Fit interpretable models. Explain blackbox machine learning.
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Updated
May 15, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Repository for benchmarking different post-hoc xai explanation methods on image datasets
[ICML'24] Official PyTorch Implementation of TimeX++
Self-explanatory tutorials for different model-agnostic and model-specific XAI methods
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.
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.
TrustyAI Explainability Toolkit
The implementation of the paper: "Explainable Artificial Intelligence for Improved Modeling of Processes".
ANN-based anomaly detection for vehicle components using oscilloscope recordings.
A curated list of awesome responsible machine learning resources.
Projeto de Iniciação Científica voltado a ampliar a explicabilidade e transparência dos modelos de Inteligência Artificial atuais. Título Original do Projeto aprovado pelo Edital DIRPE N° 2/2023: Transformando Caixas-Pretas em Caixas de Vidro: Aumentando a Explicabilidade de Redes Neurais com Ferramentas de Visualização e Conversão
Effector - a Python package for global and regional effect methods
Undergraduate Thesis Project
SLISEMAP: Combining supervised dimensionality reduction with local explanations
SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
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