A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
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Updated
May 30, 2024 - C++
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
The website for NDIF, the National Deep Inference Fabric
A curated list of awesome responsible machine learning resources.
Fit interpretable models. Explain blackbox machine learning.
ReFT: Representation Finetuning for Language Models
A game theoretic approach to explain the output of any machine learning model.
Robust multimodal brain registration via keypoints
The nnsight package enables interpreting and manipulating the internals of deep learned models.
Model interpretability and understanding for PyTorch
👋 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
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.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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 🐛 🔍
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