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eff93855-ec54-4f50-a7d8-3d7fe82d8d74 | relaxing-the-additivity-constraints-in | 2305.19838 | null | https://arxiv.org/abs/2305.19838v1 | https://arxiv.org/pdf/2305.19838v1.pdf | Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization | Bayesian Optimization (BO) is typically used to optimize an unknown function $f$ that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Although provably asymptotically optimal BO algorithms are efficient at optimizing low-dimensional functions, sca... | ['Thomas Begin', 'Patrick Thiran', 'Anthony Bardou'] | 2023-05-31 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-2.53559589e-01 1.72663778e-01 -1.92915455e-01 -2.32560202e-01
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-3.36337715e-01 6.14487350e-01 -2.53097206e-01 -8.64101499... | [6.541970729827881, 4.224836349487305] |
a18cb741-b73c-4d82-aeff-086cc821fcde | an-effective-deep-network-for-head-pose | 2210.13705 | null | https://arxiv.org/abs/2210.13705v1 | https://arxiv.org/pdf/2210.13705v1.pdf | An Effective Deep Network for Head Pose Estimation without Keypoints | Human head pose estimation is an essential problem in facial analysis in recent years that has a lot of computer vision applications such as gaze estimation, virtual reality, and driver assistance. Because of the importance of the head pose estimation problem, it is necessary to design a compact model to resolve this t... | ['Hai Tran', 'Huong Ninh', 'Minh Bui', 'Viet Tran', 'Chien Thai'] | 2022-10-25 | null | null | null | null | ['head-pose-estimation', 'gaze-estimation'] | ['computer-vision', 'computer-vision'] | [-3.08643192e-01 1.47441342e-01 1.01822019e-01 -6.77693784e-01
-5.89701176e-01 2.52523065e-01 2.31287390e-01 -6.11653268e-01
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4.52766120e-02 3.62832516e-01 5.65790176e-01 -1.77802861... | [13.653663635253906, 0.303142249584198] |
7bd374ba-3055-44d8-8510-4ebf96a42ba0 | mara-net-single-image-deraining-network-with | 2009.13990 | null | https://arxiv.org/abs/2009.13990v4 | https://arxiv.org/pdf/2009.13990v4.pdf | MCW-Net: Single Image Deraining with Multi-level Connections and Wide Regional Non-local Blocks | A recent line of convolutional neural network-based works has succeeded in capturing rain streaks. However, difficulties in detailed recovery still remain. In this paper, we present a multi-level connection and wide regional non-local block network (MCW-Net) to properly restore the original background textures in rainy... | ['Myungjoo Kang', 'Myeongho Jeon', 'Yeachan Park', 'Junho Lee'] | 2020-09-29 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.89683527e-01 -2.43933842e-01 7.60771036e-02 -4.78520989e-01
-4.19747561e-01 4.32329699e-02 1.14725865e-01 -4.18810427e-01
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2d61f716-987f-46b9-b5f2-054c50cf558c | building-change-detection-for-remote-sensing | 1909.07726 | null | https://arxiv.org/abs/1909.07726v1 | https://arxiv.org/pdf/1909.07726v1.pdf | Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model | In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual task constrained... | ['Yi Liu', 'Xue Yang', 'Chao Pang', 'Zongqian Zhan', 'Xiaomeng Zhang'] | 2019-09-17 | null | null | null | null | ['change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing', 'extracting-buildings-in-remote-sensing-images'] | ['miscellaneous', 'miscellaneous', 'miscellaneous'] | [ 1.81523770e-01 -4.87835824e-01 -6.13733232e-02 -3.31504494e-01
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8ce2fe1f-71b1-4634-b6ea-1732aee8beb8 | self-supervised-representation-learning-from | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Self-Supervised_Representation_Learning_From_Videos_for_Facial_Action_Unit_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Self-Supervised_Representation_Learning_From_Videos_for_Facial_Action_Unit_Detection_CVPR_2019_paper.pdf | Self-Supervised Representation Learning From Videos for Facial Action Unit Detection | In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations. Inspired by the fact that facial actions are the movements of facial muscles, we depict the movements as the transformation between two face images in different fram... | [' Xilin Chen', ' Shiguang Shan', ' Jiabei Zeng', 'Yong Li'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 1.14565976e-01 2.74155289e-01 -2.19933614e-01 -3.97074312e-01
-2.07937032e-01 -1.44010961e-01 6.79870963e-01 -8.04190159e-01
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2.24948321e-02 -5.94801269e-02 -9.46155190e-02 -1.16136834... | [13.591639518737793, 1.5876882076263428] |
a15b31e6-e349-429a-a8ec-17f097bdb975 | shallow-encoder-deep-decoder-sedd-networks | 2001.03017 | null | https://arxiv.org/abs/2001.03017v2 | https://arxiv.org/pdf/2001.03017v2.pdf | Shallow Encoder Deep Decoder (SEDD) Networks for Image Encryption and Decryption | This paper explores a new framework for lossy image encryption and decryption using a simple shallow encoder neural network E for encryption, and a complex deep decoder neural network D for decryption. E is kept simple so that encoding can be done on low power and portable devices and can in principle be any nonlinear ... | ['Chirag Gupta'] | 2020-01-09 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 3.74554753e-01 2.42993310e-01 1.80248439e-01 -2.15530381e-01
-3.60081702e-01 -7.02894866e-01 6.52188599e-01 -2.25552678e-01
-6.36071801e-01 7.25118160e-01 -7.33697861e-02 -7.99424171e-01
2.26204723e-01 -1.10333180e+00 -8.36002767e-01 -1.05168247e+00
-2.50872552e-01 2.97327280e-01 -2.65555084e-01 -4.14898902... | [4.440208435058594, 7.98645544052124] |
a765c336-955f-45ae-aa74-cf09dc48c898 | attention-based-scaling-adaptation-for-target | 2010.10923 | null | https://arxiv.org/abs/2010.10923v3 | https://arxiv.org/pdf/2010.10923v3.pdf | Attention-based scaling adaptation for target speech extraction | The target speech extraction has attracted widespread attention in recent years. In this work, we focus on investigating the dynamic interaction between different mixtures and the target speaker to exploit the discriminative target speaker clues. We propose a special attention mechanism without introducing any addition... | ['Yanhua Long', 'Wei Rao', 'Jiaen Liang', 'Jiangyu Han'] | 2020-10-19 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 7.75450049e-03 -2.11396471e-01 -9.55129117e-02 -1.57595173e-01
-1.12199807e+00 -3.23378354e-01 4.32814240e-01 -2.60394126e-01
-5.04914939e-01 2.59535104e-01 5.59739709e-01 -2.27267891e-01
-3.28381769e-02 -1.17935985e-01 -3.55486989e-01 -1.07757187e+00
-1.03235722e-01 -3.55731845e-01 1.51712596e-01 -1.24483436... | [14.81723690032959, 5.867146968841553] |
993070fc-fce4-4e8f-9bc7-457c79d2e2ae | geometry-based-occlusion-aware-unsupervised | 2010.10700 | null | https://arxiv.org/abs/2010.10700v1 | https://arxiv.org/pdf/2010.10700v1.pdf | Geometry-based Occlusion-Aware Unsupervised Stereo Matching for Autonomous Driving | Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove dependency on disparity groundtruth. Occlusion handling is a challenging problem in stereo matching, especially for unsupervised methods. Previo... | ['Deng Cai', 'Dan Deng', 'Liang Peng'] | 2020-10-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 3.76261413e-01 1.57769442e-01 -1.23105496e-01 -6.14171565e-01
-3.63051981e-01 -1.78963572e-01 5.31286061e-01 1.45368904e-01
-5.05962849e-01 6.37154579e-01 1.67658553e-01 -2.49828532e-01
-3.94726694e-02 -1.15457869e+00 -6.41579807e-01 -7.45386243e-01
4.80396956e-01 6.53210640e-01 6.90080404e-01 -2.34222755... | [8.7558012008667, -2.3744447231292725] |
67d372dd-c995-40c4-887e-a1faf99f0319 | deterministic-policy-optimization-by | 1711.08068 | null | http://arxiv.org/abs/1711.08068v1 | http://arxiv.org/pdf/1711.08068v1.pdf | Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces | Policy optimization methods have shown great promise in solving complex
reinforcement and imitation learning tasks. While model-free methods are
broadly applicable, they often require many samples to optimize complex
policies. Model-based methods greatly improve sample-efficiency but at the cost
of poor generalization,... | ['Daniel Levy', 'Stefano Ermon'] | 2017-11-21 | null | null | null | null | ['acrobot'] | ['playing-games'] | [ 8.29619840e-02 -6.10918961e-02 -5.51411092e-01 4.33419943e-02
-9.00086999e-01 -6.26780570e-01 7.14336395e-01 -8.90718475e-02
-8.88110578e-01 1.23829436e+00 -2.65024662e-01 -5.15910447e-01
-3.39481443e-01 -4.11403626e-01 -6.01972044e-01 -7.09987760e-01
-1.87137723e-01 5.39949596e-01 2.64170676e-01 -2.42659315... | [4.18212366104126, 2.261744499206543] |
204ed974-dd48-4bc2-8f0b-02d8e329d77e | hierarchical-personalized-federated-learning | 2303.10580 | null | https://arxiv.org/abs/2303.10580v1 | https://arxiv.org/pdf/2303.10580v1.pdf | Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks | Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it... | ['Tony Q. S. Quek', 'Howard H. Yang', 'Kun Guo', 'Chaoqun You'] | 2023-03-19 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-6.33582056e-01 -7.62819499e-03 -3.17819029e-01 8.47553685e-02
-6.03191018e-01 -3.36590230e-01 -2.56585658e-01 -4.73947048e-01
-4.75794971e-02 1.07546294e+00 -2.75128186e-01 -5.76298058e-01
-5.32701671e-01 -8.20010662e-01 -4.64445442e-01 -9.97369528e-01
-5.00173688e-01 4.39786881e-01 1.72597721e-01 2.92113334... | [5.965803146362305, 5.583015441894531] |
362de42d-0c07-4b95-800f-4cb0ca9bb529 | joint-rumour-stance-and-veracity-prediction | null | null | https://aclanthology.org/W19-6122 | https://aclanthology.org/W19-6122.pdf | Joint Rumour Stance and Veracity Prediction | The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance se... | ['Leon Derczynski', 'Emil Refsgaard Middelboe', 'Anders Edelbo Lillie'] | null | null | null | null | ws-nodalida-2019-9 | ['rumour-detection'] | ['natural-language-processing'] | [-3.70644182e-01 4.91816044e-01 -8.04844737e-01 4.41158394e-04
-1.06605482e+00 -8.60801339e-01 1.04462445e+00 2.97161549e-01
-2.92097211e-01 1.09141397e+00 6.14873052e-01 -6.72142029e-01
4.58861440e-01 -7.10544348e-01 -4.02074397e-01 -1.83255002e-01
3.01226467e-01 6.72786534e-01 3.50586176e-01 -6.28043413... | [8.290670394897461, 10.088238716125488] |
c1cd3d7d-3cc9-4246-b8a0-5151fa1e4774 | the-manifold-hypothesis-for-gradient-based-1 | 2206.07387 | null | https://arxiv.org/abs/2206.07387v1 | https://arxiv.org/pdf/2206.07387v1.pdf | The Manifold Hypothesis for Gradient-Based Explanations | When do gradient-based explanation algorithms provide meaningful explanations? We propose a necessary criterion: their feature attributions need to be aligned with the tangent space of the data manifold. To provide evidence for this hypothesis, we introduce a framework based on variational autoencoders that allows to e... | ['Ulrike Von Luxburg', 'Zeynep Akata', 'Uddeshya Upadhyay', 'Sebastian Bordt'] | 2022-06-15 | the-manifold-hypothesis-for-gradient-based | https://openreview.net/forum?id=dmq_-R2LhQk | https://openreview.net/pdf?id=dmq_-R2LhQk | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-4.04304974e-02 5.53087294e-01 -1.95672438e-01 -6.99338436e-01
-3.42090474e-03 -4.04601604e-01 6.13001943e-01 -1.18872963e-01
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1.48414612e-01 3.77950519e-01 -3.50262910e-01 -2.69845068... | [8.740891456604004, 4.695564270019531] |
ae81898b-35af-47bc-b43a-0a281ecb8589 | comparative-analysis-of-segment-anything | 2306.12510 | null | https://arxiv.org/abs/2306.12510v1 | https://arxiv.org/pdf/2306.12510v1.pdf | Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images | In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically d... | ['Abbas Sharifi', 'Ahmad Gholizadeh Lonbar', 'Elyas Irankhah', 'Kasra Danesh', 'Sara Asgarian', 'Masoumeh Farhadi Nia', 'Mohsen Ahmadi'] | 2023-06-21 | null | null | null | null | ['tumor-segmentation', 'medical-image-segmentation'] | ['computer-vision', 'medical'] | [ 5.46584189e-01 4.03737038e-01 -4.25143570e-01 -3.54922384e-01
-1.02888477e+00 -3.46588671e-01 3.37994039e-01 2.85127342e-01
-3.35999787e-01 2.75522947e-01 7.28238514e-03 -8.06616187e-01
-7.83089176e-02 -7.18519449e-01 -3.50589067e-01 -9.49843228e-01
-3.10745001e-01 7.35950708e-01 1.78572476e-01 -4.62385006... | [14.900250434875488, -2.5158543586730957] |
