20 citations · 33 across the 5 of their papers we have counts for
7 papers
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
L. Mars Gao, J. Nathan Kutz
Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under constraints allows joint discoveries of governing equations and latent coord…
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
Ruoqi Shen, Liyao Gao, Yi-An Ma
Early stopping is a simple and widely used method to prevent over-training neural networks. We develop theoretical results to reveal the relationship between the optimal early stop…
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of th…
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM combines a mechanistic stochastic simulation model GLEAM with deep learning. It uses deep learning to lea…
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao +2
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC…
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
Liyao Gao, Yifan Du, Hongshan Li +1
In the recent application of scientific modeling, machine learning models are largely applied to facilitate computational simulations of fluid systems. Rotation symmetry is a gener…