paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
157ca05b-42e5-40fb-ace2-e6b8f1fd8ec5 | constrained-deep-one-class-feature-learning | 2111.10610 | null | https://arxiv.org/abs/2111.10610v2 | https://arxiv.org/pdf/2111.10610v2.pdf | Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images | Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguishing the samples of the minority class from the majority class. Previous methods generally aim to either learn a new feature space to map tra... | ['Shandong Wu', 'Ashok Panigrahy', 'Dooman Arefan', 'Chang Liu', 'Long Gao'] | 2021-11-20 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.50951618e-01 2.36763015e-01 -3.90026122e-01 -7.06985176e-01
-5.06165862e-01 4.11441416e-01 1.44769698e-01 5.04335701e-01
-3.76257271e-01 5.55957675e-01 2.00041667e-01 2.21775532e-01
-3.48680705e-01 -8.27655315e-01 -4.79275972e-01 -1.05680728e+00
-3.07108928e-02 5.70549726e-01 -1.34040624e-01 6.53924122... | [14.865418434143066, -2.205909490585327] |
24faf5f5-9e99-46a0-8d4a-d9c6a22dfc97 | grasmos-graph-signage-model-selection-for | 2211.09642 | null | https://arxiv.org/abs/2211.09642v1 | https://arxiv.org/pdf/2211.09642v1.pdf | GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks | Signed networks, i.e., networks with positive and negative edges, commonly arise in various domains from social media to epidemiology. Modeling signed networks has many practical applications, including the creation of synthetic data sets for experiments where obtaining real data is difficult. Influential prior works p... | ['Ivona Bezáková', 'Hannah Miller', 'Angelina Brilliantova'] | 2022-11-17 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.69446683e-01 2.22915441e-01 -3.02180469e-01 -4.01346087e-01
1.64577112e-01 -4.98143554e-01 3.26542050e-01 2.15533078e-01
1.12401612e-01 1.16493547e+00 -1.58645421e-01 -4.71969008e-01
-7.29171872e-01 -1.06983984e+00 -7.87761748e-01 -5.40350437e-01
-6.93654478e-01 5.41421473e-01 2.18911543e-01 -2.57957995... | [6.734096050262451, 5.397862434387207] |
713cff69-72d9-4912-8aa5-188c420db110 | theme-matters-fashion-compatibility-learning | 1912.06227 | null | https://arxiv.org/abs/1912.06227v3 | https://arxiv.org/pdf/1912.06227v3.pdf | Theme-Matters: Fashion Compatibility Learning via Theme Attention | Fashion compatibility learning is important to many fashion markets such as outfit composition and online fashion recommendation. Unlike previous work, we argue that fashion compatibility is not only a visual appearance compatible problem but also a theme-matters problem. An outfit, which consists of a set of fashion i... | ['Jingen Liu', 'Jui-Hsin Lai', 'Dan Zeng', 'Bo Wu', 'Xin Wang', 'Tao Mei'] | 2019-12-12 | null | null | null | null | ['fashion-compatibility-learning'] | ['computer-vision'] | [-2.50020027e-01 -4.32791442e-01 -2.16537729e-01 -6.92639470e-01
-3.95794541e-01 -6.69770598e-01 4.64653254e-01 4.58132476e-02
2.69370601e-02 1.35315403e-01 5.81273437e-01 6.96594417e-02
-2.45971814e-01 -5.81407845e-01 -9.00051355e-01 -4.04829085e-01
2.30829671e-01 3.38836581e-01 -3.38559628e-01 -3.65036845... | [11.032374382019043, 0.15718328952789307] |
901b1889-b385-4493-b7f4-7ca6edd71423 | radio-sensing-with-large-intelligent-surface | 2111.02783 | null | https://arxiv.org/abs/2111.02783v2 | https://arxiv.org/pdf/2111.02783v2.pdf | Radio Sensing with Large Intelligent Surface for 6G | This paper leverages the potential of Large Intelligent Surface (LIS) for radio sensing in 6G wireless networks. Major research has been undergone about its communication capabilities but it can be exploited as a formidable tool for radio sensing. By taking advantage of arbitrary communication signals occurring in the ... | ['Elisabeth de Carvalho', 'Zheng-Hua Tan', 'Kimmo Kansanen', 'Pablo Ramirez-Espinosa', 'Cristian J. Vaca-Rubio'] | 2021-11-04 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 9.52441454e-01 4.89828706e-01 3.13017935e-01 9.65398327e-02
-6.67619705e-01 -4.78750229e-01 5.09263575e-01 -2.03311294e-01
-7.67374709e-02 3.79707158e-01 3.60577293e-02 -4.86410737e-01
-1.82216272e-01 -8.64170849e-01 -1.38291851e-01 -8.69473338e-01
-4.89339322e-01 4.71179098e-01 4.83281501e-02 -8.22887197... | [6.452203273773193, 0.9977160692214966] |
e16cb8c3-b0e9-48b0-8bed-c3085e41a85f | ganseg-learning-to-segment-by-unsupervised | 2112.01036 | null | https://arxiv.org/abs/2112.01036v3 | https://arxiv.org/pdf/2112.01036v3.pdf | GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation | Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods exist, but they depend on the comparison of pairs of images, such as from multi-views, frames of video... | ['Helge Rhodin', 'Bastian Wandt', 'Xingzhe He'] | 2021-12-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-facial-landmark-detection', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 8.86980116e-01 3.74089032e-01 -2.50551373e-01 -4.38582808e-01
-6.30519688e-01 -7.98651755e-01 6.99228883e-01 5.11298887e-02
-5.08234620e-01 6.05904877e-01 -1.49940148e-01 1.81782227e-02
3.67405325e-01 -7.45264471e-01 -1.07287419e+00 -7.17350960e-01
4.45012420e-01 5.77007711e-01 6.07895911e-01 -7.33504258... | [9.790677070617676, 0.3559904098510742] |
9156a52a-9f47-4938-b589-76df48e73596 | partial-label-learning-with-mixed-closed-set | 2307.00553 | null | https://arxiv.org/abs/2307.00553v1 | https://arxiv.org/pdf/2307.00553v1.pdf | Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples | Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and thus the true label of some collected examples could be unexpectedly outside the assigned candidate la... | ['Guowu Yang', 'Lei Feng', 'Shuo He'] | 2023-07-02 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.60458040e-01 4.19413537e-01 -5.27871609e-01 -4.86934662e-01
-9.28900361e-01 -7.59180248e-01 2.29941368e-01 3.33279312e-01
-3.31837177e-01 7.96133041e-01 -4.21108037e-01 -1.62079334e-01
-1.58214495e-01 -5.23388803e-01 -4.83779579e-01 -9.81088042e-01
-1.81424897e-02 5.63751936e-01 2.60660082e-01 3.43016118... | [9.46234130859375, 4.037433624267578] |
2ea4bda0-3d00-4d17-8cfa-4e1e766d0deb | image-models-for-large-scale-object-detection | null | null | https://aclanthology.org/2022.clib-1.22 | https://aclanthology.org/2022.clib-1.22.pdf | Image Models for large-scale Object Detection and Classification | Recent developments in computer vision applications that are based on machine learning models allow real-time object detection, segmentation and captioning in image or video streams. The paper presents the development of an extension of the 80 COCO categories into a novel ontology with more than 700 classes covering 13... | ['Svetla Koeva', 'Jordan Kralev'] | null | null | null | null | clib-2022-9 | ['real-time-object-detection'] | ['computer-vision'] | [ 2.98222363e-01 2.35087439e-01 2.20672414e-02 -5.72008550e-01
-5.55815697e-01 -7.52061069e-01 8.15152168e-01 5.62100351e-01
-7.43529022e-01 4.19829100e-01 -9.30318087e-02 -1.62436187e-01
8.71347543e-03 -7.49915659e-01 -6.66919231e-01 -1.53274924e-01
-8.86450615e-03 9.65699077e-01 8.32090318e-01 -1.10881738... | [9.538166046142578, 0.3916575014591217] |
9e93392e-418b-4b04-b441-9cc63f04f59f | a-dual-semantic-aware-recurrent-global | 2305.03602 | null | https://arxiv.org/abs/2305.03602v2 | https://arxiv.org/pdf/2305.03602v2.pdf | A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation | Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semanti... | ['Qijun Chen', 'Chengju Liu', 'Naijia Wang', 'Ronghao Dang', 'Jiagui Tang', 'Zongtao He', 'Liuyi Wang'] | 2023-05-05 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [ 1.22159012e-02 -7.56727606e-02 -3.61900359e-01 -4.73990858e-01
-3.56568635e-01 -4.50496078e-02 8.82380664e-01 -2.25127444e-01
-4.57429379e-01 3.96370202e-01 2.67753929e-01 -2.52805114e-01
-8.69577825e-02 -7.48195231e-01 -6.30233347e-01 -8.85607719e-01
8.43762457e-02 1.39401227e-01 7.21557915e-01 -4.32500541... | [4.49377965927124, 0.45354413986206055] |
fdb65d7e-66d8-4667-86f2-148147e187c6 | arabic-word-level-readability-visualization | 2210.10672 | null | https://arxiv.org/abs/2210.10672v1 | https://arxiv.org/pdf/2210.10672v1.pdf | Arabic Word-level Readability Visualization for Assisted Text Simplification | This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization. The add-on includes a lemmatization component that is connected to a five-level readability lexicon and Arabic WordNet-based substitution suggestions. The add-on can be used for assessing the reading difficulty of a... | ['Nizar Habash', 'Muhamed Al Khalil', 'Bashar Alhafni', 'Hind Saddiki', 'Reem Hazim'] | 2022-10-19 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-1.04625165e-01 5.10575175e-01 1.82618856e-01 -1.46578461e-01
-8.26204419e-01 -6.57621086e-01 3.63349020e-01 9.62339282e-01
-2.53414482e-01 3.28120023e-01 7.22988188e-01 -9.41440403e-01
-3.64950031e-01 -7.33213723e-01 -1.08450532e-01 7.37866983e-02
4.74004447e-01 5.18895149e-01 -1.01979256e-01 -8.93220127... | [10.828700065612793, 10.332642555236816] |
385cfe8a-6e81-4d5d-8427-3c46ca8b9c28 | 3d-lidar-and-stereo-fusion-using-stereo | 1904.02917 | null | http://arxiv.org/abs/1904.02917v1 | http://arxiv.org/pdf/1904.02917v1.pdf | 3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization | The complementary characteristics of active and passive depth sensing
techniques motivate the fusion of the Li-DAR sensor and stereo camera for
improved depth perception. Instead of directly fusing estimated depths across
LiDAR and stereo modalities, we take advantages of the stereo matching network
with two enhanced t... | ['Wei-Chen Chiu', 'Yi-Hsuan Tsai', 'Hou-Ning Hu', 'Tsun-Hsuan Wang', 'Min Sun', 'Chieh Hubert Lin'] | 2019-04-05 | null | null | null | null | ['stereo-matching', 'stereo-lidar-fusion'] | ['computer-vision', 'computer-vision'] | [ 4.38009292e-01 -7.09510669e-02 1.63209029e-02 -6.38533533e-01
-9.30783749e-01 -2.91258693e-01 6.91410184e-01 6.88180700e-02
-7.87007809e-01 6.40921474e-01 1.26105189e-01 -1.24536306e-01
-1.01415128e-01 -9.75541353e-01 -6.44331217e-01 -6.44288778e-01
3.89085472e-01 1.79847822e-01 2.35634655e-01 1.21747598... | [8.510809898376465, -2.605030059814453] |
56e0c9a5-d607-4013-b510-5c491fed8930 | model-based-single-image-deep-dehazing | 2111.10943 | null | https://arxiv.org/abs/2111.10943v3 | https://arxiv.org/pdf/2111.10943v3.pdf | Model-Based Single Image Deep Dehazing | Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values. Data-driven ones restore images with high PSNR values but with low contrast, and even some remaining haze. In this paper, a novel single image dehazing algorithm is introduced by fusing model... | ['Shiqian Wu', 'Haiyan Shu', 'Chaobing Zheng', 'Zhengguo Li'] | 2021-11-22 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 4.88881350e-01 -4.61191237e-01 5.17070234e-01 -1.30662695e-01
-2.73616880e-01 5.46737351e-02 6.54776096e-01 -2.46390641e-01
-3.57866138e-01 8.08955371e-01 1.07374154e-01 2.96426024e-02
-1.13014981e-01 -1.09606421e+00 -5.85742235e-01 -1.42126191e+00
-5.72016602e-03 -1.77507490e-01 4.13411915e-01 -6.74918354... | [10.900113105773926, -3.162824869155884] |
286c10da-6c49-40f5-ada1-41668478920b | change-aware-sampling-and-contrastive | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.pdf | Change-Aware Sampling and Contrastive Learning for Satellite Images | Automatic remote sensing tools can help inform many large-scale challenges such as disaster management, climate change, etc. While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled. Without labels, this data is not very useful for supervised learning algorithm... | ['Kavita Bala', 'Bharath Hariharan', 'Utkarsh Mall'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['change-detection'] | ['computer-vision'] | [ 3.66169453e-01 -2.78437763e-01 -2.46823564e-01 -8.17883015e-01
-1.08636534e+00 -5.88472068e-01 7.35596657e-01 4.04044807e-01
-6.36562884e-01 9.25641537e-01 1.71235800e-01 -1.66776866e-01
-3.56788076e-02 -9.52131391e-01 -7.11889505e-01 -8.96734595e-01
-3.92077923e-01 4.56872629e-03 2.39878535e-01 -2.50944525... | [9.646618843078613, -1.363355040550232] |
537ed428-2a2c-4441-af26-944f11899753 | agentgraph-towards-universal-dialogue | 1905.11259 | null | https://arxiv.org/abs/1905.11259v1 | https://arxiv.org/pdf/1905.11259v1.pdf | AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning | Dialogue policy plays an important role in task-oriented spoken dialogue systems. It determines how to respond to users. The recently proposed deep reinforcement learning (DRL) approaches have been used for policy optimization. However, these deep models are still challenging for two reasons: 1) Many DRL-based policies... | ['Bowen Tan', 'Zhi Chen', 'Kai Yu', 'Sishan Long', 'Milica Gasic', 'Lu Chen'] | 2019-05-27 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.77106875e-01 1.35384306e-01 -1.84246078e-01 -1.20939866e-01
