Exploration and generalization in deep learning with SwitchPath activations

This work provides a comprehensive theoretical and empirical analysis of SwitchPath, a stochastic activation function that improves learning dynamics by probabilistically toggling between a neuron standard activation and its negation. We develop theoretical foundations and demonstrate its impact in...

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Vydané v:	Machine learning Ročník 114; číslo 9; s. 200
Hlavní autori:	Di Cecco, Antonio, Papini, Andrea, Metta, Carlo, Fantozzi, Marco, Galfrè, Silvia Giulia, Morandin, Francesco, Parton, Maurizio
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Nature B.V 01.09.2025
Predmet:	Adaptability Algorithms Back propagation Decision making Decision trees Deep learning Empirical analysis Generative networks Gradient flow Machine learning Neural network algorithms Neural networks Neurons Regularization
ISSN:	0885-6125, 1573-0565, 1573-0565
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