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62e800db-0752-43ca-af59-50364d8841d7 | elevation-estimation-driven-building-3d | 2301.04581 | null | https://arxiv.org/abs/2301.04581v1 | https://arxiv.org/pdf/2301.04581v1.pdf | Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery | Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. Methods for automatic 3D urban building modeling typically employ multi-view images as input to algorithms to recover point clouds and 3D models of buildings. However, such models rel... | ['Kun fu', 'Xian Sun', 'Wenhui Diao', 'Zhirui Wang', 'Wenjie Liu', 'Deke Tang', 'Wei Chen', 'Liangjin Zhao', 'Kaiqiang Chen', 'Yongqiang Mao'] | 2023-01-11 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 6.53294921e-02 -2.08762795e-01 2.89899051e-01 -4.17574286e-01
-5.91074109e-01 -3.18561912e-01 7.14972675e-01 -2.08593801e-01
3.31854224e-01 2.90592998e-01 4.62555401e-02 -3.75530541e-01
-1.93226859e-01 -1.73799205e+00 -4.29417580e-01 -4.84129012e-01
2.36419976e-01 6.81613922e-01 3.78575176e-01 -3.20960969... | [8.168229103088379, -2.7890162467956543] |
56950e29-194a-4272-8dfb-a0e6d5c88a3d | flownet-20-evolution-of-optical-flow | 1612.01925 | null | http://arxiv.org/abs/1612.01925v1 | http://arxiv.org/pdf/1612.01925v1.pdf | FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks | The FlowNet demonstrated that optical flow estimation can be cast as a
learning problem. However, the state of the art with regard to the quality of
the flow has still been defined by traditional methods. Particularly on small
displacements and real-world data, FlowNet cannot compete with variational
methods. In this p... | ['Thomas Brox', 'Nikolaus Mayer', 'Alexey Dosovitskiy', 'Margret Keuper', 'Eddy Ilg', 'Tonmoy Saikia'] | 2016-12-06 | flownet-2-0-evolution-of-optical-flow | http://openaccess.thecvf.com/content_cvpr_2017/html/Ilg_FlowNet_2.0_Evolution_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ilg_FlowNet_2.0_Evolution_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [-2.72626877e-01 -1.94863543e-01 -3.45978700e-02 -2.27852017e-02
-2.32387856e-01 -5.35079420e-01 4.52862084e-01 -4.35369939e-01
-6.23117983e-01 1.04238319e+00 2.86431909e-01 -2.57459313e-01
1.40109479e-01 -5.56617737e-01 -6.61954045e-01 -4.74353105e-01
-3.40178490e-01 2.46801555e-01 7.17910886e-01 -2.35759333... | [8.77621078491211, -1.8085678815841675] |
0e87bf7d-2221-4c98-aad2-950c432cab2d | generic-event-boundary-captioning-a-benchmark | 2204.00486 | null | https://arxiv.org/abs/2204.00486v4 | https://arxiv.org/pdf/2204.00486v4.pdf | GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval | Cognitive science has shown that humans perceive videos in terms of events separated by the state changes of dominant subjects. State changes trigger new events and are one of the most useful among the large amount of redundant information perceived. However, previous research focuses on the overall understanding of se... | ['Mike Zheng Shou', 'Matt Feiszli', 'Stan Weixian Lei', 'Licheng Yu', 'Difei Gao', 'Yuxuan Wang'] | 2022-04-01 | null | null | null | null | ['boundary-grounding', 'boundary-captioning'] | ['computer-vision', 'computer-vision'] | [ 1.87552631e-01 -3.13320041e-01 -2.34638527e-01 -6.80628777e-01
-5.26385784e-01 -7.47082531e-01 4.18962151e-01 2.42371231e-01
-2.25583285e-01 3.31865251e-01 7.04553664e-01 -5.05110472e-02
9.46799368e-02 -4.27192971e-02 -9.11384463e-01 -2.43287891e-01
-2.38972783e-01 -1.95672885e-01 4.74961996e-01 -3.17478001... | [9.599076271057129, 0.6333010792732239] |
f947f17c-54b5-4b82-9e66-01cf5eb2e5f7 | revisiting-click-based-interactive-video | 2203.01784 | null | https://arxiv.org/abs/2203.01784v2 | https://arxiv.org/pdf/2203.01784v2.pdf | Revisiting Click-based Interactive Video Object Segmentation | While current methods for interactive Video Object Segmentation (iVOS) rely on scribble-based interactions to generate precise object masks, we propose a Click-based interactive Video Object Segmentation (CiVOS) framework to simplify the required user workload as much as possible. CiVOS builds on de-coupled modules ref... | ['Rainer Stiefelhagen', 'Michael Arens', 'Norbert Scherer-Negenborn', 'Stefan Becker', 'Sebastian Bullinger', 'Stephane Vujasinovic'] | 2022-03-03 | null | null | null | null | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 4.98275936e-01 -1.46852478e-01 2.83280760e-02 -5.60399652e-01
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1.41042277e-01 6.62380457e-01 1.37178266e+00 5.62468916... | [9.258901596069336, -0.17697866261005402] |
6d78856a-ee01-4ed1-ae6d-c2189a75a0b0 | guided-deep-generative-model-based-spatial | 2306.17197 | null | https://arxiv.org/abs/2306.17197v1 | https://arxiv.org/pdf/2306.17197v1.pdf | Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems | When adopting a model-based formulation, solving inverse problems encountered in multiband imaging requires to define spatial and spectral regularizations. In most of the works of the literature, spectral information is extracted from the observations directly to derive data-driven spectral priors. Conversely, the choi... | ['Jie Chen', 'Nicolas Dobigeon', 'Min Zhao'] | 2023-06-29 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 8.10760140e-01 2.22434893e-01 5.31415381e-02 -2.25172341e-01
-1.05328047e+00 -2.80810386e-01 7.69533515e-01 -1.71211641e-02
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-6.62943304e-01 -7.28864014e-01 -7.09437728e-01 -1.07260561e+00
2.27384850e-01 -8.81552473e-02 -1.50939345e-01 -2.46737301... | [11.542078971862793, -2.4391214847564697] |
365adf76-4eac-4140-888d-c8857c3a4d9a | learning-parallax-attention-for-stereo-image | 1903.05784 | null | http://arxiv.org/abs/1903.05784v3 | http://arxiv.org/pdf/1903.05784v3.pdf | Learning Parallax Attention for Stereo Image Super-Resolution | Stereo image pairs can be used to improve the performance of super-resolution
(SR) since additional information is provided from a second viewpoint. However,
it is challenging to incorporate this information for SR since disparities
between stereo images vary significantly. In this paper, we propose a
parallax-attentio... | ['Wei An', 'Longguang Wang', 'Jungang Yang', 'Zhengfa Liang', 'Zaiping Lin', 'Yulan Guo', 'Yingqian Wang'] | 2019-03-14 | learning-parallax-attention-for-stereo-image-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Learning_Parallax_Attention_for_Stereo_Image_Super-Resolution_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Learning_Parallax_Attention_for_Stereo_Image_Super-Resolution_CVPR_2019_paper.pdf | cvpr-2019-6 | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 4.22549516e-01 -4.16504771e-01 1.18031152e-01 -4.25867587e-01
-1.01358426e+00 -3.32040489e-01 4.83352870e-01 -5.87451458e-01
-1.91677615e-01 6.63250208e-01 6.21544003e-01 2.45010868e-01
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3.81647289e-01 5.66111356e-02 6.78754926e-01 -6.04743898... | [10.67194938659668, -2.1419496536254883] |
4dbab473-0ec1-45c8-88b0-72bc62d28450 | spatio-temporal-wind-speed-forecasting-using | 2208.13585 | null | https://arxiv.org/abs/2208.13585v2 | https://arxiv.org/pdf/2208.13585v2.pdf | Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures | This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improve local wind forecasts. Our multi-step forecasting models produce either 10-minut... | ['Paal Engelstad', 'Roy Stenbro', 'Narada Dilp Warakagoda', 'Lars Ødegaard Bentsen'] | 2022-08-29 | null | null | null | null | ['weather-forecasting', 'spatio-temporal-forecasting'] | ['miscellaneous', 'time-series'] | [-1.99714169e-01 -3.73535603e-01 2.64737636e-01 -7.01135993e-02
6.20524883e-02 -5.97942889e-01 8.51868570e-01 -1.23210968e-02
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-8.67410064e-01 -8.82722557e-01 -3.07933837e-01 -8.09422016e-01
-8.38294148e-01 -6.44608587e-02 -6.78167492e-02 -4.53395873... | [6.466240406036377, 2.9011495113372803] |
0d2eb971-bc4d-42bb-a35d-32c2c3febd5e | learning-reliable-representations-for | 2303.17117 | null | https://arxiv.org/abs/2303.17117v1 | https://arxiv.org/pdf/2303.17117v1.pdf | Learning Reliable Representations for Incomplete Multi-View Partial Multi-Label Classification | As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further facilitated this process, however, the existing multi-view contrastive learning methods crudely sepa... | ['Min Zhang', 'Liqiang Nie', 'Yong Xu', 'Jie Wen', 'Chengliang Liu'] | 2023-03-30 | null | null | null | null | ['multi-view-learning', 'multi-label-learning'] | ['computer-vision', 'methodology'] | [ 2.06331909e-01 -2.00097203e-01 -5.46720862e-01 -6.28666818e-01
-8.37077379e-01 -5.27110934e-01 4.11276609e-01 2.60902584e-01
-7.37250522e-02 3.82452756e-01 1.93974122e-01 3.45556498e-01
-2.51233757e-01 -4.84566450e-01 -1.86711207e-01 -9.83237863e-01
5.13602078e-01 2.22120926e-01 -2.61327103e-02 1.67945206... | [8.612852096557617, 4.448630332946777] |
1a0445cc-c5a4-4aa8-a0ac-1a817de18ddf | mutexmatch-semi-supervised-learning-with-1 | 2203.14316 | null | https://arxiv.org/abs/2203.14316v2 | https://arxiv.org/pdf/2203.14316v2.pdf | MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization | The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this paper, we aim to utilize low-confidence samp... | ['Yang Gao', 'Yinghuan Shi', 'Luping Zhou', 'Lei Wang', 'Lei Qi', 'Zhen Zhao', 'Yue Duan'] | 2022-03-27 | mutexmatch-semi-supervised-learning-with | https://openreview.net/forum?id=r5hq-Ooh_Ba | https://openreview.net/pdf?id=r5hq-Ooh_Ba | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [-2.59812146e-01 -6.52938858e-02 -4.76050019e-01 -6.35553896e-01
-9.01768863e-01 -3.46167296e-01 2.87194967e-01 -2.75477618e-02
-5.56236207e-01 1.12909973e+00 -1.93301424e-01 -2.63417900e-01
1.35539740e-01 -5.67275047e-01 -5.89170098e-01 -7.28272557e-01
3.34303886e-01 2.66703725e-01 9.28867906e-02 1.01449326... | [9.419179916381836, 3.7201550006866455] |
47c89756-7500-463e-821d-bbaf1e48e552 | dimension-independent-mixup-for-hard-negative | 2306.15905 | null | https://arxiv.org/abs/2306.15905v1 | https://arxiv.org/pdf/2306.15905v1.pdf | Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering | Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. W... | ['Philip S. Yu', 'Xiaolong Liu', 'Tianyu Lin', 'Chao Zhou', 'Jibing Gong', 'Liangwei Yang', 'Xi Wu'] | 2023-06-28 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 1.49261847e-01 -2.74289817e-01 -7.17775285e-01 -5.17560482e-01
-4.84837860e-01 -4.43758875e-01 5.42898238e-01 4.77679260e-02
-3.68000716e-01 8.36986601e-01 2.30902269e-01 -6.88444614e-01
-2.46916369e-01 -9.94709790e-01 -3.56462538e-01 -2.18382269e-01
-2.31837705e-01 4.45792854e-01 3.83233041e-01 -3.84755313... | [10.048665046691895, 5.631604194641113] |
97306bcb-b924-40b6-a188-1a149a1f13fc | text-detection-and-recognition-in-the-wild-a | 2006.04305 | null | https://arxiv.org/abs/2006.04305v2 | https://arxiv.org/pdf/2006.04305v2.pdf | Text Detection and Recognition in the Wild: A Review | Detection and recognition of text in natural images are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and... | ['Steven Wardell', 'Paul Fieguth', 'Mohamed A. Naiel', 'Zobeir Raisi', 'John Zelek'] | 2020-06-08 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 8.17171037e-01 -8.27048719e-01 3.15088220e-02 -1.50103554e-01
-2.62526691e-01 -4.68385547e-01 8.47048879e-01 -7.64660165e-02
-3.93953383e-01 2.26003334e-01 -2.93397047e-02 -3.37884426e-02
1.10185288e-01 -3.40934485e-01 -5.64905643e-01 -8.22968006e-01
5.40903687e-01 4.30385470e-01 3.40587974e-01 -1.33504272... | [11.963662147521973, 2.32021427154541] |
121ff187-682b-43d9-9b76-87964df2a928 | capturing-and-inferring-dense-full-body-human-1 | 2206.09553 | null | https://arxiv.org/abs/2206.09553v1 | https://arxiv.org/pdf/2206.09553v1.pdf | Capturing and Inferring Dense Full-Body Human-Scene Contact | Inferring human-scene contact (HSC) is the first step toward understanding how humans interact with their surroundings. While detecting 2D human-object interaction (HOI) and reconstructing 3D human pose and shape (HPS) have enjoyed significant progress, reasoning about 3D human-scene contact from a single image is stil... | ['Michael J. Black', 'Daniel Scharstein', 'Senya Polikovsky', 'Tsvetelina Alexiadis', 'Matvey Safroshkin', 'Markus Höschle', 'Hongwei Yi', 'Chun-Hao P. Huang'] | 2022-06-20 | capturing-and-inferring-dense-full-body-human | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Capturing_and_Inferring_Dense_Full-Body_Human-Scene_Contact_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Capturing_and_Inferring_Dense_Full-Body_Human-Scene_Contact_CVPR_2022_paper.pdf | cvpr-2022-1 | ['markerless-motion-capture', 'monocular-3d-human-pose-estimation', 'human-scene-contact-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.11977798e-01 1.15058720e-02 -1.23964660e-01 -2.56019145e-01
