Conference item
Auto-encoding sequential Monte Carlo
- Abstract:
- We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models. Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in structured probabilistic models and the flexibility of deep neural networks to model complex conditional probability distributions. We develop additional theoretical insights and introduce a new training procedure which improves both model and proposal learning. We demonstrate that our approach provides a fast, easy-to-implement and scalable means for simultaneous model learning and proposal adaptation in deep generative models.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.3MB, Terms of use)
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Authors
- Publisher:
- OpenReview
- Host title:
- Sixth International Conference on Learning Representations (ICLR), Vancouver Canada, 30th April - 3rd May, 2018
- Journal:
- International Conference on Learning Representations (ICLR) More from this journal
- Publication date:
- 2018-02-15
- Acceptance date:
- 2018-01-29
- Pubs id:
-
pubs:959092
- UUID:
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uuid:3c35d146-4402-40d4-ae1c-1e1c3d09b17a
- Local pid:
-
pubs:959092
- Deposit date:
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2019-01-11
- ARK identifier:
Terms of use
- Copyright date:
- 2018
- Notes:
- This is the publisher's version of the article. The final version is available online from OpenReview at: https://openreview.net/forum?id=BJ8c3f-0b¬eId=BJ8c3f-0b
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