7 citations · 22 across the 14 of their papers we have counts for
3 papers · 1 filter
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying m…
BAR: Bayesian Activity Recognition using variational inference
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activit…
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference
Mahesh Subedar, Ranganath Krishnan, Paulo Lopez Meyer +2
Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider…