Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 9, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
ir_explain: a Python Library of Explainable IR Methods
For calculating global feature importance using Shapley values.
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Explain a black-box module in natural language.
For OpenMOSS Mechanistic Interpretability Team's Sparse Autoencoder (SAE) research. Open-sourced and constantly updated.
A game theoretic approach to explain the output of any machine learning model.
Model interpretability and understanding for PyTorch
A curated list of awesome responsible machine learning resources.
The SINr approach to train interpretable word and graph embeddings
Knowledge Circuits in Pretrained Transformers
moDel Agnostic Language for Exploration and eXplanation
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
ReFT: Representation Finetuning for Language Models
Creating a PyTorch LSTM and Transformer to classify movies by genre and visualizing the LSTM's reasoning process
This repository is dedicated to small projects and some theoretical material that I used to get into Computer Vision using TensorFlow in a practical and efficient way.
graphpatch is a library for activation patching on PyTorch neural network 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)
Code for the paper "Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models", ISBI 2024 (Oral).
Robust multimodal brain registration via keypoints
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