Siamak Ravanbakhsh

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Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Russ GreinerProfessor of Computer Science, University of AlbertaVerified email at ualberta.ca
Jeff SchneiderCarnegie Mellon UniversityVerified email at cs.cmu.edu
François LanusseCNRS ResearcherVerified email at cea.fr
Alex SmolaBoson AIVerified email at smola.org
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Satwik KotturResearch Scientist, Facebook AIVerified email at fb.com
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Shirley HoFlatiron Institute, Center for Computational AstrophysicsVerified email at flatironinstitute.org
Chun-Liang LiResearch Scientist, GoogleVerified email at google.com
David WishartProfessor, Depts. of Computing Science and Biological Sciences - University of AlbertaVerified email at ualberta.ca
Devon R. GrahamPhD Student, University of British ColumbiaVerified email at cs.ubc.ca
Siyu HeSoftware engineer, WaymoVerified email at waymo.com
Arnab MondalPhD Candidate, Mcgill University and MilaVerified email at mail.mcgill.ca
Brendan FreyFounder & CEO, Deep Genomics; Co-Founder, Vector Institute; Professor, University of TorontoVerified email at psi.toronto.edu
Yin LiPeng Cheng LaboratoryVerified email at pcl.ac.cn
Marjan AlbooyehGraduate Research assistant at Boise State UniversityVerified email at boisestate.edu
Mehran ShakerinavaPh.D. Student, McGill School of Computer Science & MilaVerified email at mail.mcgill.ca
Jason HartfordSenior Machine Learning Research Scientist at Recursion PharmaVerified email at cs.ubc.ca
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca

Siamak Ravanbakhsh

Assistant Professor, McGill University

Verified email at cs.mcgill.ca - Homepage

AI Machine Learning


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Deep sets M Zaheer, S Kottur, S Ravanbakhsh, B Poczos, RR Salakhutdinov, ... Advances in neural information processing systems 30, 2017	2366	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	293	2015
Equivariance Through Parameter-Sharing S Ravanbakhsh, J Schneider, B Poczos International Conference on Machine Learning (ICML) 70, 2892--2901, 2017	214	2017
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	203	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	201	2019
Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos International Conference on Learning Representations (ICLR) - workshop track, 2016	200	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	150	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	85	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	65	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	52	2016
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	46	2021
Universal Equivariant Multilayer Perceptrons S Ravanbakhsh International Conference on Machine Learning (ICML), 2020	45	2020
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	37	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	29	2016
Incidence networks for geometric deep learning M Albooyeh, D Bertolini, S Ravanbakhsh International Conference on Machine Learning, 2020	26	2020
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks C Morris, G Rattan, S Kiefer, S Ravanbakhsh International Conference on Machine Learning, 2022	25	2022
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	21	2021
Equivariant Networks for Hierarchical Structures R Wang, M Albooyeh, S Ravanbakhsh Advances in Neural Information Processing Systems 33, 13806--13817, 2020	20*	2020
Subject2Vec: generative-discriminative approach from a set of image patches to a vector S Singla, M Gong, S Ravanbakhsh, F Sciurba, B Poczos, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018	20	2018
Perturbed Message Passing for Constraint Satisfaction Problems S Ravanbakhsh, R Greiner Journal of Machine Learning Research 16, 1249-1274, 2015	20	2015

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