Siamak Ravanbakhsh

According to our database, Siamak Ravanbakhsh authored at least 54 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions.

Thuan Nguyen Anh Trang, Nhat Khang Ngo, Hugo Sonnery, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy

Trans. Mach. Learn. Res., 2024

Sampling from Energy-based Policies using Diffusion.

Vineet Jain, Tara Akhound-Sadegh, Siamak Ravanbakhsh

CoRR, 2024

E(3)-Equivariant Mesh Neural Networks.

Thuan N. A. Trang, Nhat Khang Ngo, Daniel T. Levy, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy

CoRR, 2024

Learning to Reach Goals via Diffusion.

Vineet Jain, Siamak Ravanbakhsh

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh

Proceedings of the Twelfth International Conference on Learning Representations, 2024

On Diffusion Modeling for Anomaly Detection.

Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh

Proceedings of the Twelfth International Conference on Learning Representations, 2024

E(3)-Equivariant Mesh Neural Networks.

Thuan Anh Trang, Nhat Khang Ngo, Daniel T. Levy, Ngoc Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Weight-Sharing Regularization.

Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Symmetry Breaking and Equivariant Neural Networks.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

CoRR, 2023

Equivariant Adaptation of Large Pretrained Models.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh

CoRR, 2023

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks.

Daniel T. Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh

CoRR, 2023

Equivariant Adaptation of Large Pretrained Models.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Mudumba, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Lie Point Symmetry and Physics-Informed Networks.

Tara Akhound-Sadegh, Laurence Perreault Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Equivariance with Learned Canonicalization Functions.

Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

Proceedings of the International Conference on Machine Learning, 2023

2022

Transformation Coding: Simple Objectives for Equivariant Representations.

Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh

CoRR, 2022

Structuring Representations Using Group Invariants.

Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Equivariant Networks for Crystal Structures.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Utility Theory for Sequential Decision Making.

Mehran Shakerinava, Siamak Ravanbakhsh

Proceedings of the International Conference on Machine Learning, 2022

EqR: Equivariant Representations for Data-Efficient Reinforcement Learning.

Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh

Proceedings of the International Conference on Machine Learning, 2022

SpeqNets: Sparsity-aware permutation-equivariant graph networks.

Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh

Proceedings of the International Conference on Machine Learning, 2022

2021

Equivariant Networks for Pixelized Spheres.

Mehran Shakerinava, Siamak Ravanbakhsh

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Equivariant Maps for Hierarchical Structures.

Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh

CoRR, 2020

Equivariant Networks for Hierarchical Structures.

Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Universal Equivariant Multilayer Perceptrons.

Siamak Ravanbakhsh

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines.

Jakub M. Tomczak, Szymon Zareba, Siamak Ravanbakhsh, Russell Greiner

Neural Process. Lett., 2019

Incidence Networks for Geometric Deep Learning.

Marjan Albooyeh, Daniele Bertolini, Siamak Ravanbakhsh

CoRR, 2019

Deep Models for Relational Databases.

Devon R. Graham, Siamak Ravanbakhsh

CoRR, 2019

Improved Knowledge Graph Embedding Using Background Taxonomic Information.

Bahare Fatemi, Siamak Ravanbakhsh, David Poole

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Learning to Predict the Cosmological Structure Formation.

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

CoRR, 2018

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

Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabás Póczos, Kayhan N. Batmanghelich

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Deep Models of Interactions Across Sets.

Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh

Proceedings of the 35th International Conference on Machine Learning, 2018

Analysis of Cosmic Microwave Background with Deep Learning.

Siyu He, Siamak Ravanbakhsh, Shirley Ho

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Deep Sets.

Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Min-Max Propagation.

Christopher Srinivasa, Inmar E. Givoni, Siamak Ravanbakhsh, Brendan J. Frey

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Equivariance Through Parameter-Sharing.

Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Learning with Sets and Point Clouds.

Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 5th International Conference on Learning Representations, 2017

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images.

Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

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

Chun-Liang Li, Siamak Ravanbakhsh, Barnabás Póczos

CoRR, 2016

Boolean Matrix Factorization and Noisy Completion via Message Passing.

Siamak Ravanbakhsh, Barnabás Póczos, Russell Greiner

Proceedings of the 33nd International Conference on Machine Learning, 2016

Estimating Cosmological Parameters from the Dark Matter Distribution.

Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 33nd International Conference on Machine Learning, 2016

Survey Propagation beyond Constraint Satisfaction Problems.

Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Stochastic Neural Networks with Monotonic Activation Functions.

Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Perturbed message passing for constraint satisfaction problems.

Siamak Ravanbakhsh, Russell Greiner

J. Mach. Learn. Res., 2015

Boolean Matrix Factorization and Completion via Message Passing.

Siamak Ravanbakhsh, Russell Greiner

CoRR, 2015

Message Passing and Combinatorial Optimization.

Siamak Ravanbakhsh

CoRR, 2015

Embedding Inference for Structured Multilabel Prediction.

Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Accurate, fully-automated NMR spectral profiling for metabolomics.

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

CoRR, 2014

Training Restricted Boltzmann Machine by Perturbation.

Siamak Ravanbakhsh, Russell Greiner, Brendan J. Frey

CoRR, 2014

Algebra of inference in graphical models revisited.

Siamak Ravanbakhsh, Russell Greiner

CoRR, 2014

Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning.

Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Min-Max Problems on Factor Graphs.

Siamak Ravanbakhsh, Christopher Srinivasa, Brendan J. Frey, Russell Greiner

Proceedings of the 31th International Conference on Machine Learning, 2014

2012

A Generalized Loop Correction Method for Approximate Inference in Graphical Models.

Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner

Proceedings of the 29th International Conference on Machine Learning, 2012

2010

A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra.

Siamak (Moshen) Ravanbakhsh, Barnabás Póczos, Russell Greiner

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010