Search: onr:"swepub:oai:DiVA.org:liu-175056" >
A General Framework...
A General Framework for Ensemble Distribution Distillation
-
- Lindqvist, Jakob, 1992 (author)
- Chalmers University of Technology, Department of Electrical Engineering, Gothenburg, Sweden,Chalmers tekniska högskola
-
- Olmin, Amanda, 1994- (author)
- Linköpings universitet,Statistik och maskininlärning,Filosofiska fakulteten,Linköping University
-
- Lindsten, Fredrik, 1984- (author)
- Linköpings universitet,Statistik och maskininlärning,Tekniska fakulteten,Linköping University
-
show more...
-
- Svensson, Lennart, 1976 (author)
- Chalmers University of Technology, Department of Electrical Engineering, Gothenburg, Sweden,Chalmers tekniska högskola
-
show less...
-
(creator_code:org_t)
- IEEE, 2020
- 2020
- English.
-
In: 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). - : IEEE. - 9781728166629 ; 2020-September
- Related links:
-
https://liu.diva-por... (primary) (Raw object)
-
show more...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://research.cha...
-
https://research.cha...
-
show less...
Abstract
Subject headings
Close
- Ensembles of neural networks have shown to give better predictive performance and more reliable uncertainty estimates than individual networks. Additionally, ensembles allow the uncertainty to be decomposed into aleatoric (data) and epistemic (model) components, giving a more complete picture of the predictive uncertainty. Ensemble distillation is the process of compressing an ensemble into a single model, often resulting in a leaner model that still outperforms the individual ensemble members. Unfortunately, standard distillation erases the natural uncertainty decomposition of the ensemble. We present a general framework for distilling both regression and classification ensembles in a way that preserves the decomposition. We demonstrate the desired behaviour of our framework and show that its predictive performance is on par with standard distillation.
Subject headings
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- Uncertainty
- Predictive models
- Data models
- Computational modeling
- Training
- Toy manufacturing industry
- Neural networks
- Ensemble
- distillation
Publication and Content Type
- ref (subject category)
- kon (subject category)
Find in a library
To the university's database