May 15, 2024, 4:43 a.m. | Julie Keisler (EDF R\&D OSIRIS, EDF R\&D, CRIStAL, CRIStAL), El-Ghazali Talbi (CRIStAL, CRIStAL), Sandra Claudel (EDF R\&D OSIRIS, EDF R\&D), Gilles C

cs.LG updates on

arXiv:2303.12797v2 Announce Type: replace-cross
Abstract: In this paper, we propose an algorithmic framework to automatically generate efficient deep neural networks and optimize their associated hyperparameters. The framework is based on evolving directed acyclic graphs (DAGs), defining a more flexible search space than the existing ones in the literature. It allows mixtures of different classical operations: convolutions, recurrences and dense layers, but also more newfangled operations such as self-attention. Based on this search space we propose neighbourhood and evolution search operators …

abstract architectures arxiv cs.lg framework generate graphs networks neural networks ones optimization paper replace search space type

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