Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Yania Molina Souto"'
Autor:
Rafael S. Pereira, Brian Tsan, Anderson Chaves, Eduardo Ogasawara, Artur Ziviani, Florin Rusu, Yania Molina Souto, Rocio Zorilla, Fabio Porto
Publikováno v:
SSDBM
Consider a set of black-box models – each of them independently trained on a different dataset – answering the same predictive spatio-temporal query. Being built in isolation, each model traverses its own life-cycle until it is deployed to produc
Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has beco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be4e33ccabcc69b4f456c2f593bab845
http://arxiv.org/abs/1912.00134
http://arxiv.org/abs/1912.00134
Publikováno v:
IJCNN
This paper proposes a new ensemble method built upon a deep neural network architecture. We use a set of meteorological models for rain forecast as base predictors. Each meteorological model is provided to a channel of the network and, through a conv
Autor:
Paulo F. Pires, Edward Pacheco, Fabio Porto, Bruno Costa, João Guilherme Nobre Rittmeyer, Jonas Dias, Vieira Wagner Dos Santos, Gottin Vinícius Michel, Angelo E. M. Ciarlini, Yania Molina Souto
Publikováno v:
BeyondMR@SIGMOD
Demands for large-scale data analysis and processing have led to the development and widespread adoption of computing frameworks that leverage in-memory data processing, largely outperforming disk-based processing systems. One such framework is Apach
Publikováno v:
Anais do Brazilian e-Science Workshop (BreSci).
Uncertain time series analysis has recently become an important research topic, particularly when searching for features of natural phenomena using similarity functions. Natural phenomena are often modeled as time series, such as in weather forecast,