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SwitchPath: Enhancing Exploration in Neural Networks Learning Dynamics

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Antonio Di Cecco
Andrea Papini
Carlo Metta
Marco Fantozzi
Silvia Giulia Galfré
Francesco Morandin
Maurizio Parton
Published in
Discovery Science
Publisher
Springer Nature Switzerland

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Abstract

The chapter 'SwitchPath: Enhancing Exploration in Neural Networks Learning Dynamics' introduces SwitchPath, a novel stochastic activation function that toggles between standard ReLU and its negative counterpart. This function enhances neural network exploration and robustness without the computational inefficiencies of methods like Dropout. Inspired by decision trees and epsilon-greedy algorithms, SwitchPath balances exploration and exploitation, improving generalization and reducing overfitting. The manuscript presents a comprehensive methodology, including training and inference phases, and demonstrates SwitchPath’s superior performance through experiments on computer vision networks and Vision Transformers. Additionally, it explores SwitchPath’s impact on generative models and GANs, showcasing its ability to mitigate mode collapse. The chapter concludes with future research directions, highlighting the potential of SwitchPath in various advanced neural network applications.
AI Generated

This summary of the content was generated with the help of AI.

A. Di Cecco—PhD student, National PhD in AI, XXXVIII cycle, health and life sciences, UCBM.
C. Metta—EU Horizon 2020: G.A. 871042 SoBig-Data++, NextGenEU - PNRR-PEAI (M4C2, investment 1.3) FAIR and “SoBigData.it”.
F. Morandin and M. Parton—Funded by INdAM groups GNAMPA and GNSAGA.
Computational resources provided by CLAI laboratory, Chieti-Pescara, Italy.
Authors can be contacted at curiosailab@gmail.com.

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previous chapter Social Isolation, Digital Connection: COVID-19’s Impact on Twitter Ego Networks
next chapter Analyzing Explanations of Deep Graph Networks Through Node Centrality and Connectivity
Title
SwitchPath: Enhancing Exploration in Neural Networks Learning Dynamics
Authors
Antonio Di Cecco
Andrea Papini
Carlo Metta
Marco Fantozzi
Silvia Giulia Galfré
Francesco Morandin
Maurizio Parton
Copyright Year
2025
Book
Discovery Science

Print ISBN: 978-3-031-78976-2

Electronic ISBN: 978-3-031-78977-9

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-78977-9_18
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