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
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a931ed0a-2157-43af-8747-b3be6ac8830c | genq-automated-question-generation-to-support | 2305.16809 | null | https://arxiv.org/abs/2305.16809v2 | https://arxiv.org/pdf/2305.16809v2.pdf | GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children | When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent sy... | ['Art Glenberg', 'M. Adelaida Restrepo', 'Chris Blais', 'Tri Nguyen', 'Martha Michelle Soto Fernandez', 'Ligia E. Gomez', 'Arun Balajiee Lekshmi Narayanan'] | 2023-05-26 | null | null | null | null | ['reading-comprehension', 'question-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.37406141e-01 1.11254096e+00 8.68850425e-02 -8.13678741e-01
-9.62080061e-01 -7.81588793e-01 3.25169683e-01 3.88679683e-01
-1.45560592e-01 9.53047216e-01 1.08973706e+00 -5.56956947e-01
-8.09346065e-02 -5.11317134e-01 -2.67126977e-01 2.00677902e-01
6.42717481e-01 6.27051353e-01 7.99361169e-02 -3.64168435... | [12.103094100952148, 7.998887538909912] |
7a2b14b5-1792-4086-8d38-1bf7b11e4d48 | smac-simultaneous-mapping-and-clustering | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=1899 | http://proceedings.mlr.press/v80/bajaj18a/bajaj18a.pdf | SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions |
We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e.g., 2D images or 3D shapes). Our approach takes as input a heterogeneous object collection and a set of maps computed between some pairs of objects, and outp... | ['Qi-Xing Huang', 'Zihang He', 'Chandrajit Bajaj', 'Zhenxiao Liang', 'Tingran Gao'] | 2018-07-01 | null | null | null | icml-2018-7 | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.35723052e-03 -4.28257696e-02 1.05408065e-01 -2.65361369e-01
-1.06787038e+00 -8.63734961e-01 5.32385409e-01 3.42282534e-01
-4.21595946e-03 1.77809790e-01 -5.25717810e-02 1.87599123e-01
-7.20270693e-01 -6.64476037e-01 -8.68634582e-01 -8.47122192e-01
-5.65106690e-01 1.04405153e+00 5.32750547e-01 3.19849700... | [7.690502643585205, 4.513182163238525] |
0d3b1cbc-7d98-4b07-8742-f9754f462d44 | text-video-retrieval-with-disentangled | 2305.12218 | null | https://arxiv.org/abs/2305.12218v1 | https://arxiv.org/pdf/2305.12218v1.pdf | Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment | Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with natural language descriptions. Current methods either fail to leverage the local details or are computationally expensive. What's worse, they fail to leverage the heterogeneous concepts in data. In this paper, we propose th... | ['Jie Chen', 'Chang Liu', 'Li Yuan', 'Zhennan Wang', 'Jinfa Huang', 'Zesen Cheng', 'Hao Li', 'Peng Jin'] | 2023-05-20 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-1.27151787e-01 -4.07971203e-01 -2.49378994e-01 -3.78390491e-01
-5.51826894e-01 -6.97541177e-01 7.77467847e-01 3.29967588e-01
-2.73689628e-01 2.95637518e-01 6.19556546e-01 2.45055303e-01
-3.25688452e-01 -6.28427327e-01 -2.55651385e-01 -6.20799363e-01
2.23402053e-01 4.53026742e-01 9.69410613e-02 -6.96059763... | [10.451278686523438, 1.1522945165634155] |
97f726cb-bbfc-4116-bbb2-23ee996befc0 | progressive-graph-convolution-network-for-eeg | 2112.09069 | null | https://arxiv.org/abs/2112.09069v1 | https://arxiv.org/pdf/2112.09069v1.pdf | Progressive Graph Convolution Network for EEG Emotion Recognition | Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions, demonstrating that dynamic relationships between different brain regions are an essential factor affecting emotion recognition determined through electroencephalography (EEG). Moreover, in EEG emo... | ['Rui Cheng', 'Yuanfang Chen', 'Lijian Zhang', 'Wenming Zheng', 'Guangming Shi', 'Youshuo Ji', 'Yang Li', 'Fu Li', 'Yijin Zhou'] | 2021-12-14 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-2.53824413e-01 -4.81743157e-01 4.48000342e-01 -6.34758830e-01
1.56596452e-01 -4.94778514e-01 2.26723790e-01 1.30718827e-01
-2.74955422e-01 7.85684168e-01 1.95869938e-01 2.19281539e-01
-6.74148023e-01 -6.82938993e-01 -4.63203609e-01 -7.14549363e-01
-5.73302209e-01 -1.00142516e-01 -3.16924006e-01 -1.65689155... | [13.114912033081055, 3.4905002117156982] |
aa99597d-6ef3-401d-b718-2269524cd5bc | improved-skin-lesion-recognition-by-a-self | 2112.12086 | null | https://arxiv.org/abs/2112.12086v1 | https://arxiv.org/pdf/2112.12086v1.pdf | Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach | State-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets, to overcome the generalization limitations derived from the reduced size of the skin lesion imaging datasets. ImageNet is often used as the pretraining dataset, but its transferring potent... | ['Juan Carlos SanMiguel', 'Pablo Carballeira', 'Marcos Escudero Viñolo', 'Kirill Sirotkin'] | 2021-12-22 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 9.64913726e-01 1.98731706e-01 -3.41738760e-01 -3.71128529e-01
-6.15048170e-01 -5.34142494e-01 4.67327356e-01 1.08624801e-01
-7.42430151e-01 5.83645344e-01 -2.25366846e-01 -2.72733182e-01
-4.29455996e-01 -6.28232241e-01 -7.13964105e-01 -8.32046866e-01
1.51223868e-01 3.89520407e-01 3.23925227e-01 3.98001187... | [15.374006271362305, -2.7290492057800293] |
9af59514-c878-4607-b523-15c83e2803bd | fid-light-efficient-and-effective-retrieval | 2209.14290 | null | https://arxiv.org/abs/2209.14290v1 | https://arxiv.org/pdf/2209.14290v1.pdf | FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation | Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base. However, they are also more complex systems and need to handle long inputs. In this work, we introduce FiD-Light... | ['Hamed Zamani', 'Karthik Raman', 'Jiecao Chen', 'Sebastian Hofstätter'] | 2022-09-28 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-3.39180827e-02 -1.28687724e-01 -5.40732861e-01 1.05864264e-01
-1.47755527e+00 -8.08690786e-01 9.74178791e-01 6.05215192e-01
-5.83155215e-01 6.99062467e-01 5.55662811e-01 -2.27464169e-01
-1.52896181e-01 -9.01008844e-01 -8.92593324e-01 1.45273684e-02
-2.09790587e-01 6.20806992e-01 6.36678040e-01 -4.77653474... | [11.3593111038208, 7.829767227172852] |
e052bd07-7b5a-488e-8d6c-91f8717010b7 | specmar-fast-heart-rate-estimation-from-ppg | 1810.06196 | null | http://arxiv.org/abs/1810.06196v2 | http://arxiv.org/pdf/1810.06196v2.pdf | SPECMAR: Fast Heart Rate Estimation from PPG Signal using a Modified Spectral Subtraction Scheme with Composite Motion Artifacts Reference Generation | The task of heart rate estimation using photoplethysmographic (PPG) signal is
challenging due to the presence of various motion artifacts in the recorded
signals. In this paper, a fast algorithm for heart rate estimation based on
modified SPEctral subtraction scheme utilizing Composite Motion Artifacts
Reference genera... | ['Shaikh Anowarul Fattah', 'Sk. Tanvir Ahmed', 'Mohammad Tariqul Islam', 'Celia Shahnaz'] | 2018-10-15 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 4.64514911e-01 -3.84886116e-01 3.54100525e-01 1.06689304e-01
-5.16409993e-01 -2.11078808e-01 9.52173918e-02 -1.85775697e-01
-2.77279109e-01 8.16646934e-01 1.05035909e-01 1.71455249e-01
9.04425457e-02 -2.25592121e-01 -3.79213528e-03 -7.75400996e-01
-4.52165082e-02 -5.27218163e-01 -5.39389327e-02 3.16728711... | [13.970719337463379, 3.0059893131256104] |
04826ea3-af28-410b-b1db-e4e7d83fd5f2 | puffin-a-path-unifying-feed-forward | 2307.02903 | null | https://arxiv.org/abs/2307.02903v2 | https://arxiv.org/pdf/2307.02903v2.pdf | PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction | Accurately predicting vapor pressure is vital for various industrial and environmental applications. However, obtaining accurate measurements for all compounds of interest is not possible due to the resource and labor intensity of experiments. The demand for resources and labor further multiplies when a temperature-dep... | ['Nadia Shardt', 'Idelfonso B. R. Nogueira', 'Ana Mafalda Ribeiro', 'Luana P. Queiroz', 'Carine Menezes Rebello', 'Vinicius Viena Santana'] | 2023-07-06 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 3.79277617e-01 -3.26221474e-02 -4.42930400e-01 -2.50229299e-01
2.06352949e-01 -7.08408654e-01 3.48932534e-01 8.17990303e-01
-6.68013468e-02 7.13600338e-01 -4.15185004e-01 -1.00638664e+00
-5.50395429e-01 -1.28861094e+00 -7.24722564e-01 -6.40438855e-01
-1.93875089e-01 2.04738304e-01 6.49477616e-02 -2.92679280... | [5.138703346252441, 5.664463520050049] |
4b4d39da-863c-4281-a927-d3ebdcce3e2f | green-cws-extreme-distillation-and-efficient-1 | 2111.09078 | null | https://arxiv.org/abs/2111.09078v1 | https://arxiv.org/pdf/2111.09078v1.pdf | Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application | Benefiting from the strong ability of the pre-trained model, the research on Chinese Word Segmentation (CWS) has made great progress in recent years. However, due to massive computation, large and complex models are incapable of empowering their ability for industrial use. On the other hand, for low-resource scenarios,... | ['Yong liu', 'Yulan Hu'] | 2021-11-17 | green-cws-extreme-distillation-and-efficient | https://openreview.net/forum?id=9poQ2m0R-- | https://openreview.net/pdf?id=9poQ2m0R-- | null | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.62341142e-01 -1.82692438e-01 -3.17942262e-01 -3.82642418e-01
-8.90550077e-01 -4.80292231e-01 2.63854623e-01 -3.92517522e-02
-6.32619262e-01 7.05668211e-01 -6.04991987e-02 -9.15174425e-01
4.67994839e-01 -7.91382194e-01 -4.80219901e-01 -5.07021904e-01
6.12052083e-01 3.57831597e-01 4.46482748e-01 -4.69304174... | [9.989754676818848, 10.0977144241333] |
41be110a-cb89-43d3-91d0-54cd2d0cd589 | atlas-automate-online-service-configuration | 2210.16902 | null | https://arxiv.org/abs/2210.16902v1 | https://arxiv.org/pdf/2210.16902v1.pdf | Atlas: Automate Online Service Configuration in Network Slicing | Network slicing achieves cost-efficient slice customization to support heterogeneous applications and services. Configuring cross-domain resources to end-to-end slices based on service-level agreements, however, is challenging, due to the complicated underlying correlations and the simulation-to-reality discrepancy bet... | ['Tao Han', 'Nakjung Choi', 'Qiang Liu'] | 2022-10-30 | null | null | null | null | ['thompson-sampling', 'safe-exploration'] | ['methodology', 'robots'] | [-5.56502879e-01 -1.70166761e-01 -2.09510028e-01 -6.13876939e-01
-7.75961876e-01 -7.57478237e-01 -3.42621952e-01 -8.58892381e-01
-1.05212897e-01 1.12425625e+00 -2.42341235e-01 -8.85677218e-01
-4.30319965e-01 -5.43116927e-01 -4.87664670e-01 -5.85093081e-01
-5.38353801e-01 1.05272746e+00 1.86701730e-01 3.05950195... | [5.854942321777344, 1.7323272228240967] |
d33ccded-849a-44e3-8e07-c2b0278340bb | decentralized-learning-for-wireless | 1503.08855 | null | http://arxiv.org/abs/1503.08855v1 | http://arxiv.org/pdf/1503.08855v1.pdf | Decentralized learning for wireless communications and networking | This chapter deals with decentralized learning algorithms for in-network
processing of graph-valued data. A generic learning problem is formulated and
recast into a separable form, which is iteratively minimized using the
alternating-direction method of multipliers (ADMM) so as to gain the desired
degree of paralleliza... | ['Ioannis D. Schizas', 'Qing Ling', 'Gonzalo Mateos', 'Georgios B. Giannakis', 'Hao Zhu'] | 2015-03-30 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 4.75823402e-01 6.07189536e-01 -6.93929017e-01 -3.25675070e-01
-4.57753837e-01 -3.60377103e-01 1.74368739e-01 1.84724525e-01
-5.64244807e-01 1.09520912e+00 -3.19575340e-01 -6.42320454e-01
-7.51686275e-01 -8.68217230e-01 -4.81707484e-01 -8.90930057e-01
-1.19214153e+00 4.63895798e-01 -4.66049016e-01 1.85388595... | [6.182238578796387, 5.012168884277344] |
7c956b2c-a0a2-46aa-8623-81bf3a29856a | using-consumer-behavior-data-to-reduce-energy | 1510.00165 | null | http://arxiv.org/abs/1510.00165v1 | http://arxiv.org/pdf/1510.00165v1.pdf | Using consumer behavior data to reduce energy consumption in smart homes | This paper discusses how usage patterns and preferences of inhabitants can be
learned efficiently to allow smart homes to autonomously achieve energy
savings. We propose a frequent sequential pattern mining algorithm suitable for
real-life smart home event data. The performance of the proposed algorithm is
compared to ... | ['Hans-Friedrich Witschel', 'Daniel Schweizer', 'Holger Wache', 'Miguel Rodriguez', 'Michael Zehnder', 'Danilo Zanatta'] | 2015-10-01 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [-6.68286532e-02 2.59225935e-01 1.11972988e-01 -7.71728694e-01
1.41609013e-01 -2.59849340e-01 1.28450930e-01 2.40320936e-01
-2.18501434e-01 8.56385589e-01 5.61982393e-01 -1.85624212e-01
-6.14426613e-01 -1.07276821e+00 8.05192068e-02 -7.14832485e-01
-2.32426256e-01 5.95437467e-01 3.35546523e-01 -1.40962988... | [5.961394786834717, 2.550546169281006] |
