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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
28f94215-1d6e-44e6-8376-6ae88c465137 | planarrecon-real-time-3d-plane-detection-and-1 | 2206.07710 | null | https://arxiv.org/abs/2206.07710v1 | https://arxiv.org/pdf/2206.07710v1.pdf | PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos | We present PlanarRecon -- a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video. Unlike previous works that detect planes in 2D from a single image, PlanarRecon incrementally detects planes in 3D for each video fragment, which consists of a set of key frames, fro... | ['Huaizu Jiang', 'Xiaowei Zhou', 'Fengting Yang', 'Matheus Gadelha', 'Yiming Xie'] | 2022-06-15 | planarrecon-real-time-3d-plane-detection-and | http://openaccess.thecvf.com//content/CVPR2022/html/Xie_PlanarRecon_Real-Time_3D_Plane_Detection_and_Reconstruction_From_Posed_Monocular_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xie_PlanarRecon_Real-Time_3D_Plane_Detection_and_Reconstruction_From_Posed_Monocular_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-plane-detection'] | ['computer-vision'] | [ 3.65877114e-02 -1.34913847e-01 7.53880814e-02 -3.26247633e-01
-5.78609645e-01 -5.60069084e-01 3.73540699e-01 -7.52598420e-02
-2.97927618e-01 2.65376896e-01 -3.71202886e-01 1.46942690e-01
-1.35673761e-01 -9.51057971e-01 -1.02823985e+00 -3.42349917e-01
-2.91167110e-01 5.91338933e-01 9.80143964e-01 1.65989891... | [8.117650032043457, -2.3018736839294434] |
715c8be5-3e00-4dc2-aaab-5e463f6cc8d6 | experimental-demonstration-of-bandwidth | 2301.04573 | null | https://arxiv.org/abs/2301.04573v2 | https://arxiv.org/pdf/2301.04573v2.pdf | Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing | Time delay reservoir computing (TDRC) using semiconductor lasers (SLs) has proven to be a promising photonic analog approach for information processing. One appealing property is that SLs subject to delayed optical feedback and external optical injection, allow tuning the response bandwidth by changing the level of opt... | ['Ingo Fischer', 'Apostolos Argyris', 'Irene Estebanez'] | 2023-01-11 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.76122749e-01 -3.15254718e-01 -4.34178188e-02 1.44549981e-01
-2.20416799e-01 -7.46003389e-01 5.15609443e-01 -3.02173018e-01
-7.85456896e-01 8.73330891e-01 -3.38066876e-01 -3.59768450e-01
2.48917658e-03 -5.05920887e-01 -1.72055468e-01 -1.04464924e+00
-2.26902202e-01 1.40960336e-01 4.11906213e-01 -1.11363560... | [5.634557723999023, 4.864107608795166] |
640f7323-d288-4429-bf5a-014806c6bb04 | movie-recommendation-system-using-sentiment | 1811.10804 | null | https://arxiv.org/abs/1811.10804v1 | https://arxiv.org/pdf/1811.10804v1.pdf | Movie Recommendation System using Sentiment Analysis from Microblogging Data | Recommendation systems are important intelligent systems that play a vital role in providing selective information to users. Traditional approaches in recommendation systems include collaborative filtering and content-based filtering. However, these approaches have certain limitations like the necessity of prior user h... | ['Sudhanshu Kumar', 'Kanjar De', 'Shirsendu Sukanta Halder', 'Partha Pratim Roy'] | 2018-11-27 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-3.50608259e-01 -5.84600747e-01 -4.20885593e-01 -7.27779329e-01
4.65630693e-03 -4.71882939e-01 5.80945134e-01 4.35069770e-01
-6.14109337e-01 3.74627888e-01 7.30758131e-01 -4.23612654e-01
-3.85669857e-01 -1.01630926e+00 5.05865067e-02 -2.98887044e-01
3.69402081e-01 -1.35036772e-02 7.14065969e-01 -8.09652507... | [10.07238483428955, 5.83654260635376] |
287d816c-a1f5-4903-9387-6e7b22d5ca9d | cross-domain-activity-recognition-via | 2102.03353 | null | https://arxiv.org/abs/2102.03353v3 | https://arxiv.org/pdf/2102.03353v3.pdf | Cross-domain Activity Recognition via Substructural Optimal Transport | It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity recognition. Existing methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution ... | ['Xin Qin', 'Jindong Wang', 'Yiqiang Chen', 'Wang Lu'] | 2021-01-29 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 1.95964023e-01 -7.31716692e-01 -5.39496005e-01 -3.72484297e-01
-1.03309214e+00 -5.00307918e-01 4.80131418e-01 1.56363130e-01
-3.66307586e-01 9.40757275e-01 4.23019469e-01 1.04563370e-01
-3.25682074e-01 -7.68033862e-01 -7.03087747e-01 -7.73276091e-01
-1.00656167e-01 4.42342699e-01 7.39929199e-01 8.54890049... | [8.04428482055664, 1.0286751985549927] |
cf70d098-5fc2-4474-a890-9836369bcec7 | adaptively-learning-facial-expression-1 | null | null | https://doi.org/10.1109/TIP.2021.3049955 | https://doi.org/10.1109/TIP.2021.3049955 | Adaptively Learning Facial Expression Representation via C-F Labels and Distillation | Facial expression recognition is of significant importance in criminal investigation and digital entertainment. Under unconstrained conditions, existing expression datasets are highly class-imbalanced, and the similarity between expressions is high. Previous methods tend to improve the performance of facial expression ... | ['Hangyu Li; Nannan Wang; Xinpeng Ding; Xi Yang; Xinbo Gao'] | 2021-01-13 | null | null | null | ieee-transactions-on-image-processing-2021-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.91924259e-01 -9.36975703e-02 -3.36674899e-01 -8.25826526e-01
-3.80620658e-02 1.49258614e-01 2.21206099e-01 -3.02008241e-01
-4.97766793e-01 7.07448483e-01 -4.55137268e-02 1.98568434e-01
-3.66756953e-02 -8.12365115e-01 -2.80991256e-01 -7.73973465e-01
4.04080451e-02 7.91949853e-02 -2.61515468e-01 -4.01436210... | [13.601231575012207, 1.6808793544769287] |
1f5057b7-9479-4d69-9bcb-6cad86613de5 | interpretability-driven-sample-selection | 2104.06087 | null | https://arxiv.org/abs/2104.06087v1 | https://arxiv.org/pdf/2104.06087v1.pdf | Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation | In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions (e.g. label querying in an active learning setup). In this paper we propose a novel sample selection methodology based on deep features lev... | ['Dwarikanath Mahapatra'] | 2021-04-13 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [ 8.96436810e-01 5.92650712e-01 -6.22154593e-01 -6.37517512e-01
-1.42432678e+00 -3.24251443e-01 5.73566258e-01 7.70627737e-01
-5.78363597e-01 7.06664503e-01 1.65199697e-01 -1.96280047e-01
-6.95388079e-01 -5.75318038e-01 -5.90603054e-01 -9.31762815e-01
1.80526108e-01 9.51992810e-01 3.65817606e-01 4.90843147... | [14.88312816619873, -2.271135091781616] |
c7fb593b-cc94-4c03-8b83-1c0ac40b3b32 | hierarchical-resnext-models-for-breast-cancer | 1810.09025 | null | http://arxiv.org/abs/1810.09025v1 | http://arxiv.org/pdf/1810.09025v1.pdf | Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification | Microscopic histology image analysis is a cornerstone in early detection of
breast cancer. However these images are very large and manual analysis is error
prone and very time consuming. Thus automating this process is in high demand.
We proposed a hierarchical system of convolutional neural networks (CNN) that
classif... | ['Ismaël Koné', 'Lahsen Boulmane'] | 2018-10-21 | null | null | null | null | ['breast-cancer-histology-image-classification'] | ['medical'] | [ 1.00752905e-01 3.82221103e-01 5.05313650e-02 -3.02539229e-01
-8.72938335e-01 -3.12692702e-01 3.37747842e-01 5.43003440e-01
-9.14863765e-01 5.97614229e-01 -2.32065499e-01 -5.71674347e-01
1.58349108e-02 -6.71454966e-01 -4.96590972e-01 -8.35056663e-01
-1.05834939e-01 7.95801878e-01 5.70746481e-01 6.75383732... | [15.138184547424316, -2.9679644107818604] |
9e9dd344-6366-483e-9ede-0d55ca168d68 | generating-distractors-for-reading | 1809.02768 | null | http://arxiv.org/abs/1809.02768v2 | http://arxiv.org/pdf/1809.02768v2.pdf | Generating Distractors for Reading Comprehension Questions from Real Examinations | We investigate the task of distractor generation for multiple choice reading
comprehension questions from examinations. In contrast to all previous works,
we do not aim at preparing words or short phrases distractors, instead, we
endeavor to generate longer and semantic-rich distractors which are closer to
distractors ... | ['Yifan Gao', 'Piji Li', 'Michael R. Lyu', 'Irwin King', 'Lidong Bing'] | 2018-09-08 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [ 7.06064820e-01 6.48409545e-01 3.52198094e-01 -1.72385126e-01
-1.10500693e+00 -5.36458075e-01 4.95848715e-01 2.40205526e-01
-5.87416828e-01 8.08464348e-01 8.53329360e-01 -6.10294402e-01
1.53524727e-01 -6.75027132e-01 -5.99201381e-01 -3.43641639e-01
9.35793042e-01 4.00200307e-01 6.09193802e-01 -6.43662333... | [11.546570777893066, 8.222095489501953] |
c39772f9-31a6-4969-addd-18e58f652103 | iterative-geometry-encoding-volume-for-stereo | 2303.06615 | null | https://arxiv.org/abs/2303.06615v2 | https://arxiv.org/pdf/2303.06615v2.pdf | Iterative Geometry Encoding Volume for Stereo Matching | Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in matching tasks. However, all-pairs correlations lack non-local geometry knowledge and have difficulties tackling local ambiguities in ill-posed regions. In this paper, we propose Iterative Geometry Encoding Volume (IGEV-Stereo), a new deep networ... | ['Xin Yang', 'Xiaohuan Ding', 'Xianqi Wang', 'Gangwei Xu'] | 2023-03-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Iterative_Geometry_Encoding_Volume_for_Stereo_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Iterative_Geometry_Encoding_Volume_for_Stereo_Matching_CVPR_2023_paper.pdf | cvpr-2023-1 | ['stereo-matching-1'] | ['computer-vision'] | [-1.94926992e-01 -2.61696905e-01 -1.39475977e-02 -6.08932376e-01
-9.54840958e-01 -5.14528394e-01 5.07060170e-01 -1.04827024e-01
-1.84365332e-01 4.69590575e-01 4.59832132e-01 -1.23249799e-01
-2.69809127e-01 -1.01260138e+00 -9.77176547e-01 -3.46050411e-01
1.56758919e-01 6.19434237e-01 2.55972654e-01 -3.48620713... | [8.851652145385742, -2.3508188724517822] |
8cbeb63c-772f-41fa-94cc-d84f96081ac7 | did-you-miss-the-sign-a-false-negative-alarm | 1903.06391 | null | http://arxiv.org/abs/1903.06391v1 | http://arxiv.org/pdf/1903.06391v1.pdf | Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors | Object detection is an integral part of an autonomous vehicle for its
safety-critical and navigational purposes. Traffic signs as objects play a
vital role in guiding such systems. However, if the vehicle fails to locate any
critical sign, it might make a catastrophic failure. In this paper, we propose
an approach to i... | ['Niko Sünderhauf', 'Quazi Marufur Rahman', 'Feras Dayoub'] | 2019-03-15 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 7.91563243e-02 3.83289494e-02 3.67436931e-02 -1.77929431e-01
-4.35711443e-01 -5.09640157e-01 7.19290495e-01 -2.62505680e-01
-5.35704553e-01 5.94455004e-01 -6.26260877e-01 -6.62676096e-01
-1.28333256e-01 -6.55679941e-01 -6.99694157e-01 -7.91112781e-01
2.32702926e-01 5.72859526e-01 1.11864007e+00 -1.35647040... | [7.988853454589844, -0.8304251432418823] |
4ebe861f-688e-4a4e-83b0-1d21b28bdec2 | dynamic-mlp-for-fine-grained-image | 2203.03253 | null | https://arxiv.org/abs/2203.03253v1 | https://arxiv.org/pdf/2203.03253v1.pdf | Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information | Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on visual clues. Therefore, it is helpful to leverage additional information, e.g., the locations and dates for data shooting, which can be easil... | ['Jian Yang', 'Jiajun Liang', 'Shihao Zhou', 'Juntian Tao', 'Borui Zhao', 'RenJie Song', 'Xiang Li', 'Lingfeng Yang'] | 2022-03-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Dynamic_MLP_for_Fine-Grained_Image_Classification_by_Leveraging_Geographical_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Dynamic_MLP_for_Fine-Grained_Image_Classification_by_Leveraging_Geographical_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-image-classification'] | ['computer-vision'] | [-2.87199169e-02 -3.64420652e-01 -3.54543000e-01 -2.51073569e-01
-4.82511133e-01 -9.80442464e-01 7.54807830e-01 -5.83574059e-04
-3.92499506e-01 5.01524508e-01 2.16930538e-01 -2.23459050e-01
-2.86286503e-01 -5.56904495e-01 -8.11317265e-01 -9.23416436e-01
1.72222350e-02 1.40996069e-01 -2.17777923e-01 7.00678080... | [9.675597190856934, 2.040534019470215] |
a10d35fc-7e24-4fe3-82a5-4bec4a02d7c7 | time-associated-meta-learning-for-clinical | 2303.02570 | null | https://arxiv.org/abs/2303.02570v1 | https://arxiv.org/pdf/2303.02570v1.pdf | Time Associated Meta Learning for Clinical Prediction | Rich Electronic Health Records (EHR), have created opportunities to improve clinical processes using machine learning methods. Prediction of the same patient events at different time horizons can have very different applications and interpretations; however, limited number of events in each potential time window hurts ... | ['Christopher King', 'DaCheng Tao', 'Bradley Fritz', 'Yixin Chen', 'Lecheng Kong', 'Zehao Dong', 'Muhan Zhang', 'Hao liu'] | 2023-03-05 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 4.02901888e-01 -7.30714202e-02 -6.80056393e-01 -6.05893612e-01
