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Daniela Szwarcman
Daniela Szwarcman
IBM-Research
Verified email at ibm.com
Title
Cited by
Cited by
Year
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
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
Quantifying milk proteins using infrared photodetection for portable equipment
D Szwarcman, GM Penello, RMS Kawabata, MP Pires, PL Souza
Journal of Food Engineering 308, 110676, 2021
52021
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
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network
DS Civitarese, D Szwarcman, B Zadrozny, C Watson
arXiv preprint arXiv:2107.06846, 2021
32021
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
Stratigraphic Segmentation Using Convolutional Neural Networks
D Civitarese, D Szwarcman, EV Brazil
81st EAGE Conference and Exhibition 2019 Workshop Programme 2019 (1), 1-5, 2019
32019
A cyclic learning approach for improving pre-stack seismic processing
DAB Oliveira, D Szwarcman, R da Silva Ferreira, S Zaytsev, D Semin
Scientific Reports 11 (1), 1-13, 2021
22021
A modular framework for extreme weather generation
B Zadrozny, CD Watson, D Szwarcman, D Civitarese, D Oliveira, ...
arXiv preprint arXiv:2102.04534, 2021
22021
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
22020
Generating physically-consistent high-resolution climate data with hard-constrained neural networks
P Harder, Q Yang, V Ramesh, P Sattigeri, A Hernandez-Garcia, C Watson, ...
arXiv preprint arXiv:2208.05424, 2022
2022
Quantum-inspired evolutionary algorithm applied to neural architecture search
D Szwarcman, D Civitarese, M Vellasco
Applied Soft Computing 120, 108674, 2022
2022
Choose your own weather adventure: deep weather generation for “what-if” climate scenarios
C Watson, J Guevara, D Szwarcman, D Oliveira, L Tizzei, M Garcia, ...
EGU22, 2022
2022
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