JsonGrinder.jl: Automated Differentiable Neural Architecture for Embedding Arbitrary JSON Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00363523" target="_blank" >RIV/68407700:21230/22:00363523 - isvavai.cz</a>

Výsledek na webu

<a href="https://jmlr.org/papers/v23/21-0174.html" target="_blank" >https://jmlr.org/papers/v23/21-0174.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

JsonGrinder.jl: Automated Differentiable Neural Architecture for Embedding Arbitrary JSON Data

Popis výsledku v původním jazyce

Standard machine learning (ML) problems are formulated on data converted into a suitable tensor representation. However, there are data sources, for example in cybersecurity, that are naturally represented in a unifying hierarchical structure, such as XML, JSON, and Protocol Buffers. Converting this data to a tensor representation is usually done by manual feature engineering, which is laborious, lossy, and prone to bias originating from the human inability to correctly judge the importance of particular features. JsonGrinder.jl is a library automating various ML tasks on these difficult sources. Starting with an arbitrary set of JSON samples, it automatically creates a differentiable ML model (called hmilnet), which embeds raw JSON samples into a fixed-size tensor representation. This embedding network can be naturally extended by an arbitrary ML model expecting tensor inputs in order to perform classification, regression, or clustering.

Název v anglickém jazyce

JsonGrinder.jl: Automated Differentiable Neural Architecture for Embedding Arbitrary JSON Data

Popis výsledku anglicky

Standard machine learning (ML) problems are formulated on data converted into a suitable tensor representation. However, there are data sources, for example in cybersecurity, that are naturally represented in a unifying hierarchical structure, such as XML, JSON, and Protocol Buffers. Converting this data to a tensor representation is usually done by manual feature engineering, which is laborious, lossy, and prone to bias originating from the human inability to correctly judge the importance of particular features. JsonGrinder.jl is a library automating various ML tasks on these difficult sources. Starting with an arbitrary set of JSON samples, it automatically creates a differentiable ML model (called hmilnet), which embeds raw JSON samples into a fixed-size tensor representation. This embedding network can be naturally extended by an arbitrary ML model expecting tensor inputs in order to perform classification, regression, or clustering.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Machine Learning Research

ISSN

1532-4435

e-ISSN

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Svazek periodika

23

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

5

Strana od-do

1-5

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85148099476