%0 Generic
%A Luo, R
%A Wang, J
%A Yang, Y
%A Zhu, Z
%A Wang, J
%C Montreal, Canada
%D 2018
%E Bengio, S
%E Wallach, H
%E Larochelle, H
%E Grauman, K
%E CesaBianchi, N
%E Garnett, R
%F discovery:10079536
%I Neural Information Processing Systems Foundation, Inc.
%T Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
%U https://discovery.ucl.ac.uk/id/eprint/10079536/
%V 31
%X We propose a new sampling method, the thermostat-assisted continuously-tempered  Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal  distributions. It simulates the Nosé-Hoover dynamics of a continuously-tempered  Hamiltonian system built on the distribution of interest. A significant advantage of  this method is that it is not only able to efficiently draw representative i.i.d. samples  when the distribution contains multiple isolated modes, but capable of adaptively  neutralising the noise arising from mini-batches and maintaining accurate sampling.  While the properties of this method have been studied using synthetic distributions,  experiments on three real datasets also demonstrated the gain of performance over  several strong baselines with various types of neural networks plunged in.
%Z This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.