to-image-and | 2212.08044 | null | https://arxiv.org/abs/2212.08044v1 | https://arxiv.org/pdf/2212.08044v1.pdf | Are Multimodal Models Robust to Image and Text Perturbations? | Multimodal image-text models have shown remarkable performance in the past few years. However, evaluating their robustness against distribution shifts is crucial before adopting them in real-world applications. In this paper, we investigate the robustness of 9 popular open-sourced image-text models under common perturb... | ['Mu Li', 'Bo Li', 'Ding Zhao', 'Zhiqiang Tang', 'Florian Wenzel', 'Xingjian Shi', 'Yi Zhu', 'JieLin Qiu'] | 2022-12-15 | null | null | null | null | ['visual-reasoning', 'visual-reasoning', 'visual-entailment'] | ['computer-vision', 'reasoning', 'reasoning'] | [ 4.91979599e-01 -3.83172423e-01 1.55765265e-01 -9.89480838e-02
-9.30983603e-01 -8.84210765e-01 1.03450060e+00 2.51333326e-01
-4.44338143e-01 4.34738308e-01 4.45257604e-01 -1.18918672e-01
4.87050600e-02 -6.77681640e-02 -9.16284919e-01 -7.29005575e-01
2.27144241e-01 2.28766724e-01 1.68619871e-01 -3.13963979... | [11.099872589111328, 1.2445402145385742] |
3e7fae8f-fad1-4140-9783-3e1bfa5cce17 | revisiting-automatic-evaluation-of-extractive | null | null | https://aclanthology.org/2022.findings-acl.122 | https://aclanthology.org/2022.findings-acl.122.pdf | Revisiting Automatic Evaluation of Extractive Summarization Task: Can We Do Better than ROUGE? | It has been the norm for a long time to evaluate automated summarization tasks using the popular ROUGE metric. Although several studies in the past have highlighted the limitations of ROUGE, researchers have struggled to reach a consensus on a better alternative until today. One major limitation of the traditional ROUG... | ['Shubhra Kanti Karmaker', 'Naman Bansal', 'Mousumi Akter'] | null | null | null | null | findings-acl-2022-5 | ['extractive-summarization'] | ['natural-language-processing'] | [ 1.19568229e-01 2.55436897e-01 -6.11319020e-03 -2.62080401e-01
-8.12253952e-01 -6.42278731e-01 7.96076477e-01 6.26111388e-01
-7.13734210e-01 9.84317601e-01 6.12477183e-01 -1.09172404e-01
-8.76279548e-02 -6.19119346e-01 -3.45458657e-01 -2.27300063e-01
2.62733489e-01 4.70306128e-01 3.34241658e-01 -4.36824203... | [11.996589660644531, 9.321993827819824] |
1e50303e-93fb-42fb-9c41-c865ebe0b87a | integrating-user-feedback-under-identity | null | null | https://openreview.net/forum?id=SygLHbcapm | https://openreview.net/pdf?id=SygLHbcapm | Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction | Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations. However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (E... | ['Andrew McCallum', 'Nicholas Monath', 'Ari Kobren'] | 2018-11-17 | null | null | null | akbc-2019 | ['entity-resolution'] | ['natural-language-processing'] | [-4.00987744e-01 9.01311278e-01 -3.42932969e-01 -3.21656108e-01
-1.00081658e+00 -7.18963325e-01 1.93815574e-01 7.15319693e-01
-4.10342753e-01 1.30388486e+00 4.55763251e-01 3.20434012e-02
5.18385582e-02 -7.92109370e-01 -8.70911002e-01 1.91353396e-01
4.06215750e-02 7.30314672e-01 6.04422212e-01 -4.95694816... | [9.390728950500488, 8.780304908752441] |
a6ad62e2-4373-4945-af8c-557724018986 | understanding-robust-overfitting-of | 2206.08675 | null | https://arxiv.org/abs/2206.08675v2 | https://arxiv.org/pdf/2206.08675v2.pdf | Understanding Robust Overfitting of Adversarial Training and Beyond | Robust overfitting widely exists in adversarial training of deep networks. The exact underlying reasons for this are still not completely understood. Here, we explore the causes of robust overfitting by comparing the data distribution of \emph{non-overfit} (weak adversary) and \emph{overfitted} (strong adversary) adver... | ['Tongliang Liu', 'Mingming Gong', 'Chen Gong', 'Jun Yu', 'Li Shen', 'Bo Han', 'Chaojian Yu'] | 2022-06-17 | null | null | null | null | ['data-ablation'] | ['computer-vision'] | [-6.53844848e-02 2.90677756e-01 1.52126640e-01 -3.43355417e-01
-7.73328960e-01 -1.01877916e+00 1.88918531e-01 -4.51222450e-01
-5.58929682e-01 8.54854167e-01 -3.31212464e-03 -6.08654499e-01
-1.75530925e-01 -9.69304562e-01 -1.16815472e+00 -9.94315624e-01
8.84910896e-02 1.97856188e-01 1.45734809e-02 -5.31934917... | [5.601353168487549, 7.943862438201904] |
81d02928-9346-49d9-ad17-b085bdad56b4 | neural-marionette-unsupervised-learning-of | 2202.08418 | null | https://arxiv.org/abs/2202.08418v1 | https://arxiv.org/pdf/2202.08418v1.pdf | Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video | We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. Given a video stream of point cloud observation of an articulated body under arbitrary motion, our approach d... | ['Young Min Kim', 'Hyungun Choi', 'Cheol-Hui Min', 'Hojun Jang', 'Jinseok Bae'] | 2022-02-17 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 4.48307127e-01 4.17691648e-01 -3.79228950e-01 -6.23928010e-02
-5.80649137e-01 -8.06090653e-01 6.46792471e-01 -8.42420816e-01
2.34705746e-01 4.82922167e-01 7.52244413e-01 3.05980802e-01
-4.72559035e-02 -5.30733526e-01 -1.09086514e+00 -9.14765298e-01
-1.86565697e-01 7.57833660e-01 2.43293688e-01 -1.26410156... | [7.356040000915527, -0.3012984097003937] |
5b34f9fc-8051-4444-98ab-c87d825fab84 | cross-lingual-training-with-dense-retrieval | 2109.01628 | null | https://arxiv.org/abs/2109.01628v1 | https://arxiv.org/pdf/2109.01628v1.pdf | Cross-Lingual Training with Dense Retrieval for Document Retrieval | Dense retrieval has shown great success in passage ranking in English. However, its effectiveness in document retrieval for non-English languages remains unexplored due to the limitation in training resources. In this work, we explore different transfer techniques for document ranking from English annotations to multip... | ['Jimmy Lin', 'He Bai', 'Rui Zhang', 'Peng Shi'] | 2021-09-03 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-3.19385737e-01 -5.32185674e-01 -4.24742430e-01 -2.70033982e-02
-1.97109747e+00 -9.35222626e-01 9.14952815e-01 -4.20892984e-02
-8.77698481e-01 1.20211053e+00 5.69908679e-01 -2.97698349e-01
-1.78812206e-01 -7.59550989e-01 -5.53248167e-01 -2.19833925e-01
7.06479549e-02 9.82241511e-01 4.99634951e-01 -8.43028665... | [11.401276588439941, 9.769583702087402] |
e8c484d9-d14c-442a-bdf8-7abfe44ce8b9 | variational-likelihood-free-gradient-descent | null | null | https://openreview.net/forum?id=svH3klEbuXa | https://openreview.net/pdf?id=svH3klEbuXa | Variational Likelihood-Free Gradient Descent | In many scientific applications, we do not have explicit access to the likelihood function. However simulations of the process of interest, using different parameter settings, may give us access to the likelihood function implicitly. The methodology for approximating likelihoods and posterior distributions based on sim... | ['Mark Beaumont', 'Song Liu', 'Jack Simons'] | 2021-11-22 | null | null | null | pproximateinference-aabi-symposium-2022-2 | ['density-ratio-estimation'] | ['methodology'] | [-1.44129723e-01 -2.93284923e-01 2.08322018e-01 -2.61586666e-01
-7.05183566e-01 -2.68316358e-01 6.67570651e-01 4.45506632e-01
-7.53854215e-01 1.12264240e+00 -2.74789870e-01 -6.93979800e-01
-5.78281805e-02 -8.69934201e-01 -6.33753121e-01 -7.77038217e-01
-1.31989524e-01 1.06609368e+00 2.74131030e-01 2.58977234... | [6.757779598236084, 3.9725563526153564] |
ab294254-a580-470c-bd99-91e0eae44b40 | safe-mutations-for-deep-and-recurrent-neural | 1712.06563 | null | http://arxiv.org/abs/1712.06563v3 | http://arxiv.org/pdf/1712.06563v3.pdf | Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients | While neuroevolution (evolving neural networks) has a successful track record
across a variety of domains from reinforcement learning to artificial life, it
is rarely applied to large, deep neural networks. A central reason is that
while random mutation generally works in low dimensions, a random perturbation
of thousa... | ['Jay Chen', 'Joel Lehman', 'Kenneth O. Stanley', 'Jeff Clune'] | 2017-12-18 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 4.48908776e-01 1.26681656e-01 3.30245972e-01 4.42503244e-02
1.33397132e-01 -4.92924094e-01 3.06149215e-01 7.75934830e-02
-7.00699449e-01 9.07659948e-01 -4.48920220e-01 -3.41956228e-01
-1.74255416e-01 -1.09369206e+00 -6.92494214e-01 -9.38402414e-01
-1.89829573e-01 1.47251397e-01 4.73403573e-01 -7.14135528... | [8.243386268615723, 3.2333173751831055] |
4e8220a7-7372-4332-b48b-9ce75330f80e | transductive-linear-probing-a-novel-framework | 2212.05606 | null | https://arxiv.org/abs/2212.05606v1 | https://arxiv.org/pdf/2212.05606v1.pdf | Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification | Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an ... | ['Huan Liu', 'Jundong Li', 'Kaize Ding', 'Song Wang', 'Zhen Tan'] | 2022-12-11 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 5.31495214e-01 7.98958898e-01 -8.83624375e-01 -3.59080255e-01
-5.35045266e-01 -9.52423066e-02 5.19132853e-01 5.29506207e-01
-6.40333369e-02 4.47199196e-01 1.15635851e-02 -2.90446132e-01
-1.17195353e-01 -1.24432397e+00 -4.64148790e-01 -8.69974196e-01
-2.11475492e-01 5.42155862e-01 9.25261676e-02 -4.89249438... | [7.379175186157227, 6.155055046081543] |
be3f84c9-ecb0-4b5e-bed0-d24cb972e20e | object-pose-estimation-from-monocular-image | 1809.00553 | null | http://arxiv.org/abs/1809.00553v1 | http://arxiv.org/pdf/1809.00553v1.pdf | Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence | Understanding the geometry and pose of objects in 2D images is a fundamental
