no code implementations • 20 Nov 2015 • Nathan Wiebe, Christopher Granade, Ashish Kapoor, Krysta M. Svore
We provide a method for approximating Bayesian inference using rejection sampling.
no code implementations • 9 Jul 2015 • Nathan Wiebe, Ashish Kapoor, Christopher Granade, Krysta M. Svore
We present an efficient classical algorithm for training deep Boltzmann machines (DBMs) that uses rejection sampling in concert with variational approximations to estimate the gradients of the training objective function.
1 code implementation • 21 Apr 2014 • Christopher Granade, Christopher Ferrie, D. G. Cory
Here, we bound the resources required for benchmarking and show that, with prior information, we can achieve several orders of magnitude better accuracy than in traditional approaches to benchmarking.
Quantum Physics