fcd9a9ed-57fa-4116-b3a4-fbc144194766 | codetrans-towards-cracking-the-language-of | 2104.02443 | null | https://arxiv.org/abs/2104.02443v2 | https://arxiv.org/pdf/2104.02443v2.pdf | CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing | Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in software engineering. However, the applications for understanding source code language to ease the software engineering process are under-resear... | ['Burkhard Rost', 'Florian Matthes', 'Silvia Severini', 'Christoph Angerer', 'Tamas Feher', 'Tom Gibbs', 'Llion Jones', 'Wei Ding', 'Ahmed Elnaggar'] | 2021-04-06 | null | null | null | null | ['api-sequence-recommendation', 'code-summarization', 'contextual-embedding-for-source-code', 'git-commit-message-generation', 'code-documentation-generation', 'code-comment-generation', 'code-documentation-generation'] | ['computer-code', 'computer-code', 'computer-code', 'computer-code', 'computer-code', 'computer-code', 'natural-language-processing'] | [-2.15951893e-02 -1.66527912e-01 -3.15331757e-01 -4.22725201e-01
-1.08083403e+00 -3.23900461e-01 2.30640516e-01 3.03869769e-02
-1.47145446e-02 2.31852934e-01 1.20349094e-01 -6.83744907e-01
8.07898790e-02 -5.52538216e-01 -6.45251274e-01 -2.20699027e-01
8.98902193e-02 6.63949549e-02 3.27864110e-01 -2.44990081... | [7.591330051422119, 7.953886985778809] |
2c130109-0b5e-47a2-88b1-50e1337fc026 | improving-neural-topic-models-with | 2303.15350 | null | https://arxiv.org/abs/2303.15350v1 | https://arxiv.org/pdf/2303.15350v1.pdf | Improving Neural Topic Models with Wasserstein Knowledge Distillation | Topic modeling is a dominant method for exploring document collections on the web and in digital libraries. Recent approaches to topic modeling use pretrained contextualized language models and variational autoencoders. However, large neural topic models have a considerable memory footprint. In this paper, we propose a... | ['Debarshi Kumar Sanyal', 'Suman Adhya'] | 2023-03-27 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-3.46182764e-01 5.39798439e-01 -3.05843890e-01 -2.77329147e-01
-1.21438408e+00 -3.06294501e-01 8.57377410e-01 2.08244607e-01
-1.27125636e-01 7.42426336e-01 5.00989497e-01 -1.09691292e-01
-8.91545713e-02 -7.83230543e-01 -7.19985664e-01 -9.17678237e-01
1.00346558e-01 8.48661840e-01 2.23386630e-01 3.06040913... | [10.406427383422852, 6.93961238861084] |
88f8954e-d136-438e-b9f7-420f7fce564d | deep-meta-learning-for-real-time-visual | 1712.09153 | null | https://arxiv.org/abs/1712.09153v3 | https://arxiv.org/pdf/1712.09153v3.pdf | Deep Meta Learning for Real-Time Target-Aware Visual Tracking | In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds. Conventional deep convolutional feature-based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters,... | ['Janghoon Choi', 'Kyoung Mu Lee', 'Junseok Kwon'] | 2017-12-26 | deep-meta-learning-for-real-time-target-aware | http://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Deep_Meta_Learning_for_Real-Time_Target-Aware_Visual_Tracking_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Choi_Deep_Meta_Learning_for_Real-Time_Target-Aware_Visual_Tracking_ICCV_2019_paper.pdf | iccv-2019-10 | ['real-time-visual-tracking'] | ['computer-vision'] | [-2.32519314e-01 -7.29998529e-01 -1.93848088e-01 -1.89283624e-01
-3.82725418e-01 -4.99821633e-01 5.14801860e-01 -1.24270417e-01
-9.65599418e-01 3.82686108e-01 -3.89644176e-01 1.12186246e-01
-2.01324020e-02 -4.67450261e-01 -6.86795831e-01 -4.86435890e-01
-1.55376419e-01 5.05559921e-01 6.62665248e-01 1.77790895... | [6.279449939727783, -2.1389145851135254] |
51eaad51-7462-4c43-9bbd-4fbcdecdf20d | it-is-ais-turn-to-ask-human-a-question | null | null | https://openreview.net/forum?id=kKUWbb_gzI0 | https://openreview.net/pdf?id=kKUWbb_gzI0 | It is AI’s Turn to Ask Human a Question: Question and Answer Pair Generation for Children Storybooks in FairytaleQA Dataset | Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers and parents sometimes may not know what questions they should ask best help children to develop their narrative understanding abilities. We design an automated question-answer ge... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['question-answer-generation'] | ['natural-language-processing'] | [-2.01771468e-01 7.10599065e-01 4.06264514e-01 -5.84389210e-01
-1.15426719e+00 -9.97439384e-01 2.87798941e-01 3.54743540e-01
3.58086079e-01 8.43913615e-01 4.84342694e-01 -8.02672446e-01
-6.50719181e-02 -1.42243648e+00 -5.27732611e-01 3.47819269e-01
4.48270708e-01 1.04563761e+00 8.99468184e-01 -8.55515778... | [11.513737678527832, 8.02258014678955] |
a2de8ec6-4579-4c08-b2a0-9faf70f3ea3e | t-fftradnet-object-detection-with-swin-vision | 2303.16940 | null | https://arxiv.org/abs/2303.16940v1 | https://arxiv.org/pdf/2303.16940v1.pdf | T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals | Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rai... | ['Robert Laganiere', 'Martin Bouchard', 'James Giroux'] | 2023-03-29 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 5.23603201e-01 -3.28915834e-01 5.34926593e-01 -4.24295753e-01
-5.20793438e-01 -4.35100138e-01 8.90339196e-01 -9.50779691e-02
-7.50170648e-01 5.81029773e-01 -4.23763841e-01 -2.90336192e-01
-4.25286531e-01 -1.23353028e+00 -3.84342223e-01 -7.00519264e-01
-4.30300951e-01 5.46946943e-01 1.78628176e-01 -2.97572613... | [7.776658058166504, -1.2906707525253296] |
968a981e-71ae-4a00-aa3e-66d902efa8bd | safety-and-performance-why-not-both-bi | 2208.05969 | null | https://arxiv.org/abs/2208.05969v2 | https://arxiv.org/pdf/2208.05969v2.pdf | Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment | The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, which hinders the large-scale deployment on resource-restricted devices (e.g., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high perform... | ['Xiao Han', 'Leye Wang', 'Jie Zhu'] | 2022-08-11 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 2.73227721e-01 -1.49359424e-02 -2.06851110e-01 -1.31969929e-01
-3.84901017e-01 -2.51714498e-01 2.55229563e-01 4.85444553e-02
-4.37918194e-02 1.78950891e-01 -3.25671524e-01 -6.40048087e-01
-1.82386756e-01 -1.00426424e+00 -1.09365249e+00 -5.02931476e-01
-1.80557802e-01 7.01829940e-02 2.45919570e-01 1.52223691... | [6.866187572479248, 7.761033535003662] |
2decfd4a-78ba-4586-8686-3ece19d5dcdd | simulation-based-bayesian-inference-for-multi | 2109.14275 | null | https://arxiv.org/abs/2109.14275v1 | https://arxiv.org/pdf/2109.14275v1.pdf | Simulation-based Bayesian inference for multi-fingered robotic grasping | Multi-fingered robotic grasping is an undeniable stepping stone to universal picking and dexterous manipulation. Yet, multi-fingered grippers remain challenging to control because of their rich nonsmooth contact dynamics or because of sensor noise. In this work, we aim to plan hand configurations by performing Bayesian... | ['Gilles Louppe', 'Olivier Brüls', 'Norman Marlier'] | 2021-09-29 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-3.88504043e-02 1.28804922e-01 1.98253274e-01 -2.31725261e-01
-7.10381448e-01 -6.39490783e-01 4.54791337e-01 -3.53646427e-01
-3.50493550e-01 7.69397497e-01 -3.42082530e-01 -6.53012320e-02
-8.04128230e-01 -3.91179621e-01 -9.80364561e-01 -9.17752206e-01
-1.85245380e-01 9.20470417e-01 -3.06850791e-01 4.80570868... | [5.658863067626953, -0.5981153845787048] |
a0ba26ef-dd4c-474a-bf8a-8445538b9702 | d-dpcc-deep-dynamic-point-cloud-compression | 2205.01135 | null | https://arxiv.org/abs/2205.01135v1 | https://arxiv.org/pdf/2205.01135v1.pdf | D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction | The non-uniformly distributed nature of the 3D dynamic point cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry with 3D motion e... | ['Dong Wang', 'Zhu Li', 'Yiling Xu', 'Linyao Gao', 'Tingyu Fan'] | 2022-05-02 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [-2.78862923e-01 -4.00718898e-01 1.53669650e-02 1.64793665e-03
-3.66365731e-01 -8.47681612e-02 3.94119263e-01 -3.65665585e-01
-3.62055004e-01 1.26246527e-01 4.34474766e-01 -3.21870968e-02
-3.24041843e-02 -7.51882672e-01 -1.05252659e+00 -5.97234964e-01
-3.36958766e-01 -1.76414344e-02 2.10633084e-01 -9.80168805... | [10.868632316589355, -1.7608287334442139] |
c0052fb4-f08a-49ba-b7e7-7a467d07908d | towards-poisoning-of-deep-learning-algorithms | 1708.08689 | null | http://arxiv.org/abs/1708.08689v1 | http://arxiv.org/pdf/1708.08689v1.pdf | Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization | A number of online services nowadays rely upon machine learning to extract
valuable information from data collected in the wild. This exposes learning
algorithms to the threat of data poisoning, i.e., a coordinate attack in which
a fraction of the training data is controlled by the attacker and manipulated
to subvert t... | ['Andrea Paudice', 'Luis Muñoz-González', 'Battista Biggio', 'Fabio Roli', 'Vasin Wongrassamee', 'Ambra Demontis', 'Emil C. Lupu'] | 2017-08-29 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.75525653e-01 -7.65124410e-02 -1.87456664e-02 1.78287178e-01
-5.29378116e-01 -1.26233554e+00 1.02465641e+00 3.20032090e-01
-7.44556427e-01 6.70944810e-01 -3.42979431e-01 -5.87660372e-01
2.36252998e-03 -9.40697610e-01 -8.68371725e-01 -9.66694653e-01
-3.91871601e-01 6.21090353e-01 2.63635039e-01 -3.51207197... | [5.685928821563721, 7.6347551345825195] |
c405efe7-204f-44a4-9674-39486f357665 | masksketch-unpaired-structure-guided-masked | 2302.05496 | null | https://arxiv.org/abs/2302.05496v1 | https://arxiv.org/pdf/2302.05496v1.pdf | MaskSketch: Unpaired Structure-guided Masked Image Generation | Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. In this paper, we introduce MaskSketch, an image generation ... | ['Irfan Essa', 'Kate Saenko', 'Kihyuk Sohn', 'Jose Lezama', 'Dina Bashkirova'] | 2023-02-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bashkirova_MaskSketch_Unpaired_Structure-Guided_Masked_Image_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bashkirova_MaskSketch_Unpaired_Structure-Guided_Masked_Image_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-to-image-translation', 'conditional-image-generation'] | ['computer-vision', 'computer-vision'] | [ 8.55902314e-01 3.70506316e-01 -4.37167138e-02 -4.23045307e-01
-6.59137964e-01 -7.22325087e-01 1.11655259e+00 -3.06846172e-01
1.04318976e-01 6.33341253e-01 2.00929761e-01 -1.45838067e-01
4.64834571e-01 -9.58236217e-01 -1.28341854e+00 -5.14335632e-01
5.18543124e-01 5.04918993e-01 1.00583859e-01 -1.55022383... | [11.466694831848145, -0.3659377694129944] |
42e3f44b-f5cd-4c78-93e6-2fc6ee4444cf | 3d-object-detection-method-based-on-yolo-and | 2005.02132 | null | https://arxiv.org/abs/2005.02132v1 | https://arxiv.org/pdf/2005.02132v1.pdf | 3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds | Lidar based 3D object detection and classification tasks are essential for autonomous driving(AD). A lidar sensor can provide the 3D point cloud data reconstruction of the surrounding environment. However, real time detection in 3D point clouds still needs a strong algorithmic. This paper proposes a 3D object detection... | ['Xuanyu YIN', 'Kentaro SHIMIZU', 'Weimin WANG', 'Yoko SASAKI'] | 2020-04-21 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-3.41881216e-01 -6.56730890e-01 1.96046144e-01 -3.69048089e-01
-2.28784874e-01 -4.01441365e-01 2.89552659e-01 2.98325509e-01
-6.64329886e-01 -1.29487246e-01 -8.25815797e-01 -5.92682958e-01
1.96436018e-01 -1.22141016e+00 -6.76877081e-01 -3.49285662e-01
5.00168316e-02 1.09317136e+00 1.04475892e+00 -2.78895378... | [7.7261061668396, -2.635901927947998] |
91068085-b6ba-4dc5-a731-eb133a0fdcb9 | systematic-analysis-of-the-impact-of-label | 2306.15994 | null | https://arxiv.org/abs/2306.15994v1 | https://arxiv.org/pdf/2306.15994v1.pdf | Systematic analysis of the impact of label noise correction on ML Fairness | Arbitrary, inconsistent, or faulty decision-making raises serious concerns, and preventing unfair models is an increasingly important challenge in Machine Learning. Data often reflect past discriminatory behavior, and models trained on such data may reflect bias on sensitive attributes, such as gender, race, or age. On... | ['R. Ghani', 'I. Sousa', 'C. Soares', 'I. Oliveira e Silva'] | 2023-06-28 | null | null | null | null | ['fairness', 'fairness', 'decision-making'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [ 2.48491541e-01 1.40476480e-01 -6.04046345e-01 -8.72686744e-01
-6.66304708e-01 -4.75478858e-01 4.72258717e-01 7.10566938e-01
-6.80441797e-01 9.70018089e-01 1.62898347e-01 -2.71952540e-01
-1.96683675e-01 -7.04833567e-01 -3.15273970e-01 -4.80037481e-01
4.88463104e-01 5.03054440e-01 -2.48165086e-01 1.95470765... | [8.885669708251953, 5.362449645996094] |
8a91177f-c9a2-4456-b3ff-c899b135c009 | accelerated-primal-dual-methods-with-enlarged | 2307.00296 | null | https://arxiv.org/abs/2307.00296v1 | https://arxiv.org/pdf/2307.00296v1.pdf | Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems | We consider a general class of nonsmooth optimal control problems with partial differential equation (PDE) constraints, which are very challenging due to its nonsmooth objective functionals and the resulting high-dimensional and ill-conditioned systems after discretization. We focus on the application of a primal-dual ... | ['Hangrui Yue', 'Xiaoming Yuan', 'Yongcun Song'] | 2023-07-01 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [-2.42457256e-01 -8.44085123e-03 5.92109933e-02 3.09245795e-01