-6.19030774e-01 -3.51768911e-01 4.06213045e-01 -2.65973985e-01
-2.52614677e-01 2.60945559e-01 2.34166324e-01 1.86811343e-01
2.09178880e-01 -7.30504453e-01 -9.83622670e-01 -1.79581270e-01
3.51219587e-02 1.06375945e+00 5.18353820e-01 -3.13392788... | [7.032830715179443, -1.0670313835144043] |
3e0a2744-72f1-457a-afc2-40000137e6d4 | a-prospective-observational-study-to | 2106.02118 | null | https://arxiv.org/abs/2106.02118v2 | https://arxiv.org/pdf/2106.02118v2.pdf | A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals | Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously dev... | ['Christopher Tignanelli', 'Erich Kummerfeld', 'Judy Wawira Gichoya', 'Scott D. Steenburg', 'Tadashi Allen', 'Kun Huang', 'John L. Burns', 'Sean Switzer', 'Daniel Boley', 'Eric Murray', 'Nicholas Ingraham', 'Genevieve B. Melton', 'Zach Zaiman', 'Dyah Adila', 'Taihui Li', 'Le Peng', 'Ju Sun'] | 2021-06-03 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 1.85673982e-01 -2.54654288e-01 -4.60057497e-01 -3.94609213e-01
-8.10882390e-01 -5.99271715e-01 2.95226663e-01 7.95013428e-01
-4.32910532e-01 5.90339720e-01 3.75931352e-01 -1.18139672e+00
-8.55882406e-01 -4.75365162e-01 2.45533651e-03 -3.60984236e-01
-1.39359146e-01 1.34736967e+00 -1.70241281e-01 4.28645343... | [15.308751106262207, -1.9004192352294922] |
6ba35688-fb7b-4c86-9e51-9b921ba21858 | drinet-efficient-voxel-as-point-point-cloud | 2111.08318 | null | https://arxiv.org/abs/2111.08318v1 | https://arxiv.org/pdf/2111.08318v1.pdf | DRINet++: Efficient Voxel-as-point Point Cloud Segmentation | Recently, many approaches have been proposed through single or multiple representations to improve the performance of point cloud semantic segmentation. However, these works do not maintain a good balance among performance, efficiency, and memory consumption. To address these issues, we propose DRINet++ that extends DR... | ['Qifeng Chen', 'Tongyi Cao', 'Shuangjie Xu', 'Rui Wan', 'Maosheng Ye'] | 2021-11-16 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-3.65188569e-02 -2.85495073e-01 -1.96206048e-01 -5.40357649e-01
-6.26986325e-01 -1.36995539e-01 1.82368100e-01 1.16634369e-01
-8.62320289e-02 2.63482183e-01 9.46640149e-02 2.41140991e-01
2.24005952e-02 -1.17921495e+00 -9.16901112e-01 -4.54784185e-01
1.56723455e-01 3.87683183e-01 6.16754889e-01 -2.70656973... | [8.015181541442871, -3.4083328247070312] |
fa5a90de-868b-4a2a-81b8-5e26846ed188 | session-aware-information-embedding-for-e | 1707.05955 | null | http://arxiv.org/abs/1707.05955v2 | http://arxiv.org/pdf/1707.05955v2.pdf | Session-aware Information Embedding for E-commerce Product Recommendation | Most of the existing recommender systems assume that user's visiting history
can be constantly recorded. However, in recent online services, the user
identification may be usually unknown and only limited online user behaviors
can be used. It is of great importance to model the temporal online user
behaviors and conduc... | ['Si Luo', 'Yan Ming', 'Wu Chen'] | 2017-11-20 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.71198159e-01 -4.17043418e-01 -3.52706760e-01 -9.82336462e-01
-2.49150857e-01 -4.75911081e-01 4.95326102e-01 -2.20578253e-01
-3.34602803e-01 3.66061032e-01 4.29571003e-01 -5.11161208e-01
-4.07945454e-01 -1.05122626e+00 -5.30353487e-01 -3.56629372e-01
-1.17945373e-01 3.66410792e-01 4.97460775e-02 -3.41186076... | [10.159995079040527, 5.623106002807617] |
481a78d9-6813-4bd6-8e64-9994f0327ba2 | scannet-a-fast-and-dense-scanning-framework | 1707.09597 | null | http://arxiv.org/abs/1707.09597v1 | http://arxiv.org/pdf/1707.09597v1.pdf | ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images | Lymph node metastasis is one of the most significant diagnostic indicators in
breast cancer, which is traditionally observed under the microscope by
pathologists. In recent years, computerized histology diagnosis has become one
of the most rapidly expanding fields in medical image computing, which
alleviates pathologis... | ['Pheng-Ann Heng', 'Qi Dou', 'Hao Chen', 'Huangjing Lin', 'Liansheng Wang', 'Jing Qin'] | 2017-07-30 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.74210602e-01 -1.03788964e-01 -5.48889995e-01 -7.63282850e-02
-1.05711293e+00 -1.81845650e-01 5.84443621e-02 4.77856725e-01
-7.38912880e-01 6.30813777e-01 -8.90897512e-02 -7.23100722e-01
-1.65027156e-01 -7.41248310e-01 -2.98604250e-01 -1.28460610e+00
2.36752018e-01 6.31868064e-01 3.01392555e-01 1.66945845... | [15.18452262878418, -2.9556844234466553] |
8f419b78-696a-4c82-9a06-daa6bfba78da | joint-supervised-and-self-supervised-learning | 2004.07392 | null | https://arxiv.org/abs/2004.07392v1 | https://arxiv.org/pdf/2004.07392v1.pdf | Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges | Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world, where the amount of annotated data may be limited and integra... | ['Tatiana Tommasi', 'Antonio Alliegro', 'Davide Boscaini'] | 2020-04-15 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 6.39035180e-02 -1.05563521e-01 -7.32547268e-02 -3.03668082e-01
-7.44719386e-01 -7.07018733e-01 7.09002554e-01 2.77821571e-01
-2.73413092e-01 4.38476980e-01 -3.01207304e-01 1.35737509e-02
-2.43028879e-01 -8.20970178e-01 -8.41948450e-01 -4.62159574e-01
-9.19359922e-02 1.29667139e+00 6.04561627e-01 -3.89991492... | [8.016220092773438, -3.042501449584961] |
9adab9ef-da8d-4685-92f2-fbd29143051b | adversarial-feature-augmentation-for-cross | 2208.11021 | null | https://arxiv.org/abs/2208.11021v1 | https://arxiv.org/pdf/2208.11021v1.pdf | Adversarial Feature Augmentation for Cross-domain Few-shot Classification | Existing methods based on meta-learning predict novel-class labels for (target domain) testing tasks via meta knowledge learned from (source domain) training tasks of base classes. However, most existing works may fail to generalize to novel classes due to the probably large domain discrepancy across domains. To addres... | ['Andy J. Ma', 'Yanxu Hu'] | 2022-08-23 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.56488413e-01 5.22490358e-03 -4.83370215e-01 -4.16068524e-01
-1.01931727e+00 -4.38637733e-01 6.03864729e-01 -1.74200997e-01
-6.14981577e-02 1.08193672e+00 -5.31642288e-02 1.20910361e-01
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3.89744282e-01 4.32770461e-01 3.65863889e-01 -3.72966409... | [10.077657699584961, 3.0714919567108154] |
572f6c1d-73bd-4135-a31d-f56aab96a670 | towards-large-vocabulary-kazakh-russian-sign | null | null | https://aclanthology.org/2022.signlang-1.24 | https://aclanthology.org/2022.signlang-1.24.pdf | Towards Large Vocabulary Kazakh-Russian Sign Language Dataset: KRSL-OnlineSchool | This paper presents a new dataset for Kazakh-Russian Sign Language (KRSL) created for the purposes of Sign Language Processing. In 2020, Kazakhstan’s schools were quickly switched to online mode due to the COVID-19 pandemic. Every working day, the El-arna TV channel was broadcasting video lessons for grades from 1 to 1... | ['Anara Sandygulova', 'Vadim Kimmelman', 'Aigerim Kydyrbekova', 'Medet Mukushev'] | null | null | null | null | signlang-lrec-2022-6 | ['sign-language-translation'] | ['computer-vision'] | [-6.35153428e-02 1.51074141e-01 -2.53031373e-01 -3.08501303e-01
-1.05853450e+00 -7.92077184e-01 4.49776053e-01 -2.85405904e-01
-5.18622816e-01 6.59274757e-01 8.53664041e-01 -4.35681880e-01
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3.94553065e-01 4.22421575e-01 3.53784114e-01 -1.45456046... | [9.143714904785156, -6.458750247955322] |
f74d35da-d4ba-4c91-a89d-6196c0f9be52 | phrasetransformer-an-incorporation-of-local | null | null | https://link.springer.com/article/10.1007/s10489-022-04246-0 | https://link.springer.com/content/pdf/10.1007/s10489-022-04246-0.pdf | PhraseTransformer: An Incorporation of Local Context Information into Sequence-to-sequence Semantic Parsing | Semantic parsing is a challenging task mapping a natural language utterance to machine-understandable information representation. Recently, approaches using neural machine translation (NMT) have achieved many promising results, especially the Transformer. However, the typical drawback of adapting the vanilla Transforme... | ['Minh Le Nguyen', 'Vu Tran', 'Huy Tien Nguyen', 'Tung Le', 'Phuong Minh Nguyen'] | 2022-11-29 | null | null | null | applied-intelligence-2022-11 | ['nmt', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 3.20406139e-01 3.64453495e-01 -3.23516011e-01 -6.20187163e-01
-7.59218395e-01 -3.55661482e-01 2.83990473e-01 -1.23205483e-01
-4.97748405e-01 6.93621278e-01 4.51586127e-01 -5.77727675e-01
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5.56339741e-01 4.46042269e-01 1.26530752e-01 -4.44592118... | [10.678293228149414, 9.2100191116333] |
e0bf30a1-60a4-45bb-8a09-d9b7b4dfaa51 | self-supervised-relative-depth-learning-for | 1712.04850 | null | http://arxiv.org/abs/1712.04850v2 | http://arxiv.org/pdf/1712.04850v2.pdf | Self-Supervised Relative Depth Learning for Urban Scene Understanding | As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot... | ['Erik Learned-Miller', 'Huaizu Jiang', 'Greg Shakhnarovich', 'Michael Maire', 'Gustav Larsson'] | 2017-12-13 | self-supervised-relative-depth-learning-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Huaizu_Jiang_Self-Supervised_Relative_Depth_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Huaizu_Jiang_Self-Supervised_Relative_Depth_ECCV_2018_paper.pdf | eccv-2018-9 | ['road-segementation'] | ['computer-vision'] | [ 4.18050557e-01 1.70510247e-01 -3.58684599e-01 -5.27464926e-01
-5.68991780e-01 -6.43038452e-01 9.45550084e-01 -2.53232956e-01
-7.12159812e-01 5.23570716e-01 -5.72358556e-02 -2.93368958e-02
3.79899293e-01 -9.82755005e-01 -8.29409838e-01 -6.31655455e-01
-4.64258641e-02 7.87272215e-01 7.45867431e-01 -5.11343079... | [8.574723243713379, -1.7208459377288818] |
a473d871-b07f-48f1-9dbf-63333754ef75 | rats-nas-redirection-of-adjacent-trails-on | 2305.04206 | null | https://arxiv.org/abs/2305.04206v2 | https://arxiv.org/pdf/2305.04206v2.pdf | RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search | Various hand-designed CNN architectures have been developed, such as VGG, ResNet, DenseNet, etc., and achieve State-of-the-Art (SoTA) levels on different tasks. Neural Architecture Search (NAS) now focuses on automatically finding the best CNN architecture to handle the above tasks. However, the verification of a searc... | ['Kuo-Chin Fan', 'Chun-Chieh Lee', 'Jun-Wei Hsieh', 'Yu-Ming Zhang'] | 2023-05-07 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-2.62844086e-01 -2.21172825e-01 -1.63782146e-02 -1.65304810e-01
-1.20561734e-01 -2.93932080e-01 3.40006173e-01 -1.22322209e-01
-5.21636248e-01 5.94621658e-01 -1.69821545e-01 -6.21574163e-01
-1.36556819e-01 -8.59156787e-01 -7.88444519e-01 -5.73359787e-01
-2.13010967e-01 2.60860384e-01 6.85819864e-01 -3.36298496... | [8.578907012939453, 3.242985725402832] |
7d1ad0ff-ed2d-422f-b890-bfec8ba1b232 | cross-domain-transfer-via-semantic-skill | 2212.07407 | null | https://arxiv.org/abs/2212.07407v1 | https://arxiv.org/pdf/2212.07407v1.pdf | Cross-Domain Transfer via Semantic Skill Imitation | We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e.g. a robotic manipulator in a simulated kitchen. Instead of imitating low-level actions like joint velocities, our approach imitates... | ['Akshara Rai', 'Dhruv Batra', 'Joseph J. Lim', 'Franziska Meier', 'Vikash Kumar', 'Ruta Desai', 'Karl Pertsch'] | 2022-12-14 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 6.32160828e-02 2.09989905e-01 9.01468918e-02 -8.81265402e-02
-4.06962305e-01 -1.00402105e+00 7.47528732e-01 -3.24007034e-01
-5.82336605e-01 9.57515299e-01 -1.80303305e-01 -3.11080635e-01
-8.82001221e-02 -5.75463831e-01 -1.23905587e+00 -4.26715255e-01
-5.09872913e-01 7.03062415e-01 2.22560391e-01 -2.44904056... | [4.549839973449707, 0.84914231300354] |
040aa910-8901-4f4c-a9ec-51fd14ec29ca | unsupervised-chinese-word-segmentation-with | null | null | https://openreview.net/forum?id=DXbxULpbXZ4 | https://openreview.net/pdf?id=DXbxULpbXZ4 | Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation | Word Segmentation is a fundamental step for understanding Chinese language. Previous neural approaches for unsupervised Chinese Word Segmentation (CWS) only exploits shallow semantic information, which can miss important context. Large scale Pre-trained language models (PLM) have achieved great success in many areas be... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.76158482e-01 1.08286403e-01 -4.47788745e-01 -5.90058923e-01
