Statistics for topic explainable-ai

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Fit interpretable models. Explain blackbox machine learning.

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 libr...

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

[EMNLP 2024] The official GitHub repo for the survey paper "Knowledge Conflicts for LLMs: A Survey"

A list of (post-hoc) XAI for time series

💡 Adversarial attacks on explanations and how to defend them

Papers about explainability of GNNs

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Fit interpretable models. Explain blackbox machine learning.

Shapley Interactions and Shapley Values for Machine Learning

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

A toolkit for quantitative evaluation of data attribution methods.

A list of (post-hoc) XAI for time series

Explanation method for Graph Neural Networks (GNNs)

Shapley Interactions and Shapley Values for Machine Learning

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Shapley Interactions and Shapley Values for Machine Learning

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

High-Performance Symbolic Regression in Python and Julia

Fit interpretable models. Explain blackbox machine learning.

This is an official implementation for Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation. [CVPR'25]

Shapley Interactions and Shapley Values for Machine Learning

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

A list of (post-hoc) XAI for time series

source code for USENIX Security paper xNIDS

[EMNLP'2024] "XRec: Large Language Models for Explainable Recommendation"

[EMNLP 2024] The official GitHub repo for the survey paper "Knowledge Conflicts for LLMs: A Survey"

LauzHack Deep Learning Bootcamp

[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".

OpenVINO™ Explainable AI (XAI) Toolkit: Visual Explanation for OpenVINO Models

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

High-Performance Symbolic Regression in Python and Julia

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Fit interpretable models. Explain blackbox machine learning.

Shapley Interactions and Shapley Values for Machine Learning

[ACL'24] A Knowledge-grounded Interactive Evaluation Framework for Large Language Models

A Python library for explainable AI using approximate reasoning

A list of (post-hoc) XAI for time series

Arrakis is a library to conduct, track and visualize mechanistic interpretability experiments.