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The primary aim of this work is to provide new tools for machine learning and reasoning within a framework of computing with holistic data representations. Specifically, we demonstrate recursive construction of mappings and functions in high-dimensional computing with random vectors — a connectionist computing model which provides benefits similar to neural networks, but allows compositional learning...
Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization...
This paper introduces a novel design of phase locked loop (PLL) based oscillatory neural networks (ONNs) to mitigate the frequency clustering phenomenon caused by transmission delays in real systems. Theoretical analysis of the ONN reveals that transmission delays can produce frequency clustering that leads to synchronization and convergence failure. This paper describes the redesign of ONN dynamics...
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