Deep Reinforcement Learning Agent for Dynamic Pruning of Convolutional Layers-Reference-Cited by-同舟云学术

Deep Reinforcement Learning Agent for Dynamic Pruning of Convolutional Layers

Published:2023-08-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Research in Adaptive and Convergent Systems
language:
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Author:

Hadi Abir Mohammad¹^ORCID,Jang Youngsun¹^ORCID,Won Kwanghee¹^ORCID

Affiliation:

1. South Dakota State University, Brookings, South Dakota, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3599957.3606236

Reference26 articles.

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4. Radosvet Desislavov , Fernando Martínez-Plumed , and José Hernández-Orallo . 2021. Compute and energy consumption trends in deep learning inference. arXiv preprint arXiv:2109.05472 ( 2021 ). Radosvet Desislavov, Fernando Martínez-Plumed, and José Hernández-Orallo. 2021. Compute and energy consumption trends in deep learning inference. arXiv preprint arXiv:2109.05472 (2021).

5. Elena Facco , Maria d' Errico , Alex Rodriguez , and Alessandro Laio . 2017. Estimating the intrinsic dimension of datasets by a minimal neighborhood information. Scientific reports 7, 1 ( 2017 ), 12140. Elena Facco, Maria d'Errico, Alex Rodriguez, and Alessandro Laio. 2017. Estimating the intrinsic dimension of datasets by a minimal neighborhood information. Scientific reports 7, 1 (2017), 12140.

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1. Deep Convolutional Neural Network Compression based on the Intrinsic Dimension of the Training Data;ACM SIGAPP Applied Computing Review;2024-03

2. Deep Reinforcement Learning Agent for Dynamic Pruning of Convolutional Layers;Proceedings of the International Conference on Research in Adaptive and Convergent Systems;2023-08-06