64501468-85e4-442e-b5f4-8c0c120859af | kinematic-structure-correspondences-via | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Chang_Kinematic_Structure_Correspondences_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Chang_Kinematic_Structure_Correspondences_CVPR_2016_paper.pdf | Kinematic Structure Correspondences via Hypergraph Matching | In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds corresponden... | ['Yiannis Demiris', 'Martina Zambelli', 'Hyung Jin Chang', 'Tobias Fischer', 'Maxime Petit'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['hypergraph-matching'] | ['graphs'] | [ 1.86038762e-01 2.80954223e-02 2.16113627e-01 -1.51666060e-01
-2.56690651e-01 -5.63081861e-01 8.01701188e-01 1.28821328e-01
-1.26283243e-01 2.05423251e-01 6.04133196e-02 1.26249000e-01
-7.85132587e-01 -4.92252886e-01 -5.49273849e-01 -4.84678358e-01
-5.99462628e-01 4.93177772e-01 5.23590267e-01 -1.36156395... | [8.15243911743164, -1.9572609663009644] |
9ddfccec-5f44-452e-a655-4348e3231364 | autoregressive-image-generation-using | 2203.01941 | null | https://arxiv.org/abs/2203.01941v2 | https://arxiv.org/pdf/2203.01941v2.pdf | Autoregressive Image Generation using Residual Quantization | For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot sho... | ['Wook-Shin Han', 'Minsu Cho', 'Saehoon Kim', 'Chiheon Kim', 'Doyup Lee'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_Autoregressive_Image_Generation_Using_Residual_Quantization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_Autoregressive_Image_Generation_Using_Residual_Quantization_CVPR_2022_paper.pdf | cvpr-2022-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 3.13895881e-01 -4.87835817e-02 -2.70368513e-02 -8.22728649e-02
-1.15478742e+00 -3.38280678e-01 6.51828885e-01 -3.22372466e-01
-3.83734219e-02 5.17009437e-01 3.08448672e-01 -2.24171072e-01
9.53699350e-02 -1.12600720e+00 -1.10460806e+00 -6.67805552e-01
-7.32481927e-02 8.22139382e-02 1.25086168e-02 -2.14325547... | [11.17631721496582, -1.2405914068222046] |
77feda30-bb89-4f95-97d4-dc303970c5cb | diffbev-conditional-diffusion-model-for-bird | 2303.08333 | null | https://arxiv.org/abs/2303.08333v1 | https://arxiv.org/pdf/2303.08333v1.pdf | DiffBEV: Conditional Diffusion Model for Bird's Eye View Perception | BEV perception is of great importance in the field of autonomous driving, serving as the cornerstone of planning, controlling, and motion prediction. The quality of the BEV feature highly affects the performance of BEV perception. However, taking the noises in camera parameters and LiDAR scans into consideration, we us... | ['Xingang Wang', 'Yun Ye', 'Zheng Zhu', 'Jiayu Zou'] | 2023-03-15 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-9.27105919e-03 -3.91031645e-04 -2.57580519e-01 -4.79134530e-01
-3.93980622e-01 -3.66245627e-01 8.05585623e-01 3.54893208e-02
-5.61150968e-01 2.51798749e-01 2.27238685e-01 -2.68659085e-01
1.18901975e-01 -9.54971135e-01 -7.67028868e-01 -5.73523104e-01
5.42129397e-01 2.37756357e-01 6.72299683e-01 -4.76012439... | [8.172842025756836, -2.5296506881713867] |
0d248ebb-8a0b-4e2f-a62d-12615c5b127f | self-attention-based-deep-feature-fusion-for | null | null | https://ieeexplore.ieee.org/abstract/document/8982033 | https://ieeexplore.ieee.org/abstract/document/8982033 | Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification | Remote sensing scene classification aims to assign automatically each aerial image a specific sematic label. In this letter, we propose a new method, called self-attention-based deep feature fusion (SAFF), to aggregate deep layer features and emphasize the weights of the complex objects of remote sensing scene images f... | ['Ran Cao; Leyuan Fang; Ting Lu; Nanjun He'] | 2022-05-30 | null | null | null | ieee-geoscience-and-remote-sensing-letters-7 | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.98823732e-01 -4.81744051e-01 1.06218070e-01 -6.07912123e-01
-3.77675205e-01 -7.66768828e-02 4.17118073e-01 2.67162055e-01
-4.63930845e-01 4.99136955e-01 2.11439893e-01 -1.09310113e-01
-4.56319094e-01 -9.07454908e-01 -2.82688141e-01 -9.66037452e-01
-1.32939786e-01 -4.50075984e-01 -8.22707117e-02 3.29809003... | [9.862548828125, -1.4758330583572388] |
6d294856-e8f5-44b2-8be0-f6a8bf00b8d7 | community-recovery-in-hypergraphs | 1709.03670 | null | http://arxiv.org/abs/1709.03670v1 | http://arxiv.org/pdf/1709.03670v1.pdf | Community Recovery in Hypergraphs | Community recovery is a central problem that arises in a wide variety of
applications such as network clustering, motion segmentation, face clustering
and protein complex detection. The objective of the problem is to cluster data
points into distinct communities based on a set of measurements, each of which
is associat... | ['Kwangjun Ahn', 'Kangwook Lee', 'Changho Suh'] | 2017-09-12 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 7.45473921e-01 6.39845580e-02 -1.40870795e-01 -6.39102235e-02
-2.11671099e-01 -4.16073769e-01 6.12303495e-01 7.85133243e-01
-5.44662178e-01 4.99506712e-01 -2.49495819e-01 -2.24813849e-01
-1.21292077e-01 -7.59100318e-01 -5.74520051e-01 -1.25138140e+00
-3.38049978e-01 7.65409648e-01 7.49568939e-02 1.72011971... | [6.982907295227051, 5.1568098068237305] |
e4745659-d11d-4512-84ba-7cc1a3969811 | differentiable-multi-fidelity-fusion | 2306.06904 | null | https://arxiv.org/abs/2306.06904v1 | https://arxiv.org/pdf/2306.06904v1.pdf | Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning | With rapid progress in deep learning, neural networks have been widely used in scientific research and engineering applications as surrogate models. Despite the great success of neural networks in fitting complex systems, two major challenges still remain: i) the lack of generalization on different problems/datasets, a... | ['Wei W. Xing', 'Wang Kang', 'Yuwen Deng'] | 2023-06-12 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-6.24636188e-03 -5.87021410e-01 -5.36043420e-02 -3.33937049e-01
-8.98205101e-01 -2.88128942e-01 3.83847624e-01 -3.48890498e-02
-3.67397070e-01 1.06460738e+00 -4.11893666e-01 -4.48296010e-01
-4.35882419e-01 -7.32339323e-01 -1.01476729e+00 -7.80513406e-01
-7.36867785e-02 5.17918229e-01 -4.93582115e-02 -2.19380334... | [6.428447723388672, 3.500135660171509] |
1384e3d1-a9d4-4f07-9bf1-e21819201766 | syntax-guided-domain-adaptation-for-aspect | 2211.05457 | null | https://arxiv.org/abs/2211.05457v1 | https://arxiv.org/pdf/2211.05457v1.pdf | Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) aims at extracting opinionated aspect terms in review texts and determining their sentiment polarities, which is widely studied in both academia and industry. As a fine-grained classification task, the annotation cost is extremely high. Domain adaptation is a popular solution to a... | ['Jing Xiao', 'Lei Wang', 'Xuan Wang', 'Qing Liao', 'Yan Jia', 'Cuiyun Gao', 'Anguo Dong'] | 2022-11-10 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 5.27585074e-02 -3.17410618e-01 -5.06187558e-01 -7.77893245e-01
-9.24333274e-01 -8.63033235e-01 6.21838748e-01 1.73307717e-01
-2.04192847e-01 5.62649071e-01 2.82795787e-01 -2.88741708e-01
8.92643780e-02 -8.39352727e-01 -5.14673650e-01 -5.94491243e-01
7.76105642e-01 4.56688046e-01 7.03268945e-02 -6.87945187... | [11.399372100830078, 6.695052623748779] |
655bf48f-e7b9-4133-a50b-3e04ffb19ad8 | utts-unsupervised-tts-with-conditional | 2206.02512 | null | https://arxiv.org/abs/2206.02512v3 | https://arxiv.org/pdf/2206.02512v3.pdf | UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder | In this paper, we propose a novel unsupervised text-to-speech (UTTS) framework which does not require text-audio pairs for the TTS acoustic modeling (AM). UTTS is a multi-speaker speech synthesizer that supports zero-shot voice cloning, it is developed from a perspective of disentangled speech representation learning. ... | ['Dong Yu', 'Gopala Krishna Anumanchipalli', 'Chunlei Zhang', 'Jiachen Lian'] | 2022-06-06 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 3.17367017e-01 1.86557278e-01 -1.97718814e-01 -2.44872838e-01
-1.31608391e+00 -4.96139199e-01 4.70277816e-01 -6.07983589e-01
1.72750413e-01 4.68793273e-01 4.94037986e-01 -4.48277503e-01
2.89210528e-01 -5.26684165e-01 -5.97657502e-01 -8.24311912e-01
4.12099779e-01 3.62316668e-01 -2.30629086e-01 -1.67249396... | [14.995444297790527, 6.544651031494141] |
2be43fc3-52bc-411d-9e1a-b8fdc0ea80e7 | attentional-aggregation-of-deep-feature-sets | 1808.00758 | null | https://arxiv.org/abs/1808.00758v2 | https://arxiv.org/pdf/1808.00758v2.pdf | Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction | We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are una... | ['Sen Wang', 'Andrew Markham', 'Niki Trigoni', 'Bo Yang'] | 2018-08-02 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 6.79105008e-03 -3.22260223e-02 3.73011865e-02 -3.50668877e-01
-9.18499231e-01 -8.14880013e-01 6.01985335e-01 -1.59597024e-01
-5.00206463e-02 4.62146699e-01 3.77821386e-01 6.92700371e-02
-3.41388196e-01 -8.64955246e-01 -1.21033525e+00 -8.70542049e-01
-7.61893988e-02 4.26264852e-01 -7.23853931e-02 1.17236711... | [8.153120040893555, -3.5992496013641357] |
58caeb88-697f-483f-be25-3d28983670de | troubleshooting-blind-image-quality-models-in | 2105.06747 | null | https://arxiv.org/abs/2105.06747v1 | https://arxiv.org/pdf/2105.06747v1.pdf | Troubleshooting Blind Image Quality Models in the Wild | Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is hig... | ['Kede Ma', 'Zhangyang Wang', 'Tianlong Chen', 'Haotao Wang', 'Zhihua Wang'] | 2021-05-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper.pdf | cvpr-2021-1 | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 5.29027581e-02 -7.44139180e-02 2.57334024e-01 -3.31875384e-01
-1.28209376e+00 -6.37234569e-01 2.43962929e-01 -3.21547687e-01
-4.49456155e-01 6.32116318e-01 1.83862612e-01 -2.31145307e-01
-4.60729271e-01 -1.95003837e-01 -5.67031205e-01 -7.25706816e-01
-9.16416124e-02 6.45842910e-01 2.17082575e-01 -2.53020674... | [11.892057418823242, -1.8052126169204712] |
b5072041-9adf-4ae7-b8d6-277b7eb4fd2f | automated-essay-scoring-in-argumentative | 2307.04276 | null | https://arxiv.org/abs/2307.04276v1 | https://arxiv.org/pdf/2307.04276v1.pdf | Automated Essay Scoring in Argumentative Writing: DeBERTeachingAssistant | Automated Essay scoring has been explored as a research and industry problem for over 50 years. It has drawn a lot of attention from the NLP community because of its clear educational value as a research area that can engender the creation of valuable time-saving tools for educators around the world. Yet, these tools a... | ['Choong Hee Kim', 'Karan Jha', 'Tonghua Tian', 'Yann Hicke'] | 2023-07-09 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 3.32463056e-01 6.81708932e-01 -2.18693897e-01 -5.56843996e-01
-7.31480896e-01 -6.94682240e-01 4.33036655e-01 7.72803426e-01
-1.60731092e-01 5.74297845e-01 6.17692411e-01 -9.22662079e-01
-4.23613280e-01 -6.46909952e-01 -2.97937304e-01 -2.87244856e-01
8.17239463e-01 5.62500298e-01 3.61284494e-01 -3.60227078... | [11.265480041503906, 9.141261100769043] |
0d3a40b0-4b2b-4bc7-b191-5edc907ac8cf | composite-triggered-intermittent-control-for | 2305.19644 | null | https://arxiv.org/abs/2305.19644v1 | https://arxiv.org/pdf/2305.19644v1.pdf | Composite Triggered Intermittent Control for Constrained Spacecraft Attitude Tracking | This paper focuses on the spacecraft attitude control problem with intermittent actuator activation, taking into account the attitude rotation rate limitation and input saturation issue simultaneously. To address this problem, we first propose a composite event-trigger mechanism, which composed of two state-dependent t... | ['Shujian Sun', 'Weijia Wang', 'Kun Wang', 'Tao Meng', 'Jiakun Lei'] | 2023-05-31 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-6.05137609e-02 4.07578528e-01 -2.82435268e-01 4.16463107e-01
1.41072765e-01 -8.53898168e-01 3.81248057e-01 1.79905239e-02
-6.32588491e-02 1.17515528e+00 -1.20641671e-01 -4.09099221e-01
-3.73271435e-01 -4.69388932e-01 -6.37121558e-01 -1.16653299e+00
2.69257784e-01 -5.55855874e-03 -1.22846533e-02 -4.97416049... | [5.244969367980957, 2.4337925910949707] |
449353ba-125c-4bfd-b121-82eaaf86eef9 | discovery-radiomics-with-clear-dr | 1710.10675 | null | http://arxiv.org/abs/1710.10675v1 | http://arxiv.org/pdf/1710.10675v1.pdf | Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy | Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown
considerable promise in recent years as a potential tool for improving clinical
decision support in medical oncology, particularly those based around the
concept of Discovery Radiomics, where radiomic sequencers are discovered
through the analysis of ... | ['Alexander Wong', 'Graham W. Taylor', 'Devinder Kumar'] | 2017-10-29 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 6.51196539e-01 6.68297112e-02 -4.07842815e-01 -4.45384383e-01
-5.16033828e-01 -4.93682146e-01 2.88305998e-01 4.08736497e-01
-1.04820348e-01 5.82105935e-01 4.66444075e-01 -8.22316349e-01
-4.85107541e-01 -6.20054483e-01 -2.44315655e-04 -9.91927505e-01
3.09962630e-01 6.94212079e-01 -7.62673765e-02 -2.33151302... | [15.217050552368164, -2.631983757019043] |
