marcoancona / DeepExplainLinks
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)
☆757Updated 5 years ago
Alternatives and similar repositories for DeepExplain
Users that are interested in DeepExplain are comparing it to the libraries listed below
Sorting:
- Public facing deeplift repo☆865Updated 3 years ago
- A toolbox to iNNvestigate neural networks' predictions!☆1,302Updated 6 months ago
- The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Py…☆333Updated 3 years ago
- Attributing predictions made by the Inception network using the Integrated Gradients method☆639Updated 3 years ago
- ☆916Updated 2 years ago
- Code for the TCAV ML interpretability project☆645Updated 4 months ago
- Interesting resources related to XAI (Explainable Artificial Intelligence)☆839Updated 3 years ago
- Code for "High-Precision Model-Agnostic Explanations" paper☆807Updated 3 years ago
- Interpretability Methods for tf.keras models with Tensorflow 2.x☆1,035Updated last year
- Layer-wise Relevance Propagation (LRP) for LSTMs.☆225Updated 5 years ago
- Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).☆985Updated last year
- Bayesian Deep Learning Benchmarks☆672Updated 2 years ago
- Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human…☆74Updated 3 years ago
- Code for all experiments.☆318Updated 4 years ago
- ☆100Updated 7 years ago
- Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers☆98Updated 7 years ago
- ☆607Updated 2 years ago
- Data Shapley: Equitable Valuation of Data for Machine Learning☆280Updated last year
- Tensorflow tutorial for various Deep Neural Network visualization techniques☆346Updated 5 years ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆84Updated 2 years ago
- All about explainable AI, algorithmic fairness and more☆110Updated 2 years ago
- Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations☆627Updated 3 months ago
- Interpretability and explainability of data and machine learning models☆1,742Updated 7 months ago
- Building a Bayesian deep learning classifier☆490Updated 7 years ago
- apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models qui…☆518Updated 4 months ago
- Python/Keras implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defin…☆217Updated 7 years ago
- ☆134Updated 6 years ago
- For calculating global feature importance using Shapley values.☆277Updated last week
- A simple way to calibrate your neural network.☆1,162Updated 2 months ago
- Code and documentation for experiments in the TreeExplainer paper☆188Updated 6 years ago