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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
1012018
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
322019
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
272018
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
242019
Seismic facies segmentation using deep learning
D Chevitarese, D Szwarcman, RMD Silva, EV Brazil
AAPG Annual and Exhibition, 2018
242018
Efficient classification of seismic textures
DS Chevitarese, D Szwarcman, EV Brazil, B Zadrozny
2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018
212018
Semantic segmentation of seismic images
D Civitarese, D Szwarcman, EV Brazil, B Zadrozny
arXiv preprint arXiv:1905.04307, 2019
192019
Quantum-inspired neural architecture search
D Szwarcman, D Civitarese, M Vellasco
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
152019
Transfer learning applied to seismic images classification
D Chevitarese, D Szwarcman, RMD Silva, EV Brazil
AAPG Annual and Exhibition, 2018
152018
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
92022
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
92010
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
82020
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
42019
Netherlands F3 Interpretation Dataset
L Baroni, RM Silva, R S. Ferreira, D Chevitarese, D Szwarcman, ...
https://doi.org/10.5281/zenodo.1471548, 2018
42018
Speeding up the training of neural networks with cuda technology
DS Chevitarese, D Szwarcman, M Vellasco
International Conference on Artificial Intelligence and Soft Computing, 30-38, 2012
42012
Document retrieval through assertion analysis on entities and document fragments
MF Moreno, DS Civitarese, RR de Mello Brandao, ...
US Patent App. 16/258,765, 2020
32020
Effective integration of symbolic and connectionist approaches through a hybrid representation
M Moreno, D Civitarese, R Brandao, R Cerqueira
arXiv preprint arXiv:1912.08740, 2019
32019
Q-NAS revisited: Exploring evolution fitness to improve efficiency
D Szwarcman, D Civitarese, M Vellasco
2019 8th Brazilian Conference on Intelligent Systems (BRACIS), 509-514, 2019
32019
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
32019
Ore content estimation based on spatial geological data through 3D convolutional neural networks
BWWSR Carvalho, D Civitarese, D Szwarcman, P Cavalin, B Zadrozny, ...
81st EAGE Conference and Exhibition 2019 Workshop Programme 2019 (1), 1-5, 2019
32019
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