9b8597bf-5def-4193-8604-e5b7a296acff | mask-fpan-semi-supervised-face-parsing-in-the | 2212.09098 | null | https://arxiv.org/abs/2212.09098v5 | https://arxiv.org/pdf/2212.09098v5.pdf | Mask-FPAN: Semi-Supervised Face Parsing in the Wild With De-Occlusion and UV GAN | Fine-grained semantic segmentation of a person's face and head, including facial parts and head components, has progressed a great deal in recent years. However, it remains a challenging task, whereby considering ambiguous occlusions and large pose variations are particularly difficult. To overcome these difficulties, ... | ['Xikun Jiang', 'Zhongfeng Kang', 'Tianfang Zhang', 'Lei LI'] | 2022-12-18 | null | null | null | null | ['face-model', 'face-parsing'] | ['computer-vision', 'computer-vision'] | [ 1.91463754e-02 3.60485524e-01 2.18043163e-01 -8.20305943e-01
-9.04689670e-01 -5.86217582e-01 3.65049362e-01 -7.12722898e-01
-1.41092405e-01 4.21805263e-01 -1.09726883e-01 3.20475012e-01
2.15976745e-01 -4.98386443e-01 -7.80900478e-01 -7.10461795e-01
2.26367533e-01 6.98532760e-01 -2.78637987e-02 1.15281701... | [13.42341423034668, 0.5694437026977539] |
9787874d-5f65-4c86-8807-b93959dd4501 | msn-efficient-online-mask-selection-network | 2106.10452 | null | https://arxiv.org/abs/2106.10452v1 | https://arxiv.org/pdf/2106.10452v1.pdf | MSN: Efficient Online Mask Selection Network for Video Instance Segmentation | In this work we present a novel solution for Video Instance Segmentation(VIS), that is automatically generating instance level segmentation masks along with object class and tracking them in a video. Our method improves the masks from segmentation and propagation branches in an online manner using the Mask Selection Ne... | ['Humphrey Shi', 'Harsh Maheshwari', 'Shubhika Garg', 'Jiachen Li', 'Vidit Goel'] | 2021-06-19 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.53277802e-01 4.60978523e-02 -4.68251854e-02 -2.77578354e-01
-6.34803951e-01 -7.28663683e-01 2.60232687e-01 -2.78325945e-01
-5.61960399e-01 4.95429754e-01 -1.59582257e-01 -8.68367925e-02
1.04207151e-01 -6.04512930e-01 -8.92535269e-01 -4.73791778e-01
-5.42476922e-02 6.20414674e-01 1.06581151e+00 -1.62814513... | [9.147354125976562, -0.18109728395938873] |
60a0276a-029c-46e1-a1e6-e8cec87e27ea | regularization-free-estimation-in-trace | 1504.06305 | null | http://arxiv.org/abs/1504.06305v1 | http://arxiv.org/pdf/1504.06305v1.pdf | Regularization-free estimation in trace regression with symmetric positive semidefinite matrices | Over the past few years, trace regression models have received considerable
attention in the context of matrix completion, quantum state tomography, and
compressed sensing. Estimation of the underlying matrix from
regularization-based approaches promoting low-rankedness, notably nuclear norm
regularization, have enjoye... | ['Matthias Hein', 'Martin Slawski', 'Ping Li'] | 2015-04-23 | regularization-free-estimation-in-trace-1 | http://papers.nips.cc/paper/5726-regularization-free-estimation-in-trace-regression-with-symmetric-positive-semidefinite-matrices | http://papers.nips.cc/paper/5726-regularization-free-estimation-in-trace-regression-with-symmetric-positive-semidefinite-matrices.pdf | neurips-2015-12 | ['quantum-state-tomography'] | ['medical'] | [ 6.37109339e-01 1.57925576e-01 -7.14394599e-02 -1.28438488e-01
-8.19863200e-01 -4.91277039e-01 4.94034082e-01 1.05118364e-01
-6.93979323e-01 8.04118335e-01 1.31882250e-01 -4.53700632e-01
-5.98158062e-01 -3.29340637e-01 -4.99058008e-01 -1.07523382e+00
5.02528474e-02 4.24192160e-01 -1.62178904e-01 -3.03653777... | [6.753365516662598, 4.620147705078125] |
177f6d1e-c22b-4e5f-b800-f8b1792e3aae | kpeval-towards-fine-grained-semantic-based | 2303.15422 | null | https://arxiv.org/abs/2303.15422v1 | https://arxiv.org/pdf/2303.15422v1.pdf | KPEval: Towards Fine-grained Semantic-based Evaluation of Keyphrase Extraction and Generation Systems | Despite the significant advancements in keyphrase extraction and keyphrase generation methods, the predominant approach for evaluation only relies on exact matching with human references and disregards reference-free attributes. This scheme fails to recognize systems that generate keyphrases that are semantically equiv... | ['Kai-Wei Chang', 'Da Yin', 'Di wu'] | 2023-03-27 | null | null | null | null | ['keyphrase-generation', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.37875167e-02 -1.26748696e-01 -5.50559878e-01 1.52600154e-01
-9.92090940e-01 -9.77511227e-01 1.28005672e+00 7.76506543e-01
-7.10804164e-01 6.30070448e-01 7.63574541e-01 -2.14110136e-01
-3.47643375e-01 -5.32336950e-01 -2.96115130e-01 -8.62800982e-03
2.11008623e-01 2.25141525e-01 3.86280239e-01 -6.57057047... | [12.299357414245605, 8.899238586425781] |
4a8a83e3-a785-4ae0-b2b2-9cb11078841b | representations-of-time-expressions-for | null | null | https://aclanthology.org/W17-2341 | https://aclanthology.org/W17-2341.pdf | Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks | Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo... | ['Guergana Savova', 'Dmitriy Dligach', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2017-08-01 | null | null | null | ws-2017-8 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 8.11939314e-03 -1.13695621e-01 -7.36727178e-01 -5.05815506e-01
-2.23372236e-01 -4.10598576e-01 6.42210066e-01 5.41947901e-01
-8.64706933e-01 7.11824596e-01 3.01709563e-01 -4.39674288e-01
2.34793052e-01 -8.75951231e-01 -2.58502722e-01 -4.64915007e-01
-5.64302862e-01 1.89755812e-01 -7.09799826e-02 -4.10710216... | [8.549769401550293, 9.017213821411133] |
bca9f8f3-761f-4712-8256-fafd2d07ae69 | a-hybrid-mesh-neural-representation-for-3d | 2203.12613 | null | https://arxiv.org/abs/2203.12613v3 | https://arxiv.org/pdf/2203.12613v3.pdf | Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction | We propose a novel method to reconstruct the 3D shapes of transparent objects using hand-held captured images under natural light conditions. It combines the advantage of explicit mesh and multi-layer perceptron (MLP) network, a hybrid representation, to simplify the capture setting used in recent contributions. After ... | ['Weiwei Xu', 'Hujun Bao', 'Zihan Zhu', 'Jiamin Xu'] | 2022-03-23 | null | null | null | null | ['transparent-objects', 'image-matting', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.37383479e-01 1.03010975e-01 1.96772963e-01 -3.26262265e-01
-4.04239208e-01 -1.45255789e-01 3.63236576e-01 5.77821285e-02
-4.38423246e-01 5.45884192e-01 -1.84790432e-01 1.11483604e-01
-2.93220598e-02 -1.15444052e+00 -1.11932695e+00 -7.66504288e-01
1.70534998e-01 7.65994728e-01 4.31222856e-01 1.37246266... | [9.319685935974121, -3.190127372741699] |
f00ca646-ad70-42f6-a6e3-8f76fde9d8b6 | grad-fec-unequal-loss-protection-of-deep | 2307.01846 | null | https://arxiv.org/abs/2307.01846v1 | https://arxiv.org/pdf/2307.01846v1.pdf | Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence | Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in the cloud. The deep feature tensors produced by the front-end are transmitted to the cloud through a communication channel, which may be su... | ['Ivan V. Bajić', 'S. Faegheh Yeganli', 'Korcan Uyanik'] | 2023-07-04 | null | null | null | null | ['feature-importance'] | ['methodology'] | [-2.35030912e-02 -7.48406351e-02 1.62438154e-01 4.66703363e-02
7.54874051e-02 -1.33860081e-01 3.34061310e-02 2.51448471e-02
-4.86471225e-03 4.97535348e-01 5.00377640e-02 -2.41944585e-02
-3.44879985e-01 -9.27787721e-01 -2.23677188e-01 -7.83456504e-01
-3.57459813e-01 -9.19358507e-02 1.12553097e-01 1.33101255... | [5.99778938293457, 5.6057868003845215] |
8795ba58-bf7f-4620-82c3-2e291b927862 | discovering-novel-actions-in-an-open-world | 2305.16602 | null | https://arxiv.org/abs/2305.16602v1 | https://arxiv.org/pdf/2305.16602v1.pdf | Discovering Novel Actions in an Open World with Object-Grounded Visual Commonsense Reasoning | Learning to infer labels in an open world, i.e., in an environment where the target ``labels'' are unknown, is an important characteristic for achieving autonomy. Foundation models pre-trained on enormous amounts of data have shown remarkable generalization skills through prompting, particularly in zero-shot inference.... | ['Shubham Trehan', 'Sanjoy Kundu', 'Sathyanarayanan N. Aakur'] | 2023-05-26 | null | null | null | null | ['object-recognition', 'visual-commonsense-reasoning'] | ['computer-vision', 'reasoning'] | [ 3.87261182e-01 2.54799157e-01 -3.03837985e-01 -3.11506957e-01
-5.12782335e-01 -4.19015288e-01 5.94665229e-01 -4.06296313e-01
-3.52681428e-01 7.96268463e-01 2.03422338e-01 5.10778762e-02
-3.42754096e-01 -6.72944725e-01 -1.24106526e+00 -6.13428295e-01
3.15982290e-02 6.17165565e-01 2.49157161e-01 -2.66045988... | [8.559459686279297, 0.818389892578125] |
dc716ce4-2a8a-477f-8d28-bb6ed905c99f | megan-multi-explanation-graph-attention | 2211.13236 | null | https://arxiv.org/abs/2211.13236v2 | https://arxiv.org/pdf/2211.13236v2.pdf | MEGAN: Multi-Explanation Graph Attention Network | We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications. This proves crucial to improve the interpretability of graph ... | ['Pascal Friederich', 'Patrick Reiser', 'Luca Torresi', 'Jonas Teufel'] | 2022-11-23 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 5.29039681e-01 1.24102378e+00 -7.67625749e-01 -5.95923007e-01
-2.99008369e-01 -5.02876997e-01 8.06839406e-01 1.58784688e-01
3.23573232e-01 8.37729692e-01 6.42889202e-01 -7.99425066e-01
-3.14039916e-01 -5.58814585e-01 -1.04572058e+00 1.44363940e-01
7.29674548e-02 7.73350835e-01 -3.32738549e-01 -4.98460531... | [8.3828706741333, 6.169857501983643] |
cea9e5de-a322-43b5-9a6e-67e88a08caed | clozer-adaptable-data-augmentation-for-cloze-1 | null | null | https://aclanthology.org/2022.repl4nlp-1.7 | https://aclanthology.org/2022.repl4nlp-1.7.pdf | Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension | Task-adaptive pre-training (TAPT) alleviates the lack of labelled data and provides performance lift by adapting unlabelled data to downstream task. Unfortunately, existing adaptations mainly involve deterministic rules that cannot generalize well. Here, we propose Clozer, a sequence-tagging based cloze answer extracti... | ['Pascale Fung', 'Dan Su', 'Samuel Cahyawijaya', 'Zeng Min', 'Willy Chung', 'Bryan Wilie', 'Holy Lovenia'] | null | null | null | null | repl4nlp-acl-2022-5 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.87064147e-01 2.40379289e-01 5.98393977e-02 -4.23867732e-01
-1.42725825e+00 -8.49959195e-01 9.86972004e-02 2.32906029e-01
-6.20838165e-01 8.26276183e-01 2.22752139e-01 -1.03936946e+00
-2.81944156e-01 -6.66307628e-01 -7.92203248e-01 -2.64332473e-01
1.78670242e-01 8.08337212e-01 5.15163481e-01 -4.06778336... | [11.263666152954102, 8.114883422851562] |
beb68157-c214-4523-82da-94559e4cecb8 | odd-one-out-representation-learning | 2012.07966 | null | https://arxiv.org/abs/2012.07966v1 | https://arxiv.org/pdf/2012.07966v1.pdf | Odd-One-Out Representation Learning | The effective application of representation learning to real-world problems requires both techniques for learning useful representations, and also robust ways to evaluate properties of representations. Recent work in disentangled representation learning has shown that unsupervised representation learning approaches rel... | ['Bjørn Sand Jensen', 'Anders Kirk Uhrenholt', 'Salman Mohammadi'] | 2020-12-14 | null | null | null | null | ['odd-one-out'] | ['reasoning'] | [ 5.06474912e-01 2.76059002e-01 -4.57127601e-01 -4.73014534e-01
-1.12372327e+00 -6.33969069e-01 1.00210488e+00 2.94825613e-01
-2.02706650e-01 7.93843210e-01 5.50168574e-01 -3.53386909e-01
-6.33339643e-01 -5.88382900e-01 -4.25506055e-01 -6.69816017e-01
-1.18519783e-01 7.52693534e-01 -4.12382662e-01 -2.09174797... | [9.261765480041504, 4.87069034576416] |
1e36ae8a-2535-4aca-b865-a8b4e1e89400 | extraction-of-clinical-information-from-the | 1606.01093 | null | http://arxiv.org/abs/1606.01093v1 | http://arxiv.org/pdf/1606.01093v1.pdf | Extraction of clinical information from the non-invasive fetal electrocardiogram | Estimation of the fetal heart rate (FHR) has gained interest in the last
century, low heart rate variability has been studied to identify intrauterine
growth restricted fetuses (prepartum), and abnormal FHR patterns have been
associated with fetal distress during delivery (intrapartum). Several
monitoring techniques ha... | ['Joachim Behar'] | 2016-05-27 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 5.18150985e-01 1.29617840e-01 2.03331217e-01 -2.49486968e-01
-1.57116666e-01 -6.03903234e-01 -1.52885139e-01 2.44832352e-01
6.64206874e-03 6.63922369e-01 -2.01161131e-01 -4.23970997e-01
-4.42656815e-01 -6.04277670e-01 -2.69892663e-01 -7.10587442e-01
-6.45450950e-01 1.81426689e-01 6.13948330e-02 2.42245629... | [14.122944831848145, 3.101020574569702] |
