Seguir
Daniel Salles Civitarese
Daniel Salles Civitarese
IBM Research
Dirección de correo verificada de br.ibm.com
Título
Citado por
Citado por
Año
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
1722018
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
612019
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
602019
Seismic facies segmentation using deep learning
D Chevitarese, D Szwarcman, RMD Silva, EV Brazil
AAPG Annual and Exhibition, 2018
402018
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
382022
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
362018
Efficient classification of seismic textures
DS Chevitarese, D Szwarcman, EV Brazil, B Zadrozny
2018 International joint conference on neural networks (IJCNN), 1-8, 2018
332018
Quantum-inspired neural architecture search
D Szwarcman, D Civitarese, M Vellasco
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
292019
Semantic segmentation of seismic images
D Civitarese, D Szwarcman, EV Brazil, B Zadrozny
arXiv preprint arXiv:1905.04307, 2019
282019
Transfer learning applied to seismic images classification
D Chevitarese, D Szwarcman, RMD Silva, EV Brazil
AAPG Annual and Exhibition, 2018
222018
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
182023
Quantum-inspired evolutionary algorithm applied to neural architecture search
D Szwarcman, D Civitarese, M Vellasco
Applied Soft Computing 120, 108674, 2022
182022
Extreme precipitation seasonal forecast using a transformer neural network
DS Civitarese, D Szwarcman, B Zadrozny, C Watson
arXiv preprint arXiv:2107.06846, 2021
182021
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
102020
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
102019
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
Netherlands dataset: A new public dataset for machine learning in seismic interpretation: arXiv preprint, doi: 10.48550
RM Silva, L Baroni, RS Ferreira, D Civitarese, D Szwarcman, EV Brazil
arXiv, 1904
81904
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
72020
Machine learning to predict cognitive image composition
P Borrel, AB Buoro, RAA Barros, DS Chevitarese
US Patent 10,592,743, 2020
72020
Netherlands F3 Interpretation Dataset
L Baroni, RM Silva, R S. Ferreira, D Chevitarese, D Szwarcman, ...
https://doi.org/10.5281/zenodo.1471548, 2018
62018
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20