A simple implementation of the TLR-BM-Net for MRF reconstruction: "Learned Tensor Low-CP-Rank and Bloch Response Manifold Priors for Non-Cartesian MRF Reconstruction"

Optimization-Inspired Compact Deep Compressive Sensing, JSTSP2020 (PyTorch Code)

Accepted by TPAMI 2022

Accepted by CVPR 2022

COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing, TIP2021 [PyTorch Code]

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing, CVPR2018 (PyTorch Code)

Plug-and-Play Image Restoration with Deep Denoiser Prior (IEEE TPAMI 2021) (PyTorch)

Collection of reproducible deep learning for compressive sensing

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Learning in infinite dimension with neural operators.

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

DAGs with NO TEARS: Continuous Optimization for Structure Learning

Synthetic data generation for tabular data

A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.

PyTorch implementation of MoDL: Model Based Deep Learning Architecture for Inverse Problems

MoDL: Model-Based Deep Learning Architecture for Inverse Problems

Landing page for BART Webinars

Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

PyTorch Tutorial for Deep Learning Researchers

Python libraries for Google Colaboratory

Public facing notes page

Python Data Science Handbook: full text in Jupyter Notebooks

Tools and Resources collected from "Open Science and Sustainable Software for Data-driven Discovery​"

Machine Learning and Artificial Intelligence for Medicine.

Host repository for The Turing Way: a how to guide for reproducible data science

The original bloch equation simulator was a Matlab mex file created by Brian Hargreaves at Stanford University. This modification to run it as a Python C extension

A large-scale dataset of both raw MRI measurements and clinical MRI images.

Stanford University Rad229 Class Code: MRI Signals and Sequences

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.