-5.79651654e-01 -4.15840000e-01 2.12765068e-01 1.36285797e-01
-7.11342335e-01 4.19493884e-01 3.65970820e-01 -5.25539160e-01
4.98308718e-01 -7.34002948e-01 -4.37798440e-01 -3.63974184e-01
3.69876385e-01 3.91670495e-01 6.54670954e-01 -1.52477860... | [9.952720642089844, 10.109447479248047] |
78fe8d6d-6953-444f-8c95-a4aea6f82e7e | keep-learning-self-supervised-meta-learning | null | null | https://aclanthology.org/2021.eacl-main.6 | https://aclanthology.org/2021.eacl-main.6.pdf | Keep Learning: Self-supervised Meta-learning for Learning from Inference | A common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks to further improve performance. A new approach for language modelling, called dynamic evaluation, further fine-tunes a trained model during inference using trivially-pre... | ['Sai Chetan Chinthakindi', 'Akhil Kedia'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['answer-selection'] | ['natural-language-processing'] | [ 1.83138564e-01 3.15648764e-01 -2.76456356e-01 -7.96877801e-01
-9.47741210e-01 -6.03102386e-01 5.69566607e-01 1.90749571e-01
-8.57578278e-01 1.00992465e+00 5.26456460e-02 -4.00666088e-01
3.12105156e-02 -8.10260952e-01 -8.25128734e-01 -4.15229470e-01
1.11246340e-01 1.01037943e+00 5.58624387e-01 -6.32332265... | [11.120630264282227, 8.314643859863281] |
b4ad1a4e-4da6-4077-bfa3-5040ef0d922f | tuning-ir-cut-filter-for-illumination-aware | 2103.14708 | null | https://arxiv.org/abs/2103.14708v1 | https://arxiv.org/pdf/2103.14708v1.pdf | Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB | To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager. It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. To improv... | ['Yinqiang Zheng', 'Xiao Zhou', 'Junchi Yan', 'Bo Sun'] | 2021-03-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_Tuning_IR-Cut_Filter_for_Illumination-Aware_Spectral_Reconstruction_From_RGB_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_Tuning_IR-Cut_Filter_for_Illumination-Aware_Spectral_Reconstruction_From_RGB_CVPR_2021_paper.pdf | cvpr-2021-1 | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.44743860e-01 -7.02078938e-01 1.40326083e-01 -1.82744250e-01
-7.30711877e-01 -6.39908671e-01 1.38190970e-01 -6.68395936e-01
-2.80533791e-01 4.78866398e-01 6.86846823e-02 -1.67192474e-01
-3.23489249e-01 -5.84936500e-01 -5.49075723e-01 -1.06339943e+00
4.40179229e-01 -1.30958572e-01 1.06525943e-01 -2.72132128... | [10.299229621887207, -2.5225107669830322] |
74fea1c3-dfb7-4e3b-966d-ca5727aecf28 | batchgfn-generative-flow-networks-for-batch | 2306.15058 | null | https://arxiv.org/abs/2306.15058v1 | https://arxiv.org/pdf/2306.15058v1.pdf | BatchGFN: Generative Flow Networks for Batch Active Learning | We introduce BatchGFN -- a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model... | ['Yarin Gal', 'Yoshua Bengio', 'Tristan Deleu', 'Nikolay Malkin', 'Moksh Jain', 'Andrew Jesson', 'Salem Lahlou', 'Shreshth A. Malik'] | 2023-06-26 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [-4.89631034e-02 6.20738864e-01 -3.30037564e-01 -6.68101490e-01
-1.31287718e+00 -3.13059241e-01 5.05450666e-01 6.79153129e-02
-7.52873123e-01 1.11123538e+00 2.29891956e-01 -3.10702678e-02
-3.31724614e-01 -6.34448290e-01 -9.22050953e-01 -8.19453597e-01
-4.01968300e-01 9.16513324e-01 5.89144900e-02 3.20313632... | [8.226856231689453, 3.9991605281829834] |
c0ddf3b5-6d82-4940-9767-1e205d2b5f77 | continual-learning-for-automated-audio | 2107.08028 | null | https://arxiv.org/abs/2107.08028v1 | https://arxiv.org/pdf/2107.08028v1.pdf | Continual Learning for Automated Audio Captioning Using The Learning Without Forgetting Approach | Automated audio captioning (AAC) is the task of automatically creating textual descriptions (i.e. captions) for the contents of a general audio signal. Most AAC methods are using existing datasets to optimize and/or evaluate upon. Given the limited information held by the AAC datasets, it is very likely that AAC method... | ['Konstantinos Drossos', 'Jan Berg'] | 2021-07-16 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.42814517e-01 2.11701736e-01 2.18154699e-01 -3.39600325e-01
-1.04643118e+00 -8.47800732e-01 4.18619573e-01 2.85381377e-01
-4.39228356e-01 1.13624692e+00 6.61395371e-01 1.49013802e-01
-5.78359440e-02 -3.03091049e-01 -7.16210365e-01 -4.36693728e-01
1.85917527e-03 8.30132365e-01 4.17502314e-01 -5.52202873... | [15.276043891906738, 4.846409320831299] |
dacbf7a0-ba69-466f-9c6c-bd3e9f16f715 | corpus-augmentation-by-sentence-segmentation | 1905.08945 | null | https://arxiv.org/abs/1905.08945v1 | https://arxiv.org/pdf/1905.08945v1.pdf | Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation | Neural Machine Translation (NMT) has been proven to achieve impressive results. The NMT system translation results depend strongly on the size and quality of parallel corpora. Nevertheless, for many language pairs, no rich-resource parallel corpora exist. As described in this paper, we propose a corpus augmentation met... | ['Jinyi Zhang', 'Tadahiro Matsumoto'] | 2019-05-22 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.12415868e-01 -3.23867708e-01 -2.22393587e-01 -1.78467989e-01
-1.16543627e+00 -6.95452332e-01 6.79850519e-01 -4.29631293e-01
-4.26195025e-01 1.27487445e+00 3.48754108e-01 -7.70649910e-01
6.45944238e-01 -3.86507660e-01 -6.67230487e-01 -3.11908454e-01
4.34231907e-01 7.97478497e-01 -3.00216287e-01 -4.99479920... | [11.579689979553223, 10.385865211486816] |
7a01c8d1-b07b-4822-bc39-bf1112a326c1 | targeted-vae-structured-inference-and | 2009.13472 | null | https://arxiv.org/abs/2009.13472v5 | https://arxiv.org/pdf/2009.13472v5.pdf | Targeted VAE: Variational and Targeted Learning for Causal Inference | Undertaking causal inference with observational data is incredibly useful across a wide range of tasks including the development of medical treatments, advertisements and marketing, and policy making. There are two significant challenges associated with undertaking causal inference using observational data: treatment a... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Matthew James Vowels'] | 2020-09-28 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 9.13762152e-01 3.90216917e-01 -9.40619290e-01 -3.78936082e-01
-6.35835350e-01 -4.28463340e-01 7.43019581e-01 1.94503918e-01
-4.65609252e-01 1.16882527e+00 7.77544260e-01 -9.25689697e-01
-6.11398518e-01 -7.36583889e-01 -9.94910121e-01 -6.49805129e-01
-3.47787708e-01 4.43778992e-01 -3.12178701e-01 4.74198908... | [8.04552173614502, 5.381961345672607] |
1e62bfa9-1027-4042-8299-4315ec3a882c | subgraph-networks-based-contrastive-learning | 2306.03506 | null | https://arxiv.org/abs/2306.03506v1 | https://arxiv.org/pdf/2306.03506v1.pdf | Subgraph Networks Based Contrastive Learning | Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for downstream tasks. Most existing GCL methods focus on the design of graph augmentation strategies and ... | ['Xiaoniu Yang', 'Qi Xuan', 'Shanqing Yu', 'Zeyu Wang', 'Jiafei Shao', 'Jinhuan Wang'] | 2023-06-06 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.66299087e-01 6.42309964e-01 -5.83754480e-01 -1.27128139e-01
-4.32406336e-01 -4.90601957e-01 7.04062283e-01 2.17292562e-01
6.79293796e-02 6.78616703e-01 1.49461418e-01 -2.69110143e-01
-3.07562530e-01 -1.05191374e+00 -7.97538280e-01 -7.18290985e-01
-6.05825484e-01 3.26328665e-01 1.35529712e-01 -2.49962360... | [7.302801132202148, 6.234770774841309] |
d2d4fcf5-a764-4fb1-a579-7ae65821b0d5 | selective-feature-compression-for-efficient | 2104.00179 | null | https://arxiv.org/abs/2104.00179v2 | https://arxiv.org/pdf/2104.00179v2.pdf | Selective Feature Compression for Efficient Activity Recognition Inference | Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world application. In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustra... | ['Joseph Tighe', 'Davide Modolo', 'Hao Chen', 'Xinyu Li', 'Chunhui Liu'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Selective_Feature_Compression_for_Efficient_Activity_Recognition_Inference_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Selective_Feature_Compression_for_Efficient_Activity_Recognition_Inference_ICCV_2021_paper.pdf | iccv-2021-1 | ['feature-compression'] | ['computer-vision'] | [ 7.32548952e-01 -1.46808296e-01 -5.62740743e-01 -2.74923027e-01
-8.28179598e-01 -3.02672654e-01 6.00506067e-01 -2.60373324e-01
-6.66131914e-01 7.62313902e-01 4.51306045e-01 -4.79666926e-02
-2.16564074e-01 -3.97553086e-01 -8.28530073e-01 -6.08558297e-01
-2.40253046e-01 3.89651805e-01 6.18467689e-01 3.66018504... | [8.456531524658203, 0.5132519602775574] |
bbdb64f2-3835-47e7-964e-6956cdaf7760 | a-quantum-inspired-probabilistic-model-for | 2011.05511 | null | https://arxiv.org/abs/2011.05511v1 | https://arxiv.org/pdf/2011.05511v1.pdf | A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures | In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at the core of the microcosmos. Meanwhile, machine learning inverse design of materials raised intensi... | ['XueFeng Zhu', 'Yingtao Luo'] | 2020-11-11 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.64727911e-01 3.16999033e-02 -1.95831403e-01 -3.15485656e-01
-3.77940744e-01 -5.42544499e-02 4.86357540e-01 -1.07505724e-01
-1.10698916e-01 9.13650095e-01 3.87155294e-01 -1.23323165e-01
-6.33445144e-01 -1.27643073e+00 -7.91480422e-01 -1.25483680e+00
8.70743021e-02 4.54778582e-01 -1.02446407e-01 -1.40428111... | [5.418069362640381, 5.088384628295898] |
431dd1fb-150b-41c4-a999-33fd50313cab | affinitynet-semi-supervised-few-shot-learning | 1805.08905 | null | http://arxiv.org/abs/1805.08905v2 | http://arxiv.org/pdf/1805.08905v2.pdf | AffinityNet: semi-supervised few-shot learning for disease type prediction | While deep learning has achieved great success in computer vision and many
other fields, currently it does not work very well on patient genomic data with
the "big p, small N" problem (i.e., a relatively small number of samples with
high-dimensional features). In order to make deep learning work with a small
amount of ... | ['Aidong Zhang', 'Tianle Ma'] | 2018-05-22 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-1.29987270e-01 3.17134172e-01 -4.10712719e-01 -3.89982730e-01
-2.73148328e-01 -5.78196421e-02 1.87311441e-01 2.73786455e-01
-2.36878872e-01 5.44615209e-01 1.98658735e-01 -4.13097739e-01
-1.68539360e-01 -1.23324883e+00 -6.17219925e-01 -6.34909689e-01
-2.03490168e-01 4.57391560e-01 3.38223547e-01 -2.79671013... | [6.922696590423584, 6.250348091125488] |
8c5f1821-1b9e-495f-af5a-c886b43e8a29 | long-document-re-ranking-with-modular-re | 2205.04275 | null | https://arxiv.org/abs/2205.04275v2 | https://arxiv.org/pdf/2205.04275v2.pdf | Long Document Re-ranking with Modular Re-ranker | Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunks. These chunks are independently mapped to scalar scores or latent vectors, which are then pooled into a final relevance score. These encod... | ['Jamie Callan', 'Luyu Gao'] | 2022-05-09 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 3.15658636e-02 9.66575965e-02 -3.69617432e-01 -3.37336153e-01
-1.44959402e+00 -6.75018787e-01 9.63381112e-01 5.44986367e-01
-5.43735921e-01 5.25195837e-01 8.94235790e-01 1.64309561e-01
-3.12937230e-01 -4.43037450e-01 -7.26355076e-01 -2.62607276e-01
-3.00835520e-01 8.70497227e-01 1.08468622e-01 -2.94737935... | [11.441839218139648, 7.632616996765137] |
000dcad9-33ba-4c82-a394-590ea8d09b35 | systematic-literature-review-on-application | 2305.12695 | null | https://arxiv.org/abs/2305.12695v1 | https://arxiv.org/pdf/2305.12695v1.pdf | Systematic Literature Review on Application of Machine Learning in Continuous Integration | This research conducted a systematic review of the literature on machine learning (ML)-based methods in the context of Continuous Integration (CI) over the past 22 years. The study aimed to identify and describe the techniques used in ML-based solutions for CI and analyzed various aspects such as data engineering, feat... | ['Muhammad Ali Babar', 'Mansooreh Zahedi', 'Triet Huynh Minh Le', 'Ali Kazemi Arani'] | 2023-05-22 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-9.78298672e-03 5.77465072e-02 -9.57952201e-01 -2.27702975e-01
-7.81222463e-01 -3.27256352e-01 4.80062902e-01 5.90701103e-01
-4.19274807e-01 6.56686842e-01 -7.29539618e-02 -2.93415546e-01
-7.13514388e-01 -6.74816728e-01 -4.84421015e-01 -4.57775712e-01
-9.05436575e-02 3.49721700e-01 5.63392136e-03 1.82390183... | [8.43787670135498, 4.626473426818848] |
