Prototype of interactive visualisation tool for Bayesian active deep learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F24%3A00617590" target="_blank" >RIV/67985815:_____/24:00617590 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/23:00362332

Výsledek na webu

<a href="https://www.aspbooks.org/publications/535/091.pdf" target="_blank" >https://www.aspbooks.org/publications/535/091.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Prototype of interactive visualisation tool for Bayesian active deep learning

Popis výsledku v původním jazyce

In the era of big data in astronomy, we need to develop methods to analyse the data. One such method is Bayesian active deep learning (synergy of Bayesian convolutional neural networks and active learning). To improve the method's performance, we have developed a prototype of an interactive visualisation tool for a selection of an informative (contains data with high predictive uncertainty, is diverse, but not redundant) data subsample for labelling by a human expert. The tool takes as input a sample of data with the highest predictive uncertainty. These data are projected to 2-D with a dimensionality reduction technique. We visualise the projected data in an interactive scatter plot and allow a human expert to label a selected subsample of data. With this tool, she or he can select a correct subsample with all the previously mentioned characteristics. This should lower the total amount of data labelled because the Bayesian model's performance will improve faster than when the data are selected automatically.

Název v anglickém jazyce

Prototype of interactive visualisation tool for Bayesian active deep learning

Popis výsledku anglicky

In the era of big data in astronomy, we need to develop methods to analyse the data. One such method is Bayesian active deep learning (synergy of Bayesian convolutional neural networks and active learning). To improve the method's performance, we have developed a prototype of an interactive visualisation tool for a selection of an informative (contains data with high predictive uncertainty, is diverse, but not redundant) data subsample for labelling by a human expert. The tool takes as input a sample of data with the highest predictive uncertainty. These data are projected to 2-D with a dimensionality reduction technique. We visualise the projected data in an interactive scatter plot and allow a human expert to label a selected subsample of data. With this tool, she or he can select a correct subsample with all the previously mentioned characteristics. This should lower the total amount of data labelled because the Bayesian model's performance will improve faster than when the data are selected automatically.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Astronomical Data Analysis Software and Systems XXXI

ISBN

978-1-58381-957-9

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

91-94

Název nakladatele

Astronomical Society of the Pacific

Místo vydání

San Francisco

Místo konání akce

Kapské Město

Datum konání akce

24. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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