Dropout Neural Network Training Viewed from a Percolation Perspective

Abstract:In this work, we investigate the existence and effect of percolation in training deep Neural Networks (NNs) with dropout. Dropout methods are regularisation techniques for training NNs, first introduced by G. Hinton et al. (2012). These methods temporarily remove connections in the NN, randomly at each stage of training, and update the remaining subnetwork with Stochastic Gradient Descent (SGD). The process of removing connections from a network at random is similar to percolation, a paradigm model of statistical physics. If dropout were to remove enough connections such that there is no path between the input and output of the NN, then the NN could not make predictions informed by the data. We study new percolation models that mimic dropout in NNs and characterise the relationship between network topology and this path problem. The theory shows the existence of a percolative effect in dropout. We also show that this percolative effect can cause a breakdown when training NNs without biases with dropout; and we argue heuristically that this breakdown extends to NNs with biases.

Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech); Probability (math.PR); Machine Learning (stat.ML)

arXiv:2512.13853 [cs.LG]

(or arXiv:2512.13853v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.13853 Focus to learn more arXiv-issued DOI via DataCite

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