There are 14 repositories under interpretable-ai topic.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
A collection of research materials on explainable AI/ML
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
PyTorch Explain: Interpretable Deep Learning in Python.
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
All about explainable AI, algorithmic fairness and more
Explainable AI in Julia.
Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI, Trustworthy AI, and Human-Centered AI.
Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
SIDU: SImilarity Difference and Uniqueness method for explainable AI
Implementation of Beyond Neural Scaling beating power laws for deep models and prototype-based models
Explainability of Deep Learning Models
A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction
A PyTorch implementation of constrained optimization and modeling techniques
Find the samples, in the test data, on which your (generative) model makes mistakes.
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
[ICCV 2023] Learning Support and Trivial Prototypes for Interpretable Image Classification