e239ea92-c3be-4bc4-a17e-9447490dc1b1 | feature-level-collaboration-joint | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chi_Feature-Level_Collaboration_Joint_Unsupervised_Learning_of_Optical_Flow_Stereo_Depth_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chi_Feature-Level_Collaboration_Joint_Unsupervised_Learning_of_Optical_Flow_Stereo_Depth_CVPR_2021_paper.pdf | Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion | Precise estimation of optical flow, stereo depth and camera motion are important for the real-world 3D scene understanding and visual perception. Since the three tasks are tightly coupled with the inherent 3D geometric constraints, current studies have demonstrated that the three tasks can be improved through joint... | ['Xin Yang', 'Peng Guo', 'Tianyu Hao', 'Qingjie Wang', 'Cheng Chi'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['stereo-depth-estimation', 'depth-and-camera-motion'] | ['computer-vision', 'computer-vision'] | [-1.10879630e-01 -4.30071771e-01 -4.08571273e-01 -2.58422077e-01
-5.13095319e-01 -4.24949974e-01 4.20434594e-01 -6.49136543e-01
-4.36532974e-01 5.07009506e-01 3.20803612e-01 -7.39407614e-02
1.20841749e-01 -5.30391872e-01 -6.20640993e-01 -6.12627566e-01
-2.79187635e-02 -6.14215806e-02 4.31020498e-01 1.84965387... | [8.705829620361328, -1.9925874471664429] |
870ec49f-669a-4d0c-9faa-56da6cb608f7 | font-acknowledgment-and-character-extraction | 1305.4064 | null | http://arxiv.org/abs/1305.4064v1 | http://arxiv.org/pdf/1305.4064v1.pdf | Font Acknowledgment and Character Extraction of Digital and Scanned Images | The font recognition and character extraction is of immense importance as
these are many scenarios where data are in such a form, which cannot be
processed like in image form or as a hard copy. So the procedure developed in
this paper is basically related to identifying the font (Times New Roman, Arial
and Comic Sans M... | ['Syed Muhammad Arsalan Bashir'] | 2013-05-17 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 8.69066477e-01 -3.07562739e-01 4.66004223e-01 -3.32035720e-02
8.75051394e-02 -1.03362978e+00 6.32571459e-01 1.78649977e-01
-4.00779098e-01 7.98878133e-01 -1.15465716e-01 -6.07903361e-01
-1.18037052e-01 -7.09151983e-01 -2.83529669e-01 -5.88818908e-01
3.59321237e-01 1.68934166e-01 4.19291615e-01 -1.03640452... | [11.855216026306152, 2.544970750808716] |
b97a3f2c-3156-463c-bc88-7512c8c20aff | effects-of-spectral-normalization-in-multi | 2212.05331 | null | https://arxiv.org/abs/2212.05331v2 | https://arxiv.org/pdf/2212.05331v2.pdf | Effects of Spectral Normalization in Multi-agent Reinforcement Learning | A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1) The joint action space grows exponentially with the number of agents 2) This, combined with the reward sparseness and environment noise, l... | ['Pawan Kumar', 'Anuj Mahajan', 'Kinal Mehta'] | 2022-12-10 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.68249130e-02 4.88651767e-02 -2.46809751e-01 3.42284799e-01
-1.13921428e+00 -5.07589161e-01 5.38147688e-01 2.59185466e-03
-7.88918197e-01 1.27558756e+00 3.09291422e-01 -5.21528833e-02
-4.16720688e-01 -3.46416533e-02 -4.52653885e-01 -9.05313849e-01
-5.55930912e-01 5.08059025e-01 1.78219214e-01 -3.20458323... | [3.923616886138916, 2.0741233825683594] |
c4d97546-a232-4788-8492-aa0ca1b1129e | micro-expression-action-unit-detection | 1907.05023 | null | https://arxiv.org/abs/1907.05023v2 | https://arxiv.org/pdf/1907.05023v2.pdf | Micro-expression Action Unit Detection with Spatio-temporal Adaptive Pooling | Action Unit (AU) detection plays an important role for facial expression recognition. To the best of our knowledge, there is little research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Microexpression AU detection is challenging due to the small quantity of mic... | ['Guoying Zhao', 'Xiaohua Huang', 'Yante Li'] | 2019-07-11 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.49562636e-01 -4.22776997e-01 -2.05787435e-01 -4.10487860e-01
-8.34218144e-01 -2.69881755e-01 2.56635159e-01 -8.49407986e-02
-4.82827127e-01 5.00274718e-01 -1.52535051e-01 3.42633039e-01
3.16915274e-01 -7.18103409e-01 -5.21082222e-01 -1.10474849e+00
-1.69761851e-01 -4.81411278e-01 6.03949428e-02 -5.11329591... | [13.627649307250977, 1.7319908142089844] |
7d4c9fcb-fbf2-4568-979f-2103de12093f | understanding-quantum-machine-learning-also | 2306.13461 | null | https://arxiv.org/abs/2306.13461v1 | https://arxiv.org/pdf/2306.13461v1.pdf | Understanding quantum machine learning also requires rethinking generalization | Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization fail to explain the behavior of such quantum models. Our experiments reveal th... | ['Carlos Bravo-Prieto', 'Jens Eisert', 'Elies Gil-Fuster'] | 2023-06-23 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 5.06813765e-01 1.99718237e-01 -2.14110896e-01 -3.16059738e-01
-4.17414695e-01 -6.72531486e-01 8.50056946e-01 2.76830196e-01
-5.79139233e-01 7.90008545e-01 -2.05338478e-01 -7.79463053e-01
-3.70361209e-01 -1.19198632e+00 -6.19975686e-01 -9.70939398e-01
-2.68575698e-02 4.96514380e-01 1.01479180e-01 -4.80945200... | [5.60796594619751, 4.979931354522705] |
9d087765-19dd-4e68-86e8-5923790f59fe | unsupervised-adversarial-domain-adaptation-3 | 2003.02244 | null | https://arxiv.org/abs/2003.02244v2 | https://arxiv.org/pdf/2003.02244v2.pdf | Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification | Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit counterparts. We tackle situations where training data for implicit relations are lacking, and exploit domain adaptation from explicit relations (Ji et al., 2015). We present an unsupervised adversarial dom... | ['Hsin-Ping Huang', 'Junyi Jessy Li'] | 2020-03-04 | unsupervised-adversarial-domain-adaptation-2 | https://aclanthology.org/K19-1064 | https://aclanthology.org/K19-1064.pdf | conll-2019-11 | ['implicit-discourse-relation-classification', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.20827991e-01 9.34015930e-01 -5.35707474e-01 -2.64089048e-01
-4.90150601e-01 -9.94565248e-01 8.97421658e-01 9.96709913e-02
-3.99418116e-01 1.44099057e+00 4.98260796e-01 -3.29167426e-01
1.09615646e-01 -8.95653367e-01 -5.28436542e-01 -3.08745861e-01
2.13423118e-01 9.90497231e-01 2.10241139e-01 -7.48143256... | [10.724157333374023, 9.20433521270752] |
440fcb9d-aa59-48a2-aa0c-a01cf8d28b84 | neural-network-augmented-compartmental | 2212.08481 | null | https://arxiv.org/abs/2212.08481v1 | https://arxiv.org/pdf/2212.08481v1.pdf | Neural Network Augmented Compartmental Pandemic Models | Compartmental models are a tool commonly used in epidemiology for the mathematical modelling of the spread of infectious diseases, with their most popular representative being the Susceptible-Infected-Removed (SIR) model and its derivatives. However, current SIR models are bounded in their capabilities to model governm... | ['Kevin Sidak', 'Lorenz Kummer'] | 2022-12-15 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 3.02928835e-02 -1.77637532e-01 -5.03866494e-01 1.23014584e-01
1.50622860e-01 -4.22038764e-01 9.12796021e-01 2.12579533e-01
-6.42355025e-01 1.04599369e+00 5.45928106e-02 -1.02452004e+00
-6.08210087e-01 -7.62320101e-01 -7.22496986e-01 -7.34180391e-01
-6.31641269e-01 7.80657470e-01 -1.76411301e-01 -1.58953071... | [6.019142150878906, 4.362648010253906] |
0e2b3aed-1735-4cd4-8049-aed0195103bb | anomalous-event-recognition-in-videos-based | null | null | https://doi.org/10.3390/app11031344 | https://doi.org/10.3390/app11031344 | Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures | Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection. We propose a framework, Deep-network with Multiple Ranking Measures(DMRMs), which addresses context-dependency using a joint... | ['Moongu Jeon', 'Jeonghwan Gwak', 'Abhijeet Boragule', 'Shikha Dubey'] | 2021-02-02 | null | null | null | journal-2021-2 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 1.82605788e-01 -3.11066747e-01 -1.36287436e-01 -4.85178471e-01
-8.93765688e-01 -2.48488747e-02 4.46591824e-01 2.72884607e-01
-5.98912477e-01 5.00411689e-01 7.46614560e-02 1.73683427e-02
-2.65296578e-01 -5.70297062e-01 -5.88660836e-01 -7.78798163e-01
-4.66431886e-01 -1.70532629e-01 5.96997440e-01 1.02522880... | [7.8430094718933105, 1.6003310680389404] |
0a56cf15-7403-4da6-b22f-9a44b5cb2624 | deepsdf-x-sim-3-extending-deepsdf-for | 2004.09048 | null | https://arxiv.org/abs/2004.09048v3 | https://arxiv.org/pdf/2004.09048v3.pdf | Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation | Recent advances in computer graphics and computer vision have found successful application of deep neural network models for 3D shapes based on signed distance functions (SDFs) that are useful for shape representation, retrieval, and completion. However, this approach has been limited by the need to have query shapes i... | ['S. Shankar Sastry', 'Allen Y. Yang', 'Oladapo Afolabi'] | 2020-04-20 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 5.43851443e-02 -1.93478942e-01 1.68716595e-01 -3.63813937e-01
-5.86801946e-01 -6.10338926e-01 9.31612372e-01 8.43316466e-02
-3.61897916e-01 2.71550179e-01 3.00050899e-02 -2.81874359e-01
-1.13364682e-01 -8.75071406e-01 -6.77754045e-01 -3.02350193e-01
-7.22318441e-02 7.63299048e-01 6.56730607e-02 -1.09180868... | [8.358115196228027, -3.6227128505706787] |
a07628d8-71c9-4ab5-992d-8387f1dc935d | plpca-persistent-laplacian-enhanced-pca-for | 2306.06292 | null | https://arxiv.org/abs/2306.06292v1 | https://arxiv.org/pdf/2306.06292v1.pdf | PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data Analysis | Over the years, Principal Component Analysis (PCA) has served as the baseline approach for dimensionality reduction in gene expression data analysis. It primary objective is to identify a subset of disease-causing genes from a vast pool of thousands of genes. However, PCA possesses inherent limitations that hinder its ... | ['GuoWei Wei', 'Rui Wang', 'Sean Cottrell'] | 2023-06-09 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 2.17999578e-01 -2.93078959e-01 -6.73815608e-02 1.45136997e-01
-6.82911515e-01 -6.24024451e-01 4.77434933e-01 2.07616255e-01
7.96206445e-02 4.74473685e-01 2.38161355e-01 -6.72420114e-02
-5.85791469e-01 -5.59845567e-01 -2.47497678e-01 -1.18455184e+00
-3.76756161e-01 3.64937365e-01 -2.44028226e-01 -1.05158105... | [7.086864471435547, 4.975013732910156] |
f63bb61a-78ba-4a5c-8f91-8fb913fe406b | magnetic-resonance-fingerprinting | 1807.06356 | null | http://arxiv.org/abs/1807.06356v2 | http://arxiv.org/pdf/1807.06356v2.pdf | Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks | Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic
resonance parameters in a single and fast acquisition. Standard MRF
reconstructs parametric maps using dictionary matching, which lacks scalability
due to computational inefficiency. We propose to perform MRF map reconstruction
using a spatiot... | ['Sairam Geethanath', 'Olivier Scheidegger', 'Shivaprasad Chikop', 'Vimal Chandran', 'Fabian Balsiger', 'Amaresha Shridhar Konar', 'Mauricio Reyes'] | 2018-07-17 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 2.26299420e-01 -5.17103635e-02 -1.23197235e-01 -3.76423806e-01
-9.19542789e-01 -2.94985145e-01 4.40069795e-01 8.68544579e-02
-5.58184862e-01 7.36668706e-01 3.06566149e-01 -3.01068127e-02
-5.02768874e-01 -6.41715705e-01 -7.41234660e-01 -6.18777812e-01
-3.25431556e-01 7.23307788e-01 4.60717142e-01 -6.00453243... | [13.531961441040039, -2.4053547382354736] |
f181d33c-941b-4651-abd3-dfbf8a7eb5a7 | a-hierarchical-regression-chain-framework-for | 2303.08027 | null | https://arxiv.org/abs/2303.08027v1 | https://arxiv.org/pdf/2303.08027v1.pdf | A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition | As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding voc... | ['Helen Meng', 'Xunying Liu', 'Dongsheng Li', 'Kaitao Song', 'Xixin Wu', 'Jinchao Li'] | 2023-03-14 | null | null | null | null | ['a-vb-culture', 'a-vb-high', 'culture', 'a-vb-two'] | ['speech', 'speech', 'speech', 'speech'] | [-6.70893639e-02 -3.71623278e-01 -1.19727649e-01 -5.66720605e-01
-2.99404681e-01 -3.00990164e-01 2.16693014e-01 -1.18154541e-01
-1.91737473e-01 6.18484974e-01 4.47179973e-01 3.38067055e-01
-8.34048092e-02 -2.62223363e-01 -2.27442756e-01 -6.12472296e-01
-3.51394594e-01 1.03034250e-01 -4.89652991e-01 -2.70089060... | [13.472016334533691, 5.603561878204346] |
6f9cd454-a79d-4fc2-b4d0-9b668cd0fcc5 | mutual-supervised-feature-modulation-network | 2010.10744 | null | https://arxiv.org/abs/2010.10744v1 | https://arxiv.org/pdf/2010.10744v1.pdf | Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection | State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion handling strategy of popular two-stage approaches is to build a two-branch architecture with the help of additional visible body annotations. N... | ['Xu-Cheng Yin', 'Chao Zhu', 'Ye He'] | 2020-10-21 | null | null | null | null | ['body-detection', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [-2.43911415e-01 1.15166247e-01 -1.06487840e-01 -2.58482724e-01
-3.15898538e-01 4.35622633e-02 4.67851132e-01 9.89592597e-02
-3.72117400e-01 6.41931236e-01 6.52205050e-02 1.53268546e-01
5.44683158e-01 -7.67741501e-01 -5.50841630e-01 -8.15561831e-01
1.48359880e-01 1.80641264e-01 1.10959578e+00 -1.91915721... | [8.019067764282227, -0.6034425497055054] |
