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Combinations of rank-one Volterra models

Grant number: 19/21858-0
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2021
Effective date (End): November 30, 2023
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Principal researcher:Vitor Heloiz Nascimento
Grantee:Carlos Augusto Prete Junior
Home Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Learning algorithms for nonlinear models have received much attention in the past years, especially due to the success of deep neural network architectures in several applications, as image and voice recognition. Neural networks and Volterra models are two different approaches to learn nonlinear functions, which often require a large number of parameters in practical problems. Decomposable Volterra Models (DVMs) is a recent approach that can significantly reduce the number of terms of Volterra models by constraining the Volterra kernels to rank-one tensors. Even though a DVM can only approximate a limited set of nonlinear functions exactly, its complexity is greatly reduced when compared to full Volterra models. Improving the representation capability of a single DVM by using higher rank Volterra kernels is not straightforward because the space spanned by Volterra models with rank r > 1 is usually not a closed set, and the boundary is a set with nonzero measure. Thus, the best rank-r approximation for higher rank Volterra models may not exist if r > 1. In this project, we propose to use series or parallel combinations of DVMs to approximate nonlinear functions whose Volterra kernels have rank higher than one, avoiding the problem mentioned above. We will develop not only low-complexity learning algorithms for combinations of DVMs, but also distributed and/or parallel versions of these algorithms. The developed algorithms will be compared with existing low-complexity Volterra models, neural networks and the full Volterra model. We will also test the new developed techniques to solve practical problems, in particular tropical disease spread prediction problems using data from the FAPESP/UK project CADDE.

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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)
PRETE, JR., CARLOS A.; BUSS, LEWIS F.; BUCCHERI, RENATA; ABRAHIM, CLAUDIA M. M.; SALOMON, TASSILA; CRISPIM, MYUKI A. E.; OIKAWA, MARCIO K.; GREBE, EDUARD; DA COSTA, ALLYSON G.; FRAIJI, NELSON A.; CARVALHO, MARIA DO P. S. S.; WHITTAKER, CHARLES; ALEXANDER, NEAL; FARIA, NUNO R.; DYE, CHRISTOPHER; NASCIMENTO, VITOR H.; BUSCH, MICHAEL P.; SABINO, ESTER CERDEIRA. einfection by the SARS-CoV-2 Gamma variant in blood donors in Manaus, Brazi. BMC INFECTIOUS DISEASES, v. 22, n. 1 FEB 5 2022. Web of Science Citations: 0.
PRETE JR, CARLOS A.; NASCIMENTO, VITOR H.; LOPES, CASSIO G. Optimal Passive Source Localization for Acoustic Emissions. Entropy, v. 23, n. 12 DEC 2021. Web of Science Citations: 0.
FARIA, NUNO R.; MELLAN, THOMAS A.; WHITTAKER, CHARLES; CLARO, INGRA M.; CANDIDO, DARLAN DA S.; MISHRA, SWAPNIL; CRISPIM, MYUKI A. E.; SALES, FLAVIA C.; HAWRYLUK, IWONA; MCCRONE, JOHN T.; HULSWIT, RUBEN J. G.; FRANCO, LUCAS A. M.; RAMUNDO, MARIANA S.; DE JESUS, JAQUELINE G.; ANDRADE, PAMELA S.; COLETTI, THAIS M.; FERREIRA, GIULIA M.; SILVA, CAMILA A. M.; MANULI, ERIKA R.; PEREIRA, RAFAEL H. M.; PEIXOTO, PEDRO S.; KRAEMER, MORITZ U.; GABURO, JR., NELSON; CAMILO, CECILIA DA C.; HOELTGEBAUM, HENRIQUE; SOUZA, WILLIAM M.; ROCHA, ESMENIA C.; DE SOUZA, LEANDRO M.; DE PINHO, MARIANA C.; ARAUJO, LEONARDO J. T.; MALTA, V, FREDERICO S.; DE LIMA, ALINE B.; SILVA, JOICE DO P.; ZAULI, DANIELLE A. G.; FERREIRA, ALESSANDRO C. DE S.; SCHNEKENBERG, RICARDO P.; LAYDON, DANIEL J.; WALKER, PATRICK G. T.; SCHLUETER, HANNAH M.; DOS SANTOS, ANA L. P.; VIDAL, MARIA S.; DEL CARO, VALENTINA S.; FILHO, ROSINALDO M. F.; DOS SANTOS, HELEM M.; AGUIAR, RENATO S.; PROENCA-MODENA, JOSE L. P.; NELSON, BRUCE; HAY, JAMES A.; MONOD, MELODIE; MISCOURIDOU, XENIA; COUPLAND, HELEN; SONABEND, RAPHAEL; VOLLMER, MICHAELA; GANDY, AXEL; PRETE, JR., CARLOS A.; NASCIMENTO, VITOR H.; SUCHARD, MARC A.; BOWDEN, THOMAS A.; POND, SERGEI L. K.; WU, CHIEH-HSI; RATMANN, OLIVER; FERGUSON, NEIL M.; DYE, CHRISTOPHER; LOMAN, NICK J.; LEMEY, PHILIPPE; RAMBAUT, ANDREW; FRAIJI, NELSON A.; CARVALHO, MARIA DO P. S. S.; PYBUS, OLIVER G.; FLAXMAN, SETH; BHATT, SAMIR; SABINO, ESTER C. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science, v. 372, n. 6544, p. 815+, MAY 21 2021. Web of Science Citations: 224.
VERNAL, SEBASTIAN; NAHAS, ANDRESSA K.; NETO, FRANCISCO CHIARAVALLOTI; PRETE JUNIOR, CARLOS A.; CORTEZ, ANDRE L.; SABINO, ESTER CERDEIRA; LUNA, EXPEDITO JOSE DE ALBUQUERQUE. Geoclimatic, demographic and socioeconomic characteristics related to dengue outbreaks in Southeastern Brazil: an annual spatial and spatiotemporal risk model over a 12-year period. Revista do Instituto de Medicina Tropical de São Paulo, v. 63, 2021. Web of Science Citations: 0.

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