activity
20192021
most citedTraining Aware Sigmoidal Optimizer

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

collaborators

11 papers

cs.LG20211 cited

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…

cs.CV2020

KutralNet: A Portable Deep Learning Model for Fire Recognition

Angel Ayala, Bruno Fernandes, Francisco Cruz +3

Most of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to pe…

cs.CV2020

A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation

Ricardo Batista das Neves Junior, Luiz Felipe Verçosa, David Macêdo +2

The Know Your Customer (KYC) and Anti Money Laundering (AML) are worldwide practices to online customer identification based on personal identification documents, similarity and li…

cs.CL2020

Distantly-Supervised Neural Relation Extraction with Side Information using BERT

Johny Moreira, Chaina Oliveira, David Macêdo +2

Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), whi…

cs.SD2020

AM-MobileNet1D: A Portable Model for Speaker Recognition

João Antônio Chagas Nunes, David Macêdo, Cleber Zanchettin

Speaker Recognition and Speaker Identification are challenging tasks with essential applications such as automation, authentication, and security. Deep learning approaches like Sin…

cs.CV2020

Squeezed Deep 6DoF Object Detection Using Knowledge Distillation

Heitor Felix, Walber M. Rodrigues, David Macêdo +4

The detection of objects considering a 6DoF pose is a common requirement to build virtual and augmented reality applications. It is usually a complex task which requires real-time…