Siamak Ravanbakhsh
Siamak Ravanbakhsh
Assistant Professor, McGill University
Verified email at - Homepage
TitleCited byYear
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, 2017
Equivariance Through Parameter-Sharing
S Ravanbakhsh, J Schneider, B Poczos
International Conference on Machine Learning (ICML) 70, 2892--2901, 2017
Enabling dark energy science with deep generative models of galaxy images
S Ravanbakhsh, F Lanusse, R Mandelbaum, J Schneider, B Poczos
Thirty-First AAAI Conference on Artificial Intelligence, 2017
Estimating Cosmological Parameters from the Dark Matter Distribution.
S Ravanbakhsh, JB Oliva, S Fromenteau, L Price, S Ho, JG Schneider, ...
ICML, 2407-2416, 2016
Boolean Matrix Factorization and Noisy Completion via Message Passing.
S Ravanbakhsh, R Barnabas Poczos, Greiner
ICML, 945--954, 2016
Deep models of interactions across sets
J Hartford, DR Graham, K Leyton-Brown, S Ravanbakhsh
arXiv preprint arXiv:1803.02879, 2018
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
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
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, 201821458, 2019
Survey propagation beyond constraint satisfaction problems
C Srinivasa, S Ravanbakhsh, B Frey
Artificial Intelligence and Statistics, 286-295, 2016
Revisiting Algebra and Complexity of Inference in Graphical Models
S Ravanbakhsh, R Greiner
arXiv preprint arXiv:1409.7410, 2014
Training restricted boltzmann machine by perturbation
S Ravanbakhsh, R Greiner, B Frey
arXiv preprint arXiv:1405.1436, 2014
Deep Models for Relational Databases
D Graham, S Ravanbakhsh
arXiv preprint arXiv:1903.09033, 2019
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