f3b0267b-6373-44b3-94a4-7421f49bd54d | accelerating-multiframe-blind-deconvolution | 2306.12078 | null | https://arxiv.org/abs/2306.12078v1 | https://arxiv.org/pdf/2306.12078v1.pdf | Accelerating Multiframe Blind Deconvolution via Deep Learning | Ground-based solar image restoration is a computationally expensive procedure that involves nonlinear optimization techniques. The presence of atmospheric turbulence produces perturbations in individual images that make it necessary to apply blind deconvolution techniques. These techniques rely on the observation of ma... | ['C. Kuckein', 'S. Esteban Pozuelo', 'A. Asensio Ramos'] | 2023-06-21 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 5.68761408e-01 -3.28776002e-01 3.39902312e-01 -2.48540476e-01
-4.16161537e-01 -6.27109051e-01 6.03999496e-01 -9.31006446e-02
-4.75090772e-01 6.71940684e-01 -9.46199149e-02 -4.73525584e-01
3.07630078e-04 -5.11919141e-01 -8.07325780e-01 -1.01211059e+00
2.16777593e-01 2.55323887e-01 6.20373599e-02 -1.77009836... | [11.633167266845703, -2.6131534576416016] |
ce59be92-1fda-48ca-a703-dc7849b0ce61 | feeder-microgrid-management-on-an-active | 2208.10712 | null | https://arxiv.org/abs/2208.10712v1 | https://arxiv.org/pdf/2208.10712v1.pdf | Feeder Microgrid Management on an Active Distribution System during a Severe Outage | Forming a microgrid on a distribution system with large scale outage after a severe weather event is emerging as a viable solution to improve resiliency at the distribution level. This option becomes more attractive when the distribution system has high levels of distributed PV. The management of such feeder-level micr... | ['Wenyuan Tang', 'David Lubkeman', 'Ning Lu', 'Mesut Baran', 'Yiyan Li', 'Bei Xu', 'Victor Paduani', 'Rongxing Hu', 'Ashwin Shirsat', 'Valliappan Muthukaruppan'] | 2022-08-23 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.38175356e-01 -1.36244908e-01 1.00901134e-01 2.18854249e-01
-6.85248077e-02 -9.55004692e-01 3.67209643e-01 3.29587847e-01
3.22151393e-01 1.14453697e+00 -1.59823909e-01 -1.68127641e-01
-5.30813277e-01 -9.19836819e-01 -1.92472965e-01 -1.24511445e+00
-2.36350894e-01 5.05137622e-01 1.72237188e-01 -2.49091104... | [5.6724162101745605, 2.540416717529297] |
e764c025-058f-49cf-85e7-9eeea829ea33 | adversarial-learning-for-discourse-rhetorical | null | null | https://aclanthology.org/2021.acl-long.305 | https://aclanthology.org/2021.acl-long.305.pdf | Adversarial Learning for Discourse Rhetorical Structure Parsing | Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS... | ['Guodong Zhou', 'Fang Kong', 'Longyin Zhang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['drs-parsing'] | ['natural-language-processing'] | [ 3.36255819e-01 5.89503944e-01 -2.40139529e-01 -4.07390088e-01
-1.48820436e+00 -9.92497683e-01 5.70389569e-01 2.01202363e-01
-7.37342089e-02 4.85303849e-01 5.73454738e-01 -8.69573832e-01
5.02273917e-01 -8.40542734e-01 -6.40697122e-01 -4.82540756e-01
2.12051123e-01 6.18771851e-01 3.77610773e-01 -5.99826634... | [10.691483497619629, 9.428243637084961] |
c43d340c-1a32-4934-95dc-6b3069c7d5df | exploring-evolution-based-free-protein | 2206.06583 | null | https://arxiv.org/abs/2206.06583v2 | https://arxiv.org/pdf/2206.06583v2.pdf | Exploring evolution-aware & -free protein language models as protein function predictors | Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold... | ['Qiuyang Ding', 'Fei Yang', 'Hui Wang', 'Jin Su', 'Fusong Ju', 'Kevin K. Yang', 'Fajie Yuan', 'Mingyang Hu'] | 2022-06-14 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 3.20982426e-01 3.11113477e-01 -8.62744078e-02 -8.41917023e-02
-2.83882141e-01 -8.07905078e-01 3.23183566e-01 5.61443865e-01
-2.32949093e-01 1.07313144e+00 2.06252486e-01 -6.39276743e-01
-2.52153575e-02 -3.61210406e-01 -1.04623878e+00 -7.78934777e-01
1.24376165e-02 6.42785668e-01 1.97321355e-01 -5.06204128... | [4.6611456871032715, 5.649188041687012] |
2deb3b9e-d587-4e9b-876f-19d77b988e5d | multi-level-cross-view-contrastive-learning | 2204.08807 | null | https://arxiv.org/abs/2204.08807v1 | https://arxiv.org/pdf/2204.08807v1.pdf | Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System | Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, whi... | ['Xin Cao', 'Feida Zhu', 'Minghui Qiu', 'Ziyang Wang', 'Xian-Ling Mao', 'Wei Wei', 'Ding Zou'] | 2022-04-19 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-7.50377774e-02 -4.79479469e-02 -7.31023669e-01 -2.58904964e-01
-3.26286107e-01 -2.58949161e-01 1.62739247e-01 3.05511151e-02
8.19570199e-02 4.30869043e-01 4.39096123e-01 -1.15408719e-01
-7.22484469e-01 -1.07058883e+00 -5.71195841e-01 -5.97550929e-01
-8.79748464e-02 2.05430120e-01 -5.25154807e-02 -4.57676351... | [10.226811408996582, 5.62482213973999] |
f97c6739-2ef9-4bab-8ee2-889441364964 | a-comprehensive-survey-on-pose-invariant-face | 1502.04383 | null | http://arxiv.org/abs/1502.04383v3 | http://arxiv.org/pdf/1502.04383v3.pdf | A Comprehensive Survey on Pose-Invariant Face Recognition | The capacity to recognize faces under varied poses is a fundamental human
ability that presents a unique challenge for computer vision systems. Compared
to frontal face recognition, which has been intensively studied and has
gradually matured in the past few decades, pose-invariant face recognition
(PIFR) remains a lar... | ['DaCheng Tao', 'Changxing Ding'] | 2015-02-15 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.55711275e-01 -4.27308768e-01 -1.76966578e-01 -4.29052293e-01
-6.11170411e-01 -5.82502902e-01 6.08339489e-01 -9.54924226e-01
-9.12635177e-02 5.08583784e-01 1.38402849e-01 2.23332077e-01
-3.23100507e-01 -3.03664237e-01 -5.68572022e-02 -1.10324442e+00
2.49424595e-02 8.99355337e-02 -3.39274824e-01 -1.82237271... | [13.173998832702637, 0.5372790694236755] |
86b6b950-8b07-4598-b2d0-cd2308062444 | principal-uncertainty-quantification-with | 2305.10124 | null | https://arxiv.org/abs/2305.10124v1 | https://arxiv.org/pdf/2305.10124v1.pdf | Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems | Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we prop... | ['Michael Elad', 'Ehud Rivlin', 'Daniel Freedman', 'Yaniv Romano', 'Omer Belhasin'] | 2023-05-17 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 5.98830342e-01 3.64783525e-01 2.01624811e-01 -2.31582910e-01
-1.06154823e+00 -5.94466507e-01 4.36494082e-01 -1.05411597e-01
-2.45208010e-01 1.00939155e+00 2.87768155e-01 4.88058068e-02
-4.53905970e-01 -7.03552067e-01 -6.12578809e-01 -9.15822268e-01
-4.29420024e-02 2.99926251e-01 1.97685897e-01 2.37915844... | [11.582904815673828, -2.042029619216919] |
09ef715d-b355-4415-836a-446a02ebd879 | incorporating-uncertainty-from-speaker | 2302.11763 | null | https://arxiv.org/abs/2302.11763v1 | https://arxiv.org/pdf/2302.11763v1.pdf | Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification | Speech utterances recorded under differing conditions exhibit varying degrees of confidence in their embedding estimates, i.e., uncertainty, even if they are extracted using the same neural network. This paper aims to incorporate the uncertainty estimate produced in the xi-vector network front-end with a probabilistic ... | ['Tianchi Liu', 'Kong Aik Lee', 'Qiongqiong Wang'] | 2023-02-23 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-8.85093734e-02 1.03225028e-02 4.01239783e-01 -9.04491544e-01
-1.27702606e+00 -4.85617965e-01 2.36288100e-01 -6.11865055e-03
-4.80838567e-01 6.52374208e-01 3.35810333e-01 -3.34269911e-01
-5.20153232e-02 -1.08117178e-01 -4.74897027e-01 -7.12411702e-01
-1.26378655e-01 5.37561402e-02 -2.53876418e-01 3.71433526... | [14.405673027038574, 6.12004280090332] |
643fdd26-38ed-45f9-826b-f61082d0af72 | sparse-array-selection-across-arbitrary | 2004.11637 | null | https://arxiv.org/abs/2004.11637v2 | https://arxiv.org/pdf/2004.11637v2.pdf | Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning | Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search metho... | ['Ahmet M. Elbir', 'Kumar Vijay Mishra'] | 2020-04-24 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.05893779e-01 -2.19391376e-01 2.68503517e-01 -3.88746351e-01
-9.98246014e-01 -3.70721638e-01 -2.94531763e-01 1.75867409e-01
-3.58643174e-01 6.96588814e-01 1.13282517e-01 -1.99190125e-01
-3.76083344e-01 -9.07577217e-01 -9.60037410e-01 -8.94034863e-01
-2.14677244e-01 2.32647061e-01 -2.15110287e-01 -5.31610772... | [6.568282604217529, 1.1548293828964233] |
d06fbae5-6c00-4506-944c-465d0dbcacdb | an-end-to-end-visual-audio-attention-network | 2003.00832 | null | https://arxiv.org/abs/2003.00832v1 | https://arxiv.org/pdf/2003.00832v1.pdf | An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos | Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner ba... | ['Tengfei Xing', 'Kurt Keutzer', 'Hua Chai', 'Yang Gu', 'Sicheng Zhao', 'Runbo Hu', 'Pengfei Xu', 'Jufeng Yang', 'Yunsheng Ma'] | 2020-02-12 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [-1.19627126e-01 -4.63104516e-01 -4.52517159e-02 -6.00449026e-01
-7.33346522e-01 -1.71934262e-01 2.98053771e-01 -9.51604918e-02
-3.88263643e-01 3.05479020e-01 3.03523690e-01 -4.16507907e-02
3.11278731e-01 -2.04605475e-01 -6.83558047e-01 -5.30070364e-01
-1.09400496e-01 -3.49629462e-01 -3.42413843e-01 6.05956046... | [13.291775703430176, 4.967169761657715] |
8320c0c6-bb8d-4440-b5a7-4e447fc895ca | hybrid-message-passing-with-performance | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Song_Hybrid_Message_Passing_With_Performance-Driven_Structures_for_Facial_Action_Unit_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Song_Hybrid_Message_Passing_With_Performance-Driven_Structures_for_Facial_Action_Unit_CVPR_2021_paper.pdf | Hybrid Message Passing With Performance-Driven Structures for Facial Action Unit Detection | Message passing neural network has been an effective method to represent dependencies among nodes by propagating messages. However, most of message passing algorithms focus on one structure and the messages are estimated by one single approach. For the real-world data, like facial action units (AUs), the dependenci... | ['Qiang Ji', 'Wenming Zheng', 'Zijun Cui', 'Tengfei Song'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 7.28303790e-02 3.16674225e-02 -2.01873258e-01 -6.20636225e-01
-2.09957644e-01 3.47522587e-01 7.66622722e-01 1.66590333e-01
-2.56418526e-01 8.00104022e-01 -6.04103692e-02 5.07534444e-02
-9.59435254e-02 -1.11949694e+00 -7.22028673e-01 -8.03480387e-01
-2.55465448e-01 3.72926742e-01 5.85104406e-01 -1.87127724... | [7.375450134277344, 6.155650615692139] |
bd73ef24-2ee3-45e4-b6b5-3753f7163b18 | improved-techniques-for-training-single-image | 2003.11512 | null | https://arxiv.org/abs/2003.11512v2 | https://arxiv.org/pdf/2003.11512v2.pdf | Improved Techniques for Training Single-Image GANs | Recently there has been an interest in the potential of learning generative models from a single image, as opposed to from a large dataset. This task is of practical significance, as it means that generative models can be used in domains where collecting a large dataset is not feasible. However, training a model capabl... | ['Tobias Hinz', 'Stefan Wermter', 'Matthew Fisher', 'Oliver Wang'] | 2020-03-25 | null | null | null | null | ['single-image-generation'] | ['computer-vision'] | [ 5.18953145e-01 2.87458479e-01 1.45064220e-01 -2.77230918e-01
-9.31402385e-01 -5.86040676e-01 9.07147586e-01 -3.94423366e-01
-3.51787746e-01 6.78071618e-01 2.21080229e-01 -2.31709898e-01
2.57901073e-01 -7.77348340e-01 -8.01320851e-01 -5.35913169e-01
1.76428795e-01 7.07615137e-01 3.54523510e-01 7.43784104... | [11.233120918273926, -0.22748635709285736] |
6ce0eaa2-6996-4fdf-9ff8-d488d3909e29 | detecting-uncertainty-cues-in-hungarian | null | null | https://aclanthology.org/W16-5002 | https://aclanthology.org/W16-5002.pdf | Detecting Uncertainty Cues in Hungarian Social Media Texts | In this paper, we aim at identifying uncertainty cues in Hungarian social media texts. We present our machine learning based uncertainty detector which is based on a rich features set including lexical, morphological, syntactic, semantic and discourse-based features, and we evaluate our system on a small set of manuall... | ['Veronika Vincze'] | 2016-12-01 | null | null | null | ws-2016-12 | ['instance-search'] | ['computer-vision'] | [-1.65186644e-01 6.22835577e-01 -1.69604532e-02 -7.61836112e-01
-1.09385777e+00 -6.80303633e-01 1.01052499e+00 9.87105548e-01
-8.64300907e-01 1.12127376e+00 8.01814973e-01 2.91789602e-02
-8.78222957e-02 -6.03734672e-01 -4.58556950e-01 -2.65162196e-02
-1.16456293e-01 9.82035697e-01 4.67415512e-01 -4.56287205... | [10.29333209991455, 9.316666603088379] |
07e24a56-5d40-45fc-8d36-2d02884a75dc | a-survey-on-audio-synthesis-and-audio-visual | 2108.00443 | null | https://arxiv.org/abs/2108.00443v1 | https://arxiv.org/pdf/2108.00443v1.pdf | A Survey on Audio Synthesis and Audio-Visual Multimodal Processing | With the development of deep learning and artificial intelligence, audio synthesis has a pivotal role in the area of machine learning and shows strong applicability in the industry. Meanwhile, significant efforts have been dedicated by researchers to handle multimodal tasks at present such as audio-visual multimodal pr... | ['Zhaofeng Shi'] | 2021-08-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.95917249e-01 -3.87238175e-01 -1.68348089e-01 -8.73859152e-02