-1.25318217e+00 4.75297011e-02 4.24781024e-01 7.79556036e-01
-1.76940471e-01 7.01331079e-01 5.74138165e-01 -1.47830412e-01
-3.83969963e-01 -6.10842466e-01 -4.29347634e-01 -5.82154930e-01
-4.26598310e-01 5.64598322e-01 -4.84598540e-02 8.81361812... | [7.909491539001465, 6.117120265960693] |
7e18906c-99a2-40d1-8575-b6fc17605f4b | embodied-artificial-intelligence-through | 1704.01407 | null | http://arxiv.org/abs/1704.01407v3 | http://arxiv.org/pdf/1704.01407v3.pdf | Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework | In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integratio... | ['Martì Sanchez-Fibla', 'Jordi-Ysard Puigbò', 'Clément Moulin-Frier', 'Paul F. M. J. Verschure', 'Xerxes D. Arsiwalla'] | 2017-04-05 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 9.68187675e-03 4.79917914e-01 4.31529105e-01 3.14561903e-01
4.10423502e-02 -6.23762965e-01 8.73179197e-01 1.53153362e-02
-5.31352341e-01 7.02066541e-01 1.12805642e-01 -3.11796159e-01
-7.33886242e-01 -8.83518338e-01 -6.33348823e-01 -4.35991615e-01
-3.24296504e-01 3.98403466e-01 1.42917931e-01 -7.80827105... | [4.283015727996826, 1.4476736783981323] |
fd6bcfb2-0722-407c-896c-35476aca43a4 | query-reformulation-using-query-history-for | 2005.02230 | null | https://arxiv.org/abs/2005.02230v2 | https://arxiv.org/pdf/2005.02230v2.pdf | Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting | Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad-hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities i... | ['Chuan-Ju Wang', 'Ming-Feng Tsai', 'Jheng-Hong Yang', 'Sheng-Chieh Lin', 'Rodrigo Nogueira', 'Jimmy Lin'] | 2020-05-05 | null | null | null | null | ['ad-hoc-information-retrieval', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.52172875e-01 1.99320480e-01 -3.37950438e-01 -2.01576531e-01
-1.62726164e+00 -8.95734429e-01 1.12177992e+00 2.89790839e-01
-8.13818812e-01 7.28056014e-01 9.15736377e-01 -5.17227590e-01
-5.57540536e-01 -1.54355377e-01 -1.46369770e-01 -3.39267761e-01
-6.66007400e-02 9.52265203e-01 1.55453205e-01 -1.07795763... | [12.091672897338867, 7.800274848937988] |
be1ffbe0-48f2-4409-bfb8-1a7884b3942a | point-voxel-adaptive-feature-abstraction-for | 2210.15514 | null | https://arxiv.org/abs/2210.15514v2 | https://arxiv.org/pdf/2210.15514v2.pdf | Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud Classification | Great progress has been made in point cloud classification with learning-based methods. However, complex scene and sensor inaccuracy in real-world application make point cloud data suffer from corruptions, such as occlusion, noise and outliers. In this work, we propose Point-Voxel based Adaptive (PV-Ada) feature abstra... | ['Chen Zheng', 'Ninghua Yang', 'Changwei Lin', 'Lifa Zhu'] | 2022-10-27 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.33938649e-01 -6.05713725e-01 5.45689538e-02 -6.43919706e-01
-1.00877655e+00 -4.47205991e-01 3.83001417e-01 5.35005271e-01
-1.28739208e-01 3.20920080e-01 -4.85096514e-01 1.10173710e-01
-6.63136765e-02 -8.75846505e-01 -9.93670642e-01 -5.91063440e-01
-3.83242041e-01 3.16119492e-01 2.96752632e-01 9.38185006... | [7.78437614440918, -3.1042640209198] |
d736fdf9-0a91-459b-91bd-f95d7b41ad9f | automatic-detection-of-aerial-survey-ground | 2303.03041 | null | https://arxiv.org/abs/2303.03041v1 | https://arxiv.org/pdf/2303.03041v1.pdf | Automatic detection of aerial survey ground control points based on Yolov5-OBB | The use of ground control points (GCPs) for georeferencing is the most common strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time their collection represents the most time-consuming and expensive part of UAV campaigns. Recently, deep learning has been rapidly developed in the field of small o... | ['Zhuang Zhiheng', 'Chang Mengxia', 'Dong Di', 'Li Xiaopeng', 'Zheng Zhi', 'Wang Chao', 'Yang Jia', 'Cheng Chuanxiang'] | 2023-03-06 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.12419076e-01 -2.88642436e-01 3.32469344e-01 -9.76425633e-02
-5.37418008e-01 -4.99911219e-01 4.21936154e-01 1.69858322e-01
-6.19711339e-01 4.85920727e-01 -5.80983400e-01 -6.57588392e-02
-3.96006525e-01 -1.04583275e+00 -7.55514205e-01 -5.32979369e-01
-5.43167233e-01 6.66974723e-01 3.25426430e-01 -4.09268945... | [7.838160514831543, -1.5912714004516602] |
b065867e-7f5f-4572-b0d7-182daeb1c62c | revisiting-global-statistics-aggregation-for | 2112.04491 | null | https://arxiv.org/abs/2112.04491v4 | https://arxiv.org/pdf/2112.04491v4.pdf | Improving Image Restoration by Revisiting Global Information Aggregation | Global operations, such as global average pooling, are widely used in top-performance image restorers. They aggregate global information from input features along entire spatial dimensions but behave differently during training and inference in image restoration tasks: they are based on different regions, namely the cr... | ['Xin Lu', 'Chengpeng Chen', 'Liangyu Chen', 'Xiaojie Chu'] | 2021-12-08 | null | null | null | null | ['color-image-denoising', 'image-dehazing', 'grayscale-image-denoising'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.52261513e-01 -6.23667836e-01 -2.05524508e-02 -3.20312947e-01
-9.53649879e-01 -3.64583999e-01 3.17062676e-01 -3.72946084e-01
-5.70499957e-01 6.65395975e-01 4.16474164e-01 -1.88324422e-01
-2.02882141e-01 -6.39822066e-01 -8.82580638e-01 -1.29342520e+00
3.23398948e-01 -2.98098445e-01 2.55683213e-01 -1.51927605... | [11.258380889892578, -2.2924392223358154] |
0335e57d-d400-4ffb-8c2d-ec3d6bec69b3 | look-listen-and-attend-co-attention-network | 2008.05789 | null | https://arxiv.org/abs/2008.05789v1 | https://arxiv.org/pdf/2008.05789v1.pdf | Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning | When watching videos, the occurrence of a visual event is often accompanied by an audio event, e.g., the voice of lip motion, the music of playing instruments. There is an underlying correlation between audio and visual events, which can be utilized as free supervised information to train a neural network by solving th... | ['Ruize Wang', 'Ying Cheng', 'Zhihao Pan', 'Yuejie Zhang', 'Rui Feng'] | 2020-08-13 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 2.51691014e-01 -4.07959670e-01 -1.12639643e-01 -1.56549945e-01
-9.83704388e-01 -3.80838126e-01 4.48065788e-01 -1.97788015e-01
-2.38465726e-01 2.60380864e-01 5.52567899e-01 4.29508746e-01
2.83151984e-01 -1.03959166e-01 -8.48777354e-01 -7.19919860e-01
1.80347695e-03 -2.26231381e-01 3.84397835e-01 1.45650938... | [14.709012985229492, 4.927501201629639] |
b10d36be-f41a-42d4-b421-afbdabf088dd | neural-architecture-search-for-parameter | 2305.16597 | null | https://arxiv.org/abs/2305.16597v1 | https://arxiv.org/pdf/2305.16597v1.pdf | Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models | Parameter-efficient tuning (PET) methods fit pre-trained language models (PLMs) to downstream tasks by either computing a small compressed update for a subset of model parameters, or appending and fine-tuning a small number of new model parameters to the pre-trained network. Hand-designed PET architectures from the lit... | ['Greg Ver Steeg', 'Aram Galstyan', 'Govind Thattai', 'Anoop Kumar', 'Neal Lawton'] | 2023-05-26 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 6.60098568e-02 2.53401130e-01 -4.79095966e-01 -6.96756542e-01
-8.06423843e-01 -5.57106972e-01 5.08083880e-01 -1.54020861e-01
-8.63708436e-01 6.81533873e-01 2.63157934e-01 -5.51970422e-01
-8.39689225e-02 -2.90338486e-01 -7.73706913e-01 -4.42854494e-01
-4.89234217e-02 1.14483690e+00 2.24431902e-01 1.19313132... | [8.709086418151855, 3.6283881664276123] |
79a9ee22-be25-48cf-8725-afc4b6c292c1 | time-series-counterfactual-inference-with | null | null | https://openreview.net/forum?id=JVs1OrQgR3A | https://openreview.net/pdf?id=JVs1OrQgR3A | Time Series Counterfactual Inference with Hidden Confounders | We present augmented counterfactual ordinary differential equations (ACODEs), a new approach to counterfactual inference on time series data with a focus on healthcare applications. ACODEs model interventions in continuous time with differential equations, augmented by auxiliary confounding variables to reduce inferenc... | ['Yan Liu', 'Samuel A Assefa', 'Jiahao Chen', 'Guangyu Li'] | 2021-01-01 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.99545127e-01 5.72788000e-01 -4.90798831e-01 2.44243219e-02
-4.54787940e-01 3.44900452e-02 6.94824040e-01 -2.43497223e-01
-3.32797438e-01 1.78715718e+00 9.53916788e-01 -9.95918989e-01
-4.48010087e-01 -8.13011289e-01 -6.76463902e-01 -7.10170984e-01
-7.38937616e-01 6.38448834e-01 -7.17985809e-01 1.76046982... | [8.02066707611084, 5.388671398162842] |
004e500b-a9ae-4d22-8f85-9e88620806f7 | document-layout-analysis-with-aesthetic | 2111.13809 | null | https://arxiv.org/abs/2111.13809v1 | https://arxiv.org/pdf/2111.13809v1.pdf | Document Layout Analysis with Aesthetic-Guided Image Augmentation | Document layout analysis (DLA) plays an important role in information extraction and document understanding. At present, document layout analysis has reached a milestone achievement, however, document layout analysis of non-Manhattan is still a challenge. In this paper, we propose an image layer modeling method to tack... | ['Cheng Jin', 'Liang Xue', 'Zhao Zhou', 'Xiangcheng Du', 'Xin Li', 'Xingjiao Wu', 'Tianlong Ma'] | 2021-11-27 | null | null | null | null | ['document-layout-analysis', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 3.88017222e-02 -3.86677012e-02 -2.95753062e-01 -1.85221389e-01
-1.59426093e-01 -6.90511405e-01 4.18804646e-01 2.08917797e-01
-7.13025033e-02 1.42533571e-01 5.88154122e-02 -6.20129049e-01
-4.86469626e-01 -9.98316228e-01 -5.50269544e-01 -4.21365499e-01
-3.78199331e-02 3.22972208e-01 1.47848293e-01 1.06399357... | [11.673186302185059, 2.5230350494384766] |
584fa3e0-33c8-4d89-84a6-8e1fa84958b8 | guir-at-semeval-2017-task-12-a-framework-for | null | null | https://aclanthology.org/S17-2180 | https://aclanthology.org/S17-2180.pdf | GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction | Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the extraction of temporal expression and event spans with corresponding attributes and narrative container relations. Approaches i... | ['Nazli Goharian', 'Sean MacAvaney', 'Arman Cohan'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 2.41173118e-01 4.45294142e-01 -8.65501046e-01 -4.06649917e-01
-9.69822168e-01 -5.08910179e-01 7.65588105e-01 1.13459611e+00
-6.93466187e-01 1.24979103e+00 7.16368318e-01 -4.61179107e-01
-6.74756825e-01 -3.88042808e-01 -9.06376690e-02 -2.61272728e-01
-6.74398005e-01 5.44078887e-01 1.88015014e-01 -5.91759719... | [8.51390266418457, 9.005548477172852] |
d426974d-f0f4-4f8f-937a-ccd7b2c41ec7 | learning-with-fuzzy-hypergraphs-a-topical | 1906.09445 | null | https://arxiv.org/abs/1906.09445v1 | https://arxiv.org/pdf/1906.09445v1.pdf | Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization | Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such approaches fail to capture semantic similarities between sentences when they express a similar info... | ['Tommy W. S. Chow', 'Hadrien Van Lierde'] | 2019-06-22 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.44699383e-01 6.94429457e-01 -2.72476375e-01 -3.70059073e-01
-5.31705737e-01 -4.98289555e-01 5.91889024e-01 9.91263628e-01
8.45411886e-03 8.35286558e-01 6.81271911e-01 3.12667251e-01
-4.90725219e-01 -1.17604291e+00 -3.64334822e-01 -5.58301330e-01
8.08561966e-03 7.16776431e-01 3.20158899e-01 -3.55327189... | [12.555212020874023, 9.576050758361816] |
d9ac49f6-856e-4341-afda-1d2fb708004b | point-cloud-learning-with-transformer | 2104.13636 | null | https://arxiv.org/abs/2104.13636v2 | https://arxiv.org/pdf/2104.13636v2.pdf | Point Cloud Learning with Transformer | Remarkable performance from Transformer networks in Natural Language Processing promote the development of these models in dealing with computer vision tasks such as image recognition and segmentation. In this paper, we introduce a novel framework, called Multi-level Multi-scale Point Transformer (MLMSPT) that works di... | ['Xian-Feng Han', 'Qi Zhong'] | 2021-04-28 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 1.44840613e-01 -2.53301710e-01 -4.20798175e-02 -4.96106654e-01
-8.58013868e-01 -4.15664434e-01 7.56511390e-01 2.65701890e-01
4.72415164e-02 -6.45066872e-02 1.42322689e-01 -3.58890891e-02
-2.23914325e-01 -1.22491312e+00 -8.89708042e-01 -6.64671540e-01
6.75376058e-02 3.96589369e-01 6.37098014e-01 -2.24647820... | [7.986091613769531, -3.566007137298584] |
