Bayesian addition to Flux.jl

Package for fitting models according to the deep compartment modeling framework for pharmacometric applications.

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A Julia package for modelling Algorithmic Recourse Dynamics.

Layerwise Relevance Propagation in Julia.

A Julia based implementation of Plug and Play Language Models

Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event

Julia Package for Active Inference

Evidential Deep Learning Layers for Flux

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

A Julia package for people that love to learn new things about constraints

A package with exported learning networks that combine resampling methods from Imbalance.jl and classification models from MLJ

Compressed belief-state MDPs in Julia compatible with POMDPs.jl

This repo is for the final project for Comp541 Deep Learning class in Koc University.

Bayesian Safety Signal Detection in Julia

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

A powerful, no code solution for control and systems engineering

Subspace Inference package for uncertainty analysis in deep neural networks and neural ordinary differential equations using Julia

A small Julia package to facilitate working with the Trillion Dollar Words dataset.

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Lightweight module for fusing physical and neural models

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One package to train them all

Classification algorithms based on kernel nearest centers

Advanced process control for AutomationLabs

Dynamical systems identification

Neural Networks deals with hardware implementations and simulation in mind

Utilities to test implementations of the MLJ model interface and provide integration tests for the MLJ ecosystem

Empirical Bayes shrinkage in Julia

Power spectrum inference of irregularly sampled time series using Gaussian Processes in Julia

Differential Inequality Algorithms for Parametric ODEs

Monotone Cubic B-Splines (arXiv:2307.01748)

Integrate deep neural network and reverse mode automatic differentiation into Gauss process, have fun !

Deep and convolutional neural networks with feedback operations in Flux.

Julia package for Light Based Tomographic Volumetric Additive Manufacturing.

A Julia package containing GNC algorithms for autonomous aerospace systems

A collection of background subtraction algorithms for spectroscopic data

Molecular backbone geometry utilities

Operator pack for Jets.jl. Part of the COFII framework.