Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 2, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
A game theoretic approach to explain the output of any machine learning model.
For OpenMOSS Mechanistic Interpretability Team's Sparse Autoencoder (SAE) research. Open-sourced and constantly updated.
Explain a black-box module in natural language.
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)
The nnsight package enables interpreting and manipulating the internals of deep learned models.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Model interpretability and understanding for PyTorch
Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Efficient R implementation of SHAP
Creating a PyTorch LSTM and Transformer to classify movies by genre and visualizing the LSTM's reasoning process
The website for NDIF, the National Deep Inference Fabric
A curated list of awesome responsible machine learning resources.
ReFT: Representation Finetuning for Language Models
Robust multimodal brain registration via keypoints
👋 Xplique is a Neural Networks Explainability Toolbox
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.
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
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