Prototype flexible hierarchical multi-instance learning models.
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Updated
Jun 3, 2024 - Julia
Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Prototype flexible hierarchical multi-instance learning models.
A Julia implementation of choice sequence based PBT, inspired by Hypothesis
Combine multiple types in a single one
Hyperparameter optimization algorithms for use in the MLJ machine learning framework
Composable Tensor Network library in Julia
A Julia finite element tearing and interconnecting (FETI) prototype implementation.
TrixiParticles.jl: Particle-based multiphysics simulations in Julia
Contains html (and potentially other) documentation for GALAHAD to act as a web source
Quantum Toolbox in Julia
Core functionality for the MLJ machine learning framework
Interactive data visualizations and plotting in Julia
The Julia Programming Language
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
(GPU accelerated) Multi-arch (linux/amd64, linux/arm64/v8) JupyterLab Julia docker images. Please submit Pull Requests to the GitLab repository. Mirror of
Template for Julia Programming Language packages using the copier engine.
Implementation of polylogarithms in Julia
Created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman
Released February 14, 2012