#Explainable-ai
Showing 60 of 95 repositories tagged #explainable-ai, ranked by stars
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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.
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
High-Performance Symbolic Regression in Python and Julia
Debugging, monitoring and visualization for Python Machine Learning and Data Science
The Self-Coding System for Your App — Alan AI SDK for Web
A collection of research papers and software related to explainability in graph 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 libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Interpretability and explainability of data and machine learning models
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Generate Diverse Counterfactual Explanations for any machine learning model.
moDel Agnostic Language for Exploration and eXplanation
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
Semantica 🧠 • Build AI systems that can explain, trace, and justify every decision. Knowledge graphs, context graphs, reasoning engines, provenance, and governance for production AI.
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
XAI - An eXplainability toolbox for machine learning
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
OmniXAI: A Library for eXplainable AI
ICCV 2023-2025 Papers: Discover cutting-edge research from ICCV 2023-25, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!
[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.
Leave One Feature Out Importance
Papers about explainability of GNNs
Diffusion attentive attribution maps for interpreting Stable Diffusion.
Distributed High-Performance Symbolic Regression in Julia
Code, exercises and tutorials of my personal blog ! 📝
Shapley Interactions and Shapley Values for Machine Learning
Curated list of open source tooling for data-centric AI on unstructured data.
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).
Fast SHAP value computation for interpreting tree-based models
Examples of Data Science projects and Artificial Intelligence use-cases
Human-explainable AI.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Interpretability for sequence generation models 🐛 🔍
Neural network visualization toolkit for tf.keras
📍 Interactive Studio for Explanatory Model Analysis
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
Variants of Vision Transformer and its downstream tasks
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
A Python toolkit for chain-of-thought prompting 🐍
🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
PyTorch Explain: Interpretable Deep Learning in Python.
Carefully curated list of awesome data science resources.
[EMNLP 2024] The official GitHub repo for the survey paper "Knowledge Conflicts for LLMs: A Survey"
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Code for ACL 2024 paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space"
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
SHAP Plots in R
User documentation for KServe.
Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Neatly packaged AI methods for explainable ECG analysis