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 | 49 | 2019 |
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 | 36 | 2018 |
Seismic facies segmentation using deep learning D Chevitarese, D Szwarcman, RMD Silva, EV Brazil AAPG Annual and Exhibition, 2018 | 32 | 2018 |
Efficient classification of seismic textures DS Chevitarese, D Szwarcman, EV Brazil, B Zadrozny 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 31 | 2018 |
Quantum-Inspired Neural Architecture Search D Szwarcman, D Civitarese, M Vellasco 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 25 | 2019 |
Semantic Segmentation of Seismic Images D Civitarese, D Szwarcman, EV Brazil, B Zadrozny arXiv preprint arXiv:1905.04307, 2019 | 24 | 2019 |
Transfer learning applied to seismic images classification D Chevitarese, D Szwarcman, RMD Silva, EV Brazil AAPG Annual and Exhibition, 2018 | 21 | 2018 |
Foundation Models for Generalist Geospatial Artificial Intelligence J Jakubik, S Roy, CE Phillips, P Fraccaro, D Godwin, B Zadrozny, ... arXiv preprint arXiv:2310.18660, 2023 | 19 | 2023 |
Quantum-inspired evolutionary algorithm applied to neural architecture search D Szwarcman, D Civitarese, M Vellasco Applied Soft Computing 120, 108674, 2022 | 14 | 2022 |
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network DS Civitarese, D Szwarcman, B Zadrozny, C Watson arXiv preprint arXiv:2107.06846, 2021 | 14 | 2021 |
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 18, 109-122, 2022 | 12 | 2022 |
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 | 9 | 2019 |
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling Q Yang, A Hernandez-Garcia, P Harder, V Ramesh, P Sattegeri, ... arXiv preprint arXiv:2305.14452, 2023 | 7 | 2023 |
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 | 6 | 2021 |
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 | 5 | 2019 |
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 | 4 | 2020 |
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 | 3 | 2021 |
A modular framework for extreme weather generation B Zadrozny, CD Watson, D Szwarcman, D Civitarese, D Oliveira, ... arXiv preprint arXiv:2102.04534, 2021 | 3 | 2021 |
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 | 3 | 2019 |
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 | 3 | 2019 |