4,104 research outputs found
Approximation with Random Bases: Pro et Contra
In this work we discuss the problem of selecting suitable approximators from
families of parameterized elementary functions that are known to be dense in a
Hilbert space of functions. We consider and analyze published procedures, both
randomized and deterministic, for selecting elements from these families that
have been shown to ensure the rate of convergence in norm of order
, where is the number of elements. We show that both randomized and
deterministic procedures are successful if additional information about the
families of functions to be approximated is provided. In the absence of such
additional information one may observe exponential growth of the number of
terms needed to approximate the function and/or extreme sensitivity of the
outcome of the approximation to parameters. Implications of our analysis for
applications of neural networks in modeling and control are illustrated with
examples.Comment: arXiv admin note: text overlap with arXiv:0905.067
Probability of local bifurcation type from a fixed point: A random matrix perspective
Results regarding probable bifurcations from fixed points are presented in
the context of general dynamical systems (real, random matrices), time-delay
dynamical systems (companion matrices), and a set of mappings known for their
properties as universal approximators (neural networks). The eigenvalue spectra
is considered both numerically and analytically using previous work of Edelman
et. al. Based upon the numerical evidence, various conjectures are presented.
The conclusion is that in many circumstances, most bifurcations from fixed
points of large dynamical systems will be due to complex eigenvalues.
Nevertheless, surprising situations are presented for which the aforementioned
conclusion is not general, e.g. real random matrices with Gaussian elements
with a large positive mean and finite variance.Comment: 21 pages, 19 figure
Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
We generalize recent theoretical work on the minimal number of layers of
narrow deep belief networks that can approximate any probability distribution
on the states of their visible units arbitrarily well. We relax the setting of
binary units (Sutskever and Hinton, 2008; Le Roux and Bengio, 2008, 2010;
Mont\'ufar and Ay, 2011) to units with arbitrary finite state spaces, and the
vanishing approximation error to an arbitrary approximation error tolerance.
For example, we show that a -ary deep belief network with layers of width for some can approximate any probability
distribution on without exceeding a Kullback-Leibler
divergence of . Our analysis covers discrete restricted Boltzmann
machines and na\"ive Bayes models as special cases.Comment: 19 pages, 5 figures, 1 tabl
Geometry and Expressive Power of Conditional Restricted Boltzmann Machines
Conditional restricted Boltzmann machines are undirected stochastic neural
networks with a layer of input and output units connected bipartitely to a
layer of hidden units. These networks define models of conditional probability
distributions on the states of the output units given the states of the input
units, parametrized by interaction weights and biases. We address the
representational power of these models, proving results their ability to
represent conditional Markov random fields and conditional distributions with
restricted supports, the minimal size of universal approximators, the maximal
model approximation errors, and on the dimension of the set of representable
conditional distributions. We contribute new tools for investigating
conditional probability models, which allow us to improve the results that can
be derived from existing work on restricted Boltzmann machine probability
models.Comment: 30 pages, 5 figures, 1 algorith
Elementary Derivative Tasks and Neural Net Multiscale Analysis of Tasks
Neural nets are known to be universal approximators. In particular, formal
neurons implementing wavelets have been shown to build nets able to approximate
any multidimensional task. Such very specialized formal neurons may be,
however, difficult to obtain biologically and/or industrially. In this paper we
relax the constraint of a strict ``Fourier analysis'' of tasks. Rather, we use
a finite number of more realistic formal neurons implementing elementary tasks
such as ``window'' or ``Mexican hat'' responses, with adjustable widths. This
is shown to provide a reasonably efficient, practical and robust,
multifrequency analysis. A training algorithm, optimizing the task with respect
to the widths of the responses, reveals two distinct training modes. The first
mode induces some of the formal neurons to become identical, hence promotes
``derivative tasks''. The other mode keeps the formal neurons distinct.Comment: latex neurondlt.tex, 7 files, 6 figures, 9 pages [SPhT-T01/064],
submitted to Phys. Rev.
Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes
A method is provided for designing and training noise-driven recurrent neural
networks as models of stochastic processes. The method unifies and generalizes
two known separate modeling approaches, Echo State Networks (ESN) and Linear
Inverse Modeling (LIM), under the common principle of relative entropy
minimization. The power of the new method is demonstrated on a stochastic
approximation of the El Nino phenomenon studied in climate research
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