necessity for a wide range of real world applications. Driven by deep neural
networks, recent methods have brought significant improvements to object pose
estimation. However, they suffer due to scarcity of keypoint/pose-annotated
real images ... | ['Rahul M. V.', 'Jogendra Nath Kundu', 'R. Venkatesh Babu', 'Aditya Ganeshan'] | 2018-09-03 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [-1.03901282e-01 -1.83191523e-01 -1.49195280e-03 -3.68113726e-01
-8.04959655e-01 -6.73062027e-01 5.65380514e-01 -1.06542021e-01
-2.88190186e-01 1.46999255e-01 -8.07671472e-02 2.90948331e-01
-1.03299804e-01 -7.24534273e-01 -9.28287327e-01 -5.16279638e-01
3.23641092e-01 8.15990150e-01 4.67521518e-01 -8.99175033... | [7.5038604736328125, -2.6286838054656982] |
1af92796-592d-4aeb-b5fa-af38b7698ca4 | a-cross-task-flexible-transition-model-for | null | null | https://aclanthology.org/W13-4904 | https://aclanthology.org/W13-4904.pdf | A Cross-Task Flexible Transition Model for Arabic Tokenization, Affix Detection, Affix Labeling, POS Tagging, and Dependency Parsing | null | ['Stephen Tratz'] | 2013-10-01 | null | null | null | ws-2013-10 | ['transition-based-dependency-parsing'] | ['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... | [-7.444147109985352, 3.649477005004883] |
3c51d313-6475-4fe6-bbb1-99f5e3c2dfa8 | on-the-fundamental-limits-of-matrix | 2109.05408 | null | https://arxiv.org/abs/2109.05408v1 | https://arxiv.org/pdf/2109.05408v1.pdf | On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs | We study the matrix completion problem that leverages hierarchical similarity graphs as side information in the context of recommender systems. Under a hierarchical stochastic block model that well respects practically-relevant social graphs and a low-rank rating matrix model, we characterize the exact information-theo... | ['Changho Suh', 'Soheil Mohajer', 'Adel Elmahdy', 'Junhyung Ahn'] | 2021-09-12 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.18450814e-01 5.30832171e-01 -2.19470471e-01 7.29465187e-02
-4.17025208e-01 -9.15282607e-01 1.81335986e-01 2.54600972e-01
-1.68374062e-01 5.79654932e-01 1.38784826e-01 -5.06520510e-01
-8.34253728e-01 -8.92316461e-01 -7.49443233e-01 -8.57806981e-01
-6.53361499e-01 1.96196780e-01 -1.27531663e-01 -3.73666942... | [6.879227161407471, 4.9992356300354] |
94a5d300-0bef-45bd-a0ee-ba1a08aa1ae8 | online-photometric-calibration-of-automatic | 2012.14292 | null | https://arxiv.org/abs/2012.14292v2 | https://arxiv.org/pdf/2012.14292v2.pdf | Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras | Thermal infrared cameras are increasingly being used in various applications such as robot vision, industrial inspection and medical imaging, thanks to their improved resolution and portability. However, the performance of traditional computer vision techniques developed for electro-optical imagery does not directly tr... | ['Shreyansh Daftry', 'Larry Matthies', 'Manash Pratim Das'] | 2020-12-07 | null | null | null | null | ['camera-auto-calibration', 'thermal-image-denoising'] | ['computer-vision', 'computer-vision'] | [ 5.27082145e-01 -4.06901956e-01 2.66925514e-01 -3.63490820e-01
-1.38427734e-01 -6.63308978e-01 2.34430298e-01 -2.67932802e-01
-6.50212884e-01 3.65013629e-01 -5.62299907e-01 -3.26302141e-01
-1.19863480e-01 -4.20060605e-01 -4.32744950e-01 -7.91761279e-01
4.68937665e-01 3.28383058e-01 2.70625710e-01 -1.77983761... | [8.081276893615723, -2.2626638412475586] |
a2c7afc6-840f-4543-8a6c-2d1f90647b6b | sparsegnv-generating-novel-views-of-indoor | 2305.07024 | null | https://arxiv.org/abs/2305.07024v1 | https://arxiv.org/pdf/2305.07024v1.pdf | SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views | We study to generate novel views of indoor scenes given sparse input views. The challenge is to achieve both photorealism and view consistency. We present SparseGNV: a learning framework that incorporates 3D structures and image generative models to generate novel views with three modules. The first module builds a neu... | ['Ying Shan', 'Yan-Pei Cao', 'Weihao Cheng'] | 2023-05-11 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.34765363e-01 4.35220569e-01 5.16956866e-01 -6.05028749e-01
-7.45199323e-01 -7.77316034e-01 8.48750532e-01 -6.55864835e-01
3.12996536e-01 6.35715902e-01 4.24137712e-01 -2.16013249e-02
2.05862314e-01 -1.16224766e+00 -1.35499394e+00 -6.40811920e-01
4.07696694e-01 4.44858462e-01 -1.17906921e-01 3.67016206... | [9.174022674560547, -3.14700984954834] |
87820789-52e5-449b-a853-5899ae0da28b | end-to-end-learning-of-keypoint | 2106.07995 | null | https://arxiv.org/abs/2106.07995v3 | https://arxiv.org/pdf/2106.07995v3.pdf | Learning of feature points without additional supervision improves reinforcement learning from images | In many control problems that include vision, optimal controls can be inferred from the location of the objects in the scene. This information can be represented using feature points, which is a list of spatial locations in learned feature maps of an input image. Previous works show that feature points learned using un... | ['Juho Kannala', 'Alexander Ilin', 'Rinu Boney'] | 2021-06-15 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-6.98061660e-02 2.57795453e-01 -2.42050946e-01 -4.62476015e-01
-6.76156521e-01 -3.55136573e-01 7.82303631e-01 1.36149213e-01
-7.94101298e-01 5.01346648e-01 -3.48352492e-02 1.68809712e-01
-1.47336766e-01 -3.91631663e-01 -1.22190034e+00 -7.18808889e-01
-3.27173099e-02 5.79929471e-01 1.60963655e-01 9.63832811... | [4.731792449951172, 0.8028873801231384] |
1d4d19a1-1fe5-4329-9a00-927d34de8efd | subsampling-for-knowledge-graph-embedding | 2209.12801 | null | https://arxiv.org/abs/2209.12801v1 | https://arxiv.org/pdf/2209.12801v1.pdf | Subsampling for Knowledge Graph Embedding Explained | In this article, we explain the recent advance of subsampling methods in knowledge graph embedding (KGE) starting from the original one used in word2vec. | ['Katsuhiko Hayashi', 'Hidetaka Kamigaito'] | 2022-09-13 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-3.33686829e-01 2.88354099e-01 -4.15475160e-01 1.32089198e-01
2.45503336e-01 -3.00254136e-01 7.17889905e-01 9.04959962e-02
-5.67006826e-01 8.46697211e-01 8.29421759e-01 -5.51961064e-01
-3.72150183e-01 -1.29128671e+00 -2.73617804e-01 -3.09201777e-01
-2.27595121e-01 2.09428251e-01 1.84443593e-01 -7.81724274... | [8.806171417236328, 7.881773471832275] |
8a4ca13c-67be-4996-a7dd-b9ee2b7dd246 | improving-graph-based-sentence-ordering-with | 2110.06446 | null | https://arxiv.org/abs/2110.06446v1 | https://arxiv.org/pdf/2110.06446v1.pdf | Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings | Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages. In this paper, we propose a novel sentence ordering framework which introduces two cla... | ['Jinsong Su', 'Degen Huang', 'Junfeng Yao', 'Jiali Zeng', 'Yubin Ge', 'Jie zhou', 'Fandong Meng', 'Ante Wang', 'Shaopeng Lai'] | 2021-10-13 | null | https://aclanthology.org/2021.emnlp-main.186 | https://aclanthology.org/2021.emnlp-main.186.pdf | emnlp-2021-11 | ['sentence-ordering'] | ['natural-language-processing'] | [ 1.90620422e-01 2.14915007e-01 -1.52468726e-01 -8.10178816e-01
-5.02300680e-01 -5.36839843e-01 3.29656482e-01 5.42417467e-01
-1.25339836e-01 6.78758919e-01 2.58075804e-01 -2.89358944e-01
-4.11494911e-01 -8.73234868e-01 -4.93704438e-01 -4.39114392e-01
-2.30587214e-01 5.45398653e-01 3.98090005e-01 -3.38285208... | [10.982259750366211, 8.827964782714844] |
60ef7382-bb06-404c-b03b-a918cf82d7f1 | factorbase-sql-for-learning-a-multi | 1508.02428 | null | http://arxiv.org/abs/1508.02428v1 | http://arxiv.org/pdf/1508.02428v1.pdf | FactorBase: SQL for Learning A Multi-Relational Graphical Model | We describe FactorBase, a new SQL-based framework that leverages a relational
database management system to support multi-relational model discovery. A
multi-relational statistical model provides an integrated analysis of the
heterogeneous and interdependent data resources in the database. We adopt the
BayesStore desig... | ['Zhensong Qian', 'Oliver Schulte'] | 2015-08-10 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-7.43630946e-01 1.17110044e-01 -8.53565872e-01 -7.99015164e-01
-1.02168357e+00 -2.66111076e-01 5.00610709e-01 4.10100430e-01
6.80362880e-02 4.03948694e-01 5.46890944e-02 -6.83134556e-01
-6.39339328e-01 -1.29541957e+00 -9.48259473e-01 -1.43400982e-01
-3.88505578e-01 9.73890603e-01 7.19326794e-01 1.02169760... | [9.204015731811523, 7.591470241546631] |
bbb9f914-cbf8-4ca7-8c39-14ffe02aceef | bodies-at-rest-3d-human-pose-and-shape | 2004.01166 | null | https://arxiv.org/abs/2004.01166v1 | https://arxiv.org/pdf/2004.01166v1.pdf | Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data | People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight perception is complicated by occlusion from bedding. Pressure sensing mats are a promising alternative, but training data is challenging to c... | ['Ariel Kapusta', 'Henry M. Clever', 'C. Karen Liu', 'Zackory Erickson', 'Charles C. Kemp', 'Greg Turk'] | 2020-04-02 | bodies-at-rest-3d-human-pose-and-shape-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Clever_Bodies_at_Rest_3D_Human_Pose_and_Shape_Estimation_From_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Clever_Bodies_at_Rest_3D_Human_Pose_and_Shape_Estimation_From_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-1.32986484e-02 5.63740253e-01 3.00928831e-01 -3.48630160e-01
-2.16743320e-01 -1.46802759e-03 1.42622814e-01 -2.68348932e-01
