2,731 research outputs found
Correlated patterns in non-monotonic graded-response perceptrons
The optimal capacity of graded-response perceptrons storing biased and
spatially correlated patterns with non-monotonic input-output relations is
studied. It is shown that only the structure of the output patterns is
important for the overall performance of the perceptrons.Comment: 4 pages, 4 figure
A polynomial training algorithm for calculating perceptrons of optimal stability
Recomi (REpeated COrrelation Matrix Inversion) is a polynomially fast
algorithm for searching optimally stable solutions of the perceptron learning
problem. For random unbiased and biased patterns it is shown that the algorithm
is able to find optimal solutions, if any exist, in at worst O(N^4) floating
point operations. Even beyond the critical storage capacity alpha_c the
algorithm is able to find locally stable solutions (with negative stability) at
the same speed. There are no divergent time scales in the learning process. A
full proof of convergence cannot yet be given, only major constituents of a
proof are shown.Comment: 11 pages, Latex, 4 EPS figure
Replica Symmetry Breaking and the Kuhn-Tucker Cavity Method in simple and multilayer Perceptrons
Within a Kuhn-Tucker cavity method introduced in a former paper, we study
optimal stability learning for situations, where in the replica formalism the
replica symmetry may be broken, namely
(i) the case of a simple perceptron above the critical loading, and
(ii) the case of two-layer AND-perceptrons, if one learns with maximal
stability.
We find that the deviation of our cavity solution from the replica symmetric
one in these cases is a clear indication of the necessity of replica symmetry
breaking. In any case the cavity solution tends to underestimate the storage
capabilities of the networks.Comment: 32 pages, LaTex Source with 9 .eps-files enclosed, accepted by J.
Phys I (France
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
A two step algorithm for learning from unspecific reinforcement
We study a simple learning model based on the Hebb rule to cope with
"delayed", unspecific reinforcement. In spite of the unspecific nature of the
information-feedback, convergence to asymptotically perfect generalization is
observed, with a rate depending, however, in a non- universal way on learning
parameters. Asymptotic convergence can be as fast as that of Hebbian learning,
but may be slower. Moreover, for a certain range of parameter settings, it
depends on initial conditions whether the system can reach the regime of
asymptotically perfect generalization, or rather approaches a stationary state
of poor generalization.Comment: 13 pages LaTeX, 4 figures, note on biologically motivated stochastic
variant of the algorithm adde
A three-threshold learning rule approaches the maximal capacity of recurrent neural networks
Understanding the theoretical foundations of how memories are encoded and
retrieved in neural populations is a central challenge in neuroscience. A
popular theoretical scenario for modeling memory function is the attractor
neural network scenario, whose prototype is the Hopfield model. The model has a
poor storage capacity, compared with the capacity achieved with perceptron
learning algorithms. Here, by transforming the perceptron learning rule, we
present an online learning rule for a recurrent neural network that achieves
near-maximal storage capacity without an explicit supervisory error signal,
relying only upon locally accessible information. The fully-connected network
consists of excitatory binary neurons with plastic recurrent connections and
non-plastic inhibitory feedback stabilizing the network dynamics; the memory
patterns are presented online as strong afferent currents, producing a bimodal
distribution for the neuron synaptic inputs. Synapses corresponding to active
inputs are modified as a function of the value of the local fields with respect
to three thresholds. Above the highest threshold, and below the lowest
threshold, no plasticity occurs. In between these two thresholds,
potentiation/depression occurs when the local field is above/below an
intermediate threshold. We simulated and analyzed a network of binary neurons
implementing this rule and measured its storage capacity for different sizes of
the basins of attraction. The storage capacity obtained through numerical
simulations is shown to be close to the value predicted by analytical
calculations. We also measured the dependence of capacity on the strength of
external inputs. Finally, we quantified the statistics of the resulting
synaptic connectivity matrix, and found that both the fraction of zero weight
synapses and the degree of symmetry of the weight matrix increase with the
number of stored patterns.Comment: 24 pages, 10 figures, to be published in PLOS Computational Biolog
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds
Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. The best currently available denoising methods
approximate this mapping with cleverly engineered algorithms. In this work we
attempt to learn this mapping directly with plain multi layer perceptrons (MLP)
applied to image patches. We will show that by training on large image
databases we are able to outperform the current state-of-the-art image
denoising methods. In addition, our method achieves results that are superior
to one type of theoretical bound and goes a large way toward closing the gap
with a second type of theoretical bound. Our approach is easily adapted to less
extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG
artifacts, salt-and-pepper noise and noise resembling stripes, for which we
achieve excellent results as well. We will show that combining a block-matching
procedure with MLPs can further improve the results on certain images. In a
second paper, we detail the training trade-offs and the inner mechanisms of our
MLPs
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