Mill.jl
(Multiple Instance Learning Library) is a library aimed to build flexible hierarchical multi-instance learning models built on top of Flux.jl
. It is developed to be:
Watch our introductory talk from JuliaCon 2021
Run the following in REPL:
] add Mill
Julia v1.9 or later is required.
Kindly cite our work with the following entries if you find it interesting, please:
JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data
@article{Mandlik2022,
author = {Šimon Mandlík and Matěj Račinský and Viliam Lisý and Tomáš Pevný},
issn = {1533-7928},
issue = {298},
journal = {Journal of Machine Learning Research},
pages = {1-5},
title = {JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data},
volume = {23},
url = {http://jmlr.org/papers/v23/21-0174.html},
year = {2022},
}
Malicious Internet Entity Detection Using Local Graph
Inference (practical Mill.jl
application)
@article{Mandlik2024,
author = {Mandlík, Šimon and Pevný, Tomáš and Šmídl, Václav and Bajer, Lukáš},
journal = {IEEE Transactions on Information Forensics and Security},
title = {Malicious Internet Entity Detection Using Local Graph Inference},
year = {2024},
volume = {19},
pages = {3554-3566},
doi = {10.1109/TIFS.2024.3360867}
}
this implementation (fill in the used version
)
@software{Mill,
author = {Tomas Pevny and Simon Mandlik},
title = {Mill.jl framework: a flexible library for (hierarchical) multi-instance learning},
url = {https://github.com/CTUAvastLab/Mill.jl},
version = {...},
}
If you want to contribute to Mill.jl, be sure to review the contribution guidelines.
We use GitHub issues for tracking requests and bugs.