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ORCIDSiamak Ravanbakhsh
Person information
- unicode name: سیامک روانبخش
- affiliation: Carnegie Mellon University, Pittsburgh, Robotics Institute
- affiliation: University of Alberta, Edmonton, Department of Computing Science
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2020 – today
- 2024
[j3]Thuan Nguyen Anh Trang, Nhat Khang Ngo, Hugo Sonnery, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy:
Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions. Trans. Mach. Learn. Res. 2024 (2024)- [c35]Thuan Anh Trang, Nhat Khang Ngo, Daniel T. Levy, Ngoc Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy:
E(3)-Equivariant Mesh Neural Networks. AISTATS 2024: 748-756 - [c34]Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien:
Weight-Sharing Regularization. AISTATS 2024: 4204-4212 - [c33]Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh:
On Diffusion Modeling for Anomaly Detection. ICLR 2024 - [c32]Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh:
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. ICLR 2024 - [c31]Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong:
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. ICML 2024 - [c30]Vineet Jain, Siamak Ravanbakhsh:
Learning to Reach Goals via Diffusion. ICML 2024 - [i40]Thuan N. A. Trang, Nhat Khang Ngo, Daniel T. Levy, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy:
E(3)-Equivariant Mesh Neural Networks. CoRR abs/2402.04821 (2024) - [i39]Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong:
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. CoRR abs/2402.06121 (2024) - [i38]Vineet Jain, Tara Akhound-Sadegh, Siamak Ravanbakhsh:
Sampling from Energy-based Policies using Diffusion. CoRR abs/2410.01312 (2024) - 2023
- [c29]Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh:
Equivariance with Learned Canonicalization Functions. ICML 2023: 15546-15566 - [c28]Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh:
Lie Point Symmetry and Physics-Informed Networks. NeurIPS 2023 - [c27]Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Mudumba, Siamak Ravanbakhsh:
Equivariant Adaptation of Large Pretrained Models. NeurIPS 2023 - [i37]Victor Livernoche, Vineet Jain, Yashar Hezaveh
, Siamak Ravanbakhsh:
On Diffusion Modeling for Anomaly Detection. CoRR abs/2305.18593 (2023) - [i36]Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh:
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. CoRR abs/2306.11941 (2023) - [i35]Daniel T. Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh:
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. CoRR abs/2309.03139 (2023) - [i34]Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh:
Equivariant Adaptation of Large Pretrained Models. CoRR abs/2310.01647 (2023) - [i33]Vineet Jain, Siamak Ravanbakhsh:
Learning to Reach Goals via Diffusion. CoRR abs/2310.02505 (2023) - [i32]Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien:
Weight-Sharing Regularization. CoRR abs/2311.03096 (2023) - [i31]Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh:
Lie Point Symmetry and Physics Informed Networks. CoRR abs/2311.04293 (2023) - [i30]Sékou-Oumar Kaba, Siamak Ravanbakhsh:
Symmetry Breaking and Equivariant Neural Networks. CoRR abs/2312.09016 (2023) - 2022
- [c26]Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh:
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning. ICML 2022: 15908-15926 - [c25]Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh:
SpeqNets: Sparsity-aware permutation-equivariant graph networks. ICML 2022: 16017-16042 - [c24]Mehran Shakerinava, Siamak Ravanbakhsh:
Utility Theory for Sequential Decision Making. ICML 2022: 19616-19625 - [c23]Sékou-Oumar Kaba, Siamak Ravanbakhsh:
Equivariant Networks for Crystal Structures. NeurIPS 2022 - [c22]Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh:
Structuring Representations Using Group Invariants. NeurIPS 2022 - [i29]Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh:
Transformation Coding: Simple Objectives for Equivariant Representations. CoRR abs/2202.10930 (2022) - [i28]Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh:
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks. CoRR abs/2203.13913 (2022) - [i27]Mehran Shakerinava, Siamak Ravanbakhsh:
Utility Theory for Sequential Decision Making. CoRR abs/2206.13637 (2022) - [i26]Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh:
Equivariance with Learned Canonicalization Functions. CoRR abs/2211.06489 (2022) - [i25]Sékou-Oumar Kaba, Siamak Ravanbakhsh:
Equivariant Networks for Crystal Structures. CoRR abs/2211.15420 (2022) - 2021
- [c21]Mehran Shakerinava, Siamak Ravanbakhsh:
Equivariant Networks for Pixelized Spheres. ICML 2021: 9477-9488 - [i24]Mehran Shakerinava, Siamak Ravanbakhsh:
Equivariant Networks for Pixelized Spheres. CoRR abs/2106.06662 (2021) - 2020
- [c20]Siamak Ravanbakhsh:
Universal Equivariant Multilayer Perceptrons. ICML 2020: 7996-8006 - [c19]Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh:
Equivariant Networks for Hierarchical Structures. NeurIPS 2020 - [i23]Siamak Ravanbakhsh:
Universal Equivariant Multilayer Perceptrons. CoRR abs/2002.02912 (2020) - [i22]Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh:
Equivariant Maps for Hierarchical Structures. CoRR abs/2006.03627 (2020)
2010 – 2019
- 2019
- [j2]Jakub M. Tomczak, Szymon Zareba, Siamak Ravanbakhsh, Russell Greiner:
Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines. Neural Process. Lett. 50(2): 1401-1419 (2019) - [c18]Bahare Fatemi, Siamak Ravanbakhsh, David Poole:
Improved Knowledge Graph Embedding Using Background Taxonomic Information. AAAI 2019: 3526-3533 - [i21]Devon R. Graham, Siamak Ravanbakhsh:
Deep Models for Relational Databases. CoRR abs/1903.09033 (2019) - [i20]Marjan Albooyeh, Daniele Bertolini, Siamak Ravanbakhsh:
Incidence Networks for Geometric Deep Learning. CoRR abs/1905.11460 (2019) - 2018
- [c17]Siyu He, Siamak Ravanbakhsh, Shirley Ho:
Analysis of Cosmic Microwave Background with Deep Learning. ICLR (Workshop) 2018 - [c16]Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh:
Deep Models of Interactions Across Sets. ICML 2018: 1914-1923 - [c15]Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich:
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. MICCAI (1) 2018: 502-510 - [i19]Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh:
Deep Models of Interactions Across Sets. CoRR abs/1803.02879 (2018) - [i18]Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich:
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. CoRR abs/1806.11217 (2018) - [i17]Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos:
Learning to Predict the Cosmological Structure Formation. CoRR abs/1811.06533 (2018) - [i16]Bahare Fatemi, Siamak Ravanbakhsh, David Poole:
Improved Knowledge Graph Embedding using Background Taxonomic Information. CoRR abs/1812.03235 (2018) - 2017
- [c14]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI 2017: 1488-1494 - [c13]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. ICLR (Workshop) 2017 - [c12]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. ICML 2017: 2892-2901 - [c11]Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola:
Deep Sets. NIPS 2017: 3391-3401 - [c10]Christopher Srinivasa, Inmar E. Givoni, Siamak Ravanbakhsh, Brendan J. Frey:
Min-Max Propagation. NIPS 2017: 5565-5573 - [i15]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. CoRR abs/1702.08389 (2017) - [i14]Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola:
Deep Sets. CoRR abs/1703.06114 (2017) - [i13]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne C. Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. CoRR abs/1711.02033 (2017) - 2016
- [c9]Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey:
Survey Propagation beyond Constraint Satisfaction Problems. AISTATS 2016: 286-295 - [c8]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS 2016: 809-818 - [c7]Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner:
Boolean Matrix Factorization and Noisy Completion via Message Passing. ICML 2016: 945-954 - [c6]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. ICML 2016: 2407-2416 - [i12]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. CoRR abs/1601.00034 (2016) - [i11]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR abs/1609.05796 (2016) - [i10]Chun-Liang Li, Siamak Ravanbakhsh, Barnabás Póczos:
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM. CoRR abs/1611.03879 (2016) - [i9]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. CoRR abs/1611.04500 (2016) - 2015
- [j1]Siamak Ravanbakhsh, Russell Greiner:
Perturbed message passing for constraint satisfaction problems. J. Mach. Learn. Res. 16: 1249-1274 (2015) - [c5]Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans:
Embedding Inference for Structured Multilabel Prediction. NIPS 2015: 3555-3563 - [i8]Siamak Ravanbakhsh:
Message Passing and Combinatorial Optimization. CoRR abs/1508.05013 (2015) - [i7]Siamak Ravanbakhsh, Russell Greiner:
Boolean Matrix Factorization and Completion via Message Passing. CoRR abs/1509.08535 (2015) - 2014
- [c4]Siamak Ravanbakhsh, Christopher Srinivasa, Brendan J. Frey, Russell Greiner:
Min-Max Problems on Factor Graphs. ICML 2014: 1035-1043 - [c3]Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner:
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. NIPS 2014: 289-297 - [i6]Siamak Ravanbakhsh, Russell Greiner:
Perturbed Message Passing for Constraint Satisfaction Problems. CoRR abs/1401.6686 (2014) - [i5]Siamak Ravanbakhsh, Russell Greiner, Brendan J. Frey:
Training Restricted Boltzmann Machine by Perturbation. CoRR abs/1405.1436 (2014) - [i4]Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner:
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. CoRR abs/1406.0941 (2014) - [i3]Siamak Ravanbakhsh, Philip Liu, Trent C. Bjorndahl, Rupasri Mandal, Jason R. Grant, Michael Wilson, Roman Eisner, Igor Sinelnikov, Xiaoyu Hu, Claudio Luchinat, Russell Greiner, David S. Wishart:
Accurate, fully-automated NMR spectral profiling for metabolomics. CoRR abs/1409.1456 (2014) - [i2]Siamak Ravanbakhsh, Russell Greiner:
Algebra of inference in graphical models revisited. CoRR abs/1409.7410 (2014) - 2012
- [c2]Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner:
A Generalized Loop Correction Method for Approximate Inference in Graphical Models. ICML 2012 - [i1]Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner:
A Generalized Loop Correction Method for Approximate Inference in Graphical Models. CoRR abs/1206.4654 (2012) - 2010
- [c1]Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner:
A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra. AAAI 2010: 1280-1286
Coauthor Index
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last updated on 2024-11-08 20:32 CET by the dblp team
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