DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning

Learning Regularization Functionals for Inverse Problems: A Comparative Study

Code for paper "Analyzing inexact hypergradients for bilevel learning"

python codes of the paper 'FISTA-Condat-Vu: Automatic Differentiation for Hyperparameter Learning in Variational Models'

A fast algorithm that reliably computes solutions to optimization problems with complementarity constraints.

Numerical optimization in pure Rust

Code for coupled TGV regularization of multi-spectral/multi-modal inverse problems

Benchmark for bi-level optimization solvers

Nonsmooth Bilevel Parameter Learning of Imaging Variational Models

A markup-based typesetting system that is powerful and easy to learn.

A simple automation framework in Python

Implementation of the analytic deep prior (https://link.springer.com/article/10.1007%2Fs10851-019-00923-x)

Code examples for my Write Better Python Code series on YouTube.

Popular design patterns implemented in Fortran.

Free MLOps course from DataTalks.Club

Some solvers to tackle optimization problems with vanishing constraints

Learning sampling pattern with L-BFGS-B algorithm and reconstructions with PDHG.

TensorFlow for Arm

A beautiful, simple, clean, and responsive Jekyll theme for academics

Libraries of Pyomo test problems

Official Matplotlib cheat sheets

A Collection of Variational Autoencoders (VAE) in PyTorch.

NYU Deep Learning Spring 2020

NYU Deep Learning Spring 2021

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

Optimization algorithms for compresive sensing