If Neural Networks Are Allowed To Sleep And Dream, Their Performance Sensibly Increases
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About The Author

Adriano is a senior researcher in Mathematical Physics at the University of Salento. He specializes in statistical mechanics, artificial intelligence, and biological complexity.

                       

If Neural Networks Are Allowed To Sleep And Dream, Their Performance Sensibly Increases

The harmonic oscillator for associative memory and pattern recognition in Artificial Intelligence is certainly the Hopfield model (or, equivalently , its dual representation, i.e. the Restricted Boltzmann Machine (RBM) ). In a nutshell, we can store information (consisting in a set of P digital words or -generally speaking- patterns of information) by suitably modifying the synaptic interactions among neurons in the Hopfield neural network by means of the so-called Hebbian learning (or by using contrastive divergence algorithms for training...

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A New Mathematical Tool For Artificial Intelligence Borrowed From Physics

This research aims to increase our understanding  and our mathematical control of "natural" (i.e."spontaneous/emergent") information processing skills shown by Artificial Intelligence (AI), namely by neural networks and learning machines. Indeed AI is experiencing a "magic moment" as finally theorists have been overwhelmed by "big data" that can be used to train these networks and  we can check their capabilities concretely. Among a plethora of variations on theme, in particular a bulk of algorithms overall termed "Deep Learning" is showing impressive successes...

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