-2.98522890e-01 -4.57511157e-01 9.48647976e-01 1.37716413e-01
-6.99811637e-01 1.09601748e+00 3.77806783e-01 -2.32062548e-01
-8.98090377e-02 -8.15551400e-01 -2.03802481e-01 -7.51735866e-01
1.12395100e-01 1.23077166e+00 4.14016962e-01 -8.47854078... | [13.073108673095703, 8.062850952148438] |
d7fff58a-f3e1-4ece-88c6-4a15a2d01506 | solving-qsat-problems-with-neural-mcts | 2101.06619 | null | https://arxiv.org/abs/2101.06619v1 | https://arxiv.org/pdf/2101.06619v1.pdf | Solving QSAT problems with neural MCTS | Recent achievements from AlphaZero using self-play has shown remarkable performance on several board games. It is plausible to think that self-play, starting from zero knowledge, can gradually approximate a winning strategy for certain two-player games after an amount of training. In this paper, we try to leverage the ... | ['Karl Lieberherr', 'Ruiyang Xu'] | 2021-01-17 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.34718150e-01 6.64498568e-01 -9.83790960e-03 2.72763539e-02
-1.04615963e+00 -7.62146533e-01 -1.58502355e-01 3.61167565e-02
-1.26222223e-01 1.13798606e+00 -3.71054590e-01 -7.93193281e-01
-2.63934165e-01 -1.78340566e+00 -1.18944728e+00 -4.40619558e-01
-1.65778771e-01 8.51555526e-01 6.80173695e-01 -4.96254414... | [8.914429664611816, 7.052068710327148] |
beadac75-d6e8-44c0-a6fe-3a7347c11db7 | ontology-aware-learning-and-evaluation-for | 2211.12195 | null | https://arxiv.org/abs/2211.12195v1 | https://arxiv.org/pdf/2211.12195v1.pdf | Ontology-aware Learning and Evaluation for Audio Tagging | This study defines a new evaluation metric for audio tagging tasks to overcome the limitation of the conventional mean average precision (mAP) metric, which treats different kinds of sound as independent classes without considering their relations. Also, due to the ambiguities in sound labeling, the labels in the train... | ['Mark D. Plumbley', 'Wenwu Wang', 'Xinhao Mei', 'Xubo Liu', 'Qiuqiang Kong', 'Haohe Liu'] | 2022-11-22 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 1.59885481e-01 -1.27591668e-02 -3.55875604e-02 -5.01010418e-01
-7.84882307e-01 -5.01698256e-01 8.13527480e-02 6.14668310e-01
-5.60903609e-01 3.36714476e-01 2.15140164e-01 1.94300011e-01
-6.71685815e-01 -8.14521253e-01 -4.97886568e-01 -3.98870081e-01
-3.89251888e-01 1.64902925e-01 6.28038466e-01 3.62812802... | [15.285205841064453, 5.135251522064209] |
f54e76dc-9a90-45c1-867b-d7044dbf0bdd | dual-adversarial-neural-transfer-for-low | null | null | https://aclanthology.org/P19-1336 | https://aclanthology.org/P19-1336.pdf | Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition | We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the nois... | ['Rick Siow Mong Goh', 'Meng Fang', 'Hao Zhang', 'Joey Tianyi Zhou', 'Hongyuan Zhu', 'Kenneth Kwok', 'Di Jin'] | 2019-07-01 | null | null | null | acl-2019-7 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-4.78040278e-02 -2.47708112e-01 -2.52763350e-02 -5.00872850e-01
-9.96642351e-01 -6.62852585e-01 6.02348506e-01 -1.13166936e-01
-8.31586242e-01 1.16657400e+00 1.74760029e-01 -2.06010640e-01
2.14112222e-01 -8.70657206e-01 -5.47993243e-01 -3.19297463e-01
2.01556161e-01 1.58362553e-01 -1.25137657e-01 -4.04194415... | [9.907917022705078, 9.572428703308105] |
f5090954-b50f-44a1-8822-840b6ff057e0 | improving-covid-19-ct-classification-of-cnns | 2208.04718 | null | https://arxiv.org/abs/2208.04718v1 | https://arxiv.org/pdf/2208.04718v1.pdf | Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation | COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis b... | ['Xinqi Bao', 'Junkai Liao', 'Jian Jiang', 'Guangyu Jia', 'Hak-Keung Lam', 'Yujia Xu'] | 2022-08-09 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 7.70127699e-02 -5.21285832e-01 -2.37808838e-01 -1.87189728e-01
-5.38666070e-01 -1.05274186e-01 2.27971539e-01 -5.02627939e-02
-6.31073594e-01 7.75899053e-01 -2.65716668e-02 -3.82006496e-01
-9.88315493e-02 -5.70898592e-01 -2.83041686e-01 -8.62361073e-01
-1.93981484e-01 6.74323380e-01 1.90752015e-01 1.03319176... | [15.527352333068848, -1.7611263990402222] |
61a09884-35ef-4628-8a3e-a3ea400ce543 | nine-challenges-in-modern-algorithmic-trading | 2101.08813 | null | https://arxiv.org/abs/2101.08813v1 | https://arxiv.org/pdf/2101.08813v1.pdf | Nine Challenges in Modern Algorithmic Trading and Controls | This editorial article partially informs the algorithmic trading community about launching of the new journal "Algorithmic Trading and Controls" (ATC). ATC is an online open-access journal that publishes novel works on algorithmic trading and its control methodologies. In this inaugural article, we discuss nine major c... | ['Jackie Shen'] | 2021-01-21 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-2.45312467e-01 2.74117310e-02 -1.02913208e-01 1.10870056e-01
-1.65544629e-01 -1.09246981e+00 6.00064993e-01 -6.29047081e-02
-3.71253133e-01 7.79546022e-01 -6.62539825e-02 -9.52944160e-01
-3.96416247e-01 -6.66296363e-01 -3.26292962e-01 -6.19607449e-01
-8.74535143e-02 6.42388046e-01 1.38499677e-01 -3.30148339... | [4.74892520904541, 4.052894592285156] |
1ead1be1-26f4-41ac-9e6c-4fa4ed8359f0 | the-projected-covariance-measure-for | 2211.02039 | null | https://arxiv.org/abs/2211.02039v1 | https://arxiv.org/pdf/2211.02039v1.pdf | The Projected Covariance Measure for assumption-lean variable significance testing | Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test whether the regression coefficient for $X$ is non-zero. However, when the model is... | ['Richard J. Samworth', 'Rajen D. Shah', 'Ilmun Kim', 'Anton Rask Lundborg'] | 2022-11-03 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.42663985e-01 -5.89065179e-02 -4.25655007e-01 -5.08517444e-01
-8.78672004e-01 -3.93330097e-01 2.25722883e-02 3.54108028e-02
-3.13849926e-01 1.13262594e+00 -3.31649542e-01 -6.08556271e-01
-3.38231027e-01 -1.05241430e+00 -1.18016863e+00 -7.58408725e-01
-2.71970749e-01 2.75359660e-01 -2.99686760e-01 4.03894067... | [7.6062140464782715, 4.694582462310791] |
111559f9-8883-48fd-97ab-d211c49a8a43 | federated-tensor-factorization-for | 1704.03141 | null | http://arxiv.org/abs/1704.03141v1 | http://arxiv.org/pdf/1704.03141v1.pdf | Federated Tensor Factorization for Computational Phenotyping | Tensor factorization models offer an effective approach to convert massive
electronic health records into meaningful clinical concepts (phenotypes) for
data analysis. These models need a large amount of diverse samples to avoid
population bias. An open challenge is how to derive phenotypes jointly across
multiple hospi... | ['Hwanjo Yu', 'Jimeng Sun', 'Yejin Kim', 'Xiaoqian Jiang'] | 2017-04-11 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [-5.27786575e-02 -2.08456844e-01 -4.73661385e-02 -5.19714713e-01
-6.24685049e-01 -7.58204341e-01 -3.48260283e-01 5.14056027e-01
-3.29051197e-01 9.28299189e-01 2.81198770e-01 -4.65518236e-01
-3.52320671e-01 -7.83526182e-01 -4.73324537e-01 -7.16380537e-01
-1.21914968e-01 5.44998050e-01 -8.03478718e-01 7.21694976... | [6.21246862411499, 6.383845329284668] |
150cd247-a68d-4ed1-ac88-523a0c03b668 | overview-of-the-shared-task-on-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.58 | https://aclanthology.org/2022.ltedi-1.58.pdf | Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion | Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and... | ['José García-Díaz', 'Daniel García-Baena', 'Rahul Ponnusamy', 'Prasanna Kumaresan', 'Rafael Valencia-García', 'Salud María Jiménez-Zafra', 'Miguel Ángel García', 'John McCrae', 'Subalalitha Cn', 'Ruba Priyadharshini', 'Vigneshwaran Muralidaran', 'Bharathi Raja Chakravarthi'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection-for-tamil', 'hope-speech-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.49107337e-01 6.65417612e-01 -2.87718445e-01 -6.03781939e-01
-1.01255250e+00 -3.66202742e-01 1.18972826e+00 6.01160884e-01
-6.69743493e-02 6.25510931e-01 1.24124694e+00 -2.81441510e-01
3.43724489e-02 -2.53221720e-01 -1.42622083e-01 -2.24566311e-01
1.46688610e-01 3.64397228e-01 -1.90035939e-01 -5.23831487... | [9.068404197692871, 10.709474563598633] |
92cd5c38-1c8d-4d62-8ca6-197a88099c55 | radars-for-autonomous-driving-a-review-of | 2306.09304 | null | https://arxiv.org/abs/2306.09304v2 | https://arxiv.org/pdf/2306.09304v2.pdf | Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges | Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of r... | ['Soumyajit Mandal', 'Arvind Srivastav'] | 2023-06-15 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 9.27806720e-02 -7.09652543e-01 -2.02244461e-01 -4.82860446e-01
-5.39030790e-01 -3.14446002e-01 7.48256207e-01 -1.76779136e-01
-5.02840281e-01 9.68544841e-01 -1.23208225e-01 -1.73185468e-01
-3.42506051e-01 -1.01865232e+00 -2.11654931e-01 -9.21181321e-01
-3.71703893e-01 5.30379474e-01 1.89258754e-01 -2.28819564... | [7.772097587585449, -1.4063390493392944] |
8045aada-2dfe-4831-a7fa-b50998c11aa4 | learning-to-navigate-intersections-with | 2109.06783 | null | https://arxiv.org/abs/2109.06783v2 | https://arxiv.org/pdf/2109.06783v2.pdf | Learning to Navigate Intersections with Unsupervised Driver Trait Inference | Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits such as driving styl... | ['Katherine Driggs-Campbell', 'Neeloy Chakraborty', 'Haonan Chen', 'Peixin Chang', 'Shuijing Liu'] | 2021-09-14 | null | null | null | null | ['personality-trait-recognition'] | ['computer-vision'] | [-2.64010668e-01 3.74805540e-01 -4.22037661e-01 -1.01211870e+00
-5.68181157e-01 -5.68373144e-01 4.54448938e-01 -4.19733584e-01
-4.60481226e-01 2.26447880e-01 1.11222588e-01 -5.73102236e-01
-1.27493843e-01 -7.73739278e-01 -8.93313348e-01 -6.35228097e-01
1.77379727e-01 4.43719208e-01 -5.32800592e-02 -4.39046532... | [5.803346633911133, 0.9172911643981934] |
c1d0c16d-273c-489e-a001-176b9206f735 | covid-19-pneumonia-and-influenza-pneumonia | 2112.07102 | null | https://arxiv.org/abs/2112.07102v1 | https://arxiv.org/pdf/2112.07102v1.pdf | COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks | In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optima... | ['Robin Singh', 'Philip Melanchthon', 'Benjamin Prescott', 'Julianna Antonchuk'] | 2021-12-14 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [-6.52793646e-02 -2.34265178e-01 1.85687780e-01 -5.30513525e-02
-6.13244027e-02 -4.63301361e-01 8.22899789e-02 -1.64157748e-02
-5.13119519e-01 3.30553681e-01 -1.59503184e-02 -8.67108524e-01
-6.39744222e-01 -7.49223948e-01 -3.34597677e-01 -7.47381687e-01
-2.32315198e-01 6.23907864e-01 1.70934036e-01 1.74278855... | [15.556474685668945, -1.7155951261520386] |
a7095177-f74f-43bd-8407-887fd703d74d | sequence-to-sequence-load-disaggregation | 2009.12355 | null | https://arxiv.org/abs/2009.12355v1 | https://arxiv.org/pdf/2009.12355v1.pdf | Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Network | With the increased demand on economy and efficiency of measurement technology, Non-Intrusive Load Monitoring (NILM) has received more and more attention as a cost-effective way to monitor electricity and provide feedback to users. Deep neural networks has been shown a great potential in the field of load disaggregation... | ['Yanjun Feng', 'Zhi Li', 'Meng Fu', 'Gan Zhou', 'Chengwei Huang', 'Xingyao Wang'] | 2020-09-25 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.27154011e-02 -1.92018107e-01 2.12152153e-01 -5.70061088e-01
-1.93092182e-01 -3.33538353e-01 6.18987024e-01 -3.05284768e-01
-2.74821788e-01 8.52790833e-01 1.31909773e-01 -8.36220309e-02
-2.92773068e-01 -1.11965001e+00 -2.11262599e-01 -9.05368924e-01
1.69846397e-02 1.49089232e-01 -1.74857765e-01 -2.37932235... | [16.04226303100586, 7.561216354370117] |
00536379-2478-40b9-9a86-66c6a9f43378 | gallery-filter-network-for-person-search | 2210.12903 | null | https://arxiv.org/abs/2210.12903v2 | https://arxiv.org/pdf/2210.12903v2.pdf | Gallery Filter Network for Person Search | In person search, we aim to localize a query person from one scene in other gallery scenes. The cost of this search operation is dependent on the number of gallery scenes, making it beneficial to reduce the pool of likely scenes. We describe and demonstrate the Gallery Filter Network (GFN), a novel module which can eff... | ['Avideh Zakhor', 'Lucas Jaffe'] | 2022-10-24 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-4.99648862e-02 -6.06779277e-01 2.12341338e-01 -4.12904769e-01
-8.71151328e-01 -6.02996051e-01 7.12254941e-01 -2.67606050e-01
-7.84843564e-01 4.51765686e-01 5.00697792e-01 4.15237576e-01
-2.30826467e-01 -4.71837759e-01 -3.36124629e-01 -2.50377238e-01
4.60393615e-02 6.69823706e-01 5.28242350e-01 -1.35246694... | [14.826062202453613, 0.8231203556060791] |
