Bayesian Neural Networks: A Min-Max Game Framework
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866910719191023616 |
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| author | Hong, Junping Kuruoglu, Ercan Engin |
| author_facet | Hong, Junping Kuruoglu, Ercan Engin |
| contents | In deep learning, Bayesian neural networks (BNN) provide the role of robustness analysis, and the minimax method is used to be a conservative choice in the traditional Bayesian field. In this paper, we study a conservative BNN with the minimax method and formulate a two-player game between a deterministic neural network $f$ and a sampling stochastic neural network $f + r*ξ$. From this perspective, we understand the closed-loop neural networks with the minimax loss and reveal their connection to the BNN. We test the models on simple data sets, study their robustness under noise perturbation, and report some issues for searching $r$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_11126 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian Neural Networks: A Min-Max Game Framework Hong, Junping Kuruoglu, Ercan Engin Machine Learning Artificial Intelligence In deep learning, Bayesian neural networks (BNN) provide the role of robustness analysis, and the minimax method is used to be a conservative choice in the traditional Bayesian field. In this paper, we study a conservative BNN with the minimax method and formulate a two-player game between a deterministic neural network $f$ and a sampling stochastic neural network $f + r*ξ$. From this perspective, we understand the closed-loop neural networks with the minimax loss and reveal their connection to the BNN. We test the models on simple data sets, study their robustness under noise perturbation, and report some issues for searching $r$. |
| title | Bayesian Neural Networks: A Min-Max Game Framework |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2311.11126 |