ReFT: Representation Finetuning for Language Models
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Updated
May 30, 2024 - Python
ReFT: Representation Finetuning for Language Models
Robust multimodal brain registration via keypoints
Model interpretability and understanding for PyTorch
👋 Xplique is a Neural Networks Explainability Toolbox
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)
A JAX research toolkit for building, editing, and visualizing neural networks.
The NDIF server, which performs deep inference and serves nnsight requests remotely
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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
For calculating global feature importance using Shapley values.
Influence Estimation for Gradient-Boosted Decision Trees
Open and extensible benchmark for XAI methods
Knowledge Circuit Discovery in Transformers
Discovering Universal Geometry in Embeddings with ICA
Stanford NLP Python Library for Understanding and Improving PyTorch Models via Interventions
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