623edd58-9056-400b-9fd4-a48f1a960663 | satellite-galaxy-abundance-dependency-on | 2110.05498 | null | https://arxiv.org/abs/2110.05498v2 | https://arxiv.org/pdf/2110.05498v2.pdf | Satellite galaxy abundance dependency on cosmology in Magneticum simulations | Context: Modelling satellite galaxy abundance $N_s$ in Galaxy Clusters (GCs) is a key element in modelling the Halo Occupation Distribution (HOD), which itself is a powerful tool to connect observational studies with numerical simulations. Aims: To study the impact of cosmological parameters on satellite abundance both... | ['Sebastian Bocquet', 'Matteo Costanzi', 'Alexandro Saro', 'Klaus Dolag', 'Tiago Castro', 'Alessandra Fumagalli', 'Antonio Ragagnin'] | 2021-10-11 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-6.32308722e-01 -2.57767260e-01 5.05203545e-01 -7.93470666e-02
-2.32974470e-01 -5.20190239e-01 1.11653912e+00 -3.02605122e-01
-6.26900136e-01 7.54890800e-01 -2.14061603e-01 -6.55193746e-01
-3.85385081e-02 -1.23450172e+00 -5.86903334e-01 -1.45704782e+00
-4.16128755e-01 9.52681601e-01 6.14097893e-01 -4.73279834... | [7.0327372550964355, 3.4071221351623535] |
b2179e1d-f5e5-4f7c-ae58-83f65a1880a8 | indoor-localization-for-personalized-ambient | 2207.09025 | null | https://arxiv.org/abs/2207.09025v1 | https://arxiv.org/pdf/2207.09025v1.pdf | Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments | This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, ... | ['Chia Y. Han', 'Nirmalya Thakur'] | 2022-07-19 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-1.17450975e-01 -8.61027539e-02 2.50735730e-01 -5.71829021e-01
-7.13005304e-01 -1.69977948e-01 3.40570182e-01 5.51212311e-01
-4.09889430e-01 1.13118374e+00 4.24910992e-01 -4.64185119e-01
-3.61246198e-01 -1.14087319e+00 -4.41820621e-01 -6.14444137e-01
-1.78989619e-01 3.81272703e-01 2.54229248e-01 -1.34069487... | [6.571868419647217, 0.9442386627197266] |
05675967-884d-4574-8a27-a117c8ab7225 | imitation-from-arbitrary-experience-a-dual | 2302.08560 | null | https://arxiv.org/abs/2302.08560v2 | https://arxiv.org/pdf/2302.08560v2.pdf | Dual RL: Unification and New Methods for Reinforcement and Imitation Learning | The goal of reinforcement learning (RL) is to maximize the expected cumulative return. It has been shown that this objective can be represented by an optimization problem of the state-action visitation distribution under linear constraints. The dual problem of this formulation, which we refer to as dual RL, is unconstr... | ['Amy Zhang', 'Qinqing Zheng', 'Scott Niekum', 'Harshit Sikchi'] | 2023-02-16 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.12999940e-01 1.72892541e-01 -7.61880338e-01 9.02838930e-02
-1.17769206e+00 -8.46679866e-01 6.18354082e-01 -3.93384784e-01
-5.92153370e-01 1.13747680e+00 1.17364405e-02 -7.31191039e-01
-4.23173755e-01 -4.28947479e-01 -1.02289081e+00 -8.20932329e-01
-1.82120651e-01 5.07193387e-01 -2.75493979e-01 -1.84684843... | [4.1233344078063965, 2.2865819931030273] |
637f8417-1752-4d5d-bf05-5aa0866b91df | leat-towards-robust-deepfake-disruption-in | 2307.01520 | null | https://arxiv.org/abs/2307.01520v1 | https://arxiv.org/pdf/2307.01520v1.pdf | LEAT: Towards Robust Deepfake Disruption in Real-World Scenarios via Latent Ensemble Attack | Deepfakes, malicious visual contents created by generative models, pose an increasingly harmful threat to society. To proactively mitigate deepfake damages, recent studies have employed adversarial perturbation to disrupt deepfake model outputs. However, previous approaches primarily focus on generating distorted outpu... | ['Hyunsoo Yoon', 'Joonkyo Shim'] | 2023-07-04 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.20997393e-01 -1.44501448e-01 2.63440818e-01 2.72337496e-01
-7.39731252e-01 -1.21203458e+00 8.50046992e-01 -4.17200625e-01
-2.34601740e-02 7.22217143e-01 1.86251715e-01 -2.45374799e-01
-7.70039810e-03 -1.10520482e+00 -7.82316267e-01 -1.04625893e+00
1.07634321e-01 -2.29391471e-01 -6.27964959e-02 -2.00069591... | [5.5091352462768555, 7.899404048919678] |
d57624de-f8da-4efc-811a-c05eef17b349 | a-benchmark-study-on-time-series-clustering | 2004.09546 | null | https://arxiv.org/abs/2004.09546v2 | https://arxiv.org/pdf/2004.09546v2.pdf | A Benchmark Study on Time Series Clustering | This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data. Specifically, the benchmark examines eight popular clustering methods representing three c... | ['Dona M. Rizzo', 'Byung Suk Lee', 'Ali Javed'] | 2020-04-20 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.03171158e-01 -6.61172688e-01 -2.71822214e-01 -2.25714520e-01
-6.26243651e-01 -8.74559999e-01 6.41128123e-01 6.39257073e-01
-4.09704953e-01 1.91472054e-01 3.15299064e-01 -4.71598566e-01
-9.03125763e-01 -6.43912852e-01 2.76796579e-01 -6.99568510e-01
-9.39883530e-01 3.72218549e-01 1.01047188e-01 -1.78530261... | [7.232631683349609, 3.3616225719451904] |
9a447d9e-88b9-4029-abe8-fb83a7cfd1b1 | few-shot-knowledge-graph-to-text-generation | 2106.01623 | null | https://arxiv.org/abs/2106.01623v1 | https://arxiv.org/pdf/2106.01623v1.pdf | Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models | This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG). Considering the few-shot setting, we leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. We make three major technical contributions, na... | ['Ji-Rong Wen', 'Nicholas Jing Yuan', 'Zhicheng Wei', 'Wayne Xin Zhao', 'Tianyi Tang', 'Junyi Li'] | 2021-06-03 | null | https://aclanthology.org/2021.findings-acl.136 | https://aclanthology.org/2021.findings-acl.136.pdf | findings-acl-2021-8 | ['kg-to-text'] | ['natural-language-processing'] | [ 5.11656366e-02 7.76591063e-01 -8.18154395e-01 -9.56967697e-02
-9.45461094e-01 -2.82063454e-01 8.60419214e-01 3.01380754e-01
9.91386250e-02 9.80402827e-01 7.89655566e-01 -2.26756230e-01
-1.79493558e-02 -1.29557431e+00 -8.44847381e-01 -2.00994343e-01
2.59028882e-01 7.03747869e-01 -1.49773329e-01 -4.14878279... | [9.94914436340332, 8.214590072631836] |
0b23f93e-4e85-4cd0-a083-c60c03a4c56b | adaptive-support-driven-bayesian-reweighted | 2008.03877 | null | https://arxiv.org/abs/2008.03877v1 | https://arxiv.org/pdf/2008.03877v1.pdf | Adaptive support driven Bayesian reweighted algorithm for sparse signal recovery | Sparse learning has been widely studied to capture critical information from enormous data sources in the filed of system identification. Often, it is essential to understand internal working mechanisms of unknown systems (e.g. biological networks) in addition to input-output relationships. For this purpose, various fe... | ['Cheng Cheng', 'Wei Zhou', 'Junlin Li'] | 2020-08-10 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 3.35394144e-01 -5.28002977e-01 -2.10219100e-01 -3.10104519e-01
-5.37670135e-01 -6.77128360e-02 1.75215472e-02 8.46973136e-02
5.79390824e-02 8.41165721e-01 1.06980123e-01 1.63076892e-01
-4.47565943e-01 -4.47742730e-01 -4.45939153e-01 -1.03253186e+00
1.25801682e-01 -2.41525006e-02 1.56966135e-01 9.52181593... | [12.435935020446777, 0.41179534792900085] |
6d0e0d70-44ad-4e74-a7c0-11b8341c7b5d | risk-aware-meta-level-decision-making-for | 2209.05580 | null | https://arxiv.org/abs/2209.05580v1 | https://arxiv.org/pdf/2209.05580v1.pdf | Risk-aware Meta-level Decision Making for Exploration Under Uncertainty | Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, localization, action execution, as well as many other factors. For large-scale exploration applications, autonomous systems must overcome the c... | ['Ali-akbar Agha-mohammadi', 'Joel Burdick', 'Mykel J. Kochenderfer', 'Harrison Delecki', 'Mamoru Sobue', 'Oriana Peltzer', 'Amanda Bouman', 'Sung-Kyun Kim', 'Joshua Ott'] | 2022-09-12 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.56207556e-01 3.60446006e-01 -2.22896650e-01 -3.06173772e-01
-7.36663699e-01 -5.53832948e-01 3.63707244e-01 5.29793561e-01
-8.37537587e-01 1.12022746e+00 9.43575054e-02 -6.47883058e-01
-5.84479690e-01 -1.12162364e+00 -7.12255299e-01 -4.19158667e-01
-6.30959690e-01 8.14461589e-01 5.30434668e-01 -2.37547576... | [4.7596211433410645, 1.879514455795288] |
860ca1a4-ce25-4f06-8427-03c0911e8123 | self-supervised-predictive-convolutional | 2111.09099 | null | https://arxiv.org/abs/2111.09099v6 | https://arxiv.org/pdf/2111.09099v6.pdf | Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection | Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on predicting masked in... | ['Mubarak Shah', 'Thomas B. Moeslund', 'Fahad Shahbaz Khan', 'Kamal Nasrollahi', 'Radu Tudor Ionescu', 'Neelu Madan', 'Nicolae-Catalin Ristea'] | 2021-11-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['one-class-classification'] | ['miscellaneous'] | [ 1.44311890e-01 8.52707177e-02 9.97444540e-02 -4.47928190e-01
-4.47348654e-01 -1.98682383e-01 5.35695493e-01 2.59657592e-01
-2.52864957e-01 2.52780616e-01 -2.25507632e-01 -5.84071577e-02
1.29634380e-01 -6.96968675e-01 -8.31112742e-01 -7.80995846e-01
-4.38887656e-01 7.13668242e-02 4.82540935e-01 -1.61774665... | [7.696124076843262, 2.07959246635437] |
54c30fb4-d31c-4079-bafd-4e20bb83c642 | strategies-for-improving-low-resource-speech | 2306.00208 | null | https://arxiv.org/abs/2306.00208v1 | https://arxiv.org/pdf/2306.00208v1.pdf | Strategies for improving low resource speech to text translation relying on pre-trained ASR models | This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST). We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively. Using the encoder-decoder framework for ST, our ... | ['Alejandro Ciuba', 'Cecile Macaire', 'Tomas Pavlicek', 'Marek Sarvas', 'Santosh Kesiraju'] | 2023-05-31 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 9.94543210e-02 1.70535259e-02 -7.20205083e-02 -3.62083912e-01
-1.61531746e+00 -7.17575788e-01 7.23085880e-01 -3.30938876e-01
-5.98191619e-01 8.64053905e-01 5.72161078e-01 -8.12493443e-01
4.72697526e-01 3.38749923e-02 -7.66950965e-01 -3.59100580e-01
3.37618530e-01 8.20693195e-01 -2.17857122e-01 -4.57874566... | [14.48227596282959, 7.160282611846924] |
5c483df3-da6f-4024-b758-6dd106cf89b2 | speechgen-unlocking-the-generative-power-of | 2306.02207 | null | https://arxiv.org/abs/2306.02207v2 | https://arxiv.org/pdf/2306.02207v2.pdf | SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts | Large language models (LLMs) have gained considerable attention for Artificial Intelligence Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct adaptation of continuous speech to LLMs that process discrete tokens remains an unsolved challenge, hindering the application of LLMs for ... | ['Hung-Yi Lee', 'Yuan-Kuei Wu', 'Kai-Wei Chang', 'Haibin Wu'] | 2023-06-03 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.29889414e-01 4.59648252e-01 2.30015181e-02 -2.56451726e-01
-1.05867147e+00 -5.45438826e-01 8.38501155e-01 -1.96837053e-01
-1.01364583e-01 6.94551945e-01 3.65784228e-01 -4.29960072e-01
8.42967182e-02 -5.54371357e-01 -3.56591105e-01 -6.99014246e-01
1.06419079e-01 4.79618847e-01 -8.15082118e-02 -5.67130446... | [14.5322847366333, 6.887794494628906] |
7dd9b6dd-ec4f-4087-a910-787121bdcb6d | pct-net-full-resolution-image-harmonization | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guerreiro_PCT-Net_Full_Resolution_Image_Harmonization_Using_Pixel-Wise_Color_Transformations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guerreiro_PCT-Net_Full_Resolution_Image_Harmonization_Using_Pixel-Wise_Color_Transformations_CVPR_2023_paper.pdf | PCT-Net: Full Resolution Image Harmonization Using Pixel-Wise Color Transformations | In this paper, we present PCT-Net, a simple and general image harmonization method that can be easily applied to images at full resolution. The key idea is to learn a parameter network that uses downsampled input images to predict the parameters for pixel-wise color transforms (PCTs) which are applied to each pixel... | ['Björn Stenger', 'Mitsuru Nakazawa', 'Julian Jorge Andrade Guerreiro'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-harmonization'] | ['computer-vision'] | [ 3.44479591e-01 -2.76927084e-01 2.38586396e-01 -2.29195148e-01
-7.78269708e-01 -2.03377530e-01 3.81326526e-01 -3.42181742e-01
-5.70132494e-01 5.33907712e-01 1.40622780e-01 5.52249029e-02
2.08935086e-02 -8.28734696e-01 -8.96466315e-01 -7.23537982e-01
1.58203229e-01 -1.46526499e-02 3.18834484e-01 -4.62066442... | [11.029041290283203, -1.8954800367355347] |
b888c6c1-6dd0-48ec-ae82-893f6949cec8 | pixel-level-cycle-association-a-new | 2011.00147 | null | https://arxiv.org/abs/2011.00147v1 | https://arxiv.org/pdf/2011.00147v1.pdf | Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation | Domain adaptive semantic segmentation aims to train a model performing satisfactory pixel-level predictions on the target with only out-of-domain (source) annotations. The conventional solution to this task is to minimize the discrepancy between source and target to enable effective knowledge transfer. Previous domain ... | ['Alexander G. Hauptmann', 'Yueting Zhuang', 'Yi Yang', 'Yunchao Wei', 'Guoliang Kang'] | 2020-10-31 | null | http://proceedings.neurips.cc/paper/2020/hash/243be2818a23c980ad664f30f48e5d19-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/243be2818a23c980ad664f30f48e5d19-Paper.pdf | neurips-2020-12 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.40923518e-01 2.30819017e-01 -4.14844990e-01 -5.04409134e-01