3c3e8583-e33c-48b0-8d2f-c1c82223aa23 | counter-strike-deathmatch-with-large-scale | 2104.04258 | null | https://arxiv.org/abs/2104.04258v2 | https://arxiv.org/pdf/2104.04258v2.pdf | Counter-Strike Deathmatch with Large-Scale Behavioural Cloning | This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input. The agent, a deep neural network, matches the performance of the medium difficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike play style. Un... | ['Jun Zhu', 'Tim Pearce'] | 2021-04-09 | null | null | null | null | ['fps-games'] | ['playing-games'] | [ 1.60008464e-02 2.54123569e-01 -1.58744797e-01 -5.97829930e-03
-7.55230844e-01 -7.50249267e-01 8.14398885e-01 -6.30049109e-01
-9.86978292e-01 9.38044012e-01 7.20115975e-02 -3.79168004e-01
-4.91120759e-03 -5.60308278e-01 -7.80263484e-01 -4.54316884e-01
-3.46575916e-01 7.19385922e-01 7.15104163e-01 -8.12833428... | [3.7074337005615234, 1.5004788637161255] |
bbd06f1a-f3bf-4728-a20c-eec34354a52f | homados-at-semeval-2021-task-6-multi-task | null | null | https://aclanthology.org/2021.semeval-1.141 | https://aclanthology.org/2021.semeval-1.141.pdf | HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection | Among the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other related tasks, including credibility assessment, can be leveraged in multi-... | ['Piotr Przyby{\\l}a', "Konrad Kaczy{\\'n}ski"] | 2021-08-01 | null | null | null | semeval-2021 | ['propaganda-detection'] | ['natural-language-processing'] | [-2.87089944e-02 2.79720664e-01 -3.19424510e-01 -4.37812477e-01
-1.34118664e+00 -6.78841531e-01 1.16284490e+00 5.54683924e-01
-5.88477969e-01 8.02406013e-01 3.70223701e-01 -7.21646547e-01
1.72589615e-01 -4.67219979e-01 -6.30414665e-01 -3.66452903e-01
1.13763817e-01 4.67774272e-01 1.91784799e-01 -2.46172890... | [8.464176177978516, 10.657181739807129] |
30c6f112-01eb-41d6-98c3-533e59e74aa3 | do-all-roads-lead-to-rome-understanding-the | 2002.12867 | null | https://arxiv.org/abs/2002.12867v1 | https://arxiv.org/pdf/2002.12867v1.pdf | Do all Roads Lead to Rome? Understanding the Role of Initialization in Iterative Back-Translation | Back-translation provides a simple yet effective approach to exploit monolingual corpora in Neural Machine Translation (NMT). Its iterative variant, where two opposite NMT models are jointly trained by alternately using a synthetic parallel corpus generated by the reverse model, plays a central role in unsupervised mac... | ['Mikel Artetxe', 'Noe Casas', 'Gorka Labaka', 'Eneko Agirre'] | 2020-02-28 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.79446089e-01 1.30047485e-01 -2.85407186e-01 -5.37750959e-01
-1.05679321e+00 -7.64132857e-01 1.03621233e+00 -2.29351804e-01
-4.13358986e-01 8.18251550e-01 3.36593866e-01 -8.42824578e-01
4.46968645e-01 -3.59624803e-01 -9.77288246e-01 -6.10498250e-01
5.35809278e-01 8.39809000e-01 -1.75342441e-01 -4.33108807... | [11.59279727935791, 10.184125900268555] |
edae532c-3bfb-405d-aad9-4414dafaa8f0 | retinexformer-one-stage-retinex-based | 2303.06705 | null | https://arxiv.org/abs/2303.06705v1 | https://arxiv.org/pdf/2303.06705v1.pdf | Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement | When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process. Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional ... | ['Yulun Zhang', 'Radu Timofte', 'Haoqian Wang', 'Jing Lin', 'Hao Bian', 'Yuanhao Cai'] | 2023-03-12 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.43547881e-01 -5.41865528e-01 2.20536977e-01 -6.01604342e-01
-4.23444569e-01 -2.55126506e-01 5.97630024e-01 -5.51980674e-01
-1.55058131e-01 5.67471981e-01 2.33762816e-01 -1.53316066e-01
2.08586350e-01 -7.25362837e-01 -8.48993719e-01 -1.04642320e+00
5.44420421e-01 -4.07004267e-01 9.75938663e-02 -2.37972543... | [10.699371337890625, -2.5032215118408203] |
5722c615-a98e-441a-b708-6139f5d1be2f | probabilistic-knowledge-distillation-of-face | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Probabilistic_Knowledge_Distillation_of_Face_Ensembles_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Probabilistic_Knowledge_Distillation_of_Face_Ensembles_CVPR_2023_paper.pdf | Probabilistic Knowledge Distillation of Face Ensembles | Mean ensemble (i.e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model. We formalize it as feature alignment for ensemble in open-set face recognition and generalize it into Bayesian Ensemble Averaging (BEA) through the... | ['Bryan Hooi', 'Shouhong Ding', 'Jiaxiang Wu', 'Jiaying Wu', 'Miao Xiong', 'Ailin Deng', 'Shen Li', 'Jianqing Xu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-image-quality', 'face-recognition'] | ['computer-vision', 'computer-vision'] | [-9.90106445e-03 1.75728440e-01 1.47147611e-01 -7.04302669e-01
-1.10609949e+00 -5.34869075e-01 8.01503658e-01 -2.61637986e-01
3.05329692e-02 8.07066202e-01 1.93649188e-01 -1.73122168e-01
-5.62771320e-01 -7.15046346e-01 -9.27572966e-01 -1.07502878e+00
1.28474414e-01 4.63941246e-01 -3.14980626e-01 9.92693156... | [7.7538676261901855, 4.021604537963867] |
71851d07-0f7e-4c9e-968f-106db91485c9 | enhancing-aspect-extraction-for-hindi | null | null | https://aclanthology.org/2021.ecnlp-1.17 | https://aclanthology.org/2021.ecnlp-1.17.pdf | Enhancing Aspect Extraction for Hindi | Aspect extraction is not a well-explored topic in Hindi, with only one corpus having been developed for the task. In this paper, we discuss the merits of the existing corpus in terms of quality, size, sparsity, and performance in aspect extraction tasks using established models. To provide a better baseline corpus for ... | ['Manish Shrivastava', 'Alok Debnath', 'Arghya Bhattacharya'] | null | null | null | null | acl-ecnlp-2021-8 | ['aspect-extraction'] | ['natural-language-processing'] | [-1.00963879e-02 3.11380804e-01 -4.23684448e-01 -5.09836495e-01
-1.34726322e+00 -8.20077181e-01 8.92430425e-01 2.83633411e-01
-6.99137449e-01 8.68817627e-01 6.29894793e-01 -1.81945711e-01
2.77931631e-01 -5.61710894e-01 -5.06341100e-01 -3.23309332e-01
3.02389592e-01 9.16699469e-01 -1.14980854e-01 -3.72718036... | [11.40244197845459, 6.792319297790527] |
226f5c6a-22e5-4715-a73f-4256867e7c4b | sentibase-sentiment-analysis-in-twitter-on-a | null | null | https://aclanthology.org/S15-2098 | https://aclanthology.org/S15-2098.pdf | Sentibase: Sentiment Analysis in Twitter on a Budget | null | ['Aditya Joshi', 'Vasudeva Varma', 'Satarupa Guha'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['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... | [-7.181724548339844, 3.8228163719177246] |
9cc0031c-f0ad-4bb2-97b3-8ab82a5b23b4 | patient-risk-assessment-and-warning-symptom | 1809.10804 | null | http://arxiv.org/abs/1809.10804v1 | http://arxiv.org/pdf/1809.10804v1.pdf | Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks | We present an operational component of a real-world patient triage system.
Given a specific patient presentation, the system is able to assess the level
of medical urgency and issue the most appropriate recommendation in terms of
best point of care and time to treat. We use an attention-based convolutional
neural netwo... | ['Ce Zhang', 'Lorenz Kuhn', 'An-phi Nguyen', 'Ivan Girardi', 'Nora Hollenstein', 'Adam Ivankay', 'Pengfei Ji', 'Chiara Marchiori'] | 2018-09-28 | patient-risk-assessment-and-warning-symptom-1 | https://aclanthology.org/W18-5616 | https://aclanthology.org/W18-5616.pdf | ws-2018-10 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 2.30386391e-01 5.60015559e-01 -2.24779293e-01 -3.73774409e-01
-7.02803314e-01 -4.82596636e-01 3.80242229e-01 1.16446877e+00
-7.83341110e-01 3.67193758e-01 4.51740265e-01 -5.87897420e-01
-5.02663493e-01 -8.63602877e-01 -1.53624505e-01 -3.16480160e-01
-2.55968515e-02 7.85863042e-01 -5.49398325e-02 -3.62844504... | [8.309685707092285, 8.006129264831543] |
9dc117a1-edf1-469a-b495-3f9042f70300 | population-diversity-leads-to-short-running | 2204.06461 | null | https://arxiv.org/abs/2204.06461v1 | https://arxiv.org/pdf/2204.06461v1.pdf | Population Diversity Leads to Short Running Times of Lexicase Selection | In this paper we investigate why the running time of lexicase parent selection is empirically much lower than its worst-case bound of O(N*C). We define a measure of population diversity and prove that high diversity leads to low running times O(N + C) of lexicase selection. We then show empirically that genetic program... | ['William La Cava', 'Johannes Lengler', 'Thomas Helmuth'] | 2022-04-13 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.68605548e-01 1.62932817e-02 -3.86514395e-01 -2.35844657e-01
-6.56849384e-01 -8.18710685e-01 2.21736133e-01 2.45992839e-01
-6.00348532e-01 9.36627269e-01 -2.18171895e-01 -5.10418952e-01
-2.00130582e-01 -9.70788062e-01 -7.26193011e-01 -8.33363533e-01
-3.05134445e-01 6.11428022e-01 4.60769683e-01 -1.84219688... | [7.989263534545898, 7.147768974304199] |
874d19ba-e9f8-49ca-9acc-5462faad523d | robust-video-object-tracking-via-bayesian | 1604.00475 | null | http://arxiv.org/abs/1604.00475v3 | http://arxiv.org/pdf/1604.00475v3.pdf | Robust video object tracking via Bayesian model averaging based feature fusion | In this article, we are concerned with tracking an object of interest in
video stream. We propose an algorithm that is robust against occlusion, the
presence of confusing colors, abrupt changes in the object feature space and
changes in object size. We develop the algorithm within a Bayesian modeling
framework. The sta... | ['Bin Liu', 'Yi Dai'] | 2016-04-02 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 1.48720190e-01 -6.48947954e-01 1.14785545e-01 -9.13720950e-02
-1.77510321e-01 -3.90419871e-01 7.91954279e-01 1.13004580e-01
-4.11191016e-01 6.68360293e-01 -1.96526051e-01 2.45321751e-01
-4.13778335e-01 -3.32787365e-01 -5.69933712e-01 -9.40401971e-01
-2.06385151e-01 7.77007714e-02 6.01136029e-01 3.55225265... | [6.644790172576904, -1.989187479019165] |
1439c0e6-c31a-4681-83d6-a1b50471ccc5 | 3d-regnet-deep-learning-model-for-covid-19 | 2107.04055 | null | https://arxiv.org/abs/2107.04055v1 | https://arxiv.org/pdf/2107.04055v1.pdf | 3D RegNet: Deep Learning Model for COVID-19 Diagnosis on Chest CT Image | In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection. In the application of clinical medicine, lung CT images are utilized by practitioners to determine whether a patient is infected with coronavirus. However, there are some ... | ['Xinyu Liu', 'YuHan Wang', 'Haibo Qi'] | 2021-07-08 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.00308740e-01 -1.65816963e-01 2.00061388e-02 -3.30398947e-01
1.41785100e-01 -2.98413306e-01 9.66698397e-03 -1.78921402e-01
-2.58578002e-01 5.56496501e-01 5.66633977e-02 -4.30309564e-01
-2.75047898e-01 -6.59126103e-01 -2.68351138e-01 -6.95804656e-01
-3.94440562e-01 6.51292562e-01 6.70563430e-02 4.57961142... | [15.613738059997559, -1.662722110748291] |
443e11bb-b6e9-415d-b01a-dee137e79aca | realistic-face-animation-generation-from | 2103.14984 | null | https://arxiv.org/abs/2103.14984v1 | https://arxiv.org/pdf/2103.14984v1.pdf | Realistic face animation generation from videos | 3D face reconstruction and face alignment are two fundamental and highly related topics in computer vision. Recently, some works start to use deep learning models to estimate the 3DMM coefficients to reconstruct 3D face geometry. However, the performance is restricted due to the limitation of the pre-defined face templ... | ['Minshan Xie', 'Zihao Jian'] | 2021-03-27 | null | null | null | null | ['face-alignment', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.38936877e-01 -7.95294940e-02 3.52679193e-02 -6.74121141e-01
-2.65535027e-01 -3.86095531e-02 4.68993276e-01 -6.20515764e-01
-5.72955459e-02 1.86180502e-01 -3.30372527e-02 -4.38609421e-02
5.93221970e-02 -6.53092980e-01 -4.29857016e-01 -4.96479094e-01
2.14877188e-01 5.97293198e-01 -2.07092285e-01 -3.03182527... | [13.219151496887207, 0.3117883503437042] |
5b8a2fcb-ad9b-4b1a-bf38-9d339a1a3bc8 | zeroforge-feedforward-text-to-shape-without | 2306.08183 | null | https://arxiv.org/abs/2306.08183v2 | https://arxiv.org/pdf/2306.08183v2.pdf | ZeroForge: Feedforward Text-to-Shape Without 3D Supervision | Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural representations. In this work, we present ZeroForge, an approach for zero-shot text-to-shape generation t... | ['Chinmay Hegde', 'Adarsh Krishnamurthy', 'Aditya Balu', 'Anushrut Jignasu', 'Ameya Joshi', 'Minh Pham', 'Kelly O. Marshall'] | 2023-06-14 | null | null | null | null | ['text-to-shape-generation'] | ['computer-vision'] | [ 4.68398064e-01 3.59673172e-01 1.76560476e-01 -3.38276237e-01
-1.09859359e+00 -7.07099140e-01 9.76694763e-01 -1.63019598e-02
