Machine learning techniques have been extensively used in a number of applications, mainly that ones based on deep learning. However, such techniques need to have their hyperparameters fine-tuned for each specific application, being crucial to their good performance. This proposal aims at introducing Genetic Programming (GP) for parameter fine-tuning in Restricted Boltzmann Machines (RBMs), being the results validated in the context of binary image reconstruction. For comparison purposes, other metaheuristic techniques will be considered in the experimental section, as well as other public datasets. As far as we are concerned, GP-based techniques have never been used to fine-tune hyperparameters in RBMs to date. Additonally, the proposal also considers an internship abroad via FAPESP/BEPE.
News published in Agência FAPESP Newsletter about the scholarship: