Siamak Ravanbakhsh

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 61

Top Topics

Matrix Factorization

Message Passing

Constraint Satisfaction Problems

Inference In Graphical Models

Top Venues

Publications

Thuan N. A. Trang, Nhat Khang Ngo, Daniel Levy, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy
E(3)-Equivariant Mesh Neural Networks. CoRR (2024)
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 (2024)
Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh
On Diffusion Modeling for Anomaly Detection. ICLR (2024)
Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien
Weight-Sharing Regularization. AISTATS (2024)
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)
Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. ICLR (2024)
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)
Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh
Equivariant Adaptation of Large Pretrained Models. CoRR (2023)
Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh
Lie Point Symmetry and Physics-Informed Networks. NeurIPS (2023)
Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh
Lie Point Symmetry and Physics Informed Networks. CoRR (2023)
Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Mudumba, Siamak Ravanbakhsh
Equivariant Adaptation of Large Pretrained Models. NeurIPS (2023)
Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
Equivariance with Learned Canonicalization Functions. ICML (2023)
Vineet Jain, Siamak Ravanbakhsh
Learning to Reach Goals via Diffusion. CoRR (2023)
Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh
On Diffusion Modeling for Anomaly Detection. CoRR (2023)
Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. CoRR (2023)
Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. CoRR (2023)
Sékou-Oumar Kaba, Siamak Ravanbakhsh
Symmetry Breaking and Equivariant Neural Networks. CoRR (2023)
Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien
Weight-Sharing Regularization. CoRR (2023)
Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning. ICML (2022)
Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks. CoRR (2022)
Sékou-Oumar Kaba, Siamak Ravanbakhsh
Equivariant Networks for Crystal Structures. NeurIPS (2022)
Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh
Structuring Representations Using Group Invariants. NeurIPS (2022)
Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
Equivariance with Learned Canonicalization Functions. CoRR (2022)
Mehran Shakerinava, Siamak Ravanbakhsh
Utility Theory for Sequential Decision Making. ICML (2022)
Mehran Shakerinava, Siamak Ravanbakhsh
Utility Theory for Sequential Decision Making. CoRR (2022)
Sékou-Oumar Kaba, Siamak Ravanbakhsh
Equivariant Networks for Crystal Structures. CoRR (2022)
Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh
Transformation Coding: Simple Objectives for Equivariant Representations. CoRR (2022)
Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh
SpeqNets: Sparsity-aware permutation-equivariant graph networks. ICML (2022)
Mehran Shakerinava, Siamak Ravanbakhsh
Equivariant Networks for Pixelized Spheres. CoRR (2021)
Mehran Shakerinava, Siamak Ravanbakhsh
Equivariant Networks for Pixelized Spheres. ICML (2021)
Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh
Equivariant Networks for Hierarchical Structures. NeurIPS (2020)
Siamak Ravanbakhsh
Universal Equivariant Multilayer Perceptrons. ICML (2020)
Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh
Equivariant Maps for Hierarchical Structures. CoRR (2020)
Siamak Ravanbakhsh
Universal Equivariant Multilayer Perceptrons. CoRR (2020)
Bahare Fatemi, Siamak Ravanbakhsh, David Poole
Improved Knowledge Graph Embedding Using Background Taxonomic Information. AAAI (2019)
Marjan Albooyeh, Daniele Bertolini, Siamak Ravanbakhsh
Incidence Networks for Geometric Deep Learning. CoRR (2019)
Devon R. Graham, Siamak Ravanbakhsh
Deep Models for Relational Databases. CoRR (2019)
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) (2019)
Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
Learning to Predict the Cosmological Structure Formation. CoRR (2018)
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 (2018)
Bahare Fatemi, Siamak Ravanbakhsh, David Poole
Improved Knowledge Graph Embedding using Background Taxonomic Information. CoRR (2018)
Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh
Deep Models of Interactions Across Sets. ICML (2018)
Siyu He, Siamak Ravanbakhsh, Shirley Ho
Analysis of Cosmic Microwave Background with Deep Learning. ICLR (Workshop) (2018)
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)
Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh
Deep Models of Interactions Across Sets. CoRR (2018)
Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
Deep Learning with Sets and Point Clouds. ICLR (Workshop) (2017)
Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
Equivariance Through Parameter-Sharing. CoRR (2017)
Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
Equivariance Through Parameter-Sharing. ICML (2017)
Christopher Srinivasa, Inmar E. Givoni, Siamak Ravanbakhsh, Brendan J. Frey
Min-Max Propagation. NIPS (2017)
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 (2017)
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola
Deep Sets. NIPS (2017)
Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI (2017)
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola
Deep Sets. CoRR (2017)
Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR (2016)
Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner
Stochastic Neural Networks with Monotonic Activation Functions. CoRR (2016)
Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey
Survey Propagation beyond Constraint Satisfaction Problems. AISTATS (2016)
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)
Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos
Deep Learning with Sets and Point Clouds. CoRR (2016)
Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS (2016)
Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner
Boolean Matrix Factorization and Noisy Completion via Message Passing. ICML (2016)
Chun-Liang Li, Siamak Ravanbakhsh, Barnabás Póczos
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM. CoRR (2016)
Siamak Ravanbakhsh, Russell Greiner
Perturbed message passing for constraint satisfaction problems. J. Mach. Learn. Res. 16 (2015)
Siamak Ravanbakhsh, Russell Greiner
Boolean Matrix Factorization and Completion via Message Passing. CoRR (2015)
Siamak Ravanbakhsh
Message Passing and Combinatorial Optimization. CoRR (2015)
Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans
Embedding Inference for Structured Multilabel Prediction. NIPS (2015)
Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. CoRR (2014)
Siamak Ravanbakhsh, Christopher Srinivasa, Brendan J. Frey, Russell Greiner
Min-Max Problems on Factor Graphs. ICML (2014)
Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning. NIPS (2014)
Siamak Ravanbakhsh, Russell Greiner
Algebra of inference in graphical models revisited. CoRR (2014)
Siamak Ravanbakhsh, Russell Greiner
Perturbed Message Passing for Constraint Satisfaction Problems. CoRR (2014)
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 (2014)
Siamak Ravanbakhsh, Russell Greiner, Brendan J. Frey
Training Restricted Boltzmann Machine by Perturbation. CoRR (2014)
Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner
A Generalized Loop Correction Method for Approximate Inference in Graphical Models. ICML (2012)
Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner
A Generalized Loop Correction Method for Approximate Inference in Graphical Models CoRR (2012)