EvoStore: Towards Scalable Storage of Evolving Learning Models-Reference-Cited by-同舟云学术

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EvoStore: Towards Scalable Storage of Evolving Learning Models

Published:2024-06-03 Issue: Volume:738 Page:148-159
ISSN:
Container-title:Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing
language:
Short-container-title:

Author:

Underwood Robert¹^ORCID,Madhyastha Meghana²^ORCID,Burns Randal³^ORCID,Nicolae Bogdan⁴^ORCID

Affiliation:

1. Argonne National Laboratory, Lemont, Illinois, United States of America

2. Johns Hopkins University, Baltimore, Maryland, USA

3. Johns Hopkins University, Baltimore, Maryland, United States of America

4. Argonne National Laboratory, Lemont, Illinois, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3625549.3658679

Reference43 articles.

1. The TensorFlow Authors. 2022. Tensorflow SavedModel. https://www.tensorflow.org/guide/saved_model.

2. Scalable reinforcement-learning-based neural architecture search for cancer deep learning research

3. Prasanna Balaprakash, Michael Salim, Thomas D. Uram, Venkat Vishwanath, and Stefan M. Wild. 2018. DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. In HiPC'18: The IEEE 25th International Conference on High Performance Computing. IEEE, Bengaluru, India, 42--51.

4. Demystifying Parallel and Distributed Deep Learning

5. James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, null (feb 2012), 281--305.

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