2 citations · 4 across the 5 of their papers we have counts for
12 papers
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
Network complexity and computational efficiency have become increasingly significant aspects of deep learning. Sparse deep learning addresses these challenges by recovering a spars…
Sequential Bayesian Neural Subnetwork Ensembles
Sanket Jantre, Shrijita Bhattacharya, Nathan M. Urban +4
Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. H…
Variational Bayes algorithm and posterior consistency of Ising model parameter estimation
Minwoo Kim, Shrijita Bhattacharya, Tapabrata Maiti
Ising models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains chall…
Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation Details
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properti…
Black Box Variational Bayesian Model Averaging
Vojtech Kejzlar, Shrijita Bhattacharya, Mookyong Son +1
For many decades now, Bayesian Model Averaging (BMA) has been a popular framework to systematically account for model uncertainty that arises in situations when multiple competing…
Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges
Shrijita Bhattacharya, Zihuan Liu, Tapabrata Maiti
Bayesian neural network models (BNN) have re-surged in recent years due to the advancement of scalable computations and its utility in solving complex prediction problems in a wide…