22 citations · 83 across the 12 of their papers we have counts for
14 papers
Wildfire risk forecast: An optimizable fire danger index
Eduardo Rodrigues, Bianca Zadrozny, Campbell Watson
Wildfire events have caused severe losses in many places around the world and are expected to increase with climate change. Throughout the years many technologies have been develop…
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Ken C. L. Wong, Hongzhi Wang, Etienne E. Vos +3
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows…
Controlling Weather Field Synthesis Using Variational Autoencoders
Dario Augusto Borges Oliveira, Jorge Guevara Diaz, Bianca Zadrozny +1
One of the consequences of climate change is anobserved increase in the frequency of extreme cli-mate events. That poses a challenge for weatherforecast and generation algorithms,…
A comparative study of stochastic and deep generative models for multisite precipitation synthesis
Jorge Guevara, Dario Borges, Campbell Watson +1
Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating promising deep learning…
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…
Decadal Forecasts with ResDMD: a Residual DMD Neural Network
Eduardo Rodrigues, Bianca Zadrozny, Campbell Watson +1
Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society. One method that has…
Geostatistical Learning: Challenges and Opportunities
Júlio Hoffimann, Maciel Zortea, Breno de Carvalho +1
Statistical learning theory provides the foundation to applied machine learning, and its various successful applications in computer vision, natural language processing and other 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…
Learning Multi-Party Turn-Taking Models from Dialogue Logs
Maira Gatti de Bayser, Paulo Cavalin, Claudio Pinhanez +1
This paper investigates the application of machine learning (ML) techniques to enable intelligent systems to learn multi-party turn-taking models from dialogue logs. The specific M…
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…
A data-driven workflow for predicting horizontal well production using vertical well logs
Jorge Guevara, Matthias Kormaksson, Bianca Zadrozny +6
In recent work, data-driven sweet spotting technique for shale plays previously explored with vertical wells has been proposed. Here, we extend this technique to multiple formation…
Pooling Hybrid Representations for Web Structured Data Annotation
Luciano Barbosa, Breno W. Carvalho, Bianca Zadrozny
Automatically identifying data types of web structured data is a key step in the process of web data integration. Web structured data is usually associated with entities or objects…
Bus Travel Time Predictions Using Additive Models
Matthias Kormaksson, Luciano Barbosa, Marcos R. Vieira +1
Many factors can affect the predictability of public bus services such as traffic, weather and local events. Other aspects, such as day of week or hour of day, may influence bus tr…
Predicting Conditional Quantiles via Reduction to Classification
John Langford, Roberto Oliveira, Bianca Zadrozny
We show how to reduce the process of predicting general order statistics (and the median in particular) to solving classification. The accompanying theoretical statement shows that…