-8.43325615e-01 -2.56965935e-01 2.22982504e-04 8.69219527e-02
-2.39825845e-01 1.06982505e+00 -2.81818390e-01 -3.23204637e-01
-2.19734579e-01 -6.63901687e-01 -7.83706248e-01 -8.90832484e-01
-1.87617078e-01 6.99656844e-01 -1.13972075e-01 -4.84179705... | [6.577547550201416, 3.4518189430236816] |
6e285c7e-541a-4671-baae-593f6ef7fe8f | temporal-lift-pooling-for-continuous-sign | 2207.08734 | null | https://arxiv.org/abs/2207.08734v1 | https://arxiv.org/pdf/2207.08734v1.pdf | Temporal Lift Pooling for Continuous Sign Language Recognition | Pooling methods are necessities for modern neural networks for increasing receptive fields and lowering down computational costs. However, commonly used hand-crafted pooling approaches, e.g., max pooling and average pooling, may not well preserve discriminative features. While many researchers have elaborately designed... | ['Wei Feng', 'Zekang Liu', 'Liqing Gao', 'Lianyu Hu'] | 2022-07-18 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 3.07214797e-01 -7.85502613e-01 -2.33694777e-01 -3.30006927e-01
-6.96773469e-01 -3.99426669e-01 2.90851295e-01 -6.89169705e-01
-6.97673678e-01 6.63002789e-01 5.57033837e-01 -2.28784252e-02
-1.99817479e-01 -4.98281509e-01 -5.22539973e-01 -9.43065584e-01
-1.39232472e-01 -6.58488393e-01 8.66978705e-01 -4.22234058... | [9.219744682312012, -6.47070837020874] |
6c07fe6d-609b-46be-b4cd-95f9fef8d910 | language-models-can-see-plugging-visual | 2205.02655 | null | https://arxiv.org/abs/2205.02655v2 | https://arxiv.org/pdf/2205.02655v2.pdf | Language Models Can See: Plugging Visual Controls in Text Generation | Generative language models (LMs) such as GPT-2/3 can be prompted to generate text with remarkable quality. While they are designed for text-prompted generation, it remains an open question how the generation process could be guided by modalities beyond text such as images. In this work, we propose a training-free frame... | ['Nigel Collier', 'Lingpeng Kong', 'Yan Wang', 'Dani Yogatama', 'Fangyu Liu', 'Yahui Liu', 'Tian Lan', 'Yixuan Su'] | 2022-05-05 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 7.88834751e-01 6.14759982e-01 1.22305751e-01 -7.66228884e-02
-9.81939375e-01 -6.16855264e-01 1.20903826e+00 -5.38243689e-02
-2.14720234e-01 5.07696033e-01 1.79507583e-01 -4.20064270e-01
4.25589085e-01 -8.94122481e-01 -1.11035252e+00 -6.73133850e-01
4.87015694e-01 6.38164818e-01 8.00034925e-02 -2.73649096... | [11.218948364257812, 0.321459025144577] |
2013239e-bfcf-4336-a9c0-a417d2627ed3 | coupling-knowledge-based-and-data-driven | null | null | https://aclanthology.org/W12-0510 | https://aclanthology.org/W12-0510.pdf | Coupling Knowledge-Based and Data-Driven Systems for Named Entity Recognition | null | ['Jean-Yves Antoine', 'Nathalie Friburger', 'Damien Nouvel', 'Arnaud Soulet'] | 2012-04-01 | null | null | null | ws-2012-4 | ['sequential-pattern-mining'] | ['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... | [-7.257062911987305, 3.695284128189087] |
4be4d0b9-c988-413e-ad38-0ccd33c8eeac | 4d-x-ray-ct-reconstruction-using-multi-slice | 1906.06601 | null | https://arxiv.org/abs/1906.06601v1 | https://arxiv.org/pdf/1906.06601v1.pdf | 4D X-Ray CT Reconstruction using Multi-Slice Fusion | There is an increasing need to reconstruct objects in four or more dimensions corresponding to space, time and other independent parameters. The best 4D reconstruction algorithms use regularized iterative reconstruction approaches such as model based iterative reconstruction (MBIR), which depends critically on the qual... | ['Craig A. J. Kemp', 'Thilo Balke', 'Gregery T. Buzzard', 'Charles A. Bouman', 'Soumendu Majee'] | 2019-06-15 | null | null | null | null | ['low-dose-x-ray-ct-reconstruction'] | ['medical'] | [-8.02307501e-02 -1.83760524e-01 4.17543977e-01 -1.20928630e-01
-1.20277131e+00 -1.79270193e-01 3.76210153e-01 -2.32665129e-02
-5.79262555e-01 6.55213654e-01 2.46048748e-01 -1.50082469e-01
-5.08135080e-01 -7.28853047e-01 -5.29491425e-01 -1.10732937e+00
-2.32196093e-01 8.83063257e-01 2.63204992e-01 -1.67749494... | [13.234664916992188, -2.507720470428467] |
7a118f25-5fc4-4366-9777-e2788b8c6cc0 | deep-de-aliasing-for-fast-compressive-sensing | 1705.07137 | null | http://arxiv.org/abs/1705.07137v1 | http://arxiv.org/pdf/1705.07137v1.pdf | Deep De-Aliasing for Fast Compressive Sensing MRI | Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical
applications in order to reduce the scanning cost and improve the patient
experience. This can also potentially increase the image quality by reducing
the motion artefacts and contrast washout. However, once an image field of view
and the desir... | ['Yike Guo', 'Greg Slabaugh', 'Simon Arridge', 'Jennifer Keegan', 'Hao Dong', 'Pier Luigi Dragotti', 'Guang Yang', 'Fangde Liu', 'David Firmin', 'Xujiong Ye', 'Simiao Yu'] | 2017-05-19 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [ 8.23682964e-01 9.37182605e-02 8.52696821e-02 -2.75673896e-01
-9.46860015e-01 -1.80329531e-01 3.07414889e-01 -1.51674449e-01
-6.89458907e-01 7.17366159e-01 1.08015031e-01 -1.98970646e-01
-2.98023403e-01 -6.16531312e-01 -7.24704266e-01 -1.03541303e+00
-1.36737019e-01 2.38427907e-01 1.11209519e-01 8.93715322... | [13.552154541015625, -2.392425537109375] |
ccd5d32c-6c9f-47b4-8284-5b198284e242 | modality-based-factorization-for-multimodal | 1811.12624 | null | https://arxiv.org/abs/1811.12624v3 | https://arxiv.org/pdf/1811.12624v3.pdf | Modality-based Factorization for Multimodal Fusion | We propose a novel method, Modality-based Redundancy Reduction Fusion (MRRF), for understanding and modulating the relative contribution of each modality in multimodal inference tasks. This is achieved by obtaining an $(M+1)$-way tensor to consider the high-order relationships between $M$ modalities and the output laye... | ['Pascale Fung', 'Peyman Momeni', 'Elham J. Barezi'] | 2018-11-30 | modality-based-factorization-for-multimodal-1 | https://aclanthology.org/W19-4331 | https://aclanthology.org/W19-4331.pdf | ws-2019-8 | ['personality-trait-recognition'] | ['computer-vision'] | [ 3.50691557e-01 -2.82965805e-02 5.89543991e-02 -4.35745209e-01
-3.50007802e-01 -2.62721390e-01 6.64945841e-01 2.76509613e-01
-6.81791246e-01 6.54318333e-01 2.78227299e-01 1.73934162e-01
-3.50876778e-01 -5.43822825e-01 -5.24654329e-01 -7.33747244e-01
7.72799626e-02 1.93434596e-01 -2.62609780e-01 -3.80316496... | [13.21065616607666, 5.1414794921875] |
f72e84d5-516c-4255-ab37-99f7fd1fc312 | fundus-image-analysis-for-age-related-macular | 2009.01548 | null | https://arxiv.org/abs/2009.01548v1 | https://arxiv.org/pdf/2009.01548v1.pdf | Fundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report | Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population. In this report, we propose deep learning based methods for retinal analysis using color fundus images for computer aided diagnosis of AMD. We leverage the recent state of the art deep networks for building a singl... | ['Sharath M. Shankaranarayana'] | 2020-09-03 | null | null | null | null | ['fovea-detection'] | ['medical'] | [ 9.97654498e-02 2.58617967e-01 3.70496005e-01 -2.18926013e-01
-6.12418950e-01 -9.96993482e-02 2.17159480e-01 -4.38104600e-01
-5.90764344e-01 8.85888994e-01 1.05571404e-01 -6.96965635e-01
4.68502969e-01 -8.34973037e-01 -4.27942097e-01 -3.74022543e-01
1.28261641e-01 2.10511714e-01 2.43808880e-01 2.23914951... | [15.8577241897583, -4.0324296951293945] |
52c36a91-1780-41e2-b9a0-2c285fc64303 | marf-the-medial-atom-ray-field-object | 2307.00037 | null | https://arxiv.org/abs/2307.00037v1 | https://arxiv.org/pdf/2307.00037v1.pdf | MARF: The Medial Atom Ray Field Object Representation | We propose Medial Atom Ray Fields (MARFs), a novel neural object representation that enables accurate differentiable surface rendering with a single network evaluation per camera ray. Existing neural ray fields struggle with multi-view consistency and representing surface discontinuities. MARFs address both using a med... | ['Theoharis Theoharis', 'Peder Bergebakken Sundt'] | 2023-06-30 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 2.82847613e-01 4.53028917e-01 3.54599625e-01 -3.98113728e-01
-1.13561261e+00 -6.52609646e-01 5.84654868e-01 -1.41884722e-02
-1.43800765e-01 3.09507579e-01 -1.86592769e-02 -1.92183867e-01
-1.10767774e-01 -1.04584146e+00 -1.03067815e+00 -3.26086909e-01
-2.15022024e-02 8.84537637e-01 2.61714578e-01 -2.61057734... | [8.72671890258789, -3.609804391860962] |
567e9922-1717-4aac-86d7-9cc38176558f | building-scalable-video-understanding | 2301.06866 | null | https://arxiv.org/abs/2301.06866v3 | https://arxiv.org/pdf/2301.06866v3.pdf | Building Scalable Video Understanding Benchmarks through Sports | Existing benchmarks for evaluating long video understanding falls short on two critical aspects, either lacking in scale or quality of annotations. These limitations arise from the difficulty in collecting dense annotations for long videos, which often require manually labeling each frame. In this work, we introduce an... | ['Yash Kant', 'Vishvak Murahari', 'Igor Gilitschenski', 'Karthik Narasimhan', 'Alex Zhang', 'Aniket Agarwal'] | 2023-01-17 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 1.44582719e-01 -3.37128848e-01 -5.03675222e-01 -3.35173696e-01
-1.28153312e+00 -8.78243506e-01 3.19995373e-01 -8.66669342e-02
-4.48492467e-01 3.52590263e-01 6.32021308e-01 2.07307190e-01
3.53773355e-01 -1.30697101e-01 -1.01384914e+00 -2.12450415e-01
-1.99579626e-01 4.82626587e-01 3.33353192e-01 1.43118147... | [10.209755897521973, 0.8023236393928528] |
d6c5d69f-f15c-4965-ace6-0bc75bbbdcaa | learning-to-extend-program-graphs-to-work-in | 2105.14038 | null | https://arxiv.org/abs/2105.14038v1 | https://arxiv.org/pdf/2105.14038v1.pdf | Learning to Extend Program Graphs to Work-in-Progress Code | Source code spends most of its time in a broken or incomplete state during software development. This presents a challenge to machine learning for code, since high-performing models typically rely on graph structured representations of programs derived from traditional program analyses. Such analyses may be undefined f... | ['Daniel Tarlow', 'Chris J. Maddison', 'Xuechen Li'] | 2021-05-28 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 2.16588125e-01 3.86242241e-01 -7.91649163e-01 -4.55861479e-01
-5.83093762e-01 -5.85063696e-01 2.81061888e-01 9.06113625e-01
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-1.78347036e-01 -8.83545041e-01 -8.63290489e-01 3.41613501e-01
-6.09849811e-01 6.29625618e-02 1.51534438e-01 2.67128218... | [7.56673002243042, 7.8383941650390625] |
a57a271f-eacd-4ec7-8614-ea9de0140663 | weisfeiler-and-lehman-go-cellular-cw-networks | 2106.12575 | null | https://arxiv.org/abs/2106.12575v3 | https://arxiv.org/pdf/2106.12575v3.pdf | Weisfeiler and Lehman Go Cellular: CW Networks | Graph Neural Networks (GNNs) are limited in their expressive power, struggle with long-range interactions and lack a principled way to model higher-order structures. These problems can be attributed to the strong coupling between the computational graph and the input graph structure. The recently proposed Message Passi... | ['Michael Bronstein', 'Guido Montúfar', 'Pietro Liò', 'Yu Guang Wang', 'Nina Otter', 'Fabrizio Frasca', 'Cristian Bodnar'] | 2021-06-23 | null | http://proceedings.neurips.cc/paper/2021/hash/157792e4abb490f99dbd738483e0d2d4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/157792e4abb490f99dbd738483e0d2d4-Paper.pdf | neurips-2021-12 | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [ 5.63935697e-01 5.11638939e-01 -1.87364057e-01 -2.07594335e-02
3.96320187e-02 -6.69037998e-01 9.83763516e-01 2.87724465e-01
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-6.88900232e-01 -1.10212672e+00 -1.07294595e+00 -9.49803293e-01
-7.57213652e-01 7.37857044e-01 2.27385119e-01 -5.28457105... | [6.805246829986572, 6.111117839813232] |
28090b96-7c4e-422c-9bc5-244256aee313 | deep-learning-for-optimal-volt-var-control | 2211.09557 | null | https://arxiv.org/abs/2211.09557v1 | https://arxiv.org/pdf/2211.09557v1.pdf | Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources | Given their intermittency, distributed energy resources (DERs) have been commissioned with regulating voltages at fast timescales. Although the IEEE 1547 standard specifies the shape of Volt/VAR control rules, it is not clear how to optimally customize them per DER. Optimal rule design (ORD) is a challenging problem as... | ['Vassilis Kekatos', 'Spyros Chatzivasileiadis', 'Sarthak Gupta'] | 2022-11-17 | null | null | null | null | ['unity'] | ['computer-vision'] | [-5.87126911e-01 -3.40900093e-01 1.61588229e-02 1.02022197e-02
