Deep learning with differential Gaussian process flows
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 173952934
- Type
- E - Conference contribution
- DOI
-
-
- Title of conference / published proceedings
- Proceedings of Machine Learning Research: Artificial Intelligence and Statistics 2019
- First page
- 1812
- Volume
- -
- Issue
- -
- ISSN
- 2640-3498
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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A - Computer Science
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "First work to enable a Bayesian treatment of uncertainty in a differential equation formalism to generalising deep neural networks from discrete layers to continuous transformations.
This paper received the Notable Paper award at AISTATS 2019 - indicating it was the top 1% of accepted papers.
AISTATS 2019: 360 papers were accepted from 1111 submissions (32%)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
