stefanoteso / awesome-explanatory-supervisionLinks
List of relevant resources for machine learning from explanatory supervision
β157Updated 4 months ago
Alternatives and similar repositories for awesome-explanatory-supervision
Users that are interested in awesome-explanatory-supervision are comparing it to the libraries listed below
Sorting:
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" π§ (ICLR 2019)β128Updated 3 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systemsβ74Updated 3 years ago
- Local explanations with uncertainty π!β40Updated last year
- Official repository for CMU Machine Learning Department's 10732: Robustness and Adaptivity in Shifting Environmentsβ74Updated 2 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.β131Updated 4 years ago
- Towards Automatic Concept-based Explanationsβ159Updated last year
- OpenXAI : Towards a Transparent Evaluation of Model Explanationsβ247Updated 9 months ago
- β32Updated 3 years ago
- A repository for explaining feature attributions and feature interactions in deep neural networks.β187Updated 3 years ago
- XAI-Bench is a library for benchmarking feature attribution explainability techniquesβ66Updated 2 years ago
- β50Updated 2 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AAβ¦β75Updated 7 years ago
- This is a collection of papers and other resources related to fairness.β94Updated last year
- β125Updated 4 years ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)β82Updated 2 years ago
- Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Humanβ¦β72Updated 2 years ago
- Self-Explaining Neural Networksβ42Updated 5 years ago
- This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help β¦β24Updated 2 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" htβ¦β127Updated 4 years ago
- Influence Analysis and Estimation - Survey, Papers, and Taxonomyβ78Updated last year
- Model Agnostic Counterfactual Explanationsβ87Updated 2 years ago
- β134Updated 5 years ago
- An amortized approach for calculating local Shapley value explanationsβ97Updated last year
- All about explainable AI, algorithmic fairness and moreβ109Updated last year
- PyTorch Explain: Interpretable Deep Learning in Python.β155Updated last year
- Code for "Generative causal explanations of black-box classifiers"β34Updated 4 years ago
- β89Updated last month
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)β41Updated 2 years ago
- A benchmark for distribution shift in tabular dataβ52Updated 11 months ago
- Official Code Repo for the Paper: "How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions", In NeurIPS 2β¦β39Updated 2 years ago