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cs.LG2025
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta, Reese E. Jones, Ravi G. Patel +4
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differ…
cs.LG2024
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas +1
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural n…
cs.LG2024
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta +2
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagu…