Alternatives and similar repositories for kbdm

Users that are interested in kbdm are comparing it to the libraries listed below

• mini-pw / 2023L-ExploratoryDataAnalysis
  Introduction to exploratory data analysis course for Mathematics and data analysis studies in Spring 2022/2023
  ☆16Updated 2 years ago
• MI2DataLab / MI2DataLab_Seminarium
  Materiały z seminariów prowadzonych w MI^2 DataLabie.
  ☆33Updated 2 weeks ago
• mim-uw / eXplainableMachineLearning-2024
  eXplainable Machine Learning 2023/24 at MIM UW
  ☆22Updated last year
• mini-pw / 2021L-WarsztatyBadawcze
  ☆11Updated 4 years ago
• machinelearningnuremberg / Revisiting-MLPs
  ☆11Updated last year
• mini-pw / 2021L-WUM
  ☆13Updated 2 years ago
• keencelia / oo-java2
  ☆13Updated 5 years ago
• Rughalt / mini-objective-java
  Objective Programming with Java
  ☆10Updated 5 years ago
• chelseaparlett / CPSC393ParlettPelleriti
  ☆108Updated last year
• ErikinBC / SurvSet
  ☆59Updated 5 months ago
• slds-lmu / lecture_iml
  ☆17Updated 2 weeks ago
• hbaniecki / adversarial-explainable-ai
  💡 Adversarial attacks on explanations and how to defend them
  ☆330Updated 11 months ago
• mmschlk / shapiq
  Shapley Interactions and Shapley Values for Machine Learning
  ☆638Updated this week
• ModelOriented / kernelshap
  Different SHAP algorithms
  ☆55Updated 2 months ago
• understandable-machine-intelligence-lab / Quantus
  Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
  ☆631Updated 4 months ago
• chr5tphr / zennit
  Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
  ☆236Updated 3 months ago
• adrianolszewski / model-based-testing-hypotheses
  Testing hypotheses through statistical models opens a universe of new possibilities. Learn how to improve your daily work with this appro…
  ☆84Updated 2 years ago
• dobriban / stat-ml-edu
  Resources for education in statistics and machine learning: from advanced undergraduate to research level
  ☆103Updated last year
• lkopf / cosy
  [NeurIPS 2024] CoSy is an automatic evaluation framework for textual explanations of neurons.
  ☆18Updated 5 months ago
• compbiomed-unito / survhive
  Convenient, opinionated wrapper around some (deep) survival models
  ☆13Updated last month
• mini-pw / 2020Z-ProgramowanieWR
  ☆10Updated 5 years ago
• NorskRegnesentral / shapr
  Explaining the output of machine learning models with more accurately estimated Shapley values
  ☆170Updated last week
• Novartis / torchsurv
  Deep survival analysis made easy
  ☆165Updated last week
• AI4LIFE-GROUP / OpenXAI
  OpenXAI : Towards a Transparent Evaluation of Model Explanations
  ☆251Updated last year
• jim-berend / semanticlens
  Mechanistic understanding and validation of large AI models with SemanticLens
  ☆47Updated 2 months ago
• rachtibat / zennit-crp
  An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization
  ☆138Updated last year
• ClaraHapp / funData
  An S4 class for functional data in R
  ☆14Updated last year
• jaryaman / causal-inference-cheat-sheet
  Summary of useful results in Causal Inference
  ☆20Updated 4 years ago
• konstantinos-p / Bayesian-Neural-Networks-Reading-List
  A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesi…
  ☆58Updated 2 years ago
• ModelOriented / treeshap
  Compute SHAP values for your tree-based models using the TreeSHAP algorithm
  ☆90Updated last year