Heteroscedastic Uncertainty
Heteroscedastic Uncertainty is a type of uncertainty estimation in which the variability of model prediction errors is explicitly dependent on the input data, distinguishing it from homoscedastic uncertainty by allowing the noise level to vary across different regions of the input space.
87
Publications
3.4K
Citations
252
Authors
147
Institutions
Publications per year
2017–2026
38
Parents
87
1–5 of 87
252
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Institut national de recherche en sciences et technologies du numérique, France
2 publications · 142 citations
2
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2
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Wellcome Centre for Integrative Neuroimaging, United Kingdom
2 publications · 142 citations
2
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1–5 of 252
147
University College London
London, United Kingdom
3 h-index
7 publications
615 citations
ETH Zurich
Zurich, Switzerland
3 h-index
5 publications
142 citations
University of Oxford
Oxford, United Kingdom
3 h-index
3 publications
251 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
2 h-index
4 publications
68 citations
Politecnico di Milano
Milan, Italy
2 h-index
3 publications
150 citations
Siemens (United States)
Princeton, United States
2 h-index
4 publications
440 citations

University of Amsterdam
Amsterdam, The Netherlands
2 h-index
2 publications
145 citations
Harvard University
Cambridge, United States
2 h-index
2 publications
35 citations
1–8 of 147
Repository · 9 publications · 251 citations
9
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6 publications · 391 citations
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Repository · 3 publications · 59 citations
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3 publications · 69 citations
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TPAMI · 2 publications · 153 citations
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