-9.42757726e-01 -5.25683045e-01 4.01975363e-01 -1.88749701e-01
-4.47699070e-01 7.26534188e-01 -3.17038149e-01 -1.60207152e-01
2.48400196e-01 -7.85621464e-01 -7.88206637e-01 -7.12804317e-01
4.44363177e-01 3.43999326e-01 5.66366374e-01 -3.95780683... | [9.634251594543457, 1.3009954690933228] |
a4015b33-7f63-4d66-a155-e8118f603612 | slam-based-quasi-dense-reconstruction-for | 1705.09107 | null | http://arxiv.org/abs/1705.09107v1 | http://arxiv.org/pdf/1705.09107v1.pdf | SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes | Recovering surgical scene structure in laparoscope surgery is crucial step
for surgical guidance and augmented reality applications. In this paper, a
quasi dense reconstruction algorithm of surgical scene is proposed. This is
based on a state-of-the-art SLAM system, and is exploiting the initial
exploration phase that ... | ['J. M. M. Montiel', 'Alexandre Hostettler', 'Toby Collins', 'Nader Mahmoud', 'Luc Soler', 'Christophe Doignon'] | 2017-05-25 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 3.29865992e-01 3.65603119e-01 2.26151302e-01 -1.21247321e-01
-5.87862492e-01 -3.53592366e-01 2.37883672e-01 3.27080339e-01
-6.19036496e-01 6.38409615e-01 -9.71877128e-02 -3.80833596e-01
-4.20368642e-01 -4.42423850e-01 -6.98707461e-01 -5.74224830e-01
-2.96885893e-02 7.92330742e-01 1.43922195e-01 -1.61612689... | [13.824315071105957, -3.1116580963134766] |
83619106-1619-4616-92bc-8e49c53e8d1e | difface-blind-face-restoration-with-diffused | 2212.06512 | null | https://arxiv.org/abs/2212.06512v2 | https://arxiv.org/pdf/2212.06512v2.pdf | DifFace: Blind Face Restoration with Diffused Error Contraction | While deep learning-based methods for blind face restoration have achieved unprecedented success, they still suffer from two major limitations. First, most of them deteriorate when facing complex degradations out of their training data. Second, these methods require multiple constraints, e.g., fidelity, perceptual, and... | ['Chen Change Loy', 'Zongsheng Yue'] | 2022-12-13 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 7.99509734e-02 -2.88401008e-01 7.97109306e-02 -1.32937461e-01
-6.92099035e-01 -1.91322625e-01 2.56164998e-01 -3.44328880e-01
-1.46286517e-01 6.33756220e-01 1.61761224e-01 -1.27098992e-01
-9.86010879e-02 -7.29925692e-01 -6.55661583e-01 -9.72242117e-01
2.40386859e-01 -3.57062370e-02 1.99831709e-01 -1.91742241... | [11.588679313659668, -1.9850542545318604] |
cbf3bf45-619d-444f-9c30-c249f0045ef4 | orthogonal-nonnegative-tucker-decomposition | 1910.09979 | null | https://arxiv.org/abs/1910.09979v2 | https://arxiv.org/pdf/1910.09979v2.pdf | Orthogonal Nonnegative Tucker Decomposition | In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ... | ['Ye Liu', 'Xiongjun Zhang', 'Hong Yan', 'Michael K. Ng', 'Junjun Pan'] | 2019-10-21 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.23682493e-02 -4.16446924e-01 -5.44785023e-01 -8.28646868e-02
-3.36536407e-01 -2.60556579e-01 -4.07992341e-02 -6.73027039e-01
-2.23639414e-01 6.04487658e-01 1.62798971e-01 -4.33274299e-01
-4.07738358e-01 -2.03550234e-01 -7.86703378e-02 -9.59424317e-01
-3.01871896e-01 8.37882161e-02 -7.12754786e-01 -1.31008849... | [7.438353061676025, 4.47137451171875] |
5b538c79-32e7-4a15-93df-9f525966d563 | on-the-equivalence-between-node-embeddings-1 | 1910.00452 | null | https://arxiv.org/abs/1910.00452v3 | https://arxiv.org/pdf/1910.00452v3.pdf | On the Equivalence between Positional Node Embeddings and Structural Graph Representations | This work provides the first unifying theoretical framework for node (positional) embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that the relationship between structural representations and node embeddings is analogo... | ['Bruno Ribeiro', 'Balasubramaniam Srinivasan'] | 2019-10-01 | on-the-equivalence-between-positional-node | https://openreview.net/forum?id=SJxzFySKwH | https://openreview.net/pdf?id=SJxzFySKwH | iclr-2020-1 | ['triad-prediction'] | ['graphs'] | [ 1.36185125e-01 6.61673844e-01 -5.79036415e-01 -1.20350227e-01
2.04691306e-01 -7.76533544e-01 8.69244993e-01 3.67069483e-01
-6.49252832e-02 6.00340903e-01 5.23790359e-01 -8.31861734e-01
-4.01497871e-01 -1.21607745e+00 -3.66773605e-01 -6.96041644e-01
-5.79311371e-01 4.22098011e-01 -3.75535488e-02 -3.04450452... | [7.091310977935791, 6.124690532684326] |
cdd48b7a-05c8-4926-a58d-25de3b0d5678 | spoof-detection-using-x-vector-and-feature | 1904.07453 | null | https://arxiv.org/abs/1904.07453v2 | https://arxiv.org/pdf/1904.07453v2.pdf | Spoof detection using time-delay shallow neural network and feature switching | Detecting spoofed utterances is a fundamental problem in voice-based biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the pre-recorded utterance. Inspired by the state-of-the-art \emph{x}-vector based speaker verification ... | ['B. Bharathi', 'Suvidha Rupesh Kumar', 'Hema A. Murthy', 'Saranya M', 'Mari Ganesh Kumar'] | 2019-04-16 | null | null | null | null | ['voice-anti-spoofing'] | ['audio'] | [ 9.52485278e-02 -2.75349557e-01 -1.46414652e-01 -2.98771590e-01
-5.14421225e-01 -5.61139941e-01 6.86018944e-01 -1.58508033e-01
-4.26124096e-01 2.54273057e-01 -1.68816876e-02 -7.47447550e-01
-3.09527684e-02 -3.13679487e-01 -1.54243171e-01 -7.75438011e-01
1.45165950e-01 2.60545403e-01 1.65794000e-01 -2.11227641... | [14.112353324890137, 5.901759147644043] |
ffa29693-bdbc-45ee-aa3b-129790eb7b24 | sril-selective-regularization-for-class | 2305.05175 | null | https://arxiv.org/abs/2305.05175v1 | https://arxiv.org/pdf/2305.05175v1.pdf | SRIL: Selective Regularization for Class-Incremental Learning | Human intelligence gradually accepts new information and accumulates knowledge throughout the lifespan. However, deep learning models suffer from a catastrophic forgetting phenomenon, where they forget previous knowledge when acquiring new information. Class-Incremental Learning aims to create an integrated model that ... | ['Wonjun Hwang', 'Jaemin Na', 'Jisu Han'] | 2023-05-09 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 1.46864697e-01 1.45654175e-02 8.45205933e-02 -2.43850917e-01
1.62238851e-01 -2.63103098e-01 4.14316297e-01 2.46812984e-01
-8.19853544e-01 9.98048842e-01 -8.18077922e-02 2.26994187e-01
-4.43826377e-01 -8.93901527e-01 -6.41886115e-01 -7.52815127e-01
1.06330447e-01 3.00093740e-01 5.57998300e-01 5.19038662... | [9.81713581085205, 3.3640546798706055] |
a69b05f0-dd48-49b2-9c9a-c707ef8e4fb6 | conditional-random-fields-as-recurrent-neural | 1807.07464 | null | http://arxiv.org/abs/1807.07464v1 | http://arxiv.org/pdf/1807.07464v1.pdf | Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation | The Conditional Random Field as a Recurrent Neural Network layer is a
recently proposed algorithm meant to be placed on top of an existing
Fully-Convolutional Neural Network to improve the quality of semantic
segmentation. In this paper, we test whether this algorithm, which was shown to
improve semantic segmentation f... | ['Mário A. T. Figueiredo', 'Arlindo L. Oliveira', 'Miguel Monteiro'] | 2018-07-19 | null | null | null | null | ['3d-medical-imaging-segmentation', 'volumetric-medical-image-segmentation'] | ['medical', 'medical'] | [ 6.41936362e-01 4.98283744e-01 1.65452972e-01 -4.38981801e-01
-7.17620075e-01 -1.93260193e-01 3.07194114e-01 -5.23150526e-02
-7.33874261e-01 4.69904333e-01 2.63044909e-02 -5.97231388e-01
-2.77973711e-01 -7.41042197e-01 -5.75368941e-01 -6.29567742e-01
-3.11461482e-02 4.10386086e-01 5.43124676e-01 4.47799489... | [14.433799743652344, -2.499025821685791] |
a76de40b-d9e9-465b-bb4c-1320740d3d51 | convolutive-audio-source-separation-using | 1708.03989 | null | http://arxiv.org/abs/1708.03989v1 | http://arxiv.org/pdf/1708.03989v1.pdf | Convolutive Audio Source Separation using Robust ICA and an intelligent evolving permutation ambiguity solution | Audio source separation is the task of isolating sound sources that are
active simultaneously in a room captured by a set of microphones. Convolutive
audio source separation of equal number of sources and microphones has a number
of shortcomings including the complexity of frequency-domain ICA, the
permutation ambiguit... | ['Thomas Sgouros', 'Nikolaos Mitianoudis', 'Dimitrios Mallis'] | 2017-08-14 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.35059905e-01 -4.46923137e-01 6.23381793e-01 2.08320573e-01
-9.48199391e-01 -8.04077089e-01 3.16214770e-01 -6.04041517e-02
-4.28972542e-01 1.02632260e+00 3.05881858e-01 -7.35388324e-02
-7.22985566e-01 -5.67812100e-02 -3.17749113e-01 -8.87434840e-01
1.91021673e-02 1.69777721e-01 1.63140953e-01 1.41985357... | [15.163986206054688, 5.664031028747559] |
78a20a95-1fd8-45e4-aa85-78cd159d177f | video-salient-object-detection-via | 2111.02368 | null | https://arxiv.org/abs/2111.02368v1 | https://arxiv.org/pdf/2111.02368v1.pdf | Video Salient Object Detection via Contrastive Features and Attention Modules | Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neural networks or optical flow. However, these approaches require high computational cost, and tend to accumulate inaccuracies over time. In t... | ['Ming-Hsuan Yang', 'Xiaohui Shen', 'Xiaojie Jin', 'Yi-Wen Chen'] | 2021-11-03 | null | null | null | null | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.48697823e-01 -5.36963642e-01 -3.93201381e-01 -1.73458546e-01
-4.53730732e-01 -3.79587002e-02 2.77403265e-01 -2.82515376e-03
-4.57738906e-01 5.24261951e-01 2.61501938e-01 2.99913913e-01
1.92985982e-02 -4.09598380e-01 -7.21156538e-01 -1.05805826e+00
-1.91382557e-01 -4.87467587e-01 9.00636852e-01 7.09622428... | [9.546661376953125, -0.2995413541793823] |
7ec098c0-3395-4e03-a3ea-45a1828b2808 | multi-modal-fusion-for-diabetes-mellitus-and | 1604.03443 | null | http://arxiv.org/abs/1604.03443v1 | http://arxiv.org/pdf/1604.03443v1.pdf | Multi-modal Fusion for Diabetes Mellitus and Impaired Glucose Regulation Detection | Effective and accurate diagnosis of Diabetes Mellitus (DM), as well as its
early stage Impaired Glucose Regulation (IGR), has attracted much attention
recently. Traditional Chinese Medicine (TCM) [3], [5] etc. has proved that
tongue, face and sublingual diagnosis as a noninvasive method is a reasonable
way for disease ... | ['Jinxing Li', 'Jian Wu', 'David Zhang', 'Yongcheng Li'] | 2016-04-12 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 1.02562435e-01 -6.16568744e-01 -6.45999730e-01 -2.05828279e-01
-6.51482701e-01 -2.59280533e-01 2.86733508e-01 -4.02034670e-02
6.57484308e-02 6.73108518e-01 7.53901452e-02 -7.30756372e-02
-3.38730007e-01 -6.65870547e-01 1.69497997e-01 -1.06114197e+00
-1.00682259e-01 5.10546565e-01 -2.82436222e-01 1.17210068... | [15.829055786132812, -3.9758546352386475] |
21cdddbc-1226-4be9-9379-75a3db21f8b8 | conformal-link-prediction-to-control-the | 2306.14693 | null | https://arxiv.org/abs/2306.14693v1 | https://arxiv.org/pdf/2306.14693v1.pdf | Conformal link prediction to control the error rate | Most link prediction methods return estimates of the connection probability of missing edges in a graph. Such output can be used to rank the missing edges, from most to least likely to be a true edge, but it does not directly provide a classification into true and non-existent. In this work, we consider the problem of ... | ['Ariane Marandon'] | 2023-06-26 | null | null | null | null | ['link-prediction'] | ['graphs'] | [ 1.31846428e-01 5.16912341e-01 -2.60889828e-01 -3.71752322e-01
-2.06172109e-01 -5.48217297e-01 5.19160628e-01 3.91061425e-01
-8.19622576e-02 9.49886739e-01 -7.28590507e-03 -4.63989168e-01
-5.56807876e-01 -1.25742459e+00 -9.83612657e-01 -7.47424960e-01
-5.08671522e-01 6.75122142e-01 3.12551796e-01 7.07198381... | [7.212117671966553, 5.178137302398682] |
432e8a7f-f8fc-43a5-8920-ea80dada2eb2 | srg-snippet-relatedness-based-temporal-action | 1911.11306 | null | https://arxiv.org/abs/1911.11306v2 | https://arxiv.org/pdf/1911.11306v2.pdf | SRG: Snippet Relatedness-based Temporal Action Proposal Generator | Recent temporal action proposal generation approaches have suggested integrating segment- and snippet score-based methodologies to produce proposals with high recall and accurate boundaries. In this paper, different from such a hybrid strategy, we focus on the potential of the snippet score-based approach. Specifically... | ['Jinyoung Moon', 'Hyunjun Eun', 'Sumin Lee', 'Changick Kim', 'Jongyoul Park', 'Chanho Jung'] | 2019-11-26 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 4.40684050e-01 -1.58390313e-01 -5.13403893e-01 -1.51207030e-01
-1.10268652e+00 -2.63338059e-01 8.43290150e-01 3.89431655e-01
-2.48000324e-01 7.88913310e-01 5.85732341e-01 2.40874514e-01
-5.20393610e-01 -7.38552034e-01 -2.40070686e-01 -4.54236001e-01
-3.06949794e-01 4.05664146e-01 9.10804093e-01 -1.74799457... | [8.338515281677246, 0.44218024611473083] |