1.49754420e-01 6.53152168e-01 4.12934631e-01 -3.47247988e-01
5.35716340e-02 -1.11032057e+00 -7.96804309e-01 -3.51062775e-01
1.68290213e-01 8.21406841e-01 1.06173418e-01 -4.02458578... | [9.02125358581543, -3.583132028579712] |
e94bb59a-7365-4450-a529-744124eec1fe | act-aware-slot-value-predicting-in-multi | 2208.02462 | null | https://arxiv.org/abs/2208.02462v1 | https://arxiv.org/pdf/2208.02462v1.pdf | Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking | As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue. Representations of dialogue states are dependent on the domain ontology and the user's goals. In several task-oriented dialogu... | ['Biing-Hwang Juang', 'Ting-Wei Wu', 'Ruolin Su'] | 2022-08-04 | null | null | null | null | ['dialogue-state-tracking', 'machine-reading-comprehension', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.49933854e-01 6.96261287e-01 -3.56160074e-01 -6.22232378e-01
-4.35262501e-01 -8.19532990e-01 1.19766450e+00 3.12157542e-01
-2.21021101e-01 8.03992450e-01 9.41772342e-01 -6.90657735e-01
4.43281885e-03 -5.70088625e-01 4.63969886e-01 2.65345335e-01
7.16891587e-02 9.38432097e-01 3.62460911e-01 -1.11030185... | [12.927939414978027, 7.954102516174316] |
0cd25e60-e6fb-4610-b000-e8038f1cf6a4 | a-simple-and-powerful-global-optimization-for | 2209.09341 | null | https://arxiv.org/abs/2209.09341v2 | https://arxiv.org/pdf/2209.09341v2.pdf | A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation | We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only relies on independent image features and optical flows, which can be obtained using off-the-shelf se... | ['Vincent Lepetit', 'Renaud Marlet', 'Yuming Du', 'Yang Xiao', 'Nermin Samet', 'Georgy Ponimatkin'] | 2022-09-19 | null | null | null | null | ['unsupervised-object-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.31545082e-01 7.42661357e-02 -3.31883371e-01 -2.37902269e-01
-5.29884517e-01 -7.77437985e-01 4.71414804e-01 -1.77786186e-01
-6.44981146e-01 5.50302505e-01 -1.03705414e-01 -1.62711635e-01
6.90090135e-02 -3.49550575e-01 -9.10378456e-01 -7.51037419e-01
-4.29829694e-02 3.86595815e-01 7.64136136e-01 -2.58709714... | [9.100717544555664, -0.19488321244716644] |
674daf15-a943-438e-9043-81b07a3f76c6 | learning-polysemantic-spoof-trace-a-multi | 2212.03943 | null | https://arxiv.org/abs/2212.03943v1 | https://arxiv.org/pdf/2212.03943v1.pdf | Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing | Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement frame... | ['Di Huang', 'Biao Wang', 'Pengyu Li', 'Binghui Chen', 'Hongyu Yang', 'Kaicheng Li'] | 2022-12-07 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 7.51334250e-01 -4.20166969e-01 -4.44886118e-01 -1.27108008e-01
-4.47789341e-01 -6.46179676e-01 8.50675285e-01 -3.44920456e-02
4.03186768e-01 3.27456981e-01 1.57827228e-01 -3.37154865e-01
-3.03254515e-01 -8.15999389e-01 -4.00476098e-01 -9.90698695e-01
-1.64727673e-01 2.48900667e-01 -5.61855510e-02 -4.32813108... | [13.064651489257812, 1.2070266008377075] |
bb8bcac3-90eb-4e1c-b63c-664e98741c46 | joint-incremental-disfluency-detection-and-1 | null | null | https://aclanthology.org/Q14-1011 | https://aclanthology.org/Q14-1011.pdf | Joint Incremental Disfluency Detection and Dependency Parsing | We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. T... | ['Mark Johnson', 'Matthew Honnibal'] | 2014-01-01 | null | null | null | tacl-2014-1 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-2.50806153e-01 3.78166705e-01 -5.16541563e-02 -3.17464083e-01
-1.37737703e+00 -9.01924551e-01 2.31590867e-01 4.14364070e-01
-5.49666882e-01 5.59065759e-01 4.82962936e-01 -7.10047960e-01
7.00979292e-01 -2.93655664e-01 -4.20777708e-01 -2.52510816e-01
-2.79453337e-01 4.87030387e-01 7.70775914e-01 -1.48105815... | [10.476283073425293, 9.832993507385254] |
610a0c58-fee5-4a89-b025-1d0cfc916128 | cover-tree-compressed-sensing-for-fast-mr | 1706.07834 | null | http://arxiv.org/abs/1706.07834v2 | http://arxiv.org/pdf/1706.07834v2.pdf | Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery | We adopt data structure in the form of cover trees and iteratively apply
approximate nearest neighbour (ANN) searches for fast compressed sensing
reconstruction of signals living on discrete smooth manifolds. Levering on the
recent stability results for the inexact Iterative Projected Gradient (IPG)
algorithm and by us... | ['Zhouye Chen', 'Yves Wiaux', 'Mohammad Golbabaee', 'Mike E. Davies'] | 2017-06-23 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 9.84691858e-01 5.57592690e-01 -5.08859940e-02 -1.93836957e-01
-9.96458113e-01 -2.31905043e-01 1.07724860e-01 1.19802721e-01
-2.04227775e-01 6.22249842e-01 2.92441875e-01 -5.01122773e-01
-6.06437862e-01 -4.51990873e-01 -8.23388338e-01 -5.53358436e-01
-7.36813009e-01 3.16322237e-01 1.51491106e-01 -1.07081428... | [13.41998291015625, -2.4340717792510986] |
94b3a62e-0136-4b3c-9837-10226d6992b4 | inheriting-the-wisdom-of-predecessors-a | null | null | https://www.ijcai.org/proceedings/2022/572 | https://www.ijcai.org/proceedings/2022/0572.pdf | Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis | So far, aspect-based sentiment analysis (ABSA) has involved with total seven subtasks, in which, however the interactions among them have been left unexplored sufficiently. This work presents a novel multiplex cascade framework for unified ABSA and maintaining such interactions. First, we model total seven subtasks as ... | ['Donghong Ji', 'Jingye Li', 'Shengqiong Wu', 'Chenliang Li', 'Fei Li', 'Hao Fei'] | 2022-07-30 | null | null | null | conference-2022-7 | ['term-extraction', 'aspect-based-sentiment-analysis', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.35792071e-01 -1.33326724e-01 1.13617823e-01 -6.42454624e-01
-8.19412947e-01 -7.70437062e-01 4.73660290e-01 2.13708386e-01
-4.47164923e-01 2.97851771e-01 1.69362620e-01 -5.94067454e-01
-1.80835888e-01 -6.08299792e-01 -6.97709918e-01 -6.18223310e-01
2.99855649e-01 2.83557847e-02 3.42888296e-01 -5.96155345... | [11.523127555847168, 6.594694137573242] |
112ecbd2-ea85-4aa2-b6e6-29dca6186824 | a-survey-of-machine-learning-techniques-in | 2010.09680 | null | https://arxiv.org/abs/2010.09680v1 | https://arxiv.org/pdf/2010.09680v1.pdf | A Survey of Machine Learning Techniques in Adversarial Image Forensics | Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are a... | ['Kim-Kwang Raymond Choo', 'Reza M. Parizi', 'Ali Dehghantanha', 'Ehsan Nowroozi'] | 2020-10-19 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.85951924e-01 -2.02972978e-01 -1.84648469e-01 -1.67938117e-02
-5.14554203e-01 -8.40133548e-01 7.51360953e-01 4.40931678e-01
-3.96945357e-01 1.01231110e+00 -2.74906188e-01 -6.89884722e-01
1.91833615e-01 -8.95507038e-01 -6.11753941e-01 -7.81376481e-01
7.48230964e-02 -8.30795057e-03 -1.25450626e-01 1.09921746... | [12.492029190063477, 1.0879186391830444] |
d253ecd1-f2f0-482f-8cda-12b179c47e8f | unsupervised-pre-training-on-patient | 2203.12616 | null | https://arxiv.org/abs/2203.12616v2 | https://arxiv.org/pdf/2203.12616v2.pdf | Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions | Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been fully explored for clinical data analysis. Even though an immense amount of Electronic Health Record (EHR) data is recorded, data and label... | ['Nassir Navab', 'Anees Kazi', 'Chantal Pellegrini'] | 2022-03-23 | null | null | null | null | ['length-of-stay-prediction', 'unsupervised-pre-training'] | ['medical', 'methodology'] | [ 3.41948390e-01 3.36607754e-01 -2.56407142e-01 -6.22650564e-01
-8.96269262e-01 -2.90827483e-01 2.62101948e-01 7.52506077e-01
-3.08804601e-01 5.56220949e-01 5.35104752e-01 -5.05432665e-01
-2.97124803e-01 -9.27158654e-01 -7.12631047e-01 -5.27048767e-01
-2.96768099e-01 6.50140047e-01 -2.23058254e-01 1.31550118... | [7.917031288146973, 6.38846492767334] |
4a5f9645-8bd4-4bda-8a98-9f0e11ba350d | continual-facial-expression-recognition-a | 2305.06448 | null | https://arxiv.org/abs/2305.06448v1 | https://arxiv.org/pdf/2305.06448v1.pdf | Continual Facial Expression Recognition: A Benchmark | Understanding human affective behaviour, especially in the dynamics of real-world settings, requires Facial Expression Recognition (FER) models to continuously adapt to individual differences in user expression, contextual attributions, and the environment. Current (deep) Machine Learning (ML)-based FER approaches pre-... | ['Hatice Gunes', 'German I. Parisi', 'Tolga Dimlioglu', 'Nikhil Churamani'] | 2023-05-10 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.30657876e-01 1.02550704e-02 5.04321791e-02 -5.70244908e-01
5.40187247e-02 -2.15370268e-01 6.04456961e-01 9.86010954e-02
-5.93219280e-01 8.27156305e-01 -1.03377432e-01 4.87644911e-01
-1.16056584e-01 -3.40660572e-01 -3.79571497e-01 -6.61320925e-01
-4.95129704e-01 5.06798506e-01 -3.40980828e-01 -7.40948319... | [13.549811363220215, 1.999802589416504] |
e6b6cd33-d726-402d-a2f0-d2ff3efb6fa3 | multi-task-consistency-for-active-learning | 2306.12398 | null | https://arxiv.org/abs/2306.12398v1 | https://arxiv.org/pdf/2306.12398v1.pdf | Multi-Task Consistency for Active Learning | Learning-based solutions for vision tasks require a large amount of labeled training data to ensure their performance and reliability. In single-task vision-based settings, inconsistency-based active learning has proven to be effective in selecting informative samples for annotation. However, there is a lack of researc... | ['Alois C. Knoll', 'Alvaro Marcos-Ramiro', 'Michael Schmidt', 'Walter Zimmer', 'Philipp Friedrich', 'Aral Hekimoglu'] | 2023-06-21 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 6.05850637e-01 5.32254055e-02 -3.42594028e-01 -4.34594333e-01
-1.54354751e+00 -5.23780644e-01 5.44744313e-01 9.89127681e-02
-7.86342561e-01 4.72500861e-01 -2.10829392e-01 1.05345249e-01
-6.96526840e-02 -1.84713185e-01 -7.14075446e-01 -7.63620675e-01
2.46480823e-01 4.84363437e-01 7.12027311e-01 5.76375127... | [9.257956504821777, 1.2883515357971191] |
b1f26c81-1932-4549-8fe8-02c37751907b | bert-erc-fine-tuning-bert-is-enough-for | 2301.06745 | null | https://arxiv.org/abs/2301.06745v1 | https://arxiv.org/pdf/2301.06745v1.pdf | BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation | Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. How... | ['Li Wang', 'Bin Wang', 'Jian Luan', 'Yanran Li', 'Tingting Zhang', 'Jinshi Cui', 'Zhiyu Wu', 'Xiangyu Qin'] | 2023-01-17 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.05745444e-01 -3.30047339e-01 8.64086077e-02 -5.79596877e-01
-5.29741585e-01 -4.38915551e-01 7.17269242e-01 -5.12717701e-02
-6.26501739e-01 4.94544089e-01 4.75005865e-01 -1.18004337e-01
6.17608465e-02 -4.50909823e-01 -9.63397920e-02 -5.21183193e-01
2.46508002e-01 3.36442441e-01 1.05151333e-01 -4.59683150... | [13.024367332458496, 6.098376750946045] |
e73173b8-28f2-4400-9742-8e37955b5767 | is-synthetic-dataset-reliable-for | 2209.05047 | null | https://arxiv.org/abs/2209.05047v1 | https://arxiv.org/pdf/2209.05047v1.pdf | Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification? | Recent studies show that models trained on synthetic datasets are able to achieve better generalizable person re-identification (GPReID) performance than that trained on public real-world datasets. On the other hand, due to the limitations of real-world person ReID datasets, it would also be important and interesting t... | ['Cuicui Kang'] | 2022-09-12 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [-1.37157604e-01 -4.63614702e-01 1.61786601e-02 -6.33911133e-01
-4.10113275e-01 -8.49152565e-01 7.65049398e-01 2.88005620e-01
-7.42466092e-01 1.09518230e+00 -6.71652481e-02 -7.63212815e-02
-2.71637440e-01 -9.76061881e-01 -6.96202397e-01 -4.26486045e-01
-1.57914251e-01 5.76766968e-01 7.13285850e-03 -1.11374818... | [14.705273628234863, 1.0530718564987183] |
4cf7269a-dd67-4a9e-b6a0-4064e3a25b80 | a-context-aware-loss-function-for-action | 1912.01326 | null | https://arxiv.org/abs/1912.01326v3 | https://arxiv.org/pdf/1912.01326v3.pdf | A Context-Aware Loss Function for Action Spotting in Soccer Videos | In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to sp... | ['Thomas B. Moeslund', 'Adrien Deliège', 'Marc Van Droogenbroeck', 'Anthony Cioppa', 'Rikke Gade', 'Silvio Giancola', 'Bernard Ghanem'] | 2019-12-03 | a-context-aware-loss-function-for-action-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Cioppa_A_Context-Aware_Loss_Function_for_Action_Spotting_in_Soccer_Videos_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cioppa_A_Context-Aware_Loss_Function_for_Action_Spotting_in_Soccer_Videos_CVPR_2020_paper.pdf | cvpr-2020-6 | ['action-spotting'] | ['computer-vision'] | [ 5.61741352e-01 7.33100697e-02 -3.23599249e-01 -2.35569000e-01
