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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41-50 订阅
排序:
Informed machine learning  1
Informed Machine Learning
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丛书名: Cognitive Technologies
1000年
作者: Daniel Schulz Christian Bauckhage
This open access book presents the concept of Informed machine learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These rang... 详细信息
来源: 评论
System Architecture for Reading and Interpreting Physical Printouts of Medical Forms  22
System Architecture for Reading and Interpreting Physical Pr...
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22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021
作者: Snegireva, Ekaterina Khazankin, Grigory R. Mikheenko, Igor 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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An Improved Finite-time analysis of Temporal Difference learning with Deep Neural Networks  41
An Improved Finite-time Analysis of Temporal Difference Lear...
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41st International Conference on machine learning, ICML 2024
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
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ON SECOND-ORDER SCORING RULES FOR EPISTEMIC UNCERTAINTY QUANTIFICATION
arXiv
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arXiv 2023年
作者: Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium
It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture so-called aleatoric uncertainty of pr... 详细信息
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A REDUCTION-BASED FRAMEWORK FOR CONSERVATIVE BANDITS AND REINFORCEMENT learning  10
A REDUCTION-BASED FRAMEWORK FOR CONSERVATIVE BANDITS AND REI...
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10th International Conference on learning Representations, ICLR 2022
作者: Yang, Yunchang Wu, Tianhao Zhong, Han Garcelon, Evrard Pirotta, Matteo Lazaric, Alessandro Wang, Liwei Du, Simon S. Center for Data Science Peking University China University of California Berkeley United States Facebook AI Research Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China International Center for Machine Learning Research Peking University China University of Washington United States
We study bandits and reinforcement learning (RL) subject to a conservative constraint where the agent is asked to perform at least as well as a given baseline policy. This setting is particular relevant in real-world ... 详细信息
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hmBERT: Historical Multilingual Language Models for Named Entity Recognition
hmBERT: Historical Multilingual Language Models for Named En...
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2022 Conference and Labs of the Evaluation Forum, CLEF 2022
作者: Schweter, Stefan März, Luisa Schmid, Katharina Çano, Erion Bayerische Staatsbibliothek München Digital Library/ Munich Digitization Center Munich Germany Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Natural Language Processing Expert Center Data:Lab Volkswagen AG Munich Germany
Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usuall... 详细信息
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PITFALLS OF EPISTEMIC UNCERTAINTY QUANTIFICATION THROUGH LOSS MINIMISATION
arXiv
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arXiv 2022年
作者: Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The l... 详细信息
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On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification  23
On the Effectiveness of Heterogeneous Ensemble Methods for R...
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23rd IEEE International Conference on machine learning and Applications, ICMLA 2024
作者: Klüttermann, Simon Rutinowski, Jérôme Polachowski, Frederik Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel Paderborn University Paderborn Germany Tu Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany
In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace com... 详细信息
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Pitfalls in EEG-based brain effective connectivity analysis
Pitfalls in EEG-based brain effective connectivity analysis
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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011
作者: Haufe, Stefan Nikulin, Vadim V. Nolte, Guido Müller, Klaus-Robert Berlin Institute of Technology Machine Learning Germany Bernstein Focus Neurotechnology Berlin Germany Neurophysics Charité University Medicine Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Intelligent Data Analysis Fraunhofer Institute FIRST Berlin Germany
We consider the problem of estimating brain effective connectivity from electroencephalographic (EEG) measurements, which is challenging due to instantaneous correlations in the sensor data caused by volume conduction... 详细信息
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Improving Generalization and Convergence by Enhancing Implicit Regularization  38
Improving Generalization and Convergence by Enhancing Implic...
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38th Conference on Neural Information Processing Systems, NeurIPS 2024
作者: Wang, Mingze Wang, Jinbo He, Haotian Wang, Zilin Huang, Guanhua Xiong, Feiyu Li, Zhiyu Weinan, E. Wu, Lei School of Mathematical Sciences Peking University China Center for Machine Learning Research Peking University China China AI for Science Institute China School of Data Science University of Science and Technology of China China ByteDance Research China
In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence. Specifically, IRE decou...
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