67 citations · 112 across the 11 of their papers we have counts for
5 papers · 1 filter
Principal Manifold Flows
Edmond Cunningham, Adam Cobb, Susmit Jha
Normalizing flows map an independent set of latent variables to their samples using a bijective transformation. Despite the exact correspondence between samples and latent variable…
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Adam D. Cobb, Brian Jalaian
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks. Unfo…
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham +1
We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold H…
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity
Wolfgang Fruehwirt, Adam D. Cobb, Martin Mairhofer +11
As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing. To date, no low-cost, non-invasive biomarkers have been established…
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
Adam D. Cobb, Stephen J. Roberts, Yarin Gal
Current approaches in approximate inference for Bayesian neural networks minimise the Kullback-Leibler divergence to approximate the true posterior over the weights. However, this…