1 citations · 1 across the 4 of their papers we have counts for
5 papers
Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation
Chun-Yi Chang, Chih-Li Sung
Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains…
Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
Chun-Yi Chang, Chih-Li Sung
Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings…
Diffusion Non-Additive Model for Multi-Fidelity Simulations with Tunable Precision
Junoh Heo, Romain Boutelet, Wenjia Wang +1
Computer simulations are indispensable for analyzing complex systems, yet high-fidelity models often incur prohibitive computational costs. Multi-fidelity frameworks address this c…
Active Learning with Adaptive Non-Stationary Kernel for Continuous-Fidelity Surrogate Models
Romain Boutelet, Chih-Li Sung
Simulating complex physical processes across a domain of input parameters can be very computationally expensive. Multi-fidelity surrogate modeling can resolve this issue by integra…
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes
Yang Chen, Chih-Li Sung, Arpan Kusari +2
Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producin…