Conference Paper
Improvements to the Sequence Memoizer.
In proceeding of: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 69 December 2010, Vancouver, British Columbia, Canada.
Source: DBLP

Conference Paper: Deplump for Streaming Data.
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ABSTRACT: We present a generalpurpose, 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), 2931 March 2011, Snowbird, UT, USA; 01/2011  [Show abstract] [Hide abstract]
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 loglinear 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 oneor fewtimestep interactions and neglect higher order dependences, which are potentially useful in many reallife 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 timedependences 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 meanfieldlike approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the soobtained infiniteorder CRF (CRF 1) model is experimentally demonstrated.· 4.80 Impact Factor
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