G. P. Langlois

Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations

This work proposes to leverage connections between proximal operators and Hamilton--Jacobi partial differential equations (HJ PDEs) to develop deep learning architectures for learning the prior and presents numerical results in dimensions up to $64, where the recovered prior is evaluated in a single forward pass.