CV Lattes
ResearcherID
ORCID
Google Scholar Citations
Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica (IMECC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Marcos Eduardo is currently an associate professor at the Universidade Estadual de Campinas (Unicamp). He graduated in Applied and Computational Mathematics from the Universidade Estadual de Campinas (2001), and he obtained master's and doctorate degrees in Applied Mathematics from the Universidade Estadual de Campinas (2005 and 2007, respectively). He has experience in Mathematics, with an emphasis on Applied Mathematics, working mainly on the following topics: machine learning, hypercomplex-valued neural networks, deep learning, vector-valued image processing, mathematical morphology, lattice computation, and associative memory models. (Source: Lattes Curriculum)
| News published in Agência FAPESP Newsletter about the researcher |
| More itemsLess items |
| TITULO |
| Articles published in other media outlets ( ): |
| More itemsLess items |
| VEICULO: TITULO (DATA) |
| VEICULO: TITULO (DATA) |
Deep learning techniques achieved outstanding performance in various image processing and analysis tasks. Deep networks for vector-valued images represent an active research topic and include, for example, hypercomplex-valued neural networks. The nonlinearity played by some layers and activation functions in modern deep neural networks is closely related to mathematical morphology, a theo…
Artificial neural networks (ANNs), including convolutional neural networks, have demonstrated outstanding results in several application areas, including image detection and classification. Concomitantly, hypercomplex-valued neural networks showed competitive or superior performance but with fewer parameters than their equivalent neural networks defined on the real numbers. Among the adva…
Mathematical morphology is a nonlinear theory with applications in image processing and analysis. A neural network whose neurons perform elementary operations of mathematical morphology is called a morphological neural network. Such as traditional neural networks, morphological neural networks can be used for classification and regression.In this research project, we aim to contribute by …
(Only some records are available in English at this moment)
The use of hypercomplex algebras has attracted the attention of the scientific community in recent years, as they result in models with interesting algebraic and geometric properties. In fact, complex numbers are essential for the proper treatment of the phase and the information contained therein. Quaternions are widely used in computer graphics due to their efficiency in describing geom…
Associative memories (AMs) are mathematical models inspired by the human brain ability to store and recall information by association. Such as the brain, AMs are also able to recall a certain stored pattern even when the input represents an incomplete or noisy version of a stored item. The class of recurrent correlation associative memories (RCAMs), introduced by Chiueh and Goodman in the…
Neural associative memories are designed to store input-output pairs of patterns and are capable of recalling the desired output pattern upon presentation of an inclomplete or corrupted version of a certain input pattern. In this project, we propose to develop a new class of fuzzy morphological associative memories (FMAMs). The models of this class are called morphological because their n…
| 1 / 1 | Ongoing grants |
| 5 / 4 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 4 / 2 | Completed scholarships in Brazil |
| 11 / 8 | All research grants and scholarships |
| Associated processes | |