2 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 2 cited
Disentangled Multi-Fidelity Deep Bayesian Active Learning
Dongxia Wu, Ruijia Niu, Matteo Chinazzi +2
To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a di…
cs.LG2021★ 2 cited
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
Dongxia Wu, Ruijia Niu, Matteo Chinazzi +3
Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can…