c4c3813d-1675-40fe-99b0-0fbe8d87e6b8 | an-efficient-deep-learning-model-for | 2110.04980 | null | https://arxiv.org/abs/2110.04980v1 | https://arxiv.org/pdf/2110.04980v1.pdf | An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation | Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes. Recently, the emerging deep learning (DL) research has facilitated high-performance DL-AMR approaches. However, most DL-AMR models only focus on recognition accuracy, leading to ... | ['Yang Luo', 'Jialang Xu', 'Chunbo Luo', 'Fuxin Zhang'] | 2021-10-11 | null | null | null | null | ['automatic-modulation-recognition', 'intelligent-communication'] | ['time-series', 'time-series'] | [ 3.98166120e-01 -4.30030584e-01 -4.15179312e-01 -8.28282759e-02
-8.54145825e-01 1.09491758e-01 3.70851129e-01 -2.00745642e-01
-3.67519498e-01 4.02757674e-01 -8.31311345e-02 -6.36466026e-01
-1.28000394e-01 -5.97400546e-01 -2.07138196e-01 -7.35239267e-01
5.15819862e-02 -2.46729791e-01 1.03355840e-01 -1.35703087... | [6.492170810699463, 1.4782718420028687] |
0ed040cf-230d-45ef-b1ba-4909ae4afc2c | meta-reinforcement-learning-for-mastering | null | null | https://aclanthology.org/2021.metanlp-1.1 | https://aclanthology.org/2021.metanlp-1.1.pdf | Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games | Text-based games can be used to develop task-oriented text agents for accomplishing tasks with high-level language instructions, which has potential applications in domains such as human-robot interaction. Given a text instruction, reinforcement learning is commonly used to train agents to complete the intended task ow... | ['Xiaojuan Ma', 'Mingfei Sun', 'Zhenjie Zhao'] | null | null | null | null | acl-metanlp-2021-8 | ['text-based-games'] | ['playing-games'] | [ 2.35103026e-01 1.63361598e-02 -2.05932349e-01 -6.29933625e-02
-3.39743406e-01 -4.21441019e-01 7.64168620e-01 -1.02743022e-01
-6.63255394e-01 9.21797097e-01 -3.79060991e-02 -3.78646016e-01
4.08128500e-02 -7.35541403e-01 -7.59205937e-01 -6.29071772e-01
1.11580439e-01 7.08443642e-01 3.70446414e-01 -6.16623819... | [3.911623239517212, 1.45587158203125] |
cd88549c-bbfe-4634-a4aa-694e5fd0b4ce | ghn-q-parameter-prediction-for-unseen | 2208.12489 | null | https://arxiv.org/abs/2208.12489v1 | https://arxiv.org/pdf/2208.12489v1.pdf | GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks | Deep convolutional neural network (CNN) training via iterative optimization has had incredible success in finding optimal parameters. However, modern CNN architectures often contain millions of parameters. Thus, any given model for a single architecture resides in a massive parameter space. Models with similar loss cou... | ['Alexander Wong', 'Stone Yun'] | 2022-08-26 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 6.94858208e-02 3.69340986e-01 -2.92234391e-01 -3.59270722e-01
-8.71160924e-01 -6.90268219e-01 2.09918752e-01 1.02783784e-01
-4.54219699e-01 4.61605906e-01 -1.45997047e-01 -7.47591257e-01
-1.46991253e-01 -6.77541137e-01 -9.51861680e-01 -6.04954898e-01
-3.46726149e-01 1.35637224e-01 2.80937940e-01 -3.39315504... | [8.668487548828125, 3.149754285812378] |
33e9d5a9-f925-4798-bf5c-b4d12a737a00 | spatial-temporal-residual-aggregation-for | 2111.03574 | null | https://arxiv.org/abs/2111.03574v1 | https://arxiv.org/pdf/2111.03574v1.pdf | Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting | Recent learning-based inpainting algorithms have achieved compelling results for completing missing regions after removing undesired objects in videos. To maintain the temporal consistency among the frames, 3D spatial and temporal operations are often heavily used in the deep networks. However, these methods usually su... | ['Zhan Xu', 'Zili Yi', 'Qiang Tang', 'Rui Ma', 'Vishnu Sanjay Ramiya Srinivasan'] | 2021-11-05 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 6.43568709e-02 -3.59745115e-01 -1.10453099e-01 -1.35505259e-01
-8.05250823e-01 -7.09538832e-02 2.72522986e-01 -5.16682804e-01
-2.20576301e-01 9.77341115e-01 5.26197076e-01 4.02410209e-01
-2.57180899e-01 -6.36273980e-01 -1.02932620e+00 -5.63822925e-01
-9.99539346e-02 -2.38915533e-01 3.43858600e-01 -3.31584848... | [10.863327026367188, -1.3556383848190308] |
acb00304-e514-4f27-aca1-9804c03332e0 | apricot-submodular-selection-for-data | 1906.03543 | null | https://arxiv.org/abs/1906.03543v1 | https://arxiv.org/pdf/1906.03543v1.pdf | apricot: Submodular selection for data summarization in Python | We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greedy selection algorithm that offers strong theoretical guarantees on the quality of the selected set. Two submodular set functions are impleme... | ['William Stafford Noble', 'Jeffrey Bilmes', 'Jacob Schreiber'] | 2019-06-08 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-7.02152133e-01 -1.18969150e-01 -4.91986215e-01 -5.40851057e-01
-1.03236580e+00 -7.25596070e-01 -1.05337566e-03 3.50400954e-01
-2.63611406e-01 1.08444095e+00 1.58968136e-01 1.00477256e-01
-4.89543021e-01 -9.64832008e-01 -7.40083933e-01 -7.62196779e-01
-3.52424949e-01 1.03841269e+00 -1.90226898e-01 -1.91051513... | [6.675937652587891, 4.898047924041748] |
826921cb-8f9a-4764-a9aa-7ba34b781750 | computing-steiner-trees-using-graph-neural | 2108.08368 | null | https://arxiv.org/abs/2108.08368v1 | https://arxiv.org/pdf/2108.08368v1.pdf | Computing Steiner Trees using Graph Neural Networks | Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many... | ['Stephen Kobourov', 'Keaton Hamm', 'Mithun Ghosh', 'Faryad Darabi Sahneh', 'Md Asadullah Turja', 'Reyan Ahmed'] | 2021-08-18 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 7.60288164e-02 7.27973759e-01 -5.05885422e-01 -1.37013167e-01
-4.25844997e-01 -6.30655646e-01 2.45550126e-01 3.41558278e-01
-1.32377490e-01 6.83767080e-01 -2.40470678e-01 -9.35312629e-01
-4.84202087e-01 -1.28666353e+00 -9.27517593e-01 -4.71523255e-01
-7.51798093e-01 1.04554379e+00 -5.48758358e-03 -2.97515213... | [6.91955041885376, 6.027608394622803] |
94b0d072-d594-48d1-b77d-9a65ea5068dc | depthwise-separable-convolutions-versus | 2007.02683 | null | https://arxiv.org/abs/2007.02683v1 | https://arxiv.org/pdf/2007.02683v1.pdf | Depthwise Separable Convolutions Versus Recurrent Neural Networks for Monaural Singing Voice Separation | Recent approaches for music source separation are almost exclusively based on deep neural networks, mostly employing recurrent neural networks (RNNs). Although RNNs are in many cases superior than other types of deep neural networks for sequence processing, they are known to have specific difficulties in training and p... | ['Tuomas Virtanen', 'Pyry Pyykkönen', 'Styliannos I. Mimilakis', 'Konstantinos Drossos'] | 2020-07-06 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.83958584e-01 -4.95755732e-01 3.44582379e-01 7.42895454e-02
-5.64760149e-01 -6.18915081e-01 1.56186342e-01 -4.16127294e-01
-5.17491758e-01 4.41323578e-01 3.72586310e-01 -3.93601924e-01
-6.61224648e-02 -2.89055556e-01 -4.90683168e-01 -8.29312444e-01
-1.35160834e-01 -4.08011913e-01 -1.21256389e-01 -2.67856687... | [15.474184036254883, 5.566734313964844] |
2dbc4284-bd69-463b-994c-03afcdcca384 | supervised-sentence-fusion-with-single-stage | null | null | https://aclanthology.org/I13-1198 | https://aclanthology.org/I13-1198.pdf | Supervised Sentence Fusion with Single-Stage Inference | null | ['Kapil Thadani', 'Kathleen McKeown'] | 2013-10-01 | supervised-sentence-fusion-with-single-stage-1 | https://aclanthology.org/I13-1198 | https://aclanthology.org/I13-1198.pdf | ijcnlp-2013-10 | ['sentence-compression'] | ['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... | [-7.564606189727783, 3.9004316329956055] |
913316e7-ea5e-45cb-adbd-fce551a5d8ad | investigating-the-edge-of-stability | 2307.04210 | null | https://arxiv.org/abs/2307.04210v1 | https://arxiv.org/pdf/2307.04210v1.pdf | Investigating the Edge of Stability Phenomenon in Reinforcement Learning | Recent progress has been made in understanding optimisation dynamics in neural networks trained with full-batch gradient descent with momentum with the uncovering of the edge of stability phenomenon in supervised learning. The edge of stability phenomenon occurs as the leading eigenvalue of the Hessian reaches the dive... | ['Mihaela Rosca', 'Marc Peter Deisenroth', 'Rares Iordan'] | 2023-07-09 | null | null | null | null | ['q-learning', 'reinforcement-learning-1'] | ['methodology', 'methodology'] | [ 3.86267044e-02 2.43067890e-01 -3.81275773e-01 -2.06153736e-01
-4.32014823e-01 -5.13640761e-01 6.96512759e-01 2.93317229e-01
-7.05249786e-01 8.70588779e-01 4.82235625e-02 -4.86640960e-01
-4.52749670e-01 -3.90112519e-01 -9.23407018e-01 -1.11902213e+00
-4.90573406e-01 1.59369797e-01 4.57795942e-03 -5.82155228... | [4.232858657836914, 2.1995279788970947] |
fd20dcd9-daa8-4237-86ad-3b51cf4eccc0 | textual-augmentation-techniques-applied-to | 2306.07414 | null | https://arxiv.org/abs/2306.07414v1 | https://arxiv.org/pdf/2306.07414v1.pdf | Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili | In this work we investigate the impact of applying textual data augmentation tasks to low resource machine translation. There has been recent interest in investigating approaches for training systems for languages with limited resources and one popular approach is the use of data augmentation techniques. Data augmentat... | ['Vukosi Marivate', 'Catherine Gitau'] | 2023-06-12 | null | null | null | null | ['nmt', 'text-classification', 'machine-translation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 3.44273925e-01 -1.02255538e-01 -5.80882370e-01 -3.64366531e-01
-1.04742908e+00 -6.02878809e-01 9.79741454e-01 1.31737024e-01
-9.35203731e-01 1.16405320e+00 4.35311258e-01 -8.66997421e-01
3.58604491e-01 -6.22293353e-01 -7.13132203e-01 -3.47604871e-01
5.91330767e-01 1.18123162e+00 -2.39889458e-01 -1.00604177... | [11.446097373962402, 10.235816955566406] |
4cb488c0-702a-41e2-b841-bf3a62abdb3a | pose-guided-human-image-synthesis-with | 2210.03627 | null | https://arxiv.org/abs/2210.03627v1 | https://arxiv.org/pdf/2210.03627v1.pdf | Pose Guided Human Image Synthesis with Partially Decoupled GAN | Pose Guided Human Image Synthesis (PGHIS) is a challenging task of transforming a human image from the reference pose to a target pose while preserving its style. Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture o... | ['Jing Xiao', 'Xiaoyang Qu', 'Shijing Si', 'Jianzong Wang', 'Jianhan Wu'] | 2022-10-07 | null | null | null | null | ['pose-transfer', 'long-range-modeling'] | ['computer-vision', 'natural-language-processing'] | [ 2.21616596e-01 2.33507544e-01 2.66367823e-01 -3.12824130e-01
-4.08937752e-01 -1.95633829e-01 3.20114464e-01 -8.79323423e-01
-1.44766495e-01 5.41842937e-01 4.69701529e-01 5.87030709e-01
3.98394525e-01 -8.00243258e-01 -1.02998710e+00 -8.36554468e-01
5.95178068e-01 4.73003477e-01 1.84443220e-01 -4.05398995... | [11.968099594116211, -0.8593304753303528] |
b1788006-38ef-4ec2-9d4d-60dcf9aced3b | graphix-a-pre-trained-graph-edit-model-for | null | null | https://openreview.net/forum?id=uB12zutkXJR | https://openreview.net/pdf?id=uB12zutkXJR | GRAPHIX: A Pre-trained Graph Edit Model for Automated Program Repair | We present GRAPHIX, a pre-trained graph edit model for automatically detecting and fixing bugs and code quality issues in Java programs. Unlike sequence-to-sequence models, GRAPHIX leverages the abstract syntax structure of code and represents the code using a multi-head graph encoder. Along with an autoregressive tree... | ['Srinivasan H. Sengamedu', 'Thanh V Nguyen'] | 2021-09-29 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 2.4992585e-01 6.2886930e-01 -2.6738355e-01 -4.4810143e-01
-9.8335218e-01 -6.2408632e-01 1.4161451e-01 5.4620183e-01
2.5628921e-01 5.7730898e-02 1.4731520e-01 -7.0129722e-01
3.1998467e-01 -6.7126149e-01 -1.2957761e+00 1.7100336e-01
-4.1380095e-01 7.0991493e-03 1.1380436e-01 1.6455792e-02
4.5189494e-01... | [7.576473712921143, 7.832780838012695] |
66172b42-9f9b-41fe-aba5-2d4a52801f73 | graph-neural-networks-for-knowledge-enhanced | 2105.08190 | null | https://arxiv.org/abs/2105.08190v1 | https://arxiv.org/pdf/2105.08190v1.pdf | Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings | We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-based artistic representations. First, we illustrate the significant advantages of multi-task learning for fine art analysis and argue that i... | ['Nachoem Wijnberg', 'Marcel Worring', 'Monika Kackovic', 'Stevan Rudinac', 'Athanasios Efthymiou'] | 2021-05-17 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 5.58417067e-02 -8.28548819e-02 -4.02132720e-01 -1.61274359e-01
-3.51297915e-01 -9.48633075e-01 9.35779572e-01 2.41188452e-01
-6.52103275e-02 3.06012034e-01 6.08007133e-01 9.32869539e-02
-2.38075256e-01 -7.68608212e-01 -6.32763624e-01 -1.12854637e-01
1.93761766e-01 6.38658583e-01 -1.97495937e-01 -2.39901096... | [11.271492958068848, 0.4062383472919464] |
