45 citations · 71 across the 9 of their papers we have counts for
10 papers
BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis
Leandro A. Passos, Douglas Rodrigues, Danilo Jodas +3
This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on…
Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification
Mateus Roder, Leandro Aparecido Passos, João Paulo Papa +1
Quality classification of wood boards is an essential task in the sawmill industry, which is still usually performed by human operators in small to median companies in developing c…
Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
Nícolas Barbosa Gomes, Arissa Yoshida, Mateus Roder +2
Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is essential for establishing the beginning of treatment, enriching the outlook, and enhancing the overall well-…
Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization
Luiz C. F. Ribeiro, Mateus Roder, Gustavo H. de Rosa +2
The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of th…
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
Vinícius Camargo da Silva, João Paulo Papa, Kelton Augusto Pontara da Costa
Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learni…
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks
Mateus Roder, Jurandy Almeida, Gustavo H. de Rosa +3
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement…
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Luiz H. Buris, Daniel C. G. Pedronette, Joao P. Papa +3
Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real ap…
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks
Claudio Filipi Goncalves do Santos, Mateus Roder, Leandro A. Passos +1
In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improve…
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
Guilherme C. Oliveira, Gustavo H. Rosa, Daniel C. G. Pedronette +4
Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literatu…
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Leandro Aparecido Passos, João Paulo Papa, Javier Del Ser +2
This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical c…