Visualization toolkit for neural networks in PyTorch! Demo -->

All about explainable AI, algorithmic fairness and more

Code and simulations using biologically annotated neural networks

Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

Rule covering for interpretation and boosting

XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.

Simple implementations of Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP) in JAX

Code for Evaluating the Interpretability of Generative Models by Interactive Reconstruction

Explainable AI: From Simple Rules to Complex Generative Models

Concise summaries of key papers in responsible AI.

Course notes for Undergraduate Interpretable Machine Learning at UW-Madison.

Implementing text classification algorithms using the 20 newsgroups datasets, with python

Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses

Python implementation of TRANSACT, a tool to transfer non-linear predictors of drug response from model systems to tumors.

High precision anchor black box explanation algorithm

The website for NDIF, the National Deep Inference Fabric

Automatic interpretable sales forecasting for R

Low-dimensional Interpretable Kernels with Conic Discriminant Functions for Classification

Predicting categories of scientific papers with advanced machine learning techniques involving class imbalance in multi-label data and explainable machine learning.

Canonical interpretability documentation for VidSEO (WordPress plugin).