-5.22099316e-01 -8.88206720e-01 3.03689867e-01 1.27343997e-01
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-4.83230650e-01 5.09196043e-01 -5.80888391e-01 -5.39325356... | [5.762209415435791, 2.600282669067383] |
d731e221-8dfc-4811-9159-71d084e04dbc | gda-generative-data-augmentation-techniques | 2305.16663 | null | https://arxiv.org/abs/2305.16663v2 | https://arxiv.org/pdf/2305.16663v2.pdf | GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks | Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training. Such annotations would be labor-intensive to obtain in practice. Existing work adopts data augmentation techniques to generate pseudo-annota... | ['Philip S. Yu', 'Irwin King', 'Chenwei Zhang', 'Xin Zhang', 'Zeqi Tan', 'Aiwei Liu', 'Xuming Hu'] | 2023-05-26 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 4.03561860e-01 8.59522402e-01 -1.52681366e-01 -5.08969903e-01
-7.41269231e-01 -4.34928566e-01 5.17817140e-01 1.39339074e-01
-3.99674624e-01 9.92086709e-01 4.76330698e-01 -3.20164323e-01
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1.90662727e-01 4.53723758e-01 8.64395965e-03 -5.03123581... | [9.461145401000977, 8.683223724365234] |
b4420966-9b3c-4baa-809b-b92cf7a015f5 | revisiting-data-free-knowledge-distillation | 2306.02368 | null | https://arxiv.org/abs/2306.02368v1 | https://arxiv.org/pdf/2306.02368v1.pdf | Revisiting Data-Free Knowledge Distillation with Poisoned Teachers | Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model. However, the security of the synthetic or out-of-distribution (OOD) data ... | ['Jiayu Zhou', 'Ruoxi Jia', 'Lingjuan Lyu', 'Shuyang Yu', 'Yi Zeng', 'Junyuan Hong'] | 2023-06-04 | null | null | null | null | ['backdoor-defense-for-data-free-distillation'] | ['adversarial'] | [-7.90544078e-02 5.16044259e-01 -2.92559683e-01 1.98162273e-01
-8.86593878e-01 -1.48472452e+00 5.49680352e-01 1.10013127e-01
-2.26009637e-01 7.24615872e-01 -2.57210821e-01 -1.12678742e+00
-1.98050458e-02 -9.22218621e-01 -1.29987288e+00 -6.42392933e-01
4.67022844e-02 2.64498964e-02 2.96856761e-01 -1.60986274... | [5.823169231414795, 7.69917106628418] |
90286745-0124-484a-8a40-7c24de6fdd0b | artificial-intelligence-control-in-4d | null | null | https://doi.org/10.1109/ACCESS.2020.3026193 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9204607 | Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications | This article argues that an efficient artificial intelligence control algorithm needs the built-in symmetries of an industrial robot manipulator to be further characterized and exploited. The product of this enhancement is a four-dimensional (4D) discrete cylindrical grid space that can directly replace complex robot m... | ['Lihui Wang', 'Andrea de Giorgio'] | 2020-09-23 | null | null | null | null | ['industrial-robots'] | ['robots'] | [-5.72014898e-02 4.23351824e-01 -1.04320228e-01 3.62329632e-01
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-4.96724933e-01 7.72800922e-01 -7.60775030e-01 -4.31796163e-01
-1.02353251e+00 -9.67078745e-01 -3.15897644e-01 -9.13968980e-01
-3.02637577e-01 1.12845325e+00 8.93222615e-02 -3.88231367... | [4.949461460113525, 1.4949122667312622] |
29b166a8-f073-42e5-8c6c-7f61627f8a85 | knowing-when-to-quit-selective-cascaded | 2108.00377 | null | https://arxiv.org/abs/2108.00377v2 | https://arxiv.org/pdf/2108.00377v2.pdf | Knowing When to Quit: Selective Cascaded Regression with Patch Attention for Real-Time Face Alignment | Facial landmarks (FLM) estimation is a critical component in many face-related applications. In this work, we aim to optimize for both accuracy and speed and explore the trade-off between them. Our key observation is that not all faces are created equal. Frontal faces with neutral expressions converge faster than faces... | ['Roy J. Jevnisek', 'Ishay Goldin', 'Noga Levy', 'Gil Shapira'] | 2021-08-01 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.47366181e-01 1.13769092e-01 -1.07018553e-01 -4.60660875e-01
-7.42924869e-01 -2.85602242e-01 4.97841060e-01 -5.11821508e-02
-3.76774609e-01 2.00701058e-01 1.62406817e-01 1.15553908e-01
2.76933342e-01 -6.50818884e-01 -6.49234176e-01 -6.03210211e-01
-1.26089171e-01 2.63576180e-01 -1.67241111e-01 -1.18453950... | [13.460769653320312, 0.3886638283729553] |
a448443b-f787-4805-a2ff-0ace11619b8b | coronary-artery-semantic-labeling-using-edge | 2305.12327 | null | https://arxiv.org/abs/2305.12327v1 | https://arxiv.org/pdf/2305.12327v1.pdf | Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network | Coronary artery disease (CAD) is one of the primary causes leading deaths worldwide. The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis d... | ['Weihua Zhou', 'Guang-Uei Hung', 'Zhihui Xu', 'Chen Zhao'] | 2023-05-21 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-7.56152943e-02 4.29592520e-01 -6.13785803e-01 -5.07244587e-01
-5.28496087e-01 -5.11243105e-01 -1.93994552e-01 2.14573145e-01
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-3.63337368e-01 -9.48613346e-01 -1.04195019e-03 -2.37671599e-01
-9.24781412e-02 5.32672703e-01 2.25057676e-01 2.25507542... | [14.56943416595459, -2.450978994369507] |
2004dd8c-7924-47da-9bab-2c50e7cec19a | single-uhd-image-dehazing-via-interpretable | 2202.08589 | null | https://arxiv.org/abs/2202.08589v1 | https://arxiv.org/pdf/2202.08589v1.pdf | Single UHD Image Dehazing via Interpretable Pyramid Network | Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time. To address the problem, we introduce the principle of infinite approximation of Taylor's theorem with the Laplace pyramid pattern to build a model which is capable of handling 4K hazy imag... | ['Tao Wang', 'Yunliang Zhuang', 'Chen Lv', 'Xiang Chen', 'Zhuoran Zheng', 'Boxue Xiao'] | 2022-02-17 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.58665940e-01 -2.03745201e-01 5.92378438e-01 -3.48869264e-02
-3.00901473e-01 1.34257630e-01 1.93607062e-01 -1.82781547e-01
-2.25497022e-01 2.29949251e-01 -3.09451312e-01 -3.23299050e-01
1.28063247e-01 -9.46955264e-01 -8.87393057e-01 -1.08334732e+00
-8.43756273e-02 -4.00510803e-02 5.78633547e-01 -4.32482213... | [10.88658618927002, -2.88037109375] |
ed7fe6e9-6d0f-4164-a807-549363411c7b | fine-grained-analysis-of-propaganda-in-news-1 | null | null | https://aclanthology.org/D19-1565 | https://aclanthology.org/D19-1565.pdf | Fine-Grained Analysis of Propaganda in News Article | Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning sy... | ['Rostislav Petrov', "Alberto Barr{\\'o}n-Cede{\\~n}o", 'Giovanni Da San Martino', 'Preslav Nakov', 'Seunghak Yu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 3.06207180e-01 3.14150155e-01 -6.63282156e-01 -1.66472763e-01
-8.68560076e-01 -5.45740902e-01 1.29061043e+00 4.42079663e-01
-4.33457494e-01 7.75245130e-01 1.13366163e+00 -6.18060470e-01
3.39698851e-01 -8.22624266e-01 -6.87000871e-01 -4.25261706e-01
3.31316829e-01 4.30793524e-01 -3.51081975e-02 -3.24543059... | [8.541291236877441, 10.598146438598633] |
4eeb8d98-91a2-48ba-a6b9-95f95a8a58ea | robustly-pre-trained-neural-model-for-direct | 2004.06216 | null | https://arxiv.org/abs/2004.06216v1 | https://arxiv.org/pdf/2004.06216v1.pdf | Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction | Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI applications. While previous studies exist, the state-of-the-art performance has significant room for improvement. Methods: We studied several variants... | ['Hua Xu', 'Jianfu Li', 'Hong Guan', 'Murthy Devarakonda'] | 2020-04-13 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 5.91057479e-01 6.00824654e-01 -7.26653039e-01 -4.23189372e-01
-1.07504845e+00 -3.06035280e-01 6.92873895e-01 7.00506151e-01
-6.47553205e-01 8.48913252e-01 6.14956439e-01 -6.38528168e-01
-5.40279925e-01 -3.90937090e-01 -3.59185100e-01 -4.18537140e-01
-6.14351034e-01 8.54973137e-01 2.39135206e-01 -4.95369881... | [8.501730918884277, 8.971166610717773] |
1005319c-5fb0-422d-b7d5-03bee6b0faa2 | typography-with-decor-intelligent-text-style | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Typography_With_Decor_Intelligent_Text_Style_Transfer_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Typography_With_Decor_Intelligent_Text_Style_Transfer_CVPR_2019_paper.pdf | Typography With Decor: Intelligent Text Style Transfer | Text effects transfer can dramatically make the text visually pleasing. In this paper, we present a novel framework to stylize the text with exquisite decor, which is ignored by the previous text stylization methods. Decorative elements pose a challenge to spontaneously handle basal text effects and decor, which are tw... | [' Zongming Guo', ' Shuai Yang', ' Jiaying Liu', 'Wenjing Wang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['text-effects-transfer'] | ['natural-language-processing'] | [ 4.09698814e-01 -2.11181313e-01 1.67909041e-01 -1.93135977e-01
-2.58314639e-01 -8.48166347e-01 6.64591730e-01 -4.78789032e-01
-8.32846239e-02 8.34700525e-01 5.24627268e-01 -6.12397045e-02
3.24001133e-01 -5.51824927e-01 -8.37474167e-01 -6.51991129e-01
7.56066442e-01 1.29618675e-01 2.80206829e-01 -4.00765598... | [11.591793060302734, -0.4471655786037445] |
1ee51f1c-47db-4fe6-8abd-f412b285736b | low-power-object-counting-with-hierarchical | 2007.01369 | null | https://arxiv.org/abs/2007.01369v1 | https://arxiv.org/pdf/2007.01369v1.pdf | Low-Power Object Counting with Hierarchical Neural Networks | Deep Neural Networks (DNNs) can achieve state-of-the-art accuracy in many computer vision tasks, such as object counting. Object counting takes two inputs: an image and an object query and reports the number of occurrences of the queried object. To achieve high accuracy on such tasks, DNNs require billions of operation... | ['Yung-Hsiang Lu', 'George K. Thiruvathukal', 'Sara Aghajanzadeh', 'Caleb Tung', 'Shreya Ghosh', 'Isha Ghodgaonkar', 'Abhinav Goel'] | 2020-07-02 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 2.06487969e-01 -1.82048649e-01 -2.85599321e-01 -3.04439485e-01
-1.37977526e-01 -2.80341804e-01 7.13636950e-02 4.77574706e-01
-8.16978276e-01 4.33103681e-01 -3.59875530e-01 -8.95519033e-02
3.16787064e-01 -1.28060937e+00 -6.86976731e-01 -5.48401654e-01
1.32697806e-01 4.32596058e-01 1.18589318e+00 5.53383827... | [8.838510513305664, -0.09952455759048462] |
51fe47d1-8104-41d4-b995-c4e4c98bc94a | automotive-radar-mutual-interference | 2307.04326 | null | https://arxiv.org/abs/2307.04326v1 | https://arxiv.org/pdf/2307.04326v1.pdf | Automotive Radar Mutual Interference Mitigation Based on Hough Transform in Time-Frequency Domain | With the development of autonomous driving technology, automotive radar has received unprecedented attention due to its day-and-night and all-weather working capability. It is worthwhile to note that more and more vehicles are equipped with automotive radars, resulting in mutual interference between radars. The interfe... | ['Lianying Ji', 'Weichuan Zhang', 'Yanbing Li'] | 2023-07-10 | null | null | null | null | ['autonomous-driving', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 3.07061702e-01 -4.59663242e-01 1.37000546e-01 -1.00744404e-01
-2.55783558e-01 -1.01075910e-01 4.83712345e-01 -6.33496523e-01
-3.51432920e-01 6.62708938e-01 -8.26399103e-02 -2.09166706e-01
-5.20005047e-01 -7.48102248e-01 1.64750759e-02 -9.91343737e-01
3.54756676e-02 -2.84619540e-01 4.31829989e-01 -3.15794587... | [6.492772102355957, 1.1723220348358154] |
7545b150-a404-4082-b45a-439db1c62d02 | multi-view-learning-with-privileged-weighted | 2201.11306 | null | https://arxiv.org/abs/2201.11306v1 | https://arxiv.org/pdf/2201.11306v1.pdf | Multi-view learning with privileged weighted twin support vector machine | Weighted twin support vector machines (WLTSVM) mines as much potential similarity information in samples as possible to improve the common short-coming of non-parallel plane classifiers. Compared with twin support vector machines (TWSVM), it reduces the time complexity by deleting the superfluous constraints using the ... | ['Huiru Wang', 'Ruxin Xu'] | 2022-01-27 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 7.20534995e-02 -1.14831299e-01 -7.34184802e-01 -3.13141942e-01
-3.08564544e-01 -3.39483917e-01 3.34882915e-01 -3.03183168e-01
-3.73400971e-02 6.81115687e-01 -2.73899406e-01 -1.69053540e-01
-7.15165317e-01 -8.85061443e-01 -3.24372262e-01 -1.05193162e+00
4.44721192e-01 3.14285338e-01 1.69736832e-01 -2.42698044... | [8.368111610412598, 4.5124077796936035] |
