19 citations · 33 across the 8 of their papers we have counts for
9 papers
Forecasting insect abundance using time series embedding and machine learning
Gabriel R. Palma, Rodrigo F. Mello, Wesley A. C. Godoy +4
Implementing insect monitoring systems provides an excellent opportunity to create accurate interventions for insect control. However, selecting the appropriate time for an interve…
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Heitor Murilo Gomes, Maciej Grzenda, Rodrigo Mello +3
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning p…
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles
Gabriel D. Cantareira, Rodrigo F. Mello, Fernando V. Paulovich
As neural networks become the tool of choice to solve an increasing variety of problems in our society, adversarial attacks become critical. The possibility of generating data inst…
Ensuring Learning Guarantees on Concept Drift Detection with Statistical Learning Theory
Lucas Pagliosa, Rodrigo Mello
Concept Drift (CD) detection intends to continuously identify changes in data stream behaviors, supporting researchers in the study and modeling of real-world phenomena. Motivated…
Coarse-Refinement Dilemma: On Generalization Bounds for Data Clustering
Yule Vaz, Rodrigo Fernandes de Mello, Carlos Henrique Grossi
The Data Clustering (DC) problem is of central importance for the area of Machine Learning (ML), given its usefulness to represent data structural similarities from input spaces. D…
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?
Tiago S. Nazare, Rodrigo F. de Mello, Moacir A. Ponti
Recently, several techniques have been explored to detect unusual behaviour in surveillance videos. Nevertheless, few studies leverage features from pre-trained CNNs and none of th…