%%{ init: { 'theme': 'base', 'themeVariables': { 'primaryColor': '#BB2528', 'primaryTextColor': '#fff', 'primaryBorderColor': '#7C0000', 'lineColor': '#F8B229', 'secondaryColor': '#006100', 'tertiaryColor': '#e9edfb', 'fontFamily': "avenir" } } }%% flowchart TB classDef taija fill:#389836,stroke:#333,color:#fff; classDef core fill:#CB3C33,stroke:#333,color:#fff; classDef base fill:#9558B2,stroke:#333,color:#fff; %% Base base["TaijaBase.jl"] %% Meta interop["TaijaInteroperability.jl"] data["TaijaData.jl"] parallel["TaijaParallel.jl"] plotting["TaijaPlotting.jl"] %% Core ce["CounterfactualExplanations.jl"] ar["AlgorithmiRecourseDynamics.jl"] cp["ConformalPrediction.jl"] lr["LaplaceRedux.jl"] jem["JointEnergyModels.jl"] class base base; class interop,data,parallel,plotting taija; class ce,cp,lr,jem,ar core; %% Graph subgraph "Meta Packages" data & plotting & parallel & interop end subgraph "Core Packages" ce & cp & lr & jem & ar end
About
Taija currently covers a range of approaches towards making AI systems more trustworthy:
- Model Explainability (CounterfactualExplanations.jl)
- Algorithmic Recourse (CounterfactualExplanations.jl, AlgorithmicRecourseDynamics.jl)
- Predictive Uncertainty Quantification (ConformalPrediction.jl, LaplaceRedux.jl)
- Effortless Bayesian Deep Learning (LaplaceRedux.jl)
- Hybrid Learning (JointEnergyModels.jl)
Various meta packages can be used to extend the core functionality:
- Plotting (TaijaPlotting.jl)
- Datasets for testing and benchmarking (TaijaData.jl)
- Parallelization (TaijaParallel.jl)
- Interoperability with other programming languages (TaijaInteroperability.jl)
The TaijaBase.jl package provides common symbols, types and functions that are used across all or multiple Taija packages.
Why Taija?
Taija stands for Trustworthy Artificial Intelligence in Julia. When thinking about a logo that embodies trustworthiness, we quickly landed on 🐶.