Minimax Bayesian Neural Networks
Robustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field. Recently, researchers have applied the closed-loop idea to neural networks via the minimax m...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/1099-4300/27/4/340 |
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| author | Junping Hong Ercan Engin Kuruoglu |
| author_facet | Junping Hong Ercan Engin Kuruoglu |
| author_sort | Junping Hong |
| collection | DOAJ |
| description | Robustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field. Recently, researchers have applied the closed-loop idea to neural networks via the minimax method and proposed the closed-loop neural networks. In this paper, we study more conservative BNNs with the minimax method, which formulates a two-player game between a deterministic neural network and a sampling stochastic neural network. From this perspective, we reveal the connection between the closed-loop neural and the BNNs. We test the models on some simple data sets and study their robustness under noise perturbation, etc. |
| format | Article |
| id | doaj-art-bdd3c8ce9bf64dadb9dd40d4caf9466c |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-bdd3c8ce9bf64dadb9dd40d4caf9466c2025-08-20T02:28:34ZengMDPI AGEntropy1099-43002025-03-0127434010.3390/e27040340Minimax Bayesian Neural NetworksJunping Hong0Ercan Engin Kuruoglu1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaTsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaRobustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field. Recently, researchers have applied the closed-loop idea to neural networks via the minimax method and proposed the closed-loop neural networks. In this paper, we study more conservative BNNs with the minimax method, which formulates a two-player game between a deterministic neural network and a sampling stochastic neural network. From this perspective, we reveal the connection between the closed-loop neural and the BNNs. We test the models on some simple data sets and study their robustness under noise perturbation, etc.https://www.mdpi.com/1099-4300/27/4/340Bayesian neural networksrobustnessnoise perturbationminimax gameclosed-loop neural networksmaximal coding rate distortion |
| spellingShingle | Junping Hong Ercan Engin Kuruoglu Minimax Bayesian Neural Networks Entropy Bayesian neural networks robustness noise perturbation minimax game closed-loop neural networks maximal coding rate distortion |
| title | Minimax Bayesian Neural Networks |
| title_full | Minimax Bayesian Neural Networks |
| title_fullStr | Minimax Bayesian Neural Networks |
| title_full_unstemmed | Minimax Bayesian Neural Networks |
| title_short | Minimax Bayesian Neural Networks |
| title_sort | minimax bayesian neural networks |
| topic | Bayesian neural networks robustness noise perturbation minimax game closed-loop neural networks maximal coding rate distortion |
| url | https://www.mdpi.com/1099-4300/27/4/340 |
| work_keys_str_mv | AT junpinghong minimaxbayesianneuralnetworks AT ercanenginkuruoglu minimaxbayesianneuralnetworks |