39,590 research outputs found
Finite Connectivity Attractor Neural Networks
We study a family of diluted attractor neural networks with a finite average
number of (symmetric) connections per neuron. As in finite connectivity spin
glasses, their equilibrium properties are described by order parameter
functions, for which we derive an integral equation in replica symmetric (RS)
approximation. A bifurcation analysis of this equation reveals the locations of
the paramagnetic to recall and paramagnetic to spin-glass transition lines in
the phase diagram. The line separating the retrieval phase from the spin-glass
phase is calculated at zero temperature. All phase transitions are found to be
continuous.Comment: 17 pages, 4 figure
Discriminative Cooperative Networks for Detecting Phase Transitions
The classification of states of matter and their corresponding phase
transitions is a special kind of machine-learning task, where physical data
allow for the analysis of new algorithms, which have not been considered in the
general computer-science setting so far. Here we introduce an unsupervised
machine-learning scheme for detecting phase transitions with a pair of
discriminative cooperative networks (DCN). In this scheme, a guesser network
and a learner network cooperate to detect phase transitions from fully
unlabeled data. The new scheme is efficient enough for dealing with phase
diagrams in two-dimensional parameter spaces, where we can utilize an active
contour model -- the snake -- from computer vision to host the two networks.
The snake, with a DCN "brain", moves and learns actively in the parameter
space, and locates phase boundaries automatically
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