Portrait de Pablo Lemos

Pablo Lemos

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Laurence Perreault-Levasseur
Co-supervisor
Yashar Hezaveh
Sujets de recherche
Biologie computationnelle
Modèles génératifs
Modèles probabilistes
Site web
Google Scholar
GitHub

Publications

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
Yashar Hezaveh
Benjamin Wandelt
2023-10-13
Monthly Notices of the Royal Astronomical Society: Letters (publié)
doi.org
arxiv.org
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pablo Lemos
Adam Coogan
Yashar Hezaveh
Laurence Perreault-Levasseur
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pablo Lemos
Adam Coogan
Yashar Hezaveh
Laurence Perreault-Levasseur
2023-04-24
ICML.cc/2023/Conference (poster)
doi.org
openreview.net
A theory of continuous generative flow networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (voir plus)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.
2023-01-30
ArXiv (prépublication)
doi.org
arxiv.org
A theory of continuous generative flow networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
Nikolay Malkin
2023-01-01
ICML (publié)
doi.org
openreview.net
A theory of continuous generative flow networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (voir plus)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.
2023-01-01
ICML (publié)
doi.org
openreview.net