PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)

Fixes 500+ mislabeled MURA images

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to b…

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

A Python implementation of global optimization with gaussian processes.

Implementation of Conditional Neural Networks in Pytorch.

PMLB: A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms.

Code and documentation for experiments in the TreeExplainer paper

Code for AAAI 2018 accepted paper: "Beyond Sparsity: Tree Regularization of Deep Models for Interpretability"

Latex code for making neural networks diagrams

Interpreting CNN Knowledge via an Explanatory Graph

Uniform Manifold Approximation and Projection