27614019-9e09-4511-9d47-7e00ee33496f | domain-generalization-in-robust-invariant | 2304.03431 | null | https://arxiv.org/abs/2304.03431v1 | https://arxiv.org/pdf/2304.03431v1.pdf | Domain Generalization In Robust Invariant Representation | Unsupervised approaches for learning representations invariant to common transformations are used quite often for object recognition. Learning invariances makes models more robust and practical to use in real-world scenarios. Since data transformations that do not change the intrinsic properties of the object cause the... | ['Ramesh Raskar', 'Keshav Gupta', 'Ritvik Kapila', 'Gauri Gupta'] | 2023-04-07 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 4.85175073e-01 -2.66949743e-01 -3.23807955e-01 -7.39156246e-01
-2.40785941e-01 -7.00663865e-01 7.08611786e-01 -1.02332048e-01
-2.09291190e-01 5.75203240e-01 2.10467786e-01 1.32360190e-01
-3.29471260e-01 -7.04041541e-01 -6.23925507e-01 -8.78919125e-01
1.24421485e-01 5.52960217e-01 2.96083122e-01 -1.25314564... | [9.779410362243652, 2.8864362239837646] |
28d26eba-bfd0-4d16-8645-f137241ef513 | unsupervised-video-object-segmentation-with | 1812.07712 | null | http://arxiv.org/abs/1812.07712v1 | http://arxiv.org/pdf/1812.07712v1.pdf | Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation | Unsupervised video object segmentation is a crucial application in video
analysis without knowing any prior information about the objects. It becomes
tremendously challenging when multiple objects occur and interact in a given
video clip. In this paper, a novel unsupervised video object segmentation
approach via distra... | ['C. -C. Jay Kuo', 'Ming-Sui Lee', 'Yueru Chen', 'Ye Wang', 'Siyang Li', 'Qin Huang', 'Kaitai Zhang', 'Jongmoo Choi'] | 2018-12-19 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 5.41987598e-01 -1.80707574e-01 -2.60981858e-01 -3.02245200e-01
-3.89293432e-01 -4.44467276e-01 4.48677182e-01 -8.80896598e-02
-6.77117765e-01 7.75127470e-01 -2.81795859e-01 4.05050777e-02
2.10057601e-01 -5.14001131e-01 -9.69329834e-01 -8.52536440e-01
-8.42924714e-02 3.98639351e-01 1.15570986e+00 1.97100669... | [9.11983585357666, -0.33697596192359924] |
3336d9e3-6d05-4e04-ad53-1ce22db93045 | situation-recognition-with-graph-neural | 1708.04320 | null | http://arxiv.org/abs/1708.04320v1 | http://arxiv.org/pdf/1708.04320v1.pdf | Situation Recognition with Graph Neural Networks | We address the problem of recognizing situations in images. Given an image,
the task is to predict the most salient verb (action), and fill its semantic
roles such as who is performing the action, what is the source and target of
the action, etc. Different verbs have different roles (e.g. attacking has
weapon), and eac... | ['Ruiyu Li', 'Sanja Fidler', 'Raquel Urtasun', 'Jiaya Jia', 'Renjie Liao', 'Makarand Tapaswi'] | 2017-08-14 | situation-recognition-with-graph-neural-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Situation_Recognition_With_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Situation_Recognition_With_ICCV_2017_paper.pdf | iccv-2017-10 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.18420601e-01 3.65333945e-01 -3.46252412e-01 -5.05717576e-01
-1.22679889e-01 -7.86926448e-01 8.13867390e-01 4.12772119e-01
-4.43792552e-01 3.27777624e-01 8.41900289e-01 -2.72762179e-01
-3.62142585e-02 -7.51836181e-01 -7.56249070e-01 -4.81809258e-01
-1.26514331e-01 4.84001040e-01 3.36141229e-01 -2.58042395... | [10.297636985778809, 1.436782956123352] |
29c87ddc-ad2a-48f0-8881-dd92c5f4ec89 | adaptation-to-criticality-through | 1712.05284 | null | http://arxiv.org/abs/1712.05284v3 | http://arxiv.org/pdf/1712.05284v3.pdf | Adaptation to criticality through organizational invariance in embodied agents | Many biological and cognitive systems do not operate deep within one or other
regime of activity. Instead, they are poised at critical points located at
phase transitions in their parameter space. The pervasiveness of criticality
suggests that there may be general principles inducing this behaviour, yet
there is no wel... | ['Manuel G. Bedia', 'Miguel Aguilera'] | 2017-12-13 | null | null | null | null | ['acrobot'] | ['playing-games'] | [ 2.09410250e-01 2.97408164e-01 -4.02569696e-02 1.18199594e-01
2.64081806e-01 -4.10568118e-01 1.20646942e+00 2.89681613e-01
-4.73514229e-01 9.08508360e-01 -3.50991368e-01 -1.48353606e-01
-5.77291429e-01 -6.49005234e-01 -6.49693191e-01 -1.23460305e+00
-3.02218080e-01 4.14383918e-01 5.71539104e-01 -7.97414601... | [5.5561604499816895, 4.140189170837402] |
a272d3aa-5c4b-4cfb-a073-b74de1134cff | smoothed-dilated-convolutions-for-improved | 1808.08931 | null | http://arxiv.org/abs/1808.08931v2 | http://arxiv.org/pdf/1808.08931v2.pdf | Smoothed Dilated Convolutions for Improved Dense Prediction | Dilated convolutions, also known as atrous convolutions, have been widely
explored in deep convolutional neural networks (DCNNs) for various dense
prediction tasks. However, dilated convolutions suffer from the gridding
artifacts, which hampers the performance. In this work, we propose two simple
yet effective degriddi... | ['Zhengyang Wang', 'Shuiwang Ji'] | 2018-08-27 | null | null | null | null | ['audio-generation'] | ['audio'] | [-6.14687160e-04 4.21219915e-01 3.56657535e-01 -3.94479871e-01
2.47775495e-01 -3.16581249e-01 5.93909681e-01 -2.84054369e-01
-5.62835574e-01 3.69589806e-01 1.75333411e-01 -3.67958844e-01
9.91799384e-02 -9.42249894e-01 -8.07155550e-01 -8.77428114e-01
-1.05588138e-01 -4.44537073e-01 4.01575685e-01 -1.87733278... | [9.010910034179688, 2.3521082401275635] |
3685cfb2-1dc9-40e3-85f1-036ac074e000 | escaping-data-scarcity-for-high-resolution | 2203.16669 | null | https://arxiv.org/abs/2203.16669v1 | https://arxiv.org/pdf/2203.16669v1.pdf | Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination | In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult problem. Recent methods attempting to fill the gap via synthesis have achieved promising results, but their performance is still limited by th... | ['Vishal M. Patel', 'Pengfei Guo', 'Yiqun Mei'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.pdf | cvpr-2022-1 | ['heterogeneous-face-recognition', 'face-hallucination'] | ['computer-vision', 'computer-vision'] | [ 9.69893187e-02 1.29449695e-01 -1.12697072e-01 -4.25650060e-01
-8.50448310e-01 -3.41528386e-01 5.99765599e-01 -4.51073796e-01
2.20001303e-02 9.07538235e-01 3.40028405e-01 1.37061730e-01
-2.59194244e-02 -7.58181334e-01 -5.98191023e-01 -6.76002264e-01
3.27103436e-01 4.65019673e-01 -3.26082438e-01 -1.96482360... | [13.018500328063965, 0.28498971462249756] |
777335d0-e731-4d32-9e75-22c8c641c74b | integrating-dictionary-and-web-n-grams-for | null | null | https://aclanthology.org/O13-5002 | https://aclanthology.org/O13-5002.pdf | Integrating Dictionary and Web N-grams for Chinese Spell Checking | null | ['Hsun-wen Chiu', 'Jian-Cheng Wu', 'Jason S. Chang'] | 2013-12-01 | integrating-dictionary-and-web-n-grams-for-1 | https://aclanthology.org/O13-5002 | https://aclanthology.org/O13-5002.pdf | roclingijclclp-2013-12 | ['chinese-spell-checking'] | ['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... | [-7.2309250831604, 3.8548083305358887] |
f381ff9e-7ba4-42cc-afee-72a1e44971e1 | towards-inferring-network-properties-from | 2302.02470 | null | https://arxiv.org/abs/2302.02470v1 | https://arxiv.org/pdf/2302.02470v1.pdf | Towards inferring network properties from epidemic data | Epidemic propagation on networks represents an important departure from traditional massaction models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the complexity becomes tracta... | ['Wasiur R. KhudaBukhsh', 'Luc Berthouze', 'István Z. Kiss'] | 2023-02-05 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.30247974e-01 -6.89576790e-02 -2.48976156e-01 1.64539456e-01
-1.56525508e-01 -5.13875902e-01 7.94276357e-01 4.22590762e-01
-4.63678539e-01 9.91469204e-01 -2.11476311e-02 -6.23312294e-01
-7.26657152e-01 -8.47877443e-01 -5.37595749e-01 -9.02019143e-01
-6.15546346e-01 6.37616932e-01 -3.22098918e-02 -1.63165972... | [6.047408103942871, 4.392537593841553] |
3fceb611-3319-4240-9417-73de4e3218d8 | sata-sparsity-aware-training-accelerator-for | 2204.05422 | null | https://arxiv.org/abs/2204.05422v3 | https://arxiv.org/pdf/2204.05422v3.pdf | SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks | Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. Recently, SNNs with backpropagation through time (BPTT) have achieved a higher accuracy result on image recognition task... | ['Priyadarshini Panda', 'Youngeun Kim', 'Abhiroop Bhattacharjee', 'Abhishek Moitra', 'Ruokai Yin'] | 2022-04-11 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 2.81569898e-01 -3.11434746e-01 -2.15666622e-01 -3.71909738e-01
3.23386014e-01 -1.53095990e-01 3.60067397e-01 -8.01371131e-03
-9.78886724e-01 4.87518221e-01 -3.90525401e-01 -6.37175798e-01
-8.50036442e-02 -9.83173907e-01 -9.11126614e-01 -9.09301817e-01
-4.25205380e-02 4.81542870e-02 1.10344045e-01 -1.79901365... | [8.266880989074707, 2.5672757625579834] |
0ef34964-9961-4163-9c8a-73538af73b0a | led2-net-monocular-360-layout-estimation-via | 2104.00568 | null | https://arxiv.org/abs/2104.00568v2 | https://arxiv.org/pdf/2104.00568v2.pdf | LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering | Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360 layout estimation as a problem of predicting depth o... | ['Yi-Hsuan Tsai', 'Wei-Chen Chiu', 'Min Sun', 'Yu-Hsuan Yeh', 'Fu-En Wang'] | 2021-04-01 | null | null | null | null | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 2.99474120e-01 2.54393935e-01 5.48622720e-02 -4.45615232e-01
-7.07248688e-01 -7.31633067e-01 5.06585240e-01 2.23482221e-01
-1.32777184e-01 8.16768557e-02 4.93719965e-01 -5.93593180e-01
1.15872324e-01 -1.08287370e+00 -8.61430407e-01 -3.20874065e-01
1.38660835e-03 2.31677711e-01 -1.40235975e-01 1.49153680... | [8.728928565979004, -2.863719940185547] |
900251af-7c96-4b56-a734-aac83ea803d4 | safe-deep-reinforcement-learning-based | null | null | https://www.sciencedirect.com/science/article/pii/S0306261920302841#ab015 | https://reader.elsevier.com/reader/sd/pii/S0306261920302841?token=F8F3B5C3A9B94B9464C8804C4DD3B0571D1931074703DF28ECF8A15E49EB9192D322DC47A3959181F2ABE761702CFD2B&originRegion=eu-west-1&originCreation=20211015062006 | Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks | Reinforcement learning-based schemes are being recently applied for model-free voltage control in active distribution networks. However, existing reinforcement learning methods face challenges when it comes to continuous state and action spaces problems or problems with operation constraints. To address these limitatio... | ['Lin Gaoa', 'Zihao Wu', 'Chen Wang', 'Deliang Liang', 'Peng Kou'] | 2020-04-15 | null | null | null | elsevier-applied-energy-2020-4 | ['safe-exploration'] | ['robots'] | [-3.71564388e-01 1.16409771e-01 -5.89952052e-01 -3.54212150e-02
-3.45787734e-01 -4.22302514e-01 1.99479073e-01 1.27929151e-01
-2.16424108e-01 1.29605091e+00 -1.75585136e-01 -4.76784021e-01
-3.81058067e-01 -1.06849694e+00 -3.27053785e-01 -1.05489862e+00
-5.05654216e-01 2.57867366e-01 -2.39436850e-02 -3.53693753... | [5.526538372039795, 2.4853403568267822] |
376f9f0a-4607-4dca-9f0d-872b96149526 | tipcb-a-simple-but-effective-part-based | 2105.11628 | null | https://arxiv.org/abs/2105.11628v1 | https://arxiv.org/pdf/2105.11628v1.pdf | TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search | Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description. The significant feature gap between two modalities makes this task very challenging. Many existing methods attempt to utilize local alignment to address this prob... | ['Ruili Wang', 'yuhui Zheng', 'zhenxing Wang', 'Yujiang Lu', 'Guoqing Zhang', 'Yuhao Chen'] | 2021-05-25 | null | null | null | null | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [ 1.26787812e-01 -6.82341754e-01 -2.16688409e-01 -3.07287306e-01
-9.59557891e-01 -3.36297333e-01 7.64110148e-01 -1.22171596e-01
-7.18019605e-01 2.96543986e-01 2.71999955e-01 1.11628525e-01
-1.43814772e-01 -6.01879239e-01 -5.28507292e-01 -6.74028218e-01
5.54430008e-01 4.11523372e-01 2.84323633e-01 -5.55373877... | [14.644929885864258, 0.8322553634643555] |
ae84864d-b572-4f14-b8cc-4b97dbbb61d4 | point-cloud-video-anomaly-detection-based-on | 2306.04466 | null | https://arxiv.org/abs/2306.04466v1 | https://arxiv.org/pdf/2306.04466v1.pdf | Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder | Video anomaly detection has great potential in enhancing safety in the production and monitoring of crucial areas. Currently, most video anomaly detection methods are based on RGB modality, but its redundant semantic information may breach the privacy of residents or patients. The 3D data obtained by depth camera and L... | ['Wenguang Wang', 'Tengjiao He'] | 2023-06-04 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-2.52369076e-01 -3.38148326e-01 4.21690792e-01 -2.11668611e-01
-3.15172493e-01 -2.03887090e-01 2.56247163e-01 3.56628776e-01
-4.79268134e-01 9.86587256e-02 9.79931056e-02 1.02674058e-02