6923d9ff-824c-4b30-87a9-3adc33fae96b | cic-fbk-approach-to-native-language | null | null | https://aclanthology.org/W17-5042 | https://aclanthology.org/W17-5042.pdf | CIC-FBK Approach to Native Language Identification | We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017. Our approach combines features commonly used in previous NLI research, i.e., word n-grams, lemma n-grams, part-of-speech n-grams, and function words, with recently introduced character n-grams from misspelled wo... | ['Carlo Strapparava', 'Lingzhen Chen', 'Ilia Markov', 'Grigori Sidorov'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 1.36224300e-01 2.42091957e-02 -4.18325663e-01 -3.05684179e-01
-9.08671856e-01 -9.06317353e-01 7.11000085e-01 4.72213805e-01
-8.71987879e-01 8.75247598e-01 7.32177734e-01 -5.73380291e-01
-1.17885545e-01 -5.56456208e-01 -3.83516669e-01 -2.76127040e-01
-2.40355149e-01 4.92211968e-01 1.06417410e-01 -8.68041888... | [10.437905311584473, 10.461492538452148] |
0f704dac-eef6-4ba1-a66a-ea42a0dc896c | learning-populations-of-parameters | 1709.02707 | null | http://arxiv.org/abs/1709.02707v2 | http://arxiv.org/pdf/1709.02707v2.pdf | Learning Populations of Parameters | Consider the following estimation problem: there are $n$ entities, each with
an unknown parameter $p_i \in [0,1]$, and we observe $n$ independent random
variables, $X_1,\ldots,X_n$, with $X_i \sim $ Binomial$(t, p_i)$. How
accurately can one recover the "histogram" (i.e. cumulative density function)
of the $p_i$'s? Whi... | ['Kevin Tian', 'Gregory Valiant', 'Weihao Kong'] | 2017-09-08 | learning-populations-of-parameters-1 | http://papers.nips.cc/paper/7160-learning-populations-of-parameters | http://papers.nips.cc/paper/7160-learning-populations-of-parameters.pdf | neurips-2017-12 | ['sports-analytics'] | ['computer-vision'] | [-1.89578682e-01 1.51203498e-01 -4.61430073e-01 -1.99867412e-01
-1.05822122e+00 -7.91145682e-01 -1.71013772e-01 4.09318328e-01
-6.87358260e-01 9.10734057e-01 -2.37861469e-01 -4.89133686e-01
-7.08654583e-01 -1.27888286e+00 -9.62034106e-01 -7.19910741e-01
-7.33266890e-01 7.78849959e-01 -1.57807246e-01 9.74128619... | [6.748971939086914, 4.673530578613281] |
d10ae1bb-5f2a-4c91-bb3e-7507bce5720a | federated-sparse-training-lottery-aware-model | 2208.13092 | null | https://arxiv.org/abs/2208.13092v2 | https://arxiv.org/pdf/2208.13092v2.pdf | Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge | Limited computation and communication capabilities of clients pose significant challenges in federated learning (FL) over resource-limited edge nodes. A potential solution to this problem is to deploy off-the-shelf sparse learning algorithms that train a binary sparse mask on each client with the expectation of trainin... | ['Salman Avestimehr', 'Yue Niu', 'Saurav Prakash', 'Souvik Kundu', 'Sara Babakniya'] | 2022-08-27 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 8.29305202e-02 1.36452228e-01 -5.25890589e-01 -1.49015471e-01
-9.07058418e-01 -3.09453785e-01 5.10176681e-02 -3.52807313e-01
5.55587523e-02 6.50798082e-01 1.42686874e-01 -3.34619850e-01
-3.49711239e-01 -5.38915277e-01 -7.56744504e-01 -8.01170886e-01
-8.34292024e-02 6.02315545e-01 -6.05085269e-02 2.63296306... | [5.900199890136719, 6.108227252960205] |
f5969316-f25c-4375-bd6b-f3f284b8fc57 | would-you-ask-it-that-way-measuring-and | 2205.12768 | null | https://arxiv.org/abs/2205.12768v1 | https://arxiv.org/pdf/2205.12768v1.pdf | Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering | Knowledge graph question answering (KGQA) facilitates information access by leveraging structured data without requiring formal query language expertise from the user. Instead, users can express their information needs by simply asking their questions in natural language (NL). Datasets used to train KGQA models that wo... | ['Krisztian Balog', 'Trond Linjordet'] | 2022-05-25 | null | null | null | null | ['graph-question-answering', 'natural-questions'] | ['graphs', 'miscellaneous'] | [-2.03572318e-01 4.35806841e-01 1.46711916e-01 -5.60305893e-01
-1.30489171e+00 -1.14168620e+00 5.89812338e-01 1.83064535e-01
-5.50186932e-01 8.62186670e-01 3.34546298e-01 -6.52997673e-01
1.99732166e-02 -1.12042785e+00 -6.71465695e-01 1.59706146e-01
5.99395275e-01 1.04083645e+00 4.29519564e-01 -8.26504409... | [10.86252212524414, 7.929474830627441] |
86c7f048-7ed6-4b49-b392-e6389a9b4b21 | deep-structured-implicit-functions | 1912.06126 | null | https://arxiv.org/abs/1912.06126v2 | https://arxiv.org/pdf/1912.06126v2.pdf | Local Deep Implicit Functions for 3D Shape | The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Local Deep Imp... | ['Thomas Funkhouser', 'Avneesh Sud', 'Aaron Sarna', 'Kyle Genova', 'Forrester Cole'] | 2019-12-12 | local-deep-implicit-functions-for-3d-shape | http://openaccess.thecvf.com/content_CVPR_2020/html/Genova_Local_Deep_Implicit_Functions_for_3D_Shape_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Genova_Local_Deep_Implicit_Functions_for_3D_Shape_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-shape-representation'] | ['computer-vision'] | [-1.38452798e-01 2.88274854e-01 2.15841740e-01 -5.19344449e-01
-9.07373130e-01 -6.75923824e-01 4.97725546e-01 -7.44296834e-02
9.71331894e-02 3.49015683e-01 1.83939099e-01 -1.51481032e-01
-8.46011490e-02 -1.02554977e+00 -1.03708339e+00 -3.23286533e-01
-2.30111957e-01 1.01264405e+00 1.60081148e-01 6.40755445... | [8.587191581726074, -3.657292127609253] |
d90c634f-b752-4fb8-88d2-c2b637a3217b | procc-progressive-cross-primitive-consistency | 2211.12417 | null | https://arxiv.org/abs/2211.12417v3 | https://arxiv.org/pdf/2211.12417v3.pdf | ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning | Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions. Existing works either learn the joint composit... | ['Ziming Liu', 'Haozhao Wang', 'Jingcai Guo', 'Song Guo', 'Wenchao Xu', 'Fushuo Huo'] | 2022-11-19 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.73754889e-01 -4.12082709e-02 -2.81046420e-01 -1.38835430e-01
-4.97406632e-01 -2.23908067e-01 8.86118054e-01 -1.57947317e-01
-3.29605162e-01 2.01696187e-01 1.97847918e-01 7.26595595e-02
2.23454952e-01 -6.50033474e-01 -8.74574542e-01 -9.69326437e-01
3.26216787e-01 4.77683574e-01 6.64456487e-01 -1.23036131... | [10.233449935913086, 2.2483973503112793] |
e8b754fe-18eb-4945-853d-937cb182b12e | prepositional-phrase-attachment-over-word | null | null | https://aclanthology.org/W17-6305 | https://aclanthology.org/W17-6305.pdf | Prepositional Phrase Attachment over Word Embedding Products | We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task). Our model exploits tensor products of word embeddings, capturing all possible conjunctions of latent embeddings. Our results on a wide range of datasets and task settings show that tensor products are t... | ['Pranava Swaroop Madhyastha', 'Xavier Carreras', 'Ariadna Quattoni'] | 2017-09-01 | null | null | null | ws-2017-9 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-2.10105851e-01 7.52873719e-02 -4.55047131e-01 -5.76492906e-01
-9.58504200e-01 -7.77404785e-01 3.48186195e-01 3.42537940e-01
-8.94572020e-01 4.70102668e-01 5.39677858e-01 -5.29711246e-01
-1.25131279e-01 -3.10623497e-01 -4.93099719e-01 -3.74976605e-01
-3.72099608e-01 9.41983640e-01 3.12492430e-01 -3.80490601... | [10.494112968444824, 9.463590621948242] |
40e697f0-7a59-4415-be6c-7db6b865c6bf | a-study-of-low-resource-speech-commands | 2110.03894 | null | https://arxiv.org/abs/2110.03894v3 | https://arxiv.org/pdf/2110.03894v3.pdf | Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Classification | In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to modify the acoustic signals (from the target domain) to repurpose a pretrained SCR model (from the source domain). To solve the label mismatch... | ['Yu Tsao', 'Pin-Yu Chen', 'Sabato Marco Siniscalchi', 'Hu Hu', 'Chao-Han Huck Yang', 'Pin-Jui Ku', 'Hao Yen'] | 2021-10-08 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 5.06517529e-01 -3.43596607e-01 5.11186600e-01 -5.94459176e-01
-1.15169513e+00 -6.80540740e-01 5.20230412e-01 -6.64352655e-01
-7.05219388e-01 5.64152896e-01 1.83416009e-01 -1.34794042e-01
4.15134639e-01 -2.98734158e-01 -6.47016764e-01 -6.71951652e-01
3.83108705e-01 4.81695801e-01 1.16697684e-01 -4.65443105... | [14.63351058959961, 6.599139213562012] |
9bfd8d5d-bde4-42e9-940a-152801476052 | solving-visual-analogies-using-neural | 2111.10361 | null | https://arxiv.org/abs/2111.10361v1 | https://arxiv.org/pdf/2111.10361v1.pdf | Solving Visual Analogies Using Neural Algorithmic Reasoning | We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs. This program synthesis task can be easily solved via symbolic search. Using a variation of the `neural ana... | ['Tirtharaj Dash', 'Ashwin Srinivasan', 'Lovekesh Vig', 'Gautam Shroff', 'Atharv Sonwane'] | 2021-11-19 | null | null | null | null | ['visual-analogies'] | ['computer-vision'] | [ 5.72782159e-01 2.88164526e-01 4.64291833e-02 -5.09608805e-01
9.32432413e-02 -1.07631707e+00 1.08197665e+00 2.89195538e-01
-2.09927067e-01 6.13073289e-01 -1.04014210e-01 -7.19888389e-01
-1.30306333e-01 -1.10864687e+00 -1.07818627e+00 -2.25642800e-01
1.68234244e-01 2.36828208e-01 3.31636280e-01 -2.82604396... | [10.580831527709961, 2.309551477432251] |
8d7eafbb-e81d-471f-a533-2738a879961f | face-inverse-rendering-via-hierarchical | 2301.06733 | null | https://arxiv.org/abs/2301.06733v2 | https://arxiv.org/pdf/2301.06733v2.pdf | Face Inverse Rendering via Hierarchical Decoupling | Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable to generalize well for real-world situations, due to the gap between synthetic d... | ['Jiawan Zhang', 'Wenjing Dai', 'Xiaojie Guo', 'Meng Wang'] | 2023-01-17 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 1.61112875e-01 2.88436515e-03 2.34988227e-01 -6.46171093e-01
-4.44015294e-01 -2.99715638e-01 4.83169794e-01 -6.95397139e-01
1.14028446e-01 6.06199563e-01 5.48022240e-02 -1.25308514e-01
2.31905654e-01 -8.57496500e-01 -5.85904479e-01 -6.61463916e-01
4.40579414e-01 3.00841451e-01 -2.86624044e-01 -2.43805200... | [12.87390422821045, -0.131914883852005] |
376a86d7-29e3-497e-bb51-a4f632419233 | a-unified-one-shot-prosody-and-speaker | 2211.06535 | null | https://arxiv.org/abs/2211.06535v1 | https://arxiv.org/pdf/2211.06535v1.pdf | A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units | We present a unified system to realize one-shot voice conversion (VC) on the pitch, rhythm, and speaker attributes. Existing works generally ignore the correlation between prosody and language content, leading to the degradation of naturalness in converted speech. Additionally, the lack of proper language features prev... | ['Alexander Rudnicky', 'Shinji Watanabe', 'Li-Wei Chen'] | 2022-11-12 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 6.01684228e-02 1.24737872e-02 -4.39210802e-01 -3.16797853e-01
-9.19302702e-01 -6.37946427e-01 7.83976912e-02 -6.01381820e-04
1.59851819e-01 4.63309526e-01 8.09156179e-01 -1.05908751e-01
4.47834313e-01 -5.78659654e-01 -3.36594015e-01 -2.97872573e-01
2.35235408e-01 -1.64727315e-01 -5.24571538e-02 -3.31167907... | [14.955738067626953, 6.554706573486328] |
be4ce4ea-ce4a-4ac5-9172-a316e378f67b | knns-of-semantic-encodings-for-rating | 2302.00412 | null | https://arxiv.org/abs/2302.00412v2 | https://arxiv.org/pdf/2302.00412v2.pdf | KNNs of Semantic Encodings for Rating Prediction | This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by semantic similarity. This textual, memory-based approach to rating ... | ['Lucas Dixon', 'Thomas Bonald', 'Raghuram Vadapalli', 'Léo Laugier'] | 2023-02-01 | null | null | null | null | ['collaborative-filtering', 'semantic-textual-similarity'] | ['miscellaneous', 'natural-language-processing'] | [ 2.14825094e-01 2.88183868e-01 -1.06053519e+00 -8.57432723e-01
-5.84958076e-01 -3.82227868e-01 8.63069355e-01 7.88415194e-01
-8.42111707e-02 3.46830100e-01 1.14521599e+00 -5.57748079e-01
-8.64373207e-01 -7.26668298e-01 -1.01673774e-01 1.35940194e-01
-9.88807436e-03 7.74138153e-01 4.92348552e-01 -9.02335823... | [10.033125877380371, 5.759647846221924] |
72582cec-f07f-4fe9-b533-70a036604e70 | content-and-style-aware-generation-of-text | 2204.05539 | null | https://arxiv.org/abs/2204.05539v1 | https://arxiv.org/pdf/2204.05539v1.pdf | Content and Style Aware Generation of Text-line Images for Handwriting Recognition | Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic... | ['Mauricio Villegas', 'Alicia Fornés', 'Marçal Rusiñol', 'Pau Riba', 'Lei Kang'] | 2022-04-12 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.10474682e-01 -3.79637837e-01 -1.83810666e-02 -4.92672026e-01
-4.51954126e-01 -7.94160664e-01 6.81143403e-01 -3.95039380e-01
-1.77550524e-01 6.34783745e-01 -3.68863642e-01 -1.82075649e-02
3.75617951e-01 -6.33990943e-01 -5.68861187e-01 -7.46549368e-01
6.59520388e-01 7.29202569e-01 1.84827790e-01 -2.89834924... | [11.842345237731934, 2.2734744548797607] |
