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Proceedings of the 26th Annual International Conference on Machine Learning
Abstract
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a model of the data's probability density can assist in identifying clusters. ...
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Cited by
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Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665
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Cai, J., Luo, J., Wang, S., & Yang, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.11.077 · Cited by 2,061
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Metadata
- Publisher
- ACM
- Published
- 2009-06-14
- OpenAlex
- W4214740783
- Cited by
- 399
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Proceedings of the 26th Annual International Conference on Machine Learning. https://app.cochat.ai/papers/10.1145/1553374
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- Proceedings of the 26th Annual International Conference on Machine Learning. (2009). https://doi.org/10.1145/1553374
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