-1.43762574e-01 -8.42378855e-01 -8.08202922e-01 -7.91548669e-01
-4.82189327e-01 1.23770922e-01 2.25087762e-01 -1.01584427... | [7.825022220611572, 1.5123647451400757] |
c0afb2c5-6693-4a3c-b6ec-dd78ceef3715 | none-class-ranking-loss-for-document-level | 2205.00476 | null | https://arxiv.org/abs/2205.00476v2 | https://arxiv.org/pdf/2205.00476v2.pdf | None Class Ranking Loss for Document-Level Relation Extraction | Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation". For good docu... | ['Wee Sun Lee', 'Yang Zhou'] | 2022-05-01 | null | null | null | null | ['video-super-resolution', 'document-level-relation-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 3.97529930e-01 1.42493606e-01 -6.85749590e-01 -7.13790417e-01
-9.10965621e-01 -5.17821670e-01 2.84103304e-01 7.96249866e-01
-3.01616162e-01 9.40798879e-01 -1.68960720e-01 -9.64659303e-02
-2.26353839e-01 -9.23658550e-01 -4.87732232e-01 -8.20386589e-01
1.45376951e-01 5.26276648e-01 -4.33990359e-02 -4.09739502... | [9.245142936706543, 8.628490447998047] |
0f055f64-409e-4692-80ea-7e572abf0d0d | solo-or-ensemble-choosing-a-cnn-architecture | 1904.12724 | null | http://arxiv.org/abs/1904.12724v1 | http://arxiv.org/pdf/1904.12724v1.pdf | Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification | Convolutional neural networks (CNNs) deliver exceptional results for computer
vision, including medical image analysis. With the growing number of available
architectures, picking one over another is far from obvious. Existing art
suggests that, when performing transfer learning, the performance of CNN
architectures on... | ['Fábio Perez', 'Sandra Avila', 'Eduardo Valle'] | 2019-04-29 | null | null | null | null | ['skin-lesion-classification', 'skin-lesion-identification'] | ['medical', 'medical'] | [ 2.12801844e-01 -2.87632775e-02 2.97729727e-02 -1.88159510e-01
-8.42328906e-01 -4.80387002e-01 7.72136748e-01 9.84780639e-02
-1.09917057e+00 8.28779817e-01 2.15766117e-01 -3.74629706e-01
-4.55306619e-01 -6.11266196e-01 -4.60096985e-01 -9.52147841e-01
-1.09650768e-01 4.12315458e-01 2.34390110e-01 -1.57509148... | [15.48631477355957, -2.829659938812256] |
1bae1fdc-c0d3-41a5-8f09-90da7339875a | a-performance-consistent-and-computation | 2205.01239 | null | https://arxiv.org/abs/2205.01239v1 | https://arxiv.org/pdf/2205.01239v1.pdf | A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation | The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in p... | ['Chunyan Wang', 'Juncheng Tong'] | 2022-05-02 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-6.25246689e-02 -1.69892684e-01 4.01249342e-02 -4.88640457e-01
-5.09036303e-01 2.13234201e-02 3.44116837e-01 -3.57751437e-02
-7.58803666e-01 7.87025154e-01 -2.66480327e-01 -1.22765221e-01
-1.70879066e-01 -7.42350459e-01 -2.09162712e-01 -1.15129507e+00
-5.03030159e-02 3.95088553e-01 2.12805226e-01 6.98694885... | [14.65245532989502, -2.4611451625823975] |
b87e6f28-c7e8-4887-ad1e-d26c1ab65de0 | pointersect-neural-rendering-with-cloud-ray | 2304.12390 | null | https://arxiv.org/abs/2304.12390v1 | https://arxiv.org/pdf/2304.12390v1.pdf | Pointersect: Neural Rendering with Cloud-Ray Intersection | We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface normal estimation, rendering room-scale point clouds, inverse rendering, and ray tracing with global ill... | ['Oncel Tuzel', 'Kwang Moo Yi', 'Anurag Ranjan', 'Wei-Yu Chen', 'Jen-Hao Rick Chang'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 3.89996588e-01 -1.84548512e-01 5.00169516e-01 -2.68114626e-01
-7.72625566e-01 -4.95520204e-01 6.40282273e-01 1.29734814e-01
-2.23115623e-01 3.35238993e-01 -5.36418557e-01 -3.73244613e-01
2.55601436e-01 -1.43518829e+00 -1.07984757e+00 -5.08697391e-01
2.54956126e-01 1.13188696e+00 4.66547221e-01 -3.82969141... | [8.985250473022461, -3.256187677383423] |
6920bdf5-a659-4306-a4fb-a4f7fa9406b0 | hitrans-a-transformer-based-context-and | null | null | https://aclanthology.org/2020.coling-main.370 | https://aclanthology.org/2020.coling-main.370.pdf | HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations | Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers. Different from the traditional non-conversational emotion detection, the model for EDC should be context-sensitive (e.g., understanding the whole conversation rather than one utterance) and... | ['Yijiang Liu', 'Meishan Zhang', 'Fei Li', 'Donghong Ji', 'Jingye Li'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.56270027e-01 8.25499147e-02 2.69536704e-01 -9.97433901e-01
-1.17381716e+00 -4.66176808e-01 4.56472874e-01 -6.69977739e-02
1.88698322e-02 3.34602982e-01 7.43106604e-01 -1.22007497e-01
5.45576632e-01 -3.42049003e-01 -2.59902775e-01 -7.13719308e-01
1.88700318e-01 3.00165385e-01 1.83907524e-02 -4.09726858... | [13.117166519165039, 6.0189290046691895] |
473e4467-5117-4f63-b167-8013472f86bf | incorporating-intra-class-variance-to-fine | 1703.00196 | null | http://arxiv.org/abs/1703.00196v1 | http://arxiv.org/pdf/1703.00196v1.pdf | Incorporating Intra-Class Variance to Fine-Grained Visual Recognition | Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the ... | ['Ling-Yu Duan', 'Tiejun Huang', 'Yihang Lou', 'Shiqi Wang', 'Feng Gao', 'Yan Bai'] | 2017-03-01 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 1.24763750e-01 -7.34481514e-01 -5.36474325e-02 -8.60827625e-01
-8.51704478e-01 -3.88934016e-01 8.53777468e-01 -1.86392426e-01
-1.47338808e-01 4.96527165e-01 2.05593213e-01 2.37288728e-01
-5.37456512e-01 -6.09630287e-01 -5.76045394e-01 -7.96787381e-01
2.87590951e-01 -3.35158743e-02 9.46104452e-02 9.51321200... | [9.652022361755371, 2.0200607776641846] |
7ce84a80-078e-4ba6-8500-f905ddc02c95 | advanced-deep-learning-methodologies-for-skin | 2003.06356 | null | https://arxiv.org/abs/2003.06356v1 | https://arxiv.org/pdf/2003.06356v1.pdf | Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages | Technology-assisted platforms provide reliable solutions in almost every field these days. One such important application in the medical field is the skin cancer classification in preliminary stages that need sensitive and precise data analysis. For the proposed study the Kaggle skin cancer dataset is utilized. The pro... | ['Asma Khatoon', 'Muhammad Ali Farooq', 'Viktor Varkarakis', 'Peter Corcoran'] | 2020-03-13 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 6.05312586e-01 8.87653511e-03 1.74304456e-01 -3.18622701e-02
-6.64265633e-01 -1.82946876e-01 5.50679862e-01 5.52834451e-01
-8.05516660e-01 8.46665084e-01 -1.83350265e-01 -1.29462704e-01
-5.91423035e-01 -7.91875899e-01 -2.73678273e-01 -9.69884574e-01
-7.16396514e-03 -1.17493741e-01 2.51458049e-01 -3.29272270... | [15.546486854553223, -2.952207088470459] |
bc4c1704-bd9f-446f-9a06-312f93bd0b3a | language-models-with-image-descriptors-are | 2205.10747 | null | https://arxiv.org/abs/2205.10747v4 | https://arxiv.org/pdf/2205.10747v4.pdf | Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners | The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence... | ['Heng Ji', 'Mohit Bansal', 'Shih-Fu Chang', 'Derek Hoiem', 'Chenguang Zhu', 'ZiYi Yang', 'Shuohang Wang', 'Xudong Lin', 'Jie Lei', 'Luowei Zhou', 'Ruochen Xu', 'Manling Li', 'Zhenhailong Wang'] | 2022-05-22 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 4.08516794e-01 5.07844500e-02 -4.44557369e-01 -5.52272677e-01
-1.13183606e+00 -4.38284874e-01 6.98365033e-01 -4.26342577e-01
-2.81456679e-01 6.22021317e-01 5.39689243e-01 -2.90966094e-01
5.52519321e-01 -4.57123220e-01 -1.28912508e+00 -3.07331264e-01
6.99785277e-02 2.70134956e-01 3.34451795e-01 4.80069928... | [10.394372940063477, 0.7570582628250122] |
4852f8d1-a688-48c7-a6f5-3a1433aef04f | improving-low-resource-named-entity-1 | null | null | https://aclanthology.org/2021.ccl-1.101 | https://aclanthology.org/2021.ccl-1.101.pdf | Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising | “Deep neural networks have achieved state-of-the-art performances on named entity recognition(NER) with sufficient training data while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem we propose a novel data augmentation method based on pre-trained language model (PLM) and curri... | ['Zhang Yujie', 'Chen Yufeng', 'Xu Jinan', 'Liu Jian', 'Zhu Wenjing'] | null | null | null | null | ccl-2021-8 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.25856400e-01 9.97173265e-02 5.08721694e-02 -4.11969125e-01
-1.24645853e+00 -7.49503851e-01 7.27837086e-01 -2.48862077e-02
-1.17506218e+00 1.04395020e+00 4.09313947e-01 -4.41583365e-01
1.85262531e-01 -7.94021189e-01 -9.36279058e-01 -4.02220935e-01
6.06775880e-01 5.47815084e-01 -1.93541929e-01 -8.76797438... | [9.782722473144531, 9.531045913696289] |
30592227-5b02-41fd-be44-05d24f2149c6 | evaluating-out-of-distribution-performance-on | 2210.07448 | null | https://arxiv.org/abs/2210.07448v2 | https://arxiv.org/pdf/2210.07448v2.pdf | Evaluating Out-of-Distribution Performance on Document Image Classifiers | The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto standard benchmark for document classification, yet to our knowledge all studies that use this corp... | ['Kevin Leach', 'David Kuang', 'Yutong Ai', 'Gordon Lim', 'Stefan Larson'] | 2022-10-14 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-2.74847001e-01 -4.47510809e-01 -6.05128050e-01 -3.91120404e-01
-9.55513537e-01 -1.40239394e+00 9.16324556e-01 4.92175937e-01
-1.95100173e-01 6.53014362e-01 -5.78784645e-02 -8.32537770e-01
-3.50056827e-01 -6.89449728e-01 -5.25788009e-01 -5.85997999e-01
2.21028045e-01 9.48871136e-01 2.89277941e-01 -2.12610945... | [9.319167137145996, 4.171878814697266] |
ec349837-d1c0-441c-ac4f-65a100bd60b5 | computer-aided-diagnosis-and-prediction-in | 2206.14683 | null | https://arxiv.org/abs/2206.14683v2 | https://arxiv.org/pdf/2206.14683v2.pdf | Computer-aided diagnosis and prediction in brain disorders | Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and ... | ['Esther E. Bron', 'Stefan Klein', 'Wiro J. Niessen', 'Frederik Barkhof', 'Marion Smits', 'Daniel Bos', 'Sebastian R. van der Voort', 'Vikram Venkatraghavan'] | 2022-06-29 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 3.29183936e-01 4.89445686e-01 -1.27334772e-02 -5.12776554e-01
-4.99467283e-01 -1.01693146e-01 2.80292869e-01 5.49935937e-01
-5.98596334e-01 8.27072680e-01 3.84305388e-01 -5.65054178e-01
-4.87071365e-01 -6.60367787e-01 -1.87130690e-01 -5.40857255e-01
-4.97101098e-01 1.11912274e+00 2.43708953e-01 1.46353364... | [14.203989028930664, -1.8021234273910522] |
1c401153-240e-4a38-8b09-b50560ba6bf0 | smddh-singleton-mention-detection-using-deep | 2301.09361 | null | https://arxiv.org/abs/2301.09361v1 | https://arxiv.org/pdf/2301.09361v1.pdf | SMDDH: Singleton Mention detection using Deep Learning in Hindi Text | Mention detection is an important component of coreference resolution system, where mentions such as name, nominal, and pronominals are identified. These mentions can be purely coreferential mentions or singleton mentions (non-coreferential mentions). Coreferential mentions are those mentions in a text that refer to th... | ['Kamlesh Dutta', 'Pardeep Singh', 'Kusum Lata'] | 2023-01-23 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-6.93358108e-02 3.48758578e-01 -3.27163160e-01 -5.15016019e-01
-8.10616195e-01 -7.76509047e-01 6.04951441e-01 4.87873763e-01
-8.00539672e-01 9.90330637e-01 8.11110973e-01 -1.53610513e-01
-2.37457931e-01 -7.98056722e-01 -3.44496906e-01 -6.38453245e-01
-5.52139208e-02 7.07919955e-01 2.15155482e-01 -6.05253994... | [9.322796821594238, 9.538413047790527] |
6bbd2ca6-1d26-4248-8ac7-09ecd8d8a3f8 | skeleon-based-typing-style-learning-for | 2012.03212 | null | https://arxiv.org/abs/2012.03212v1 | https://arxiv.org/pdf/2012.03212v1.pdf | Skeleon-Based Typing Style Learning For Person Identification | We present a novel architecture for person identification based on typing-style, constructed of adaptive non-local spatio-temporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movem... | ['Dan Raviv', 'David Mendlovic', 'Lior Gelberg'] | 2020-12-06 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-9.84720811e-02 -6.07551277e-01 2.77627800e-02 -3.38048220e-01
1.10770985e-02 -4.59329784e-01 4.16938961e-01 -1.30766109e-01
-7.29088545e-01 5.48395693e-01 3.07985604e-01 2.86794037e-01
-1.61450468e-02 -6.41463995e-01 -3.14231277e-01 -5.65238714e-01
-2.34064534e-01 4.88955379e-01 1.25106618e-01 -4.98230845... | [14.496756553649902, 1.068596363067627] |
3f56989c-dad4-4af1-8bff-db08451f405b | sanet-scene-agnostic-network-for-camera | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_SANet_Scene_Agnostic_Network_for_Camera_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_SANet_Scene_Agnostic_Network_for_Camera_Localization_ICCV_2019_paper.pdf | SANet: Scene Agnostic Network for Camera Localization | This paper presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other.Despite recent advancement in learning based methods, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and ro... | [' Ping Tan', ' Yasutaka Furukawa', ' Honghua Li', ' Chengzhou Tang', ' Ziqian Bai', 'Luwei Yang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['camera-localization'] | ['computer-vision'] | [ 4.84691113e-02 -2.51906693e-01 -2.22315431e-01 -7.08029568e-01
-7.73305893e-01 -7.06751466e-01 2.32250899e-01 1.34247661e-01
-6.04518116e-01 1.96929514e-01 -8.12624991e-02 -2.30300322e-01
-6.84586987e-02 -7.95069337e-01 -1.12606680e+00 -5.13061523e-01
3.12472284e-02 6.53475702e-01 3.31652969e-01 -2.02594548... | [7.641895771026611, -2.177433729171753] |
7dbb3039-c270-4d26-8d76-f8030200a6f4 | multiphase-flow-prediction-with-deep-neural | 1910.09657 | null | https://arxiv.org/abs/1910.09657v1 | https://arxiv.org/pdf/1910.09657v1.pdf | Multiphase flow prediction with deep neural networks | This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able to handle permeability heterogeneity in high dimensional systems, and can learn the interplay of viscous, gravity, and capillary forces fro... | ['Meng Tang', 'Gege Wen', 'Sally M. Benson'] | 2019-10-21 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.85129344e-01 -1.14654511e-01 -3.35043669e-02 -1.42405868e-01
-3.04110795e-01 -2.13293791e-01 3.89244854e-01 5.14506876e-01
-2.67142594e-01 1.03633428e+00 -2.12293953e-01 -7.19321132e-01
-1.65251985e-01 -1.27277732e+00 -8.70321214e-01 -6.83861434e-01
-3.96984786e-01 9.00187671e-01 3.61388057e-01 -2.34530866... | [6.353893280029297, 3.3192012310028076] |
a8f9b722-cbd8-42f0-bf9b-6885814ee6f5 | a-de-raining-semantic-segmentation-network | 2104.07877 | null | https://arxiv.org/abs/2104.07877v1 | https://arxiv.org/pdf/2104.07877v1.pdf | A De-raining semantic segmentation network for real-time foreground segmentation | Few researches have been proposed specifically for real-time semantic segmentation in rainy environments. However, the demand in this area is huge and it is challenging for lightweight networks. Therefore, this paper proposes a lightweight network which is specially designed for the foreground segmentation in rainy env... | ['Yihui Zhang', 'Fanyi Wang'] | 2021-04-16 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 2.64788792e-02 -3.35874379e-01 3.13089013e-01 -5.41070282e-01
1.43992691e-03 -3.36205289e-02 -1.29838884e-01 -5.21414638e-01
-7.51426339e-01 9.06737030e-01 -4.42382365e-01 -5.77552438e-01
3.16710353e-01 -1.01362550e+00 -6.81547225e-01 -1.12186456e+00
-1.13926210e-01 1.29846632e-01 7.53319740e-01 -1.00590251... | [10.913066864013672, -3.2525134086608887] |
94a4344b-44ff-41db-ac24-40d2fcfe1378 | an-evaluation-on-large-language-model-outputs | 2304.08637 | null | https://arxiv.org/abs/2304.08637v1 | https://arxiv.org/pdf/2304.08637v1.pdf | An Evaluation on Large Language Model Outputs: Discourse and Memorization | We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when mea... | ['Si-Qing Chen', 'Qilong Gu', 'Alex Sokolov', 'Xun Wang', 'Adrian de Wynter'] | 2023-04-17 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.25471277e-02 6.16575897e-01 -1.15900166e-01 -2.17603762e-02
-9.14791822e-01 -8.74335825e-01 9.45569515e-01 7.63206482e-01
-5.06110251e-01 1.24999952e+00 7.46678531e-01 -7.38269925e-01
-2.84639329e-01 -1.00389564e+00 -9.83638108e-01 -1.96999758e-01
4.47975427e-01 1.26389652e-01 -2.55494624e-01 1.60557888... | [11.775555610656738, 9.147092819213867] |
c7ec79c2-eb22-4297-9e79-3ce97b0585a8 | video-enhancement-of-people-wearing-polarized | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Ye_Video_Enhancement_of_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Ye_Video_Enhancement_of_2013_CVPR_paper.pdf | Video Enhancement of People Wearing Polarized Glasses: Darkening Reversal and Reflection Reduction | With the wide-spread of consumer 3D-TV technology, stereoscopic videoconferencing systems are emerging. However, the special glasses participants wear to see 3D can create distracting images. This paper presents a computational framework to reduce undesirable artifacts in the eye regions caused by these 3D glasses. Mor... | ['Ruigang Yang', 'Cha Zhang', 'Mao Ye'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['video-enhancement'] | ['computer-vision'] | [ 1.01339862e-01 1.80201083e-01 2.95055270e-01 -2.25399971e-01
-3.87148187e-02 -2.96317458e-01 3.08587641e-01 -1.06863546e+00
3.09633072e-02 5.08419454e-01 4.93044496e-01 -5.46442270e-02
2.82688230e-01 -1.35785624e-01 -4.86558110e-01 -8.06604922e-01
2.91741282e-01 -3.35227400e-01 2.16483936e-01 4.72570285... | [10.39669418334961, -2.7276058197021484] |
2894acc2-19e4-4d89-91de-c39d8e32ac36 | auto-card-efficient-and-robust-codec-avatar | 2304.11835 | null | https://arxiv.org/abs/2304.11835v1 | https://arxiv.org/pdf/2304.11835v1.pdf | Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence | Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras wi... | ['Yingyan Lin', 'Xiaoliang Dai', 'Peizhao Zhang', 'Jason Saragih', 'Chenghui Li', 'Yuecheng Li', 'Yonggan Fu'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.pdf | cvpr-2023-1 | ['architecture-search'] | ['methodology'] | [ 2.63665140e-01 1.22144751e-01 2.83405393e-01 -1.59684956e-01
-6.04093313e-01 -4.49565649e-01 4.97718602e-01 -7.64669657e-01
-2.41448343e-01 2.75264651e-01 9.14702639e-02 -7.16250688e-02
2.24333867e-01 -3.27076703e-01 -7.47269988e-01 -4.61626709e-01
-3.98777910e-02 5.68393916e-02 -3.03544104e-01 -4.28263634... | [12.94336223602295, -0.4313945472240448] |
d429edcd-2f79-4060-a11a-34c994a0fe1e | learning-from-missing-data-using-selection | 1509.09130 | null | http://arxiv.org/abs/1509.09130v1 | http://arxiv.org/pdf/1509.09130v1.pdf | Learning From Missing Data Using Selection Bias in Movie Recommendation | Recommending items to users is a challenging task due to the large amount of
missing information. In many cases, the data solely consist of ratings or tags
voluntarily contributed by each user on a very limited subset of the available
items, so that most of the data of potential interest is actually missing.
Current ap... | ['Olivier Cappé', 'Claire Vernade'] | 2015-09-30 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-4.59592268e-02 -1.16078973e-01 -7.88982213e-01 -5.34833729e-01
-6.65733278e-01 -6.38305247e-01 3.95955086e-01 1.10727087e-01
-2.94333637e-01 7.95200408e-01 8.89201522e-01 -1.39711112e-01
-3.61629426e-01 -1.03622091e+00 -6.37513638e-01 -5.72965980e-01
1.26062036e-01 5.77680409e-01 -1.31458347e-03 -1.41202196... | [9.819479942321777, 5.569430351257324] |
48a8d9bc-54d0-42e2-bf51-a7ebdfc0d6db | japanese-lexical-simplification-for-non | null | null | https://aclanthology.org/W16-4912 | https://aclanthology.org/W16-4912.pdf | Japanese Lexical Simplification for Non-Native Speakers | This paper introduces Japanese lexical simplification. Japanese lexical simplification is the task of replacing difficult words in a given sentence to produce a new sentence with simple words without changing the original meaning of the sentence. We purpose a method of supervised regression learning to estimate difficu... | ['Muhaimin Hading', 'Maki Sakamoto', 'Yuji Matsumoto'] | 2016-12-01 | null | null | null | ws-2016-12 | ['embeddings-evaluation'] | ['natural-language-processing'] | [ 1.04560852e-02 1.97651774e-01 1.94665000e-01 -7.94410706e-01
-5.88556468e-01 -2.50849456e-01 -6.64576888e-02 3.11676294e-01
-1.05373776e+00 1.17731714e+00 7.49627471e-01 -1.64771914e-01
2.19684556e-01 -5.40244877e-01 -3.76066118e-02 -6.98679090e-01
3.70047867e-01 2.22806156e-01 -1.33720875e-01 -6.11415684... | [10.89494514465332, 10.392009735107422] |
38ef2f42-fbf5-41a8-a7e1-f69e11119a06 | auto-exposure-fusion-for-single-image-shadow | 2103.01255 | null | https://arxiv.org/abs/2103.01255v2 | https://arxiv.org/pdf/2103.01255v2.pdf | Auto-Exposure Fusion for Single-Image Shadow Removal | Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful state-of-the-art deep neural networks could hardly recover traceless shadow-removed background. This paper proposes a new solution for this ta... | ['Song Wang', 'Yang Liu', 'Wei Feng', 'Hongkai Yu', 'Felix Juefei-Xu', 'Qing Guo', 'Changqing Zhou', 'Lan Fu'] | 2021-03-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.pdf | cvpr-2021-1 | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.70371151e-01 -2.46581227e-01 4.26349759e-01 -5.07678032e-01
-3.43120426e-01 -4.06691045e-01 2.58690447e-01 -5.95657587e-01
-2.03253627e-01 7.87309587e-01 -9.28574353e-02 -5.29305816e-01
3.90470654e-01 -8.49733412e-01 -7.63710797e-01 -1.01607549e+00
2.76897162e-01 -6.27139509e-02 8.92042875e-01 -3.66784632... | [10.84470272064209, -4.075271129608154] |
36595914-3c4d-40f5-8928-e1f602f5df27 | 190910063 | 1909.10063 | null | https://arxiv.org/abs/1909.10063v1 | https://arxiv.org/pdf/1909.10063v1.pdf | Algorithms for certain classes of Tamil Spelling correction | Tamil language has an agglutinative, diglossic, alpha-syllabary structure which provides a significant combinatorial explosion of morphological forms all of which are effectively used in Tamil prose, poetry from antiquity to the modern age in an unbroken chain of continuity. However, for the language understanding, spe... | ['Muthiah Annamalai', 'T. Shrinivasan'] | 2019-09-22 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 2.58305162e-01 -4.66922224e-01 5.51391626e-03 -5.32943159e-02
-3.61268848e-01 -1.07088530e+00 2.83491194e-01 4.58127946e-01
-6.31803572e-01 1.09160125e+00 1.39446393e-01 -9.41657305e-01
-7.68863410e-02 -6.63136005e-01 -3.01750571e-01 -3.76820117e-01
2.79669374e-01 5.79566956e-01 2.10544080e-01 -9.90767479... | [10.646358489990234, 10.527134895324707] |
8f1f0249-726d-4ae1-bae7-dcd3182d6004 | policy-architectures-for-compositional | 2203.05960 | null | https://arxiv.org/abs/2203.05960v1 | https://arxiv.org/pdf/2203.05960v1.pdf | Policy Architectures for Compositional Generalization in Control | Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment. Learning goal-conditioned policies is a natural paradigm to solve such tasks. However, current approaches struggle to learn and generalize as task complexity increases, such as varia... | ['Aravind Rajeswaran', 'Chelsea Finn', 'Vikash Kumar', 'Allan Zhou'] | 2022-03-10 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.84061769e-02 1.53195709e-01 -9.46695432e-02 -3.46363544e-01
-4.79144365e-01 -8.43309224e-01 8.37244570e-01 -1.60730526e-01
-4.40441847e-01 9.78813827e-01 4.28051949e-01 -1.64456472e-01
-2.77699143e-01 -5.77463210e-01 -1.06595707e+00 -4.28662300e-01
-2.21439078e-01 8.46854270e-01 1.15346313e-01 -4.71149594... | [4.358019828796387, 1.0232455730438232] |
903f7db0-95ee-4b4f-8d48-5a479575516a | chinese-lexical-analysis-with-deep-bi-gru-crf | 1807.01882 | null | http://arxiv.org/abs/1807.01882v1 | http://arxiv.org/pdf/1807.01882v1.pdf | Chinese Lexical Analysis with Deep Bi-GRU-CRF Network | Lexical analysis is believed to be a crucial step towards natural language
understanding and has been widely studied. Recent years, end-to-end lexical
analysis models with recurrent neural networks have gained increasing
attention. In this report, we introduce a deep Bi-GRU-CRF network that jointly
models word segmenta... | ['Shuqi Sun', 'Ke Sun', 'Zhenyu Jiao'] | 2018-07-05 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 4.45868634e-02 9.68525335e-02 -2.73233950e-01 -3.31812799e-01
-1.06512821e+00 -6.01027966e-01 1.84927240e-01 2.45563969e-01
-1.14200568e+00 7.77308822e-01 1.24371506e-01 -6.62985384e-01
7.58137047e-01 -6.48805082e-01 -4.37247247e-01 -4.56067264e-01
1.60822675e-01 6.46457672e-01 2.03989252e-01 2.16214880... | [9.993976593017578, 9.982598304748535] |
5f617432-55f5-4093-9574-e98ba85a1d08 | generative-voxelnet-learning-energy-based | 2012.13522 | null | https://arxiv.org/abs/2012.13522v1 | https://arxiv.org/pdf/2012.13522v1.pdf | Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis | 3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a deep 3D energy-ba... | ['Ying Nian Wu', 'Song-Chun Zhu', 'Wenguan Wang', 'Ruiqi Gao', 'Zilong Zheng', 'Jianwen Xie'] | 2020-12-25 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-1.39029965e-01 -6.89157546e-02 2.20796913e-01 -1.72072813e-01
-6.40132904e-01 -3.22599590e-01 7.96182215e-01 -1.25312179e-01
4.10608500e-01 5.57363749e-01 1.65739954e-01 -7.29780942e-02
4.66621928e-02 -1.39568210e+00 -6.40036404e-01 -8.94378006e-01
2.25548476e-01 1.00481796e+00 3.41793358e-01 -9.85103846... | [8.870844841003418, -3.6339805126190186] |
