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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...
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...
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