activity
20202023
most citedTraining Aware Sigmoidal Optimizer

1 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.NE20231 cited

A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations

Sergio F. Chevtchenko, Yeshwanth Bethi, Teresa B. Ludermir +1

Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains…

cs.LG2021

Metodos de Agrupamentos em dois Estagios

Jefferson Souza, Teresa Ludermir

This work investigates the use of two-stage clustering methods. Four techniques were proposed: SOMK, SOMAK, ASCAK and SOINAK. SOMK is composed of a SOM (Self-Organizing Maps) follo…

cs.NE2021

Otimizacao de Redes Neurais atraves de Algoritmos Geneticos Celulares

Anderson da Silva, Teresa Ludermir

This works proposes a methodology to searching for automatically Artificial Neural Networks (ANN) by using Cellular Genetic Algorithm (CGA). The goal of this methodology is to find…

cs.NE2021

Um Metodo para Busca Automatica de Redes Neurais Artificiais

Anderson P. da Silva, Teresa B. Ludermir, Leandro M. Almeida

This paper describes a method that automatically searches Artificial Neural Networks using Cellular Genetic Algorithms. The main difference of this method for a common genetic algo…

cs.NE2021

Uso de GSO cooperativos com decaimentos de pesos para otimizacao de redes neurais

Danielle Silva, Teresa Ludermir

Training of Artificial Neural Networks is a complex task of great importance in supervised learning problems. Evolutionary Algorithms are widely used as global optimization techniq…

cs.LG2021

Distance Metric Learning through Minimization of the Free Energy

Dusan Stosic, Darko Stosic, Teresa B. Ludermir +1

Distance metric learning has attracted a lot of interest for solving machine learning and pattern recognition problems over the last decades. In this work we present a simple appro…