c2fa6b89-ba3b-4419-941d-1092083aec63 | open-images-v5-text-annotation-and-yet | 2106.12326 | null | https://arxiv.org/abs/2106.12326v1 | https://arxiv.org/pdf/2106.12326v1.pdf | Open Images V5 Text Annotation and Yet Another Mask Text Spotter | A large scale human-labeled dataset plays an important role in creating high quality deep learning models. In this paper we present text annotation for Open Images V5 dataset. To our knowledge it is the largest among publicly available manually created text annotations. Having this annotation we trained a simple Mask-R... | ['Vladislav Sovrasov', 'Sergei Nosov', 'Ilya Krylov'] | 2021-06-23 | null | null | null | null | ['text-spotting', 'text-annotation'] | ['computer-vision', 'natural-language-processing'] | [ 2.17900157e-01 2.26243272e-01 -2.09843352e-01 -4.38383877e-01
-9.88786757e-01 -5.12252450e-01 7.08167434e-01 2.70591732e-02
-6.51977420e-01 5.16751111e-01 2.35665247e-01 -3.31866950e-01
4.41952288e-01 -4.00253922e-01 -6.90238476e-01 -3.71846616e-01
6.41626418e-01 1.00798106e+00 3.37111115e-01 2.84606993... | [11.935914993286133, 2.2648611068725586] |
880d9901-4d5f-476d-954c-612090bb0660 | multilingual-holistic-bias-extending | 2305.13198 | null | https://arxiv.org/abs/2305.13198v1 | https://arxiv.org/pdf/2305.13198v1.pdf | Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale | We introduce a multilingual extension of the HOLISTICBIAS dataset, the largest English template-based taxonomy of textual people references: MULTILINGUALHOLISTICBIAS. This extension consists of 20,459 sentences in 50 languages distributed across all 13 demographic axes. Source sentences are built from combinations of 1... | ['Carleigh Wood', 'Daniel Licht', 'Cynthia Gao', 'Elahe Kalbassi', 'Christophe Ropers', 'Prangthip Hansanti', 'Eric Smith', 'Pierre Andrews', 'Marta R. Costa-jussà'] | 2023-05-22 | null | null | null | null | ['joint-multilingual-sentence-representations'] | ['natural-language-processing'] | [-2.99001813e-01 4.39675674e-02 -5.56348324e-01 -4.18614328e-01
-7.56374419e-01 -8.43348384e-01 1.20300031e+00 3.70151073e-01
-8.71112704e-01 1.18154657e+00 1.11557639e+00 -5.07077694e-01
9.98762846e-02 -7.14284062e-01 -4.14434612e-01 -2.71490753e-01
6.11268222e-01 9.48638499e-01 -5.54483593e-01 -8.35879862... | [9.621809005737305, 10.239100456237793] |
57a3df99-0034-45b3-98d5-873ccf406a0f | progressive-pose-attention-transfer-for | 1904.03349 | null | https://arxiv.org/abs/1904.03349v3 | https://arxiv.org/pdf/1904.03349v3.pdf | Progressive Pose Attention Transfer for Person Image Generation | This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. ... | ['Zhen Zhu', 'Miao Yu', 'Bofei Wang', 'Baoguang Shi', 'Xiang Bai', 'Tengteng Huang'] | 2019-04-06 | progressive-pose-attention-transfer-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Progressive_Pose_Attention_Transfer_for_Person_Image_Generation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Progressive_Pose_Attention_Transfer_for_Person_Image_Generation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['pose-transfer'] | ['computer-vision'] | [ 2.53363345e-02 6.72228485e-02 3.43988270e-01 -3.77101064e-01
-4.33228105e-01 -6.13966346e-01 6.01751983e-01 -6.72050595e-01
-3.01335394e-01 8.46540451e-01 2.10689411e-01 2.99486816e-01
3.08258325e-01 -8.79691899e-01 -9.06661630e-01 -5.89801788e-01
3.20650548e-01 6.02636576e-01 -1.65203065e-01 -1.42640755... | [12.027148246765137, -0.8077887892723083] |
83c93ae5-fca8-484d-a417-c839ddf08140 | fast-3d-line-segment-detection-from | 1901.02532 | null | http://arxiv.org/abs/1901.02532v1 | http://arxiv.org/pdf/1901.02532v1.pdf | Fast 3D Line Segment Detection From Unorganized Point Cloud | This paper presents a very simple but efficient algorithm for 3D line segment
detection from large scale unorganized point cloud. Unlike traditional methods
which usually extract 3D edge points first and then link them to fit for 3D
line segments, we propose a very simple 3D line segment detection algorithm
based on po... | ['Yahui Liu', 'Xiaohu Lu', 'Kai Li'] | 2019-01-08 | null | null | null | null | ['line-segment-detection', 'line-detection'] | ['computer-vision', 'computer-vision'] | [-8.95812958e-02 -3.12126189e-01 -1.03082575e-01 -4.34936286e-04
-5.54710329e-01 -6.09406710e-01 5.87363169e-02 4.90181684e-01
-1.45883232e-01 6.13320321e-02 -5.98866045e-01 -4.08732027e-01
2.70115227e-01 -7.41014361e-01 -5.43789089e-01 -3.97977740e-01
-9.10619274e-02 6.74917698e-01 6.53180003e-01 2.24463195... | [7.9415602684021, -2.980980396270752] |
3b999da2-4c07-43e1-8443-06a769a4a64d | cmx-cross-modal-fusion-for-rgb-x-semantic | 2203.04838 | null | https://arxiv.org/abs/2203.04838v3 | https://arxiv.org/pdf/2203.04838v3.pdf | CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers | Scene understanding based on image segmentation is a crucial component for autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting informative features from the supplementary modality (X-modality). In this work, we propose CMX, a transformer-based cross-modal fusion framework f... | ['Ruiping Liu', 'Huayao Liu', 'Jiaming Zhang', 'Rainer Stiefelhagen', 'Xinxin Hu', 'Kailun Yang'] | 2022-03-09 | null | null | null | null | ['multispectral-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.78144503e-01 -6.33884519e-02 -1.07812181e-01 -8.02900374e-01
-1.21094823e+00 -3.23038071e-01 6.04600251e-01 -2.73001283e-01
-4.55764621e-01 4.01379406e-01 -2.27190480e-02 -1.25286683e-01
-2.35356212e-01 -9.91047263e-01 -8.65955889e-01 -8.14049304e-01
5.18809080e-01 2.08667353e-01 1.81169406e-01 -2.87366420... | [9.429872512817383, -1.19434654712677] |
963f1f1f-b783-4e79-a66f-5a2fafd27e1e | deeplung-3d-deep-convolutional-nets-for | 1709.05538 | null | http://arxiv.org/abs/1709.05538v1 | http://arxiv.org/pdf/1709.05538v1.pdf | DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification | In this work, we present a fully automated lung CT cancer diagnosis system,
DeepLung. DeepLung contains two parts, nodule detection and classification.
Considering the 3D nature of lung CT data, two 3D networks are designed for the
nodule detection and classification respectively. Specifically, a 3D Faster
R-CNN is des... | ['Chaochun Liu', 'Wentao Zhu', 'Wei Fan', 'Xiaohui Xie'] | 2017-09-16 | null | null | null | null | ['automated-pulmonary-nodule-detection-and'] | ['medical'] | [-2.01566666e-01 6.03846788e-01 -6.02200091e-01 -2.11415663e-01
-1.03918839e+00 -1.24919206e-01 2.91501820e-01 -3.49706262e-01
-2.47651413e-01 2.35874474e-01 2.80636072e-01 -5.89531481e-01
7.48576820e-02 -7.95233727e-01 -3.91233116e-01 -7.19765604e-01
8.98343846e-02 8.75401974e-01 7.20158577e-01 2.74918169... | [15.40347957611084, -2.139723300933838] |
8924f7ae-ccf3-4d7a-8ca6-7a81165025f7 | thinking-hallucination-for-video-captioning | 2209.13853 | null | https://arxiv.org/abs/2209.13853v1 | https://arxiv.org/pdf/2209.13853v1.pdf | Thinking Hallucination for Video Captioning | With the advent of rich visual representations and pre-trained language models, video captioning has seen continuous improvement over time. Despite the performance improvement, video captioning models are prone to hallucination. Hallucination refers to the generation of highly pathological descriptions that are detache... | ['Partha Pratim Mohanta', 'Nasib Ullah'] | 2022-09-28 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.35967648e-01 -2.70157233e-02 -9.26687866e-02 -1.11355223e-01
-1.05295157e+00 -5.22157133e-01 7.93393552e-01 1.70669332e-01
-2.28259563e-01 7.16631770e-01 8.42416465e-01 7.34021366e-02
3.05980563e-01 -1.42643943e-01 -8.19659472e-01 -3.96429718e-01
3.18267107e-01 2.93080926e-01 9.36665684e-02 -1.65331990... | [10.681403160095215, 0.7942745685577393] |
42554e91-ef8e-41df-82a2-c2c9aaa8c843 | long-tailed-continual-learning-for-visual | 2307.00183 | null | https://arxiv.org/abs/2307.00183v1 | https://arxiv.org/pdf/2307.00183v1.pdf | Long-Tailed Continual Learning For Visual Food Recognition | Deep learning based food recognition has achieved remarkable progress in predicting food types given an eating occasion image. However, there are two major obstacles that hinder deployment in real world scenario. First, as new foods appear sequentially overtime, a trained model needs to learn the new classes continuous... | ['Fengqing Zhu', 'Heather A. Eicher-Miller', 'Jack Ma', 'Luotao Lin', 'Jiangpeng He'] | 2023-07-01 | null | null | null | null | ['food-recognition', 'continual-learning'] | ['computer-vision', 'methodology'] | [ 1.95157558e-01 -3.09610844e-01 -4.18531030e-01 -5.27275860e-01
-2.79737949e-01 -3.06861699e-01 -9.21840779e-03 7.50042081e-01
-4.38250929e-01 6.36729777e-01 8.66642371e-02 8.63742828e-02
-7.85179716e-03 -9.72903728e-01 -1.11080289e+00 -5.81756830e-01
-1.59671307e-01 3.51659447e-01 -8.78194720e-02 -7.95514435... | [11.535948753356934, 4.370182991027832] |
1cde2ef9-e9af-4f0d-9b46-361f178b7ed5 | subdomain-adaptation-with-manifolds | 2005.03229 | null | https://arxiv.org/abs/2005.03229v1 | https://arxiv.org/pdf/2005.03229v1.pdf | Subdomain Adaptation with Manifolds Discrepancy Alignment | Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into accoun... | ['Tze-Yun Leong', 'Pengfei Wei', 'Yiping Ke', 'Xinghua Qu'] | 2020-05-06 | null | null | null | null | ['subdomain-adaptation'] | ['methodology'] | [-3.50924551e-01 -1.41522765e-01 -4.70078103e-02 -3.42666209e-01
-9.30476665e-01 -5.64554751e-01 5.49400687e-01 -1.76708087e-01
-4.04036269e-02 7.50294089e-01 1.77393794e-01 -1.28918467e-03
-4.23383921e-01 -8.00377131e-01 -7.60867417e-01 -7.90674686e-01
1.30000129e-01 4.71795440e-01 -8.09773058e-02 1.06505211... | [10.358701705932617, 3.120917320251465] |
7a61951c-135f-4913-b434-59f876a5f148 | topological-relational-learning-on-graphs | 2110.15529 | null | https://arxiv.org/abs/2110.15529v1 | https://arxiv.org/pdf/2110.15529v1.pdf | Topological Relational Learning on Graphs | Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are vulnerable to graph perturbations. To address these challenges, we propose a novel topological neural framework of topological relational i... | ['Yulia R. Gel', 'Baris Coskunuzer', 'Yuzhou Chen'] | 2021-10-29 | null | http://proceedings.neurips.cc/paper/2021/hash/e334fd9dac68f13fa1a57796148cf812-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/e334fd9dac68f13fa1a57796148cf812-Paper.pdf | neurips-2021-12 | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.02058973e-03 3.98410857e-01 -2.81908870e-01 -1.91409081e-01
-1.17657065e-01 -4.97780055e-01 6.07928753e-01 4.20480758e-01
9.67037007e-02 4.43272442e-01 1.52557507e-01 -4.49961782e-01
-3.38116318e-01 -1.26645339e+00 -9.32766855e-01 -8.44821751e-01
-7.39400148e-01 3.21274161e-01 3.57070982e-01 -3.83601248... | [6.96617317199707, 6.16331672668457] |
157afc10-2e38-461d-9816-36d7e31da023 | topology-free-type-structures-with | 2212.07246 | null | https://arxiv.org/abs/2212.07246v2 | https://arxiv.org/pdf/2212.07246v2.pdf | Topology-Free Type Structures with Conditioning Events | We establish the existence of the universal type structure in presence of conditioning events without any topological assumption, namely, a type structure that is terminal, belief-complete, and non-redundant, by performing a construction \`a la Heifetz & Samet (1998). In doing so, we answer affirmatively to a longstand... | ['Pierfrancesco Guarino'] | 2022-12-14 | null | null | null | null | ['type'] | ['speech'] | [-9.51648876e-02 9.24105644e-01 1.72707781e-01 2.04319227e-03
1.74768627e-01 -8.91140759e-01 9.32965517e-01 3.09565216e-01
-2.37399697e-01 7.66843438e-01 1.99264303e-01 -7.81566441e-01
-5.26519060e-01 -1.24390852e+00 -8.02316964e-01 -8.12368095e-01
-5.21483779e-01 3.62714946e-01 4.35694188e-01 -3.97934169... | [8.211579322814941, 5.883911609649658] |
53d6c816-38b6-4d38-a85c-fa909cb73436 | semi-supervised-image-classification-with | 2108.13673 | null | https://arxiv.org/abs/2108.13673v1 | https://arxiv.org/pdf/2108.13673v1.pdf | Semi-supervised Image Classification with Grad-CAM Consistency | Consistency training, which exploits both supervised and unsupervised learning with different augmentations on image, is an effective method of utilizing unlabeled data in semi-supervised learning (SSL) manner. Here, we present another version of the method with Grad-CAM consistency loss, so it can be utilized in train... | ['Seunghyuk Cho', 'Juyong Lee'] | 2021-08-31 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [-1.73592776e-01 4.15185153e-01 -4.03357774e-01 -7.94921458e-01
-9.49430585e-01 -5.90581238e-01 2.32402995e-01 -8.75410885e-02
-7.62367189e-01 1.11777949e+00 -5.34059517e-02 -2.87430793e-01
8.17974284e-02 -4.59746212e-01 -1.09161794e+00 -6.68427587e-01
9.87100303e-02 3.30900788e-01 9.28023010e-02 6.08438626... | [9.438549041748047, 3.6645028591156006] |
86dca52f-faa1-474d-b91b-b50b27a7a1d9 | an-interpretable-model-for-scene-graph | 1811.09543 | null | http://arxiv.org/abs/1811.09543v1 | http://arxiv.org/pdf/1811.09543v1.pdf | An Interpretable Model for Scene Graph Generation | We propose an efficient and interpretable scene graph generator. We consider
three types of features: visual, spatial and semantic, and we use a late fusion
strategy such that each feature's contribution can be explicitly investigated.