-8.09914947e-01 -6.39542639e-01 6.56809211e-01 -1.39312129e-02
-5.02252460e-01 7.02614963e-01 6.48066461e-01 2.42972031e-01
1.05707578e-01 -2.68081754e-01 -7.75900483e-01 -6.01461768e-01
-4.97323692e-01 5.88847511e-02 6.93859637e-01 -3.03700520... | [8.305116653442383, 0.48820042610168457] |
a6ccc840-4c45-40f7-8cff-98de073ca9fe | end-to-end-speaker-attributed-asr-with | 2104.02128 | null | https://arxiv.org/abs/2104.02128v1 | https://arxiv.org/pdf/2104.02128v1.pdf | End-to-End Speaker-Attributed ASR with Transformer | This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio. Firstly, we thoroughly update the model architecture that was previously designed based on a long s... | ['Takuya Yoshioka', 'Zhuo Chen', 'Zhong Meng', 'Xiaofei Wang', 'Yashesh Gaur', 'Guoli Ye', 'Naoyuki Kanda'] | 2021-04-05 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.11794956e-01 -1.03640422e-01 3.67718250e-01 -5.12198389e-01
-1.81271493e+00 -1.59483567e-01 1.44536555e-01 -2.07723960e-01
-4.49150622e-01 2.85389304e-01 3.35379928e-01 -2.20593557e-01
3.32861245e-01 5.94787821e-02 -7.38042474e-01 -8.04828703e-01
2.56510749e-02 3.72901082e-01 1.41783897e-02 3.74754071... | [14.631935119628906, 6.151705265045166] |
579cdff2-ea1b-40a3-becd-c7995bb818a6 | scope-and-arbitration-in-machine-learning | 2303.06386 | null | https://arxiv.org/abs/2303.06386v1 | https://arxiv.org/pdf/2303.06386v1.pdf | Scope and Arbitration in Machine Learning Clinical EEG Classification | A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit the label of their parent recording. We hypothesised that window labe... | ['David Western', 'Luke J. W. Canham', 'Yixuan Zhu'] | 2023-03-11 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 7.51059353e-01 3.92657936e-01 -7.11797178e-02 -7.74828315e-01
-1.30403471e+00 -6.36290014e-01 2.29258478e-01 6.27129018e-01
-6.40453160e-01 9.77589846e-01 1.68893382e-01 -7.03985810e-01
-9.05023366e-02 -1.83812767e-01 -4.63978976e-01 -6.27621830e-01
-4.34548080e-01 3.72469395e-01 3.56547743e-01 4.83192027... | [13.335137367248535, 3.4931297302246094] |
85b95c98-63fe-40ab-9ec1-3a4bc9563c33 | graformer-graph-oriented-transformer-for-3d | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_GraFormer_Graph-Oriented_Transformer_for_3D_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_GraFormer_Graph-Oriented_Transformer_for_3D_Pose_Estimation_CVPR_2022_paper.pdf | GraFormer: Graph-Oriented Transformer for 3D Pose Estimation | In 2D-to-3D pose estimation, it is important to exploit the spatial constraints of 2D joints, but it is not yet well modeled. To better model the relation of joints for 3D pose estimation, we propose an effective but simple network, called GraFormer, where a novel transformer architecture is designed via embedding ... | ['Yunjie Tian', 'Weiqiang Wang', 'Weixi Zhao'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [-4.73113954e-01 5.81998527e-01 4.27119248e-02 -3.77530485e-01
7.32911751e-02 -6.13860749e-02 4.91222829e-01 -4.53924946e-02
-5.18248379e-01 2.96971649e-01 3.94317001e-01 1.42741874e-01
-2.61048764e-01 -7.65769899e-01 -1.02868247e+00 -5.89058757e-01
-5.29951155e-01 6.61590815e-01 3.61894131e-01 -3.76996279... | [7.058216571807861, -0.6285943984985352] |
4ec01265-5ff3-4164-a3c2-0b839c29a77f | filtering-aggression-from-the-multilingual | null | null | https://aclanthology.org/W18-4423 | https://aclanthology.org/W18-4423.pdf | Filtering Aggression from the Multilingual Social Media Feed | This paper describes the participation of team DA-LD-Hildesheim from the Information Retrieval Lab(IRLAB) at DA-IICT Gandhinagar, India in collaboration with the University of Hildesheim, Germany and LDRP-ITR, Gandhinagar, India in a shared task on Aggression Identification workshop in COLING 2018. The objective of the... | ['M', 'Prasenjit Majumder', 'ip', 'Thomas l', 'S Modha'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-4.46392089e-01 1.93239432e-02 2.50040084e-01 -3.00418168e-01
-4.89176989e-01 -4.20839220e-01 4.61342216e-01 5.62970221e-01
-9.24128711e-01 5.09199321e-01 4.72372562e-01 -1.60686001e-01
-5.34939110e-01 -8.07307243e-01 -1.83348116e-02 -4.69209373e-01
-9.67720672e-02 7.10177541e-01 -5.56992218e-02 -7.19519496... | [8.822958946228027, 10.774117469787598] |
d2f2865c-48c4-4034-9e66-4e3b64b6dfc3 | cascades-of-regression-tree-fields-for-image | 1404.2086 | null | http://arxiv.org/abs/1404.2086v2 | http://arxiv.org/pdf/1404.2086v2.pdf | Cascades of Regression Tree Fields for Image Restoration | Conditional random fields (CRFs) are popular discriminative models for
computer vision and have been successfully applied in the domain of image
restoration, especially to image denoising. For image deblurring, however,
discriminative approaches have been mostly lacking. We posit two reasons for
this: First, the blur k... | ['Jeremy Jancsary', 'Uwe Schmidt', 'Stefan Roth', 'Carsten Rother', 'Sebastian Nowozin'] | 2014-04-08 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.71713281e-01 -4.07714635e-01 2.65197784e-01 -3.22634250e-01
-8.99430394e-01 -4.00615662e-01 6.88669980e-01 -2.72252619e-01
-4.06415910e-01 6.23045802e-01 1.19332217e-01 -1.09887868e-01
-2.25080386e-01 -3.88191819e-01 -7.77082622e-01 -9.88604248e-01
2.42521971e-01 2.99763650e-01 5.83587997e-02 5.34207746... | [11.577362060546875, -2.4904654026031494] |
2ec1f28b-8ec7-42d4-b8d7-627b26201f3e | lagr-label-aligned-graphs-for-better | null | null | https://openreview.net/forum?id=WtK3-ws0P3_ | https://openreview.net/pdf?id=WtK3-ws0P3_ | LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing | Semantic parsing is the task of producing structured meaning representations for natural language sentences.
Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['systematic-generalization'] | ['reasoning'] | [ 9.43816483e-01 1.09336436e+00 -8.60843062e-02 -9.66461003e-01
-1.04155016e+00 -1.08468330e+00 3.49531561e-01 2.68823117e-01
-1.68681085e-01 8.27600300e-01 2.64236093e-01 -5.38133919e-01
1.88031659e-01 -9.33831155e-01 -1.14021456e+00 -5.26370347e-01
-6.12570941e-02 6.30149484e-01 2.84304079e-02 3.54303457... | [10.512675285339355, 9.096501350402832] |
8631d0b6-b315-4cc8-a9be-d89973c43b1d | toward-a-unified-framework-for-unsupervised | 2212.10097 | null | https://arxiv.org/abs/2212.10097v1 | https://arxiv.org/pdf/2212.10097v1.pdf | Toward a Unified Framework for Unsupervised Complex Tabular Reasoning | Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume th... | ['Jianyong Wang', 'Ning Liu', 'Bowen Dong', 'Zhichao Duan', 'Xiuxing Li', 'Zhenyu Li'] | 2022-12-20 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 2.06451282e-01 4.93779957e-01 -4.66037571e-01 -5.57765663e-01
-1.07795060e+00 -7.87919641e-01 5.34574747e-01 1.24375597e-01
2.68067658e-01 7.60658920e-01 6.23402596e-02 -8.13677132e-01
2.90693104e-01 -1.27404034e+00 -1.09421742e+00 -2.74817087e-02
4.41956282e-01 7.23484814e-01 2.60937810e-01 -3.46890271... | [9.578991889953613, 7.742423057556152] |
3b4016b9-92a7-4029-b449-de99aa6da478 | handling-motion-blur-in-multi-frame-super | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Ma_Handling_Motion_Blur_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Ma_Handling_Motion_Blur_2015_CVPR_paper.pdf | Handling Motion Blur in Multi-Frame Super-Resolution | Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family... | ['Ziyang Ma', 'Xin Tao', 'Li Xu', 'Jiaya Jia', 'Renjie Liao', 'Enhua Wu'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 4.50918734e-01 -4.14820462e-01 2.25764796e-01 -8.13772678e-02
-6.92455888e-01 -1.47790387e-01 2.57105708e-01 -9.51757967e-01
-3.13467026e-01 1.06127787e+00 5.09141207e-01 2.28637233e-01
-2.58521169e-01 -3.02988619e-01 -5.30420661e-01 -6.66713059e-01
2.91369915e-01 -3.97301644e-01 2.68635839e-01 9.37302932... | [11.457064628601074, -2.645368814468384] |
2dc072d5-6275-4ff8-a97c-8cc6e86be181 | using-rhetorical-structure-theory-for | null | null | https://aclanthology.org/W17-3608 | https://aclanthology.org/W17-3608.pdf | Using Rhetorical Structure Theory for Detection of Fake Online Reviews | null | ['Olu Popoola'] | 2017-09-01 | null | null | null | ws-2017-9 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.248286724090576, 3.568211317062378] |
b9305e95-2352-4415-a01a-24d806b1240a | annotation-and-classification-of-evidence-and-1 | 2107.06990 | null | https://arxiv.org/abs/2107.06990v1 | https://arxiv.org/pdf/2107.06990v1.pdf | Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing | Automated writing evaluation systems can improve students' writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e... | ['Richard Correnti', 'Lindsay C. Matsumura', 'Diane Litman', 'Elaine Wang', 'Tazin Afrin'] | 2021-07-14 | annotation-and-classification-of-evidence-and | https://aclanthology.org/2020.bea-1.7 | https://aclanthology.org/2020.bea-1.7.pdf | ws-2020-7 | ['automated-writing-evaluation'] | ['natural-language-processing'] | [ 1.29248992e-01 5.49058974e-01 -5.38121164e-01 -3.38399738e-01
-8.59300792e-01 -1.05407333e+00 6.01652741e-01 1.03219008e+00
-4.86313730e-01 7.49518752e-01 6.85949624e-01 -8.96078169e-01
-3.76721114e-01 -8.52771938e-01 -4.34236139e-01 1.48296922e-01
1.13427472e+00 2.21194446e-01 2.27086455e-01 -4.96366322... | [11.274574279785156, 9.24040699005127] |
b81b6199-e112-4462-ad18-a413418cd804 | interactive-models-for-post-editing | null | null | https://aclanthology.org/2021.triton-1.19 | https://aclanthology.org/2021.triton-1.19.pdf | Interactive Models for Post-Editing | Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections. Automatic Post-Editing (APE) models have been introduced to perform these corrections without human intervention. However, no system has been able to fully automate the Post-Editing (PE) process. Moreover, while n... | ['Ruslan Mitkov', 'Marie Escribe'] | null | null | null | null | triton-2021-7 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 5.81638336e-01 4.99335796e-01 -5.14156297e-02 -4.06969845e-01
-8.54922295e-01 -7.55917549e-01 8.84918511e-01 7.58364648e-02
-4.77556139e-01 5.89753747e-01 1.09592550e-01 -9.67593193e-01
2.59415209e-01 -3.20572436e-01 -7.65731990e-01 1.98086262e-01
5.48549891e-01 8.65819216e-01 2.69552283e-02 -3.45507205... | [11.63212776184082, 10.27624225616455] |
fa355070-ce4c-4cb5-acfa-7e0c574d5f3e | random-graph-matching-at-otter-s-threshold | 2209.12313 | null | https://arxiv.org/abs/2209.12313v2 | https://arxiv.org/pdf/2209.12313v2.pdf | Random graph matching at Otter's threshold via counting chandeliers | We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex. For two Erd\H{o}s-R\'enyi graphs $\mathcal{G}(n,q)$ whose edges are correlated through a latent vertex correspondence, we show that this algorithm correctly... | ['Sophie H. Yu', 'Jiaming Xu', 'Yihong Wu', 'Cheng Mao'] | 2022-09-25 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.59951913e-01 2.85532236e-01 -1.76880032e-01 -5.96635044e-03
-6.61554217e-01 -4.06918555e-01 4.56211269e-02 5.01140833e-01
-2.58999228e-01 3.92728448e-01 -5.48256755e-01 -5.35178900e-01
-6.01677775e-01 -1.40847552e+00 -4.76665705e-01 -7.30007052e-01
-7.74720669e-01 8.31406176e-01 4.49230492e-01 -3.32619905... | [6.814311504364014, 5.16959810256958] |
6acefab2-1bd4-4ccd-b755-afa97ca9a2a4 | weighted-sparse-subspace-representation-a | 2106.04330 | null | https://arxiv.org/abs/2106.04330v1 | https://arxiv.org/pdf/2106.04330v1.pdf | Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning | Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work, we first propose a novel spectral-based subspace clustering algorithm that seeks to represent each point as a sparse convex combination of... | ['Nicos G. Pavlidis', 'Hankui Peng'] | 2021-06-08 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 3.48160595e-01 -2.20793232e-01 -3.05956781e-01 -2.38613307e-01
-8.89011800e-01 -7.15674996e-01 4.39100713e-01 2.76715606e-01
-5.11185050e-01 4.88023371e-01 -1.99731410e-01 -7.14120567e-02
-3.22121739e-01 -2.52022445e-01 -4.69143301e-01 -1.02971184e+00
-3.00425917e-01 8.73805642e-01 1.60886750e-01 4.46413368... | [7.764566898345947, 4.424886703491211] |
4a7e690a-f0ab-42b6-8144-d125259ec315 | recurrent-attention-models-for-depth-based | 1611.07212 | null | http://arxiv.org/abs/1611.07212v1 | http://arxiv.org/pdf/1611.07212v1.pdf | Recurrent Attention Models for Depth-Based Person Identification | We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning tas... | ['Li Fei-Fei', 'Albert Haque', 'Alexandre Alahi'] | 2016-11-22 | recurrent-attention-models-for-depth-based-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Haque_Recurrent_Attention_Models_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Haque_Recurrent_Attention_Models_CVPR_2016_paper.pdf | cvpr-2016-6 | ['person-identification'] | ['computer-vision'] | [-1.65676102e-01 -1.49124563e-01 1.17643684e-01 -1.95563257e-01
