Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866912862238146560 |
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| author | Wang, Han Kawasaki, Eiji Damblin, Guillaume Daniel, Geoffrey |
| author_facet | Wang, Han Kawasaki, Eiji Damblin, Guillaume Daniel, Geoffrey |
| contents | We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Bayesian modeling of a neural network's final layer has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can be used to enhance a canonically trained deep neural network with uncertainty-aware capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01761 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition Wang, Han Kawasaki, Eiji Damblin, Guillaume Daniel, Geoffrey Machine Learning We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Bayesian modeling of a neural network's final layer has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can be used to enhance a canonically trained deep neural network with uncertainty-aware capabilities. |
| title | Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.01761 |