Hierarchical Multiple Instance Learning | Tomas Pevny | JuliaCon 2021

Описание: This talk was presented as part of JuliaCon 2021.

Abstract:
Learning from raw data input is one of the key components of many successful applications of machine learning methods. While machine learning problems are often formulated on data that naturally translate into a vector representation suitable for classifiers, there are data sources with a unifying hierarchical structure, such as JSON. This talk will describe Mill.jl and JsonGrinder.jl, which offers a theoretically justified approach to solve machine learning problems with these data sources.

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Contents
00:00 Introduction and Motivation
01:07 Possible solutions to the device ID problem
02:52 HMIL approach
06:32 Comparison to SOTA
07:20 Theoretical guarantees
08:29 Mill.jl: node types
09:11 Mill.jl: representing leafs
10:58 Mill.jl: representing dicts
11:59 Mill.jl: representing arrays
13:19 Mill.jl: working with the whole sample
14:26 Mill.jl: training a model with JsonGrinder.jl and Flux.jl
19:58 Summary

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