-1.77606881e-01 5.10650635e-01 3.69807035e-01 3.69823724e-01
5.83590150e-01 -8.74674261e-01 -1.05678689e+00 -1.29856635e-02
-6.04330860e-02 1.06233084e+00 -4.11891332e-03 -4.41510946... | [7.019432067871094, -1.1348596811294556] |
ed22fa76-77da-45fd-93f8-1e87caf8081b | orbits-online-recovery-of-missing-blocks-in | null | null | http://www.vldb.org/pvldb/vol14/p294-khayati.pdf | http://www.vldb.org/pvldb/vol14/p294-khayati.pdf | ORBITS: Online Recovery of Missing Blocks in Multiple Time Series Streams | With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real... | ['Philippe Cudré-Mauroux', 'Zakhar Tymchenko', 'Ines Arous', 'Mourad Khayati'] | 2020-11-01 | null | null | null | proceedings-of-the-vldb-endowment-pvldb-2020 | ['multivariate-time-series-imputation', 'time-series-streams'] | ['time-series', 'time-series'] | [ 3.34568292e-01 -3.18796158e-01 -2.96637695e-02 -4.61441837e-02
-1.09403121e+00 -6.99095726e-01 1.05200954e-01 6.59718394e-01
-1.62419915e-01 5.10725200e-01 1.05959825e-01 -1.76294535e-01
-3.90209943e-01 -8.93651187e-01 -7.94995189e-01 -5.54198861e-01
-5.49115956e-01 4.34047908e-01 4.45471674e-01 7.86739588... | [7.303322792053223, 3.0315585136413574] |
8c09505a-af9a-4066-b6c4-4aece09ddafc | query-tracking-for-e-commerce-conversational | 1810.03274 | null | http://arxiv.org/abs/1810.03274v1 | http://arxiv.org/pdf/1810.03274v1.pdf | Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective | With the development of dialog techniques, conversational search has
attracted more and more attention as it enables users to interact with the
search engine in a natural and efficient manner. However, comparing with the
natural language understanding in traditional task-oriented dialog which
focuses on slot filling an... | ['Yunlun Yang', 'Yu Gong', 'Xi Chen'] | 2018-10-08 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 5.59041984e-02 3.26011419e-01 -4.68155026e-01 -4.56645578e-01
-4.83867586e-01 -5.70836365e-01 7.47380197e-01 1.67771339e-01
-5.29356062e-01 4.14602846e-01 4.44780201e-01 -4.50000316e-01
-5.02285399e-02 -5.50430536e-01 -7.58418441e-02 9.10897180e-03
3.57552290e-01 9.95453775e-01 4.04210001e-01 -8.27213407... | [12.217796325683594, 7.814226150512695] |
75a837e1-853c-48f6-ab06-af809e42e0e7 | yh-technologies-at-activitynet-challenge-2018 | 1807.00686 | null | http://arxiv.org/abs/1807.00686v1 | http://arxiv.org/pdf/1807.00686v1.pdf | YH Technologies at ActivityNet Challenge 2018 | This notebook paper presents an overview and comparative analysis of our
systems designed for the following five tasks in ActivityNet Challenge 2018:
temporal action proposals, temporal action localization, dense-captioning
events in videos, trimmed action recognition, and spatio-temporal action
localization. | ['Xue Li', 'Ting Yao'] | 2018-06-29 | null | null | null | null | ['dense-captioning', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision'] | [ 4.94295806e-01 -4.54721181e-03 -7.18498349e-01 -2.56364614e-01
-7.19275594e-01 -4.53731269e-01 9.07117963e-01 -2.65459061e-01
-6.54588044e-01 8.24561596e-01 1.30938840e+00 4.36879188e-01
-5.76585494e-02 2.26094171e-01 -5.67003548e-01 -6.09258175e-01
-9.26068664e-01 8.52042250e-03 6.44712031e-01 3.66157323... | [8.262192726135254, 0.4519444406032562] |
78357169-0af5-4ec3-9134-77a3d6f55ade | comparing-acoustic-based-approaches-for | 2106.01555 | null | https://arxiv.org/abs/2106.01555v2 | https://arxiv.org/pdf/2106.01555v2.pdf | Comparing Acoustic-based Approaches for Alzheimer's Disease Detection | Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD. In this paper, we study the performance and generalizability of three approaches for AD detection from speech on the recent ADReSSo challenge dataset: 1) using conventional acoustic features 2) using novel pre-train... | ['Jekaterina Novikova', 'Aparna Balagopalan'] | 2021-06-03 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 6.30979910e-02 5.64685091e-04 3.19619365e-02 -4.58453685e-01
-1.61805713e+00 -1.88552096e-01 6.49959505e-01 3.15827817e-01
-7.35032499e-01 3.91456097e-01 7.71145463e-01 2.54488271e-02
-1.04148082e-01 -4.85263973e-01 -5.29997945e-02 -3.80442828e-01
-3.99106383e-01 4.47313845e-01 3.79751444e-01 -2.14381330... | [13.883306503295898, 5.372596740722656] |
8e4b5034-f916-4095-90b9-792827b803c3 | look-back-again-dual-parallel-attention | null | null | https://dl.acm.org/doi/10.1145/3460426.3463674 | https://dl.acm.org/doi/pdf/10.1145/3460426.3463674 | Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition | Nowadays, it is a trend that using a parallel-decoupled encoderdecoder (PDED) framework in scene text recognition for its flexibility and efficiency. However, due to the inconsistent information content between queries and keys in the parallel positional attention module (PPAM) used in this kind of framework(queries: p... | ['Junbo Guo', 'Hongtao Xie', 'Guoqing Jin', 'Zilong Fu'] | 2021-08-01 | null | null | null | icmr-2021-8 | ['scene-text-recognition'] | ['computer-vision'] | [-3.29063125e-02 -4.63940948e-01 -1.34680672e-02 -9.56006050e-02
-3.94671351e-01 -4.32868928e-01 9.23254013e-01 -2.12981656e-01
-4.09790993e-01 1.62185967e-01 4.03034478e-01 -2.08170921e-01
9.90086943e-02 -4.56350565e-01 -7.49728084e-01 -7.08189249e-01
6.07805669e-01 1.19910436e-02 1.83658987e-01 -8.86145383... | [11.792877197265625, 2.0484097003936768] |
9746fc63-aa05-4479-82fb-e5330c320441 | collaborative-intelligence-challenges-and | 2102.06841 | null | https://arxiv.org/abs/2102.06841v1 | https://arxiv.org/pdf/2102.06841v1.pdf | Collaborative Intelligence: Challenges and Opportunities | This paper presents an overview of the emerging area of collaborative intelligence (CI). Our goal is to raise awareness in the signal processing community of the challenges and opportunities in this area of growing importance, where key developments are expected to come from signal processing and related disciplines. T... | ['Yonghong Tian', 'Weisi Lin', 'Ivan V. Bajić'] | 2021-02-13 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 7.38484800e-01 -1.41478553e-01 3.21519822e-01 -3.90011758e-01
-4.81843203e-01 -2.53812969e-01 1.76344797e-01 1.18791468e-01
-3.74080479e-01 5.11663318e-01 4.07636613e-01 8.78016651e-02
-5.39851665e-01 -3.83846164e-01 1.10665634e-01 -5.75389624e-01
-9.52014625e-01 -3.81512403e-01 -2.49685839e-01 -1.48203388... | [15.40478229522705, 5.575064659118652] |
9b5c944e-e45d-4010-b616-038f15856f25 | unsupervised-full-constituency-parsing-with-1 | null | null | https://openreview.net/forum?id=R73K-lxO9eU | https://openreview.net/pdf?id=R73K-lxO9eU | Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence | Unsupervised constituency parsing has been explored much but is still far from being solved as currently mainstream unsupervised constituency parser only captures the unlabeled structure of sentences. Properties in the substitution of constituents make it possible to detect constituents in a particular label. We propos... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['constituency-parsing'] | ['natural-language-processing'] | [ 3.56493711e-01 6.47794604e-01 -5.37902594e-01 -1.01722944e+00
-1.02577090e+00 -1.23151672e+00 4.50592667e-01 3.44251841e-01
-2.25637525e-01 8.99684787e-01 5.42930663e-01 -3.85330528e-01
3.99146795e-01 -8.53534281e-01 -6.98923290e-01 -4.78527606e-01
1.65750772e-01 5.32659590e-01 3.62780780e-01 -8.60827118... | [10.367127418518066, 9.667684555053711] |
9999c650-3900-400a-9dc0-eeec9dd03fe9 | global-spectral-filter-memory-network-for | 2210.05567 | null | https://arxiv.org/abs/2210.05567v2 | https://arxiv.org/pdf/2210.05567v2.pdf | Global Spectral Filter Memory Network for Video Object Segmentation | This paper studies semi-supervised video object segmentation through boosting intra-frame interaction. Recent memory network-based methods focus on exploiting inter-frame temporal reference while paying little attention to intra-frame spatial dependency. Specifically, these segmentation model tends to be susceptible to... | ['Yujiu Yang', 'Yansong Tang', 'Yitong Wang', 'Xinyuan Zhao', 'Jiahao Wang', 'Ran Yu', 'Yong liu'] | 2022-10-11 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.01095930e-01 -1.55822635e-01 -5.44147074e-01 -3.50608140e-01
-5.52654326e-01 -2.75893629e-01 4.24886227e-01 -2.74803996e-01
-3.43735099e-01 5.77192664e-01 3.55026960e-01 1.75522909e-01
1.47263687e-02 -6.11416578e-01 -9.30792093e-01 -8.31803381e-01
-1.80796355e-01 -2.88340241e-01 7.65961885e-01 -1.66559853... | [9.241135597229004, -0.03387390822172165] |
93a68f6c-a8d1-4319-91a0-eaf95a6ed743 | learning-word-embeddings-for-data-sparse-and | null | null | https://aclanthology.org/N18-4007 | https://aclanthology.org/N18-4007.pdf | Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets | This research proposal describes two algorithms that are aimed at learning word embeddings for data sparse and sentiment rich data sets. The goal is to use word embeddings adapted for domain specific data sets in downstream applications such as sentiment classification. The first approach learns word embeddings in a su... | ['Prathusha Kameswara Sarma'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['learning-word-embeddings'] | ['methodology'] | [ 6.93806857e-02 1.55034224e-02 -5.97170115e-01 -7.09644735e-01
-4.74962413e-01 -7.29258955e-01 6.25943840e-01 4.90508050e-01
-6.24571204e-01 2.52562970e-01 7.41322458e-01 -1.25684187e-01
-2.10571945e-01 -6.42316401e-01 2.32727528e-01 -8.23174715e-01
