3 citations · 3 across the 2 of their papers we have counts for
4 papers
Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously
Abhishek Keripale, Ponkrshnan Thiagarajan, Susanta Ghosh
Steerable convolutional neural networks (Steerable-CNNs) guarantee SE(3)-equivariance by parameterizing kernels as linear combinations of steerable basis functions, but their deter…
Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar +4
Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham density functional theory calculations, as i…
Electronic structure prediction of medium and high entropy alloys across composition space
Shashank Pathrudkar, Stephanie Taylor, Abhishek Keripale +6
We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated allo…
Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural Networks
Ponkrshnan Thiagarajan, Susanta Ghosh
Bayesian neural networks (BNNs) are state-of-the-art machine learning methods that can naturally regularize and systematically quantify uncertainties using their stochastic paramet…