research-article

Free access

The sequence memoizer

Authors:

Cédric Archambeau,

Lancelot James,

Yee Whye TehAuthors Info & Claims

Communications of the ACM, Volume 54, Issue 2

Pages 91 - 98

https://doi.org/10.1145/1897816.1897842

Published: 01 February 2011 Publication History

All formats PDF

Abstract

Probabilistic models of sequences play a central role in most machine translation, automated speech recognition, lossless compression, spell-checking, and gene identification applications to name but a few. Unfortunately, real-world sequence data often exhibit long range dependencies which can only be captured by computationally challenging, complex models. Sequence data arising from natural processes also often exhibits power-law properties, yet common sequence models do not capture such properties. The sequence memoizer is a new hierarchical Bayesian model for discrete sequence data that captures long range dependencies and power-law characteristics, while remaining computationally attractive. Its utility as a language model and general purpose lossless compressor is demonstrated.

References

[1]

Bartlett, N., Pfau, D., Wood, F. Forgetting counts: Constant memory inference for a dependent hierarchical Pitman--Yor process. In 27th International Conference on Machine Learning, to appear (2010).

[2]

Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C. A neural probabilistic language model. J. Mach. Learn. Res. 3 (2003), 1137--1155.

Digital Library

[3]

Chen, S.F., Goodman, J. An empirical study of smoothing techniques for language modeling. Comput. Speech Lang. 13, 4 (1999), 359--394.

Digital Library

[4]

Cleary, J.G., Teahan, W.J. Unbounded length contexts for PPM. Comput. J. 40 (1997), 67--75.

[5]

Doucet, A., de Freitas, N., Gordon, N.J. Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer-Verlag, New York, May 2001.

[6]

Gasthaus, J., Teh, Y.W. Improvements to the sequence memoizer. In Advances in Neural Information Processing Systems 23, to appear (2010).

[7]

Gasthaus, J., Wood, F., Teh, Y.W. Lossless compression based on the sequence memoizer. Data Compression Conference 2010. J.A. Storer, M.W. Marcellin, eds. Los Alamitos, CA, USA, 2010, 337--345. IEEE Computer Society.

Digital Library

[8]

Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B. Bayesian data analysis. Chapman & Hall, CRC, 2nd edn, 2004.

[9]

Giegerich, R., Kurtz, S. From Ukkonen to McCreight and Weiner: A unifying view of linear-lime suffix tree construction. Algorithmica 19, 3 (1997), 331--353.

[10]

Goldwater, S., Griffiths, T.L., Johnson, M. Interpolating between types and tokens by estimating power law generators. In Advances in Neural Information Processing Systems 18 (2006), MIT Press, 459--466.

[11]

MacKay, D.J.C., Peto, L.B. A hierarchical Dirichlet language model. Nat. Lang. Eng. 1, 2 (1995), 289--307.

[12]

Mahoney, M. Large text compression benchmark. URL: http://www.mattmahoney.net/text/text.html (2009).

[13]

Mnih, A., Yuecheng, Z., Hinton, G. Improving a statistical language model through non-linear prediction. Neurocomputing 72, 7--9 (2009), 1414--1418.

Digital Library

[14]

Pitman, J. Coalescents with multiple collisions. Ann. Probab. 27 (1999), 1870--1902.

[15]

Pitman, J., Yor, M. The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator. Ann. Probab. 25 (1997), 855--900.

[16]

Robert, C.P., Casella, G. Monte Carlo Statistical Methods. Springer Verlag, 2004.

[17]

Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J. (reprinted in ACM SIGMOBILE Mobile Computing and Communications Review 2001) (1948).

Digital Library

[18]

Teh, Y.W. A hierarchical Bayesian language model based on Pitman-Yor processes. In Proceedings of the Association for Computational Linguistics (2006), 985--992.

Digital Library

[19]

Willems, F.M.J. The context-tree weighting method: Extensions. IEEE Trans. Inform. Theory 44, 2 (1998), 792--798.

Digital Library

[20]

Willems, F.M.J. CTW website. URL: http://www.ele.tue.nl/ctw/ (2009).

[21]

Wood, F., Archambeau, C., Gasthaus, J., James, L., Teh, Y.W. A stochastic memoizer for sequence data. In 26th International Conference on Machine Learning (2009), 1129--1136.

Digital Library

[22]

Wu, P., Teahan, W.J. A new PPM variant for Chinese text compression. Nat. Lang. Eng. 14, 3 (2007), 417--430.

