| Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017 | 3043 | 2017 |
| Accurate, fully-automated NMR spectral profiling for metabolomics S Ravanbakhsh, P Liu, R Mandal, JR Grant, M Wilson, R Eisner, ... PLoS ONE 10 (5), e0124219, 2015 | 338* | 2015 |
| Equivariance Through Parameter-Sharing S Ravanbakhsh, J Schneider, B Poczos International Conference on Machine Learning (ICML) 70, 2892--2901, 2017 | 284 | 2017 |
| Learning to predict the cosmological structure formation S He, Y Li, Y Feng, S Ho, S Ravanbakhsh, W Chen, B Póczos Proceedings of the National Academy of Sciences 116 (28), 13825-13832, 2019 | 264 | 2019 |
| Cmu deeplens: deep learning for automatic image-based galaxy–galaxy strong lens finding F Lanusse, Q Ma, N Li, TE Collett, CL Li, S Ravanbakhsh, R Mandelbaum, ... Monthly Notices of the Royal Astronomical Society 473 (3), 3895-3906, 2018 | 231 | 2018 |
| Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos International Conference on Learning Representations (ICLR) - workshop track, 2016 | 217 | 2016 |
| Deep models of interactions across sets J Hartford, DR Graham, K Leyton-Brown, S Ravanbakhsh International Conference on Machine Learning 80, 1909-1918, 2018 | 179 | 2018 |
| Estimating cosmological parameters from the dark matter distribution S Ravanbakhsh, J Oliva, S Fromenteau, L Price, S Ho, J Schneider, ... International conference on machine learning, 2407-2416, 2016 | 98 | 2016 |
| Enabling dark energy science with deep generative models of galaxy images S Ravanbakhsh, F Lanusse, R Mandelbaum, J Schneider, B Poczos AAAI Conference on Artificial Intelligence, 1488-1494, 2017 | 78 | 2017 |
| Deep generative models for galaxy image simulations F Lanusse, R Mandelbaum, S Ravanbakhsh, CL Li, P Freeman, B Póczos Monthly Notices of the Royal Astronomical Society 504 (4), 5543-5555, 2021 | 71 | 2021 |
| Equivariance with learned canonicalization functions SO Kaba, AK Mondal, Y Zhang, Y Bengio, S Ravanbakhsh International Conference on Machine Learning, 15546-15566, 2023 | 67 | 2023 |
| Boolean Matrix Factorization and Noisy Completion via Message Passing. S Ravanbakhsh, R Barnabas Poczos, Greiner International Conference on Machine Learning (ICML), 945-954, 2016 | 61 | 2016 |
| Universal Equivariant Multilayer Perceptrons S Ravanbakhsh International Conference on Machine Learning (ICML), 2020 | 56 | 2020 |
| SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks C Morris, G Rattan, S Kiefer, S Ravanbakhsh International Conference on Machine Learning, 2022 | 44 | 2022 |
| Improved knowledge graph embedding using background taxonomic information B Fatemi, S Ravanbakhsh, D Poole Proceedings of the AAAI conference on artificial intelligence 33 (01), 3526-3533, 2019 | 44 | 2019 |
| Recovering the wedge modes lost to 21-cm foregrounds S Gagnon-Hartman, Y Cui, A Liu, S Ravanbakhsh Monthly Notices of the Royal Astronomical Society 504 (4), 4716-4729, 2021 | 33 | 2021 |
| Incidence networks for geometric deep learning M Albooyeh, D Bertolini, S Ravanbakhsh International Conference on Machine Learning, 2020 | 33 | 2020 |
| Stochastic Neural Networks with Monotonic Activation Functions S Ravanbakhsh, B Poczos, J Schneider, D Schuurmans, R Greiner 19th International Conference on Artificial Intelligence and Statistics 41 …, 2016 | 31 | 2016 |
| Equivariant Networks for Crystal Structures SO Kaba, S Ravanbakhsh Advances in Neural Information Processing Systems, 2022 | 29 | 2022 |
| EqR: Equivariant representations for data-efficient reinforcement learning AK Mondal, V Jain, K Siddiqi, S Ravanbakhsh International Conference on Machine Learning, 15908-15926, 2022 | 28 | 2022 |
Barnabas PoczosAssociate professor, Carnegie Mellon Universitycs.cmu.edu의 이메일 확인됨
