Jun 5, 2021 · We propose a Complex-valued Attentional MEta Learner (CAMEL) for the problem of few-shot signal recognition by leveraging attention and meta-learning in the ...
Sep 13, 2024 · Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for ...
Apr 18, 2024 · Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition. Ying Peng, Tongji University, China; Yihong Dong ...
Oct 12, 2023 · Implementation of the transformer proposed in Building Blocks for a Complex-Valued Transformer Architecture, plus a few other proposals from related papers.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition. Preprint. Full-text available. Jun 2021. Yihong Dong ...
Jun 16, 2023 · [28] proposes a complex-valued meta- learning framework for signal recognition. As a byproduct, they de- fine a complex-valued attention.
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Signal Transformer: Complex-Valued Attention and Meta-Learning for Signal Recognition · Ying Peng · Yihong Dong · et al.
A deep learning model which incorporates the transformer model as a backbone and develop complex attention and encoder/decoder network operating on ...
This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this ...
May 31, 2023 · The paper discusses the development of a Complex-valued Attentional MEta Learner (CAMEL) for few-shot signal recognition.