Implicit Generative Prior for Bayesian Neural Networks

TL;DR

This paper introduces a novel Bayesian neural network framework using implicit generative priors and a combined variational inference and gradient ascent approach, improving uncertainty quantification and computational efficiency.

Contribution

It proposes the NA-EB framework that employs implicit generative priors and a hybrid inference method, advancing Bayesian neural network modeling for complex data.

Findings

Outperforms existing methods in prediction accuracy.

Provides reliable uncertainty quantification.

Demonstrates effectiveness on diverse datasets.

Abstract

Predictive uncertainty quantification is crucial for reliable decision-making in various applied domains. Bayesian neural networks offer a powerful framework for this task. However, defining meaningful priors and ensuring computational efficiency remain significant challenges, especially for complex real-world applications. This paper addresses these challenges by proposing a novel neural adaptive empirical Bayes (NA-EB) framework. NA-EB leverages a class of implicit generative priors derived from low-dimensional distributions. This allows for efficient handling of complex data structures and effective capture of underlying relationships in real-world datasets. The proposed NA-EB framework combines variational inference with a gradient ascent algorithm. This enables simultaneous hyperparameter selection and approximation of the posterior distribution, leading to improved computational efficiency. We establish the theoretical foundation of the framework through posterior and classification consistency. We demonstrate the practical applications of our framework through extensive evaluations on a variety of tasks, including the two-spiral problem, regression, 10 UCI datasets, and image classification tasks on both MNIST and CIFAR-10 datasets. The results of our experiments highlight the superiority of our proposed framework over existing methods, such as sparse variational Bayesian and generative models, in terms of prediction accuracy and uncertainty quantification.