understandable-machine-intelligence-lab / Quantus
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
☆573Updated 2 months ago
Alternatives and similar repositories for Quantus:
Users that are interested in Quantus are comparing it to the libraries listed below
- 👋 Xplique is a Neural Networks Explainability Toolbox☆657Updated 3 months ago
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆237Updated 5 months ago
- Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.☆208Updated 5 months ago
- An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization☆122Updated 7 months ago
- Papers and code of Explainable AI esp. w.r.t. Image classificiation☆200Updated 2 years ago
- 💡 Adversarial attacks on explanations and how to defend them☆304Updated last month
- A toolbox to iNNvestigate neural networks' predictions!☆1,276Updated last year
- Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty i…☆257Updated 4 months ago
- OmniXAI: A Library for eXplainable AI☆892Updated 5 months ago
- MetaQuantus is an XAI performance tool to identify reliable evaluation metrics☆32Updated 9 months ago
- For calculating global feature importance using Shapley values.☆259Updated this week
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms☆286Updated last year
- A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also in…☆742Updated 4 years ago
- Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human…☆72Updated 2 years ago
- A Library for Uncertainty Quantification.☆894Updated 3 weeks ago
- A collection of research materials on explainable AI/ML☆1,442Updated 2 months ago
- All about explainable AI, algorithmic fairness and more☆107Updated last year
- Shapley Interactions and Shapley Values for Machine Learning☆299Updated this week
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆74Updated 2 years ago
- 👽 Out-of-Distribution Detection with PyTorch☆270Updated this week
- 👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.☆311Updated last week
- A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.☆86Updated 2 years ago
- ☆119Updated 2 years ago
- A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.☆558Updated 11 months ago
- Experiments on Tabular Data Models☆271Updated last year
- A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).☆132Updated 3 years ago
- A framework for prototyping and benchmarking imputation methods☆170Updated last year
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true cla…☆235Updated last year
- Interesting resources related to XAI (Explainable Artificial Intelligence)☆819Updated 2 years ago
- Interpretability and explainability of data and machine learning models☆1,652Updated 6 months ago