Learning Dropout Parameters for Convolutional Neural Networks

• Grant number: 16/21243-7
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: February 01, 2017
• End date: 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: Gustavo Kunde Rohde
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Institution abroad: 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

(5)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

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. (14/16250-9, 14/12236-1, 13/07375-0, 15/25739-4, 16/21243-7)

PEREIRA, DANILLO ROBERTO; REBOUCAS FILHO, PEDRO P.; DE ROSA, GUSTAVO HENRIQUE; PAPA, JOAO PAULO; DE ALBUQUERQUE, VICTOR HUGO C.; IEEE. Stroke Lesion Detection Using Convolutional Neural Networks. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 6-pg., 2018-01-01. (14/16250-9, 14/12236-1, 15/25739-4, 16/21243-7)

ROSA, GUSTAVO H.; AFONSO, LUIS C. S.; BALDASSIN, ALEXANDRO; PAPA, JOAO P.; YANG, XIN-SHE; FELSBERG, M; HEYDEN, A; KRUGER, N. Quaternionic Flower Pollination Algorithm. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, v. 10425, p. 12-pg., 2017-01-01. (14/16250-9, 15/25739-4, 14/12236-1, 16/21243-7)

PASSOS, LEANDRO A.; PEREIRA, CLAYTON R.; REZENDE, EDMAR R. S.; CARVALHO, TIAGO J.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO P.; IEEE. Parkinson Disease Identification using Residual Networks and Optimum-Path Forest. 2018 IEEE 12TH INTERNATIONAL SYMPOSIUM ON APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS (SACI), v. N/A, p. 5-pg., 2018-01-01. (13/07375-0, 16/21243-7, 14/12236-1, 15/25739-4, 14/16250-9)

PEREIRA, CLAYTON R.; PASSOS, LEANDRO A.; LOPES, RICARDO R.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO PAULO; FELSBERG, M; HEYDEN, A; KRUGER, N. Parkinson's Disease Identification Using Restricted Boltzmann Machines. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, v. 10425, p. 11-pg., 2017-01-01. (14/16250-9, 15/25739-4, 14/12236-1, 16/21243-7)