-1.61125306e-02 5.01651704e-01 -2.57079691e-01 -6.50826871... | [10.429464340209961, 8.601731300354004] |
77b8429a-bacd-428f-b03f-81d7015c2e7a | mtcue-learning-zero-shot-control-of-extra | 2305.15904 | null | https://arxiv.org/abs/2305.15904v1 | https://arxiv.org/pdf/2305.15904v1.pdf | MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation | Efficient utilisation of both intra- and extra-textual context remains one of the critical gaps between machine and human translation. Existing research has primarily focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speak... | ['Carolina Scarton', 'Robert Flynn', 'Sebastian Vincent'] | 2023-05-25 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.50119078e-01 6.58939872e-03 -6.42017245e-01 -3.85169625e-01
-1.19325626e+00 -7.95037270e-01 1.13407505e+00 1.95665643e-01
-6.21080577e-01 8.92180562e-01 7.63093829e-01 -7.68437326e-01
4.05609548e-01 -5.88141918e-01 -5.71078777e-01 -3.00604522e-01
4.46296334e-01 6.18381023e-01 -4.33939695e-01 -5.50252438... | [11.602646827697754, 10.275280952453613] |
b39ebd4c-2cc8-43c4-81a0-e39f58be3163 | hybrid-quantum-classical-generative | 2212.11614 | null | https://arxiv.org/abs/2212.11614v2 | https://arxiv.org/pdf/2212.11614v2.pdf | Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation | Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counter... | ['Muhammad Usman', 'Sarah M. Erfani', 'Maxwell T. West', 'Shu Lok Tsang'] | 2022-12-22 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.98263824e-01 3.43337834e-01 1.88751131e-01 9.28875208e-02
-1.10033882e+00 -7.02739656e-01 9.88494992e-01 -2.64963567e-01
-4.56697792e-01 9.09271777e-01 -3.69570643e-01 -3.25664371e-01
7.77795017e-02 -1.18234229e+00 -7.42474556e-01 -1.24119413e+00
2.27063254e-01 3.97525162e-01 -3.97610925e-02 -5.89608490... | [5.626674175262451, 4.96585750579834] |
f6bd75a6-c654-45e3-9282-34a2fbb44766 | sn-computer-science-towards-offensive | 2108.10939 | null | https://arxiv.org/abs/2108.10939v2 | https://arxiv.org/pdf/2108.10939v2.pdf | Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and Posts | Offensive Language detection in social media platforms has been an active field of research over the past years. In non-native English spoken countries, social media users mostly use a code-mixed form of text in their posts/comments. This poses several challenges in the offensive content identification tasks, and consi... | ['Uthayasanker Thayasivam', 'Charangan Vasantharajan'] | 2021-08-24 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-4.24527407e-01 -2.59528667e-01 -4.17752624e-01 9.74904895e-02
-1.18080485e+00 -6.35950744e-01 6.87301874e-01 2.85423417e-02
-6.13151371e-01 4.35531288e-01 3.65374982e-01 -5.18654764e-01
2.90105969e-01 -4.10790682e-01 -3.82625669e-01 -3.14225048e-01
8.56236443e-02 3.79660368e-01 -1.07429080e-01 -7.15859890... | [9.004748344421387, 10.618927001953125] |
c88f7955-eb02-4010-886a-5e4eec1900d0 | improved-chord-recognition-by-combining | 1808.05335 | null | http://arxiv.org/abs/1808.05335v1 | http://arxiv.org/pdf/1808.05335v1.pdf | Improved Chord Recognition by Combining Duration and Harmonic Language Models | Chord recognition systems typically comprise an acoustic model that predicts
chords for each audio frame, and a temporal model that casts these predictions
into labelled chord segments. However, temporal models have been shown to only
smooth predictions, without being able to incorporate musical information about
chord... | ['Filip Korzeniowski', 'Gerhard Widmer'] | 2018-08-16 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 3.81616622e-01 3.37049067e-01 -1.37791991e-01 -1.59709886e-01
-7.26137578e-01 -7.88121700e-01 4.31248397e-01 9.20853913e-02
-2.33706459e-01 3.09833109e-01 6.75829113e-01 -3.92042726e-01
-9.17569771e-02 -6.47325039e-01 -4.45109308e-01 -3.68053973e-01
-3.96065474e-01 1.72132850e-01 5.84760725e-01 -4.03808445... | [15.891481399536133, 5.317929267883301] |
9b624ed2-19ed-4ae8-9481-a269c7090ac4 | towards-fairness-aware-multi-objective | 2207.12138 | null | https://arxiv.org/abs/2207.12138v1 | https://arxiv.org/pdf/2207.12138v1.pdf | Towards Fairness-Aware Multi-Objective Optimization | Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such a... | ['Yaochu Jin', 'Wenli Du', 'Wei Du', 'Lianbo Ma', 'Guo Yu'] | 2022-07-22 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-4.85689081e-02 -2.23164022e-01 -8.32105577e-01 -8.26732695e-01
-4.46126789e-01 -3.48492652e-01 2.24804699e-01 6.26984477e-01
-8.76518667e-01 1.05865633e+00 4.08209383e-01 -4.27648008e-01
-6.71806455e-01 -5.87837279e-01 1.69570401e-01 -4.18929279e-01
-6.56968355e-02 3.41375083e-01 -9.49121177e-01 -2.99337268... | [8.994924545288086, 5.305873870849609] |
b93eec45-1741-4c62-8681-3c35e9e262eb | opal-offline-primitive-discovery-for-1 | 2010.13611 | null | https://arxiv.org/abs/2010.13611v3 | https://arxiv.org/pdf/2010.13611v3.pdf | OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning | Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited. However, in many practical applications, the situation is reversed: an agent may have access to large amounts of undi... | ['Ofir Nachum', 'Sergey Levine', 'Pulkit Agrawal', 'Aviral Kumar', 'Anurag Ajay'] | 2020-10-26 | opal-offline-primitive-discovery-for | https://openreview.net/forum?id=V69LGwJ0lIN | https://openreview.net/pdf?id=V69LGwJ0lIN | iclr-2021-1 | ['few-shot-imitation-learning'] | ['methodology'] | [-1.35168597e-01 -7.33508542e-02 -4.00744468e-01 3.17921564e-02
-6.06179118e-01 -9.25861597e-01 6.47867858e-01 3.25988859e-01
-6.99577093e-01 8.53054404e-01 2.31307149e-01 -4.59290653e-01
-8.33289474e-02 -6.96844757e-01 -6.12022042e-01 -7.59901345e-01
-5.69002628e-01 4.53741729e-01 1.33526132e-01 -3.15075666... | [4.15925931930542, 1.8207002878189087] |
acbb3218-dcee-4848-8d17-e8240dc1c839 | model-based-validation-as-probabilistic | 2305.09930 | null | https://arxiv.org/abs/2305.09930v1 | https://arxiv.org/pdf/2305.09930v1.pdf | Model-based Validation as Probabilistic Inference | Estimating the distribution over failures is a key step in validating autonomous systems. Existing approaches focus on finding failures for a small range of initial conditions or make restrictive assumptions about the properties of the system under test. We frame estimating the distribution over failure trajectories fo... | ['Mykel J. Kochenderfer', 'Anthony Corso', 'Harrison Delecki'] | 2023-05-17 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-3.18722755e-01 -8.26577321e-02 -3.13936383e-01 -8.45354721e-02
-9.38164353e-01 -5.90086758e-01 7.43833899e-01 -6.92155585e-02
-2.32880235e-01 9.61022735e-01 -5.07939100e-01 -9.18347359e-01
1.95793621e-02 -6.31744683e-01 -9.86686826e-01 -4.21834886e-01
-5.11902213e-01 9.72088397e-01 5.49414277e-01 -2.14050248... | [4.83386754989624, 2.1098368167877197] |
93644853-4413-4522-b438-5c48fdaaffbe | distributional-reinforcement-learning-for | 1905.06125 | null | https://arxiv.org/abs/1905.06125v1 | https://arxiv.org/pdf/1905.06125v1.pdf | Distributional Reinforcement Learning for Efficient Exploration | In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second ... | ['Yao-Liang Yu', 'Kaiwen Wu', 'Linglong Kong', 'Shangtong Zhang', 'Borislav Mavrin', 'Hengshuai Yao'] | 2019-05-13 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-7.58714795e-01 4.85696435e-01 -1.59727246e-01 6.48300350e-02
-1.14411700e+00 -5.46083272e-01 3.82055223e-01 -1.73587799e-01
-9.48972166e-01 1.39184606e+00 1.51427642e-01 -4.55403924e-01
-4.90689814e-01 -7.70788312e-01 -1.01875234e+00 -8.38737607e-01
-3.67518157e-01 5.72413385e-01 2.80421674e-01 -6.73549533... | [4.080782890319824, 2.4855148792266846] |
24801ebd-f032-4e44-917a-49a5fab0d996 | global-table-extractor-gte-a-framework-for | 2005.00589 | null | https://arxiv.org/abs/2005.00589v2 | https://arxiv.org/pdf/2005.00589v2.pdf | Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context | Documents are often used for knowledge sharing and preservation in business and science, within which are tables that capture most of the critical data. Unfortunately, most documents are stored and distributed as PDF or scanned images, which fail to preserve logical table structure. Recent vision-based deep learning ap... | ['Xu Zhong', 'Nancy Xin Ru Wang', 'Lucian Popa', 'Xinyi Zheng', 'Doug Burdick'] | 2020-05-01 | null | null | null | null | ['table-recognition', 'cell-detection', 'table-detection', 'table-extraction'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous'] | [-1.21977299e-01 1.52550675e-02 -2.25358665e-01 -2.08152607e-02
-1.00010514e+00 -8.52976382e-01 7.04630315e-01 5.43721259e-01
-2.19338179e-01 7.68727422e-01 2.28292167e-01 -2.65043825e-01
2.69903898e-01 -1.01978350e+00 -1.00301492e+00 -4.34032798e-01
1.98564753e-01 1.06284642e+00 1.82181358e-01 6.03594407... | [11.694283485412598, 3.013561725616455] |
e11ac91d-7296-437e-8dfc-ec78de42a1f4 | observational-and-interventional-causal | 2212.02435 | null | https://arxiv.org/abs/2212.02435v1 | https://arxiv.org/pdf/2212.02435v1.pdf | Observational and Interventional Causal Learning for Regret-Minimizing Control | We explore how observational and interventional causal discovery methods can be combined. A state-of-the-art observational causal discovery algorithm for time series capable of handling latent confounders and contemporaneous effects, called LPCMCI, is extended to profit from casual constraints found through randomized ... | ['Christian Reiser'] | 2022-12-05 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.21435475e-01 7.25445151e-01 -8.93732488e-01 1.34925935e-02
