Alexandre Adam

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

doi.org

arxiv.org

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

doi.org

arxiv.org

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

doi.org

arxiv.org

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

doi.org

arxiv.org

Amortizing intractable inference in diffusion models for vision, language, and control

Moksh J. Jain

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (see more)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (preprint)

doi.org

arxiv.org

Improved off-policy training of diffusion samplers

We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (see more)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.

2024-02-07

ArXiv (preprint)

arxiv.org

Improved off-policy training of diffusion samplers

We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (see more)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.

2024-02-07

ArXiv (preprint)

arxiv.org

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

Yashar Hezaveh

Laurence Perreault-Levasseur

2024-01-01

J. Open Source Softw. (published)

doi.org

arxiv.org

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling

2024-01-01

arXiv.org (preprint)

doi.org

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling

We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (see more)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.

2024-01-01

NeurIPS (published)

doi.org

arxiv.org

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife

Yashar Hezaveh