| Learning with a Wasserstein loss C Frogner, C Zhang, H Mobahi, M Araya-Polo, T Poggio arXiv preprint arXiv:1506.05439, 2015 | 321 | 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 | 155 | 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 | 55 | 2014 |
| 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 | 17 | 2016 |
| Approximate inference with wasserstein gradient flows C Frogner, T Poggio International Conference on Artificial Intelligence and Statistics, 2581-2590, 2020 | 11 | 2020 |
| Discovering Weakly-Interacting Factors in a Complex Stochastic Process. C Frogner, A Pfeffer NIPS, 481-488, 2007 | 11 | 2007 |
| Learning with a Wasserstein Loss Advances in Neural Information Processing Systems (NIPS) C Frogner, C Zhang, H Mobahi, M Araya-Polo, T Poggio | 7 | 2015 |
| Learning entropic wasserstein embeddings C Frogner, F Mirzazadeh, J Solomon International Conference on Learning Representations (ICLR), 2019 | 5 | 2019 |
| Incorporating unlabeled data into distributionally robust learning C Frogner, S Claici, E Chien, J Solomon arXiv preprint arXiv:1912.07729, 2019 | 4 | 2019 |
| Fast and flexible inference of joint distributions from their marginals C Frogner, T Poggio International Conference on Machine Learning, 2002-2011, 2019 | 4 | 2019 |
| Learning embeddings into entropic wasserstein spaces C Frogner, F Mirzazadeh, J Solomon arXiv preprint arXiv:1905.03329, 2019 | 3 | 2019 |
| Heuristics for Automatically Decomposing a Stochastic Process for Factored Inference C Frogner, A Pfeffer | | 2007 |
| Regularized Least Squares C Frogner | | |
