The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling annotated data
Abstract
We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in correspondence latent Dirichlet allocation, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type. We conduct experiments on the Corel database of images and captions, assessing performance in terms of held-out likelihood, automatic annotation, and text-based image retrieval.
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Citations
Cites
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
Self-citationParametric empirical Bayes inference: theory and applications
Morris, C. N. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477920 · Cited by 1,448 · cited 1×
“With K draws from the prior distribution, however, we are in a good position to take the empirical Bayes perspective [9] and compute a maximum likelihood estimate of η.”
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Cited by
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Self-citationFraming Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,314
Blei, D. M., & McAuliffe, J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.0783 · Cited by 1,259
Self-citationIdentifying miRNAs, targets and functions
Liu, B., Li, J., & Cairns, M. J. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbs075 · Cited by 563
Metadata
- Authors
- Blei, D. M., & Jordan, M. I.
- Venue
- Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
- Publisher
- ACM
- Published
- 2003-07-28
- Pages
- 127-134
- OpenAlex
- W2020842694
- Cited by
- 1,054
- References
- 2
- FWCI
- 28.38
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. https://doi.org/10.1145/860435.860460
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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.
