Upcoming Events

Parallel Bio-Inspired Methods for Model Optimization and Pattern Recognition

August 23, 2013 10:30AM to 11:30AM
Presenter 
Youssef Nashed, Postdoc Interviewee
Location 
Building 240, Room 1404-1405
Type 
Seminar
Series 
Abstract:
Nature based computational models are usually inherently parallel. The collaborative intelligence in those models emerges from the simultaneous instruction processing by simple independent units (neurons, ants, swarm members, etc...). This talk will present the benefits of such parallel models in terms of efficiency and accuracy. First, the viability of a parallel implementation of bio-inspired meta-heuristics for function optimization on consumer level graphic cards is described in detail. Then, in an effort to expose those parallel models to the research community, the meta-heuristic implementations were abstracted and grouped in an open source parameter/function optimization library, libCudaOptimize. This library was proven effective in various applications from different fields. Varying from detecting pathological structures in medical images to finding traffic signs in videos obtained from autonomous vehicles. It has also shown significant gains in both execution time and optimization accuracy, thanks to its parallel implementation.

The talk will also present a novel parallel model of the human neocortex. This model, termed HQSOM, is able to detect, classify, and predict patterns in time-series data, in an unsupervised way. Experimental results for a gesture recognition task show promising results on videos acquired by the Microsoft Kinect sensor. Moreover, the model was tested successfully on different modalities of data, ranging from audio signals, to handwritten digits classification problems.