2 papers
cs.LG2018
Langevin-gradient parallel tempering for Bayesian neural learning
Rohitash Chandra, Konark Jain, Ratneel V. Deo +1
Bayesian neural learning feature a rigorous approach to estimation and uncertainty quantification via the posterior distribution of weights that represent knowledge of the neural n…
cs.LG2018
Surrogate-assisted parallel tempering for Bayesian neural learning
Rohitash Chandra, Konark Jain, Arpit Kapoor +1
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) metho…