21 citations · 31 across the 8 of their papers we have counts for
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cs.LG2026
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…
cs.LG2024★ 4 cited
Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks
Revanth Mattey, Susanta Ghosh
The gradient flow of the Ginzburg-Landau free energy functional leads to the Allen Cahn equation that is widely used for modeling phase separation. Machine learning methods f…
cs.LG2022★ 21 cited
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…