understandable-machine-intelligence-lab / QuantusLinks
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
β634Updated 5 months ago
Alternatives and similar repositories for Quantus
Users that are interested in Quantus are comparing it to the libraries listed below
Sorting:
- π Xplique is a Neural Networks Explainability Toolboxβ726Updated 3 weeks ago
- OpenXAI : Towards a Transparent Evaluation of Model Explanationsβ252Updated last year
- Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.β238Updated 5 months ago
- An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximizationβ140Updated last year
- Papers and code of Explainable AI esp. w.r.t. Image classificiationβ225Updated 3 years ago
- OmniXAI: A Library for eXplainable AIβ961Updated last year
- MetaQuantus is an XAI performance tool to identify reliable evaluation metricsβ40Updated last year
- A toolbox to iNNvestigate neural networks' predictions!β1,306Updated 9 months ago
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmsβ299Updated 2 years ago
- A Library for Uncertainty Quantification.β924Updated 8 months ago
- β501Updated last year
- Reliability diagrams visualize whether a classifier model needs calibrationβ164Updated 3 years ago
- For calculating global feature importance using Shapley values.β282Updated 3 weeks ago
- Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty iβ¦β268Updated 3 months ago
- A collection of research materials on explainable AI/MLβ1,599Updated last month
- Shapley Interactions and Shapley Values for Machine Learningβ666Updated this week
- π Influenciae is a Tensorflow Toolbox for Influence Functionsβ64Updated last year
- Build and train Lipschitz constrained networks: TensorFlow implementation of k-Lipschitz layersβ100Updated 10 months ago
- Open-source framework for uncertainty and deep learning models in PyTorchβ466Updated this week
- The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a β¦β368Updated last year
- Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Humanβ¦β75Updated 3 years ago
- A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).β139Updated 4 years ago
- Generate Diverse Counterfactual Explanations for any machine learning model.β1,482Updated 6 months ago
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true claβ¦β255Updated 2 years ago
- scikit-activeml: A Comprehensive and User-friendly Active Learning Libraryβ181Updated last week
- Experiments on Tabular Data Modelsβ281Updated 2 years ago
- β213Updated 4 years ago
- A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.β627Updated 6 months ago
- A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also inβ¦β761Updated 5 years ago
- β122Updated 3 years ago