1 code implementation • 9 Feb 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
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
1 code implementation • 7 Feb 2024 • Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function.
no code implementations • 6 Feb 2024 • Pablo Lemos, Sammy Sharief, Nikolay Malkin, Laurence Perreault-Levasseur, Yashar Hezaveh
The proposed approach enables the estimation of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset.
1 code implementation • 6 Feb 2024 • Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
1 code implementation • 6 Dec 2023 • Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur
We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization.
no code implementations • 29 Nov 2023 • Noe Dia, M. J. Yantovski-Barth, Alexandre Adam, Micah Bowles, Pablo Lemos, Anna M. M. Scaife, Yashar Hezaveh, Laurence Perreault-Levasseur
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner.
no code implementations • 23 Oct 2023 • Pablo Lemos, Liam Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Regaldo-Saint Blancard, David Spergel
We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset.
no code implementations • 20 Oct 2023 • Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
1 code implementation • 6 Feb 2023 • Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur
We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces.
1 code implementation • 30 Jan 2023 • Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.
1 code implementation • 18 Jul 2022 • Pablo Lemos, Miles Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
1 code implementation • 11 Jul 2022 • T. Lucas Makinen, Tom Charnock, Pablo Lemos, Natalia Porqueres, Alan Heavens, Benjamin D. Wandelt
We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference.
1 code implementation • 25 May 2022 • Harry T. J. Bevins, William J. Handley, Pablo Lemos, Peter H. Sims, Eloy de Lera Acedo, Anastasia Fialkov, Justin Alsing
Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic Dawn among other phenomena.
no code implementations • 23 May 2022 • David Yallup, Will Handley, Mike Hobson, Anthony Lasenby, Pablo Lemos
The true posterior distribution of a Bayesian neural network is massively multimodal.
no code implementations • 4 Feb 2022 • Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia
We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation.