explainability

A game theoretic approach to explain the output of any machine learning model.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Fit interpretable models. Explain blackbox machine learning.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

[Pattern Recognition 25] CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

Interpretable Causal Diffusion Language Models

🗺️ Data Cleaning and Textual Data Visualization 🗺️

Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.

Interpret text data with LLMs (sklearn compatible).

Collection of NLP model explanations and accompanying analysis tools

Official implementation of the paper The Hidden Language of Diffusion Models

Time series explainability via self-supervised model behavior consistency

Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)

Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.

Linear probe found representations of scene attributes in a text-to-image diffusion model