Jul 2, 2024 · Approach: We introduce a Gated Cross-Attention Network (GCAN) to enhance prediction accuracy. This approach leverages cross-attention mechanisms to weigh and ...
Temporal-spatial cross attention network for recognizing imagined ...
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Jul 4, 2024 · The Transformer is a deep neural network that utilizes the self-attention mechanism to effectively capture long dependencies in sequential data.
5 days ago · In addition, we propose a pure attention network without any conventional activation function, termed as GRA-Net. Experiments conducted on typical computer ...
Jul 23, 2024 · A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All ...
Jul 19, 2024 · The attention mechanism can enable neural networks (NN) to focus on their input (or feature) subsets, thereby concentrating the learning process on important ...
Jul 10, 2024 · This work presents a novel technique for tabular data called adaptive multiscale attention deep neural network architecture (also named excited attention).
4 days ago · Transformers are a type of deep learning model that utilizes self-attention mechanisms to process and generate sequences of data efficiently, ...
4 days ago · In this paper, we aim to improve the robustness of Keyword Spotting (KWS) systems in noisy environments while keeping a small memory footprint. We propose a new ...
Jul 2, 2024 · The Transformer neural network is a powerful deep learning model that was introduced in a landmark paper titled "attention is all you need" by Vaswani et al.
Jul 1, 2024 · The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent ...
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