Bayesian Neural Networks: A Min-Max Game Framework
Junping Hong Ercan Engin Kuruoglu
Bayesian neural networks use random variables to describe the neural networks
rather than deterministic neural networks and are mostly trained by variational
inference which updates the mean and variance at the same time. Here, we
formulate the Bayesian neural networks as a minimax game problem. We do the
experiments on the MNIST data set and the primary result is comparable to the
existing closed-loop transcription neural network. Finally, we reveal the
connections between Bayesian neural networks and closed-loop transcription
neural networks, and show our framework is rather practical, and provide
another view of Bayesian neural networks.