Focused quantization for sparse CNNs
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9088
- Type
- E - Conference contribution
- DOI
-
-
- Title of conference / published proceedings
- Advances in Neural Information Processing Systems
- First page
- 5585
- Volume
- 32
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Compliant
- Month of publication
- January
- 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
-
4
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- To the best of our knowledge, this paper still offers state-of-the-art results in terms of model size and accuracy for ResNets (neural networks for image classification). Work such as this is important in reducing the cost of machine-learning techniques and expanding the application areas that they can be applied to.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -