Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets-Reference-Cited by-同舟云学术

Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets

Published:2023-06 Issue:3 Volume:7 Page:818-828
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Adeel Ahsan¹^ORCID,Adetomi Adewale²,Ahmed Khubaib²,Hussain Amir³,Arslan Tughrul⁴,Phillips W. A.⁵

Affiliation:

1. Nuffield Department of Surgical Sciences, Oxford Computational Neuroscience, University of Oxford, Oxford, U.K.

2. CMI Lab, University of Wolverhampton, Wolverhampton, U.K.

3. Edinburgh Napier University, Edinburgh, U.K.

4. School of Engineering, University of Edinburgh, Edinburgh, U.K.

5. Department of Psychology, University of Stirling, Stirling, U.K.

Funder

Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

Reference65 articles.

2. Winner-take-all autoencoders;makhzani;Proc Adv Neural Inf Process Syst,0

4. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks;hoefler;J Mach Learn Res,2021

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