Model interpretability and understanding for PyTorch
-
Updated
May 29, 2024 - Python
Model interpretability and understanding for PyTorch
A curated list of awesome responsible machine learning resources.
A game theoretic approach to explain the output of any machine learning model.
Fit interpretable models. Explain blackbox machine learning.
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
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 JAX research toolkit for building, editing, and visualizing neural networks.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
ReFT: Representation Finetuning for Language Models
Universal Neurons in GPT2 Language Models
Creating a PyTorch LSTM to classify movies by genre and visualizing the model's reasoning process
The NDIF server, which performs deep inference and serves nnsight requests remotely
CVPR 2023: Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification
Interpretability for sequence generation models 🐛 🔍
Explain model and feature dependencies by decomposition of SHAP values
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Official Implementation of ARACHNET: INTERPRETABLE SUB-ARACHNOID SPACE SEGMENTATION USING AN ADDITIVE CONVOLUTIONAL NEURAL NETWORK
Influence Estimation for Gradient-Boosted Decision Trees
Add a description, image, and links to the interpretability topic page so that developers can more easily learn about it.
To associate your repository with the interpretability topic, visit your repo's landing page and select "manage topics."