44 citations · 129 across the 8 of their papers we have counts for
9 papers
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network
Daniel Salles Civitarese, Daniela Szwarcman, Bianca Zadrozny +1
An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at s…
A modular framework for extreme weather generation
Bianca Zadrozny, Campbell D. Watson, Daniela Szwarcman +4
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes cruc…
Workflow Provenance in the Lifecycle of Scientific Machine Learning
Renan Souza, Leonardo G. Azevedo, Vítor Lourenço +10
Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains,…
Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation
Marcio Moreno, Daniel Civitarese, Rafael Brandao +1
In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration. Such description…
Managing Machine Learning Workflow Components
Marcio Moreno, Vítor Lourenço, Sandro Rama Fiorini +4
Machine Learning Workflows (MLWfs) have become essential and a disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be comp…
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
Renan Souza, Leonardo Azevedo, Vítor Lourenço +10
Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of…
Semantic Segmentation of Seismic Images
Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil +1
Almost all work to understand Earth's subsurface on a large scale relies on the interpretation of seismic surveys by experts who segment the survey (usually a cube) into layers; a…
Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation
Reinaldo Mozart Silva, Lais Baroni, Rodrigo S. Ferreira +3
Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three ma…
Penobscot Dataset: Fostering Machine Learning Development for Seismic Interpretation
Lais Baroni, Reinaldo Mozart Silva, Rodrigo S. Ferreira +3
We have seen in the past years the flourishing of machine and deep learning algorithms in several applications such as image classification and segmentation, object detection and r…