تقرير
Bayesian posterior approximation with stochastic ensembles
| العنوان: | Bayesian posterior approximation with stochastic ensembles |
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| المؤلفون: | Balabanov, Oleksandr, Mehlig, Bernhard, Linander, Hampus |
| المصدر: | CVPR (2023) 13701-13711 |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning |
| الوصف: | We introduce ensembles of stochastic neural networks to approximate the Bayesian posterior, combining stochastic methods such as dropout with deep ensembles. The stochastic ensembles are formulated as families of distributions and trained to approximate the Bayesian posterior with variational inference. We implement stochastic ensembles based on Monte Carlo dropout, DropConnect and a novel non-parametric version of dropout and evaluate them on a toy problem and CIFAR image classification. For both tasks, we test the quality of the posteriors directly against Hamiltonian Monte Carlo simulations. Our results show that stochastic ensembles provide more accurate posterior estimates than other popular baselines for Bayesian inference. Comment: 19 pages, CVPR 2023 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/CVPR52729.2023.01317 |
| URL الوصول: | http://arxiv.org/abs/2212.08123 |
| رقم الأكسشن: | edsarx.2212.08123 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/CVPR52729.2023.01317 |
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