31 citations · 57 across the 18 of their papers we have counts for
3 papers · 1 filter
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon +2
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeuti…
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Sanket Jantre, Nathan M. Urban, Xiaoning Qian +1
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…