101 citations · 292 across the 18 of their papers we have counts for
19 papers
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…
Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!
Felipe Costa Farias, Teresa Bernarda Ludermir, Carmelo José Albanez Bastos-Filho
The model selection procedure is usually a single-criterion decision making in which we select the model that maximizes a specific metric in a specific set, such as the Validation…
Embarrassingly Parallel Independent Training of Multi-Layer Perceptrons with Heterogeneous Architectures
Felipe Costa Farias, Teresa Bernarda Ludermir, Carmelo Jose Albanez Bastos-Filho
The definition of a Neural Network architecture is one of the most critical and challenging tasks to perform. In this paper, we propose ParallelMLPs. ParallelMLPs is a procedure to…
Distinction Maximization Loss: Efficiently Improving Out-of-Distribution Detection and Uncertainty Estimation by Replacing the Loss and Calibrating
David Macêdo, Cleber Zanchettin, Teresa Ludermir
Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classification acc…
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…
Configurable sublinear circuits for quantum state preparation
Israel F. Araujo, Daniel K. Park, Teresa B. Ludermir +3
The theory of quantum algorithms promises unprecedented benefits of harnessing the laws of quantum mechanics for solving certain computational problems. A persistent obstacle to us…
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…
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…
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…
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…
Enhanced Isotropy Maximization Loss: Seamless and High-Performance Out-of-Distribution Detection Simply Replacing the SoftMax Loss
David Macêdo, Teresa Ludermir
Current out-of-distribution detection approaches usually present special requirements (e.g., collecting outlier data and hyperparameter validation) and produce side effects (e.g.,…
Training Aware Sigmoidal Optimizer
David Macêdo, Pedro Dreyer, Teresa Ludermir +1
Proper optimization of deep neural networks is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defin…
Similarity Based Stratified Splitting: an approach to train better classifiers
Felipe Farias, Teresa Ludermir, Carmelo Bastos-Filho
We propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split the data. The splits are generated using simila…
Quantum One-class Classification With a Distance-based Classifier
Nicolas M. de Oliveira, Lucas P. de Albuquerque, Wilson R. de Oliveira +2
The advancement of technology in Quantum Computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current qu…
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to pr…
Entropic Out-of-Distribution Detection
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., cl…
Paths to collapse for isolated skyrmions in few-monolayer ferromagnetic films
Dusan Stosic, Jeroen Mulkers, Bartel Van Waeyenberge +2
Magnetic skyrmions are topological spin configurations in materials with chiral Dzyaloshinskii-Moriya interaction (DMI), that are potentially useful for storing or processing infor…
Quantum perceptron over a field and neural network architecture selection in a quantum computer
Adenilton J. da Silva, Teresa B. Ludermir, Wilson R. de Oliveira
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in thi…
Weightless neural network parameters and architecture selection in a quantum computer
Adenilton J. da Silva, Wilson R. de Oliveira, Teresa B. Ludermir
Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques…