A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

PyTorch library for solving imaging inverse problems using deep learning

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Implementation of deep implicit attention in PyTorch

Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.

Official implementation for the ECCV 2022 paper "Streaming Multiscale Deep Equilibrium Models"

Official implementation of DEQ-MPI: A deep equilibrium reconstruction model for magnetic particle imaging

Physics-Informed Deep Equilibrium Models. With an application to the 4 tanks system.

Implementing Deep Equilibrium model on the Mnist dataset using Jax and Flax