Alternatives and similar repositories for awesome-explainable-ai:

Users that are interested in awesome-explainable-ai are comparing it to the libraries listed below

• AI4LIFE-GROUP / LLM_Explainer
  Code for paper: Are Large Language Models Post Hoc Explainers?
  ☆30Updated 8 months ago
• AI4LIFE-GROUP / OpenXAI
  OpenXAI : Towards a Transparent Evaluation of Model Explanations
  ☆240Updated 7 months ago
• Trustworthy-ML-Lab / Label-free-CBM
  A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled concept dat…
  ☆91Updated 11 months ago
• mateoespinosa / cem
  Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper…
  ☆59Updated last month
• pietrobarbiero / pytorch_explain
  PyTorch Explain: Interpretable Deep Learning in Python.
  ☆152Updated 10 months ago
• MaheepChaudhary / Causality-in-Trustworthy-Machine-Learning
  The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era comp…
  ☆92Updated last year
• acmi-lab / cmu-10732-robustness-adaptivity-shift
  Official repository for CMU Machine Learning Department's 10732: Robustness and Adaptivity in Shifting Environments
  ☆73Updated 2 years ago
• weitianxin / awesome-distribution-shift
  A curated list of papers and resources about the distribution shift in machine learning.
  ☆111Updated last year
• wwweiwei / awesome-self-supervised-learning-for-tabular-data
  A collection of research materials on SSL for non-sequential tabular data (SSL4NSTD)
  ☆183Updated last month
• opendataval / opendataval
  OpenDataVal: a Unified Benchmark for Data Valuation in Python (NeurIPS 2023)
  ☆96Updated last month
• vanderschaarlab / Datagnosis
  A Data-Centric library providing a unified interface for state-of-the-art methods for hardness characterisation of data points.
  ☆24Updated 2 weeks ago
• dmitrykazhdan / concept-based-xai
  Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
  ☆54Updated 2 years ago
• YyzHarry / SubpopBench
  [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
  ☆108Updated last year
• SinaMohseni / Awesome-XAI-Evaluation
  Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems
  ☆74Updated 2 years ago
• Varal7 / conformal-language-modeling
  Conformal Language Modeling
  ☆28Updated last year
• dilyabareeva / quanda
  A toolkit for quantitative evaluation of data attribution methods.
  ☆42Updated this week
• flecue / xai-aaai2022
  ☆31Updated 3 years ago
• mertyg / post-hoc-cbm
  Code for the paper "Post-hoc Concept Bottleneck Models". Spotlight @ ICLR 2023
  ☆74Updated 10 months ago
• AmeenAli / XAI_Transformers
  Official Code Implementation of the paper : XAI for Transformers: Better Explanations through Conservative Propagation
  ☆63Updated 3 years ago
• ZaydH / influence_analysis_papers
  Influence Analysis and Estimation - Survey, Papers, and Taxonomy
  ☆72Updated last year
• pietrobarbiero / entropy-lens
  ☆16Updated 2 years ago
• jjbrophy47 / instance_based_interpretability
  Existing literature about training-data analysis.
  ☆17Updated 3 years ago
• abacusai / xai-bench
  XAI-Bench is a library for benchmarking feature attribution explainability techniques
  ☆63Updated 2 years ago
• PatrickZH / Awesome-Coreset-Selection
  Awesome coreset/core-set/subset/sample selection works.
  ☆173Updated 8 months ago
• yewsiang / ConceptBottleneck
  Concept Bottleneck Models, ICML 2020
  ☆194Updated 2 years ago
• LevinRoman / tabular-transfer-learning
  A repo for transfer learning with deep tabular models
  ☆102Updated 2 years ago
• BingzhaoZhu / XTab
  ☆33Updated 2 years ago
• ZhangTP1996 / TapTap
  ☆64Updated last year
• uvanlp / valda
  A Python Data Valuation Package
  ☆30Updated 2 years ago
• MadryLab / trak
  A fast, effective data attribution method for neural networks in PyTorch
  ☆202Updated 4 months ago