-4.23953384e-01 -3.98011595e-01 6.04294121e-01 2.53688842e-01
-3.81910533e-01 1.32134545e+00 4.82780367e-01 -7.95912921e-01
-9.80400562e-01 -7.62064934e-01 -9.08173859e-01 -8.40019703e-01
-7.97203898e-01 6.56104803e-01 -4.11674500e-01 4.01729941... | [7.827875137329102, 5.257971286773682] |
10a69b52-f55b-4102-9d3d-c19dc4629c9d | optimized-high-resolution-3d-dense-u-net | null | null | https://www.mdpi.com/2076-3417/9/3/404/htm | https://www.mdpi.com/2076-3417/9/3/404/pdf | Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation | The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach. We introduce 3D Dense-U-Net neural network architecture ... | ['Malay Kishore Dutta', 'Kamil Říha', 'Václav Uher', 'Radim Burget', 'Martin Kolařík'] | 2019-01-25 | null | null | null | applied-sciences-2019-1 | ['unet-segmentation'] | ['computer-vision'] | [ 2.36271873e-01 5.40571988e-01 3.30577604e-02 -5.41177869e-01
-3.53293717e-01 6.95978478e-02 1.77704692e-01 3.22622955e-01
-9.30622637e-01 4.89937246e-01 -2.75034219e-01 -6.19857788e-01
9.95967984e-02 -1.02365386e+00 -4.62796420e-01 -2.28448182e-01
-2.40221769e-01 1.11565149e+00 5.74994743e-01 9.08426121... | [14.36684513092041, -2.489110231399536] |
21f5685f-329b-4352-8e2e-7b698dc5325f | mortality-prediction-with-adaptive-feature | 2301.07107 | null | https://arxiv.org/abs/2301.07107v2 | https://arxiv.org/pdf/2301.07107v2.pdf | Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset | Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identifying modifiable risk factors based on the Electronic Medical Records (EMR) collected along with the follow-up visits are of great importance... | ['Tao Wang', 'Wenjie Ruan', 'Xinju Zhao', 'Wen Tang', 'Yasha Wang', 'Xinyu Ma', 'Zhihao Yu', 'Xianfeng Jiao', 'Junyi Gao', 'Chaohe Zhang', 'Liantao Ma'] | 2023-01-17 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-3.50768059e-01 -1.25712365e-01 -9.04838145e-02 -4.43556279e-01
-4.26399767e-01 8.68082121e-02 5.48272058e-02 4.94993895e-01
-1.68152526e-01 1.07315874e+00 5.62659621e-01 -2.93323994e-01
-6.17102802e-01 -8.92320573e-01 -1.91575646e-01 -6.90797508e-01
-7.84423590e-01 7.98013628e-01 -6.74632192e-01 6.07297681... | [7.952764511108398, 6.031208038330078] |
4608ffb3-8dd1-4983-82de-58ba2947ce16 | adversarial-self-supervised-scene-flow | 2011.00551 | null | https://arxiv.org/abs/2011.00551v1 | https://arxiv.org/pdf/2011.00551v1.pdf | Adversarial Self-Supervised Scene Flow Estimation | This work proposes a metric learning approach for self-supervised scene flow estimation. Scene flow estimation is the task of estimating 3D flow vectors for consecutive 3D point clouds. Such flow vectors are fruitful, \eg for recognizing actions, or avoiding collisions. Training a neural network via supervised learning... | ['Pascal Mettes', 'Olaf Booij', 'Joris van Vugt', 'Victor Zuanazzi'] | 2020-11-01 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.61434039e-01 -3.35450321e-01 -2.56951541e-01 -2.37711310e-01
-6.90754712e-01 -6.96052730e-01 5.39527476e-01 -8.86083916e-02
-3.82986397e-01 5.92621565e-01 3.67500447e-02 -9.96819884e-02
4.12986390e-02 -7.61123896e-01 -7.96044350e-01 -6.05881035e-01
-4.32679474e-01 5.55979133e-01 4.39772993e-01 -5.89731373... | [8.563060760498047, -1.9957183599472046] |
b733c4de-9c4d-4ab9-af1e-b086699d6cee | vp-slam-a-monocular-real-time-visual-slam | 2210.12756 | null | https://arxiv.org/abs/2210.12756v2 | https://arxiv.org/pdf/2210.12756v2.pdf | VP-SLAM: A Monocular Real-time Visual SLAM with Points, Lines and Vanishing Points | Traditional monocular Visual Simultaneous Localization and Mapping (vSLAM) systems can be divided into three categories: those that use features, those that rely on the image itself, and hybrid models. In the case of feature-based methods, new research has evolved to incorporate more information from their environment ... | ['Petros Maragos', 'Panagiotis Mermigkas', 'Andreas Georgis'] | 2022-10-23 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.71150580e-01 -2.86981285e-01 -5.93780167e-02 -1.90108970e-01
-2.43232265e-01 -5.44720769e-01 8.23444307e-01 -7.36309737e-02
-6.07361734e-01 5.64222097e-01 -4.14534330e-01 -3.28919113e-01
-8.46707001e-02 -7.74103224e-01 -6.23992622e-01 -5.34773827e-01
4.95841764e-02 8.36008608e-01 4.71289515e-01 -4.62215960... | [7.413369655609131, -2.142005443572998] |
7bb18f43-3281-46af-92d6-b87cbddc5a63 | lstm-knowledge-transfer-for-hrv-based-sleep | 1809.06221 | null | http://arxiv.org/abs/1809.06221v1 | http://arxiv.org/pdf/1809.06221v1.pdf | LSTM knowledge transfer for HRV-based sleep staging | Automated sleep stage classification using heart-rate variability is an
active field of research. In this work limitations of the current
state-of-the-art are addressed through the use of deep learning techniques and
their efficacy is demonstrated. First, a temporal model is proposed for the
inference of sleep stages f... | [] | 2018-09-12 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability', 'sleep-staging'] | ['medical', 'medical', 'medical'] | [ 1.98455080e-01 9.15179178e-02 -1.66089892e-01 -6.40166044e-01
-6.26104295e-01 -6.46343604e-02 -6.82030842e-02 -6.20714948e-02
-9.10778761e-01 1.08625174e+00 -1.93931639e-01 -2.62721866e-01
-1.57862291e-01 -3.60198677e-01 -2.08901241e-01 -6.79901958e-01
-4.05034721e-01 2.59787858e-01 -2.24575460e-01 2.33169913... | [13.536994934082031, 3.4653046131134033] |
1ca9f837-e881-4bec-b63b-4a653a23fef1 | scattering-spectra-models-for-physics | 2306.17210 | null | https://arxiv.org/abs/2306.17210v1 | https://arxiv.org/pdf/2306.17210v1.pdf | Scattering Spectra Models for Physics | Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra mode... | ['Stéphane Mallat', 'Brice Ménard', 'Erwan Allys', 'Rudy Morel', 'Sihao Cheng'] | 2023-06-29 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 4.15031314e-01 -5.79855323e-01 4.74530496e-02 -3.45077336e-01
-7.13255763e-01 -6.49387956e-01 7.90694594e-01 3.60953569e-01
-1.16667002e-01 7.22331822e-01 5.42294532e-02 -2.05949828e-01
-9.51230347e-01 -7.70655453e-01 -4.04191792e-01 -1.24735582e+00
-3.88293296e-01 8.06934357e-01 2.78024793e-01 -1.29465625... | [7.141607761383057, 3.9522688388824463] |
05b1e514-973b-4ef0-a34f-66dd69ae59a3 | simcgnn-simple-contrastive-graph-neural | 2302.03997 | null | https://arxiv.org/abs/2302.03997v1 | https://arxiv.org/pdf/2302.03997v1.pdf | SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation | Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers. Existing graph-based SBR methods all lack the ability to differentiate between sessions with the same last item, and suffer from severe popularity bias. Inspi... | ['Jinpeng Chen', 'Yongheng Wang', 'Xiongnan Jin', 'Josiah Poon', 'Feifei Kou', 'Fan Zhang', 'Xudong Zhang', 'Yuan Cao'] | 2023-02-08 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-5.57650533e-03 -2.61184335e-01 -6.22706473e-01 -5.42203665e-01
-2.31642947e-01 -4.11814123e-01 4.21375096e-01 4.18912411e-01
-3.77184987e-01 5.35130858e-01 3.69887829e-01 -5.91348171e-01
-5.00234187e-01 -9.64134753e-01 -3.73661011e-01 -3.77953827e-01
-7.50978053e-01 3.01324487e-01 1.23619981e-01 -2.61422724... | [10.181093215942383, 5.617000102996826] |
56202457-29b7-46f5-82f1-6fbc377b48e9 | end-to-end-active-speaker-detection | 2203.14250 | null | https://arxiv.org/abs/2203.14250v2 | https://arxiv.org/pdf/2203.14250v2.pdf | End-to-End Active Speaker Detection | Recent advances in the Active Speaker Detection (ASD) problem build upon a two-stage process: feature extraction and spatio-temporal context aggregation. In this paper, we propose an end-to-end ASD workflow where feature learning and contextual predictions are jointly learned. Our end-to-end trainable network simultane... | ['Bernard Ghanem', 'Chen Zhao', 'Moritz Cordes', 'Juan Leon Alcazar'] | 2022-03-27 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 8.55351686e-02 1.50754884e-01 1.75482780e-01 -4.55709696e-01
-1.10075819e+00 -3.98546904e-01 7.86666453e-01 3.69829327e-01
-4.37101483e-01 1.58992112e-01 5.93290865e-01 2.94497423e-02
-3.67872208e-01 -5.06055832e-01 -5.09289265e-01 -5.71663737e-01
-6.29462004e-01 1.07885897e-01 3.04708987e-01 -2.44876929... | [14.731039047241211, 5.002188682556152] |
19b103b2-df44-4e5a-860a-5e866bf0ad30 | discoscene-spatially-disentangled-generative | 2212.11984 | null | https://arxiv.org/abs/2212.11984v1 | https://arxiv.org/pdf/2212.11984v1.pdf | DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis | Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects. This work presents DisCoScene: a 3Daware generative model for high-quality and controllable scene synthesis. The key ingredient of our... | ['Sergey Tulyakov', 'Bolei Zhou', 'Hsin-Ying Lee', 'Yujun Shen', 'Ceyuan Yang', 'Aliaksandr Siarohin', 'Ivan Skorokhodov', 'Sida Peng', 'Zifan Shi', 'Menglei Chai', 'Yinghao Xu'] | 2022-12-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 0.2918554 -0.21979433 0.21060976 -0.25862485 -0.4506362 -0.8852473
0.84491 -0.20331359 0.2203015 0.36710334 0.15685663 0.03209176
0.0265527 -0.99941045 -0.94175243 -0.9253097 0.40254888 0.42543778
0.3214671 -0.35112146 -0.09792456 0.808605 -1.7639511 0.08029699
1.0034003 0.8432254 0.7... | [9.281058311462402, -3.138479471206665] |
