Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data

no code implementations • 30 Jan 2024 • Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming.

Dimensionality Reduction Representation Learning

Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models

no code implementations • 12 Sep 2023 • Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon, Kristofer Reyes, Nathan M. Urban

This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model.

Experimental Design Uncertainty Quantification

Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks

no code implementations • 6 Sep 2023 • Sanket Jantre, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness.

Bayesian Inference Uncertainty Quantification +1