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检索条件"机构=Department of Mathematical Modeling and Data Analysis"
34 条 记 录,以下是1-10 订阅
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Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?  41
Is Epistemic Uncertainty Faithfully Represented by Evidentia...
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41st International Conference on Machine Learning, ICML 2024
作者: Jürgens, Mira Meinert, Nis Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modeling Ghent University Belgium Neustrelitz Germany Germany
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but... 详细信息
来源: 评论
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation  36
Pitfalls of Epistemic Uncertainty Quantification through Los...
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36th Conference on Neural Information Processing Systems, NeurIPS 2022
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
来源: 评论
Machine Learning Models and Technology for Classification of Forest on Satellite data  20
Machine Learning Models and Technology for Classification of...
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20th International Conference on Smart Technologies, EUROCON 2023
作者: Salii, Yevhenii Kuzin, Volodymyr Hohol, Anton Kussul, Nataliia Yailymova, Hanna National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Mathematical Modeling and Data Analysis Kyiv Ukraine National University of 'Kyiv-Mohyla Academy' Department of Informatics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Space Information Technologies and Systems Space Research Institute NASU-SSAU Department of Mathematical Modeling and Data Analysis Kyiv Ukraine
The paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, we propose 4 machine learning models. Two of them are based on Rand... 详细信息
来源: 评论
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
arXiv
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arXiv 2024年
作者: Jürgens, Mira Meinert, Nis Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Department of Data Analysis and Mathematical Modeling Ghent University Belgium Neustrelitz Germany Germany
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but... 详细信息
来源: 评论
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
来源: 评论
A CALIBRATION TEST FOR EVALUATING SET-BASED EPISTEMIC UNCERTAINTY REPRESENTATIONS
arXiv
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arXiv 2025年
作者: Jürgens, Mira Mortier, Thomas Hüllermeier, Eyke Bengs, Viktor Waegeman, Willem Department of Data Analysis and Mathematical Modeling Ghent University Belgium Department of Environment Ghent University Belgium Department of Informatics Munich Center for Machine Learning LMU Munich Germany
The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilistic predictors, also known as credal s... 详细信息
来源: 评论
Is epistemic uncertainty faithfully represented by evidential deep learning methods?  24
Is epistemic uncertainty faithfully represented by evidentia...
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Proceedings of the 41st International Conference on Machine Learning
作者: Mira Jürgens Nis Meinert Viktor Bengs Eyke Hüllermeier Willem Waegeman Department of Data Analysis and Mathematical Modeling Ghent University Belgium Institue of Communications and Navigation German Aerospace Center (DLR) Neustrelitz Germany Department of Informatics University of Munich (LMU) Germany
Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but...
来源: 评论
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
来源: 评论
Machine Learning Models and Technology for Classification of Forest on Satellite data
Machine Learning Models and Technology for Classification of...
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Eurocon
作者: Yevhenii Salii Volodymyr Kuzin Anton Hohol Nataliia Kussul Hanna Yailymova Department of Mathematical Modeling and Data Analysis National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Department of Informatics National University of “Kyiv-Mohyla Academy” Kyiv Ukraine Department of Space Information Technologies and Systems Space Research Institute NASU-SSAU Department of Mathematical Modeling and Data Analysis National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine
The paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, we propose 4 machine learning models. Two of them are based on Rand...
来源: 评论
Assessing Ukraine’s Solar Power Potential: A Comprehensive analysis Using Satellite data and Fuzzy Logic
Assessing Ukraine’s Solar Power Potential: A Comprehensive ...
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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)
作者: Sofiia Drozd Nataliia Kussul Department of Mathematical Modeling and Data Analysis National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Department of Space Information Systems and Technologies Space Research Institute NASU-SSAU Kyiv Ukraine
This study evaluates the land suitability for the placement of solar power stations in Ukraine, utilizing satellite data on climate factors (Global Horizontal Irradiance, temperature, precipitation, wind speed), topog... 详细信息
来源: 评论