| Learning with a Wasserstein loss C Frogner, C Zhang, H Mobahi, M Araya-Polo, T Poggio Advances in Neural Information Processing Systems 2015, 2015 | 707 | 2015 |
| 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 | 295 | 2017 |
| 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 | 106 | 2014 |
| Learning embeddings into entropic wasserstein spaces C Frogner, F Mirzazadeh, J Solomon International Conference on Learning Representations (ICLR), 2019 | 48* | 2019 |
| Approximate inference with wasserstein gradient flows C Frogner, T Poggio International Conference on Artificial Intelligence and Statistics, 2581-2590, 2020 | 34 | 2020 |
| Incorporating unlabeled data into distributionally robust learning C Frogner, S Claici, E Chien, J Solomon Journal of Machine Learning Research 22 (56), 1-46, 2021 | 27 | 2021 |
| Predicting geological features in 3D seismic data T Dahlke, M Araya-Polo, C Zhang, C Frogner, T Poggio Advances in Neural Information Processing Systems (NIPS) 29, 2016 | 20 | 2016 |
| Fast and flexible inference of joint distributions from their marginals C Frogner, T Poggio International Conference on Machine Learning, 2002-2011, 2019 | 18 | 2019 |
| Discovering weakly-interacting factors in a complex stochastic process C Frogner, A Pfeffer Advances in Neural Information Processing Systems 20, 2007 | 11 | 2007 |
| Machine-learning based automated fault detection in seismic traces: 76th Conference and Exhibition, EAGE C Zhang, C Frogner, M Araya-Polo, D Hohl Extended Abstracts, http://dx. doi. org/10.3997/2214-4609.20141500, 2014 | 9 | 2014 |
| Support Vector Machines C Frogner MIT, 2011 | 2 | 2011 |
| Heuristics for Automatically Decomposing a Stochastic Process for Factored Inference C Frogner, A Pfeffer | | 2007 |
| Regularized Least Squares C Frogner | | |
