About
+Taija currently covers a range of approaches towards making AI systems more trustworthy:
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- 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:
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- 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?
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Taija stands for Trustworthy Artificial Intelligence in Julia. When thinking about a logo that embodies trustworthiness, we quickly landed on š¶.
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