Learning with a Wasserstein loss C Frogner, C Zhang, H Mobahi, M Araya, TA Poggio Advances in neural information processing systems 28, 2015 | 686 | 2015 |
Deep-learning tomography M Araya-Polo, J Jennings, A Adler, T Dahlke The Leading Edge 37 (1), 58-66, 2018 | 539 | 2018 |
Automated fault detection without seismic processing M Araya-Polo, T Dahlke, C Frogner, C Zhang, T Poggio, D Hohl The Leading Edge 36 (3), 208-214, 2017 | 294 | 2017 |
Automatic differentiation: applications, theory, and implementations M Bücker Springer, 2006 | 221 | 2006 |
Deep learning for seismic inverse problems: Toward the acceleration of geophysical analysis workflows A Adler, M Araya-Polo, T Poggio IEEE Signal Processing Magazine 38 (2), 89-119, 2021 | 105 | 2021 |
Machine-learning based automated fault detection in seismic traces C Zhang, C Frogner, M Araya-Polo, D Hohl 76th EAGE Conference and Exhibition 2014 2014 (1), 1-5, 2014 | 104 | 2014 |
Assessing accelerator-based HPC reverse time migration M Araya-Polo, J Cabezas, M Hanzich, M Pericas, F Rubio, I Gelado, ... IEEE Transactions on Parallel and Distributed Systems 22 (1), 147-162, 2010 | 99 | 2010 |
3D seismic imaging through reverse-time migration on homogeneous and heterogeneous multi-core processors M Araya-Polo, F Rubio, R De la Cruz, M Hanzich, JM Cela, DP Scarpazza Scientific Programming 17 (1-2), 185-198, 2009 | 74 | 2009 |
Deep learning-driven velocity model building workflow SFMF Mauricio Araya-Polo The Leading Edge 38 (11), 872a1-872a9, 2019 | 69 | 2019 |
Automatic Differentiation: Applications, Theory, and Implementations M Bucker, G Corliss, P Hovland, U Naumann, B Norris Lecture Notes in Computational Science and Engineering 50, 2006 | 51 | 2006 |
Deep learning–driven permeability estimation from 2D images M Araya-Polo, FO Alpak, S Hunter, R Hofmann, N Saxena Computational Geosciences 24 (2), 571-580, 2020 | 40 | 2020 |
Exploiting memory customization in FPGA for 3D stencil computations M Shafiq, M Pericas, R De la Cruz, M Araya-Polo, N Navarro, E Ayguadé 2009 International Conference on Field-Programmable Technology, 38-45, 2009 | 36 | 2009 |
Algorithm 942: semi-stencil R De La Cruz, M Araya-Polo ACM Transactions on Mathematical Software (TOMS) 40 (3), 1-39, 2014 | 35 | 2014 |
The adjoint data-flow analyses: Formalization, properties, and applications L Hascoëet, M Araya-Polo Automatic Differentiation: Applications, Theory, and Implementations, 135-146, 2006 | 33 | 2006 |
Minibatch least-squares reverse time migration in a deep-learning framework J Vamaraju, J Vila, M Araya-Polo, D Datta, M Sidahmed, MK Sen Geophysics 86 (2), S125-S142, 2021 | 32 | 2021 |
Deep learning: algorithms and applications W Pedrycz, SM Chen Springer, 2020 | 32 | 2020 |
A survey of sparse matrix-vector multiplication performance on large matrices M Grossman, C Thiele, M Araya-Polo, F Frank, FO Alpak, V Sarkar arXiv preprint arXiv:1608.00636, 2016 | 31 | 2016 |
Deep learning joint inversion of seismic and electromagnetic data for salt reconstruction Y Sun, B Denel, N Daril, L Evano, P Williamson, M Araya-Polo SEG Technical Program Expanded Abstracts 2020, 550-554, 2020 | 30 | 2020 |
Deep recurrent architectures for seismic tomography A Adler, M Araya-Polo, T Poggio 81st EAGE conference and exhibition 2019 2019 (1), 1-5, 2019 | 30 | 2019 |
Introducing the Semi-stencil Algorithm R De La Cruz, M Araya-Polo, JM Cela Parallel Processing and Applied Mathematics: 8th International Conference …, 2010 | 30 | 2010 |