f7ed5d38-dd73-4453-8874-b972aaa88cfe | learning-to-guide-a-saturation-based-theorem | 2106.03906 | null | https://arxiv.org/abs/2106.03906v1 | https://arxiv.org/pdf/2106.03906v1.pdf | Learning to Guide a Saturation-Based Theorem Prover | Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the design of learning mechanisms that can be integrated into theorem provers to improve their performance automatically. In this work, we intro... | ['Achille Fokoue', 'Michael Witbrock', 'Kavitha Srinivas', 'Ndivhuwo Makondo', 'Pavan Kapanipathi', 'Shajith Ikbal', 'Cristina Cornelio', 'Vernon Austil', 'Bassem Makni', 'Maxwell Crouse', 'Ibrahim Abdelaziz'] | 2021-06-07 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.49995068e-01 5.59688091e-01 -3.95725161e-01 -1.06677867e-01
-7.85428822e-01 -7.17797041e-01 6.04364574e-01 3.75274330e-01
2.17229174e-03 7.35984862e-01 -1.30665690e-01 -1.26885962e+00
-2.40658432e-01 -1.17407286e+00 -1.34173119e+00 -5.91273233e-02
-3.88724893e-01 6.10730290e-01 4.53462213e-01 -2.71762103... | [8.895305633544922, 7.1019744873046875] |
888f03f2-56ad-4e62-88e7-f2cd51e18008 | understanding-the-importance-of-heart-sound | 2005.10480 | null | https://arxiv.org/abs/2005.10480v2 | https://arxiv.org/pdf/2005.10480v2.pdf | A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation | Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed. Some researchers in the field argue the segmentation step is an unwanted ... | ['Houman Ghaemmaghami', 'Sridha Sridharan', 'Tharindu Fernando', 'Theekshana Dissanayake', 'Clinton Fookes', 'Simon Denman'] | 2020-05-21 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 7.64463782e-01 5.31025767e-01 -5.97904697e-02 -3.13697577e-01
-3.82376760e-01 -3.38918895e-01 1.86523274e-01 3.16283107e-01
-1.05578840e-01 4.41386431e-01 2.53499858e-02 -6.01495206e-01
-2.85796732e-01 -5.63355267e-01 -4.57368940e-02 -7.13089705e-01
4.58565503e-02 5.16083598e-01 2.48831790e-02 1.36570945... | [14.27788257598877, 3.2880403995513916] |
649337f7-5be2-4488-831e-d4a2e97a6245 | contact-aware-retargeting-of-skinned-motion | 2109.07431 | null | https://arxiv.org/abs/2109.07431v1 | https://arxiv.org/pdf/2109.07431v1.pdf | Contact-Aware Retargeting of Skinned Motion | This paper introduces a motion retargeting method that preserves self-contacts and prevents interpenetration. Self-contacts, such as when hands touch each other or the torso or the head, are important attributes of human body language and dynamics, yet existing methods do not model or preserve these contacts. Likewise,... | ['Jun Saito', 'Jimei Yang', 'Aaron Hertzmann', 'Duygu Ceylan', 'Ruben Villegas'] | 2021-09-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Villegas_Contact-Aware_Retargeting_of_Skinned_Motion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Villegas_Contact-Aware_Retargeting_of_Skinned_Motion_ICCV_2021_paper.pdf | iccv-2021-1 | ['motion-retargeting'] | ['computer-vision'] | [ 7.65315965e-02 9.65425465e-03 -3.86490941e-01 7.71175250e-02
-4.44168091e-01 -5.16543210e-01 4.47132677e-01 -3.79032075e-01
-4.32986617e-01 3.52430165e-01 8.72842610e-01 2.26577088e-01
1.66937813e-01 -3.91370416e-01 -8.49396169e-01 -3.39455813e-01
-1.78274691e-01 3.52546483e-01 5.85461676e-01 -3.71105731... | [7.361909866333008, -0.41498738527297974] |
a996da2b-f283-481d-9a3a-6032f68b4f58 | a-kinematic-chain-space-for-monocular-motion | 1702.00186 | null | http://arxiv.org/abs/1702.00186v1 | http://arxiv.org/pdf/1702.00186v1.pdf | A Kinematic Chain Space for Monocular Motion Capture | This paper deals with motion capture of kinematic chains (e.g. human
skeletons) from monocular image sequences taken by uncalibrated cameras. We
present a method based on projecting an observation into a kinematic chain
space (KCS). An optimization of the nuclear norm is proposed that implicitly
enforces structural pro... | ['Bastian Wandt', 'Hanno Ackermann', 'Bodo Rosenhahn'] | 2017-02-01 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.60887259e-01 -9.80957299e-02 -1.64868996e-01 5.03793266e-03
9.60319936e-02 -6.26294911e-01 5.67864418e-01 -4.63859469e-01
-6.68138206e-01 6.86386466e-01 -1.90125033e-01 1.13893244e-02
-1.21065918e-02 -3.49456817e-01 -8.11852872e-01 -8.39297473e-01
1.19821325e-01 7.23810494e-01 5.39732575e-01 7.91200176... | [7.442643642425537, -1.4216597080230713] |
6cb13d94-52c8-4d9e-a507-1526c8201a07 | potential-based-credit-assignment-for | 2305.18380 | null | https://arxiv.org/abs/2305.18380v1 | https://arxiv.org/pdf/2305.18380v1.pdf | Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles | While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This paper introduces the concept of collaborative reinforcement learning (RL) to generate challenging test cases for AV planning and decision-makin... | ['Hao Shen', 'Chih-Hong Cheng', 'Utku Ayvaz'] | 2023-05-28 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 5.82397506e-02 4.46842551e-01 1.09645603e-02 -2.75547475e-01
-4.98772532e-01 -5.08015156e-01 8.72562051e-01 3.25889409e-01
-5.94559550e-01 1.38281131e+00 -4.73858677e-02 -4.86514211e-01
-4.96153921e-01 -8.42794955e-01 -5.18221855e-01 -7.17056751e-01
-3.67079765e-01 6.61328971e-01 3.58041674e-01 -4.75149781... | [4.533801078796387, 1.9693312644958496] |
19292532-82fe-4f43-92ba-906937de66c6 | target-driven-structured-transformer-planner | 2207.11201 | null | https://arxiv.org/abs/2207.11201v1 | https://arxiv.org/pdf/2207.11201v1.pdf | Target-Driven Structured Transformer Planner for Vision-Language Navigation | Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In... | ['Si Liu', 'Huaxia Xia', 'Haibing Ren', 'Lirong Yang', 'Wenguan Wang', 'Chen Gao', 'Jinyu Chen', 'Yusheng Zhao'] | 2022-07-19 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 3.09691802e-02 1.63002953e-01 1.84872255e-01 -4.19165403e-01
-8.43731940e-01 -6.11998022e-01 5.98087430e-01 -2.20231131e-01
-3.77575606e-01 5.84059596e-01 5.43700039e-01 -7.16753960e-01
-1.92087833e-02 -6.52779400e-01 -8.48028421e-01 -5.79930604e-01
-1.88235775e-01 3.00050884e-01 9.15643200e-02 -4.58253235... | [4.508424758911133, 0.5073919892311096] |
e752bd1a-912d-4e28-946c-beff6c4297d5 | bmad-benchmarks-for-medical-anomaly-detection | 2306.11876 | null | https://arxiv.org/abs/2306.11876v2 | https://arxiv.org/pdf/2306.11876v2.pdf | BMAD: Benchmarks for Medical Anomaly Detection | Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditio... | ['Xingyu Li', 'Zhaoxiang Zhang', 'Yinsheng He', 'Hanqiu Deng', 'Hanshi Sun', 'Jinan Bao'] | 2023-06-20 | null | null | null | null | ['medical-diagnosis', 'anomaly-detection'] | ['medical', 'methodology'] | [ 7.25588277e-02 -2.01372519e-01 2.64726188e-02 -1.42729729e-01
-5.88731945e-01 -1.94629416e-01 1.40158966e-01 6.84032381e-01
-5.10525852e-02 2.94762135e-01 -2.42685780e-01 -3.82359117e-01
-1.64081350e-01 -4.85240817e-01 -1.17546998e-01 -8.01202834e-01
-2.39696845e-01 3.02353024e-01 3.02397192e-01 1.63038686... | [7.627323627471924, 2.054788112640381] |
2d21d0e6-5e31-4eab-8b80-13879404cf8d | cross-lingual-wolastoqey-english-definition | null | null | https://aclanthology.org/2021.ranlp-main.17 | https://aclanthology.org/2021.ranlp-main.17.pdf | Cross-Lingual Wolastoqey-English Definition Modelling | Definition modelling is the task of automatically generating a dictionary-style definition given a target word. In this paper, we consider cross-lingual definition generation. Specifically, we generate English definitions for Wolastoqey (Malecite-Passamaquoddy) words. Wolastoqey is an endangered, low-resource polysynth... | ['Paul Cook', 'Diego Bear'] | null | null | https://aclanthology.org/2021.ranlp-1.17 | https://aclanthology.org/2021.ranlp-1.17.pdf | ranlp-2021-9 | ['definition-modelling'] | ['natural-language-processing'] | [ 2.90284604e-01 2.00738922e-01 -2.48658001e-01 -2.03867808e-01
-8.29267502e-01 -1.07028723e+00 7.97103584e-01 2.45156348e-01
-7.00450540e-01 1.20840228e+00 4.15860921e-01 -6.08904302e-01
1.27230957e-01 -9.38408613e-01 -5.10189116e-01 -8.40675682e-02
5.22886038e-01 5.54833710e-01 -4.73321646e-01 -6.31173313... | [10.948023796081543, 9.689929962158203] |
9a48574f-579d-4c74-9e24-e49ecf98001a | multimodal-attention-fusion-for-target | 2102.01326 | null | https://arxiv.org/abs/2102.01326v1 | https://arxiv.org/pdf/2102.01326v1.pdf | Multimodal Attention Fusion for Target Speaker Extraction | Target speaker extraction, which aims at extracting a target speaker's voice from a mixture of voices using audio, visual or locational clues, has received much interest. Recently an audio-visual target speaker extraction has been proposed that extracts target speech by using complementary audio and visual clues. Altho... | ['Shoko Araki', 'Tomohiro Nakatani', 'Marc Delcroix', 'Keisuke Kinoshita', 'Tsubasa Ochiai', 'Hiroshi Sato'] | 2021-02-02 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.58830658e-02 -1.55296922e-01 1.48826852e-01 -5.24963364e-02
-1.48885691e+00 -4.45562631e-01 5.48064113e-01 2.30428621e-01
-2.22664773e-01 7.19365060e-01 3.82390797e-01 6.31418079e-02
-1.24171667e-01 -6.21380173e-02 -3.47073197e-01 -8.78987849e-01
5.55601493e-02 3.02714646e-01 5.02621651e-01 -3.13409567... | [14.494242668151855, 5.295816421508789] |
4d9fe829-3cfb-48f8-a79b-58d9198e3f60 | classification-of-household-materials-via | 1805.04051 | null | http://arxiv.org/abs/1805.04051v3 | http://arxiv.org/pdf/1805.04051v3.pdf | Classification of Household Materials via Spectroscopy | Recognizing an object's material can inform a robot on the object's fragility
or appropriate use. To estimate an object's material during manipulation, many
prior works have explored the use of haptic sensing. In this paper, we explore
a technique for robots to estimate the materials of objects using spectroscopy.
