Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization-Reference-Cited by-同舟云学术

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization

Published:2023-07-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Lange Robert¹^ORCID,Schaul Tom²^ORCID,Chen Yutian²^ORCID,Lu Chris³^ORCID,Zahavy Tom²^ORCID,Dalibard Valentin²^ORCID,Flennerhag Sebastian²^ORCID

Affiliation:

1. Technical Univ. Berlin, Berlin, Germany

2. Google DeepMind, London, United Kingdom

3. University of Oxford, Oxford, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583131.3590496

Reference45 articles.

1. Marcin Andrychowicz , Misha Denil , Sergio Gomez , Matthew W Hoffman , David Pfau , Tom Schaul , Brendan Shillingford , and Nando De Freitas . 2016. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 ( 2016 ). Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 (2016).

2. Sebastian Pineda Arango , Hadi S Jomaa , Martin Wistuba , and Josif Grabocka . 2021 . Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml. arXiv preprint arXiv:2106.06257 (2021). Sebastian Pineda Arango, Hadi S Jomaa, Martin Wistuba, and Josif Grabocka. 2021. Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml. arXiv preprint arXiv:2106.06257 (2021).

3. Samy Bengio , Yoshua Bengio , Jocelyn Cloutier , and Jan Gescei . 1992. On the optimization of a synaptic learning rule . In Optimality in Biological and Artificial Networks? Routledge , 281--303. Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gescei. 1992. On the optimization of a synaptic learning rule. In Optimality in Biological and Artificial Networks? Routledge, 281--303.

4. James Bradbury , Roy Frostig , Peter Hawkins , Matthew James Johnson , Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. 2018 . JAX: composable transformations of Python +NumPy programs. (2018). http://github.com/google/jax James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. 2018. JAX: composable transformations of Python+NumPy programs. (2018). http://github.com/google/jax

5. Yutian Chen , Matthew W. Hoffman , Sergio Gómez Colmenarejo , Misha Denil , Timothy P. Lillicrap , Matt Botvinick , and Nando de Freitas . 2017 . Learning to Learn without Gradient Descent by Gradient Descent . In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.) , Vol. 70 . PMLR, 748--756. https://proceedings.mlr.press/v70/chen17e.html Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, and Nando de Freitas. 2017. Learning to Learn without Gradient Descent by Gradient Descent. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 748--756. https://proceedings.mlr.press/v70/chen17e.html

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