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
Run the following in REPL:
] add Mill
Julia v1.10 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 = {{\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 = {...}, }
If you want to contribute to Mill.jl, be sure to review the contribution guidelines.
We use GitHub issues for tracking requests and bugs.