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Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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 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
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
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.
Book repository for The Turing Way: a how to guide for reproducible, ethical and collaborative 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