Approaching limitations of digital computing technologies have spurred research in
neuromorphic and other unconventional approaches to computing. Here we argue that if we …

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural
networks (ANNs) have exploited biological properties to solve complex problems. However …

We consider restricted Boltzmann machine (RBMs) trained over an unstructured dataset
made of blurred copies of definite but unavailable “archetypes” and we show that there …

In this work we approach attractor neural networks from a machine learning perspective: we
look for optimal network parameters by applying a gradient descent over a regularized loss …

Dense Associative Memories or Modern Hopfield Networks have many appealing properties
of associative memory. They can do pattern completion, store a large number of memories …

We consider dense, associative neural-networks trained by a teacher (ie, with supervision)
and we investigate their computational capabilities analytically, via statistical-mechanics …

Hopfield networks are artificial neural networks which store memory patterns on the states of
their neurons by choosing recurrent connection weights and update rules such that the …

The recent progresses in Machine Learning opened the door to actual applications of
learning algorithms but also to new research directions both in the field of Machine Learning …

Abstract Restricted Boltzmann machines (RBMs) with a binary visible layer of size N and a
Gaussian hidden layer of size P have been proved to be equivalent to a Hopfield neural …

Understanding the glassy nature of neural networks is pivotal both for theoretical and
computational advances in Machine Learning and Theoretical Artificial Intelligence. Keeping …