Conference Paper

Improvements to the Sequence Memoizer.

Conference: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada.
Source: DBLP
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    ABSTRACT: We present a general-purpose, lossless compressor for streaming data. This compressor is based on the deplump probabilistic compressor for batch data. Approximations to the inference procedure used in the probabilistic model underpinning deplump are introduced that yield the computational asyptotics necessary for stream compression. We demonstrate the performance of this streaming deplump variant relative to the batch compressor on a benchmark corpus and find that it performs equivalently well despite these approximations. We also explore the performance of the streaming variant on corpora that are too large to be compressed by batch deplump and demonstrate excellent compression performance.
    2011 Data Compression Conference (DCC 2011), 29-31 March 2011, Snowbird, UT, USA; 01/2011
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    ABSTRACT: We interpret results from a study where data was modeled using constant space approxi-mations to the sequence memoizer. The sequence memoizer (SM) is a non-constant-space, Bayesian nonparametric model in which the data are the sufficient statistic in the stream-ing setting. We review approximations to the probabilistic model underpinning the SM that yield the computational asymptotic complexities necessary for modeling very large (streaming) datasets with fixed computational resource. Results from modeling a benchmark corpus are shown for both the effectively parametric, approximate models and the fully nonparametric SM. We find that the approximations perform nearly as well in terms of predictive likelihood. We argue from this single example that, due to the lack of sufficiency, Bayesian nonparametric models may, in general, not be suitable as models of streaming data, and propose that nonstationary parametric models and estimators for the same inspired by Bayesian nonparametric models may be worth investigating more fully.
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    ABSTRACT: Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations can capture only one-or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF (CRF 1) model is experimentally demonstrated.


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