Mohsin Hasan

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 … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-09-25

NeurIPS.cc/2024/Conference (poster)

doi.org

openreview.net

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 … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

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 … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

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 … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

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 … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

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 … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-05-31

ArXiv (prépublication)

doi.org

arxiv.org

Estimating Expectations without Sampling: Neural Stein Estimation

Mohsin Hasan

Dinghuai Zhang

Cheikh Ahmed

Awa Khouna

Yoshua Bengio

We propose a method for estimating the expected value of a given function …

2024-05-27