Classifier Weighted Mixture models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866912178283479040 |
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| author | Argouarc'h, Elouan Desbouvries, François Barat, Eric Kawasaki, Eiji Dautremer, Thomas |
| author_facet | Argouarc'h, Elouan Desbouvries, François Barat, Eric Kawasaki, Eiji Dautremer, Thomas |
| contents | This paper proposes an extension of standard mixture stochastic models, by replacing the constant mixture weights with functional weights defined using a classifier. Classifier Weighted Mixtures enable straightforward density evaluation, explicit sampling, and enhanced expressivity in variational estimation problems, without increasing the number of components nor the complexity of the mixture components. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classifier Weighted Mixture models Argouarc'h, Elouan Desbouvries, François Barat, Eric Kawasaki, Eiji Dautremer, Thomas Machine Learning This paper proposes an extension of standard mixture stochastic models, by replacing the constant mixture weights with functional weights defined using a classifier. Classifier Weighted Mixtures enable straightforward density evaluation, explicit sampling, and enhanced expressivity in variational estimation problems, without increasing the number of components nor the complexity of the mixture components. |
| title | Classifier Weighted Mixture models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.02989 |