Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

Conference on Neural Information Processing Systems (NeurIPS)


Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the function space such as periodicity or dependence on contexts such as inputs. To this end, this paper introduces two innovations: (i) a Gaussian process-based hierarchical model for network weights based on unit priors that can flexibly encode correlated weight structures, and (ii) input-dependent versions of these weight priors that can provide convenient ways to regularize the function space through the use of kernels defined on contextual inputs. We show these models provide desirable test-time uncertainty estimates on out-of-distribution data and demonstrate cases of modeling inductive biases for neural networks with kernels which help both interpolation and extrapolation from training data.

Related Publications

All Publications

NeurIPS - December 5, 2021

Local Differential Privacy for Regret Minimization in Reinforcement Learning

Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

NeurIPS - December 5, 2021

Hierarchical Skills for Efficient Exploration

Jonas Gehring, Gabriel Synnaeve, Andreas Krause, Nicolas Usunier

NeurIPS - December 5, 2021

Interpretable agent communication from scratch (with a generic visual processor emerging on the side)

Roberto Dessì, Eugene Kharitonov, Marco Baroni

Workshop on Online Abuse and Harms (WHOAH) at ACL - November 30, 2021

Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection

Lambert Mathias, Shaoliang Nie, Bertie Vidgen, Aida Davani, Zeerak Waseem, Douwe Kiela, Vinodkumar Prabhakaran

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy