Few-shot Federated Learning in Randomized Neural Networks via Hyperdimensional Computing-Reference-Cited by-同舟云学术

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Few-shot Federated Learning in Randomized Neural Networks via Hyperdimensional Computing

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Rosato Antonello¹,Panella Massimo¹,Osipov Evgeny²,Kleyko Denis³

Affiliation:

1. University of Rome “La Sapienza”,Dept. of Information Engineering, Electronics and Telecommunications (DIET),Rome,Italy,00184

2. Luleå University of Technology,Luleå,Sweden

3. University of California,Berkeley,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892007.pdf?arnumber=9892007

Reference45 articles.

1. Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics

2. Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks

3. Multiplicative Binding, Representation Operators & Analogy;gayler;Advances in Analogy Research Integration of Theory and Data from the Cognitive Computational and Neural Sciences,0

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On Hyperdimensional Computing-based Federated Learning: A Case Study;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

2. Recent Progress and Development of Hyperdimensional Computing (HDC) for Edge Intelligence;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-03

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