We study the key factors about these features that have the most impact on the
perfo... | ['Ahmed Elgammal', 'Andrew Tao', 'Kevin Shih', 'Ji Zhang', 'Bryan Catanzaro'] | 2018-11-21 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 4.33293194e-01 4.72553670e-01 5.59215471e-02 -3.23766857e-01
-3.94416332e-01 -6.17119014e-01 7.90994585e-01 4.27357048e-01
-2.65303820e-01 4.55829442e-01 3.61572623e-01 -5.36481977e-01
-2.35715937e-02 -8.71430159e-01 -1.05028999e+00 -3.42247277e-01
-6.33362383e-02 3.19425732e-01 4.97661948e-01 -5.06837666... | [10.479741096496582, 1.6156872510910034] |
99da35d3-e6d8-414a-96f6-78862fa3168d | calling-out-bluff-attacking-the-robustness-of | 2007.06796 | null | https://arxiv.org/abs/2007.06796v5 | https://arxiv.org/pdf/2007.06796v5.pdf | Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems | Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. This number is increasing further due to COVID-19 and the associated automation of education and testing. Despite such wide usage, the AI-based testing literature of these "intelligent" models is... | ['Junyi Jessy Li', 'Anubha Kabra', 'Rajiv Ratn Shah', 'Yaman Kumar', 'Mehar Bhatia'] | 2020-07-14 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.24139123e-01 -1.43382717e-02 2.53220111e-01 -4.28341836e-01
-5.65841675e-01 -1.02044773e+00 3.70039046e-01 2.11610749e-01
-6.33041680e-01 8.27730417e-01 -1.94780037e-01 -4.54906642e-01
-4.78255361e-01 -8.68237257e-01 -4.29959655e-01 -1.79928839e-01
3.55857491e-01 3.48919243e-01 3.33425194e-01 -5.66327691... | [11.310174942016602, 9.324749946594238] |
eecc2fc1-6196-4855-99df-4fcf4f87d57e | unsupervised-and-semi-supervised-learning | 1511.06390 | null | http://arxiv.org/abs/1511.06390v2 | http://arxiv.org/pdf/1511.06390v2.pdf | Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks | In this paper we present a method for learning a discriminative classifier
from unlabeled or partially labeled data. Our approach is based on an objective
function that trades-off mutual information between observed examples and their
predicted categorical class distribution, against robustness of the classifier
to an ... | ['Jost Tobias Springenberg'] | 2015-11-19 | null | null | null | null | ['unsupervised-image-classification', 'unsupervised-mnist'] | ['computer-vision', 'methodology'] | [ 7.34784126e-01 5.32943487e-01 2.24398822e-01 -4.00628954e-01
-1.14343965e+00 -1.17485261e+00 1.05925977e+00 -4.71698374e-01
-6.07525632e-02 6.40453637e-01 9.80694145e-02 -9.44331437e-02
-7.60306269e-02 -7.74811745e-01 -1.06121898e+00 -1.16617906e+00
9.43934172e-02 7.29626060e-01 -2.92804927e-01 2.28748843... | [11.595945358276367, -0.1322363168001175] |
a88fa5a7-0f8f-4298-935f-233f28975925 | cot-mae-v2-contextual-masked-auto-encoder | 2304.03158 | null | https://arxiv.org/abs/2304.03158v1 | https://arxiv.org/pdf/2304.03158v1.pdf | CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval | Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-encoder utilizes contextual embedding to assist in the reconstruction of passages. However, it only uses a single auto-encoding pre-task for ... | ['Songlin Hu', 'Fuzheng Zhang', 'Zijia Lin', 'Meng Lin', 'Peng Wang', 'Guangyuan Ma', 'Xing Wu'] | 2023-04-05 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-5.52617013e-02 -3.40872854e-01 -3.23181778e-01 -2.21921042e-01
-1.27791226e+00 -3.92501801e-01 8.86392117e-01 2.35152543e-01
-2.06597462e-01 5.26069641e-01 1.05167675e+00 2.15191126e-01
2.25466654e-01 -8.13430727e-01 -8.52100432e-01 -4.53385651e-01
4.03695166e-01 3.96738023e-01 -1.58610329e-01 -5.72962224... | [11.40931224822998, 7.7848076820373535] |
9551a2de-5f17-4f84-bd63-88ee2e306f9f | pragmatic-information-in-translation-a-corpus | 2007.05234 | null | https://arxiv.org/abs/2007.05234v1 | https://arxiv.org/pdf/2007.05234v1.pdf | Pragmatic information in translation: a corpus-based study of tense and mood in English and German | Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German tense and mood in translation. Human translators do not find this correspondence easy, and as we will show through careful analysis, there ar... | ['Ekaterina Lapshinova-Koltunski', 'Alexander Fraser', 'Anita Ramm'] | 2020-07-10 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-1.71559289e-01 -1.38288230e-01 -5.83644092e-01 -5.81997991e-01
-7.07813919e-01 -1.01597607e+00 7.20803142e-01 3.91472697e-01
-5.28045774e-01 9.74616528e-01 5.33280015e-01 -8.21197748e-01
1.03606611e-01 -6.66869819e-01 -6.00784957e-01 -3.75931375e-02
7.46844634e-02 5.85555911e-01 -5.43089569e-01 -9.84033108... | [11.4947509765625, 10.252410888671875] |
eb674dc1-47b6-4a14-a98f-620167124e5a | ultra-sensitive-flexible-sponge-sensor-array | 2205.03238 | null | https://arxiv.org/abs/2205.03238v2 | https://arxiv.org/pdf/2205.03238v2.pdf | Ultra-sensitive Flexible Sponge-Sensor Array for Muscle Activities Detection and Human Limb Motion Recognition | Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive robots, etc. Lightweight wearable sensors, including inertial sensors, surface electromyography sensors, and flexible strain/press... | ['Vivian W. Q. Lou', 'Wen Jung Li', 'Ning Xi', 'Roy Vellaisamy', 'Ho-Yin Chan', 'Meng Chen', 'Keer Wang', 'Clio Cheng', 'Yifan Liu', 'Jiao Suo'] | 2022-04-30 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.77669358e-01 -2.83984188e-02 -6.07769847e-01 5.00242174e-01
1.72646400e-02 -2.38552943e-01 -2.48351887e-01 -6.79662883e-01
-7.38246500e-01 7.99092650e-01 6.17719173e-01 2.17135921e-01
1.28743902e-01 -3.56101662e-01 -2.95361131e-01 -7.27785230e-01
-3.56893629e-01 -1.18778639e-01 6.07689977e-01 -1.17190100... | [6.889420509338379, 0.23096978664398193] |
dcba0208-e859-473e-a508-8242fae840ab | revisiting-the-transferability-of-supervised | 2112.00496 | null | https://arxiv.org/abs/2112.00496v3 | https://arxiv.org/pdf/2112.00496v3.pdf | Revisiting the Transferability of Supervised Pretraining: an MLP Perspective | The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervised counterparts. This paper revisits this phenomenon and sheds new light on understanding the transferability gap between unsupervised and s... | ['Wanli Ouyang', 'Donglian Qi', 'Rui Zhao', 'Lei Bai', 'Feng Zhu', 'Shixiang Tang', 'Yizhou Wang'] | 2021-12-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-image-classification'] | ['computer-vision'] | [ 4.92065996e-01 1.35336757e-01 -4.82159048e-01 -5.98130405e-01
-1.05971172e-01 -4.69286501e-01 4.66121852e-01 3.57815742e-01
-8.26823056e-01 3.82967621e-01 -1.44965097e-01 -2.53969938e-01
6.26914427e-02 -5.55767238e-01 -9.45920408e-01 -7.09518969e-01
-3.00798137e-02 3.26940119e-01 2.59096056e-01 8.27023312... | [9.524393081665039, 2.4727234840393066] |
0da34164-e47b-4ff7-b75a-0330aa910fce | multiverse-at-the-edge-interacting-real-world | 2305.10350 | null | https://arxiv.org/abs/2305.10350v1 | https://arxiv.org/pdf/2305.10350v1.pdf | Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming | Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a 'digital twin'. In this paper, we consi... | ['Kaushik Chowdhury', 'Stratis Ioannidis', 'Jennifer Dy', 'Suyash Pradhan', 'Debashri Roy', 'Utku Demir', 'Batool Salehi'] | 2023-05-10 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 1.40548553e-02 9.75828171e-02 -7.91130960e-03 -2.51652032e-01
-8.29345345e-01 -4.24820751e-01 5.01164138e-01 -3.85110199e-01
-1.35576770e-01 8.32043827e-01 -2.36967266e-01 -8.83549035e-01
-4.20625061e-01 -1.28612506e+00 -6.25118375e-01 -7.24825203e-01
-4.66539711e-01 4.93833363e-01 3.51149619e-01 -5.95834255... | [6.22395658493042, 1.1517428159713745] |
5927448c-93ff-472e-aa16-80da7fc769c4 | transformer-based-self-supervised-multimodal | 2303.17611 | null | https://arxiv.org/abs/2303.17611v1 | https://arxiv.org/pdf/2303.17611v1.pdf | Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition | Recently, wearable emotion recognition based on peripheral physiological signals has drawn massive attention due to its less invasive nature and its applicability in real-life scenarios. However, how to effectively fuse multimodal data remains a challenging problem. Moreover, traditional fully-supervised based approach... | ['Ali Amad', 'Mohamed Daoudi', 'Yujin WU'] | 2023-03-29 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 5.25686800e-01 -3.11572224e-01 6.35201037e-02 -7.57229686e-01
-1.11080122e+00 -2.97912657e-01 2.29293972e-01 1.51668549e-01
-5.81290424e-01 8.31104040e-01 1.35518476e-01 2.87465334e-01
-1.01761326e-01 -2.13672355e-01 -5.45698643e-01 -8.28055978e-01
1.28974626e-02 -1.01892754e-01 -5.02211273e-01 2.48350278... | [13.213845252990723, 4.863786220550537] |
434006e5-db27-4122-91a1-8ff8010c2eb4 | exploit-cam-by-itself-complementary-learning | 2303.02449 | null | https://arxiv.org/abs/2303.02449v1 | https://arxiv.org/pdf/2303.02449v1.pdf | Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation | Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable of generating fine-grained masks for semantic segmentation. To guide CAM to find more non-discriminating object patterns, this paper turns ... | ['Bo Han', 'Tongliang Liu', 'Wankou Yang', 'Xian Zhang', 'Marcus Kalander', 'Junjie Ye', 'Fei Zhang', 'Jiren Mai'] | 2023-03-04 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 5.30170023e-01 5.09170294e-01 -2.99965173e-01 -3.59150320e-01
-5.44435799e-01 -6.09041691e-01 6.69159889e-01 -1.10242464e-01
-5.40894032e-01 6.04067087e-01 -2.72289187e-01 -5.15011400e-02
1.32124633e-01 -8.17705631e-01 -9.92708623e-01 -8.37682009e-01
3.62585597e-02 4.64488328e-01 9.08875406e-01 -2.68294245... | [9.612992286682129, 0.6770159006118774] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.