JutulDarcyRules: ChainRules extension to Jutul and JutulDarcy

A high-performance library for gradient based quantum optimal control

Operator layers for Flux.jl that allow for the construction of Neural Operator models by using Flux's API. Useful for discretization-independent spatio-temporal ML models.

Linear models

A package for wrapping iterative MLJ models in a control strategy

Useful sparse codes

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Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.

Differentiable 2d rasterizer in Julia

MLJ.jl interface for JLBoost.jl

A Julia package for parameterized convex approximators including parameterized log-sum-exp (PLSE) network.

MLJ.jl interface for JLBoost.jl

A Julia package for building production-ready measures (metrics) for statistics and machine learning

Fast accurate creation, detection and analysis of quantum vortex distributions.

Connecting MLJ and MLFlow

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DNA Motif discovery that includes the discovery of flexible (long or gapped) motifs.

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A Julia package for kernel functions of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

A decision-making framework for the cost-efficient design of experiments, balancing the value of acquired experimental evidence and incurred costs.

A Package for Estimating Stochastic Frontier Models using Julia

Neural-Network Outlier Detection Algorithms for Julia

A Julia based implementation of Plug and Play Language Models

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Julia package for MPI reconstruction

Layerwise Relevance Propagation in Julia.

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A Julia package for modelling Algorithmic Recourse Dynamics.

Jet operator pack for seismic modeling dependent on WaveFD.jl. Part of the COFII framework.

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Bayesian addition to Flux.jl

Forked Successor to github.com/JuliaFolds/FLoops.jl

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Independent Hypothesis Weighting for multiple testing with side-information in Julia

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A Julia package for integration of packages of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

MPI based parallel for method of moments (MoM) and MLFMA (https://github.com/deltaeecs/MoM_AllinOne.jl.jl)

Optimally tuned ridge regression when features can be partitioned into groups.

Julia implementation of the ISOKANN algorithm for the computation of invariant subspaces of Koopman operators