bac51659-2717-4b1a-b6ce-e2c7fcccf991 | in-situ-monitoring-additive-manufacturing | 2301.00554 | null | https://arxiv.org/abs/2301.00554v1 | https://arxiv.org/pdf/2301.00554v1.pdf | In-situ monitoring additive manufacturing process with AI edge computing | In-situ monitoring system can be used to monitor the quality of additive manufacturing (AM) processes. In the case of digital image correlation (DIC) based in-situ monitoring systems, high-speed cameras were used to capture images of high resolutions. This paper proposed a novel in-situ monitoring system to accelerate ... | ['Liwei Chen', 'Yuqing Hou', 'Yikai Zhang', 'Hui Li', 'Wenkang Zhu'] | 2023-01-02 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 5.32470942e-01 -1.89651385e-01 3.78992945e-01 8.92852992e-02
-3.80864292e-01 2.02696826e-02 -5.92892952e-02 -1.34642452e-01
-1.75903693e-01 3.62652242e-01 -5.35482764e-01 -2.23845392e-02
-5.15862167e-01 -1.06925821e+00 -6.04251266e-01 -4.98051465e-01
2.29618564e-01 4.10696715e-01 3.43848854e-01 -1.93573654... | [10.972984313964844, -2.1345794200897217] |
acf67b5d-c4f5-440b-977f-d461b29a994d | generalist-equivariant-transformer-towards-3d | 2306.01474 | null | https://arxiv.org/abs/2306.01474v2 | https://arxiv.org/pdf/2306.01474v2.pdf | Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning | Many processes in biology and drug discovery involve various 3D interactions between different molecules, such as protein and protein, protein and small molecule, etc. Designing a generalist model to learn universal molecular interactions is valuable yet challenging, given that different molecules are usually represent... | ['Yang Liu', 'Wenbing Huang', 'Xiangzhe Kong'] | 2023-06-02 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 2.6293585e-01 -6.1612416e-02 -2.1707812e-01 -4.2545781e-01
-4.8952329e-01 -5.3120017e-01 5.8588916e-01 3.3808339e-01
-2.0394111e-02 1.0838958e+00 3.0083555e-01 -4.0232402e-01
-2.1186908e-01 -7.4542814e-01 -1.1744449e+00 -1.0283538e+00
1.5948053e-02 6.8639100e-01 -7.8188907e-03 -8.2664385e-02
9.4277561e-02... | [5.103739261627197, 5.838719844818115] |
46fcd26f-45b9-4785-b6a2-ab107ec9f69e | tacoformer-token-channel-compounded-cross | 2306.13592 | null | https://arxiv.org/abs/2306.13592v1 | https://arxiv.org/pdf/2306.13592v1.pdf | TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition | Recently, emotion recognition based on physiological signals has emerged as a field with intensive research. The utilization of multi-modal, multi-channel physiological signals has significantly improved the performance of emotion recognition systems, due to their complementarity. However, effectively integrating emoti... | ['Xinda Li'] | 2023-06-23 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 0.01101531 -0.696022 0.21215975 -0.5246184 -0.9581585 -0.3156082
0.3022957 0.14945237 -0.47645628 0.8281086 0.30202046 0.36713526
-0.2612656 -0.26818335 -0.5410425 -0.83340985 -0.02241809 -0.46930593
-0.36676037 -0.06498421 0.17977136 0.07277316 -1.5773698 0.75479984
1.1143876 1.5178552 0.1... | [13.233315467834473, 4.882299423217773] |
601d4675-3421-4328-b8a7-8a814bce4aa0 | continual-predictive-learning-from-videos | 2204.05624 | null | https://arxiv.org/abs/2204.05624v1 | https://arxiv.org/pdf/2204.05624v1.pdf | Continual Predictive Learning from Videos | Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at all times. In practice, however, different prediction tasks may arrive sequentially so that the environments may change persistently through... | ['Xiaokang Yang', 'Mingsheng Long', 'Yunbo Wang', 'Siyu Gao', 'Han Lu', 'Wendong Zhang', 'Geng Chen'] | 2022-04-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Continual_Predictive_Learning_From_Videos_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Continual_Predictive_Learning_From_Videos_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-prediction'] | ['computer-vision'] | [ 2.28800476e-01 -1.98784530e-01 -3.07265986e-02 1.86662357e-02
-4.95625079e-01 -2.66186327e-01 7.70706534e-01 -1.34756416e-02
-3.40532571e-01 1.04669487e+00 1.88160446e-02 -2.61000893e-03
-4.19665515e-01 -4.72168118e-01 -1.28365636e+00 -6.74648762e-01
-3.19903970e-01 8.54514837e-01 6.24648392e-01 9.45865214... | [8.426531791687012, 0.5204747319221497] |
671f8389-72af-4055-b363-ec314753f7ad | alzheimers-disease-diagnosis-based-on | 1810.10941 | null | http://arxiv.org/abs/1810.10941v1 | http://arxiv.org/pdf/1810.10941v1.pdf | Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis | Dementia is a syndrome characterised by the decline of different cognitive
abilities. Alzheimer's Disease (AD) is the most common dementia affecting
cognitive domains such as memory and learning, perceptual-motion or executive
function. High rate of deaths and high cost for detection, treatments and
patient's care coun... | ['Juan Manuel Fernández Montenegro'] | 2018-10-25 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [-1.18751310e-01 -1.41163856e-01 3.61593783e-01 -3.78087491e-01
1.03980556e-01 -3.02506536e-01 5.33281922e-01 2.46049553e-01
-9.13998246e-01 1.02066803e+00 9.13048387e-02 1.85953081e-01
-4.31138784e-01 -7.05606461e-01 -1.07166126e-01 -5.91991007e-01
-5.74328423e-01 2.22853780e-01 7.97323138e-02 -5.37169695... | [13.333396911621094, 3.2887208461761475] |
44bc46d0-ac07-415c-a076-e266bdb7182c | st-sql-semi-supervised-self-training-for-text | null | null | https://openreview.net/forum?id=CPdvNQGrOr0 | https://openreview.net/pdf?id=CPdvNQGrOr0 | ST-SQL: Semi-Supervised Self-Training for Text-to-SQL via Column Specificity Meta-Learning | The few-shot problem is an urgent challenge for the generalization capability of the single-table text-to-SQL task. Current few-shot methods neglect the potential information of unlabeled data and have a domain bias due to the same weight of samples. Motivated by this, this paper proposes a Self-Training text-to-SQL (S... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [ 8.70153755e-02 -1.70162350e-01 -6.98004067e-01 -7.48977482e-01
-9.15103674e-01 -2.71794558e-01 4.69055027e-01 2.85135478e-01
-3.33722174e-01 7.57020593e-01 1.50136098e-01 -1.66527539e-01
2.22746328e-01 -1.01153100e+00 -6.75262630e-01 -3.75982583e-01
3.77469480e-01 8.90202582e-01 5.67405999e-01 -3.81816953... | [10.081921577453613, 3.5285208225250244] |
49519d5e-d530-4c47-b39f-356f9422f921 | handling-rare-items-in-data-to-text | null | null | https://aclanthology.org/W18-6543 | https://aclanthology.org/W18-6543.pdf | Handling Rare Items in Data-to-Text Generation | Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that... | ['Claire Gardent', 'Anastasia Shimorina'] | 2018-11-01 | null | null | null | ws-2018-11 | ['kg-to-text'] | ['natural-language-processing'] | [ 3.03396881e-01 -3.23507786e-02 -2.17245504e-01 -1.20413356e-01
-8.51602137e-01 -7.68780351e-01 1.30480385e+00 3.95262450e-01
-8.65386486e-01 9.64056790e-01 6.94986641e-01 -4.68031824e-01
7.97971711e-02 -7.65725434e-01 -7.28827298e-01 -2.97992796e-01
1.67108908e-01 5.57133973e-01 3.10846537e-01 -2.45578289... | [11.544432640075684, 9.06477165222168] |
4ff20331-1dfa-48ae-b427-88262273f332 | an-intelligent-safety-system-for-human | 1812.03953 | null | http://arxiv.org/abs/1812.03953v2 | http://arxiv.org/pdf/1812.03953v2.pdf | An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles | Nowadays, automobile manufacturers make efforts to develop ways to make cars
fully safe. Monitoring driver's actions by computer vision techniques to detect
driving mistakes in real-time and then planning for autonomous driving to avoid
vehicle collisions is one of the most important issues that has been
investigated i... | ['Hadi Abdi Khojasteh', 'Ebrahim Ansari', 'Parvin Razzaghi', 'Alireza Abbas Alipour'] | 2018-12-10 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [-3.83980870e-02 2.35053077e-01 3.97102982e-02 -5.62871039e-01
-1.65078595e-01 -2.70642102e-01 5.85295737e-01 -5.05194187e-01
-4.25841421e-01 7.89652094e-02 -1.34083077e-01 -6.06573045e-01
4.41730544e-02 -5.75013101e-01 -4.03431714e-01 -6.07696474e-01
5.66661716e-01 7.64854178e-02 5.08432865e-01 -7.27431595... | [7.7683587074279785, -0.776201069355011] |
938f2f12-010c-48e8-89ee-14b5ab43ab0f | continuous-and-interactive-factual-knowledge | null | null | https://openreview.net/forum?id=GxT3-eeWLNx | https://openreview.net/pdf?id=GxT3-eeWLNx | Continuous and Interactive Factual Knowledge Learning in Verification Dialogues | Knowledge bases (KBs) used in applications such as dialogue systems need to be continuously expanded in order to serve the users well. This process is known as knowledge base completion (KBC). A piece of knowledge or a fact is often represented as a triple (s, r, t), meaning that the entity s and the entity t have the ... | ['Anonymous'] | 2020-10-15 | null | null | null | neurips-workshop-hamlets-2020-12 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-2.29431883e-01 6.64328456e-01 -2.21418068e-01 -3.43288958e-01
-3.42768162e-01 -6.79652512e-01 5.19720376e-01 4.27278012e-01
-2.42209449e-01 1.50914800e+00 1.81942895e-01 -2.93128431e-01
-1.40508011e-01 -9.43542957e-01 -6.42326176e-01 -3.26368362e-01
5.29680736e-02 5.56134880e-01 5.93698144e-01 -6.38059855... | [9.487417221069336, 8.315291404724121] |
55db41ec-d5eb-49a6-9058-d2b3c18025fe | cross-lingual-cross-age-group-adaptation-for | 2306.14517 | null | https://arxiv.org/abs/2306.14517v1 | https://arxiv.org/pdf/2306.14517v1.pdf | Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition | Speech emotion recognition plays a crucial role in human-computer interactions. However, most speech emotion recognition research is biased toward English-speaking adults, which hinders its applicability to other demographic groups in different languages and age groups. In this work, we analyze the transferability of e... | ['Pascale Fung', 'Zihan Liu', 'Rita Frieske', 'Willy Chung', 'Holy Lovenia', 'Samuel Cahyawijaya'] | 2023-06-26 | null | null | null | null | ['emotion-recognition', 'speech-emotion-recognition'] | ['computer-vision', 'speech'] | [-6.30591750e-01 -8.60810503e-02 -5.27274907e-02 -6.79938734e-01
-4.07774478e-01 -2.27129668e-01 3.00224513e-01 -9.39679742e-02
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-7.38854036e-02 2.54669264e-02 -3.97570193e-01 -2.94457823... | [13.539072036743164, 5.833860874176025] |
74fb0b79-1bf8-4c1f-8b5e-a78f0136c1ad | revisiting-random-channel-pruning-for-neural | 2205.05676 | null | https://arxiv.org/abs/2205.05676v1 | https://arxiv.org/pdf/2205.05676v1.pdf | Revisiting Random Channel Pruning for Neural Network Compression | Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical problem, each being claimed effective in some ways. Yet, a benchmark to compare those algorithms directly is lacking, mainly due to the complex... | ['Luc van Gool', 'Radu Timofte', 'Shuhang Gu', 'Wen Li', 'Kamil Adamczewski', 'Yawei Li'] | 2022-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Revisiting_Random_Channel_Pruning_for_Neural_Network_Compression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Revisiting_Random_Channel_Pruning_for_Neural_Network_Compression_CVPR_2022_paper.pdf | cvpr-2022-1 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.84079725e-01 -1.85213700e-01 -5.19044757e-01 -3.00321966e-01
-3.55360895e-01 -2.12323949e-01 5.91783404e-01 2.04949722e-01
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-3.02335650e-01 2.65661001e-01 4.65056062e-01 -3.26090962... | [8.525535583496094, 3.239650249481201] |
1a593d0e-69ac-4367-a13b-64aad5a465f6 | bayesian-convolutional-neural-network-based | 2005.08460 | null | https://arxiv.org/abs/2005.08460v1 | https://arxiv.org/pdf/2005.08460v1.pdf | Bayesian convolutional neural network based MRI brain extraction on nonhuman primates | Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory ... | ['Rasmus M. Birn', 'Mary E. Meyerand', 'Jonathan A. Oler', 'Gengyan Zhao', 'Fang Liu', 'Ned H. Kalin'] | 2020-05-18 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 6.47947341e-02 1.30182996e-01 2.98632950e-01 -6.89425230e-01
-5.17762005e-01 -1.93942249e-01 4.68865901e-01 3.22785340e-02
-1.01415420e+00 9.83382583e-01 -2.64798880e-01 -1.22200653e-01
-2.53676295e-01 -4.41273272e-01 -5.78206778e-01 -8.59369397e-01
-3.67684931e-01 9.20358062e-01 4.30661410e-01 5.91496527... | [14.18805980682373, -2.298013687133789] |
0aaa416c-85d1-4a6f-a7ed-bc127072b153 | bayesian-optimization-for-radio-resource | 2012.08469 | null | https://arxiv.org/abs/2012.08469v2 | https://arxiv.org/pdf/2012.08469v2.pdf | Bayesian Optimization for Radio Resource Management: Open Loop Power Control | We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resource management (RRM) problems. We believe that BOGP is a powerful tool that has been somewhat overlooked in RRM research, although it elegant... | ['Jakob Hoydis', 'Alvaro Valcarce Rial', 'Lorenzo Maggi'] | 2020-12-15 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 7.97909200e-02 1.51850462e-01 -4.02396992e-02 -9.07820165e-02
-6.17495120e-01 -3.04776728e-01 4.01595294e-01 -3.54116201e-01
-1.41292885e-01 1.21586525e+00 -8.19661394e-02 -9.14950728e-01
-8.24244261e-01 -2.98892707e-01 -9.20782760e-02 -1.19169784e+00
-5.21441638e-01 5.23297369e-01 -3.88809204e-01 -1.01604261... | [6.351013660430908, 3.716660737991333] |
