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Bayesian Neural Networks: A Min-Max Game Framework

arXiv 2311.11126 ↗First posted 18 Nov 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

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*xi. 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 47 references that are in the map. First posted on arXiv 18 Nov 2023.

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References · 29

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17 more
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