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https://github.com/ctuavastlab/mill.jl
Build flexible hierarchical multi-instance learning models.
https://github.com/ctuavastlab/mill.jl
flux hierarchical-data json julia machine-learning multi-instance-learning
Last synced: 5 days ago
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Build flexible hierarchical multi-instance learning models.
- Host: GitHub
- URL: https://github.com/ctuavastlab/mill.jl
- Owner: CTUAvastLab
- License: mit
- Created: 2018-04-05T05:41:40.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-12-16T01:46:23.000Z (23 days ago)
- Last Synced: 2024-12-24T16:28:38.031Z (14 days ago)
- Topics: flux, hierarchical-data, json, julia, machine-learning, multi-instance-learning
- Language: Julia
- Homepage: https://ctuavastlab.github.io/Mill.jl/stable/
- Size: 8.67 MB
- Stars: 88
- Watchers: 7
- Forks: 8
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
README
---
[](https://github.com/CTUAvastLab/Mill.jl/blob/master/LICENSE.md)
[](https://CTUAvastLab.github.io/Mill.jl/stable)
[](https://github.com/CTUAvastLab/Mill.jl/actions/workflows/ci.yml)
[](https://codecov.io/gh/CTUAvastLab/Mill.jl)
`Mill.jl` (Multiple Instance Learning Library) is a library aimed to build flexible hierarchical multi-instance learning models built on top of [`Flux.jl`](https://fluxml.ai). It is developed to be:
* **flexible** and **versatile**
* as **general** as possible
* **fast**
* and dependent on only handful of other packages
[**Watch our introductory talk from JuliaCon 2021** ](https://www.youtube.com/watch?v=Bf0CvltIDbE)
## Installation
Run the following in REPL:
```julia
] add Mill
```
Julia v1.10 or later is required.
## Getting Started
- [Documentation](https://ctuavastlab.github.io/Mill.jl/stable/)
- [API Reference](https://ctuavastlab.github.io/Mill.jl/stable/api/aggregation/)
- [Examples](https://ctuavastlab.github.io/Mill.jl/stable/examples/musk/musk/)
## Citation
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*](https://jmlr.org/papers/v23/21-0174.html)
```
@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*](https://ieeexplore.ieee.org/document/10418120) (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 = {...},
}
```
## Contribution guidelines
If you want to contribute to Mill.jl, be sure to review the
[contribution guidelines](CONTRIBUTING.md).
We use [GitHub issues](https://github.com/CTUAvastLab/Mill.jl/issues) for
tracking requests and bugs.