Siamak Ravanbakhsh
Siamak Ravanbakhsh
Assistant Professor, McGill University
Verified email at - Homepage
Cited by
Cited by
Deep sets
M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ...
Advances in neural information processing systems 30, 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
Deep learning with sets and point clouds
S Ravanbakhsh, J Schneider, B Poczos
International Conference on Learning Representations (ICLR) - workshop track, 2016
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
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
Equivariance Through Parameter-Sharing
S Ravanbakhsh, J Schneider, B Poczos
International Conference on Machine Learning (ICML) 70, 2892--2901, 2017
Deep models of interactions across sets
J Hartford, DR Graham, K Leyton-Brown, S Ravanbakhsh
International Conference on Machine Learning 80, 1909-1918, 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
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
Boolean Matrix Factorization and Noisy Completion via Message Passing.
S Ravanbakhsh, R Barnabas Poczos, Greiner
International Conference on Machine Learning (ICML), 945-954, 2016
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
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
Universal Equivariant Multilayer Perceptrons
S Ravanbakhsh
International Conference on Machine Learning (ICML), 2020
Incidence networks for geometric deep learning
M Albooyeh, D Bertolini, S Ravanbakhsh
International Conference on Machine Learning, 2020
Perturbed Message Passing for Constraint Satisfaction Problems
S Ravanbakhsh, R Greiner
Journal of Machine Learning Research 16, 1249-1274, 2015
Embedding inference for structured multilabel prediction
F Mirzazadeh, S Ravanbakhsh, N Ding, D Schuurmans
Neural Information Processing Systems, 2015
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
Determination of the optimal tubulin isotype target as a method for the development of individualized cancer chemotherapy
S Ravanbakhsh, M Gajewski, R Greiner, JA Tuszynski
Theoretical Biology and Medical Modelling 10 (1), 1-18, 2013
Equivariant Networks for Hierarchical Structures
R Wang, M Albooyeh, S Ravanbakhsh
Advances in Neural Information Processing Systems 33, 13806--13817, 2020
Subject2Vec: generative-discriminative approach from a set of image patches to a vector
S Singla, M Gong, S Ravanbakhsh, F Sciurba, B Poczos, ...
International Conference on Medical Image Computing and Computer-Assisted …, 2018
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