Efficient Approximate Inference with Walsh-Hadamard Variational Inference

no code implementations • 29 Nov 2019 • Simone Rossi, Sebastien Marmin, Maurizio Filippone

Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes.

Bayesian Inference Gaussian Processes +1

Walsh-Hadamard Variational Inference for Bayesian Deep Learning

no code implementations • NeurIPS 2020 • Simone Rossi, Sebastien Marmin, Maurizio Filippone

Over-parameterized models, such as DeepNets and ConvNets, form a class of models that are routinely adopted in a wide variety of applications, and for which Bayesian inference is desirable but extremely challenging.

Bayesian Inference Variational Inference