Siamak Ravanbakhsh

Laurence Perreault-Levasseur

Biographie

Siamak Ravanbakhsh est professeur adjoint à l’École d’informatique de l’Université McGill depuis août 2019. Avant de se joindre à McGill et à Mila – Institut québécois d’intelligence artificielle, il a occupé un poste similaire à l’Université de la Colombie-Britannique. De 2015 à 2017, il a été stagiaire postdoctoral au Département d’apprentissage automatique et à l’Institut de robotique de l’Université Carnegie Mellon, et il a obtenu un doctorat de l’Université de l’Alberta. Il s’intéresse aux problèmes de l’apprentissage de la représentation et de l’inférence dans l’IA.

Ses recherches actuelles portent sur le rôle de la symétrie et de l’invariance dans l’apprentissage profond des représentations.

Étudiants actuels

Tara Akhound-Sadegh

Doctorat - McGill

Co-superviseur⋅e :

Fabricio Dos Santos

Stagiaire de recherche - McGill

Han Du Han

Maîtrise professionnelle - McGill

Jikael Gagnon

Stagiaire de recherche - McGill

Julie Hlavacek-Larrondo

Visiteur de recherche indépendant

Vineet Jain

Doctorat - McGill

Oumar Kaba

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Mary Letey

Collaborateur·rice de recherche

Daniel Levy

Doctorat - McGill

Xiusi Li

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Khang Ngo

Maîtrise recherche - McGill

Siba Smarak Panigrahi

Collaborateur·rice alumni - McGill

Postdoctorat - McGill

Kusha Sareen

Maîtrise recherche - McGill

Doctorat - McGill

Collaborateur·rice alumni - McGill

Alan Yang

Maîtrise professionnelle - McGill

Publications

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models

Daniel Levy

Siba Smarak Panigrahi

Sékou-Oumar Kaba

Qiang Zhu

Mikhail Galkin

Santiago Miret

2024-10-07

NeurIPS.cc/2024/Workshop/AI4Mat (spotlight)

Sampling from Energy-based Policies using Diffusion

Vineet Jain

Tara Akhound-Sadegh

2024-10-02

ArXiv (prépublication)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Tara Akhound-Sadegh

Jarrid Rector-Brooks

Joey Bose

Sarthak Mittal

Pablo Lemos

Cheng-Hao Liu

Marcin Sendera

Gauthier Gidel

Yoshua Bengio

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (voir plus)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient---and no data samples---to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is *simulation-free*, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-05-01

ICML.cc/2024/Conference (poster)

Weight-Sharing Regularization

Mehran Shakerinava

Motahareh Sohrabi

Simon Lacoste-Julien

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)

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

Thuan Nguyen Anh Trang

Khang Nhat Ngo

Hugo Sonnery

Thieu Vo

Truong Son Hy

Self-attention models have made great strides toward accurately modeling a wide array of data modalities, including, more recently, graph-st… (voir plus)ructured data. This paper demonstrates that adaptive hierarchical attention can go a long way toward successfully applying transformers to graphs. Our proposed model Sequoia provides a powerful inductive bias towards long-range interaction modeling, leading to better generalization. We propose an end-to-end mechanism for a data-dependent construction of a hierarchy which in turn guides the self-attention mechanism. Using adaptive hierarchy provides a natural pathway toward sparse attention by constraining node-to-node interactions with the immediate family of each node in the hierarchy (e.g., parent, children, and siblings). This in turn dramatically reduces the computational complexity of a self-attention layer from quadratic to log-linear in terms of the input size while maintaining or sometimes even surpassing the standard transformer's ability to model long-range dependencies across the entire input. Experimentally, we report state-of-the-art performance on long-range graph benchmarks while remaining computationally efficient. Moving beyond graphs, we also display competitive performance on long-range sequence modeling, point-clouds classification, and segmentation when using a fixed hierarchy. Our source code is publicly available at https://github.com/HySonLab/HierAttention

2024-04-11

TMLR (accepté)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Tara Akhound-Sadegh

Jarrid Rector-Brooks

Joey Bose

Sarthak Mittal

Pablo Lemos

Cheng-Hao Liu

Marcin Sendera

Gauthier Gidel

Yoshua Bengio

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (voir plus)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-02-09

ArXiv (prépublication)