-7.39500225e-01 -4.07833159e-01 3.97543043e-01 7.03447359e-03
-7.00231940e-02 3.98571312e-01 3.65850359e-01 -2.59529110e-02
1.30661547e-01 -4.28348482e-01 -2.45929152e-01 -9.78281856e-01
1.62454560e-01 -2.32799370e-02 -2.02793732e-01 -1.40040517... | [14.327959060668945, 5.025004863739014] |
1c50c41f-d4b1-4cef-a3ef-dae568de0115 | mega-moving-average-equipped-gated-attention | 2209.10655 | null | https://arxiv.org/abs/2209.10655v3 | https://arxiv.org/pdf/2209.10655v3.pdf | Mega: Moving Average Equipped Gated Attention | The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences. In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential)... | ['Luke Zettlemoyer', 'Jonathan May', 'Graham Neubig', 'Liangke Gui', 'Junxian He', 'Xiang Kong', 'Chunting Zhou', 'Xuezhe Ma'] | 2022-09-21 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 3.22341859e-01 1.86509751e-02 -4.76521939e-01 -2.86936224e-01
-9.38233376e-01 -6.03355110e-01 6.97807014e-01 -9.17041227e-02
-5.27118444e-01 9.13437605e-01 3.04620832e-01 -8.92856777e-01
2.44620487e-01 -3.74189645e-01 -1.14575779e+00 -6.03259563e-01
-3.25913489e-01 7.89032161e-01 3.37947905e-01 -4.41588938... | [10.854175567626953, 6.99728536605835] |
af17c3d1-c6ff-491a-87e4-0eccbdd2a4f1 | learning-diverse-tone-styles-for-image | 2207.05430 | null | https://arxiv.org/abs/2207.05430v2 | https://arxiv.org/pdf/2207.05430v2.pdf | Learning Diverse Tone Styles for Image Retouching | Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjecti... | ['WangMeng Zuo', 'Xiaohe Wu', 'Ming Liu', 'Jiawei Zhang', 'Haolin Wang'] | 2022-07-12 | null | null | null | null | ['image-retouching'] | ['computer-vision'] | [ 3.35654914e-01 -1.97354227e-01 8.31005350e-03 -3.58740509e-01
-4.83857602e-01 -7.09024429e-01 4.01098043e-01 -4.56971139e-01
-7.30105862e-02 4.60693538e-01 3.03561240e-01 -1.66609243e-01
3.08702528e-01 -7.57988274e-01 -7.10920691e-01 -6.89290762e-01
8.46820056e-01 -2.04945914e-02 -4.22127210e-02 -3.59348238... | [11.5408296585083, -0.6135421991348267] |
e570e99e-9858-4c9e-a7ce-be01b983542c | se-bridge-speech-enhancement-with-consistent | 2305.13796 | null | https://arxiv.org/abs/2305.13796v1 | https://arxiv.org/pdf/2305.13796v1.pdf | SE-Bridge: Speech Enhancement with Consistent Brownian Bridge | We propose SE-Bridge, a novel method for speech enhancement (SE). After recently applying the diffusion models to speech enhancement, we can achieve speech enhancement by solving a stochastic differential equation (SDE). Each SDE corresponds to a probabilistic flow ordinary differential equation (PF-ODE), and the traje... | ['Hao Huang', 'Gulila Altenbek', 'Fuchun Sun', 'Mengfan Fu', 'Zhibin Qiu'] | 2023-05-23 | null | null | null | null | ['speech-enhancement', 'speaker-verification'] | ['speech', 'speech'] | [ 1.90425143e-01 1.87973425e-01 2.62782246e-01 5.39106987e-02
-1.16053438e+00 -2.11112484e-01 6.45989358e-01 -4.02536839e-01
-2.87049741e-01 5.92953801e-01 5.67196667e-01 -6.18356228e-01
1.14158413e-03 -4.54515070e-01 -5.39795756e-01 -8.66340578e-01
-5.98943532e-02 -4.34098803e-02 1.46072984e-01 -4.42763269... | [15.047028541564941, 6.051145553588867] |
3b0b000c-9850-45e9-b8a8-5a644745dedf | sanom-results-for-oaei-2019 | 2006.05219 | null | https://arxiv.org/abs/2006.05219v1 | https://arxiv.org/pdf/2006.05219v1.pdf | SANOM Results for OAEI 2019 | Simulated annealing-based ontology matching (SANOM) participates for the second time at the ontology alignment evaluation initiative (OAEI) 2019. This paper contains the configuration of SANOM and its results on the anatomy and conference tracks. In comparison to the OAEI 2017, SANOM has improved significantly, and its... | ['Yao-Hua Tan', 'Majid Mohammadi', 'Wout Hofman', 'Amir Ahooye Atashin'] | 2020-06-09 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 1.85257792e-01 7.14016736e-01 -3.49095166e-01 -2.54710093e-02
-6.24395072e-01 9.86887217e-02 3.19515109e-01 7.77379990e-01
-6.83521748e-01 5.17141223e-01 4.03795004e-01 6.74757212e-02
-8.23973119e-01 -7.06872702e-01 -1.73352018e-01 -2.26536632e-01
-1.59317479e-01 1.28482366e+00 4.69874501e-01 -5.62721550... | [9.185012817382812, 8.065192222595215] |
47279e00-f7a7-4960-986c-87d374e53553 | coordvit-a-novel-method-of-improve-vision | null | null | https://ieeexplore.ieee.org/document/10049941 | https://ieeexplore.ieee.org/document/10049941 | CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate | Recently, in speech emotion recognition, a Transformer-based method using spectrogram images instead of sound data showed improved accuracy than Convolutional Neural Networks (CNNs). Vision Transformer (ViT), a Transformer-based method, achieves high classification accuracy by using divided patches from the input image... | ['Seung-Ho Lee', 'Jeongyoon Kim'] | 2023-03-10 | null | null | null | international-conference-on-electronics | ['speech-emotion-recognition'] | ['speech'] | [ 7.12970197e-02 -1.45468414e-01 4.35022384e-01 -1.13903679e-01
-4.04970884e-01 -5.99635132e-02 2.96091110e-01 -3.98748904e-01
-3.79399031e-01 4.48886961e-01 1.45833284e-01 -1.13020763e-01
2.80739427e-01 -8.62574995e-01 -5.30606508e-01 -9.21446383e-01
6.03396773e-01 -4.23828721e-01 1.04230352e-01 -1.46418408... | [14.47581958770752, 5.763065338134766] |
1b292449-62ad-4f14-8588-2cb8ddbd429f | wav2shape-hearing-the-shape-of-a-drum-machine | 2007.10299 | null | https://arxiv.org/abs/2007.10299v1 | https://arxiv.org/pdf/2007.10299v1.pdf | wav2shape: Hearing the Shape of a Drum Machine | Disentangling and recovering physical attributes, such as shape and material, from a few waveform examples is a challenging inverse problem in audio signal processing, with numerous applications in musical acoustics as well as structural engineering. We propose to address this problem via a combination of time--frequen... | ['Vincent Lostanlen', 'Han Han'] | 2020-07-20 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.76436114e-01 -5.84703274e-02 7.31005728e-01 1.64178479e-02
-1.05821812e+00 -6.90150023e-01 3.05774868e-01 -2.32252672e-01
-4.21131365e-02 5.91682613e-01 2.64051199e-01 6.11206517e-02
-4.83840764e-01 -8.40803683e-01 -8.66781533e-01 -9.85043347e-01
-1.06825441e-01 5.28375626e-01 -3.49974066e-01 -2.00988248... | [15.69200325012207, 5.867941856384277] |
cbf306d7-193b-41d1-9bdc-ea3957c089b0 | kelp-a-kernel-based-learning-platform-for | null | null | https://aclanthology.org/P15-4004 | https://aclanthology.org/P15-4004.pdf | KeLP: a Kernel-based Learning Platform for Natural Language Processing | null | ['Roberto Basili', 'Simone Filice', 'Giuseppe Castellucci', 'Danilo Croce'] | 2015-07-01 | kelp-a-kernel-based-learning-platform-for-1 | https://aclanthology.org/P15-4004 | https://aclanthology.org/P15-4004.pdf | ijcnlp-2015-7 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.265670299530029, 3.7381439208984375] |
43da5d2c-06b7-4310-bfe9-99198c624a42 | a-deep-neural-network-for-multi-species-fish | 2109.10664 | null | https://arxiv.org/abs/2109.10664v1 | https://arxiv.org/pdf/2109.10664v1.pdf | A deep neural network for multi-species fish detection using multiple acoustic cameras | Underwater acoustic cameras are high potential devices for many applications in ecology, notably for fisheries management and monitoring. However how to extract such data into high value information without a time-consuming entire dataset reading by an operator is still a challenge. Moreover the analysis of acoustic im... | ['Thomas Corpetti', 'Laurent Beaulaton', 'Marie Nevoux', 'François Martignac', 'Guglielmo Fernandez', 'Garcia Fernandez'] | 2021-09-22 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [ 7.60799497e-02 -1.02909461e-01 8.48848820e-01 -2.70234048e-01
-3.87803048e-01 -5.57306349e-01 4.96589899e-01 3.02110523e-01
-1.32741511e+00 3.25094759e-01 -4.58270371e-01 1.80774167e-01
-2.13415638e-01 -7.84837306e-01 -7.44303465e-01 -9.33482051e-01
-6.22522473e-01 4.06898171e-01 6.66601598e-01 -1.12426691... | [8.551814079284668, -1.1948878765106201] |
87233faa-f22e-40aa-88e1-5f78bdc61bb6 | a-codec-information-assisted-framework-for | 2210.08229 | null | https://arxiv.org/abs/2210.08229v1 | https://arxiv.org/pdf/2210.08229v1.pdf | A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution | Online processing of compressed videos to increase their resolutions attracts increasing and broad attention. Video Super-Resolution (VSR) using recurrent neural network architecture is a promising solution due to its efficient modeling of long-range temporal dependencies. However, state-of-the-art recurrent VSR models... | ['Li Song', 'Rong Xie', 'Youliang Yan', 'Jiaming Guo', 'Xueyi Zou', 'Hengsheng Zhang'] | 2022-10-15 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.25649053e-01 -5.53439081e-01 -2.82185644e-01 -2.34208956e-01
-4.80825722e-01 1.68687925e-02 2.08425641e-01 -2.22126499e-01
-4.19369996e-01 5.63419163e-01 3.35735828e-01 -1.27204254e-01
-4.09279298e-03 -5.27726769e-01 -6.52017891e-01 -7.00610816e-01
-2.77801573e-01 -5.16958773e-01 4.83284235e-01 -3.32272649... | [11.080592155456543, -1.8377848863601685] |
a30d14d3-ce22-4efe-b4fb-5f8828dd3284 | conformal-loss-controlling-prediction | 2301.02424 | null | https://arxiv.org/abs/2301.02424v1 | https://arxiv.org/pdf/2301.02424v1.pdf | Conformal Loss-Controlling Prediction | Conformal prediction is a learning framework controlling prediction coverage of prediction sets, which can be built on any learning algorithm for point prediction. This work proposes a learning framework named conformal loss-controlling prediction, which extends conformal prediction to the situation where the value of ... | ['Hongyue Li', 'Xiaojun Yang', 'Zhong Ji', 'Ping Wang', 'Di Wang'] | 2023-01-06 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 2.93698549e-01 5.16590118e-01 -2.78014779e-01 -6.81270063e-01
-9.11237597e-01 -2.79317468e-01 4.80277658e-01 5.67990005e-01
-2.19492719e-01 7.36589313e-01 -1.12956561e-01 -4.39363182e-01
-9.58462775e-01 -1.21922040e+00 -8.26039016e-01 -8.17467809e-01
-6.15289211e-01 6.03756726e-01 4.89563137e-01 -8.65506157... | [7.978555679321289, 4.28490686416626] |
29576732-6f3c-4bb9-ab38-64b45d5d1aa1 | leftright-hand-segmentation-in-egocentric | 1607.06264 | null | http://arxiv.org/abs/1607.06264v1 | http://arxiv.org/pdf/1607.06264v1.pdf | Left/Right Hand Segmentation in Egocentric Videos | Wearable cameras allow people to record their daily activities from a
user-centered (First Person Vision) perspective. Due to their favorable
location, wearable cameras frequently capture the hands of the user, and may
thus represent a promising user-machine interaction tool for different
applications. Existent First P... | ['Emilia Barakova', 'Alejandro Betancourt', 'Lucio Marcenaro', 'Matthias Rauterberg', 'Carlo Regazzoni', 'Pietro Morerio'] | 2016-07-21 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 3.02424014e-01 -3.51246715e-01 -3.40916693e-01 -1.71548892e-02
-1.71896160e-01 -7.50743568e-01 5.40322006e-01 -6.51232898e-02
-4.73919660e-01 7.32686043e-01 -6.59486577e-02 -1.97473206e-02
1.13909267e-01 -3.25774491e-01 -4.90194350e-01 -1.00933516e+00
4.34048355e-01 5.04055023e-01 7.54947186e-01 1.64062411... | [6.6623945236206055, -0.7040334343910217] |
c6b121d1-9fcd-42e6-bc65-4e20c2d1c611 | a-theoretically-grounded-benchmark-for | 2203.12184 | null | https://arxiv.org/abs/2203.12184v2 | https://arxiv.org/pdf/2203.12184v2.pdf | A Theoretically Grounded Benchmark for Evaluating Machine Commonsense | Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community. Current CSR benchmarks use multiple-choice (and in relatively fewer cases, generative) question-answering instances to evaluate machine commonsense. Recent progress in transformer-based ... | ['Mayank Kejriwal', 'Deborah L. McGuinness', 'Yasaman Razeghi', 'Alice M. Mulvehill', 'Ke Shen', 'Henrique Santos'] | 2022-03-23 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 2.78938681e-01 3.29402983e-01 -1.82522401e-01 -3.35514635e-01
-1.00764298e+00 -6.91009283e-01 8.75733018e-01 2.20157593e-01
-2.00174779e-01 6.65875733e-01 5.11046767e-01 -5.26596487e-01
-2.57662177e-01 -1.00024831e+00 -3.39062959e-01 -2.39641443e-01
3.92208040e-01 7.53675461e-01 1.56624354e-02 -9.05526221... | [10.057890892028809, 8.074295997619629] |
79e4a4e8-2679-4a06-aed6-cc61e899bc6d | baseline-needs-more-love-on-simple-word | 1805.09843 | null | http://arxiv.org/abs/1805.09843v1 | http://arxiv.org/pdf/1805.09843v1.pdf | Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms | Many deep learning architectures have been proposed to model the
compositionality in text sequences, requiring a substantial number of
parameters and expensive computations. However, there has not been a rigorous
evaluation regarding the added value of sophisticated compositional functions.
