Energy-Based Dropout in Restricted Boltzmann Machines: Why Not Go Random
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Data
2020-01-01
Autores
Roder, Mateus
de Rosa, Gustavo Henrique
de Albuquerque, Victor Hugo C.
Rossi, Andre L. D.
Papa, Joao P.
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Resumo
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing. Nevertheless, such models suffer from a common problem known as overfitting, which limits the network from predicting unseen data effectively. Regularization approaches arise in an attempt to address such a shortcoming. Among them, one can refer to the well-known Dropout, which tackles the problem by randomly shutting down a set of neurons and their connections according to a certain probability. Therefore, this approach does not consider any additional knowledge to decide which units should be disconnected. In this paper, we propose an energy-based Dropout (E-Dropout) that makes conscious decisions whether a neuron should be dropped or not. Specifically, we design this regularization method by correlating neurons and the model’s energy as an importance level for further applying it to energy-based models, such as Restricted Boltzmann Machines (RBMs). The experimental results over several benchmark datasets revealed the proposed approach’s suitability compared to the traditional Dropout and the standard RBMs.
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Computational modeling, Dropout, energy-based dropout, Image reconstruction, machine learning, Mathematical model, Neurons, regularization, restricted boltzmann machines, Standards, Task analysis, Training
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IEEE Transactions on Emerging Topics in Computational Intelligence.