A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
DeepInverse: a 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
TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction (2025)
My personal website
Physics-Informed Deep Equilibrium Models. With an application to the 4 tanks system.
The Recursive Semantic Refinement Network is an approach to abstractive summarization that frames the task as an iterative regression problem in a semantic space. Instead of generating text word-by-word, the network refines a fixed-size semantic embedding of the summary until it converges to a state semantically close to the ground-truth embedding.
Implementing Deep Equilibrium model on the Mnist dataset using Jax and Flax
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