NDLI: A Predictive Coding Framework for Learning to Predict Changes in Streaming Data

Content Provider	IEEE Xplore Digital Library
Author	Banerjee, B. Dutta, J.K.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Memphis, Memphis, TN, USA (Banerjee, B.; Dutta, J.K.)
Abstract	Streaming sensorial data poses major computational challenges, such as, lack of storage, inapplicability of offline algorithms, and the necessity to capture nonstationary data distributions with concept drifts. Our goal is to build a learner framework that uses the current data and the knowledge from historical data to predict the next data in an efficient, unsupervised and online manner. Labeled streaming data is scarce, hence prediction of data instead of labels is a more realistic problem. We present a learner model, called SELP, for learning in variances as features from explanations of surprises due to prediction errors in streaming spatiotemporal data. This model runs a relentless cycle of Surprise → Explain → Learn → Predict involving the real external world and its internal model. The learner is continuously updated, independent of a trigger, proportional to its surprise. It implements a more efficient version of predictive coding, a form of biologically-plausible information coding paradigm, by predicting changes in the data instead of the data itself. Experimental results obtained from deploying our implementation on synthesized and real-world data are qualitatively comparable to that of traditional predictive coding on similar data sets. The results also offer insights into the learner design. This research lays out the foundations for an agent-based framework with an internal model grounded to the data stream.
Sponsorship	Toshiba
Starting Page	497
Ending Page	504
File Size	1004046
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.134
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	explain surprise Computational modeling concept drift Neurons Predictive models Predictive coding Prediction algorithms Data models Feedforward neural networks generative model
Content Type	Text
Resource Type	Article

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