31,853 research outputs found
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.Comment: 13 pages, 7 figure
BM3D Frames and Variational Image Deblurring
A family of the Block Matching 3-D (BM3D) algorithms for various imaging
problems has been recently proposed within the framework of nonlocal patch-wise
image modeling [1], [2]. In this paper we construct analysis and synthesis
frames, formalizing the BM3D image modeling and use these frames to develop
novel iterative deblurring algorithms. We consider two different formulations
of the deblurring problem: one given by minimization of the single objective
function and another based on the Nash equilibrium balance of two objective
functions. The latter results in an algorithm where the denoising and
deblurring operations are decoupled. The convergence of the developed
algorithms is proved. Simulation experiments show that the decoupled algorithm
derived from the Nash equilibrium formulation demonstrates the best numerical
and visual results and shows superiority with respect to the state of the art
in the field, confirming a valuable potential of BM3D-frames as an advanced
image modeling tool.Comment: Submitted to IEEE Transactions on Image Processing on May 18, 2011.
implementation of the proposed algorithm is available as part of the BM3D
package at http://www.cs.tut.fi/~foi/GCF-BM3
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees,
have been successfully used for regression in many applications and research
studies. Furthermore, these methods have been extended in order to deal with
uncertainty in the output variable, using for example a quantile loss in Random
Forests (Meinshausen, 2006). To the best of our knowledge, no extension has
been provided yet for dealing with uncertainties in the input variables, even
though such uncertainties are common in practical situations. We propose here
such an extension by showing how standard regression trees optimizing a
quadratic loss can be adapted and learned while taking into account the
uncertainties in the inputs. By doing so, one no longer assumes that an
observation lies into a single region of the regression tree, but rather that
it belongs to each region with a certain probability. Experiments conducted on
several data sets illustrate the good behavior of the proposed extension.Comment: 9 page
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