16 citations · 31 across the 7 of their papers we have counts for
5 papers · 1 filter
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates
Liu Yang, Tingwei Meng, George Em Karniadakis
We propose a simple but effective modification of the discriminators, namely measure-conditional discriminators, as a plug-and-play module for different GANs. By taking the generat…
Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models
Liu Yang, Constantinos Daskalakis, George Em Karniadakis
We propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., m…
A GMM based algorithm to generate point-cloud and its application to neuroimaging
Liu Yang, Rudrasis Chakraborty
Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, reg…
Potential Flow Generator with Optimal Transport Regularity for Generative Models
Liu Yang, George Em Karniadakis
We propose a potential flow generator with optimal transport regularity, which can be easily integrated into a wide range of generative models including different versions of…
Neural-net-induced Gaussian process regression for function approximation and PDE solution
Guofei Pang, Liu Yang, George Em Karniadakis
Neural-net-induced Gaussian process (NNGP) regression inherits both the high expressivity of deep neural networks (deep NNs) as well as the uncertainty quantification property of G…