Enhancing Sequential Model Performance with Squared Sigmoid TanH (SST) Activation Under Data Constraints

Activation functions enable neural networks to learn complex representations by introducing non-linearities. While feedforward models commonly use rectified linear units, sequential models like recurrent neural networks, long short-term memory (LSTMs) and gated recurrent units (GRUs) still rely on S...

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Veröffentlicht in:	arXiv.org 2024-02
Hauptverfasser:	Subramanian, Barathi, Jeyaraj, Rathinaraja, Rakhmonov Akhrorjon Akhmadjon Ugli, Kim, Jeonghong
Format:	Artikel
Sprache:	eng
Schlagworte:	Constraint modelling Datasets Gradient flow Neural networks Recurrent neural networks
Online-Zugang:	Volltext
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