303 research outputs found
Thermal Equilibrium with the Wiener Potential: Testing the Replica Variational Approximation
We consider the statistical mechanics of a classical particle in a
one-dimensional box subjected to a random potential which constitutes a Wiener
process on the coordinate axis. The distribution of the free energy and all
correlation functions of the Gibbs states may be calculated exactly as a
function of the box length and temperature. This allows for a detailed test of
results obtained by the replica variational approximation scheme. We show that
this scheme provides a reasonable estimate of the averaged free energy.
Furthermore our results shed more light on the validity of the concept of
approximate ultrametricity which is a central assumption of the replica
variational method.Comment: 6 pages, 1 file LaTeX2e generating 2 eps-files for 2 figures
automaticall
On-Line AdaTron Learning of Unlearnable Rules
We study the on-line AdaTron learning of linearly non-separable rules by a
simple perceptron. Training examples are provided by a perceptron with a
non-monotonic transfer function which reduces to the usual monotonic relation
in a certain limit. We find that, although the on-line AdaTron learning is a
powerful algorithm for the learnable rule, it does not give the best possible
generalization error for unlearnable problems. Optimization of the learning
rate is shown to greatly improve the performance of the AdaTron algorithm,
leading to the best possible generalization error for a wide range of the
parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.
Inference and Optimization of Real Edges on Sparse Graphs - A Statistical Physics Perspective
Inference and optimization of real-value edge variables in sparse graphs are
studied using the Bethe approximation and replica method of statistical
physics. Equilibrium states of general energy functions involving a large set
of real edge-variables that interact at the network nodes are obtained in
various cases. When applied to the representative problem of network resource
allocation, efficient distributed algorithms are also devised. Scaling
properties with respect to the network connectivity and the resource
availability are found, and links to probabilistic Bayesian approximation
methods are established. Different cost measures are considered and algorithmic
solutions in the various cases are devised and examined numerically. Simulation
results are in full agreement with the theory.Comment: 21 pages, 10 figures, major changes: Sections IV to VII updated,
Figs. 1 to 3 replace
Equilibration through local information exchange in networks
We study the equilibrium states of energy functions involving a large set of
real variables, defined on the links of sparsely connected networks, and
interacting at the network nodes, using the cavity and replica methods. When
applied to the representative problem of network resource allocation, an
efficient distributed algorithm is devised, with simulations showing full
agreement with theory. Scaling properties with the network connectivity and the
resource availability are found.Comment: v1: 7 pages, 1 figure, v2: 4 pages, 2 figures, simplified analysis
and more organized results, v3: minor change
Statistical Mechanics of Learning: A Variational Approach for Real Data
Using a variational technique, we generalize the statistical physics approach
of learning from random examples to make it applicable to real data. We
demonstrate the validity and relevance of our method by computing approximate
estimators for generalization errors that are based on training data alone.Comment: 4 pages, 2 figure
Perceptron capacity revisited: classification ability for correlated patterns
In this paper, we address the problem of how many randomly labeled patterns
can be correctly classified by a single-layer perceptron when the patterns are
correlated with each other. In order to solve this problem, two analytical
schemes are developed based on the replica method and Thouless-Anderson-Palmer
(TAP) approach by utilizing an integral formula concerning random rectangular
matrices. The validity and relevance of the developed methodologies are shown
for one known result and two example problems. A message-passing algorithm to
perform the TAP scheme is also presented
Charge Symmetry Breaking and QCD
Charge symmetry breaking (CSB) in the strong interaction occurs because of
the difference between the masses of the up and down quarks. The use of
effective field theories allows us to follow this influence of confined quarks
in hadronic and nuclear systems. The progress in observing and understanding
CSB is reviewed with particular attention to the recent successful observations
of CSB in measurements involving the production of a single neutral pion and to
the related theoretical progress.Comment: 41 pages, 10 figures, for Nov. 2006 edition Annual Review of Nuclear
and Particle Physic
Generalizing with perceptrons in case of structured phase- and pattern-spaces
We investigate the influence of different kinds of structure on the learning
behaviour of a perceptron performing a classification task defined by a teacher
rule. The underlying pattern distribution is permitted to have spatial
correlations. The prior distribution for the teacher coupling vectors itself is
assumed to be nonuniform. Thus classification tasks of quite different
difficulty are included. As learning algorithms we discuss Hebbian learning,
Gibbs learning, and Bayesian learning with different priors, using methods from
statistics and the replica formalism. We find that the Hebb rule is quite
sensitive to the structure of the actual learning problem, failing
asymptotically in most cases. Contrarily, the behaviour of the more
sophisticated methods of Gibbs and Bayes learning is influenced by the spatial
correlations only in an intermediate regime of , where
specifies the size of the training set. Concerning the Bayesian case we show,
how enhanced prior knowledge improves the performance.Comment: LaTeX, 32 pages with eps-figs, accepted by J Phys
Implicitly Constrained Semi-Supervised Least Squares Classification
We introduce a novel semi-supervised version of the least squares classifier.
This implicitly constrained least squares (ICLS) classifier minimizes the
squared loss on the labeled data among the set of parameters implied by all
possible labelings of the unlabeled data. Unlike other discriminative
semi-supervised methods, our approach does not introduce explicit additional
assumptions into the objective function, but leverages implicit assumptions
already present in the choice of the supervised least squares classifier. We
show this approach can be formulated as a quadratic programming problem and its
solution can be found using a simple gradient descent procedure. We prove that,
in a certain way, our method never leads to performance worse than the
supervised classifier. Experimental results corroborate this theoretical result
in the multidimensional case on benchmark datasets, also in terms of the error
rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium
on Intelligent Data Analysis (2015), Saint-Etienne, Franc
Optimal Resource Allocation in Random Networks with Transportation Bandwidths
We apply statistical physics to study the task of resource allocation in
random sparse networks with limited bandwidths for the transportation of
resources along the links. Useful algorithms are obtained from recursive
relations. Bottlenecks emerge when the bandwidths are small, causing an
increase in the fraction of idle links. For a given total bandwidth per node,
the efficiency of allocation increases with the network connectivity. In the
high connectivity limit, we find a phase transition at a critical bandwidth,
above which clusters of balanced nodes appear, characterised by a profile of
homogenized resource allocation similar to the Maxwell's construction.Comment: 28 pages, 11 figure
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