-2.23303437e-01 -5.55662036e-01 6.30360126e-01 1.01553552e-01
-2.59263128e-01 2.01682776e-01 6.51635945e-01 1.89872041e-01
-6.00813963e-02 -5.88444412e-01 -3.49543542e-01 -2.27552816e-01
-6.22866511e-01 2.75891334e-01 -2.31629729e-01 -1.76837891... | [7.09236478805542, -0.9200131893157959] |
321d96af-33f6-474a-a985-f31495b04d1b | weighted-clustering-ensemble-a-review | 1910.02433 | null | https://arxiv.org/abs/1910.02433v2 | https://arxiv.org/pdf/1910.02433v2.pdf | Weighted Clustering Ensemble: A Review | Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving both the robustness and the stability of results from individual clustering methods. Weighted clustering ensemble arises naturally from clustering ensemble. One of the arguments for weighted clustering ensemble is that elements (... | ['Mimi Zhang'] | 2019-10-06 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 2.41889119e-01 -4.64994282e-01 2.25499883e-01 -6.00199759e-01
-6.88257039e-01 -7.60459661e-01 4.38780546e-01 2.20302135e-01
-2.98447281e-01 5.07573783e-01 3.09689313e-01 -2.03195170e-01
-9.02223706e-01 -6.53211415e-01 2.04999104e-01 -1.51584494e+00
-3.06789935e-01 4.09803569e-01 -1.18386880e-01 6.20809160... | [7.605652809143066, 4.555628776550293] |
11322dcf-e42b-4480-9c0e-1739a0ed246c | proceedings-of-the-2nd-workshop-on-logic-and | 2211.09923 | null | https://arxiv.org/abs/2211.09923v1 | https://arxiv.org/pdf/2211.09923v1.pdf | Proceedings of the 2nd Workshop on Logic and Practice of Programming (LPOP) | This proceedings contains abstracts and position papers for the work presented at the second Logic and Practice of Programming (LPOP) Workshop. The workshop was held online, virtually in place of Chicago, USA, on November 15, 2010, in conjunction with the ACM SIGPLAN Conference on Systems, Programming, Languages, and A... | ['Yanhong A. Liu', 'Peter Van Roy', 'David S. Warren'] | 2022-11-17 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [-1.21095195e-01 4.12407130e-01 -1.50659174e-01 -3.19462270e-01
-1.31416962e-01 -8.16472948e-01 5.60726643e-01 6.33365333e-01
-1.18341751e-01 5.32475948e-01 2.12624725e-02 -7.62542188e-01
9.06218216e-02 -8.09210539e-01 -5.40145397e-01 1.92480907e-01
-5.09522200e-01 6.00283146e-02 6.51493371e-01 -3.04879993... | [8.685700416564941, 6.726314067840576] |
a780dced-2ba5-4457-8eca-1bfa1db4ed9b | most-a-multi-oriented-scene-text-detector | 2104.01070 | null | https://arxiv.org/abs/2104.01070v2 | https://arxiv.org/pdf/2104.01070v2.pdf | MOST: A Multi-Oriented Scene Text Detector with Localization Refinement | Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect ratios and varying scales. To tackle such difficulties, we propose in t... | ['Xiang Bai', 'Yongpan Wang', 'Cong Yao', 'Wenqing Cheng', 'Jun Tang', 'Humen Zhong', 'Zhibo Yang', 'Minghui Liao', 'Minghang He'] | 2021-04-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-text-detection'] | ['computer-vision'] | [ 5.69761992e-01 -5.07509887e-01 1.60325050e-01 -2.27769330e-01
-6.88470364e-01 -2.87069291e-01 8.61817360e-01 4.20719266e-01
-6.39935136e-01 2.17606962e-01 -9.74907503e-02 -2.17573628e-01
-1.37105985e-02 -6.39974952e-01 -2.86538512e-01 -8.61410618e-01
4.16771084e-01 3.16063076e-01 9.04742599e-01 -1.99445650... | [12.011011123657227, 2.3204824924468994] |
a18576be-61d9-4810-81cb-645501d6b688 | infinite-action-contextual-bandits-with | 2302.08551 | null | https://arxiv.org/abs/2302.08551v2 | https://arxiv.org/pdf/2302.08551v2.pdf | Infinite Action Contextual Bandits with Reusable Data Exhaust | For infinite action contextual bandits, smoothed regret and reduction to regression results in state-of-the-art online performance with computational cost independent of the action set: unfortunately, the resulting data exhaust does not have well-defined importance-weights. This frustrates the execution of downstream d... | ['Paul Mineiro', 'Yinglun Zhu', 'Mark Rucker'] | 2023-02-16 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.04120561e-01 3.07659477e-01 -6.13001108e-01 -3.43409777e-01
-1.20686221e+00 -8.70809317e-01 1.90393806e-01 1.37031332e-01
-4.95985150e-01 1.28083014e+00 2.52632856e-01 -6.37860775e-01
-9.03306961e-01 -6.02703631e-01 -9.42712188e-01 -7.03129590e-01
-1.52633682e-01 5.62255144e-01 -1.55421898e-01 -5.62247150... | [4.535826683044434, 3.293484926223755] |
326769b9-6de0-4c51-9309-be19fdd55ad0 | neural-face-identification-in-a-2d-wireframe-1 | 2203.04229 | null | https://arxiv.org/abs/2203.04229v1 | https://arxiv.org/pdf/2203.04229v1.pdf | Neural Face Identification in a 2D Wireframe Projection of a Manifold Object | In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs. To reconstruct the 3D models depicted by a single 2D line drawing, an important key is finding the edge loops in the line drawing which correspond to the actual faces of the 3D object. In this paper, we approach ... | ['Zihan Zhou', 'Jia Zheng', 'Kehan Wang'] | 2022-03-08 | neural-face-identification-in-a-2d-wireframe | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Neural_Face_Identification_in_a_2D_Wireframe_Projection_of_a_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Neural_Face_Identification_in_a_2D_Wireframe_Projection_of_a_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.97284418e-01 2.10835859e-01 -2.39827279e-02 -2.45872065e-02
-2.88406700e-01 -7.01721489e-01 4.57070112e-01 -1.62388325e-01
5.13460577e-01 1.61113754e-01 -1.25056699e-01 -4.65024084e-01
-1.93485525e-02 -7.50479877e-01 -5.03070951e-01 -1.81206539e-01
2.25400776e-02 7.86881745e-01 1.74619272e-01 -1.68882668... | [8.686009407043457, -3.500701904296875] |
4285e33d-fb7d-4f3a-a2b8-bcee0894098d | frankmocap-a-monocular-3d-whole-body-pose | 2108.06428 | null | https://arxiv.org/abs/2108.06428v1 | https://arxiv.org/pdf/2108.06428v1.pdf | FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration | Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert of subtle movements of face, hands, and body. In this paper, we present FrankMocap, a fast and accurate whole-body 3D pose estimation system... | ['Hanbyul Joo', 'Takaaki Shiratori', 'Yu Rong'] | 2021-08-13 | null | null | null | null | ['3d-pose-estimation', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision'] | [-4.24078226e-01 -6.06211834e-02 -3.72021049e-02 -1.88114196e-01
-5.67846179e-01 -5.52230895e-01 3.39546531e-01 -4.86825019e-01
-1.71989739e-01 2.78435767e-01 3.15810770e-01 2.91338801e-01
3.43626797e-01 -3.07468772e-01 -6.04020476e-01 -3.14557403e-01
-6.07225895e-02 6.03050590e-01 2.45803714e-01 -1.79041281... | [7.050110816955566, -0.9125545024871826] |
6305a0c8-13cc-4b23-b4c5-19f2f61633e7 | deep-stock-predictions | 2006.04992 | null | https://arxiv.org/abs/2006.04992v1 | https://arxiv.org/pdf/2006.04992v1.pdf | Deep Stock Predictions | Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider the design of a trading strategy that performs portfolio optimization us... | ['Akash Doshi', 'Somnath Rakshit', 'Sina Rafati', 'Puneet Sachdeva', 'Alexander Issa'] | 2020-06-08 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-1.94968387e-01 -8.80196914e-02 -9.68471915e-03 -4.94007826e-01
-4.05502707e-01 -4.09804970e-01 6.24345541e-01 -2.56576657e-01
-5.19113243e-01 5.04551351e-01 8.21665749e-02 -7.77173102e-01
-1.96483806e-01 -9.74778891e-01 -7.12562799e-01 -5.54758430e-01
-1.15707844e-01 4.12011266e-01 1.02752179e-01 -2.69046158... | [4.468695640563965, 4.194979190826416] |
c7dcd470-c9a0-4c5b-b892-ac1b462cb0ba | leveraging-generative-ai-models-for-synthetic | 2305.05247 | null | https://arxiv.org/abs/2305.05247v1 | https://arxiv.org/pdf/2305.05247v1.pdf | Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy | The widespread adoption of electronic health records and digital healthcare data has created a demand for data-driven insights to enhance patient outcomes, diagnostics, and treatments. However, using real patient data presents privacy and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic data g... | ['Shashank Kumar', 'Aryan Jadon'] | 2023-05-09 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.78176814e-01 7.82362759e-01 -1.19477473e-01 -7.05038428e-01
-7.29704320e-01 -4.39133734e-01 1.73263550e-01 3.50393951e-01
-2.45830137e-02 1.05814362e+00 7.56863117e-01 -3.05797011e-01
-1.53106242e-01 -8.76850486e-01 -2.32000098e-01 -6.25656068e-01
1.24573961e-01 8.05690706e-01 -1.05827379e+00 1.62185326... | [6.24810266494751, 6.754917144775391] |
e8d9c8ad-efa8-4561-8570-f1c551f91ef9 | data-efficient-goal-oriented-conversation | 1910.01302 | null | https://arxiv.org/abs/1910.01302v1 | https://arxiv.org/pdf/1910.01302v1.pdf | Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks | Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. Data-driven dialogue system development has to be adapted to meet this requirement --- therefore, reducing the amount of data and annotations necessar... | ['Arash Eshghi', 'Igor Shalyminov', 'Sungjin Lee', 'Oliver Lemon'] | 2019-10-03 | data-efficient-goal-oriented-conversation-1 | https://aclanthology.org/D19-1183 | https://aclanthology.org/D19-1183.pdf | ijcnlp-2019-11 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.33768424e-01 1.00518644e+00 9.29601565e-02 -3.84770453e-01
-9.19191062e-01 -5.01174748e-01 9.35291529e-01 7.42373690e-02
-3.66328359e-01 1.26296580e+00 4.09797579e-01 -2.59300381e-01
1.06707819e-01 -9.07336593e-01 -1.71000779e-01 -3.05039167e-01
1.26417980e-01 1.13047349e+00 4.18750077e-01 -1.18064606... | [12.723152160644531, 7.965518951416016] |
c7cd3382-ff9b-40f8-bc22-d924c00f8917 | nvidia-unibz-submission-for-epic-kitchens-100 | 2206.10869 | null | https://arxiv.org/abs/2206.10869v1 | https://arxiv.org/pdf/2206.10869v1.pdf | NVIDIA-UNIBZ Submission for EPIC-KITCHENS-100 Action Anticipation Challenge 2022 | In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge. Our modelings, the higher-order recurrent space-time transformer and the message-passing neural network with edge learning, are both recurrent-based architectures which observe only 2.5 seconds in... | ['Simon See', 'Ka-Chun Cheung', 'Cheng-Kuang Lee', 'Sze-Sen Poon', 'Yi-Kwan Wong', 'Giuseppe Fiameni', 'Oswald Lanz', 'Tsung-Ming Tai'] | 2022-06-22 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.64442730e-01 4.09567326e-01 -3.95837665e-01 -8.84081796e-02
-1.14111710e+00 -2.16216192e-01 8.93486857e-01 2.48386189e-02
-3.11748534e-01 5.95592618e-01 8.10695112e-01 -5.62613487e-01
6.50970042e-02 -3.75550240e-01 -8.09928954e-01 -2.91724265e-01
-5.21494150e-01 4.50080872e-01 2.43893296e-01 -3.03864986... | [8.075424194335938, 0.5675957798957825] |
15f4b808-930f-45a6-a76a-9341268541e1 | illinois-cross-lingual-wikifier-grounding | null | null | https://aclanthology.org/C16-2031 | https://aclanthology.org/C16-2031.pdf | Illinois Cross-Lingual Wikifier: Grounding Entities in Many Languages to the English Wikipedia | We release a cross-lingual wikification system for all languages in Wikipedia. Given a piece of text in any supported language, the system identifies names of people, locations, organizations, and grounds these names to the corresponding English Wikipedia entries. The system is based on two components: a cross-lingual ... | ['Chen-Tse Tsai', 'Dan Roth'] | 2016-12-01 | illinois-cross-lingual-wikifier-grounding-1 | https://aclanthology.org/C16-2031 | https://aclanthology.org/C16-2031.pdf | coling-2016-12 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-7.88317382e-01 2.79441297e-01 -4.49871153e-01 -2.86474556e-01
-1.02324855e+00 -8.03928137e-01 6.30965531e-01 4.15632367e-01
-8.41028452e-01 1.12617254e+00 5.64033210e-01 -2.57555932e-01
2.35353500e-01 -1.09678185e+00 -6.74821019e-01 -1.13834878e-02
1.16541728e-01 5.29767394e-01 2.68119156e-01 -4.09485459... | [9.712125778198242, 9.50732421875] |
c68c43ba-5306-4944-9d5e-e18d7b9d126b | a-new-comprehensive-benchmark-for-semi-1 | 2305.13611 | null | https://arxiv.org/abs/2305.13611v1 | https://arxiv.org/pdf/2305.13611v1.pdf | A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation | Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task fo... | ['Yanning Zhang', 'Peng Wang', 'Yue Lu', 'Congqi Cao'] | 2023-05-23 | a-new-comprehensive-benchmark-for-semi | http://openaccess.thecvf.com//content/CVPR2023/html/Cao_A_New_Comprehensive_Benchmark_for_Semi-Supervised_Video_Anomaly_Detection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cao_A_New_Comprehensive_Benchmark_for_Semi-Supervised_Video_Anomaly_Detection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [-8.39001909e-02 -5.53364933e-01 1.07082695e-01 -1.87898025e-01
