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Learning Dropout Parameters for Convolutional Neural Networks

Grant number: 16/21243-7
Support type:Scholarships abroad - Research Internship - Master's degree
Effective date (Start): February 01, 2017
Effective date (End): May 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:João Paulo Papa
Grantee:Gustavo Henrique de Rosa
Supervisor abroad: Gustavo Kunde Rohde
Home Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
Local de pesquisa : University of Virginia (UVa), United States  
Associated to the scholarship:15/25739-4 - On the Study of Semantics in Deep Learning Models, BP.MS

Abstract

Deep learning-based approaches have been paramount in the last years, mainly due to their outstanding results in several application domains, that range from face and object recognition to handwritten digits identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanism. However, one main shortcoming of such models concerns their overfitting problem, which prevents the network from predicting unseen data effectively. In this proposal, we address this problem by means of proper selecting regularization parameters by means of meta-heuristic-driven techniques, which provide a simple and elegant solution to a number of optimization problems. The proposed approach will be validated in the context of nuclei detection in general-purpose cell images under the supervision of Prof. Gustavo Rohde, University of Virginia.

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PASSOS, LEANDRO A.; DE SOUZA, JR., LUIS A.; MENDEL, ROBERT; EBIGBO, ALANNA; PROBST, ANDREAS; MESSMANN, HELMUT; PALM, CHRISTOPH; PAPA, JOAO PAULO. Barrett's esophagus analysis using infinity Restricted Boltzmann Machines. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 59, p. 475-485, FEB 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.