From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference

Researchers established a new theoretical framework connecting Bayesian neural networks to Gaussian processes, developing improved convergence results and identifiability properties. They introduced a scalable computational method using Nyström approximation for training and prediction, demonstrating competitive performance on real-world datasets.