-1.03729896e-01 -2.98538119e-01 4.66110080e-01 1.58075213e-01
-9.34797823e-02 3.27778190e-01 4.44557471e-03 -3.95016223e-01
6.19911142e-02 -5.26444912e-01 -6.29059732e-01 -8.04242671e-01
-3.37858617e-01 1.01475380e-01 4.87376422e-01 -2.98068643... | [7.839961528778076, 1.5766037702560425] |
35f12f90-03a4-44d9-a495-950528649394 | syllable-based-sequence-to-sequence-speech | 1804.10752 | null | http://arxiv.org/abs/1804.10752v2 | http://arxiv.org/pdf/1804.10752v2.pdf | Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese | Sequence-to-sequence attention-based models have recently shown very
promising results on automatic speech recognition (ASR) tasks, which integrate
an acoustic, pronunciation and language model into a single neural network. In
these models, the Transformer, a new sequence-to-sequence attention-based model
relying entir... | ['Bo Xu', 'Shuang Xu', 'Shiyu Zhou', 'Linhao Dong'] | 2018-04-28 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 4.68201727e-01 -1.06011666e-01 -3.05313990e-02 -2.45785251e-01
-1.24247169e+00 -2.07737967e-01 4.83491331e-01 -5.04569232e-01
-5.55627823e-01 6.31115913e-01 2.19633833e-01 -1.05007148e+00
7.06851840e-01 -2.05922008e-01 -8.58175159e-01 -7.48400509e-01
4.38703626e-01 4.96619523e-01 -1.18535221e-01 -3.59883398... | [14.443045616149902, 6.966318607330322] |
3f98b582-4a46-4c5b-b1bb-2dfa48273c75 | informing-unsupervised-pretraining-with | 1909.02339 | null | https://arxiv.org/abs/1909.02339v2 | https://arxiv.org/pdf/1909.02339v2.pdf | Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity | Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through languag... | ['Goran Glavaš', 'Edoardo Maria Ponti', 'Ivan Vulić', 'Anne Lauscher', 'Anna Korhonen'] | 2019-09-05 | null | https://aclanthology.org/2020.coling-main.118 | https://aclanthology.org/2020.coling-main.118.pdf | coling-2020-8 | ['lexical-simplification'] | ['natural-language-processing'] | [ 2.44687200e-01 3.51635695e-01 -5.19055486e-01 -4.93811250e-01
-7.60487378e-01 -4.96060431e-01 5.23634315e-01 7.60500133e-01
-9.91332769e-01 6.26642764e-01 5.77662587e-01 -5.51972926e-01
-1.68498710e-01 -7.78142333e-01 -6.18713796e-01 -5.03021955e-01
1.85038224e-01 7.33208418e-01 -7.07708150e-02 -6.68415129... | [10.721789360046387, 8.916720390319824] |
a16a1ec1-c4d6-4b59-938e-ba104bd8b855 | wuyun-exploring-hierarchical-skeleton-guided | 2301.04488 | null | https://arxiv.org/abs/2301.04488v2 | https://arxiv.org/pdf/2301.04488v2.pdf | WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning | Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated ... | ['Lingyun Sun', 'Songruoyao Wu', 'Qihao Liang', 'Xu Tan', 'Zhijie Huang', 'Tieyao Zhang', 'Xinda Wu', 'Kejun Zhang'] | 2023-01-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.49239177e-01 -1.32477239e-01 2.87716240e-02 1.93576366e-01
-1.03825223e+00 -9.74276781e-01 2.03772172e-01 -3.18300724e-01
1.32511988e-01 8.54030669e-01 7.17143059e-01 1.07465848e-01
-3.15403640e-01 -8.29569519e-01 -4.77558702e-01 -6.58684254e-01
2.03424394e-01 3.90768111e-01 -2.66734332e-01 -6.50457203... | [16.047391891479492, 5.5526227951049805] |
6e44391d-1b81-45de-a00b-4df9a1bff6f0 | han-ecg-an-interpretable-atrial-fibrillation | 2002.05262 | null | https://arxiv.org/abs/2002.05262v1 | https://arxiv.org/pdf/2002.05262v1.pdf | HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks | Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of more than 3 million people in the U.S. and over 33 million people around the world and is associated with a five-fold increased risk of stroke and mortality. like other problems in healthcare domain, artificial intellige... | ['U. Rajendra Acharya', 'Fatemeh Afghah', 'Sajad Mousavi'] | 2020-02-12 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 2.95031995e-01 -5.90221770e-02 4.00551967e-02 -9.95863155e-02
-4.91076142e-01 -2.34011665e-01 -9.41813886e-02 1.79047719e-01
8.81398842e-02 7.91437984e-01 3.80523086e-01 -5.06512821e-01
-3.46766084e-01 -5.97764790e-01 -4.66083316e-03 -7.14279234e-01
-3.40250939e-01 1.45551980e-01 -2.26523787e-01 -2.10944843... | [14.316999435424805, 3.2947115898132324] |
dade9dee-3afd-4494-91ac-d4c6c392bd2c | iranian-modal-music-dastgah-detection-using | 2203.15335 | null | https://arxiv.org/abs/2203.15335v3 | https://arxiv.org/pdf/2203.15335v3.pdf | Iranian Modal Music (Dastgah) detection using deep neural networks | Music classification and genre detection are topics in music information retrieval (MIR) that many articles have been published regarding their utilities in the modern world. However, this contribution is insufficient in non-western music, such as Iranian modal music. In this work, we have implemented several deep neur... | ['Milad Dadgar', 'Farzad Didehvar', 'Danial Ebrat'] | 2022-03-29 | null | null | null | null | ['music-classification', 'music-information-retrieval'] | ['music', 'music'] | [-2.98764348e-01 -5.69044709e-01 7.85325542e-02 3.12873274e-01
-5.42350054e-01 -5.53937495e-01 4.10108954e-01 -3.40391189e-01
-3.10512483e-01 3.77446651e-01 5.45868635e-01 -2.50717886e-02
-6.27463579e-01 -7.21119642e-01 -2.73535609e-01 -6.56452417e-01
-3.13151032e-01 3.53867888e-01 -3.98006022e-01 -5.63657939... | [15.896685600280762, 5.182823181152344] |
b0281f7d-3c0b-4111-b4eb-bed931847434 | cpmlho-hyperparameter-tuning-via-cutting | 2212.06150 | null | https://arxiv.org/abs/2212.06150v1 | https://arxiv.org/pdf/2212.06150v1.pdf | CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization | The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function. Finding the best response function is... | ['Chen Zhu', 'Mana Zheng', 'Shaoyu Dou', 'Yang Jiao', 'Shuo Yang'] | 2022-12-11 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-5.05992651e-01 -1.75571725e-01 -3.44357789e-01 -4.72153455e-01
-3.12226802e-01 -2.70497829e-01 -3.27153862e-01 -4.30404574e-01
-6.98994398e-01 8.25996578e-01 -1.75318584e-01 -9.15327147e-02
-5.36835134e-01 -8.42710018e-01 -5.35537302e-01 -1.01791668e+00
3.34767193e-01 4.28953052e-01 3.34331989e-01 -1.67583480... | [6.872664928436279, 4.009604454040527] |
abebd6b6-ba8c-4a31-9be8-ba610e058a24 | on-estimation-and-inference-of-large | 2211.01921 | null | https://arxiv.org/abs/2211.01921v2 | https://arxiv.org/pdf/2211.01921v2.pdf | On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis and its equivalence with Quasi Maximum Likelihood estimation | We provide an alternative derivation of the asymptotic results for the Principal Components estimator of a large approximate factor model and we prove that the derived estimator of the loadings is asymptotically equivalent to their Quasi Maximum Likelihood estimator. This result holds regardless of the specific second ... | ['Matteo Barigozzi'] | 2022-11-03 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.88005358e-01 -1.24481127e-01 -6.38441667e-02 5.98571226e-02
-3.34485352e-01 -8.26441467e-01 3.88409227e-01 -2.47403830e-01
-4.78167892e-01 5.77963650e-01 8.19228441e-02 -6.05762064e-01
-8.06252301e-01 -6.95735872e-01 -6.39998198e-01 -8.02506387e-01
-2.44867533e-01 1.52527899e-01 -1.91504940e-01 -5.36934957... | [6.4563889503479, 4.159118175506592] |
a4c14d53-da09-419f-948b-8b800ca634a7 | explaining-the-performance-of-collaborative | 2303.11172 | null | https://arxiv.org/abs/2303.11172v1 | https://arxiv.org/pdf/2303.11172v1.pdf | Explaining the Performance of Collaborative Filtering Methods With Optimal Data Characteristics | The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the cha... | ['Marwan Bikdash', 'Samin Poudel'] | 2023-03-17 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-2.62686461e-01 -5.90467632e-01 -2.80977219e-01 -4.44798559e-01
-1.99711561e-01 -7.29789674e-01 6.21756673e-01 2.67912507e-01
-4.93456095e-01 2.12883383e-01 6.71361566e-01 -1.53196409e-01
-7.73603499e-01 -9.47113037e-01 -4.06778336e-01 -3.62964123e-01
-3.49084198e-01 2.42435634e-01 2.55401045e-01 -4.14828688... | [10.026398658752441, 5.731016635894775] |
5b145131-e3b6-41eb-ab7f-da4ab0cc9a52 | weakly-supervised-contrastive-learning-1 | 2110.04770 | null | https://arxiv.org/abs/2110.04770v1 | https://arxiv.org/pdf/2110.04770v1.pdf | Weakly Supervised Contrastive Learning | Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent achievement of contrastive learning. Most of the existing contrastive learning frameworks adopt the instance discrimination as the pretext task, which treating every single instance as a differ... | ['Chang Xu', 'Xiaogang Wang', 'ChangShui Zhang', 'Chen Qian', 'Shan You', 'Fei Wang', 'Mingkai Zheng'] | 2021-10-10 | weakly-supervised-contrastive-learning | http://openaccess.thecvf.com//content/ICCV2021/html/Zheng_Weakly_Supervised_Contrastive_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zheng_Weakly_Supervised_Contrastive_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification', 'semi-supervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.33198535e-01 9.80469659e-02 -4.88206565e-01 -2.08943784e-01
-4.90003884e-01 -2.80490220e-01 7.48947859e-01 2.73338616e-01
-4.18897092e-01 6.37425005e-01 -2.35979501e-02 -6.75157532e-02
-1.37430608e-01 -8.90467465e-01 -6.76291943e-01 -9.85069215e-01
1.38026699e-01 2.26865381e-01 4.10122067e-01 -5.91166504... | [9.516084671020508, 2.761770486831665] |
5db2cbbb-31bc-44aa-93c7-1b534e893a26 | approximating-dtw-with-a-convolutional-neural | 2301.12873 | null | https://arxiv.org/abs/2301.12873v1 | https://arxiv.org/pdf/2301.12873v1.pdf | Approximating DTW with a convolutional neural network on EEG data | Dynamic Time Wrapping (DTW) is a widely used algorithm for measuring similarities between two time series. It is especially valuable in a wide variety of applications, such as clustering, anomaly detection, classification, or video segmentation, where the time-series have different timescales, are irregularly sampled, ... | ['Laurent Heutte', 'Alain Rakotomamonjy', 'Romain Picot-Clemente', 'Hugo Lerogeron'] | 2023-01-30 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 7.56343231e-02 -2.12346137e-01 1.00066036e-01 -3.32969069e-01
-6.07073665e-01 -6.27143383e-01 5.76875210e-01 4.30092484e-01
-6.15821600e-01 4.61660802e-01 -5.07446192e-02 -2.09987283e-01
-6.25942111e-01 -5.23203135e-01 -6.05467558e-01 -9.42650378e-01
-5.77685058e-01 3.42732459e-01 2.31164679e-01 -1.47475615... | [7.401034832000732, 3.3485796451568604] |
ca6e9db6-36bb-4a0b-bd30-3f3581d3ce44 | edbb-demo-biometrics-and-behavior-analysis | 2211.09210 | null | https://arxiv.org/abs/2211.09210v2 | https://arxiv.org/pdf/2211.09210v2.pdf | edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms | We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education. The edBB platform aims to study the challenges associated to user recognition and behavior understanding in digital platforms. This platform has been developed for data collection, acquiring signals from ... | ['Javier Ortega-Garcia', 'Julian Fierrez', 'Luis F. Gomez', 'Ruben Tolosana', 'Aythami Morales', 'Roberto Daza'] | 2022-11-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 5.50167225e-02 -1.35455355e-01 -4.79945773e-03 -2.48617426e-01
-1.70924485e-01 -3.92472595e-01 1.87560678e-01 4.67644364e-01
-3.72861505e-01 6.04620397e-01 -1.58052072e-01 -2.50313431e-01
-3.57880712e-01 -3.60941559e-01 -3.59974891e-01 -6.46424294e-01
1.56281367e-01 -1.64078012e-01 -2.42156461e-01 -2.03836337... | [13.49421215057373, 2.694260835647583] |
dd85b7cc-c9b3-4df6-96f4-a026a5821817 | boxvis-video-instance-segmentation-with-box | 2303.14618 | null | https://arxiv.org/abs/2303.14618v1 | https://arxiv.org/pdf/2303.14618v1.pdf | BoxVIS: Video Instance Segmentation with Box Annotations | It is expensive and labour-extensive to label the pixel-wise object masks in a video. As a results, the amount of pixel-wise annotations in existing video instance segmentation (VIS) datasets is small, limiting the generalization capability of trained VIS models. An alternative but much cheaper solution is to use bound... | ['Lei Zhang', 'Minghan Li'] | 2023-03-26 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.38545293e-01 1.29118413e-01 -5.81713915e-01 -5.22469640e-01
-1.00618124e+00 -5.08369207e-01 3.82047832e-01 -8.72599781e-02
-5.48717618e-01 6.15746796e-01 -3.65480602e-01 -1.13628760e-01
2.35923082e-01 -5.12555718e-01 -1.08545530e+00 -5.56594074e-01
4.57265321e-03 2.55634785e-01 7.94817209e-01 1.84405148... | [9.273626327514648, 0.06983296573162079] |
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