Recently a research trend of learning algorithms by means of deep learning techniques has started. Most of these are different implementations of the controller-interface abstraction: they use a neural controller as a “processor" and provide different interfaces for input, output and memory management. In this trend, we consider of particular interest the Neural Random-Access Machines, called NRAM, because this model is also able to solve problems which require indirect memory references. In this paper we propose a version of the Neural Random-Access Machines, where the core neural controller is trained with Differential Evolution meta-heuristic instead of the usual backpropagation algorithm. Some experimental results showing that this approach is effective and competitive are also presented.
Neural Random Access Machines Optimized by Differential Evolution
Baioletti M.;Belli V.;Di Bari G.;Poggioni V.
2018
Abstract
Recently a research trend of learning algorithms by means of deep learning techniques has started. Most of these are different implementations of the controller-interface abstraction: they use a neural controller as a “processor" and provide different interfaces for input, output and memory management. In this trend, we consider of particular interest the Neural Random-Access Machines, called NRAM, because this model is also able to solve problems which require indirect memory references. In this paper we propose a version of the Neural Random-Access Machines, where the core neural controller is trained with Differential Evolution meta-heuristic instead of the usual backpropagation algorithm. Some experimental results showing that this approach is effective and competitive are also presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
