Advanced search
Start date

On the Study of Semantics in Deep Learning Models

Grant number: 15/25739-4
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): March 01, 2016
Effective date (End): July 01, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:João Paulo Papa
Grantee:Gustavo Henrique de Rosa
Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
Associated scholarship(s):16/21243-7 - Learning Dropout Parameters for Convolutional Neural Networks, BE.EP.MS


Deep learning architectures have been extensively studied in the last years, mainly due to their discriminative power and effectiveness in many crucial problems in computer vision, such as face and people identification, as well as object recognition, just to name a few. However, one problem related to these models concerns with their number of parameters, which can easily reach thousands of hundreds. Another drawback is related to the need for large datasets for training purposes, as well as their high probability of overtraining, mainly because of their complex architecture. Although some recent works have proposed different solutions to alleviate this problem, such approaches still require parameters, which need to be fine-tuned, and also are skilled-dependent. This proposal aims at "learning how these techniques do learn", i.e., to learn their good points and shortcomings. Techniques such as Restricted Boltzmann Machines and Deep Belief Nets will be studied in order to obtain a better understanding of their working mechanism through meta-heuristic-based optimization. This proposal also comprises an internship at Middlesex University, United Kingdom.

News published in Agência FAPESP Newsletter about the scholarship:
Articles published in other media outlets (0 total):
More itemsLess items

Scientific publications (7)
(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)
DE ROSA, GUSTAVO H.; PAPA, JOAO P.; YANG, XIN-S. Handling dropout probability estimation in convolution neural networks using meta-heuristics. SOFT COMPUTING, v. 22, n. 18, SI, p. 6147-6156, . (14/12236-1, 14/16250-9, 15/25739-4)
PEREIRA, CLAYTON R.; PEREIRA, DANILO R.; ROSA, GUSTAVO H.; ALBUQUERQUE, VICTOR H. C.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO P.. Handwritten dynamics dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification. ARTIFICIAL INTELLIGENCE IN MEDICINE, v. 87, p. 67-77, . (13/07375-0, 14/16250-9, 16/19403-6, 10/15566-1, 14/12236-1, 15/25739-4)
PAPA, JOAO PAULO; ROSA, GUSTAVO H.; PEREIRA, DANILLO R.; YANG, XIN-SHE. Quaternion-based Deep Belief Networks fine-tuning. APPLIED SOFT COMPUTING, v. 60, p. 328-335, . (14/16250-9, 14/12236-1, 15/25739-4)
AMORIM, WILLIAN PARAGUASSU; TETILA, EVERTON CASTELAO; PISTORI, HEMERSON; PAPA, JOAO PAULO. Semi-supervised learning with convolutional neural networks for UAV images automatic recognition. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 164, . (14/12236-1, 13/07375-0, 15/25739-4, 16/19403-6)
AMORIM, WILLIAN PARAGUASSU; ROSA, GUSTAVO HENRIQUE; THOMAZELLA, ROGERIO; COGO CASTANHO, JOSE EDUARDO; LOFRANO DOTTO, FABIO ROMANO; RODRIGUES JUNIOR, OSWALDO PONS; MARANA, APARECIDO NILCEU; PAPA, JOAO PAULO. Semi-supervised learning with connectivity-driven convolutional neural networks. PATTERN RECOGNITION LETTERS, v. 128, p. 16-22, . (14/12236-1, 13/07375-0, 15/25739-4, 16/19403-6)
PAPA, JOAO PAULO; ROSA, GUSTAVO HENRIQUE; PAPA, LUCIENE PATRICI. A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes. PATTERN RECOGNITION LETTERS, v. 100, p. 59-66, . (13/07375-0, 14/16250-9, 16/19403-6, 10/15566-1, 14/12236-1, 15/25739-4)
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, . (14/16250-9, 14/12236-1, 13/07375-0, 15/25739-4, 16/21243-7)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
ROSA, Gustavo Henrique de. Meta-Heuristic Optimization-based Regularization for Deep Learning Architectures. 2018. Master's Dissertation - Universidade Estadual Paulista (Unesp). Instituto de Biociências Letras e Ciências Exatas. São José do Rio Preto São José do Rio Preto.

Please report errors in scientific publications list by writing to: