research
∙
05/31/2023
Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning
Recently, there has been significant interest in operator learning, i.e....
share
research
∙
05/25/2023
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Particle-based deep generative models, such as gradient flows and score-...
research
∙
02/02/2023
Convolutional Neural Operators
Although very successfully used in machine learning, convolution based n...
research
∙
10/03/2022
Module-wise Training of Residual Networks via the Minimizing Movement Scheme
Greedy layer-wise or module-wise training of neural networks is compelli...
research
∙
06/10/2021
A Neural Tangent Kernel Perspective of GANs
Theoretical analyses for Generative Adversarial Networks (GANs) generall...
research
∙
06/08/2021
LEADS: Learning Dynamical Systems that Generalize Across Environments
When modeling dynamical systems from real-world data samples, the distri...
research
∙
10/09/2020
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Forecasting complex dynamical phenomena in settings where only partial k...
research
∙
09/17/2020
A Principle of Least Action for the Training of Neural Networks
Neural networks have been achieving high generalization performance on m...
research
∙
12/18/2019
Unsupervised Adversarial Image Inpainting
We consider inpainting in an unsupervised setting where there is neither...
research
∙
06/04/2019
Optimal Unsupervised Domain Translation
Domain Translation is the problem of finding a meaningful correspondence...
research
∙
02/26/2019
Learning Dynamical Systems from Partial Observations
We consider the problem of forecasting complex, nonlinear space-time pro...
research
∙
11/21/2017
