Augmenting Transfer Learning with Semantic Reasoning
- Submitting institution
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University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 149528341
- Type
- E - Conference contribution
- DOI
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10.24963/ijcai.2019/246
- Title of conference / published proceedings
- Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI2019)
- First page
- 1779
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- 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
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper on applying ontology reasoning for improving transfer learning by suggesting when and what to transfer. Previous works are either limited by the expressivity, or not able to ensure positive domain transfer. This work has been shown to be robust to both intra-domain transfer learning, including air quality forecasting from Beijing to Hangzhou and bus delay prediction from London to Dublin, and inter-domain transfer learning tasks, such as from bus delay prediction in London to air quality prediction in Beijing.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
