Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
☆82Dec 8, 2022Updated 3 years ago
Alternatives and similar repositories for Fooling-LIME-SHAP
Users that are interested in Fooling-LIME-SHAP are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- reference implementation for "explanations can be manipulated and geometry is to blame"☆37Jul 24, 2022Updated 3 years ago
- Local explanations with uncertainty 💐!☆42Aug 8, 2023Updated 2 years ago
- bayesian lime☆18Aug 3, 2024Updated last year
- ☆33Jun 22, 2024Updated last year
- Supervised Local Modeling for Interpretability☆29Oct 27, 2018Updated 7 years ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click. Zero configuration with optimized deployments.
- [ICLR 2022] Boosting Randomized Smoothing with Variance Reduced Classifiers☆11Mar 29, 2022Updated 4 years ago
- A Python library for pruning and visualizing Keras Neural Networks' structure and weights☆10Dec 24, 2019Updated 6 years ago
- S-LIME: Stabilized-LIME for model explanation (KDD 2021). pip install stabilized-lime☆16Mar 7, 2026Updated last month
- Explaining Image Classifiers by Counterfactual Generation☆28Apr 23, 2022Updated 4 years ago
- Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!☆29Jul 13, 2019Updated 6 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆44Aug 1, 2021Updated 4 years ago
- For calculating Shapley values via linear regression.☆73Jun 6, 2021Updated 4 years ago
- Literature Survey about the available Diffusion Models capable of Time-Series Forecasting☆16Mar 28, 2025Updated last year
- ☆12Mar 26, 2021Updated 5 years ago
- Managed Kubernetes at scale on DigitalOcean • AdDigitalOcean Kubernetes includes the control plane, bandwidth allowance, container registry, automatic updates, and more for free.
- Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function☆16Aug 7, 2017Updated 8 years ago
- ☆10Aug 22, 2025Updated 8 months ago
- This repository provides a PyTorch implementation of "Fooling Neural Network Interpretations via Adversarial Model Manipulation". Our pap…☆23Dec 19, 2020Updated 5 years ago
- Code for Fong and Vedaldi 2017, "Interpretable Explanations of Black Boxes by Meaningful Perturbation"☆32Sep 25, 2019Updated 6 years ago
- A public repository for corrupt0 datathon's court data☆11Jul 6, 2019Updated 6 years ago
- ☆14Jun 22, 2020Updated 5 years ago
- This repository contains the code for all figures in the paper "General Pitfalls of Model-agnostic Interpretation Methods for Machine Lea…☆15Aug 17, 2021Updated 4 years ago
- The code snippets for the SW chapter of the "Interpretable AI" book.☆19Apr 10, 2019Updated 7 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆77Mar 26, 2022Updated 4 years ago
- AI Agents on DigitalOcean Gradient AI Platform • AdBuild production-ready AI agents using customizable tools or access multiple LLMs through a single endpoint. Create custom knowledge bases or connect external data.
- ☆13Apr 23, 2019Updated 7 years ago
- Deep Weighted Averaging Classifiers☆22Feb 4, 2019Updated 7 years ago
- CCS 2023 | Explainable malware and vulnerability detection with XAI in paper "FINER: Enhancing State-of-the-art Classifiers with Feature …☆11Aug 20, 2024Updated last year
- Explanation Optimization☆13Oct 16, 2020Updated 5 years ago
- Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …☆16May 9, 2019Updated 6 years ago
- ☆16May 9, 2022Updated 3 years ago
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆254Aug 17, 2024Updated last year
- ☆52Aug 29, 2020Updated 5 years ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆25Sep 4, 2022Updated 3 years ago
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- An amortized approach for calculating local Shapley value explanations☆104Nov 29, 2023Updated 2 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆39Jun 21, 2022Updated 3 years ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,508Jul 13, 2025Updated 9 months ago
- Layer-wise Relevance Propagation (LRP) for LSTMs.☆225Apr 24, 2020Updated 6 years ago
- Code and documentation for experiments in the TreeExplainer paper☆192Oct 4, 2019Updated 6 years ago
- ☆14Dec 4, 2023Updated 2 years ago
- ☆20Jan 23, 2020Updated 6 years ago