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检索条件"机构=Research Center of Machine Learning and Data Analysis"
298 条 记 录,以下是101-110 订阅
排序:
A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks
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Simulation Modelling Practice and Theory 2025年 143卷
作者: Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea
The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (ME...
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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping
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Geoderma 2025年 459卷
作者: Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany —Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara S
Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are neede... 详细信息
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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet machine learning Model  23
Stroke Home Rehabilitation Approach Using Mobile Application...
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7th International Conference on Medical and Health Informatics, ICMHI 2023
作者: Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand
Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabiliti... 详细信息
来源: 评论
ReInform: Selecting paths with reinforcement learning for contextualized link prediction
arXiv
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arXiv 2022年
作者: Speranskaya, Marina Methias, Sameh Roth, Benjamin Center for Information and Language Processing LMU Munich Germany Technical University of Munich Munich Germany Research Group Data Mining and Machine Learning University of Vienna Vienna Austria
We propose to use reinforcement learning to inform transformer-based contextualized link prediction models by providing paths that are most useful for predicting the correct answer. This is in contrast to previous app... 详细信息
来源: 评论
Region-Aware Metric learning for Open World Semantic Segmentation via Meta-Channel Aggregation
arXiv
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arXiv 2022年
作者: Dong, Hexin Chen, Zifan Yuan, Mingze Xie, Yutong Zhao, Jie Yu, Fei Dong, Bin Zhang, Li Center for Data Science Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Beijing China
As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution ... 详细信息
来源: 评论
BLESSEMFLOOD21: ADVANCING FLOOD analysis WITH A HIGH-RESOLUTION GEOREFERENCED dataSET FOR HUMANITARIAN AID SUPPORT
arXiv
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arXiv 2024年
作者: Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany
Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained t... 详细信息
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An improved finite-time analysis of temporal difference learning with deep neural networks  24
An improved finite-time analysis of temporal difference lear...
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Proceedings of the 41st International Conference on machine learning
作者: Zhifa Ke Zaiwen Wen Junyu Zhang Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research and Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore
Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical under...
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An Improved Finite-time analysis of Temporal Difference learning with Deep Neural Networks
arXiv
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arXiv 2024年
作者: Ke, Zhifa Wen, Zaiwen Zhang, Junyu Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore
Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical under... 详细信息
来源: 评论
System Architecture for Reading and Interpreting Physical Printouts of Medical Forms
System Architecture for Reading and Interpreting Physical Pr...
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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)
作者: Ekaterina Snegireva Grigory R. Khazankin Igor Mikheenko Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Meshalkin National Medical Research Center Novosibirsk Russia
This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results ... 详细信息
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HERALD: A NATURAL LANGUAGE ANNOTATED LEAN 4 dataSET
arXiv
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arXiv 2024年
作者: Gao, Guoxiong Wang, Yutong Jiang, Jiedong Gao, Qi Qin, Zihan Xu, Tianyi Dong, Bin Peking University China National University of Singapore Singapore Center for Data Science Peking University China Beijing International Center for Mathematical Research The New Cornerstone Science Laboratory Peking University China Center for Machine Learning Research Peking University China
Verifiable formal languages like Lean have profoundly impacted mathematical reasoning, particularly through the use of large language models (LLMs) for automated reasoning. A significant challenge in training LLMs for... 详细信息
来源: 评论