no code implementations • 17 Aug 2023 • Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
In this paper, we explore two well-established shrinkage techniques, Lasso and Horseshoe, for model compression in Bayesian neural networks.
no code implementations • 1 Jun 2022 • Sanket Jantre, Sandeep Madireddy, Shrijita Bhattacharya, Tapabrata Maiti, Prasanna Balaprakash
Deep neural network ensembles that appeal to model diversity have been used successfully to improve predictive performance and model robustness in several applications.
no code implementations • 25 Aug 2021 • Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection.
no code implementations • 19 Nov 2020 • Shrijita Bhattacharya, Zihuan Liu, Tapabrata Maiti
This paper develops a variational Bayesian neural network estimation methodology and related statistical theory.
no code implementations • 29 Jun 2020 • Shrijita Bhattacharya, Tapabrata Maiti
However there are few results which revolve around the theoretical properties of VB, especially in non-parametric problems.