Chaitanya Tumuluri

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This paper integrates the evidential reasoning methodology with the parallel distributed learning paradigm of artificial neural networks (ANN). As such, this work presents an algorithm for the detection and, if possible, subsequent correction of the errors in the neuron responses in the output layer of the multiple adaptive linear element (MADALINE) ANN. A(More)
This paper presents two novel (GIST and GEST) networks, which combine unsupervised featureextraction and Hebbian learning, for tracking emergent correlations in the evolution of spatiotemporal distributions. The networks were successfully tested on the challenging Data Mapping problem, using an execution driven simulation of their implementation in hardware.
Traditionally, in distributed memory architectures, locality maintenance and load balancing are seen as user level activities involving compiler and runtime system support in software. Such software solutions require an explicit phase of execution, requiring the application to suspend its activities. This paper presents the rst (to our knowledge)(More)
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