1c5931be-c089-4c2c-b36e-ab045df7e47f | rocketqav2-a-joint-training-method-for-dense | 2110.07367 | null | https://arxiv.org/abs/2110.07367v2 | https://arxiv.org/pdf/2110.07367v2.pdf | RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking | In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we... | ['Ji-Rong Wen', 'Haifeng Wang', 'Hua Wu', 'Qiaoqiao She', 'Wayne Xin Zhao', 'Jing Liu', 'Yingqi Qu', 'Ruiyang Ren'] | 2021-10-14 | null | https://aclanthology.org/2021.emnlp-main.224 | https://aclanthology.org/2021.emnlp-main.224.pdf | emnlp-2021-11 | ['passage-re-ranking'] | ['natural-language-processing'] | [-7.12634549e-02 -4.34173971e-01 -1.83807239e-01 -2.66744196e-01
-1.50976527e+00 -6.23960674e-01 5.90001166e-01 5.84631085e-01
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7.57535547e-02 6.28124297e-01 3.29546511e-01 -3.54892224... | [11.454859733581543, 7.708746433258057] |
91ad9afd-60dd-4c75-a260-0b03d0cd84a4 | pre-training-co-evolutionary-protein | 2110.15527 | null | https://arxiv.org/abs/2110.15527v1 | https://arxiv.org/pdf/2110.15527v1.pdf | Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model | Understanding protein sequences is vital and urgent for biology, healthcare, and medicine. Labeling approaches are expensive yet time-consuming, while the amount of unlabeled data is increasing quite faster than that of the labeled data due to low-cost, high-throughput sequencing methods. In order to extract knowledge ... | ['Tie-Yan Liu', 'Tao Qin', 'Bin Shao', 'Pan Deng', 'Jianwei Zhu', 'Yingce Xia', 'Siyuan Liu', 'He Zhang', 'Fusong Ju', 'Huanhuan Xia', 'Lijun Wu', 'Shizhuo Zhang', 'Liang He'] | 2021-10-29 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.22338438e-01 1.42293513e-01 -3.93774390e-01 -4.35149878e-01
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9.57689956e-02 5.95731020e-01 1.13825187e-01 -1.10156924... | [4.673201084136963, 5.638831615447998] |
35d41c86-be20-49ae-8b08-41cf42d245d3 | when-cnns-meet-random-rnns-towards-multi | 2004.12349 | null | https://arxiv.org/abs/2004.12349v2 | https://arxiv.org/pdf/2004.12349v2.pdf | When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition | Recognizing objects and scenes are two challenging but essential tasks in image understanding. In particular, the use of RGB-D sensors in handling these tasks has emerged as an important area of focus for better visual understanding. Meanwhile, deep neural networks, specifically convolutional neural networks (CNNs), ha... | ['Nevrez Imamoglu', 'Ali Caglayan', 'Ryosuke Nakamura', 'Ahmet Burak Can'] | 2020-04-26 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.83578539e-01 -3.60554516e-01 -1.34210750e-01 -5.98388076e-01
-7.65723884e-01 -4.18952197e-01 4.79841411e-01 -2.88655668e-01
-5.54886460e-01 5.39044023e-01 -3.69693860e-02 -1.68521628e-02
-1.95353463e-01 -9.51557398e-01 -6.41136706e-01 -1.04437184e+00
4.76172507e-01 -1.20261438e-01 2.27668092e-01 1.68886945... | [9.633014678955078, -0.8843486905097961] |
dae8d5a9-072e-482f-ad33-f17cfd0c7779 | image-edge-restoring-filter | 2112.13540 | null | https://arxiv.org/abs/2112.13540v1 | https://arxiv.org/pdf/2112.13540v1.pdf | Image Edge Restoring Filter | In computer vision, image processing and computer graphics, image smoothing filtering is a very basic and important task and to be expected possessing good edge-preserving smoothing property. Here we address the problem that the edge-preserving ability of many popular local smoothing filters needs to be improved. In th... | ['Zhihang Wang', 'Yongpeng Li', 'Qian Liu'] | 2021-12-27 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 1.55256823e-01 -3.57595742e-01 4.11687940e-01 -2.54170120e-01
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-5.25105119e-01 6.94487751e-01 5.88100433e-01 -2.14193821e-01
-1.12717681e-01 -7.76303172e-01 -2.98969328e-01 -7.86920428e-01
1.07268706e-01 -7.47796655e-01 8.90483260e-01 -2.69251555... | [11.09510612487793, -2.575838565826416] |
08a3744d-6636-46c0-a74c-fe8c5f060eef | rethinking-domain-generalization-for-face | 2303.13662 | null | https://arxiv.org/abs/2303.13662v1 | https://arxiv.org/pdf/2303.13662v1.pdf | Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment | This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations. Most prior works regard domain-specific signals as a negative impact, and apply metric learning or adversarial losses to remove them from feature representation. Thou... | ['Wen-Sheng Chu', 'Yixuan Li', 'Xiaoming Liu', 'Yaojie Liu', 'Yiyou Sun'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Rethinking_Domain_Generalization_for_Face_Anti-Spoofing_Separability_and_Alignment_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Rethinking_Domain_Generalization_for_Face_Anti-Spoofing_Separability_and_Alignment_CVPR_2023_paper.pdf | cvpr-2023-1 | ['metric-learning', 'face-anti-spoofing', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 6.35523498e-01 -5.88425610e-04 -2.83773720e-01 -3.21667373e-01
-6.39604390e-01 -7.92398989e-01 6.74608469e-01 -4.39623088e-01
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-2.60742128e-01 -4.98077124e-01 -7.88470089e-01 -9.19468701e-01
-1.51834235e-01 -4.62823026e-02 1.40379919e-02 -2.34304383... | [13.067424774169922, 1.186003565788269] |
e2bdc041-b2e2-4ec2-8b94-3469912f636f | ai-predicts-independent-construction-safety | 1908.05972 | null | https://arxiv.org/abs/1908.05972v2 | https://arxiv.org/pdf/1908.05972v2.pdf | AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes | This paper significantly improves on, and finishes to validate, an approach proposed in previous research in which safety outcomes were predicted from attributes with machine learning. Like in the original study, we use Natural Language Processing (NLP) to extract fundamental attributes from raw incident reports and ma... | ['Antoine J. -P. Tixier', 'Matthew R. Hallowell', 'Henrietta Baker'] | 2019-08-16 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [ 2.32075527e-01 3.45493495e-01 -5.58783472e-01 -3.89324635e-01
-8.37286294e-01 -4.45850164e-01 5.15147090e-01 1.14072526e+00
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-4.13641751e-01 -9.44338679e-01 -4.93986219e-01 -4.62684363e-01
-2.28098392e-01 4.15088207e-01 8.55447426e-02 6.31863400... | [8.139104843139648, 5.198251247406006] |
c8ca79e4-134d-4ac4-b787-9e24dd441e43 | robust-reflection-removal-with-reflection | 2103.04273 | null | https://arxiv.org/abs/2103.04273v2 | https://arxiv.org/pdf/2103.04273v2.pdf | Robust Reflection Removal with Reflection-free Flash-only Cues | We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent t... | ['Qifeng Chen', 'Chenyang Lei'] | 2021-03-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lei_Robust_Reflection_Removal_With_Reflection-Free_Flash-Only_Cues_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lei_Robust_Reflection_Removal_With_Reflection-Free_Flash-Only_Cues_CVPR_2021_paper.pdf | cvpr-2021-1 | ['reflection-removal'] | ['computer-vision'] | [ 7.04195023e-01 -3.88882279e-01 4.71937358e-01 5.09569719e-02
-9.81723785e-01 -4.75570917e-01 2.46204272e-01 -4.27696943e-01
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2.24419340e-01 -8.05712044e-01 5.17613351e-01 -1.00799263... | [10.509469985961914, -2.6640737056732178] |
746a4c75-95d5-49be-98b8-ddf0590b4982 | the-power-of-many-a-physarum-swarm-steiner | 2110.08233 | null | https://arxiv.org/abs/2110.08233v1 | https://arxiv.org/pdf/2110.08233v1.pdf | The Power of Many: A Physarum Swarm Steiner Tree Algorithm | We create a novel Physarum Steiner algorithm designed to solve the Euclidean Steiner tree problem. Physarum is a unicellular slime mold with the ability to form networks and fuse with other Physarum organisms. We use the simplicity and fusion of Physarum to create large swarms which independently operate to solve the S... | ['Laura P. Schaposnik', 'Fidel I. Schaposnik Massolo', 'Sheryl Hsu'] | 2021-10-15 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 6.10003807e-02 1.03529036e-01 4.62660104e-01 3.02854002e-01
5.39520800e-01 -1.26032937e+00 5.11996627e-01 2.05900222e-01
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-6.99485838e-01 9.38789129e-01 7.79356658e-01 -5.44962645... | [5.635213375091553, 4.127933025360107] |
93f321f8-7296-4b94-aa15-22999974d936 | unsupervised-generation-of-long-form | null | null | https://aclanthology.org/2022.pandl-1.3 | https://aclanthology.org/2022.pandl-1.3.pdf | Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates | We explore the task of generating long-form technical questions from textbooks. Semi-structured metadata of a textbook — the table of contents and the index — provide rich cues for technical question generation. Existing literature for long-form question generation focuses mostly on reading comprehension assessment, an... | ['Tapas Nayak', 'Pratik Saini', 'Arpita Kundu', 'Subhasish Ghosh', 'Indrajit Bhattacharya'] | null | null | null | null | pandl-coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 3.33892018e-01 7.23586619e-01 1.77572295e-01 4.80048470e-02
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-4.33864981e-01 -9.24661219e-01 -7.14903891e-01 2.93088049e-01
7.80094504e-01 4.77965236e-01 4.52595711e-01 -5.50939381... | [11.45769214630127, 8.066893577575684] |
d7270265-52c4-4c30-bc46-a85df29225a7 | local-and-global-topics-in-text-modeling-of | 2104.01115 | null | https://arxiv.org/abs/2104.01115v1 | https://arxiv.org/pdf/2104.01115v1.pdf | Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites | Topic models are popular models for analyzing a collection of text documents. The models assert that documents are distributions over latent topics and latent topics are distributions over words. A nested document collection is where documents are nested inside a higher order structure such as stories in a book, articl... | ['Robert E. Weiss', 'Jason Wang'] | 2021-03-30 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [-2.38160402e-01 3.39470208e-01 -6.91125333e-01 -1.67281613e-01
-1.10866547e+00 -8.80861521e-01 8.34877372e-01 7.58205354e-01
-9.93506610e-02 5.31980693e-01 9.78796840e-01 -5.31275153e-01
-4.07669276e-01 -1.27658141e+00 -6.14660025e-01 -6.11598849e-01
-2.45965913e-01 7.50522971e-01 5.92582881e-01 6.69341013... | [10.34074878692627, 7.014677047729492] |
85b5babd-12c7-4c88-8cd3-9d084d1bcac5 | one-stage-shape-instantiation-from-a-single | 1907.10763 | null | https://arxiv.org/abs/1907.10763v1 | https://arxiv.org/pdf/1907.10763v1.pdf | One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud | Shape instantiation which predicts the 3D shape of a dynamic target from one or more 2D images is important for real-time intra-operative navigation. Previously, a general shape instantiation framework was proposed with manual image segmentation to generate a 2D Statistical Shape Model (SSM) and with Kernel Partial Lea... | ['Guang-Zhong Yang', 'Jian-Qing Zheng', 'Peichao Li', 'Zhao-Yang Wang', 'Xiao-Yun Zhou'] | 2019-07-24 | null | null | null | null | ['image-to-3d'] | ['computer-vision'] | [ 1.22799441e-01 5.80506444e-01 7.16744736e-02 -5.97530007e-01
-7.64780343e-01 -2.03639045e-01 5.43152809e-01 3.80088061e-01
-6.11331463e-01 3.44132453e-01 -4.02731389e-01 -4.33044046e-01
-2.51645088e-01 -5.40645719e-01 -6.86955214e-01 -5.67347288e-01
-1.13149360e-01 9.37036455e-01 4.03623074e-01 -9.06767771... | [14.064542770385742, -2.5804617404937744] |
ea671d61-4b3b-498b-8b4a-92a6ad161a6f | task-adaptive-pre-training-and-self-training | 2109.06466 | null | https://arxiv.org/abs/2109.06466v2 | https://arxiv.org/pdf/2109.06466v2.pdf | Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding | Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data. However, it's unclear whether they learn similar representations or they can be effectively combined. In this paper, ... | ['Xifeng Yan', 'Wenhu Chen', 'Semih Yavuz', 'Shiyang Li'] | 2021-09-14 | null | https://aclanthology.org/2021.findings-emnlp.86 | https://aclanthology.org/2021.findings-emnlp.86.pdf | findings-emnlp-2021-11 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 5.6523401e-01 4.2579386e-01 -7.8283262e-01 -8.7820506e-01
-1.0385566e+00 -7.7721006e-01 8.4415203e-01 1.5129682e-01
-4.9528354e-01 9.8763359e-01 4.3466070e-01 -5.4988605e-01
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1.1294833e-01... | [10.812566757202148, 8.158119201660156] |
eb54244d-4df7-43e8-96c6-b42d3217251a | causality-driven-hierarchical-structure | 2210.06964 | null | https://arxiv.org/abs/2210.06964v1 | https://arxiv.org/pdf/2210.06964v1.pdf | Causality-driven Hierarchical Structure Discovery for Reinforcement Learning | Hierarchical reinforcement learning (HRL) effectively improves agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e.g., subgoals or options). However, how to automatically discover high-quality hierarchical structures is still a great challenge. Previous ... | ['Yunji Chen', 'Qi Guo', 'Ling Li', 'Zidong Du', 'Xishan Zhang', 'Ruizhi Chen', 'Qi Yi', 'Jiaming Guo', 'Ke Tang', 'Rui Zhang', 'Xing Hu', 'Shaohui Peng'] | 2022-10-13 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-4.39007461e-01 -1.59497689e-02 -2.35914662e-01 3.26676704e-02
