Doubly Stochastic Variational Inference for Deep Gaussian Processes
- Submitting institution
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University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14717
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017)
- First page
- 4591
- Volume
- 30
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Technical exception
- Month of publication
- December
- Year of publication
- 2017
- URL
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-
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published at top-tier machine learning conference (acceptance rate 22%); considered the state of the art in efficient and scalable learning and inference in deep Gaussian processes; allows for hierarchical probabilistic representation learning, which will be critical for safe AI where guarantees are required to deploy bigger AI systems; contributed greatly to the understanding of deep Gaussian processes and forms the basis of many follow-up papers (by other research groups) in the field (e.g., https://arxiv.org/abs/1905.03350, https://link.springer.com/chapter/10.1007/978-3-030-46147-8_35) and applications in climate science (https://arxiv.org/abs/1903.07320).
- Author contribution statement
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- Non-English
- No
- English abstract
- -