Representation learning to overcome scarce data in machine learning. Application to chemosensory receptors

Matej Hladiš; Sébastien Fiorucci; Jérôme Golebiowski; Jérémie Topin

Conference Papers Year : 2021

Representation learning to overcome scarce data in machine learning. Application to chemosensory receptors

(1) , (1) , (2) , (1)

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Matej Hladiš

Function : Author

Institut de Chimie de Nice

Sébastien Fiorucci

Function : Author
PersonId : 184057
IdHAL : sebastien-fiorucci
ORCID : 0000-0003-2245-0478
IdRef : 110498488

Institut de Chimie de Nice

CV

Jérôme Golebiowski

Function : Author
PersonId : 767896
ORCID : 0000-0002-3675-1952

Daegu Gyeongbuk Institute of Science and Technology

Jérémie Topin

Function : Author
PersonId : 1081477

Institut de Chimie de Nice

Domains

Machine Learning [stat.ML] Cheminformatics Theoretical and/or physical chemistry Neurons and Cognition [q-bio.NC]

Sébastien FIORUCCI : Connect in order to contact the contributor

https://hal.univ-cotedazur.fr/hal-03908186

Submitted on : Tuesday, December 20, 2022-2:35:53 PM

Last modification on : Thursday, April 11, 2024-1:08:14 PM

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hal-03908186 , version 1 (20-12-2022)

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HAL Id : hal-03908186 , version 1

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Matej Hladiš, Sébastien Fiorucci, Jérôme Golebiowski, Jérémie Topin. Representation learning to overcome scarce data in machine learning. Application to chemosensory receptors. 23e congrès du GGMM et 10e journées de la SFCi, Nov 2021, Lille, France. ⟨hal-03908186⟩

Representation learning to overcome scarce data in machine learning. Application to chemosensory receptors

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