776057f0-735e-4a73-a400-963b15ed3cd8 | shallow-pooling-for-sparse-labels | 2109.00062 | null | https://arxiv.org/abs/2109.00062v2 | https://arxiv.org/pdf/2109.00062v2.pdf | Shallow pooling for sparse labels | Recent years have seen enormous gains in core IR tasks, including document and passage ranking. Datasets and leaderboards, and in particular the MS MARCO datasets, illustrate the dramatic improvements achieved by modern neural rankers. When compared with traditional test collections, the MS MARCO datasets employ substa... | ['Charles L. A. Clarke', 'Xinyi Yan', 'Alexandra Vtyurina', 'Negar Arabzadeh'] | 2021-08-31 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.02263948e-02 -1.86671332e-01 -4.32328105e-01 -4.82841939e-01
-1.51705217e+00 -1.05612588e+00 6.20278656e-01 4.24361438e-01
-7.98998296e-01 6.73631608e-01 8.69620025e-01 -3.22999388e-01
-6.36148036e-01 -6.54656410e-01 -8.48348260e-01 -2.60647535e-01
-1.23073600e-01 8.77588272e-01 2.06373170e-01 -6.00903749... | [11.48799991607666, 7.579523086547852] |
4f3fa442-1712-4363-ab6c-66b58fdde7dc | cross-clinic-de-identification-of-swedish | null | null | https://aclanthology.org/2022.legal-1.10 | https://aclanthology.org/2022.legal-1.10.pdf | Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats | Privacy preservation of sensitive information is one of the main concerns in clinical text mining. Due to the inherent privacy risks of handling clinical data, the clinical corpora used to create the clinical Named Entity Recognition (NER) models underlying clinical de-identification systems cannot be shared. This situ... | ['Marina Santini', 'Thomas Vakili', 'Olle Bridal'] | null | null | null | null | legal-lrec-2022-6 | ['de-identification'] | ['natural-language-processing'] | [ 2.16299355e-01 3.37963998e-01 -2.89261080e-02 -4.71606731e-01
-8.54724407e-01 -6.98994040e-01 2.29132578e-01 9.75838006e-01
-9.01358366e-01 9.45909202e-01 6.37933612e-01 -5.67613482e-01
-3.96903247e-01 -3.07412505e-01 -1.93526506e-01 -6.60152256e-01
1.34954154e-01 6.50555015e-01 -1.57850131e-01 3.81241918... | [6.809483051300049, 7.027385234832764] |
c94fe118-e2da-4e09-8dea-994459e18fae | retico-an-incremental-framework-for-spoken | null | null | https://aclanthology.org/2020.sigdial-1.6 | https://aclanthology.org/2020.sigdial-1.6.pdf | Retico: An incremental framework for spoken dialogue systems | In this paper we present the newest version of retico - a python-based incremental dialogue framework to create state-of-the-art spoken dialogue systems and simulations. Retico provides a range of incremental modules that are based on services like Google ASR, Google TTS and Rasa NLU. Incremental networks can be create... | ['Thilo Michael'] | null | null | null | null | sigdial-acl-2020-7 | ['spoken-dialogue-systems'] | ['speech'] | [-4.79898900e-01 7.00786352e-01 3.23479801e-01 -7.37054527e-01
-4.55352634e-01 -7.34606445e-01 9.19690669e-01 -1.27581581e-01
-1.04838185e-01 6.98707998e-01 7.73652136e-01 -9.07915652e-01
2.31094271e-01 -7.25875735e-01 -1.40365958e-01 1.31461844e-01
-1.34570867e-01 1.09488583e+00 3.80889773e-01 -1.27939868... | [12.886287689208984, 7.935369968414307] |
43847a2c-27c0-4d31-9f3d-1ac4db4f287e | interpretable-machine-learning-accelerated | 2304.03928 | null | https://arxiv.org/abs/2304.03928v1 | https://arxiv.org/pdf/2304.03928v1.pdf | Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation | Crops are constantly challenged by different environmental conditions. Seed treatment by nanomaterials is a cost-effective and environmentally-friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected ... | ['Fang Cheng', 'Yingchao He', 'Da Liu', 'Maozhen Qu', 'Sam F. Y. Li', 'Dan Luo', 'Hengjie Yu'] | 2023-04-08 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 6.28679633e-01 -4.35149223e-01 -4.45011288e-01 2.80311882e-01
7.89478123e-02 -1.01229954e+00 -9.42205265e-02 1.03761101e+00
2.32886627e-01 7.62597084e-01 -8.03840384e-02 -8.14693391e-01
-3.49494904e-01 -1.11121583e+00 -5.86347520e-01 -1.07474029e+00
-3.36489119e-02 -3.58198255e-01 2.20242068e-02 -6.94494724... | [9.269200325012207, -1.5725524425506592] |
2b2243e4-232f-4984-ab62-9ecfa5c92429 | home-activity-monitoring-using-low-resolution | 1811.05416 | null | http://arxiv.org/abs/1811.05416v1 | http://arxiv.org/pdf/1811.05416v1.pdf | Home Activity Monitoring using Low Resolution Infrared Sensor | Action monitoring in a home environment provides important information for
health monitoring and may serve as input into a smart home environment. Visual
analysis using cameras can recognise actions in a complex scene, such as
someones living room. However, although there the huge potential benefits and
importance, spe... | ['Timothy Volonakis', 'Lili Tao', 'Melvyn Smith', 'Kevin Chetty', 'Bo Tan', 'Yanguo Jing'] | 2018-11-13 | null | null | null | null | ['home-activity-monitoring'] | ['miscellaneous'] | [ 6.52284563e-01 2.67090220e-02 2.63874352e-01 -3.61283392e-01
-6.85508788e-01 -1.52110577e-01 3.28212202e-01 -8.65854695e-02
-8.99302900e-01 8.22099447e-01 6.36479855e-01 2.04674825e-01
1.10836394e-01 -6.37175739e-01 -2.76601791e-01 -8.86265218e-01
-1.07062191e-01 -4.96251099e-02 6.36800945e-01 1.36806592... | [7.264708042144775, 0.5409824252128601] |
56706ea5-eb26-4bf1-a595-46ef6e6d8f1e | brain-decoding-from-functional-mri-using-long | 1809.05561 | null | http://arxiv.org/abs/1809.05561v1 | http://arxiv.org/pdf/1809.05561v1.pdf | Brain decoding from functional MRI using long short-term memory recurrent neural networks | Decoding brain functional states underlying different cognitive processes
using multivariate pattern recognition techniques has attracted increasing
interests in brain imaging studies. Promising performance has been achieved
using brain functional connectivity or brain activation signatures for a
variety of brain decod... | ['Yong Fan', 'Hongming Li'] | 2018-09-14 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 7.04621732e-01 -6.37366951e-01 -5.16891852e-02 -5.99617064e-01
-3.07800710e-01 -2.81143844e-01 6.94334149e-01 -1.75409645e-01
-6.39696240e-01 5.68152785e-01 3.71932834e-01 -1.32396206e-01
-4.77831423e-01 -3.63883346e-01 -3.47749382e-01 -5.91271460e-01
-4.09840524e-01 1.83186501e-01 -6.75675422e-02 1.33520648... | [12.607540130615234, 3.389878034591675] |
3fd0ab4e-4632-42c2-9296-b5311c31c25d | learning-the-dimensionality-of-word | 1511.05392 | null | http://arxiv.org/abs/1511.05392v3 | http://arxiv.org/pdf/1511.05392v3.pdf | Learning the Dimensionality of Word Embeddings | We describe a method for learning word embeddings with data-dependent
dimensionality. Our Stochastic Dimensionality Skip-Gram (SD-SG) and Stochastic
Dimensionality Continuous Bag-of-Words (SD-CBOW) are nonparametric analogs of
Mikolov et al.'s (2013) well-known 'word2vec' models. Vector dimensionality is
made dynamic b... | ['Sachin Ravi', 'Eric Nalisnick'] | 2015-11-17 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-6.38069332e-01 -5.47728762e-02 -5.69101334e-01 -2.27937549e-01
-3.80505055e-01 -7.79500008e-01 1.12951946e+00 1.01427577e-01
-5.70378602e-01 3.68524790e-01 1.01037896e+00 -7.99175560e-01
-1.75488248e-01 -7.77082562e-01 -1.28416657e-01 -7.27618694e-01
-4.07665670e-01 4.40448672e-01 4.11042683e-02 -3.48298371... | [10.461138725280762, 8.685569763183594] |
3638bdab-0802-4e61-aa23-41c68046dfb7 | word-level-persian-lipreading-dataset | 2304.04068 | null | https://arxiv.org/abs/2304.04068v1 | https://arxiv.org/pdf/2304.04068v1.pdf | Word-level Persian Lipreading Dataset | Lip-reading has made impressive progress in recent years, driven by advances in deep learning. Nonetheless, the prerequisite such advances is a suitable dataset. This paper provides a new in-the-wild dataset for Persian word-level lipreading containing 244,000 videos from approximately 1,800 speakers. We evaluated the ... | ['Nasser Mozayani', 'Hossein Zeinali', 'Samin Heydarian', 'Ali Lashini', 'Javad Peymanfard'] | 2023-04-08 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-1.21879233e-02 1.12052642e-01 -6.85339510e-01 -4.41669710e-02
-1.43368840e+00 8.54782537e-02 5.53332448e-01 -2.32588485e-01
-5.52264094e-01 7.49058664e-01 4.75470692e-01 -2.25500949e-02
4.19528872e-01 -1.92797974e-01 -4.88549858e-01 -6.32311106e-01
9.93064865e-02 7.99202546e-02 2.87623703e-01 2.92089768... | [14.32300853729248, 5.0104756355285645] |
5db3251c-aea1-4c0b-910b-40cf0b061011 | e-2-net-an-edge-enhanced-network-for-accurate | 2007.09791 | null | https://arxiv.org/abs/2007.09791v1 | https://arxiv.org/pdf/2007.09791v1.pdf | E$^2$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans | Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, ... | ['Yu-Xing Tang', 'Ronald M. Summers', 'Yingying Zhu', 'Youbao Tang', 'Jing Xiao'] | 2020-07-19 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-3.47916722e-01 2.58729249e-01 -2.65568256e-01 -2.53677696e-01
-3.35943848e-01 -1.97172180e-01 2.38272205e-01 -2.36961190e-02
-9.00290906e-02 2.69189596e-01 3.04308981e-01 -3.76084954e-01
1.32663712e-01 -8.16773713e-01 -1.95562795e-01 -8.17439079e-01
-4.62205142e-01 5.44918954e-01 3.68874818e-01 2.39078432... | [14.496053695678711, -2.719008207321167] |
d5942524-ecdd-4a4e-978a-773328b70f68 | rethinking-pseudo-labels-for-semi-supervised | 2106.00168 | null | https://arxiv.org/abs/2106.00168v2 | https://arxiv.org/pdf/2106.00168v2.pdf | Rethinking Pseudo Labels for Semi-Supervised Object Detection | Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels as supervisory signals. However, when using pseudo labels, there is a lack of consideration in localization precision and amplified class imb... | ['Larry S. Davis', 'Abhinav Shrivastava', 'Zuxuan Wu', 'Hengduo Li'] | 2021-06-01 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.42969710e-01 1.61793441e-01 -4.42193091e-01 -6.82342827e-01
-1.47041333e+00 -8.04249704e-01 6.86074793e-01 3.14813018e-01
-7.10822582e-01 6.95657611e-01 -1.72076434e-01 5.46464510e-02
1.21594518e-01 -1.17779918e-01 -8.36222172e-01 -7.44248867e-01
2.89635122e-01 4.96940017e-01 4.89750057e-01 3.42179269... | [9.248537063598633, 1.2629212141036987] |
94f7c48e-4087-4739-8e5f-36d3041d7802 | double-graph-based-reasoning-for-document | 2009.13752 | null | https://arxiv.org/abs/2009.13752v1 | https://arxiv.org/pdf/2009.13752v1.pdf | Double Graph Based Reasoning for Document-level Relation Extraction | Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first ... | ['Lei LI', 'Runxin Xu', 'Shuang Zeng', 'Baobao Chang'] | 2020-09-29 | null | https://aclanthology.org/2020.emnlp-main.127 | https://aclanthology.org/2020.emnlp-main.127.pdf | emnlp-2020-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.39994860e-01 7.07922578e-01 -3.38362634e-01 -3.05972576e-01
-7.37852633e-01 -6.72047853e-01 6.75883770e-01 7.29243696e-01
-2.93790195e-02 8.63659859e-01 4.46987629e-01 -6.97513282e-01
-2.21861050e-01 -1.32322216e+00 -6.02530241e-01 4.26116735e-02
-3.52130353e-01 3.52510780e-01 4.53228831e-01 -1.61107615... | [9.210741996765137, 8.584807395935059] |
26770479-baf1-45bb-bd60-aa475dd9f475 | i-can-read-your-mind-control-mechanism | 2205.03556 | null | https://arxiv.org/abs/2205.03556v1 | https://arxiv.org/pdf/2205.03556v1.pdf | I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks | Recent years have witnessed the fast advance of security research for networked dynamical system (NDS). Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i.e., how to protect control mechanism secre... | ['Xinping Guan', 'Lin Cai', 'Yushan Li', 'Jianping He'] | 2022-05-07 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-7.46132014e-03 -1.41321095e-02 -4.89491105e-01 2.08645985e-01
-4.81628329e-02 -1.19608223e+00 5.43437898e-01 -1.93175673e-01
-1.50270626e-01 8.79743516e-01 -1.84952602e-01 -7.50229418e-01
-6.63370252e-01 -8.24577510e-01 -4.84267890e-01 -1.08276141e+00
-6.51057601e-01 -3.04342538e-01 7.20261633e-02 -3.51315647... | [5.800440311431885, 7.15212345123291] |
738b62b2-8f87-4725-86ba-c4e06ed18a41 | recovering-surveillance-video-using-rf-cues | 2212.13340 | null | https://arxiv.org/abs/2212.13340v1 | https://arxiv.org/pdf/2212.13340v1.pdf | Recovering Surveillance Video Using RF Cues | Video capture is the most extensively utilized human perception source due to its intuitively understandable nature. A desired video capture often requires multiple environmental conditions such as ample ambient-light, unobstructed space, and proper camera angle. In contrast, wireless measurements are more ubiquitous a... | ['Rabih Younes', 'Xiang Li'] | 2022-12-27 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 5.94553709e-01 -2.57719547e-01 3.09508648e-02 -3.89741451e-01
-7.40212262e-01 -8.78980994e-01 4.46848154e-01 -5.22683144e-01
-1.25700414e-01 7.11420238e-01 2.32595518e-01 -8.04100856e-02
2.24894620e-02 -7.55701602e-01 -1.06506610e+00 -6.03967845e-01
1.98665962e-01 -3.35378647e-01 -8.75381976e-02 3.82203870... | [10.6797456741333, -1.4250322580337524] |