We d... | ['Sonia Chernova', 'Nathan Luskey', 'Zackory Erickson', 'Charles C. Kemp'] | 2018-05-10 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.09700298e-01 -3.55413742e-02 -1.82792749e-02 -2.52389044e-01
-5.98969162e-01 -5.01895905e-01 -1.71024472e-01 3.07985634e-01
-2.12723643e-01 5.26856422e-01 -4.44671363e-01 3.82981971e-02
-1.33939907e-01 -9.44673121e-01 -1.02661347e+00 -3.17904115e-01
-2.59283651e-02 4.29200143e-01 2.18576252e-01 -5.28523251... | [5.835573673248291, -0.8115377426147461] |
7cd6fb67-7ac6-4546-a0b5-87dd33829616 | twice-mixing-a-rank-learning-based-quality | 2102.00670 | null | https://arxiv.org/abs/2102.00670v1 | https://arxiv.org/pdf/2102.00670v1.pdf | Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement | To improve the quality of underwater images, various kinds of underwater image enhancement (UIE) operators have been proposed during the past few years. However, the lack of effective objective evaluation methods limits the further development of UIE techniques. In this paper, we propose a novel rank learning guided no... | ['Xinghao Ding', 'Yue Huang', 'Xueyang Fu', 'Zhenqi Fu'] | 2021-02-01 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 3.86076927e-01 -1.05757855e-01 4.49408829e-01 -5.45760274e-01
-8.26919854e-01 -2.07037315e-01 3.99661154e-01 -3.96936871e-02
-6.07503176e-01 7.27736235e-01 3.14027481e-02 1.31807998e-01
-2.18217522e-01 -9.50534225e-01 -7.58219898e-01 -8.78456712e-01
-1.10778064e-01 1.21380672e-01 1.54769093e-01 -3.83243829... | [10.716172218322754, -3.510483980178833] |
52b1c309-8cee-45ac-9a4f-8f077ae1c479 | bert-proof-syntactic-structures-investigating | null | null | https://aclanthology.org/2021.findings-acl.288 | https://aclanthology.org/2021.findings-acl.288.pdf | BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing | null | ['Maximin Coavoux'] | null | null | null | null | findings-acl-2021-8 | ['constituency-parsing'] | ['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... | [-7.213083744049072, 3.8001513481140137] |
9c4794cd-18db-4fcc-b2bb-7a07f8cc1e12 | energy-analysis-of-bursting-hindmarsh-rose | 2203.11252 | null | https://arxiv.org/abs/2203.11252v1 | https://arxiv.org/pdf/2203.11252v1.pdf | Energy analysis of bursting Hindmarsh-Rose neurons with time-delayed coupling | Mathematical modeling is an important tool to study the role of delay in neural systems and to evaluate its effects on the signaling activity of coupled neurons. Models for delayed neurons are often used to represent the dynamics of real neurons, but rarely to assess the energy required to maintain these dynamics. In t... | ['Fernando Vadillo', 'Abdelmalik Moujahid'] | 2022-03-21 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 1.85636044e-01 -1.63065270e-01 1.54945537e-01 3.84556472e-01
1.71838880e-01 -7.60088861e-01 4.99763936e-01 5.30222893e-01
-9.04728830e-01 8.83103132e-01 -3.72418582e-01 -4.79115397e-02
-1.10969141e-01 -6.12201154e-01 -6.36603951e-01 -1.27834809e+00
-2.10233405e-01 -4.79876772e-02 5.38215995e-01 -5.22423387... | [8.014143943786621, 2.8544862270355225] |
9df0c625-5867-4269-bf76-b8d1cb377c01 | 300-sparsans-at-semeval-2018-task-9-hypernymy | null | null | https://aclanthology.org/S18-1152 | https://aclanthology.org/S18-1152.pdf | 300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes | This paper describes 300-sparsians{'}s participation in SemEval-2018 Task 9: Hypernym Discovery, with a system based on sparse coding and a formal concept hierarchy obtained from word embeddings. Our system took first place in subtasks (1B) Italian (all and entities), (1C) Spanish entities, and (2B) music entities. | ['P{\\\'e}ter F{\\"o}ldi{\\\'a}k', "M{\\'a}rton Makrai", "G{\\'a}bor Berend"] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [-2.25452960e-01 7.98272848e-01 -1.20884247e-01 -2.03238860e-01
-2.15619057e-01 -5.14917254e-01 7.26497769e-01 7.64797926e-01
-9.41670120e-01 9.88794446e-01 6.03920639e-01 -3.07595789e-01
-3.40130240e-01 -7.70510674e-01 -4.82032537e-01 -1.21008888e-01
-4.98413384e-01 1.08935928e+00 6.69920594e-02 -5.13868868... | [9.80933952331543, 8.708108901977539] |
10b8c8d3-4918-4f3a-89b2-831b98d8fc32 | fence-gan-towards-better-anomaly-detection | 1904.01209 | null | http://arxiv.org/abs/1904.01209v1 | http://arxiv.org/pdf/1904.01209v1.pdf | Fence GAN: Towards Better Anomaly Detection | Anomaly detection is a classical problem where the aim is to detect anomalous
data that do not belong to the normal data distribution. Current
state-of-the-art methods for anomaly detection on complex high-dimensional data
are based on the generative adversarial network (GAN). However, the traditional
GAN loss is not d... | ['Farhan Akram', 'Connie Kou Khor Li', 'Sojeong Park', 'Amadeus Aristo Winarto', 'Hwee Kuan Lee', 'Cuong Phuc Ngo'] | 2019-04-02 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 1.58117294e-01 -2.27148551e-02 3.54059905e-01 -3.18532050e-01
-4.22170520e-01 -4.09242064e-01 4.72397149e-01 1.63058028e-01
-2.35320017e-01 7.21798182e-01 -3.75622392e-01 -2.00007379e-01
7.72835612e-02 -9.56271231e-01 -6.38434172e-01 -8.72882485e-01
-6.31813854e-02 6.01021409e-01 2.29297891e-01 -1.44563958... | [7.59529447555542, 2.361971139907837] |
6ba71002-ff32-45ed-9eb1-912f65621356 | study-on-the-concept-and-development-of-a | 2208.09697 | null | https://arxiv.org/abs/2208.09697v1 | https://arxiv.org/pdf/2208.09697v1.pdf | Study on the Concept and Development of a Mobile Incubator | Creating the best possible conditions is essential for proper cell growth. Incubators, a type of biotechnological instrument, are used to simulate this condition and maintain the cells within them. The processes involved in creating a mobile incubator, which are essential for monitoring a cell culture's physiological p... | ['Huseyin Uvet', 'Abdurrahim Yilmaz', 'Ufuk Gorkem Kirabali', 'Atasangu Yilmaz', 'Rahmetullah Varol', 'Nesim Bilici', 'Fehmi Can Ay'] | 2022-08-20 | null | null | null | null | ['culture'] | ['speech'] | [ 3.84896100e-02 -3.13310832e-01 6.47955984e-02 4.72449452e-01
2.98644602e-01 -4.65629369e-01 9.34817195e-02 7.88392961e-01
-5.20814776e-01 8.84060681e-01 -5.24104357e-01 -2.85460770e-01
5.94199955e-01 -7.08206773e-01 -4.27709848e-01 -1.12197721e+00
2.45244727e-01 8.22508708e-02 2.80019641e-01 1.81003228... | [13.87893295288086, -3.0372865200042725] |
964716f1-790b-4a77-a02e-6db57b7a3f07 | look-further-to-recognize-better-learning | 1907.12924 | null | https://arxiv.org/abs/1907.12924v1 | https://arxiv.org/pdf/1907.12924v1.pdf | Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition | Service robots are expected to operate effectively in human-centric environments for long periods of time. In such realistic scenarios, fine-grained object categorization is as important as basic-level object categorization. We tackle this problem by proposing an open-ended object recognition approach which concurrentl... | ['S. Hamidreza Kasaei'] | 2019-07-26 | null | null | null | null | ['3d-object-recognition', 'object-categorization'] | ['computer-vision', 'computer-vision'] | [-8.12638551e-02 1.16061959e-02 -2.17949778e-01 -5.92070580e-01
-3.63063633e-01 -3.47393364e-01 1.01984251e+00 4.77002472e-01
-4.27195340e-01 4.29031819e-01 -5.13785258e-02 2.69438535e-01
-2.45710894e-01 -7.66937315e-01 -5.52084684e-01 -9.50322986e-01
-2.64944166e-01 1.15529013e+00 4.82894897e-01 2.70202719... | [7.617640972137451, -1.19450044631958] |
18348197-7d0c-497b-aca7-1f9218d3d195 | few-shot-class-incremental-learning-for-named | null | null | https://aclanthology.org/2022.acl-long.43 | https://aclanthology.org/2022.acl-long.43.pdf | Few-Shot Class-Incremental Learning for Named Entity Recognition | Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, we study a more challenging but practical problem, i.e., few-shot class-incremental learning for NER, where an NER model is t... | ['Ricardo Henao', 'Ruiyi Zhang', 'Subrata Mitra', 'Sungchul Kim', 'Handong Zhao', 'Tong Yu', 'Rui Wang'] | null | null | null | null | acl-2022-5 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.03897461e-01 4.22720641e-01 -1.27344012e-01 -4.03828681e-01
-8.89492750e-01 -4.64430571e-01 2.72037029e-01 9.25497413e-02
-8.01322162e-01 1.08761322e+00 3.75594586e-01 2.27471832e-02
4.92457598e-01 -9.94077146e-01 -7.00434446e-01 -4.19008166e-01
1.81045219e-01 6.58229828e-01 4.45768803e-01 -2.05750287... | [9.693087577819824, 9.303074836730957] |
9022178c-8d55-4bf3-8f49-bfb5ecd5bab3 | ssmd-semi-supervised-medical-image-detection | 2106.01544 | null | https://arxiv.org/abs/2106.01544v1 | https://arxiv.org/pdf/2106.01544v1.pdf | SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation | Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the... | ['Yizhou Yu', 'Weimin Li', 'Shu Zhang', 'Gang Wang', 'Haofeng Li', 'Chengdi Wang', 'Hong-Yu Zhou'] | 2021-06-03 | null | null | null | null | ['semi-supervised-object-detection', 'medical-image-detection'] | ['computer-vision', 'computer-vision'] | [ 4.42642182e-01 3.38564813e-01 -4.53766763e-01 -5.47879815e-01
-9.80836272e-01 -3.95821817e-02 2.78949708e-01 1.27252281e-01
-3.49000275e-01 4.42691982e-01 6.11795904e-03 -1.64483823e-02
-3.14529217e-03 -3.01607937e-01 -4.75963712e-01 -9.62366223e-01
2.24896312e-01 3.24256212e-01 3.72971803e-01 1.49279267... | [14.759422302246094, -2.0814921855926514] |
41f56950-39d8-45a0-9422-f3a3b36d87ce | a-survey-and-approach-to-chart-classification | 2307.04147 | null | https://arxiv.org/abs/2307.04147v1 | https://arxiv.org/pdf/2307.04147v1.pdf | A Survey and Approach to Chart Classification | Charts represent an essential source of visual information in documents and facilitate a deep understanding and interpretation of information typically conveyed numerically. In the scientific literature, there are many charts, each with its stylistic differences. Recently the document understanding community has begun ... | ['David S Doermann', 'Mohammed Javed', 'Anurag Dhote'] | 2023-07-09 | null | null | null | null | ['classification-1'] | ['methodology'] | [-1.12625413e-01 -3.88699055e-01 -4.51558352e-01 -1.89766377e-01
-5.73409081e-01 -8.07977498e-01 8.96966338e-01 5.21510065e-01
3.44548523e-01 3.40704232e-01 6.45213604e-01 -7.60679066e-01
2.03270555e-01 -5.66106081e-01 -5.40157139e-01 -2.55630672e-01
-2.72608757e-01 4.47512507e-01 -3.77964526e-01 -1.08794849... | [11.33210277557373, 2.197071075439453] |
7b915e0b-7af0-4cf2-bd1f-b6639f0f4138 | detector-free-weakly-supervised-group | 2204.02139 | null | https://arxiv.org/abs/2204.02139v1 | https://arxiv.org/pdf/2204.02139v1.pdf | Detector-Free Weakly Supervised Group Activity Recognition | Group activity recognition is the task of understanding the activity conducted by a group of people as a whole in a multi-person video. Existing models for this task are often impractical in that they demand ground-truth bounding box labels of actors even in testing or rely on off-the-shelf object detectors. Motivated ... | ['Suha Kwak', 'Minsu Cho', 'Jinsung Lee', 'Dongkeun Kim'] | 2022-04-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_Detector-Free_Weakly_Supervised_Group_Activity_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_Detector-Free_Weakly_Supervised_Group_Activity_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['group-activity-recognition'] | ['computer-vision'] | [ 2.48542905e-01 -2.72515923e-01 -4.14650857e-01 -3.15720677e-01