Digital Library

[23]

Zipf, G. Selective Studies and the Principle of Relative Frequency in Language. Harvard University Press, Cambridge, MA, 1932.

Cited By

James L(2022)Single-Block Recursive Poisson–Dirichlet Fragmentations of Normalized Generalized Gamma ProcessesMathematics10.3390/math1004056110:4(561)Online publication date: 11-Feb-2022
https://doi.org/10.3390/math10040561
Murali RPatnaik SCranefield SEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Mining International Political Norms from the GDELT DatabaseProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3399035(1943-1945)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3399035
Antoniano-Villalobos IBorgonovo ELu X(2020)Nonparametric estimation of probabilistic sensitivity measuresStatistics and Computing10.1007/s11222-019-09887-930:2(447-467)Online publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1007/s11222-019-09887-9
Show More Cited By

Index Terms

The sequence memoizer
1. Computing methodologies
2. Mathematics of computing
  1. Probability and statistics

Recommendations

Improvements to the sequence memoizer
NIPS'10: Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1

The sequence memoizer is a model for sequence data with state-of-the-art performance on language modeling and compression. We propose a number of improvements to the model and inference algorithm, including an enlarged range of hyperparameters, a memory-...
Zero-Correlation Zone Sequence Set Constructed from a Perfect Sequence and a Complementary Sequence Pair

The present paper introduces the construction of a class of sequence sets with zero-correlation zones called zero-correlation zone sequence sets. The proposed zero-correlation zone sequence set can be generated from an arbitrary perfect sequence and an ...
Binary Zero-Correlation Zone Sequence Set Constructed from an M-Sequence

The present paper introduces an improved construction of a class of binary sequences having a zero-correlation zone (hereafter binary ZCZ sequence set). The cross-correlation function and the side-lobe of the auto-correlation function of the proposed ...

Comments

Information & Contributors

Information

Published In

cover image Communications of the ACM

Communications of the ACM Volume 54, Issue 2

February 2011

115 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1897816

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 February 2011

Published in CACM Volume 54, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Popular
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
6,758
Total Downloads

Downloads (Last 12 months)298
Downloads (Last 6 weeks)17

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

James L(2022)Single-Block Recursive Poisson–Dirichlet Fragmentations of Normalized Generalized Gamma ProcessesMathematics10.3390/math1004056110:4(561)Online publication date: 11-Feb-2022
https://doi.org/10.3390/math10040561
Murali RPatnaik SCranefield SEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Mining International Political Norms from the GDELT DatabaseProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3399035(1943-1945)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3399035
Antoniano-Villalobos IBorgonovo ELu X(2020)Nonparametric estimation of probabilistic sensitivity measuresStatistics and Computing10.1007/s11222-019-09887-930:2(447-467)Online publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1007/s11222-019-09887-9
Camerlenghi FLijoi APrünster I(2020)Bayesian Nonparametric Prediction with Multi-sample DataNonparametric Statistics10.1007/978-3-030-57306-5_11(113-121)Online publication date: 12-Nov-2020
https://doi.org/10.1007/978-3-030-57306-5_11
Lawless CArbel J(2019)A simple proof of Pitman–Yor’s Chinese restaurant process from its stick-breaking representationDependence Modeling10.1515/demo-2019-00037:1(45-52)Online publication date: 8-Mar-2019
https://doi.org/10.1515/demo-2019-0003
Camerlenghi FLijoi AOrbanz PPrünster I(2019)Distribution theory for hierarchical processesThe Annals of Statistics10.1214/17-AOS167847:1Online publication date: 1-Feb-2019
https://doi.org/10.1214/17-AOS1678
Camerlenghi FLijoi APrünster I(2018)Bayesian nonparametric inference beyond the Gibbs‐type frameworkScandinavian Journal of Statistics10.1111/sjos.1233445:4(1062-1091)Online publication date: 21-May-2018
https://doi.org/10.1111/sjos.12334
Kleiman-Weiner MTenenbaum JZhou P(2018)Non-parametric Bayesian inference of strategies in repeated gamesThe Econometrics Journal10.1111/ectj.1211221:3(298-315)Online publication date: 13-Sep-2018
https://doi.org/10.1111/ectj.12112
Petitjean FBuntine WWebb GZaidi N(2018)Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processesMachine Language10.1007/s10994-018-5718-0107:8-10(1303-1331)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10994-018-5718-0
Messias JWhiteson S(2017)Dynamic-depth context tree weightingProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3294996.3295092(3330-3339)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.5555/3294996.3295092
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents