Subaging in underparametrized Deep Neural Networks

Carolina Herrera Segura; Edison Montoya; Diego Tapias

doi:10.1088/2632-2153/ac8f1b

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arXiv · 2209.02517

Subaging in underparametrized Deep Neural Networks

Carolina Herrera Segura·Edison Montoya·Diego Tapias

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Abstract

We consider a simple classification problem to show that the dynamics of finite-width Deep Neural Networks in the underparametrized regime gives rise to effects similar to those associated with glassy systems, namely a slow evolution of the loss function and aging. Remarkably, the aging is sublinear in the waiting time (subaging) and the power-law exponent characterizing it is robust to different architectures under the constraint of a constant total number of parameters. Our results are maintained in the more complex scenario of the MNIST database. We find that for this database there is a unique exponent ruling the subaging behavior in the whole phase.

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BibTeX RIS

Carolina Herrera Segura, Edison Montoya, Diego Tapias. 2022-09-06. Subaging in underparametrized Deep Neural Networks. https://doi.org/10.1088/2632-2153%2Fac8f1b

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