Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search-Reference-Cited by-同舟云学术

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Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search

Published:2024-07-14 Issue: Volume:17 Page:1888-1894
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
language:
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Author:

Coleti Mark¹^ORCID,Gunaratne Chathika¹^ORCID,Young Steven¹^ORCID,Varadarajan Swetha²^ORCID,Patton Robert¹^ORCID,Potok Tom³^ORCID

Affiliation:

1. Oak Ridge National Laboratory, Oak Ridge, United States of America

2. ACM, Bolingbrook, United States of America

3. Oak Ridge National Laboratory, Oak Ridgbe, United States of America

Funder

Department of Energy, Office of Science

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638530.3664149

Reference15 articles.

1. Coletti, M., Lunga, D., Bassett, J. K., and Rose, A. Evolving larger convolutional layer kernel sizes for a settlement detection deep-learner on summit. In Workshop for Deeplearning for Supercomputers (November 2019), The International Conference for High Performance Computing, Networking, Storage, and Analysis.

2. Coletti, M. A., Scott, E. O., and Bassett, J. K. Library for evolutionary algorithms in python (leap). In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (New York, NY, USA, 2020), GECCO '20, Association for Computing Machinery, pp. 1571--1579.

3. Dask Development Team. Dask: Library for dynamic task scheduling, 2016.

4. Kincade, K. Berkeley lab deploys next-gen supercomputer, perlmutter, bolstering u.s. scientific research, May 2021.

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