-4.68132019e-01 -6.55146897e-01 8.25869262e-01 3.05150300e-01
-4.83906984e-01 3.93809319e-01 5.25220513e-01 1.91102296e-01
1.12847701e-01 -4.86019462e-01 -7.24728823e-01 -7.43298531e-01
-2.17491806e-01 1.41270518e-01 4.20791626e-01 1.46167114... | [8.238384246826172, 0.6250066757202148] |
27630196-3e82-4ea5-9111-4d10e8515b91 | consistent-jumpy-predictions-for-videos-and | 1807.02033 | null | http://arxiv.org/abs/1807.02033v3 | http://arxiv.org/pdf/1807.02033v3.pdf | Consistent Generative Query Networks | Stochastic video prediction models take in a sequence of image frames, and
generate a sequence of consecutive future image frames. These models typically
generate future frames in an autoregressive fashion, which is slow and requires
the input and output frames to be consecutive. We introduce a model that
overcomes the... | ['S. M. Ali Eslami', 'Edward Lockhart', 'Fabio Viola', 'Murray Shanahan', 'Marta Garnelo', 'Danilo J. Rezende', 'Ananya Kumar'] | 2018-07-05 | null | null | null | iclr-2019-5 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.38265687e-01 2.10385188e-03 -8.04910213e-02 -1.94180548e-01
-7.60949910e-01 -6.98695064e-01 6.51397407e-01 -4.55538690e-01
1.59947313e-02 5.40130258e-01 3.27868789e-01 -2.04203710e-01
4.02395308e-01 -6.49441004e-01 -1.06503105e+00 -4.90921170e-01
-9.02469233e-02 1.12414867e-01 4.65449959e-01 3.17629367... | [9.637214660644531, -2.103463888168335] |
29357dcf-c3e3-433c-8af1-2ad2a9df18a0 | balanced-training-of-energy-based-models-with | 2306.00684 | null | https://arxiv.org/abs/2306.00684v3 | https://arxiv.org/pdf/2306.00684v3.pdf | Balanced Training of Energy-Based Models with Adaptive Flow Sampling | Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density. Although very flexible, EBMs lack a specified normalization constant of the model, making the likelihood of the model computationally intractable. Several approximate samplers and variational infer... | ['Marylou Gabrié', 'Éric Moulines', 'Louis Grenioux'] | 2023-06-01 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 1.59215033e-01 -1.85475918e-03 -4.71721381e-01 -4.44513917e-01
-5.94709277e-01 -2.64267117e-01 7.56438732e-01 -5.66690415e-02
-4.10949737e-01 1.12983072e+00 7.62818158e-02 -3.50621015e-01
3.99117172e-02 -1.22775578e+00 -7.79858947e-01 -7.75532603e-01
2.78197765e-01 6.08172834e-01 2.18355581e-01 3.71444613... | [7.041081428527832, 3.8995015621185303] |
32ef5f03-8f83-4f47-85bf-09fe04c7d6db | generative-models-improve-radiomics-1 | 2109.02252 | null | https://arxiv.org/abs/2109.02252v1 | https://arxiv.org/pdf/2109.02252v1.pdf | Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study | Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic featu... | ['Leonard Wee', 'Andre Dekker', 'Inigo Bermejo', 'Junhua Chen'] | 2021-09-06 | null | null | null | null | ['deep-attention', 'lung-cancer-diagnosis', 'deep-attention'] | ['computer-vision', 'medical', 'natural-language-processing'] | [ 3.08031261e-01 2.33533323e-01 -9.64532793e-02 -3.85074198e-01
-1.21976566e+00 -1.60395294e-01 5.05118966e-01 9.85710844e-02
-6.41086102e-01 8.39086771e-01 3.87378722e-01 -4.76762027e-01
-2.21367136e-01 -1.12892818e+00 -6.76893115e-01 -1.10980308e+00
1.51509121e-01 7.39546180e-01 8.36697593e-02 -3.95787843... | [15.241116523742676, -2.243168592453003] |
83a54b07-034d-4032-b661-fb0b5d704f27 | text-guided-high-definition-consistency | 2305.05901 | null | https://arxiv.org/abs/2305.05901v1 | https://arxiv.org/pdf/2305.05901v1.pdf | Text-guided High-definition Consistency Texture Model | With the advent of depth-to-image diffusion models, text-guided generation, editing, and transfer of realistic textures are no longer difficult. However, due to the limitations of pre-trained diffusion models, they can only create low-resolution, inconsistent textures. To address this issue, we present the High-definit... | ['Tiantong He', 'Zhibin Tang'] | 2023-05-10 | null | null | null | null | ['text-guided-generation'] | ['computer-vision'] | [ 3.12535375e-01 -9.97610986e-02 4.12047356e-01 -3.29999149e-01
-5.50104678e-01 -4.53097105e-01 8.00876737e-01 -1.43084720e-01
7.09430426e-02 5.25436878e-01 1.99012637e-01 1.99503183e-01
-5.41936532e-02 -1.08633792e+00 -7.35476673e-01 -6.08207166e-01
2.95907676e-01 4.11126941e-01 4.43344772e-01 -2.82839268... | [9.449670791625977, -3.0935611724853516] |
66e10c18-abb6-406b-978c-8cd5186c003f | perpetual-humanoid-control-for-real-time | 2305.06456 | null | https://arxiv.org/abs/2305.06456v2 | https://arxiv.org/pdf/2305.06456v2.pdf | Perpetual Humanoid Control for Real-time Simulated Avatars | We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e.g. pose estimates from video or generated from language) and unexpected falls. Our controller scales up to learning ten thousand motion clips without using any extern... | ['Weipeng Xu', 'Kris Kitani', 'Alexander Winkler', 'Jinkun Cao', 'Zhengyi Luo'] | 2023-05-10 | null | null | null | null | ['humanoid-control'] | ['robots'] | [ 1.33805033e-02 2.20607594e-01 1.06320448e-01 4.16934907e-01
-8.03976178e-01 -4.81518775e-01 3.23756456e-01 -6.57623947e-01
-5.56302667e-01 9.39636230e-01 1.75393611e-01 2.30737895e-01
1.61662802e-01 -4.89627540e-01 -1.19704914e+00 -5.57956636e-01
-5.29578447e-01 8.63621116e-01 4.71530229e-01 -3.08725178... | [5.01881742477417, 0.7476443648338318] |
97dffa07-d48c-4e6a-b5a7-aa42fa5dc619 | a-mid-level-video-representation-based-on | 1605.03804 | null | http://arxiv.org/abs/1605.03804v1 | http://arxiv.org/pdf/1605.03804v1.pdf | A Mid-level Video Representation based on Binary Descriptors: A Case Study for Pornography Detection | With the growing amount of inappropriate content on the Internet, such as
pornography, arises the need to detect and filter such material. The reason for
this is given by the fact that such content is often prohibited in certain
environments (e.g., schools and workplaces) or for certain publics (e.g.,
children). In rec... | ['Arnaldo de A. Araújo', 'Silvio Jamil F. Guimarães', 'Sandra Avila', 'Carlos Caetano', 'William Robson Schwartz'] | 2016-05-12 | null | null | null | null | ['video-description', 'pornography-detection'] | ['computer-vision', 'computer-vision'] | [ 1.98425457e-01 -3.46940517e-01 -1.93089887e-01 1.16136946e-01
-5.80321431e-01 -3.62128109e-01 5.52523196e-01 6.82405710e-01
-2.16427132e-01 3.56269300e-01 1.13184281e-01 2.53519714e-01
-3.08054239e-01 -1.02533913e+00 -3.69493306e-01 -9.08556283e-01
1.91125423e-01 -7.82717690e-02 4.49421495e-01 -6.59779087... | [12.0521821975708, 0.49912768602371216] |
d60bc799-5b05-43ab-923e-970d793b4741 | cross-lingual-word-embeddings-beyond-zero | 2011.01682 | null | https://arxiv.org/abs/2011.01682v1 | https://arxiv.org/pdf/2011.01682v1.pdf | Cross-lingual Word Embeddings beyond Zero-shot Machine Translation | We explore the transferability of a multilingual neural machine translation model to unseen languages when the transfer is grounded solely on the cross-lingual word embeddings. Our experimental results show that the translation knowledge can transfer weakly to other languages and that the degree of transferability depe... | ['Ali Basirat', 'Shifei Chen'] | 2020-11-03 | null | null | null | null | ['zero-shot-machine-translation'] | ['natural-language-processing'] | [-3.96326900e-01 -3.57394628e-02 -7.46473372e-01 -3.33064735e-01
-8.30701411e-01 -9.67298210e-01 7.59818673e-01 -2.44307920e-01
-5.13429344e-01 1.05660105e+00 5.44593394e-01 -8.72811258e-01
3.68943393e-01 -7.27744520e-01 -1.03046012e+00 -2.42128730e-01
1.65810347e-01 5.73273242e-01 -1.18019015e-01 -6.12896144... | [11.280980110168457, 10.135885238647461] |
5fe15454-c9a6-40a1-ac00-e723b10cdc2d | assessing-gender-bias-in-predictive | 2203.10264 | null | https://arxiv.org/abs/2203.10264v1 | https://arxiv.org/pdf/2203.10264v1.pdf | Assessing Gender Bias in Predictive Algorithms using eXplainable AI | Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair r... | ['Silvia Ramis', 'Cristina Manresa-Yee'] | 2022-03-19 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.09074932e-01 4.42705989e-01 -3.80765796e-01 -8.48651648e-01
7.63069279e-03 -3.12429100e-01 5.12718558e-01 2.59999465e-02
-5.90435922e-01 9.96431410e-01 -9.19719338e-02 -3.02566975e-01
7.31421774e-03 -7.79405236e-01 -5.76531112e-01 -6.31290257e-01
2.47876987e-01 6.55126721e-02 -5.77079654e-01 -3.90563086... | [12.978278160095215, 1.406218409538269] |
308a1b21-3a8f-41e6-be22-66ca949ae5aa | lpyolo-low-precision-yolo-for-face-detection | 2207.10482 | null | https://arxiv.org/abs/2207.10482v1 | https://arxiv.org/pdf/2207.10482v1.pdf | LPYOLO: Low Precision YOLO for Face Detection on FPGA | In recent years, number of edge computing devices and artificial intelligence applications on them have advanced excessively. In edge computing, decision making processes and computations are moved from servers to edge devices. Hence, cheap and low power devices are required. FPGAs are very low power, inclined to do pa... | ['Hasan Şakir Bilge', 'Sefa Burak Okcu', 'Bestami Günay'] | 2022-07-21 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-2.38191802e-03 -6.84903935e-02 -1.58993617e-01 -2.92819530e-01
5.20228922e-01 -2.79159158e-01 1.43268526e-01 -2.63961852e-01
-6.45884573e-01 4.07904953e-01 -6.94476128e-01 -6.29959464e-01
2.15994433e-01 -1.01618648e+00 -5.02464771e-01 -4.17250454e-01
1.38223782e-01 -2.36271560e-01 5.59072316e-01 1.88410487... | [8.254861831665039, 2.642193078994751] |
92462d61-bca5-41f8-822d-0fd2469949a4 | learning-how-to-infer-partial-mdps-for-in | 2302.04250 | null | https://arxiv.org/abs/2302.04250v2 | https://arxiv.org/pdf/2302.04250v2.pdf | Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration | To generalize across tasks, an agent should acquire knowledge from past tasks that facilitate adaptation and exploration in future tasks. We focus on the problem of in-context adaptation and exploration, where an agent only relies on context, i.e., history of states, actions and/or rewards, rather than gradient-based u... | ['Hado van Hasselt', 'Nan Rosemary Ke', 'Chentian Jiang'] | 2023-02-08 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 3.40414703e-01 2.76496112e-01 -3.71233612e-01 -2.59412020e-01
-8.05849552e-01 -6.04521453e-01 8.99015963e-01 1.11667790e-01
-8.74823272e-01 1.37678862e+00 -7.88520277e-02 -3.30892354e-01
-1.55602386e-02 -6.99152946e-01 -9.86785710e-01 -7.25071192e-01
-1.02604240e-01 9.52835143e-01 3.61041605e-01 -6.16661645... | [4.141273021697998, 1.9219046831130981] |
2a5e6cb8-62a7-4257-9542-d0feb048d9dd | impact-of-redundancy-on-resilience-in | 2211.08622 | null | https://arxiv.org/abs/2211.08622v1 | https://arxiv.org/pdf/2211.08622v1.pdf | Impact of Redundancy on Resilience in Distributed Optimization and Learning | This report considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with the server to find a minimum of the aggregate of the local cost... | ['Nitin H. Vaidya', 'Nirupam Gupta', 'Shuo Liu'] | 2022-11-16 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-5.25255799e-01 -2.38075331e-02 2.30652109e-01 1.42428493e-02
-5.76718152e-01 -5.51717401e-01 -2.04215106e-02 4.10167485e-01
-6.08254015e-01 8.10505390e-01 -4.09996271e-01 -3.92646864e-02
-5.71735799e-01 -6.63939953e-01 -8.91182005e-01 -1.00795305e+00
-7.93523490e-01 5.72877765e-01 6.13592528e-02 -3.00450325... | [6.108375072479248, 4.900088787078857] |
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