Delayed Memory Unit: Modeling Temporal Dependency Through Delay Gate

Recurrent neural networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applications. Nevertheless, vanilla RNNs are confronted with the well-known issue of gradient vanishing and exploding, posing a s...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-11, p.1-11
Hauptverfasser:	Sun, Pengfei, Wu, Jibin, Zhang, Malu, Devos, Paul, Botteldooren, Dick
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational efficiency Computational modeling Delay gate delay line Delay lines Delays Logic gates Long short term memory recurrent neural network (RNN) Recurrent neural networks Speech recognition Sun time series analysis Transformers
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