This paper presents a framework for imposing meaningful functional priors using scalable Markov chain Monte Carlo (MCMC) sampling from an ap- proximation to the ...

Specifying sensible priors for Bayesian neural networks (BNNs) is key to obtain state-of-the-art predictive performance while obtaining sound predictive ...

Missing: Imposing | Show results with:Imposing

Duration: 58:09
Posted: Oct 8, 2023

Specifying sensible priors for Bayesian neural networks (BNNs) is key to obtain state-of-the-art predictive performance while obtaining sound predictive ...

Our proposal is to impose such functional priors on well-established architectures of neural networks by means of minimising the Wasserstein distance ...

Oct 21, 2024 · Function-space priors in Bayesian Neural Networks provide a more intuitive approach to embedding beliefs directly into the model's output, ...

Function-space priors in Bayesian Neural Networks provide a more intuitive approach to embedding beliefs directly into the model's output, thereby enhancing ...

The Bayesian treatment of neural networks dictates that a prior distribution is considered over the weight and bias parameters of the network.

This work presents an approach to specify a more principled prior for Bayesian Neural Networks that can leverage the well studied kernel design techniques ...