The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A spectral algorithm for learning Hidden Markov Models
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177697196 · Cited by 4,297
Elements of Information Theory
Jacobs, K. (1992) · Discrete Stochastics · doi:10.1007/978-3-0348-8645-1_7 · Cited by 2,918
Subspace Identification for Linear Systems
Van Overschee, P., & De Moor, B. (1996) · Springer eBooks · doi:10.1007/978-1-4613-0465-4 · Cited by 2,586
(1991) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(91)90038-5 · Cited by 2,437
On the method of bounded differences
McDiarmid, C. (1989) · Surveys in Combinatorics, 1989 · doi:10.1017/cbo9781107359949.008 · Cited by 1,425
Baum, L. E., & Eagon, J. A. (1967) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1967-11751-8 · Cited by 1,036
Cited by
Tensor decompositions for learning latent variable models
(2012) · arXiv (Cornell University) · doi:10.5555/2627435.2697055 · Cited by 779
Self-citationKernel Mean Embedding of Distributions: A Review and Beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., & Schölkopf, B. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000060 · Cited by 409
A Latent Variable Model Approach to PMI-based Word Embeddings
Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00106 · Cited by 286
Causal Effect Inference with Deep Latent-Variable Models
Louizos, C., Shalit, U., Mooij, J. M., Sontag, D. A., Zemel, R. S., & Welling, M. (2017) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1705.08821 · Cited by 267
Rodosthenous, T., Shahrezaei, V., & Evangelou, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa530 · Cited by 67
Metadata
- Authors
- Hsu, D., Kakade, S. M., & Zhang, T.
- Venue
- Journal of Computer and System Sciences
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 78
- Issue
- 5
- Pages
- 1460-1480
- ISSN
- 0022-0000 (ISSN-L)
- OpenAlex
- W1825640910
- Cited by
- 278
- References
- 32
- FWCI
- 28.81
- Type
- journal article
- Language
- en
- Open access
- bronze
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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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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.