d1b178f8-d548-4377-8feb-e8427ad6e237 | handsoff-labeled-dataset-generation-with-no | 2212.12645 | null | https://arxiv.org/abs/2212.12645v2 | https://arxiv.org/pdf/2212.12645v2.pdf | HandsOff: Labeled Dataset Generation With No Additional Human Annotations | Recent work leverages the expressive power of generative adversarial networks (GANs) to generate labeled synthetic datasets. These dataset generation methods often require new annotations of synthetic images, which forces practitioners to seek out annotators, curate a set of synthetic images, and ensure the quality of ... | ['Arjun Seshadri', 'Achal Dave', 'Mariya I. Vasileva', 'Austin Xu'] | 2022-12-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_HandsOff_Labeled_Dataset_Generation_With_No_Additional_Human_Annotations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_HandsOff_Labeled_Dataset_Generation_With_No_Additional_Human_Annotations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['keypoint-detection'] | ['computer-vision'] | [ 7.45290637e-01 4.98439580e-01 -2.85779964e-02 -4.80767846e-01
-1.29324436e+00 -8.86404455e-01 6.88552260e-01 -6.32900894e-01
-2.91023374e-01 8.93331170e-01 -1.21631168e-01 -2.01789916e-01
6.27577305e-01 -7.60127902e-01 -1.02308166e+00 -3.56233150e-01
5.05835712e-01 8.45211446e-01 1.28481448e-01 -1.49392396... | [11.407658576965332, -0.298392653465271] |
6c6056fb-d771-4e90-b6f5-28727899e25b | ssd-kd-a-self-supervised-diverse-knowledge | 2203.11490 | null | https://arxiv.org/abs/2203.11490v2 | https://arxiv.org/pdf/2203.11490v2.pdf | SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images | Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence. ... | ['Z. Jane Wang', 'Chunyan Miao', 'Tim K. Lee', 'Yuheng Wang', 'Yongwei Wang'] | 2022-03-22 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 2.92936206e-01 3.72980461e-02 -6.98558211e-01 -1.14810802e-01
-6.16774678e-01 -1.14517935e-01 3.31949532e-01 2.96674877e-01
-3.95892560e-01 5.60631990e-01 -1.41729549e-01 -2.32672408e-01
-4.19013172e-01 -1.01202500e+00 -2.58087546e-01 -9.19766128e-01
3.56235474e-01 4.32707332e-02 4.08775330e-01 7.36639416... | [15.547673225402832, -2.845900297164917] |
47a5aadd-0a4e-4010-9c9d-9b2a7905f086 | zero-shot-cross-lingual-transfer-is-a-hard | null | null | https://aclanthology.org/2021.insights-1.7 | https://aclanthology.org/2021.insights-1.7.pdf | Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing | The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages. While zero-shot cross-lingual transfer relies on multilingual word embeddings to apply a model trained on one language to another, Yarowski and Ngai (2001) propose the ... | ['Mark Steedman', 'Sabine Weber'] | null | null | null | null | emnlp-insights-2021-11 | ['multilingual-word-embeddings'] | ['methodology'] | [ 1.77679174e-02 5.06643116e-01 -2.01629266e-01 -6.29836440e-01
-9.75175083e-01 -8.47716391e-01 8.26166391e-01 2.54004091e-01
-1.02619445e+00 1.05609322e+00 3.83522242e-01 -5.35340846e-01
2.01943874e-01 -8.30364466e-01 -7.13846326e-01 -2.24904805e-01
1.71764553e-01 9.09081101e-01 6.43580109e-02 -2.31586844... | [10.597139358520508, 9.809503555297852] |
0f3d54ac-e0b3-438b-8bd1-b403159106ab | 190600672 | 1906.00672 | null | https://arxiv.org/abs/1906.00672v3 | https://arxiv.org/pdf/1906.00672v3.pdf | Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS | Neural TTS has demonstrated strong capabilities to generate human-like speech with high quality and naturalness, while its generalization to out-of-domain texts is still a challenging task, with regard to the design of attention-based sequence-to-sequence acoustic modeling. Various errors occur in those inputs with uns... | ['Yan Deng', 'Mutian He', 'Lei He'] | 2019-06-03 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 4.25623983e-01 3.33646238e-02 4.65186566e-01 -4.00088876e-01
-7.98537374e-01 -4.56316292e-01 2.72610784e-01 -4.13944483e-01
-3.95423383e-01 5.51022828e-01 1.13232605e-01 -5.87215185e-01
2.19158813e-01 -3.23074460e-01 -6.85797334e-01 -6.88564062e-01
3.47582310e-01 2.89326549e-01 4.98302191e-01 -4.11199898... | [14.683048248291016, 6.7006306648254395] |
fe6e9ba1-ac4c-4e4f-babf-6d126d278165 | real-time-scene-text-detection-with-1 | 2202.10304 | null | https://arxiv.org/abs/2202.10304v1 | https://arxiv.org/pdf/2202.10304v1.pdf | Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion | Recently, segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field, because of their superiority in detecting the text instances of arbitrary shapes and extreme aspect ratios, profiting from the pixel-level descriptions. However, the vast majority of the existing ... | ['Xiang Bai', 'Cong Yao', 'Zhaoyi Wan', 'Zhisheng Zou', 'Minghui Liao'] | 2022-02-21 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.98994863e-01 -6.09154165e-01 2.33481318e-01 -2.12664440e-01
-4.73545462e-01 -4.23240155e-01 6.09489501e-01 4.83746171e-01
-6.30424857e-01 2.20506545e-02 -2.23813921e-01 -7.36186206e-02
1.64020061e-01 -8.77856612e-01 -2.66697437e-01 -7.43427396e-01
5.23758829e-01 2.71376252e-01 1.03655016e+00 -1.23983614... | [12.061545372009277, 2.254253387451172] |
99a3600f-4032-41bb-9d29-a4ef731d736b | dynamic-advisor-based-ensemble-dynabe-case | 1805.12111 | null | http://arxiv.org/abs/1805.12111v4 | http://arxiv.org/pdf/1805.12111v4.pdf | Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies | Stock trend prediction is a challenging task due to the market's noise, and
machine learning techniques have recently been successful in coping with this
challenge. In this research, we create a novel framework for stock prediction,
Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas
based on... | ['Zhengyang Dong'] | 2018-05-24 | null | null | null | null | ['stock-trend-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-5.79581439e-01 -3.34328651e-01 -7.70886764e-02 -3.32253724e-01
-2.68369943e-01 -7.92490065e-01 6.78861976e-01 -1.57370120e-01
-2.75658578e-01 9.77249563e-01 -1.54965287e-02 -5.62701881e-01
-1.02845831e-02 -1.04071641e+00 -3.97284716e-01 -6.41359746e-01
-1.22566260e-01 7.96127737e-01 4.49380785e-01 -4.54999298... | [4.5749382972717285, 4.160758972167969] |
4e4de728-1f55-43b3-9f97-5b2f804ec91f | motif-based-graph-self-supervised-learning | 2110.00987 | null | https://arxiv.org/abs/2110.00987v2 | https://arxiv.org/pdf/2110.00987v2.pdf | Motif-based Graph Self-Supervised Learning for Molecular Property Prediction | Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success in various molecular generation and prediction tasks. In cases where labeled data is scarce, GNNs can be pre-trained on unlabeled molecular d... | ['Chee-Kong Lee', 'Chengqiang Lu', 'Hao Wang', 'Qi Liu', 'Zaixi Zhang'] | 2021-10-03 | null | http://proceedings.neurips.cc/paper/2021/hash/85267d349a5e647ff0a9edcb5ffd1e02-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/85267d349a5e647ff0a9edcb5ffd1e02-Paper.pdf | neurips-2021-12 | ['retrosynthesis'] | ['medical'] | [ 6.20032489e-01 5.36637120e-02 -6.55830204e-01 -3.38364929e-01
-5.05785108e-01 -7.33646333e-01 5.80149293e-01 4.47322786e-01
7.54285082e-02 1.01023877e+00 7.11450279e-02 -6.34601533e-01
-4.62502465e-02 -1.30366051e+00 -1.15461397e+00 -8.29398990e-01
-1.60468131e-01 3.03931713e-01 2.15483934e-01 -3.57217163... | [5.117594242095947, 5.925605773925781] |
5aa6e6c0-addd-415c-ad2a-66acf51092e2 | short-text-conversation-based-on-deep-neural | 1907.03070 | null | https://arxiv.org/abs/1907.03070v1 | https://arxiv.org/pdf/1907.03070v1.pdf | Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures | With the development of Natural Language Processing, Automatic question-answering system such as Waston, Siri, Alexa, has become one of the most important NLP applications. Nowadays, enterprises try to build automatic custom service chatbots to save human resources and provide a 24-hour customer service. Evaluation of ... | ['Chia-Hui Chang', 'Hsiang-En Cherng'] | 2019-07-06 | null | null | null | null | ['short-text-conversation'] | ['natural-language-processing'] | [-3.35922718e-01 1.73950478e-01 3.67438644e-01 -4.62631375e-01
-5.17423928e-01 -3.55924845e-01 6.24997973e-01 5.24289943e-02
-5.83352149e-01 9.15688396e-01 5.13639212e-01 -1.66547775e-01
1.97586119e-01 -7.91802645e-01 -5.89457690e-04 -2.88664132e-01
3.74398410e-01 5.67253828e-01 5.79479575e-01 -7.14694798... | [12.755526542663574, 7.726679801940918] |
efe93516-74ae-4cd3-8af2-7381d2076fa8 | materobot-material-recognition-in-wearable | 2302.14595 | null | https://arxiv.org/abs/2302.14595v1 | https://arxiv.org/pdf/2302.14595v1.pdf | MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments | Wearable robotics can improve the lives of People with Visual Impairments (PVI) by providing additional sensory information. Blind people typically recognize objects through haptic perception. However, knowing materials before touching is under-explored in the field of assistive technology. To fill this gap, in this wo... | ['Rainer Stiefelhagen', 'Kunyu Peng', 'Kailun Yang', 'Jiaming Zhang', 'Junwei Zheng'] | 2023-02-28 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [-9.77056548e-02 1.31106928e-01 -1.47834972e-01 3.35328653e-02
-6.16494477e-01 -3.19263488e-01 -3.21409076e-01 -2.45286867e-01
-3.17346245e-01 6.41713738e-01 2.00884104e-01 5.87960556e-02
-3.10608387e-01 -5.31617939e-01 -7.53627598e-01 -5.22410631e-01
1.79639563e-01 5.19072227e-02 6.88114464e-02 -1.93783596... | [7.741673946380615, -1.4363559484481812] |
8d6cee78-e241-41d7-9cdf-28cef974e31a | spts-single-point-text-spotting | 2112.07917 | null | https://arxiv.org/abs/2112.07917v6 | https://arxiv.org/pdf/2112.07917v6.pdf | SPTS: Single-Point Text Spotting | Existing scene text spotting (i.e., end-to-end text detection and recognition) methods rely on costly bounding box annotations (e.g., text-line, word-level, or character-level bounding boxes). For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotati... | ['Lianwen Jin', 'Xiang Bai', 'Chunhua Shen', 'Dahua Lin', 'Jing Li', 'Shenggao Zhu', 'Songxuan Lai', 'Mingxin Huang', 'Jiaxin Zhang', 'Yuliang Liu', 'Xinyu Wang', 'Dezhi Peng'] | 2021-12-15 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 6.95126414e-01 -2.94100434e-01 -5.48767447e-02 -3.08811188e-01
-9.33603704e-01 -5.41710734e-01 7.51114964e-01 3.38422805e-01
-5.11712849e-01 2.37369537e-01 -1.20378047e-01 -3.62297416e-01
3.57092470e-01 -5.49733758e-01 -9.03710306e-01 -5.47697783e-01
4.05809343e-01 7.12959588e-01 5.48972130e-01 -1.11787423... | [12.001815795898438, 2.254969835281372] |
6e6cbf5e-98e5-494b-a1b9-386050075e65 | integrating-physiological-time-series-and | 2003.11059 | null | https://arxiv.org/abs/2003.11059v2 | https://arxiv.org/pdf/2003.11059v2.pdf | Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction | Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more. To date, most methods designed to learn predictive models from ICU EHR data have focused on a single modality. In this ... | ['Satya Narayan Shukla', 'Benjamin M. Marlin'] | 2020-03-24 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 3.49740423e-02 -3.42320740e-01 -9.71578583e-02 -3.54527116e-01
-8.49081993e-01 -2.44614363e-01 2.51337886e-01 1.00683033e+00
-8.01236331e-02 9.31983531e-01 6.14902020e-01 -5.24427831e-01
-2.79182315e-01 -5.44938684e-01 -4.13476467e-01 -4.41457510e-01
-3.52319807e-01 4.76134777e-01 -5.41013777e-01 2.55941510... | [7.971783638000488, 6.209127902984619] |
9d7068df-fe91-4abb-a4aa-cec50af73c4e | cross-domain-neural-pitch-and-periodicity | 2301.12258 | null | https://arxiv.org/abs/2301.12258v2 | https://arxiv.org/pdf/2301.12258v2.pdf | Cross-domain Neural Pitch and Periodicity Estimation | Pitch is a foundational aspect of our perception of audio signals. Pitch contours are commonly used to analyze speech and music signals and as input features for many audio tasks, including music transcription, singing voice synthesis, and prosody editing. In this paper, we describe a set of techniques for improving th... | ['Bryan Pardo', 'Nathan Pruyne', 'Caedon Hsieh', 'Max Morrison'] | 2023-01-28 | null | null | null | null | ['music-transcription', 'singing-voice-synthesis'] | ['music', 'speech'] | [-1.84897751e-01 -5.22046983e-01 -1.66715294e-01 -1.26715645e-01
-9.23319817e-01 -6.70806885e-01 -1.94226220e-01 -1.81904390e-01
-3.96597028e-01 4.10956681e-01 6.39253110e-02 -3.50613207e-01
1.67428911e-01 -5.31517029e-01 -4.46985573e-01 -6.04523659e-01
-3.07217479e-01 1.29170701e-01 8.91629457e-02 8.62190053... | [15.516680717468262, 5.812486171722412] |
bbdee48d-6cd3-4e3b-bf5f-801372abaf1b | modifying-optimal-sat-based-approach-to-multi | 1707.00228 | null | http://arxiv.org/abs/1707.00228v1 | http://arxiv.org/pdf/1707.00228v1.pdf | Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants | In multi-agent path finding (MAPF) the task is to find non-conflicting paths
