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Daniel Salles Civitarese
Daniel Salles Civitarese
IBM Research
Verified email at br.ibm.com
Title
Cited by
Cited by
Year
Correlation analysis of performance measures for multi-label classification
RB Pereira, A Plastino, B Zadrozny, LHC Merschmann
Information Processing & Management 54 (3), 359-369, 2018
1512018
Provenance data in the machine learning lifecycle in computational science and engineering
R Souza, L Azevedo, V Lourenço, E Soares, R Thiago, R Brandão, ...
2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), 1-10, 2019
532019
Netherlands dataset: A new public dataset for machine learning in seismic interpretation
RM Silva, L Baroni, RS Ferreira, D Civitarese, D Szwarcman, EV Brazil
arXiv preprint arXiv:1904.00770, 2019
452019
Deep learning applied to seismic facies classification: A methodology for training
DS Chevitarese, D Szwarcman, RMG e Silva, EV Brazil
Saint Petersburg 2018 2018 (1), 1-5, 2018
352018
Seismic facies segmentation using deep learning
D Chevitarese, D Szwarcman, RMD Silva, EV Brazil
AAPG Annual and Exhibition, 2018
312018
Efficient classification of seismic textures
DS Chevitarese, D Szwarcman, EV Brazil, B Zadrozny
2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018
302018
Quantum-inspired neural architecture search
D Szwarcman, D Civitarese, M Vellasco
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
252019
Workflow provenance in the lifecycle of scientific machine learning
R Souza, LG Azevedo, V Lourenço, E Soares, R Thiago, R Brandão, ...
Concurrency and Computation: Practice and Experience 34 (14), e6544, 2022
242022
Semantic segmentation of seismic images
D Civitarese, D Szwarcman, EV Brazil, B Zadrozny
arXiv preprint arXiv:1905.04307, 2019
242019
Transfer learning applied to seismic images classification
D Chevitarese, D Szwarcman, RMD Silva, EV Brazil
AAPG Annual and Exhibition, 2018
212018
Quantum-inspired evolutionary algorithm applied to neural architecture search
D Szwarcman, D Civitarese, M Vellasco
Applied Soft Computing 120, 108674, 2022
142022
Extreme precipitation seasonal forecast using a transformer neural network
DS Civitarese, D Szwarcman, B Zadrozny, C Watson
arXiv preprint arXiv:2107.06846, 2021
132021
Exploring data streaming to improve 3d FFT implementation on multiple GPUs
CP da Silva, LF Cupertino, D Chevitarese, MAC Pacheco, C Bentes
2010 22nd International Symposium on Computer Architecture and High …, 2010
102010
Managing machine learning workflow components
M Moreno, V Lourenço, SR Fiorini, P Costa, R Brandão, D Civitarese, ...
International Journal of Semantic Computing 14 (02), 295-309, 2020
92020
Machine learning to predict cognitive image composition
P Borrel, AB Buoro, RAA Barros, DS Chevitarese
US Patent 10,592,743, 2020
72020
Penobscot dataset: Fostering machine learning development for seismic interpretation
L Baroni, RM Silva, RS Ferreira, D Civitarese, D Szwarcman, EV Brazil
arXiv preprint arXiv:1903.12060, 2019
72019
Ai foundation models for weather and climate: Applications, design, and implementation
SK Mukkavilli, DS Civitarese, J Schmude, J Jakubik, A Jones, N Nguyen, ...
arXiv preprint arXiv:2309.10808, 2023
62023
Vacuum Ultraviolet Laser Induced Breakdown Spectroscopy (VUV-LIBS) with machine learning for pharmaceutical analysis
MB Alli, D Szwarcman, DS Civitarese, P Hayden
Journal of Physics: Conference Series 1289 (1), 012031, 2019
52019
Speeding up the training of neural networks with cuda technology
DS Chevitarese, D Szwarcman, M Vellasco
Artificial Intelligence and Soft Computing: 11th International Conference …, 2012
52012
Enabling robust horizon picking from small training sets
AB Mattos, D Civitarese, D Szwarcman, M Oliveira, S Zaytsev, DG Semin, ...
IEEE Transactions on Geoscience and Remote Sensing 59 (6), 5317-5324, 2020
42020
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Articles 1–20