A framework for collision probability distribution estimation via temporal difference learning
-
Updated
May 13, 2024 - Python
A framework for collision probability distribution estimation via temporal difference learning
SLISEMAP: Combining supervised dimensionality reduction with local explanations
A package for Counterfactual Explanations and Algorithmic Recourse in Julia.
AntakIA is THE tool to explain an ML model or replace it with a collection of basic explainable models.
Implementation for the paper Explaining Text Similarity in Transformer Models
An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.
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..
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI and Human-Centered AI.
PhysioEx, a PyTorch Lightning based library for Interpretable physiological signal classifiers
A project focusing on binary classification using Explainable Artificial Intelligence (XAI) methods, specifically SHAP (SHapley Additive exPlanations), and Grid Search for hyperparameter tuning. The project utilizes EfficientNetV2-B0 architecture on the Cat VS Dog dataset.
User documentation for KServe.
Robust regression algorithm that can be used for explaining black box models (Python implementation)
Papers about explainability of GNNs
A PyTorch implementation of constrained optimization and modeling techniques
Fit interpretable models. Explain blackbox machine learning.
List of relevant resources for machine learning from explanatory supervision
Distributed High-Performance Symbolic Regression in Julia
Add a description, image, and links to the explainable-ai topic page so that developers can more easily learn about it.
To associate your repository with the explainable-ai topic, visit your repo's landing page and select "manage topics."