for multiple agents. In this paper we focus on finding suboptimal solutions for
MAPF for the sum-of-costs variant. Recently, a SAT-based approached was
developed to solve this problem and proved beneficial in many cases when
compared to other ... | ['Eli Boyarski', 'Pavel Surynek', 'Ariel Felner', 'Roni Stern'] | 2017-07-02 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-9.07919109e-02 2.78972656e-01 -3.29646587e-01 -7.82308728e-02
-5.30278802e-01 -8.72412801e-01 2.11841747e-01 2.62438834e-01
-1.09431349e-01 1.61159599e+00 -2.33472452e-01 -3.51403654e-01
-9.12957191e-01 -1.01660323e+00 -2.83749372e-01 -4.06025022e-01
-7.79065013e-01 1.35860598e+00 6.16781473e-01 -5.06775200... | [4.988466262817383, 1.8631861209869385] |
267b4e0e-cde4-4123-9cf1-978c1e2481fc | symmetric-dense-inception-network-for | null | null | https://openreview.net/forum?id=91RHZVOKj1M | https://openreview.net/pdf?id=91RHZVOKj1M | Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images | Deep-learning-based automatic analysis of the multiplex immunohistochemistry (mIHC) enables distinct cell populations to be localized on a large scale, providing insights into disease biology and therapeutic targets. However, standard deep-learning pipelines performed cell detection and classification as two-stage task... | ['Yinyin Yuan', 'Jonathan A. Ledermann', 'Teresa Marafioti', 'Ayse U. Akarca', 'Tami Grunewald', 'Hanyun Zhang'] | 2021-07-20 | null | null | null | miccai-workshop-compay-2021-9 | ['cell-detection'] | ['computer-vision'] | [ 2.11895183e-01 -1.11939706e-01 -2.45017275e-01 -1.47394225e-01
-1.14388585e+00 -5.26743174e-01 3.50556046e-01 7.53883064e-01
-7.40251005e-01 9.72978830e-01 -3.83531332e-01 -2.37760842e-01
1.75596967e-01 -6.28482699e-01 -3.85759085e-01 -1.35845923e+00
2.34168861e-02 5.40891349e-01 1.07542679e-01 3.06510419... | [15.058904647827148, -3.0920567512512207] |
9665d908-2363-42eb-8ade-4d8a23c17fc9 | robust-sound-guided-image-manipulation | 2208.14114 | null | https://arxiv.org/abs/2208.14114v3 | https://arxiv.org/pdf/2208.14114v3.pdf | Robust Sound-Guided Image Manipulation | Recent successes suggest that an image can be manipulated by a text prompt, e.g., a landscape scene on a sunny day is manipulated into the same scene on a rainy day driven by a text input "raining". These approaches often utilize a StyleCLIP-based image generator, which leverages multi-modal (text and image) embedding ... | ['Sang Ho Yoon', 'Sangpil Kim', 'Jinkyu Kim', 'Gyeongrok Oh', 'Wonmin Byeon', 'Seung Hyun Lee'] | 2022-08-30 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 5.03611803e-01 -1.66828245e-01 2.09888890e-01 -3.35343033e-01
-5.83308458e-01 -7.90446162e-01 9.15161669e-01 -3.16714942e-01
-1.53369695e-01 3.61828685e-01 4.02465880e-01 -2.36635968e-01
3.87561023e-01 -8.72743785e-01 -9.09308791e-01 -7.12949991e-01
3.29143316e-01 -1.98258385e-01 -2.15201408e-01 -3.37104797... | [11.556941032409668, -0.20375262200832367] |
37f6e94f-36b0-4ec2-93cb-9280bdfff8be | recurrent-ladder-networks | 1707.09219 | null | http://arxiv.org/abs/1707.09219v4 | http://arxiv.org/pdf/1707.09219v4.pdf | Recurrent Ladder Networks | We propose a recurrent extension of the Ladder networks whose structure is
motivated by the inference required in hierarchical latent variable models. We
demonstrate that the recurrent Ladder is able to handle a wide variety of
complex learning tasks that benefit from iterative inference and temporal
modeling. The arch... | ['Tele Hotloo Hao', 'Isabeau Prémont-Schwarz', 'Alexander Ilin', 'Rinu Boney', 'Harri Valpola', 'Antti Rasmus'] | 2017-07-28 | recurrent-ladder-networks-1 | http://papers.nips.cc/paper/7182-recurrent-ladder-networks | http://papers.nips.cc/paper/7182-recurrent-ladder-networks.pdf | neurips-2017-12 | ['music-modeling'] | ['music'] | [ 1.63866282e-01 -1.29778206e-01 -5.65507948e-01 -2.58019000e-01
-4.43891257e-01 -3.68552715e-01 7.20941901e-01 -2.58456856e-01
4.08184305e-02 5.05794346e-01 3.41942698e-01 -9.66870710e-02
-5.73324084e-01 -3.79064530e-01 -4.16983783e-01 -8.54668081e-01
-8.70752633e-01 4.91465747e-01 3.01915050e-01 -3.90938632... | [8.579971313476562, 0.9350833296775818] |
100b566a-4a87-4ef5-b405-96a4c4584fcf | predicting-electricity-infrastructure-induced | 2206.02930 | null | https://arxiv.org/abs/2206.02930v2 | https://arxiv.org/pdf/2206.02930v2.pdf | Predicting Electricity Infrastructure Induced Wildfire Risk in California | This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down points triggered by grid infrastructure collected between 2015 to 2019 in Pacific Gas & Electricity territory along with various weather, ve... | ['Duncan S. Callaway', 'Baihong Jin', 'Zheng Zhang', 'Meghana Bharadwaj', 'Mengqi Yao'] | 2022-06-06 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.08446017e-01 -4.72439587e-01 -4.95539725e-01 6.80610240e-02
-4.14865643e-01 -6.09850526e-01 8.67129028e-01 5.37797272e-01
-9.19600353e-02 9.37052608e-01 4.56217438e-01 -1.10361278e+00
-5.68335950e-01 -1.39182734e+00 -8.01703557e-02 -9.42349553e-01
-6.87031806e-01 2.34483808e-01 -1.93490699e-01 -3.90049577... | [6.202621936798096, 2.809185028076172] |
fa997e03-8176-4ee3-a632-9ee4fbc3e9d9 | facial-expression-recognition-at-the-edge-cpu | 2305.15422 | null | https://arxiv.org/abs/2305.15422v1 | https://arxiv.org/pdf/2305.15422v1.pdf | Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU | Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have shown promise in their ability to classify human facial expressions, however, large CNNs are not well-suited to be implemented on resource- and... | ['Ramtin Zand', 'Lareb Khan', 'Heath Smith', 'Mohammadreza Mohammadi'] | 2023-05-17 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-8.12350735e-02 -3.00418407e-01 -1.76462993e-01 -6.38072550e-01
1.04999661e-01 -2.22051933e-01 1.83641925e-01 -2.31116965e-01
-6.32439733e-01 4.11731005e-01 -3.16063315e-01 -3.89869779e-01
2.91871339e-01 -8.29851210e-01 -3.35320234e-01 -6.25144780e-01
-7.60751292e-02 -7.33698457e-02 -1.31016061e-01 -1.48650691... | [8.427695274353027, 2.7749664783477783] |
17babf5c-1044-402f-8ba4-9069b9aa7883 | a-probabilistic-rotation-representation-for | 2305.18947 | null | https://arxiv.org/abs/2305.18947v1 | https://arxiv.org/pdf/2305.18947v1.pdf | A Probabilistic Rotation Representation for Symmetric Shapes With an Efficiently Computable Bingham Loss Function | In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation, it cannot represent the ambiguity of the observation. In order to handle the ambiguity, the Bingham distribution is one promising solution. However, it requires co... | ['Koichi Nishiwaki', 'Takuya Ikeda', 'Hiroya Sato'] | 2023-05-30 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-4.53729302e-01 -2.25399837e-01 -3.30263793e-01 -4.96646643e-01
-6.09702587e-01 -2.72157103e-01 3.75829607e-01 -8.06264207e-02
-4.57488745e-01 8.39352608e-01 -4.44206089e-01 -1.39319301e-01
-1.81011707e-01 -9.41950440e-01 -8.81105065e-01 -7.57511914e-01
1.18451342e-01 5.14751673e-01 1.00541957e-01 5.90406470... | [8.474921226501465, -1.0333870649337769] |
61bb8260-2afd-4234-b227-0f9fb3cfd8d8 | toist-task-oriented-instance-segmentation | 2210.10775 | null | https://arxiv.org/abs/2210.10775v1 | https://arxiv.org/pdf/2210.10775v1.pdf | TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation | Current referring expression comprehension algorithms can effectively detect or segment objects indicated by nouns, but how to understand verb reference is still under-explored. As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs... | ['Ya-Qin Zhang', 'Guyue Zhou', 'Hao Zhao', 'Xiaoxue Chen', 'Yongliang Shi', 'Beiwen Tian', 'Pengfei Li'] | 2022-10-19 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 5.07488549e-01 4.92884755e-01 -2.29424447e-01 -7.11918890e-01
-1.07330692e+00 -6.68125927e-01 5.48601687e-01 1.12929985e-01
-6.84700072e-01 5.05367994e-01 1.23680115e-01 -3.10962290e-01
-1.66043907e-01 -7.81317592e-01 -9.03449535e-01 -4.69155997e-01
1.68867067e-01 8.52345705e-01 -5.93437534e-03 -2.92220235... | [10.379800796508789, 1.3293808698654175] |
3637919b-d153-46f0-acc0-1b11e998be4d | fusionnet-3d-object-classification-using | 1607.05695 | null | http://arxiv.org/abs/1607.05695v4 | http://arxiv.org/pdf/1607.05695v4.pdf | FusionNet: 3D Object Classification Using Multiple Data Representations | High-quality 3D object recognition is an important component of many vision
and robotics systems. We tackle the object recognition problem using two data
representations, to achieve leading results on the Princeton ModelNet
challenge. The two representations: 1. Volumetric representation: the 3D object
is discretized s... | ['Vishakh Hegde', 'Reza Zadeh'] | 2016-07-19 | null | null | null | null | ['3d-object-classification', '3d-object-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.32236469e-01 1.67898148e-01 -6.79393560e-02 -2.74727136e-01
-3.88279170e-01 -4.70793545e-01 8.68157685e-01 -6.11623898e-02
-3.06427777e-01 3.95817459e-01 5.25836349e-02 -4.49144602e-01
1.52473360e-01 -9.64797497e-01 -8.70293260e-01 -5.19470632e-01
-2.17934698e-01 3.39787275e-01 2.74270982e-01 1.68013766... | [8.132647514343262, -3.6849288940429688] |
3c3d88d3-55ba-42d6-970b-c4d375b20dfa | predictive-modeling-of-hospital-readmission | 2106.08488 | null | https://arxiv.org/abs/2106.08488v1 | https://arxiv.org/pdf/2106.08488v1.pdf | Predictive Modeling of Hospital Readmission: Challenges and Solutions | Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital... | ['Xingquan Zhu', 'Shuwen Wang'] | 2021-06-16 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 1.38671309e-01 -1.00118622e-01 -7.58720517e-01 -3.47823411e-01
-5.41370273e-01 -3.43105011e-02 -5.53409420e-02 1.01914179e+00
-1.76413089e-01 7.61746645e-01 6.24050021e-01 -6.49260938e-01
-8.18535924e-01 -7.09003747e-01 -1.55273721e-01 -6.11284256e-01
-2.33783513e-01 8.40863109e-01 -5.29029012e-01 1.87512130... | [7.982918739318848, 6.221606731414795] |
00356dd1-9599-48b9-9a3b-a119a046125e | deep-learning-based-generalized-models-for | 2011.06739 | null | https://arxiv.org/abs/2011.06739v3 | https://arxiv.org/pdf/2011.06739v3.pdf | Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables | Depression detection using vocal biomarkers is a highly researched area. Articulatory coordination features (ACFs) are developed based on the changes in neuromotor coordination due to psychomotor slowing, a key feature of Major Depressive Disorder. However findings of existing studies are mostly validated on a single d... | ['Carol Espy-Wilson', 'Nadee Seneviratne'] | 2020-11-13 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-2.59958059e-02 -2.94025332e-01 -2.19964847e-01 -3.74107033e-01
-9.18150604e-01 -3.78932655e-01 5.64539850e-01 2.90439934e-01
-5.70658624e-01 3.87029141e-01 4.23664957e-01 5.70222028e-02
-2.21520752e-01 -4.25199330e-01 1.23183727e-02 -3.34736526e-01
-1.23515785e-01 3.85391936e-02 -2.84012645e-01 -2.17198789... | [13.799832344055176, 5.067220211029053] |
0479f8a3-0b2d-43a7-9f39-20184704d6e4 | analysis-of-impact-of-emotions-on-target | 2208.07091 | null | https://arxiv.org/abs/2208.07091v1 | https://arxiv.org/pdf/2208.07091v1.pdf | Analysis of impact of emotions on target speech extraction and speech separation | Recently, the performance of blind speech separation (BSS) and target speech extraction (TSE) has greatly progressed. Most works, however, focus on relatively well-controlled conditions using, e.g., read speech. The performance may degrade in more realistic situations. One of the factors causing such degradation may be... | ['Jan Černocký', 'Ladislav Mošner', 'Tsubasa Ochiai', 'Marc Delcroix', 'Martin Kocour', 'Kateřina Žmolíková', 'Ján Švec'] | 2022-08-15 | null | null | null | null | ['speech-separation', 'speech-extraction'] | ['speech', 'speech'] | [-3.13609272e-01 -2.98384219e-01 3.84011418e-01 -3.30845296e-01
-8.61952960e-01 -6.45244360e-01 3.32067221e-01 -2.79040188e-01
-1.74705788e-01 4.98406351e-01 3.72783363e-01 -1.51925668e-01
1.38325244e-01 9.99368131e-02 -4.32714760e-01 -9.86076057e-01
-4.99685071e-02 -1.91163458e-02 -2.10076839e-01 -6.35827407... | [14.53093433380127, 6.003451347351074] |
6397d4a3-68f6-49b6-b1a2-c6831586cb03 | contrastive-weighted-learning-for-near | 2211.03073 | null | https://arxiv.org/abs/2211.03073v2 | https://arxiv.org/pdf/2211.03073v2.pdf | Contrastive Weighted Learning for Near-Infrared Gaze Estimation | Appearance-based gaze estimation has been very successful with the use of deep learning. Many following works improved domain generalization for gaze estimation. However, even though there has been much progress in domain generalization for gaze estimation, most of the recent work have been focused on cross-dataset per... | ['Adam Lee'] | 2022-11-06 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.98352247e-01 -2.85589248e-02 -9.62765068e-02 -6.60486519e-01
