Siamak Ravanbakhsh

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Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Russ GreinerProfessor of Computing Science, University of Alberta; CIFAR AI ChairVerified 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, AppleVerified email at apple.com
Arnab MondalPhD Candidate, Mcgill University and MilaVerified email at mail.mcgill.ca
Sékou-Oumar KabaMila - Quebec Artificial Intelligence Institute & McGill UniversityVerified email at mila.quebec
Devon R. GrahamPhD Student, University of British ColumbiaVerified email at cs.ubc.ca
David WishartProfessor, Depts. of Computing Science and Biological Sciences - University of AlbertaVerified email at ualberta.ca
Siyu HeSoftware engineer, WaymoVerified email at waymo.com
Mehran ShakerinavaPh.D. Student, McGill School of Computer Science & 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
Jason HartfordDame Kathleen Ollerenshaw Fellow, University of Manchester; Research Scientist at Valence LabsVerified 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	2825	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	319	2015
Equivariance Through Parameter-Sharing S Ravanbakhsh, J Schneider, B Poczos International Conference on Machine Learning (ICML) 70, 2892--2901, 2017	259	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	244	2019
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	215	2018
Deep learning with sets and point clouds S Ravanbakhsh, J Schneider, B Poczos International Conference on Learning Representations (ICLR) - workshop track, 2016	214	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	171	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	92	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	76	2017
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	60	2021
Boolean Matrix Factorization and Noisy Completion via Message Passing. S Ravanbakhsh, R Barnabas Poczos, Greiner International Conference on Machine Learning (ICML), 945-954, 2016	60	2016
Universal Equivariant Multilayer Perceptrons S Ravanbakhsh International Conference on Machine Learning (ICML), 2020	52	2020
Equivariance with learned canonicalization functions SO Kaba, AK Mondal, Y Zhang, Y Bengio, S Ravanbakhsh International Conference on Machine Learning, 15546-15566, 2023	46	2023
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	43	2019
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks C Morris, G Rattan, S Kiefer, S Ravanbakhsh International Conference on Machine Learning, 2022	38	2022
Incidence networks for geometric deep learning M Albooyeh, D Bertolini, S Ravanbakhsh International Conference on Machine Learning, 2020	30	2020
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	30	2016
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	28	2021
EqR: Equivariant representations for data-efficient reinforcement learning AK Mondal, V Jain, K Siddiqi, S Ravanbakhsh International Conference on Machine Learning, 15908-15926, 2022	25	2022
Equivariant Networks for Hierarchical Structures R Wang, M Albooyeh, S Ravanbakhsh Advances in Neural Information Processing Systems 33, 13806--13817, 2020	23*	2020

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