29 citations · 64 across the 6 of their papers we have counts for
5 papers · 1 filter
Bayesian graph convolutional neural networks via tempered MCMC
Rohitash Chandra, Ayush Bhagat, Manavendra Maharana +1
Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data. More recently, there ha…
Evaluation of deep learning models for multi-step ahead time series prediction
Rohitash Chandra, Shaurya Goyal, Rishabh Gupta
Time series prediction with neural networks has been the focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in…
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…
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…
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…