Work

Minimax Bayesian Neural Networks

Year: 2025

Type: article

Abstract: 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.

Source: Entropy

DOI: 10.3390/e27040340

Authors: Junping Hong, Ercan Engin Kuruoglu

Institutions: Tsinghua Shenzhen International Graduate School, Tsinghua University

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Science, Technology and Innovation Commission of Shenzhen Municipality