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Laurence Perreault-Levasseur

Membre académique associé

Professeure adjointe, Université de Montréal, Département de physique

Sujets de recherche

Apprentissage profond

Modèles génératifs

Modèles probabilistes

Réseaux de neurones en graphes

Systèmes dynamiques

Vision par ordinateur

Biographie

Laurence Perreault-Levasseur est titulaire de la Chaire de recherche du Canada en cosmologie computationnelle et en intelligence artificielle. Elle est professeure adjointe à l'Université de Montréal et membre associée de Mila – Institut québécois d’intelligence artificielle, où elle mène des recherches sur le développement et l'application de méthodes d'apprentissage automatique à la cosmologie. Elle est également chercheuse invitée au Flatiron Institute, à New York. Auparavant, elle a été chargée de recherche au Center for Computational Astrophysics du Flatiron Institute et boursière postdoctorale du KIPAC à l'Université de Stanford. Laurence Perreault-Levasseur a obtenu un doctorat de l'Université de Cambridge, où elle a travaillé sur les applications des méthodes de la théorie des champs effectifs ouverts au formalisme de l'inflation. Elle est titulaire d'une licence et d'une maîtrise en sciences de l'Université McGill.

Étudiants actuels

Doctorat - UdeM

Tara Akhound-Sadegh

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siamak Ravanbakhsh

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

andreas.filipp@umontreal.ca

Maîtrise recherche - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Michael Matesic

Doctorat - UdeM

Guillaume Payeur

Collaborateur·rice de recherche - UdeM

guillaume.payeur@mail.mcgill.ca

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Sacha Perry-Fagant

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Sammy Nasser Sharief

Maîtrise recherche - UdeM

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - McGill

Co-superviseur⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Publications

Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra

C. L. Rhea

J. Hlavacek-Larrondo

Ralph P. Kraft

Ákos Bogdán

Alexandre Adam

Laurence Perreault-Levasseur

2023-11-29

ArXiv (prépublication)

The search for the lost attractor

Mario Pasquato

Syphax Haddad

Pierfrancesco Di Cintio

Alexandre Adam

Pablo Lemos

No'e Dia

Mircea Petrache

Ugo Niccolo Di Carlo

Alessandro A. Trani

Laurence Perreault-Levasseur

2023-11-27

ArXiv (prépublication)

Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise

Ronan Legin

Alexandre Adam

Laurence Perreault-Levasseur

Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.

2023-11-03

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

Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models

Ronan Legin

Matthew Ho

Pablo Lemos

Laurence Perreault-Levasseur

Shirley Ho

Benjamin Wandelt

2023-10-13

Monthly Notices of the Royal Astronomical Society: Letters (publié)

Time Delay Cosmography with a Neural Ratio Estimator

Eve Campeau-Poirier

Laurence Perreault-Levasseur

Adam Coogan

We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant (…

2023-09-27

ArXiv (prépublication)

Lie Point Symmetry and Physics-Informed Networks

Tara Akhound-Sadegh

Laurence Perreault-Levasseur

Johannes Brandstetter

Max Welling

Siamak Ravanbakhsh

Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equiv… (voir plus)ariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplored. We explore the integration of PDE symmetries, known as Lie point symmetries, in a major family of neural solvers known as physics-informed neural networks (PINNs). We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function. Intuitively, our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions.. Effectively, this means that once the network learns a solution, it also learns the neighbouring solutions generated by Lie point symmetries. Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.

AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

Connor J Stone

Stéphane Courteau

Jean-Charles Cuillandre

Laurence Perreault-Levasseur

Nikhil Arora

2023-08-16

Monthly Notices of the Royal Astronomical Society (publié)

Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey

Aritra Ghosh

C. Urry

Aayush Mishra

Laurence Perreault-Levasseur

P. Natarajan

D. Sanders

Daisuke Nagai

Chuan Tian

Nico Cappelluti

J. Kartaltepe

M. Powell

Amrit Rau

Ezequiel Treister

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for ∼… (voir plus)8 million galaxies in the Hyper Suprime-Cam Wide survey with z ≤ 0.75 and m ≤ 23. GaMPEN is a machine-learning framework that estimates Bayesian posteriors for a galaxy’s bulge-to-total light ratio (L B /L T ), effective radius (R e ), and flux (F). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with 1% of our data set. This two-step process will be critical for applying machine-learning algorithms to future large imaging surveys, such a

2023-08-14

The Astrophysical Journal (publié)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

Laurence Perreault-Levasseur

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines

Alexandre Adam

Laurence Perreault-Levasseur

Max Welling

Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass densit… (voir plus)y in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.

2023-06-27

The Astrophysical Journal (publié)

Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise

Ronan Legin

Alexandre Adam

Laurence Perreault-Levasseur

2023-06-06

The Astrophysical Journal Letters (publié)

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Pablo Lemos

Adam Coogan

Laurence Perreault-Levasseur

2023-04-24

ICML.cc/2023/Conference (poster)