-4.03911263e-01 -4.63742018e-01 2.97010750e-01 1.21527780e-02
-2.06485227e-01 1.25275660e+00 5.67336857e-01 -2.42503345e-01
-6.02744758e-01 -9.99343693e-01 -6.62177563e-01 -7.03725159e-01
-9.22507465e-01 6.35031521e-01 3.31770629e-01 -6.48824990... | [3.99023175239563, 1.675085186958313] |
01b41ac5-8ef9-4ff9-82fc-cf09bc2200d1 | dechorate-a-calibrated-room-impulse-response | 2104.13168 | null | https://arxiv.org/abs/2104.13168v1 | https://arxiv.org/pdf/2104.13168v1.pdf | dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing | This paper presents dEchorate: a new database of measured multichannel Room Impulse Responses (RIRs) including annotations of early echo timings and 3D positions of microphones, real sources and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods... | ['Sharon Gannot', 'Nancy Bertin', 'Antoine Deleforge', 'Cédric Foy', 'Pinchas Tandeitnik', 'Diego Di Carlo'] | 2021-04-27 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 8.46731663e-02 -6.50098801e-01 1.11791289e+00 -3.72066915e-01
-1.43368161e+00 -9.66623008e-01 3.82342637e-01 6.94345459e-02
-2.73534179e-01 2.86809444e-01 7.51298189e-01 -3.85954410e-01
-6.09371886e-02 -1.56950448e-02 -1.73933744e-01 -8.82584751e-01
-4.97023672e-01 9.95917171e-02 1.35311961e-01 -2.03564450... | [15.084566116333008, 5.811712741851807] |
67b72918-8041-405c-9513-5887c1b6a5bf | roft-real-time-optical-flow-aided-6d-object | 2111.03821 | null | https://arxiv.org/abs/2111.03821v1 | https://arxiv.org/pdf/2111.03821v1.pdf | ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking | 6D object pose tracking has been extensively studied in the robotics and computer vision communities. The most promising solutions, leveraging on deep neural networks and/or filtering and optimization, exhibit notable performance on standard benchmarks. However, to our best knowledge, these have not been tested thoroug... | ['Lorenzo Natale', 'Ugo Pattacini', 'Giulia Pasquale', 'Yuriy Onyshchuk', 'Nicola A. Piga'] | 2021-11-06 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-1.50736764e-01 -5.92705548e-01 -1.42753720e-01 3.98092344e-02
-2.95397848e-01 -7.71443665e-01 4.33239222e-01 -1.50631607e-01
-8.14910412e-01 1.95870027e-01 -4.05839801e-01 -1.85318142e-02
-6.60075247e-02 -2.11005956e-01 -9.15664852e-01 -6.26734018e-01
-3.47047448e-01 6.60049558e-01 5.98229110e-01 9.69216675... | [6.8664774894714355, -2.2472951412200928] |
f26765ed-0e06-477e-983a-2df6758ce4e2 | unsupervised-doppler-radar-based-activity | 2103.10478 | null | https://arxiv.org/abs/2103.10478v2 | https://arxiv.org/pdf/2103.10478v2.pdf | Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare | Passive radio frequency (RF) sensing and monitoring of human daily activities in elderly care homes is an emerging topic. Micro-Doppler radars are an appealing solution considering their non-intrusiveness, deep penetration, and high-distance range. Unsupervised activity recognition using Doppler radar data has not rece... | ['Bo Tan', 'Yanguo Jing', 'Wenda Li', 'Sara Sharifzadeh', 'Yordanka Karayaneva'] | 2021-03-18 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.58925170e-01 -8.83253291e-03 4.08052295e-01 -5.40083162e-02
-4.59377408e-01 1.12224254e-04 7.33098865e-01 -1.30520388e-02
-8.16275835e-01 9.38117921e-01 4.96018797e-01 -1.09411605e-01
-8.71218920e-01 -8.34035158e-01 2.28650421e-01 -1.13029969e+00
-5.46355724e-01 3.30474675e-01 -3.92553955e-02 -1.11416623... | [13.949509620666504, 1.547965168952942] |
afea1aab-ebcc-4ef9-8499-03a24c2b4148 | probabilistic-knowledge-graph-embeddings | null | null | https://openreview.net/forum?id=rJ4qXnCqFX | https://openreview.net/pdf?id=rJ4qXnCqFX | Probabilistic Knowledge Graph Embeddings | We develop a probabilistic extension of state-of-the-art embedding models for link prediction in relational knowledge graphs. Knowledge graphs are collections of relational facts, where each fact states that a certain relation holds between two entities, such as people, places, or objects. We argue that knowledge graph... | ['Stephan Mandt', 'Robert Bamler', 'Farnood Salehi'] | 2018-09-27 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.88910440e-01 6.71007514e-01 -4.96790260e-01 -2.25119755e-01
-5.56851327e-01 -5.98408282e-01 9.86974239e-01 5.68623900e-01
-3.72299135e-01 7.23913968e-01 4.02446330e-01 -2.88497567e-01
-5.63478649e-01 -1.12409413e+00 -9.00741696e-01 -4.64800239e-01
-2.09235236e-01 8.76645029e-01 4.58375126e-01 -4.20602225... | [8.753222465515137, 7.604669094085693] |
2365a4fa-968e-4698-b64e-5c42d68e01b5 | convolutional-neural-networks-for-fast | 1809.04440 | null | https://arxiv.org/abs/1809.04440v2 | https://arxiv.org/pdf/1809.04440v2.pdf | Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching | Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a t... | ['Yunsheng Bai', 'Hao Ding', 'Yizhou Sun', 'Wei Wang'] | 2018-09-10 | null | null | null | null | ['set-matching', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 1.91835426e-02 -1.20420471e-01 -2.78421324e-02 -3.10713947e-01
-3.44704300e-01 -6.07023299e-01 4.62584943e-01 1.14567089e+00
-3.87809992e-01 2.33189374e-01 -1.52314156e-01 -3.02341759e-01
-4.52714413e-01 -1.27090549e+00 -3.52574706e-01 -4.39740509e-01
-3.90232176e-01 5.75912774e-01 1.18865713e-01 -2.12122679... | [7.1628899574279785, 6.029603004455566] |
8da5544d-0e29-4ac1-8f27-7e6a59921993 | online-simulator-based-experimental-design | 2303.02227 | null | https://arxiv.org/abs/2303.02227v1 | https://arxiv.org/pdf/2303.02227v1.pdf | Online simulator-based experimental design for cognitive model selection | The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discriminate between theories expressed as computational models. Research on this subject has mostly been restricted to optimal experiment design with... | ['Andrew Howes', 'Samuel Kaski', 'Luigi Acerbi', 'Suyog Chandramouli', 'Gregoire Clarte', 'Aini Putkonen', 'Alexander Aushev'] | 2023-03-03 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 4.64789212e-01 -3.56694400e-01 -2.00401306e-01 -4.23401207e-01
-6.85501993e-01 -6.62431538e-01 4.76534724e-01 4.20051217e-01
-9.04545188e-01 1.06847203e+00 -3.20799083e-01 -6.37986422e-01
-6.09201670e-01 -4.87254947e-01 -4.37850267e-01 -2.81537473e-01
-2.01181218e-01 5.32337368e-01 1.52879521e-01 -2.28201076... | [4.934492588043213, 3.081486463546753] |
2f916580-7be0-40fd-9b44-9be9a6b57eb3 | interpretable-learning-for-self-driving-cars | 1703.10631 | null | http://arxiv.org/abs/1703.10631v1 | http://arxiv.org/pdf/1703.10631v1.pdf | Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention | Deep neural perception and control networks are likely to be a key component
of self-driving vehicles. These models need to be explainable - they should
provide easy-to-interpret rationales for their behavior - so that passengers,
insurance companies, law enforcement, developers etc., can understand what
triggered a pa... | ['John Canny', 'Jinkyu Kim'] | 2017-03-30 | interpretable-learning-for-self-driving-cars-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Kim_Interpretable_Learning_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Kim_Interpretable_Learning_for_ICCV_2017_paper.pdf | iccv-2017-10 | ['steering-control'] | ['computer-vision'] | [ 1.98898658e-01 5.31120479e-01 -1.39764488e-01 -7.70108402e-01
-3.13589373e-03 -5.14115870e-01 8.72801542e-01 -8.99853632e-02
-1.61943808e-02 5.82075536e-01 4.32082117e-01 -8.14324439e-01
1.57203376e-01 -5.77360153e-01 -1.28145647e+00 -3.61987025e-01
9.00356937e-03 -7.00532552e-03 4.60416824e-01 -3.58987153... | [8.924098014831543, 5.266473770141602] |
24d44c1c-f5eb-498a-9a0e-e8e0cf7b6b60 | enrich4all-a-first-luxembourgish-bert-model | null | null | https://aclanthology.org/2022.sigul-1.27 | https://aclanthology.org/2022.sigul-1.27.pdf | ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot | Machine Translation (MT)-empowered chatbots are not established yet, however, we see an amazing future breaking language barriers and enabling conversation in multiple languages without time-consuming language model building and training, particularly for under-resourced languages. In this paper we focus on the under-r... | ['Dimitra Anastasiou'] | null | null | null | null | sigul-lrec-2022-6 | ['visual-dialogue', 'visual-dialogue'] | ['computer-vision', 'natural-language-processing'] | [-4.36706066e-01 5.67144394e-01 5.92518561e-02 -4.54240024e-01
-8.62152100e-01 -9.17719662e-01 8.71216118e-01 -1.75433636e-01
-4.72983420e-01 9.50433850e-01 2.59505987e-01 -9.18800235e-01
2.06355885e-01 -4.08778727e-01 1.43207327e-01 -1.15592621e-01
3.84524792e-01 1.36271691e+00 1.82880118e-01 -7.53388703... | [12.690877914428711, 7.96993350982666] |
3ca6ddc7-fc81-4e4b-a439-22d44c3a1bb4 | unitrec-a-unified-text-to-text-transformer | 2305.15756 | null | https://arxiv.org/abs/2305.15756v1 | https://arxiv.org/pdf/2305.15756v1.pdf | UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation | Prior study has shown that pretrained language models (PLM) can boost the performance of text-based recommendation. In contrast to previous works that either use PLM to encode user history as a whole input text, or impose an additional aggregation network to fuse multi-turn history representations, we propose a unified... | ['Kam-Fai Wong', 'Yiming Du', 'Huimin Wang', 'Zhiming Mao'] | 2023-05-25 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 1.12946056e-01 -3.39801520e-01 -6.20894551e-01 -4.51801479e-01
-9.33578849e-01 -5.14311433e-01 9.04050946e-01 -1.96344536e-02
-4.63192642e-01 2.20694020e-01 9.28861260e-01 -5.49795389e-01
1.33878797e-01 -5.63533485e-01 -6.18328273e-01 -1.92493454e-01
2.62375861e-01 4.33745921e-01 -5.72570413e-02 -2.11898118... | [10.230107307434082, 5.757397174835205] |
28458a56-f035-43e4-b10b-fdad1e871fb5 | entropy-dissipation-informed-neural-network | 2303.11205 | null | https://arxiv.org/abs/2303.11205v1 | https://arxiv.org/pdf/2303.11205v1.pdf | Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs | We extend the concept of self-consistency for the Fokker-Planck equation (FPE) to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of particles under drift and diffusion, MVE accounts for the additional inter-particle interactions, which are often highly singular in physical s... | ['Zhenfu Wang', 'Zebang Shen'] | 2023-02-11 | null | null | null | null | ['type'] | ['speech'] | [-3.92359704e-01 -2.63267070e-01 5.16502023e-01 2.44554356e-02
-1.30563574e-02 -3.38095665e-01 5.53538978e-01 3.13856870e-01
-5.92948020e-01 1.31641746e+00 -4.14560348e-01 6.34227023e-02
-2.16005281e-01 -9.12813246e-01 -7.51596689e-01 -1.11142576e+00
-4.07110184e-01 6.34767830e-01 6.33227304e-02 -3.95862490... | [6.476781845092773, 3.542633056640625] |
79278216-39d2-4fa2-b0f5-b40f8f22c4c7 | testing-human-ability-to-detect-deepfake | 2212.05056 | null | https://arxiv.org/abs/2212.05056v3 | https://arxiv.org/pdf/2212.05056v3.pdf | Testing Human Ability To Detect Deepfake Images of Human Faces | Deepfakes are computationally-created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020 a workshop consulting AI experts from aca... | ['Bennett Kleinberg', 'Shane D. Johnson', 'Sergi D. Bray'] | 2022-12-07 | null | null | null | null | ['human-detection-of-deepfakes'] | ['miscellaneous'] | [ 2.49392986e-01 4.52201873e-01 5.09032048e-02 -1.49398714e-01
-6.59466028e-01 -8.96144390e-01 6.19171560e-01 8.39512572e-02
-6.99261904e-01 4.74941969e-01 2.50854045e-01 -5.73780537e-01
3.00844759e-01 -7.12580204e-01 -5.88815153e-01 -3.41570169e-01
3.95518810e-01 -2.69083232e-02 7.64585659e-02 3.28156748... | [12.454084396362305, 1.1748028993606567] |
3d2c9d50-4aba-4b46-8c9b-4be563b4c321 | edpn-enhanced-deep-pyramid-network-for-blurry | 2105.04872 | null | https://arxiv.org/abs/2105.04872v1 | https://arxiv.org/pdf/2105.04872v1.pdf | EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration | Image deblurring has seen a great improvement with the development of deep neural networks. In practice, however, blurry images often suffer from additional degradations such as downscaling and compression. To address these challenges, we propose an Enhanced Deep Pyramid Network (EDPN) for blurry image restoration from... | ['Zhiwei Xiong', 'Yueyi Zhang', 'Jie Huang', 'Zeyu Xiao', 'Ruikang Xu'] | 2021-05-11 | null | null | null | null | ['image-deblocking'] | ['computer-vision'] | [ 2.37848774e-01 -7.31774747e-01 1.49600551e-01 -7.29491338e-02
-6.26447320e-01 -2.66815543e-01 3.72914970e-01 -5.67775309e-01
-3.46226618e-02 7.51735508e-01 8.81487906e-01 -8.06336664e-03
-1.72943518e-01 -2.95576811e-01 -7.42861390e-01 -7.56218612e-01
-2.29102615e-02 -5.72646856e-01 2.36698449e-01 -2.71459073... | [11.463809967041016, -2.5396111011505127] |
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