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, 3391-3401, 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
arXiv preprint arXiv:1611.04500, 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
Equivariance Through Parameter-Sharing
S Ravanbakhsh, J Schneider, B Poczos
International Conference on Machine Learning (ICML) 70, 2892--2901, 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
Enabling dark energy science with deep generative models of galaxy images
S Ravanbakhsh, F Lanusse, R Mandelbaum, J Schneider, B Poczos
arXiv preprint arXiv:1609.05796, 2016
Deep models of interactions across sets
J Hartford, DR Graham, K Leyton-Brown, S Ravanbakhsh
arXiv preprint arXiv:1803.02879, 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
Boolean Matrix Factorization and Noisy Completion via Message Passing.
S Ravanbakhsh, R Barnabas Poczos, Greiner
ICML, 945--954, 2016
Perturbed Message Passing for Constraint Satisfaction Problems
S Ravanbakhsh, R Greiner
Journal of Machine Learning Research 16, 1249-1274, 2015
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), 29, 2013
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
Improved knowledge graph embedding using background taxonomic information
B Fatemi, S Ravanbakhsh, D Poole
Proceedings of the AAAI Conference on Artificial Intelligence 33, 3526-3533, 2019
Embedding inference for structured multilabel prediction
F Mirzazadeh, S Ravanbakhsh, N Ding, D Schuurmans
Advances in Neural Information Processing Systems, 3555-3563, 2015
Augmentative message passing for traveling salesman problem and graph partitioning
S Ravanbakhsh, R Rabbany, R Greiner
Advances in Neural Information Processing Systems, 289-297, 2014
Min-Max Problems on Factor-Graphs
S Ravanbakhsh, S Christopher, B Frey, R Greiner
International Conference on Machine Learning (ICML), 2014
Incidence networks for geometric deep learning
M Albooyeh, D Bertolini, S Ravanbakhsh
arXiv:1905.11460, 2020
Survey propagation beyond constraint satisfaction problems
C Srinivasa, S Ravanbakhsh, B Frey
Artificial Intelligence and Statistics, 286-295, 2016
Equivariant Entity-Relationship Networks
D Graham, J Wang, S Ravanbakhsh
arXiv preprint arXiv:1903.09033, 2020
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