axa-rev-research / locality-interpretable-surrogateView external linksLinks
Repository of the paper "Defining Locality for Surrogates in Post-hoc Interpretablity" published at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)
☆17Nov 9, 2021Updated 4 years ago
Alternatives and similar repositories for locality-interpretable-surrogate
Users that are interested in locality-interpretable-surrogate are comparing it to the libraries listed below
Sorting:
- Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!☆29Jul 13, 2019Updated 6 years ago
- Prediction Explanations Clustering☆10Oct 19, 2023Updated 2 years ago
- Article for Special Edition of Information: Machine Learning with Python☆14Jan 8, 2025Updated last year
- Code repository for the ECCV 2022 (Oral) paper "Cartoon Explanations of Image Classifiers"☆10Nov 24, 2025Updated 2 months ago
- A graphical EDA tool☆14Jan 9, 2023Updated 3 years ago
- Colorspace thresholding to detect fire in videos/images☆10Jan 13, 2018Updated 8 years ago
- lightgbmのfeature-transform（特徴量の非線形化）をすることで、80,000を超える特徴量を線形回帰でも表現できることを示します☆10Nov 7, 2017Updated 8 years ago
- The official implementation of paper "Drop-Activation: Implicit Parameter Reduction and Harmonious Regularization".☆10May 30, 2019Updated 6 years ago
- Initier la mise à disposition, pour tout citoyen, de techniques d’Intelligence Artificielle destinées à appréhender le nombre important d…☆11Aug 20, 2024Updated last year
- implement some outlier detection algorithms☆11Sep 25, 2015Updated 10 years ago
- Blind Justice Code for the paper "Blind Justice: Fairness with Encrypted Sensitive Attributes", ICML 2018☆14Mar 20, 2019Updated 6 years ago
- Code for anything posted on f1-predictor.com☆13Sep 11, 2019Updated 6 years ago
- Intelligent Water Drops Algorithm for TSP.☆10Dec 12, 2020Updated 5 years ago
- ☆13Aug 12, 2019Updated 6 years ago
- Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curv…☆16Aug 7, 2022Updated 3 years ago
- The ts302_team final solution to the KDD CUP 2019 AutoML Track problem.☆15Jul 3, 2020Updated 5 years ago
- Implementation of tree-structured neural networks in PyTorch.☆14Nov 15, 2021Updated 4 years ago
- A Python library for anomaly detection☆13Aug 28, 2017Updated 8 years ago
- PDF and notebooks of GUDHI presentation @ NIPS 2017☆15Jan 22, 2018Updated 8 years ago
- XBOS Anomaly Detection☆15Oct 23, 2021Updated 4 years ago
- COVID-19 statistics in Taiwan☆14Apr 13, 2023Updated 2 years ago
- Documentation de l'algorithme d'orientation COVID19☆13Nov 15, 2020Updated 5 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Jan 10, 2023Updated 3 years ago
- Temporary repo to split the pseudo livrable☆17May 7, 2020Updated 5 years ago
- The 1st place solution for AutoSpeech 2019.☆17Jun 9, 2020Updated 5 years ago
- automated machine learning toolkit☆15Apr 8, 2018Updated 7 years ago
- ☆19Dec 27, 2020Updated 5 years ago
- Multi-Class Classification | Transfer Learning With PySpark☆13Nov 12, 2019Updated 6 years ago
- 'Keep Calm and Trust your Model' : On Explainability of Machine Learning Models☆15Jul 29, 2017Updated 8 years ago
- an easy way to define preprocessing data pipeline (similar to sklean-pandas but for Spark ML)☆17Feb 6, 2019Updated 7 years ago
- Self-contained Jupyter notebook that walks through loading raw ECG, designing digital filters, visualising spectra, cleaning noise and ex…☆19Nov 17, 2019Updated 6 years ago
- PyTorch implementation of SmoothTaylor☆15Sep 5, 2021Updated 4 years ago
- A slider control using d3.js☆15Dec 27, 2017Updated 8 years ago
- Homomorphic Random Forest library☆17Apr 12, 2023Updated 2 years ago
- Code for KDD CUP 2019 Auto-ML track☆21Jul 25, 2019Updated 6 years ago
- Code for the paper 'Efficient Variational Inference for Gaussian Process Regression Networks'☆22Jan 2, 2014Updated 12 years ago
- Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.☆21Feb 2, 2026Updated 2 weeks ago
- ☆22Jun 3, 2019Updated 6 years ago
- A user interface to interpret machine learning models.☆71Jan 14, 2020Updated 6 years ago