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Found 13 papers, 0 papers with code
Parallel Learning by Multitasking Neural Networks
A modern challenge of Artificial Intelligence is learning multiple patterns at once (i. e. parallel learning).
Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting
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 function.
Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning
We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations.
Dense Hebbian neural networks: a replica symmetric picture of supervised learning
We consider dense, associative neural-networks trained by a teacher (i. e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations.
Pavlov Learning Machines
As well known, Hebb's learning traces its origin in Pavlov's Classical Conditioning, however, while the former has been extensively modelled in the past decades (e. g., by Hopfield model and countless variations on theme), as for the latter modelling has remained largely unaddressed so far; further, a bridge between these two pillars is totally lacking.
Recurrent neural networks that generalize from examples and optimize by dreaming
The gap between the huge volumes of data needed to train artificial neural networks and the relatively small amount of data needed by their biological counterparts is a central puzzle in machine learning.
Supervised Hebbian Learning
In neural network's Literature, Hebbian learning traditionally refers to the procedure by which the Hopfield model and its generalizations store archetypes (i. e., definite patterns that are experienced just once to form the synaptic matrix).
The emergence of a concept in shallow neural networks
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 exists a critical sample size beyond which the RBM can learn archetypes, namely the machine can successfully play as a generative model or as a classifier, according to the operational routine.
The relativistic Hopfield model with correlated patterns
In this work we introduce and investigate the properties of the "relativistic" Hopfield model endowed with temporally correlated patterns.
Mathematical Physics Mathematical Physics
A statistical-inference approach to reconstruct inter-cellular interactions in cell-migration experiments
In the first experiment, cell migrate in a wound-healing model: when applied to this experiment, the inference method predicts the existence of cell-cell interactions, correctly mirroring the strong intercellular contacts which are present in the experiment.
Neural networks with redundant representation: detecting the undetectable
We consider a three-layer Sejnowski machine and show that features learnt via contrastive divergence have a dual representation as patterns in a dense associative memory of order P=4.
Dreaming neural networks: rigorous results
Recently a daily routine for associative neural networks has been proposed: the network Hebbian-learns during the awake state (thus behaving as a standard Hopfield model), then, during its sleep state, optimizing information storage, it consolidates pure patterns and removes spurious ones: this forces the synaptic matrix to collapse to the projector one (ultimately approaching the Kanter-Sompolinksy model).
Dreaming neural networks: forgetting spurious memories and reinforcing pure ones
The standard Hopfield model for associative neural networks accounts for biological Hebbian learning and acts as the harmonic oscillator for pattern recognition, however its maximal storage capacity is $\alpha \sim 0. 14$, far from the theoretical bound for symmetric networks, i. e. $\alpha =1$.
