interpretability
There are 622 repositories under interpretability topic.
facet
Human-explainable AI.
FastTreeSHAP
Fast SHAP value computation for interpreting tree-based models
mli-resources
H2O.ai Machine Learning Interpretability Resources
yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
explainx
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
laser
The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
pyss3
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
inseq
Interpretability for sequence generation models 🐛 🔍
modelStudio
📍 Interactive Studio for Explanatory Model Analysis
awesome-fairness-in-ai
A curated list of awesome Fairness in AI resources
CLIP_Surgery
CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
adversarial-explainable-ai
💡 Adversarial attacks on explanations and how to defend them
diffusers-interpret
Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
nnsight
The nnsight package enables interpreting and manipulating the internals of deep learned models.
sage
For calculating global feature importance using Shapley values.
OpenXAI
OpenXAI : Towards a Transparent Evaluation of Model Explanations
LRP_for_LSTM
Layer-wise Relevance Propagation (LRP) for LSTMs.
Awesome-Computer-Vision
Awesome Resources for Advanced Computer Vision Topics
ferret
A python package for benchmarking interpretability techniques on Transformers.
QAConv
[ECCV 2020] QAConv: Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting, and [CVPR 2022] GS: Graph Sampling Based Deep Metric Learning
zennit
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
MentalLLaMA
This repository introduces MentaLLaMA, the first open-source instruction following large language model for interpretable mental health analysis.
PyCEbox
⬛ Python Individual Conditional Expectation Plot Toolbox
ConceptBottleneck
Concept Bottleneck Models, ICML 2020
cnn-interpretability
🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease
Visualizing-CNNs-for-monocular-depth-estimation
official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"
visual-attribution
Pytorch Implementation of recent visual attribution methods for model interpretability
GSAT
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
GraphXAI
GraphXAI: Resource to support the development and evaluation of GNN explainers
imodelsX
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
thermostat
Collection of NLP model explanations and accompanying analysis tools
reverse-engineering-neural-networks
A collection of tools for reverse engineering neural networks.
knowledge-neurons
A library for finding knowledge neurons in pretrained transformer models.
secml
A Python library for Secure and Explainable Machine Learning
interpretability-by-parts
Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)
timbertrek
Explore and compare 1K+ accurate decision trees in your browser!