| 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 | 91 | 2018 |
| 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 | 29 | 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 | 23 | 2018 |
| Seismic facies segmentation using deep learning D Chevitarese, D Szwarcman, RMD Silva, EV Brazil AAPG Annual and Exhibition, 2018 | 23 | 2018 |
| 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 | 21 | 2019 |
| Efficient classification of seismic textures DS Chevitarese, D Szwarcman, EV Brazil, B Zadrozny 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 20 | 2018 |
| Semantic segmentation of seismic images D Civitarese, D Szwarcman, EV Brazil, B Zadrozny arXiv preprint arXiv:1905.04307, 2019 | 16 | 2019 |
| Quantum-inspired neural architecture search D Szwarcman, D Civitarese, M Vellasco 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 13 | 2019 |
| Transfer learning applied to seismic images classification D Chevitarese, D Szwarcman, RMD Silva, EV Brazil AAPG Annual and Exhibition, 2018 | 12 | 2018 |
| 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 | 8 | 2020 |
| 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 | 8 | 2010 |
| 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 | 5 | 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 | 4 | 2019 |
| Netherlands F3 Interpretation Dataset L Baroni, RM Silva, R S. Ferreira, D Chevitarese, D Szwarcman, ... https://doi.org/10.5281/zenodo.1471548, 2018 | 4 | 2018 |
| 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 | 4 | 2012 |
| 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 | 3 | 2019 |
| 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 |
| 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 | 3 | 2019 |
| Smart sensing: a system and a method for distributed and fault tolerant hierarchical autonomous cognitive instrumentation MF Moreno, RR de Mello Brandao, DS Chevitarese US Patent App. 15/335,050, 2018 | 3 | 2018 |
Daniela SzwarcmanIBM-ResearchVerified email at ibm.com
