69 citations · 318 across the 21 of their papers we have counts for
7 papers · 1 filter
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…
Improving Deep Image Clustering With Spatial Transformer Layers
Thiago V. M. Souza, Cleber Zanchettin
Image clustering is an important but challenging task in machine learning. As in most image processing areas, the latest improvements came from models based on the deep learning ap…
Additive Margin SincNet for Speaker Recognition
João Antônio Chagas Nunes, David Macêdo, Cleber Zanchettin
Speaker Recognition is a challenging task with essential applications such as authentication, automation, and security. The SincNet is a new deep learning based model which has pro…
Heartbeat Anomaly Detection using Adversarial Oversampling
Jefferson L. P. Lima, David Macêdo, Cleber Zanchettin
Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to…
Squeezed Very Deep Convolutional Neural Networks for Text Classification
Andréa B. Duque, Luã Lázaro J. Santos, David Macêdo +1
Most of the research in convolutional neural networks has focused on increasing network depth to improve accuracy, resulting in a massive number of parameters which restricts the t…
Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition
Cleison Correia de Amorim, David Macêdo, Cleber Zanchettin
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Tempo…
Hierarchical Attentional Hybrid Neural Networks for Document Classification
Jader Abreu, Luis Fred, David Macêdo +1
Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, th…