14 citations · 23 across the 3 of their papers we have counts for
4 papers
Compact Bayesian Neural Networks via pruned MCMC sampling
Ratneel Deo, Scott Sisson, Jody M. Webster +1
Bayesian Neural Networks (BNNs) offer robust uncertainty quantification in model predictions, but training them presents a significant computational challenge. This is mainly due t…
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…
Multi-core parallel tempering Bayeslands for basin and landscape evolution
Rohitash Chandra, R. Dietmar Müller, Danial Azam +4
The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geo-physical inversion problems. Badlands…
Stacked transfer learning for tropical cyclone intensity prediction
Ratneel Vikash Deo, Rohitash Chandra, Anuraganand Sharma
Tropical cyclone wind-intensity prediction is a challenging task considering drastic changes climate patterns over the last few decades. In order to develop robust prediction model…