In this paper, we conduct a ... | ['Ricardo Henao', 'Wenlin Wang', 'Lawrence Carin', 'Yizhe Zhang', 'Qinliang Su', 'Martin Renqiang Min', 'Guoyin Wang', 'Dinghan Shen', 'Chunyuan Li'] | 2018-05-24 | baseline-needs-more-love-on-simple-word-1 | https://aclanthology.org/P18-1041 | https://aclanthology.org/P18-1041.pdf | acl-2018-7 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 4.53343928e-01 -1.65445969e-01 -2.18799114e-01 -4.01882231e-01
-6.79371893e-01 -6.68210566e-01 8.91350687e-01 5.15184820e-01
-7.54842103e-01 3.00321192e-01 5.10036528e-01 -6.93480730e-01
-1.53257204e-02 -4.49623883e-01 -3.91097337e-01 -6.60966098e-01
1.70814767e-02 1.44881830e-01 8.03387091e-02 -1.00314222... | [10.69913101196289, 8.601259231567383] |
f8efab9c-cc0a-4c05-a368-a793a4019a6f | online-nonnegative-matrix-factorization-with-1 | 1604.02634 | null | http://arxiv.org/abs/1604.02634v2 | http://arxiv.org/pdf/1604.02634v2.pdf | Online Nonnegative Matrix Factorization with Outliers | We propose a unified and systematic framework for performing online
nonnegative matrix factorization in the presence of outliers. Our framework is
particularly suited to large-scale data. We propose two solvers based on
projected gradient descent and the alternating direction method of multipliers.
We prove that the se... | ['Renbo Zhao', 'Vincent Y. F. Tan'] | 2016-04-10 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 0.04690439 -0.33323166 0.06091622 -0.22466573 -0.90877026 -0.46608096
0.06297755 -0.2874176 -0.3874195 0.66834056 0.09090465 -0.3292489
-0.22185549 -0.16530623 -0.9023168 -0.92853427 -0.20856003 0.32646367
-0.29791966 0.02492271 0.11503962 0.4889313 -1.1843462 -0.10470826
0.95422596 0.8891002 0.0... | [7.157547950744629, 4.497538089752197] |
71fa5a01-1b02-413d-a152-7d1eb7f0a359 | sensitivity-analysis-in-unconditional | 2303.14298 | null | https://arxiv.org/abs/2303.14298v2 | https://arxiv.org/pdf/2303.14298v2.pdf | Sensitivity Analysis in Unconditional Quantile Effects | This paper proposes a framework to analyze the effects of counterfactual policies on the unconditional quantiles of an outcome variable. For a given counterfactual policy, we obtain identified sets for the effect of both marginal and global changes in the proportion of treated individuals. To conduct a sensitivity anal... | ['Julian Martinez-Iriarte'] | 2023-03-24 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 7.15402961e-02 2.30749860e-01 -5.47263086e-01 -2.03174084e-01
-4.74006295e-01 -3.57923329e-01 6.58587456e-01 6.49573624e-01
-5.72453678e-01 1.00897706e+00 7.13980019e-01 -9.42482769e-01
-4.45508808e-01 -1.09093857e+00 -7.73957253e-01 -4.20953512e-01
9.26281512e-02 1.57619134e-01 -1.55182496e-01 2.69099861... | [8.021946907043457, 5.240142822265625] |
630017dc-cf51-4f0e-b85d-c577090aa44f | maskcl-semantic-mask-driven-contrastive | 2305.13600 | null | https://arxiv.org/abs/2305.13600v1 | https://arxiv.org/pdf/2305.13600v1.pdf | MaskCL: Semantic Mask-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change | This paper considers a novel and challenging problem: unsupervised long-term person re-identification with clothes change. Unfortunately, conventional unsupervised person re-id methods are designed for short-term cases and thus fail to perceive clothes-independent patterns due to simply being driven by RGB prompt. To t... | ['Jun Guo', 'Chun-Guang Li', 'Peng Xu', 'Mingkun Li'] | 2023-05-23 | null | null | null | null | ['person-re-identification', 'unsupervised-long-term-person-re', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.71391624e-01 -1.84704781e-01 8.65823701e-02 -5.21627486e-01
-1.56041265e-01 -5.32725990e-01 7.57141113e-01 -1.99073091e-01
-6.92477465e-01 4.09980267e-01 4.98178184e-01 5.20059228e-01
-9.30706114e-02 -2.95218050e-01 -5.58754742e-01 -6.14455760e-01
2.93046206e-01 7.53020227e-01 -4.91037630e-02 -2.36852288... | [14.716933250427246, 0.9990857839584351] |
013c206a-c9f9-4a7b-8648-9505725bd92f | a-statistical-model-for-melody-reduction | 2105.05385 | null | https://arxiv.org/abs/2105.05385v1 | https://arxiv.org/pdf/2105.05385v1.pdf | A Statistical Model for Melody Reduction | A commonly-cited reason for the poor performance of automatic chord estimation (ACE) systems within music information retrieval (MIR) is that non-chord tones (i.e., notes outside the supporting harmony) contribute to error during the labeling process. Despite the prevalence of machine learning approaches in MIR, there ... | ['Claire Arthur', 'Tianxue Hu'] | 2021-05-12 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 1.47285491e-01 -9.45879295e-02 -1.19298801e-01 1.12314142e-01
-8.00364614e-01 -1.10413802e+00 2.96992868e-01 2.84321666e-01
-1.45820394e-01 2.43039489e-01 5.97444654e-01 -2.40582138e-01
-6.07930899e-01 -6.13991022e-01 1.20248109e-01 -2.69444346e-01
-3.07640694e-02 5.48768044e-01 -2.82116383e-02 -5.35652101... | [15.953985214233398, 5.343047618865967] |
d2a1df8f-80a2-4f6c-a4fb-815698aede94 | image-and-text-fusion-for-upmc-food-101-using | null | null | https://ieeexplore.ieee.org/abstract/document/9290622 | http://artelab.dista.uninsubria.it/res/research/papers/2020/2020-IVCNZ-Gallo-Food101.pdf | Image and Text fusion for UPMC Food-101 \\using BERT and CNNs | The modern digital world is becoming more and more multimodal. Looking on the internet, images are often associated with the text, so classification problems with these two modalities are very common.
In this paper, we examine multimodal classification using textual information and visual representations of the same c... | ['and Riccardo La Grassa', 'Nicola Landro', 'Gianmarco Ria', 'Ignazio Gallo'] | 2020-12-17 | null | null | null | null | ['multimodal-text-and-image-classification'] | ['methodology'] | [ 1.19094633e-01 -5.80281973e-01 7.83525407e-03 -2.31248319e-01
-8.49756062e-01 -5.76429129e-01 1.08651102e+00 6.15931928e-01
-5.72767556e-01 8.94899189e-01 2.99967706e-01 -5.16037419e-02
2.56284848e-02 -4.89684880e-01 -3.14706802e-01 -6.36085033e-01
5.07764332e-03 4.25619781e-01 2.03791946e-01 -6.38606250... | [13.119930267333984, 5.161306381225586] |
d775acdf-9770-4c2b-81fe-6d6b6fb701f3 | bert-got-a-date-introducing-transformers-to-1 | null | null | https://openreview.net/forum?id=9onUW-cjTl8 | https://openreview.net/pdf?id=9onUW-cjTl8 | BERT got a Date: Introducing Transformers to Temporal Tagging | Temporal expressions in text play a significant role in language understanding, and correctly identifying them is fundamental to various retrieval and natural language processing systems.
Previous works have slowly shifted from rule-based to neural architectures, capable of tagging expressions with higher accuracy. How... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['temporal-tagging'] | ['natural-language-processing'] | [ 3.54018509e-02 -4.56815623e-02 -6.96203470e-01 -5.31952739e-01
-7.13155568e-01 -8.12845111e-01 7.74644673e-01 3.04405242e-01
-6.20586038e-01 7.36153722e-01 1.75668836e-01 -3.89002621e-01
1.03863530e-01 -6.49845481e-01 -4.53149378e-01 -3.94079775e-01
-2.58092761e-01 4.61745977e-01 4.13445860e-01 -2.84172386... | [9.921918869018555, 9.214543342590332] |
fd475198-f89e-4da9-a1ef-474ec28da6fc | a-comparative-study-of-texture-attributes-for | 1812.08263 | null | http://arxiv.org/abs/1812.08263v1 | http://arxiv.org/pdf/1812.08263v1.pdf | A comparative study of texture attributes for characterizing subsurface structures in seismic volumes | In this paper, we explore how to computationally characterize subsurface
geological structures presented in seismic volumes using texture attributes.
