Conference item
Generative models as distributions of functions
- Abstract:
-
Generative models are typically trained on grid-like data such as images. As a result, the size of these models usually scales directly with the underlying grid resolution. In this paper, we abandon discretized grids and instead parameterize individual data points by continuous functions. We then build generative models by learning distributions over such functions. By treating data points as functions, we can abstract away from the specific type of data we train on and construct models that ...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Version of record, pdf, 9.2MB)
-
- Publication website:
- https://proceedings.mlr.press/v151/dupont22a.html
Authors
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 151
- Pages:
- 2989-3015
- Publication date:
- 2022-05-03
- Acceptance date:
- 2022-01-18
- Event title:
- 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022)
- Event location:
- Virtual event
- Event website:
- https://aistats.org/aistats2022/
- Event start date:
- 2022-03-28
- Event end date:
- 2022-03-30
- ISSN:
-
2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1243097
- Local pid:
- pubs:1243097
- Deposit date:
- 2022-12-08
Terms of use
- Copyright holder:
- Dupont et al.
- Copyright date:
- 2022
- Rights statement:
- Copyright 2021 by the author(s).
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record