E(3)-Equivariant Mesh Neural Networks

Thuan N.a. Trang

Nhat-Khang Ngô

Daniel Levy

Thieu N. Vo

Truong Son Hy

Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have address the need for geometric… (voir plus) deep learning on 3D mesh. However, we observe that the complexities in many of these architectures does not translate to practical performance, and simple deep models for geometric graphs are competitive in practice. Motivated by this observation, we minimally extend the update equations of E(n)-Equivariant Graph Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face information, and further improve it to account for long-range interactions through hierarchy. The resulting architecture, Equivariant Mesh Neural Network (EMNN), outperforms other, more complicated equivariant methods on mesh tasks, with a fast run-time and no expensive pre-processing. Our implementation is available at https://github.com/HySonLab/EquiMesh

2024-02-07

ArXiv (prépublication)

On Diffusion Modeling for Anomaly Detection

Victor Livernoche

Vineet Jain

Yashar Hezaveh

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (voir plus)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.

2024-01-16

ICLR.cc/2024/Conference (spotlight)

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory

Arnab Kumar Mondal

Siba Smarak Panigrahi

Sai Rajeswar

Kaleem Siddiqi

The accurate modeling of dynamics in interactive environments is critical for successful long-range prediction. Such a capability could adva… (voir plus)nce Reinforcement Learning (RL) and Planning algorithms, but achieving it is challenging. Inaccuracies in model estimates can compound, resulting in increased errors over long horizons. We approach this problem from the lens of Koopman theory, where the nonlinear dynamics of the environment can be linearized in a high-dimensional latent space. This allows us to efficiently parallelize the sequential problem of long-range prediction using convolution while accounting for the agent’s action at every time step. Our approach also enables stability analysis and better control over gradients through time. Taken together, these advantages result in significant improvement over the existing approaches, both in the efficiency and the accuracy of modeling dynamics over extended horizons. We also show that this model can be easily incorporated into dynamics modeling for model-based planning and model-free RL and report promising experimental results.

2024-01-16

ICLR.cc/2024/Conference (poster)

E(3)-Equivariant Mesh Neural Networks

Thuan Nguyen Anh Trang

Khang Nhat Ngo

Daniel Levy

Thieu Vo

Truong Son Hy

Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have addressed the need for geometr… (voir plus)ic deep learning on 3D meshes. However, we observe that the complexities in many of these architectures do not translate to practical performance, and simple deep models for geometric graphs are competitive in practice. Motivated by this observation, we minimally extend the update equations of E(n)-Equivariant Graph Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face information and further improve it to account for long-range interactions through a hierarchy. The resulting architecture, Equivariant Mesh Neural Network (EMNN), outperforms other, more complicated equivariant methods on mesh tasks, with a fast run-time and no expensive preprocessing. Our implementation is available at https://github.com/HySonLab/EquiMesh.

2024-01-01

AISTATS (publié)

Symmetry Breaking and Equivariant Neural Networks

Sékou-Oumar Kaba

Using symmetry as an inductive bias in deep learning has been proven to be a principled approach for sample-efficient model design. However,… (voir plus) the relationship between symmetry and the imperative for equivariance in neural networks is not always obvious. Here, we analyze a key limitation that arises in equivariant functions: their incapacity to break symmetry at the level of individual data samples. In response, we introduce a novel notion of 'relaxed equivariance' that circumvents this limitation. We further demonstrate how to incorporate this relaxation into equivariant multilayer perceptrons (E-MLPs), offering an alternative to the noise-injection method. The relevance of symmetry breaking is then discussed in various application domains: physics, graph representation learning, combinatorial optimization and equivariant decoding.

2023-11-28

NeurIPS.cc/2023/Workshop/NeurReps (présentation orale)

Physics-Informed Transformer Networks

Fabricio Dos Santos

F. Dos

Tara Akhound-Sadegh

Physics-informed neural networks (PINNs) have been recognized as a viable alternative to conventional numerical solvers for Partial Differen… (voir plus)tial Equations (PDEs). The main appeal of PINNs is that since they directly enforce the PDE equation, one does not require access to costly ground truth solutions for training the model. However, a key challenge is their limited generalization across varied initial conditions. Addressing this, our study presents a novel Physics-Informed Transformer (PIT) model for learning the solution operator for PDEs. Using the attention mechanism, PIT learns to leverage the relationships between its initial condition and query points, resulting in a significant improvement in generalization. Moreover, in contrast to existing physics-informed networks, our model is invariant to the discretization of the input domain, providing great flexibility in problem specification and training. We validated our proposed method on the 1D Burgers’ and the 2D Heat equations, demonstrating notable improvement over standard PINN models for operator learning with negligible computational overhead.

2023-10-31

NeurIPS.cc/2023/Workshop/DLDE (poster)