Scholarship 16/25776-0 - Aprendizado computacional, Visualização de dados - BV FAPESP
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Autoencoders neural networks optimization by visual analytics data

Grant number: 16/25776-0
Support Opportunities:Scholarships in Brazil - Master
Start date until: May 01, 2017
End date until: August 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal Investigator:Alexandre Xavier Falcão
Grantee:Bárbara Caroline Benato
Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM
Associated scholarship(s):17/25327-3 - Visual analytics for user-assisted label propagation in neural-network image classifier design, BE.EP.MS

Abstract

Artificial Neural Networks, specifically those with a deep architecture (with more than two hidden layers), are usually affected by an occurrence known as super-training of data. In the super-training of data, the premature convergence of the network generates quite satisfactory results in the training and validation sets, but with a lower performance expected on the test set. Among the many ways of handle with this problem, such as regularization and use of larger training sets, one has been rarely employed is the one that makes use of information visualization techniques. These techniques incorporate the user's knowledge into the network training. Such visualization tools enable a greater understanding of the network learning process, allowing to intervene in a certain region, parameters, connections and neurons of the network in order to improve its effectiveness. Depending on the network configuration, the user can check how the samples are grouped in the final classification process, as well as visualize the weights and activations of the neurons and how they affect the final classification result. In this sense, the present proposal of a master's research project aims to use information visualization techniques to assist in the training process of Autoencoder Neural Networks, since this technique has been widely used in the literature for the most diverse tasks, such as classification of images, signals and dimensionality reduction. The present proposal also contemplates a period of internship abroad by BEPE (Bolsa de Pesquisa e Estágio no Exterior) at the University of Groningen, Netherlands, under the guidance of Prof. Alexandru Telea, who has experience in the information visualization area. Given the student already has previous experience in deep learning due to her scientific initiation supported by FAPESP, this internship abroad will contribute to her learning in the data visualization area, as well as allowing a scientific maturity and experience with other research groups. (AU)

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Scientific publications (4)
(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)
BENATO, BARBARA C.; GOMES, JANCARLO F.; TELEA, ALEXANDRU C.; FALCAO, ALEXANDRE X.. Semi-automatic data annotation guided by feature space projection. PATTERN RECOGNITION, v. 109, . (16/25776-0, 14/12236-1, 17/25327-3)
BENATO, BARBARA C.; TELEA, ALEXANDRU C.; FALCAO, ALEXANDRE X.; IEEE. Semi-Supervised Learning with Interactive Label Propagation guided by Feature Space Projections. PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 8-pg., . (16/25776-0, 14/12236-1, 17/25327-3)
PEIXINHO, ALAN Z.; BENATO, BARBARA C.; NONATO, LUIS G.; FALCAO, ALEXANDRE X.; IEEE. Delaunay Triangulation Data Augmentation guided by Visual Analytics for Deep Learning. PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 8-pg., . (14/12236-1, 16/25776-0)
MARTINS, SAMUEL BOTTER; BENATO, BARBARA CAROLINE; SILVA, BRUNA FERREIRA; YASUDA, CLARISSA LYN; FALCAO, ALEXANDRE XAVIER; MORI, K; HAHN, HK. Modeling Normal Brain Asymmetry in MR Images Applied to Anomaly Detection without Segmentation and Data Annotation. MEDICAL IMAGING 2019: COMPUTER-AIDED DIAGNOSIS, v. 10950, p. 10-pg., . (14/12236-1, 13/07559-3, 16/25776-0, 15/10369-7)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
BENATO, Bárbara Caroline. Data annotation guided by feature projection. 2019. Master's Dissertation - Universidade Estadual de Campinas (UNICAMP). Instituto de Computação Campinas, SP.

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