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Siamak Ravanbakhsh
Siamak Ravanbakhsh
Assistant Professor, McGill University
Bestätigte E-Mail-Adresse bei cs.mcgill.ca - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Deep sets
M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ...
Advances in neural information processing systems 30, 2017
31572017
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
345*2015
Equivariance Through Parameter-Sharing
S Ravanbakhsh, J Schneider, B Poczos
International Conference on Machine Learning (ICML) 70, 2892--2901, 2017
2952017
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
2662019
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
2392018
Deep learning with sets and point clouds
S Ravanbakhsh, J Schneider, B Poczos
International Conference on Learning Representations (ICLR) - workshop track, 2016
2212016
Deep models of interactions across sets
J Hartford, DR Graham, K Leyton-Brown, S Ravanbakhsh
International Conference on Machine Learning 80, 1909-1918, 2018
1852018
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
982016
Equivariance with learned canonicalization functions
SO Kaba, AK Mondal, Y Zhang, Y Bengio, S Ravanbakhsh
International Conference on Machine Learning, 15546-15566, 2023
822023
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
792017
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
742021
Boolean Matrix Factorization and Noisy Completion via Message Passing.
S Ravanbakhsh, R Barnabas Poczos, Greiner
International Conference on Machine Learning (ICML), 945-954, 2016
622016
Universal Equivariant Multilayer Perceptrons
S Ravanbakhsh
International Conference on Machine Learning (ICML), 2020
562020
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
C Morris, G Rattan, S Kiefer, S Ravanbakhsh
International Conference on Machine Learning, 2022
502022
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
412019
Iterated denoising energy matching for sampling from Boltzmann densities
T Akhound-Sadegh, J Rector-Brooks, AJ Bose, S Mittal, P Lemos, CH Liu, ...
International Conference on Machine Learning (ICML), 2024
402024
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
382021
Equivariant Networks for Crystal Structures
SO Kaba, S Ravanbakhsh
Advances in Neural Information Processing Systems, 2022
322022
EqR: Equivariant representations for data-efficient reinforcement learning
AK Mondal, V Jain, K Siddiqi, S Ravanbakhsh
International Conference on Machine Learning, 15908-15926, 2022
322022
Incidence networks for geometric deep learning
M Albooyeh, D Bertolini, S Ravanbakhsh
International Conference on Machine Learning, 2020
322020
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