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Siamak Ravanbakhsh

University of Alberta

Equivariant Networks for Pixelized Spheres


Jun 12, 2021
Mehran Shakerinava, Siamak Ravanbakhsh

* Accepted to ICML 2021 

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Equivariant Maps for Hierarchical Structures


Jun 05, 2020
Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh


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Universal Equivariant Multilayer Perceptrons


Feb 07, 2020
Siamak Ravanbakhsh


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Incidence Networks for Geometric Deep Learning


May 27, 2019
Marjan Albooyeh, Daniele Bertolini, Siamak Ravanbakhsh


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Deep Models for Relational Databases


Mar 21, 2019
Devon Graham, Siamak Ravanbakhsh


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Improved Knowledge Graph Embedding using Background Taxonomic Information


Dec 07, 2018
Bahare Fatemi, Siamak Ravanbakhsh, David Poole


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Learning to Predict the Cosmological Structure Formation


Nov 15, 2018
Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos

* 7 pages, 5 figures, 1 table 

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Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector


Jun 28, 2018
Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank Sciurba, Barnabas Poczos, Kayhan N. Batmanghelich

* MICCAI 2018 

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Deep Models of Interactions Across Sets


Jun 08, 2018
Jason Hartford, Devon R Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh


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Deep Sets


Apr 14, 2018
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola

* NIPS 2017 

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Estimating Cosmological Parameters from the Dark Matter Distribution


Nov 06, 2017
Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

* ICML 2016 

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Equivariance Through Parameter-Sharing


Jun 13, 2017
Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

* icml'17 

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Deep Learning with Sets and Point Clouds


Feb 24, 2017
Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos


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Enabling Dark Energy Science with Deep Generative Models of Galaxy Images


Nov 30, 2016
Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum, Jeff Schneider, Barnabas Poczos


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Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM


Nov 11, 2016
Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos


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Stochastic Neural Networks with Monotonic Activation Functions


Jul 22, 2016
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner

* AISTATS 2016 

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Boolean Matrix Factorization and Noisy Completion via Message Passing


Feb 04, 2016
Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner


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Message Passing and Combinatorial Optimization


Aug 20, 2015
Siamak Ravanbakhsh

* Ravanbakhsh, S. (2015), Message Passing and Combinatorial Optimization, PhD thesis, University of Alberta 

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Revisiting Algebra and Complexity of Inference in Graphical Models


May 03, 2015
Siamak Ravanbakhsh, Russell Greiner


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Perturbed Message Passing for Constraint Satisfaction Problems


Feb 03, 2015
Siamak Ravanbakhsh, Russell Greiner

* JMLR 16(Jul):1249-1274, 2015 

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Accurate, fully-automated NMR spectral profiling for metabolomics


Sep 08, 2014
Siamak Ravanbakhsh, Philip Liu, Trent Bjorndahl, Rupasri Mandal, Jason R. Grant, Michael Wilson, Roman Eisner, Igor Sinelnikov, Xiaoyu Hu, Claudio Luchinat, Russell Greiner, David S. Wishart

* PLoS ONE 10(5): e0124219, 2015 

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Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning


Jun 04, 2014
Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner


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Training Restricted Boltzmann Machine by Perturbation


May 06, 2014
Siamak Ravanbakhsh, Russell Greiner, Brendan Frey


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A Generalized Loop Correction Method for Approximate Inference in Graphical Models


Jun 18, 2012
Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner

* ICML2012 

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