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Functional Gradient Descent for n-Tuple Regression

Published in 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2021

This paper is about a n-tuple network architecture that is suitable for nonstationary online learning.

Recommended citation: Katopodis, R. F. ; Lima, P. M. V. ; França, F. M. G. . Functional Gradient Descent for n-Tuple Regression. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2021, Bruges. Proc of ESANN 2021. Brussels: i6doc.com, 2021. p. 505-511.

Reinforcement Learning with Weightless Neural Networks (Master’s Dissertation)

Published in , 2022

My Master’s dissertation, on the challenges of employing weightless networks in reinforcement learning tasks.

Presentation at ESANN 2021

Published: October 09, 2021

This is my 15-minute oral presentation of the paper “Functional Gradient Descent for n-Tuple regression” at ESANN 2021.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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