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We consider an attention-based model that recognizes objects via a sequence of glimpses, and analyze the variation in classification accuracy with the number of glimpses. The problem of object recognition is formulated as a partially observable Markov decision process where the environment is partially observable and glimpses are actions. We show that voting from random attentional policies provides...
Outlier detection has been an active area of research for a few decades. We propose a new definition of outlier that is useful for high-dimensional data. According to this definition, given a dictionary of atoms learned using the sparse coding objective, the outlierness of a data point depends jointly on two factors: the frequency of each atom in reconstructing all data points (or its negative log...
Learning features invariant to arbitrary transformations in the data is a requirement for any recognition system, biological or artificial. It is now widely accepted that simple cells in the primary visual cortex respond to features while the complex cells respond to features invariant to different transformations. We present a novel two-layered feedforward neural model that learns features in the...
The detection of abnormal/unusual events based on dynamically varying spatial data has been of great interest in many real world applications. It is a challenging task to detect abnormal events as they occur rarely and it is very difficult to predict or reconstruct them. Here we address the issue of the detection of propagating phase gradient in the sequence of brain images obtained by EEG arrays...
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...
Surveillance sensors are a major source of unstructured Big Data. Discovering and recognizing spatiotemporal objects (e.g., events) in such data is of paramount importance to the security and safety of facilities and individuals. Hierarchical feature learning is at the crux to the problems of discovery and recognition. We present a multilayered convergent neural architecture for storing repeating...
Surveillance sensors are a major source of unstructured Big Data. Discovering and recognizing spatiotemporal objects (e.g., events) in such data is of paramount importance to the security and safety of facilities and individuals. What kind of computational model is necessary for discovering spatiotemporal objects at the level of abstraction they occur? Hierarchical invariant feature learning is the...
Streaming data from different kinds of sensors contributes to Big Data in a significant way. Recognizing the norms and abnormalities in such spatiotemporal data is a challenging problem. We present a general-purpose biologically-plausible computational model, called SELP, for learning the norms or invariances as features in an unsupervised and online manner from explanations of saliencies or surprises...
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