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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:

  • flexible and versatile
  • as general as possible
  • fast
  • and dependent on only handful of other packages

Watch our introductory talk from JuliaCon 2021

Installation

Run the following in REPL:

] add Mill

Julia v1.10 or later is required.

Getting Started

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

    @article{Mandlik2022,
      author = {{\v{S}}imon Mandl{\'{i}}k and Mat{\v{e}}j Ra{\v{c}}insk{\'{y}} and Viliam Lis{\'{y}} and Tom{\'{a}}{\v{s}} Pevn{\'{y}}},
      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 = {{\v{S}}imon Mandl{\'{i}}k and Tom{\'{a}}{\v{s}} Pevn{\'{y}} and V{\'{a}}clav {\v{S}}m{\'{i}}dl and Luk{\'{a}}{\v{s}} Bajer},
      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  = {{\v{S}}imon Mandl{\'{i}}k and Tom{\'{a}}{\v{s}} Pevn{\'{y}}},
      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.

We use GitHub issues for tracking requests and bugs.

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Build flexible hierarchical multi-instance learning models.

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