-6.20418668e-01 -4.03546154e-01 2.57508099e-01 -5.62692761e-01
-4.10512835e-01 6.32286727e-01 1.88826043e-02 -1.29377797e-01
-3.57248858e-02 5.89003637e-02 -7.15498447e-01 -9.12248790e-01
2.56178677e-01 -1.64435640e-01 -1.12926938e-01 -1.48915306... | [14.141717910766602, 0.05177021026611328] |
ee0f51a1-9a01-48ac-8f69-047bcb713ba8 | person-in-wifi-fine-grained-person-perception | 1904.00276 | null | http://arxiv.org/abs/1904.00276v1 | http://arxiv.org/pdf/1904.00276v1.pdf | Person-in-WiFi: Fine-grained Person Perception using WiFi | Fine-grained person perception such as body segmentation and pose estimation
has been achieved with many 2D and 3D sensors such as RGB/depth cameras, radars
(e.g., RF-Pose) and LiDARs. These sensors capture 2D pixels or 3D point clouds
of person bodies with high spatial resolution, such that the existing
Convolutional ... | ['Stanislav Panev', 'Fei Wang', 'Sanping Zhou', 'Dong Huang', 'Jinsong Han'] | 2019-03-30 | person-in-wifi-fine-grained-person-perception-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Person-in-WiFi_Fine-Grained_Person_Perception_Using_WiFi_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Person-in-WiFi_Fine-Grained_Person_Perception_Using_WiFi_ICCV_2019_paper.pdf | iccv-2019-10 | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 4.97652829e-01 1.93168595e-01 5.12519181e-01 -2.85653859e-01
-4.51049894e-01 -6.15453243e-01 1.66624248e-01 -3.22886556e-01
-4.45130408e-01 4.70352322e-01 -5.85153466e-03 1.30383894e-01
1.14021957e-01 -1.21799994e+00 -1.06329381e+00 -4.54267740e-01
-2.86589712e-01 6.35593593e-01 9.48344991e-02 1.34934619... | [6.836877346038818, 0.3930681347846985] |
c86d4ee3-275e-4f17-b771-66971aaee6ff | open-set-recognition-of-breast-cancer | 2201.02923 | null | https://arxiv.org/abs/2201.02923v1 | https://arxiv.org/pdf/2201.02923v1.pdf | Open-Set Recognition of Breast Cancer Treatments | Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, predicting cancer treatments can naturally be formulated in terms of an open-set recognition probl... | ['Yuan Luo', 'Diego Klabjan', 'Alexander Cao'] | 2022-01-09 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.09770358e-01 3.04672033e-01 -4.39977199e-01 -2.43802696e-01
-1.23479903e+00 -4.70280528e-01 5.55273294e-01 1.50302708e-01
-2.51609176e-01 1.00725281e+00 4.81699891e-02 -4.54417080e-01
-2.04269215e-01 -6.66323364e-01 -8.63859832e-01 -9.64594722e-01
3.17114264e-01 8.79005134e-01 -5.05951345e-01 1.91601038... | [6.125430107116699, 5.77226448059082] |
6e0ff0ad-551f-44a9-a02b-028bf7867584 | enabling-my-robot-to-play-pictionary | 1608.03369 | null | http://arxiv.org/abs/1608.03369v1 | http://arxiv.org/pdf/1608.03369v1.pdf | Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition | Freehand sketching is an inherently sequential process. Yet, most approaches
for hand-drawn sketch recognition either ignore this sequential aspect or
exploit it in an ad-hoc manner. In our work, we propose a recurrent neural
network architecture for sketch object recognition which exploits the long-term
sequential and... | ['Babu R. Venkatesh', 'Ravi Kiran Sarvadevabhatla', 'Jogendra Kundu'] | 2016-08-11 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 1.40827954e-01 -5.20457923e-01 -2.90724903e-01 -3.40803117e-01
-3.32640201e-01 -6.88005924e-01 7.45529652e-01 -3.02170753e-01
-2.97751874e-01 1.72802210e-01 9.70445946e-02 -2.60151207e-01
-1.66291386e-01 -8.36552978e-01 -5.61558902e-01 -3.11839819e-01
-2.02506594e-02 5.88702142e-01 2.17626587e-01 -2.43440211... | [11.734039306640625, 0.430096834897995] |
4b225d67-24ae-4fe2-b94b-351eb6834615 | bootstrapping-ternary-relation-extractors | 1511.08952 | null | https://arxiv.org/abs/1511.08952v2 | https://arxiv.org/pdf/1511.08952v2.pdf | Bootstrapping Ternary Relation Extractors | Binary relation extraction methods have been widely studied in recent years. However, few methods have been developed for higher n-ary relation extraction. One limiting factor is the effort required to generate training data. For binary relations, one only has to provide a few dozen pairs of entities per relation, as t... | ['Ndapandula Nakashole'] | 2015-11-29 | null | null | null | null | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 1.73553348e-01 5.04055142e-01 -5.63960254e-01 -3.20897162e-01
-5.89392304e-01 -6.36936784e-01 5.95812559e-01 8.03091109e-01
-4.40548837e-01 1.36833930e+00 -1.71846300e-01 -5.21228194e-01
-2.94604599e-01 -1.22795045e+00 -4.28409398e-01 -5.92105500e-02
-7.22040609e-02 9.22810137e-01 -6.78572208e-02 -2.39371151... | [9.301706314086914, 8.653947830200195] |
d3c8f6b0-66eb-4e3e-adb4-9bde1d70d80d | natural-response-generation-for-chinese | 2302.08817 | null | https://arxiv.org/abs/2302.08817v1 | https://arxiv.org/pdf/2302.08817v1.pdf | Natural Response Generation for Chinese Reading Comprehension | Machine reading comprehension (MRC) is an important area of conversation agents and draws a lot of attention. However, there is a notable limitation to current MRC benchmarks: The labeled answers are mostly either spans extracted from the target corpus or the choices of the given candidates, ignoring the natural aspect... | ['Jia Li', 'Baoyuan Wang', 'Yinan Bao', 'Hongguang Li', 'Nuo Chen'] | 2023-02-17 | null | null | null | null | ['response-generation', 'reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.5977042e-01 3.1626269e-01 2.5378585e-01 -6.7994153e-01
-1.4370737e+00 -6.2675822e-01 7.2492415e-01 -4.1475713e-01
-2.4167152e-01 1.0712588e+00 9.2025304e-01 -2.3048125e-01
3.5834795e-01 -7.7718896e-01 -5.7797575e-01 -3.2326326e-01
4.8501903e-01 8.3791494e-01 1.4402555e-01 -7.4025983e-01
1.7603071e-01... | [12.299242973327637, 8.310821533203125] |
0c22b5ee-7690-4253-8dde-4b24fcc22d35 | billion-scale-similarity-search-with-gpus | 1702.08734 | null | http://arxiv.org/abs/1702.08734v1 | http://arxiv.org/pdf/1702.08734v1.pdf | Billion-scale similarity search with GPUs | Similarity search finds application in specialized database systems handling
complex data such as images or videos, which are typically represented by
high-dimensional features and require specific indexing structures. This paper
tackles the problem of better utilizing GPUs for this task. While GPUs excel at
data-paral... | ['Hervé Jégou', 'Matthijs Douze', 'Jeff Johnson'] | 2017-02-28 | null | null | null | null | ['image-similarity-search'] | ['computer-vision'] | [ 2.35276371e-02 -4.86995667e-01 -2.63635188e-01 -1.94574088e-01
-8.74336064e-01 -6.05030119e-01 6.09693348e-01 5.16097784e-01
-7.35364079e-01 2.05892444e-01 1.32577941e-01 -4.05616820e-01
-4.16140825e-01 -9.40274894e-01 -6.12570405e-01 -4.73821700e-01
-6.89063817e-02 6.55839980e-01 5.50076485e-01 -2.79073119... | [8.565876960754395, 3.425962448120117] |
7cd0ae01-5954-49da-ba54-28233ddc3ee2 | pdsum-prototype-driven-continuous | 2302.05550 | null | https://arxiv.org/abs/2302.05550v1 | https://arxiv.org/pdf/2302.05550v1.pdf | PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream | Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set. With the rapid development of online platforms for generating and distributing text-rich documents, there arises an urgent need for continuous... | ['Jiawei Han', 'Hou Pong Chan', 'Susik Yoon'] | 2023-02-10 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.76423478e-01 -4.11702275e-01 -5.55776954e-02 -8.93981382e-02
-9.27488804e-01 -7.14773715e-01 5.98752320e-01 1.17361069e+00
-1.69150963e-01 7.90572762e-01 6.54344380e-01 5.39270997e-01
-4.25012529e-01 -6.16508007e-01 -2.24497870e-01 -5.73137820e-01
-2.04962820e-01 6.49436414e-01 5.75046778e-01 -3.36411893... | [12.61446762084961, 9.558448791503906] |
f20aee3f-f7ac-4fa8-b795-3f03a0bc78fb | eulerian-phase-based-motion-magnification-for | 2212.04923 | null | https://arxiv.org/abs/2212.04923v1 | https://arxiv.org/pdf/2212.04923v1.pdf | Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings | Efficient and accurate detection of subtle motion generated from small objects in noisy environments, as needed for vital sign monitoring, is challenging, but can be substantially improved with magnification. We developed a complex Gabor filter-based decomposition method to amplify phases at different spatial wavelengt... | ['Tauhidur Rahman', 'Suren Jayasuriya', 'Deepak Ganesan', 'Stephanie Carreiro', 'Toral Surti', 'Md Farhan Tasnim Oshim'] | 2022-12-03 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 4.49766129e-01 -4.77026761e-01 2.63559788e-01 8.27560127e-02
-4.91068214e-01 -6.18604720e-01 -3.36521447e-01 -5.03355153e-02
-6.82969987e-01 8.49979699e-01 1.48805588e-01 -3.81359607e-01
-1.45257354e-01 -2.25181386e-01 1.14901468e-01 -1.05352664e+00
-4.44367975e-01 -1.40652031e-01 4.43174034e-01 3.02245378... | [13.944354057312012, 2.959150791168213] |
8ceb1e13-5e05-4a0f-92f9-15deebe1f421 | fast-distributed-inference-serving-for-large | 2305.05920 | null | https://arxiv.org/abs/2305.05920v1 | https://arxiv.org/pdf/2305.05920v1.pdf | Fast Distributed Inference Serving for Large Language Models | Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT. The interactive nature of these applications demand low job completion time (JCT) for model inference. Existing LLM serving systems use run-to-completion processing for inference jobs, which suffers from head-of-l... | ['Xin Jin', 'Xuanzhe Liu', 'Gang Huang', 'Zili Zhang', 'Yinmin Zhong', 'Bingyang Wu'] | 2023-05-10 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.29832551e-01 -1.57269880e-01 -4.98391896e-01 -4.69092160e-01
-6.55591905e-01 -2.99667269e-01 4.66789901e-01 5.31052835e-02
-5.27862012e-01 4.33083385e-01 -1.01625808e-01 -7.46052980e-01
9.45934728e-02 -8.56237471e-01 -5.67958713e-01 -4.50650871e-01
-1.55786827e-01 1.02072060e+00 5.65960050e-01 -3.69569622... | [8.62181282043457, 3.470515727996826] |
0aeb8c8b-1ee2-403c-b587-b68a76e9ce9f | deep-reparametrization-of-multi-frame-super | 2108.08286 | null | https://arxiv.org/abs/2108.08286v1 | https://arxiv.org/pdf/2108.08286v1.pdf | Deep Reparametrization of Multi-Frame Super-Resolution and Denoising | We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep repara... | ['Radu Timofte', 'Luc van Gool', 'Fisher Yu', 'Martin Danelljan', 'Goutam Bhat'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bhat_Deep_Reparametrization_of_Multi-Frame_Super-Resolution_and_Denoising_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bhat_Deep_Reparametrization_of_Multi-Frame_Super-Resolution_and_Denoising_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-frame-super-resolution', 'burst-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.56113076e-01 -1.09169737e-01 1.85067430e-02 -2.88429409e-01
-1.07881308e+00 -2.00061537e-02 8.63018095e-01 -2.96712101e-01
-3.25006366e-01 5.50506473e-01 4.94956166e-01 3.51009935e-01
-4.08052266e-01 -6.88704312e-01 -9.14023995e-01 -1.22503912e+00
1.03418216e-01 1.23898476e-01 -4.90765199e-02 -1.01745032... | [11.540752410888672, -2.3683090209960938] |
3dbc92bb-28f2-4fe5-af51-7b31431f4d3d | centering-based-neural-coherence-modeling | null | null | https://aclanthology.org/2020.emnlp-main.604 | https://aclanthology.org/2020.emnlp-main.604.pdf | Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments | Previous neural coherence models have focused on identifying semantic relations between adjacent sentences. However, they do not have the means to exploit structural information. In this work, we propose a coherence model which takes discourse structural information into account without relying on human annotations. We... | ['Michael Strube', 'Sungho Jeon'] | null | null | null | null | emnlp-2020-11 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 9.17691216e-02 5.58241665e-01 -3.09057325e-01 -2.09532842e-01
-7.35768616e-01 -6.82847619e-01 9.74839032e-01 5.08192718e-01
-3.24857384e-01 4.74969029e-01 1.02052295e+00 -4.21578139e-01
3.84565033e-02 -8.25869322e-01 -5.48793852e-01 -1.74240232e-01
2.47417465e-01 3.48537713e-01 3.45995903e-01 -3.52788627... | [11.219230651855469, 9.31920337677002] |
e9e686d9-eff3-4dfe-955e-b292600f5b10 | labeling-documents-with-timestamps-learning | null | null | https://aclanthology.org/P12-1011 | https://aclanthology.org/P12-1011.pdf | Labeling Documents with Timestamps: Learning from their Time Expressions | null | ['Nathanael Chambers'] | 2012-07-01 | null | null | null | acl-2012-7 | ['document-dating'] | ['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... | [-7.31355619430542, 3.650650978088379] |
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