30 citations · 30 across the 6 of their papers we have counts for
3 papers · 1 filter
Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
Rajit Rajpal, Benedict Leimkuhler, Yuanhao Jiang
Bayesian neural networks (BNNs) require scalable sampling algorithms to approximate posterior distributions over parameters. Existing stochastic gradient Markov Chain Monte Carlo (…
A Langevin sampling algorithm inspired by the Adam optimizer
Benedict Leimkuhler, René Lohmann, Peter Whalley
We present a framework for adaptive-stepsize MCMC sampling based on time-rescaled Langevin dynamics, in which the stepsize variation is dynamically driven by an additional degree o…
Efficient Langevin sampling with position-dependent diffusion
Eugen Bronasco, Benedict Leimkuhler, Dominic Phillips +1
We introduce a numerical method for Brownian dynamics with position dependent diffusion tensor which is second order accurate for sampling the invariant measure while requiring onl…