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2020 – today
- 2024
[i12]Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Salles Civitarese, Shraddha Singh, Julian Kuehnert, Kumar Ankur, Aman Gupta, Christopher Phillips, Romeo Kienzler, Daniela Szwarcman, Vishal Gaur, Rajat Shinde, Rohit Lal, Arlindo Da Silva, Jorge Luis Guevara Diaz, Anne Jones, Simon Pfreundschuh, Amy Lin, Aditi Sheshadri, Udaysankar Nair, Valentine Anantharaj, Hendrik F. Hamann, Campbell D. Watson, Manil Maskey, Tsengdar J. Lee, Juan Bernabé-Moreno, Rahul Ramachandran:
Prithvi WxC: Foundation Model for Weather and Climate. CoRR abs/2409.13598 (2024)- 2023
- [c6]Romeo Kienzler, Johannes Schmude, Naomi Simumba, Benedikt Blumenstiel, Marcus Freitag, Daiki Kimura, Zoltan Arnold Nagy, Michael Behrendt, Hendrik F. Hamann, S. Karthik Mukkavilli, Daniel Salles Civitarese:
TensorBank: Tensor Lakehouse for Foundation Model Training. IEEE Big Data 2023: 3350-3354 - [i11]Romeo Kienzler, Benedikt Blumenstiel, Zoltan Arnold Nagy, S. Karthik Mukkavilli, Johannes Schmude, Marcus Freitag, Michael Behrendt, Daniel Salles Civitarese, Naomi Simumba, Daiki Kimura, Hendrik F. Hamann:
TensorBank: Tensor Lakehouse for Foundation Model Training. CoRR abs/2309.02094 (2023) - [i10]S. Karthik Mukkavilli, Daniel Salles Civitarese, Johannes Schmude, Johannes Jakubik, Anne Jones, Nam Nguyen, Christopher Phillips, Sujit Roy, Shraddha Singh, Campbell D. Watson, Raghu K. Ganti, Hendrik F. Hamann, Udaysankar Nair, Rahul Ramachandran, Kommy Weldemariam:
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation. CoRR abs/2309.10808 (2023) - 2022
- [j4]Daniela Szwarcman
, Daniel Civitarese, Marley M. B. R. Vellasco:
Quantum-inspired evolutionary algorithm applied to neural architecture search. Appl. Soft Comput. 120: 108674 (2022) - [j3]Renan Souza, Leonardo Guerreiro Azevedo, Vítor Lourenço, Elton F. S. Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Márcio Ferreira Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco A. S. Netto:
Workflow provenance in the lifecycle of scientific machine learning. Concurr. Comput. Pract. Exp. 34(14) (2022) - 2021
- [j2]Andréa Britto Mattos, Daniel Civitarese, Daniela Szwarcman, Matheus Oliveira, Semen Zaytsev, Daniil Semin, Dário A. B. Oliveira:
Enabling Robust Horizon Picking From Small Training Sets. IEEE Trans. Geosci. Remote. Sens. 59(6): 5317-5324 (2021) - [i9]Bianca Zadrozny, Campbell D. Watson, Daniela Szwarcman, Daniel Civitarese, Dário A. B. Oliveira, Eduardo Rodrigues, Jorge Guevara Diaz:
A modular framework for extreme weather generation. CoRR abs/2102.04534 (2021) - [i8]Daniel Salles Civitarese, Daniela Szwarcman, Bianca Zadrozny, Campbell D. Watson:
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network. CoRR abs/2107.06846 (2021) - 2020
- [j1]Márcio Ferreira Moreno, Vítor Lourenço, Sandro Rama Fiorini, Polyana B. da Costa, Rafael Brandão, Daniel Civitarese, Renato Cerqueira:
Managing Machine Learning Workflow Components. Int. J. Semantic Comput. 14(2): 295-309 (2020) - [c5]Márcio Ferreira Moreno, Vítor Lourenço, Sandro Rama Fiorini, Polyana B. da Costa, Rafael Brandão, Daniel Civitarese, Renato Cerqueira:
Managing Machine Learning Workflow Components. ICSC 2020: 25-30 - [i7]Renan Souza, Leonardo Guerreiro Azevedo, Vítor Lourenço, Elton F. S. Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Márcio Ferreira Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco A. S. Netto:
Workflow Provenance in the Lifecycle of Scientific Machine Learning. CoRR abs/2010.00330 (2020)
2010 – 2019
- 2019
- [c4]Daniela Szwarcman, Daniel Civitarese, Marley M. B. R. Vellasco:
Q-NAS Revisited: Exploring Evolution Fitness to Improve Efficiency. BRACIS 2019: 509-514 - [c3]Daniela Szwarcman, Daniel Civitarese, Marley M. B. R. Vellasco:
Quantum-Inspired Neural Architecture Search. IJCNN 2019: 1-8 - [c2]Márcio Ferreira Moreno, Daniel Civitarese, Rafael Brandão, Renato Cerqueira:
Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation. NeSy@IJCAI 2019 - [c1]Renan Souza, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco Aurélio Stelmar Netto, Leonardo Azevedo, Vítor Lourenço, Elton F. de Souza Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Márcio Ferreira Moreno:
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. WORKS@SC 2019: 1-10 - [i6]Lais Baroni, Reinaldo Mozart Da Gama e Silva, Rodrigo S. Ferreira, Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil:
Penobscot Dataset: Fostering Machine Learning Development for Seismic Interpretation. CoRR abs/1903.12060 (2019) - [i5]Reinaldo Mozart Da Gama e Silva, Lais Baroni, Rodrigo S. Ferreira, Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil:
Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation. CoRR abs/1904.00770 (2019) - [i4]Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil, Bianca Zadrozny:
Semantic Segmentation of Seismic Images. CoRR abs/1905.04307 (2019) - [i3]Renan Souza, Leonardo Azevedo, Vítor Lourenço, Elton F. de Souza Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Márcio Ferreira Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco A. S. Netto:
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. CoRR abs/1910.04223 (2019) - [i2]Márcio Ferreira Moreno, Vítor Lourenço, Sandro Rama Fiorini, Polyana B. da Costa, Rafael Brandão, Daniel Civitarese, Renato Cerqueira:
Managing Machine Learning Workflow Components. CoRR abs/1912.05665 (2019) - [i1]Márcio Ferreira Moreno, Daniel Civitarese, Rafael Brandão, Renato Cerqueira:
Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation. CoRR abs/1912.08740 (2019)
Coauthor Index
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