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Authors: Olga Grebenkova 1 ; 2 ; Oleg Bakhteev 1 ; 3 and Vadim Strijov 3

Affiliations: 1 Moscow Institute of Physics and Technology (MIPT), Russia ; 2 Skolkovo Institute of Science and Technology (Skoltech), Russia ; 3 FRC CSC RAS, Russia

Keyword(s): Model Complexity Control, Hypernetworks, Variational Model Optimization, Bayesian Inference.

Abstract: The paper is devoted to deep learning model complexity. It is estimated by Bayesian inference and based on a computational budget. The idea of the proposed method is to represent deep learning model parameters in the form of hypernetwork output. A hypernetwork is a supplementary model which generates parameters of the selected model. This paper considers the minimum description length from a Bayesian point of view. We introduce prior distributions of deep learning model parameters to control the model complexity. The paper analyzes and compares three types of regularization to define the parameter distribution. It infers and generalizes the model evidence as a criterion that depends on the required model complexity. Finally, it analyzes this method in the computational experiments on the Wine, MNIST, and CIFAR-10 datasets.

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Paper citation in several formats:
Grebenkova, O.; Bakhteev, O. and Strijov, V. (2023). Deep Learning Model Selection With Parametric Complexity Control. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 65-74. DOI: 10.5220/0011626900003393

@conference{icaart23,
author={Olga Grebenkova. and Oleg Bakhteev. and Vadim Strijov.},
title={Deep Learning Model Selection With Parametric Complexity Control},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2023},
pages={65-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011626900003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Deep Learning Model Selection With Parametric Complexity Control
SN - 978-989-758-623-1
IS - 2184-433X
AU - Grebenkova, O.
AU - Bakhteev, O.
AU - Strijov, V.
PY - 2023
SP - 65
EP - 74
DO - 10.5220/0011626900003393
PB - SciTePress

- Science and Technology Publications, Lda.
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