End-to-end Differentiable Proving
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14280
- Type
- E - Conference contribution
- DOI
-
-
- Title of conference / published proceedings
- Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA
- First page
- 3791
- Volume
- 30
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- 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
-
1
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Out of 3240 submissions, this paper has been selected as one of 40 papers for oral presentation at the Neural Information Processing Systems (NIPS) conference in 2017 (1.2% acceptance rate). It is one of the first papers providing a principled way of combining deep and symbolic learning and has since lead to various works in the community that build upon the approach for natural language processing, computer vision and reinforcement learning problems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -