#Interpretability
Showing 60 of 85 repositories tagged #interpretability, ranked by stars
A game theoretic approach to explain the output of any machine learning model.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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.
Model interpretability and understanding for PyTorch
A collection of infrastructure and tools for research in neural network interpretability.
A curated list of awesome responsible machine learning resources.
๐ Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
StellarGraph - Machine Learning on Graphs
Algorithms for explaining machine learning models
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
A JAX research toolkit for building, editing, and visualizing neural networks.
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
[ICCV 2017] Torch code for Grad-CAM
Interpretable ML package ๐ for concise, transparent, and accurate predictive modeling (sklearn-compatible).
moDel Agnostic Language for Exploration and eXplanation
Model explainability that works seamlessly with ๐ค transformers. Explain your transformers model in just 2 lines of code.
XAI - An eXplainability toolbox for machine learning
Interpretability Methods for tf.keras models with Tensorflow 2.x
The nnsight package enables interpreting and manipulating the internals of deep learned models.
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
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Shapley Interactions and Shapley Values for Machine Learning
Visualization toolkit for neural networks in PyTorch! Demo -->
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Code for the TCAV ML interpretability project
๐ญ Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Fast SHAP value computation for interpreting tree-based models
Human-explainable AI.
H2O.ai Machine Learning Interpretability Resources
Interpretability for sequence generation models ๐ ๐
Chat2Graph: Graph Native Agentic System.
An awesome repository & A comprehensive survey on interpretability of LLM attention heads.
The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:
๐ Interactive Studio for Explanatory Model Analysis
This repository introduces MentaLLaMA, the first open-source instruction following large language model for interpretable mental health analysis.
Wanna know what your model sees? Here's a package for applying EigenCAM (like GradCAM) and generating heatmap from the new YOLO models
Diffusers-Interpret ๐ค๐งจ๐ต๏ธโโ๏ธ: Model explainability for ๐ค Diffusers. Get explanations for your generated images.
Concept Bottleneck Models, ICML 2020
Awesome Resources for Advanced Computer Vision Topics
Interpretable Causal Diffusion Language Models
A Python library for Secure and Explainable Machine Learning
Interpret text data with LLMs (sklearn compatible).
PyTorch Explain: Interpretable Deep Learning in Python.
[NeurIPS 2024] Knowledge Circuits in Pretrained Transformers
Explore and compare 1K+ accurate decision trees in your browser!
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Editing machine learning models to reflect human knowledge and values
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" ๐ง (ICLR 2019)
[ICLR 2025] Code and Data Repo for Paper "Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation"
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
A simple PyTorch implementation of influence functions.
Robust multimodal image registration via keypoints
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.