Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors-Reference-Cited by-同舟云学术

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Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors

Published:2024-06-28 Issue:27 Volume:15 Page:6974-6985
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Manzhos Sergei¹^ORCID,Chen Qun Gao²^ORCID,Lee Wen-Ya²^ORCID,Heejoo Yoon¹,Ihara Manabu¹,Chueh Chu-Chen³^ORCID

Affiliation:

1. School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan

2. Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan

3. Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan

Funder

National Taiwan University

Japan Science and Technology Agency

National Science and Technology Council

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.4c01413

Reference54 articles.

1. Neural Networks: Tricks of the Trade

2. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

3. Approximation of continuous functions of several variables by an arbitrary nonlinear continuous function of one variable, linear functions, and their superpositions

4. An Introduction to Neural Networks for the Social Sciences

5. Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies

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