102 citations · 114 across the 3 of their papers we have counts for
3 papers
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou +3
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approach…
Forward-Mode Automatic Differentiation in Julia
Jarrett Revels, Miles Lubin, Theodore Papamarkou
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently develop…
The Controlled Thermodynamic Integral for Bayesian Model Comparison
Chris J. Oates, Theodore Papamarkou, Mark Girolami
Bayesian model comparison relies upon the model evidence, yet for many models of interest the model evidence is unavailable in closed form and must be approximated. Many of the est…