For this purpose, we conduct a comparative study of typical texture attributes
presented in the image processing literature. We focus on spatial attributes in
this study ... | ['Suhail Al-Dharrab', 'Mohamed Deriche', 'Zhen Wang', 'Zhiling Long', 'Yuting Hu', 'Ghassan AlRegib', 'Haibin Di', 'Yazeed Alaudah', 'Muhammad Ali Qureshi', 'Motaz Alfarraj', 'Asjad Amin'] | 2018-12-19 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 4.00778860e-01 1.23755671e-01 2.56824315e-01 -5.13461649e-01
-5.42704642e-01 -3.50686580e-01 4.90776122e-01 3.59097540e-01
-2.78513789e-01 6.41388059e-01 6.04184903e-02 -2.83957541e-01
-4.27790284e-01 -1.36774421e+00 -2.54051238e-01 -7.73825169e-01
-3.51229846e-01 8.00631523e-01 2.32831553e-01 -1.47123143... | [7.236192226409912, 2.0665860176086426] |
1278257d-747a-4ba2-aa63-123b212a991e | generative-goal-driven-user-simulation-for | null | null | https://aclanthology.org/D12-1007 | https://aclanthology.org/D12-1007.pdf | Generative Goal-Driven User Simulation for Dialog Management | null | ['Ben Allison', 'Mark Steedman', 'Aciel Eshky'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['user-simulation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.29979944229126, 3.8672454357147217] |
68d10b05-1822-41ce-a9fd-e04c37f5eb39 | dynamic-feature-pruning-and-consolidation-for | 2211.14742 | null | https://arxiv.org/abs/2211.14742v1 | https://arxiv.org/pdf/2211.14742v1.pdf | Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification | Occluded person re-identification (ReID) is a challenging problem due to contamination from occluders, and existing approaches address the issue with prior knowledge cues, eg human body key points, semantic segmentations and etc, which easily fails in the presents of heavy occlusion and other humans as occluders. In th... | ['Wei Yang', 'Qiang Hu', 'Junqing Yu', 'Hang Zhou', 'Yuteng Ye'] | 2022-11-27 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 9.54416841e-02 7.75335729e-03 8.67498815e-02 -1.87695861e-01
-7.37154186e-01 -1.67697176e-01 4.17497694e-01 1.71032384e-01
-4.75523889e-01 6.91210270e-01 3.49872380e-01 6.31344914e-01
-5.92941465e-03 -6.04073286e-01 -6.93516374e-01 -6.09952509e-01
2.59295911e-01 4.28817093e-01 1.48784965e-01 7.45300427... | [14.676971435546875, 0.8835920095443726] |
3812d3b2-93d7-4025-9867-029545e76c14 | taking-a-respite-from-representation-learning | 2209.13492 | null | https://arxiv.org/abs/2209.13492v3 | https://arxiv.org/pdf/2209.13492v3.pdf | Taking a Respite from Representation Learning for Molecular Property Prediction | Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite booming techniques in molecular representation learning, fundamentals underlying molecular property prediction haven't been carefully examined yet. In this study, we conducted a systematic ... | ['Fusheng Wang', 'Dimitris Samaras', 'Iwao Ojima', 'Hehe Wang', 'Zhibo Yang', 'Jianyuan Deng'] | 2022-09-26 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.74355447e-01 -2.10083872e-01 -7.31272697e-01 -1.99635446e-01
-7.26257145e-01 -6.82603419e-01 4.57980633e-01 7.13515639e-01
-1.68263003e-01 1.38449335e+00 1.50339752e-01 -5.95750749e-01
-6.49930060e-01 -8.03915620e-01 -7.78580308e-01 -8.09798539e-01
-5.26254833e-01 1.41456097e-01 -1.37542158e-01 -2.50121266... | [5.15069055557251, 5.809345722198486] |
2bac2874-2f35-40a9-bd2d-52b5515948a7 | 1st-solution-places-for-cvpr-2023-ug-textbf-2 | 2306.08963 | null | https://arxiv.org/abs/2306.08963v1 | https://arxiv.org/pdf/2306.08963v1.pdf | 1st Solution Places for CVPR 2023 UG$^{\textbf{2}}$+ Challenge Track 2.1-Text Recognition through Atmospheric Turbulence | In this technical report, we present the solution developed by our team VIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 of the CVPR 2023 UG$^{2}$+ challenge. Our solution involves an efficient multi-stage framework that restores a high-quality image from distorted frames. Specifically, a fr... | ['Luxin Yan', 'Yi Chang', 'Shuning Cao', 'Xueyao Xiao', 'Shengqi Xu'] | 2023-06-15 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [ 2.74929136e-01 -8.34928334e-01 3.04209858e-01 -1.50398761e-01
-7.47906089e-01 -4.67377752e-01 4.71357107e-01 -2.99142569e-01
-2.74645150e-01 5.04273295e-01 3.57336104e-01 -2.03663275e-01
2.92333085e-02 -4.12356704e-01 -3.40201050e-01 -8.51821065e-01
5.01513004e-01 -1.43367663e-01 -2.27655154e-02 -1.60343960... | [11.121133804321289, -2.034360408782959] |
96debf53-60af-4e1f-8f84-cce95396bec7 | seeing-behind-objects-for-3d-multi-object | 2012.08197 | null | https://arxiv.org/abs/2012.08197v2 | https://arxiv.org/pdf/2012.08197v2.pdf | Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences | Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track t... | ['Matthias Nießner', 'Angela Dai', 'Niloy J. Mitra', 'Yu-Shiang Wong', 'Norman Müller'] | 2020-12-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Muller_Seeing_Behind_Objects_for_3D_Multi-Object_Tracking_in_RGB-D_Sequences_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.33942643e-01 -4.15299028e-01 1.11221984e-01 1.74104907e-02
-6.43475831e-01 -1.07838285e+00 2.88134664e-01 -1.79994985e-01
-2.49778807e-01 2.41953596e-01 -8.62928331e-02 2.45215386e-01
1.69321433e-01 -4.09593552e-01 -9.94010091e-01 -6.36551499e-01
-2.07030233e-02 7.24186718e-01 6.93010926e-01 2.61827447... | [7.036330699920654, -2.281648874282837] |
5fa9857c-3f12-48eb-b6d8-a458918a2643 | efficiency-in-ambiguity-two-models-of | null | null | https://aclanthology.org/W15-0118 | https://aclanthology.org/W15-0118.pdf | Efficiency in Ambiguity: Two Models of Probabilistic Semantics for Natural Language | null | ['Daoud Clarke', 'Bill Keller'] | 2015-04-01 | null | null | null | ws-2015-4 | ['learning-semantic-representations'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.392161846160889, 3.883859872817993] |
5abeb78e-b0ae-4a71-96c8-560133a5be01 | deep-image-matting | 1703.03872 | null | http://arxiv.org/abs/1703.03872v3 | http://arxiv.org/pdf/1703.03872v3.pdf | Deep Image Matting | Image matting is a fundamental computer vision problem and has many
applications. Previous algorithms have poor performance when an image has
similar foreground and background colors or complicated textures. The main
reasons are prior methods 1) only use low-level features and 2) lack high-level
context. In this paper,... | ['Brian Price', 'Scott Cohen', 'Ning Xu', 'Thomas Huang'] | 2017-03-10 | deep-image-matting-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Xu_Deep_Image_Matting_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Xu_Deep_Image_Matting_CVPR_2017_paper.pdf | cvpr-2017-7 | ['semantic-image-matting'] | ['computer-vision'] | [ 3.95371377e-01 -3.36647183e-01 -7.22174440e-03 -3.85365099e-01
-4.81852889e-01 -1.21256649e-01 3.41094524e-01 -2.75766194e-01
-2.01509416e-01 5.22093415e-01 1.00446217e-01 -2.17941374e-01
5.58737755e-01 -9.09354508e-01 -1.01569331e+00 -6.67712629e-01
2.90587544e-01 2.99428552e-01 6.03653848e-01 -4.91782986... | [10.653619766235352, -0.9164931774139404] |
e4b1224f-95e9-4f75-9cc1-6104297d52e0 | retrosynthesis-prediction-with-conditional-1 | 2001.01408 | null | https://arxiv.org/abs/2001.01408v1 | https://arxiv.org/pdf/2001.01408v1.pdf | Retrosynthesis Prediction with Conditional Graph Logic Network | Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely o... | ['Connor W. Coley', 'Hanjun Dai', 'Le Song', 'Bo Dai', 'Chengtao Li'] | 2020-01-06 | retrosynthesis-prediction-with-conditional | http://papers.nips.cc/paper/9090-retrosynthesis-prediction-with-conditional-graph-logic-network | http://papers.nips.cc/paper/9090-retrosynthesis-prediction-with-conditional-graph-logic-network.pdf | neurips-2019-12 | ['retrosynthesis'] | ['medical'] | [ 7.22588658e-01 3.14534307e-01 -6.25942945e-01 -3.31435025e-01
-1.95005298e-01 -7.61644185e-01 6.89442337e-01 6.70318365e-01
-7.80229196e-02 8.75254154e-01 -1.38223946e-01 -8.16045582e-01
1.99069027e-02 -1.16181827e+00 -7.14449644e-01 -6.50626063e-01
1.12384446e-01 5.33663273e-01 2.85936385e-01 -6.30233660... | [4.515755653381348, 6.095261573791504] |
1a48dd04-8686-4491-b3c3-98c03004bfaf | dynamic-perceiver-for-efficient-visual | 2306.11248 | null | https://arxiv.org/abs/2306.11248v1 | https://arxiv.org/pdf/2306.11248v1.pdf | Dynamic Perceiver for Efficient Visual Recognition | Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``easy'' samples can be generated at earlier exits, negating the need for executing deeper layers. Current multi-exit networks typically implemen... | ['Gao Huang', 'Shiji Song', 'Junlan Feng', 'Chao Deng', 'Yifan Pu', 'Xuran Pan', 'Yulin Wang', 'Zeyu Liu', 'Dongchen Han', 'Yizeng Han'] | 2023-06-20 | null | null | null | null | ['action-recognition-in-videos', 'classification-1'] | ['computer-vision', 'methodology'] | [ 2.59331495e-01 -8.46435204e-02 -3.38013411e-01 -6.70523286e-01
-5.23614347e-01 -6.22012913e-01 4.49285477e-01 8.47467035e-02
-5.12032330e-01 3.48017544e-01 -8.17093477e-02 -3.90878618e-01
1.02301121e-01 -7.66531825e-01 -6.79081023e-01 -6.28062129e-01
2.49429587e-02 -2.67340280e-02 3.42214584e-01 9.58608687... | [9.4284029006958, 1.443318247795105] |
b7ae2839-dcaa-48ab-8685-5a0ba41091da | meta-ordinal-regression-forest-for-learning | 2012.03480 | null | https://arxiv.org/abs/2012.03480v1 | https://arxiv.org/pdf/2012.03480v1.pdf | Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules | Deep learning-based methods have achieved promising performance in early detection and classification of lung nodules, most of which discard unsure nodules and simply deal with a binary classification -- malignant vs benign. Recently, an unsure data model (UDM) was proposed to incorporate those unsure nodules by formul... | ['Hongming Shan', 'Junping Zhang', 'Haiping Zhu', 'Yiming Lei'] | 2020-12-07 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 1.88181221e-01 1.68958455e-01 -7.46927440e-01 -5.19298136e-01
-6.26627445e-01 2.57435024e-01 5.78991771e-01 -9.96511951e-02
-1.28093407e-01 5.59421718e-01 2.14340776e-01 -4.70406085e-01
-4.67110962e-01 -1.04078531e+00 -2.76081860e-01 -8.99894476e-01
-6.35991246e-02 8.03504765e-01 3.50400239e-01 -1.76910292... | [15.340761184692383, -2.153601884841919] |
bc2533d2-9c4a-461d-90da-6c9a11f23de7 | joint-featurewise-weighting-and-lobal | 2007.12829 | null | https://arxiv.org/abs/2007.12829v1 | https://arxiv.org/pdf/2007.12829v1.pdf | Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering | Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance. It remains challenging to effectively exploit complementary information across multiple views since the original data often contai... | ['Shi-Xun Lina', 'Ting Shu', 'Guo Zhongb'] | 2020-07-25 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.49581003e-01 -6.08897150e-01 -1.10079125e-01 -1.89673543e-01
-6.75212085e-01 -7.53946424e-01 1.18604176e-01 -1.17074117e-01
9.76555347e-02 9.43790078e-02 3.65605146e-01 3.63493502e-01
-4.42500323e-01 -4.48814243e-01 -1.91436499e-01 -1.22003269e+00
3.41573983e-01 2.04030514e-01 -4.22425717e-02 7.02415481... | [8.239219665527344, 4.646291255950928] |
f40db8ef-a41d-4cb9-bece-4575e1d0ddf3 | mixbin-towards-budgeted-binarization | 2211.06739 | null | https://arxiv.org/abs/2211.06739v1 | https://arxiv.org/pdf/2211.06739v1.pdf | MixBin: Towards Budgeted Binarization | Binarization has proven to be amongst the most effective ways of neural network compression, reducing the FLOPs of the original model by a large extent. However, such levels of compression are often accompanied by a significant drop in the performance. There exist some approaches that reduce this performance drop by fa... | ['Deepak K. Gupta', 'Dilip K. Prasad', 'Neeraj Anand', 'Udbhav Bamba'] | 2022-11-12 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.67839551e-01 -6.44133091e-02 -3.10363144e-01 -3.68413121e-01
-2.86340028e-01 -3.69836330e-01 3.69875044e-01 4.20226380e-02
-8.13186586e-01 8.39627564e-01 -4.44689065e-01 -4.96382475e-01
-4.50592816e-01 -9.53632772e-01 -1.04249144e+00 -9.24212158e-01
2.35207658e-02 4.89645541e-01 5.21961212e-01 1.56478751... | [8.578322410583496, 3.1624035835266113] |
6b3acdf3-3050-4109-a373-65a086add788 | ssc-semantic-scan-context-for-large-scale | 2107.00382 | null | https://arxiv.org/abs/2107.00382v2 | https://arxiv.org/pdf/2107.00382v2.pdf | SSC: Semantic Scan Context for Large-Scale Place Recognition | Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, norma... | ['Yong liu', 'Tianxin Huang', 'Xiangrui Zhao', 'Xin Kong', 'Lin Li'] | 2021-07-01 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-7.27657601e-02 -5.29044390e-01 -2.07538918e-01 -6.32936180e-01
-7.26387799e-01 -4.11098003e-01 6.79893732e-01 2.96959281e-01
-2.44844347e-01 2.34573916e-01 -4.85754423e-02 1.33445501e-01
-5.36994450e-02 -1.10052884e+00 -7.89272547e-01 -5.14797807e-01
1.80050567e-01 5.24859011e-01 5.04668415e-01 -1.38688281... | [7.608547687530518, -2.468963861465454] |
1218f17e-3a0a-47c8-9cb5-e1308671167c | how-to-not-train-your-dragon-training-free | 2305.16925 | null | https://arxiv.org/abs/2305.16925v1 | https://arxiv.org/pdf/2305.16925v1.pdf | How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers | Object goal navigation is an important problem in Embodied AI that involves guiding the agent to navigate to an instance of the object category in an unknown environment -- typically an indoor scene. Unfortunately, current state-of-the-art methods for this problem rely heavily on data-driven approaches, \eg, end-to-end... | ['Fisher Yu', 'Bernard Ghanem', 'Suryansh Kumar', 'Guohao Li', 'Junting Chen'] | 2023-05-26 | null | null | null | null | ['simultaneous-localization-and-mapping', 'navigate'] | ['computer-vision', 'reasoning'] | [ 2.56362349e-01 1.10089175e-01 4.87168096e-02 -2.53328621e-01
-6.47990763e-01 -5.55696011e-01 7.50434577e-01 3.73921216e-01
-5.07884860e-01 5.18649578e-01 -3.19782533e-02 -2.22412273e-01
-3.88972223e-01 -8.91333699e-01 -1.04104757e+00 -4.61435467e-01
-3.12550396e-01 7.44684041e-01 5.12535691e-01 -4.98560220... | [4.600912094116211, 0.5822535157203674] |
613bf636-1204-4f91-81b5-4497ce148fb2 | learning-semantic-representations-of-users | null | null | https://aclanthology.org/P15-1098 | https://aclanthology.org/P15-1098.pdf | Learning Semantic Representations of Users and Products for Document Level Sentiment Classification | null | ['Ting Liu', 'Duyu Tang', 'Bing Qin'] | 2015-07-01 | learning-semantic-representations-of-users-1 | https://aclanthology.org/P15-1098 | https://aclanthology.org/P15-1098.pdf | ijcnlp-2015-7 | ['learning-semantic-representations'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.376448154449463, 3.78114914894104] |
e5065152-798a-4428-b6cd-d93ad712bad9 | dual-cross-polarized-gpr-measurement-method | 2205.08142 | null | https://arxiv.org/abs/2205.08142v1 | https://arxiv.org/pdf/2205.08142v1.pdf | Dual-Cross-Polarized GPR Measurement Method for Detection and Orientation Estimation of Shallowly Buried Elongated Object | Detecting a shallowly buried and elongated object and estimating its orientation using a commonly adopted co-polarized GPR system is challenging due to the presence of strong ground clutter that masks the target reflection. A cross-polarized configuration can be used to suppress ground clutter and reveal the object ref... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Lai Fern Ow', 'Wenhao Luo', 'Yee Hui Lee', 'Hai-Han Sun'] | 2022-05-17 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.21713769e-01 -1.76043242e-01 6.08622849e-01 7.25900382e-03
-7.09036231e-01 -5.16126275e-01 1.01363584e-01 -1.11827582e-01
-1.81870237e-01 7.79297352e-01 -1.32332876e-01 -2.01578960e-01
-5.99950910e-01 -7.45280743e-01 -3.60096037e-01 -1.41764808e+00
-2.31079742e-01 1.85446367e-01 3.18085462e-01 -1.67616352... | [6.803504943847656, 1.320444107055664] |
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