19,870 research outputs found
Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible
System Identification of multi-rotor UAVs using echo state networks
Controller design for aircraft with unusual configurations presents unique challenges, particularly in extracting valid mathematical models of the MRUAVs behaviour. System Identification is a collection of techniques for extracting an accurate mathematical model of a dynamic system from experimental input-output data. This can entail parameter identification only (known as grey-box modelling) or more generally full parameter/structural identification of the nonlinear mapping (known as black-box). In this paper we propose a new method for black-box identification of the non-linear dynamic model of a small MRUAV using Echo State Networks (ESN), a novel approach to train Recurrent Neural Networks (RNN)
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the \mbox{Tornado} framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
\mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure
Grand-Canonical Adaptive Resolution Centroid Molecular Dynamics: Implementation and Application
We have implemented the Centroid Molecular Dynamics scheme (CMD) into the
Grand Canonical-like version of the Adaptive Resolution Simulation Molecular
Dynamics (GC-AdResS) method. We have tested the implementation on two different
systems, liquid parahydrogen at extreme thermodynamic conditions and liquid
water at ambient conditions; the reproduction of structural as well as
dynamical results of reference systems are highly satisfactory. The capability
of performing GC-AdResS CMD simulations allows for the treatment of a system
characterized by some quantum features and open boundaries. This latter
characteristic not only is of computational convenience, allowing for
equivalent results of much larger and computationally more expensive systems,
but also suggests a tool of analysis so far not explored, that is the
unambiguous identification of the essential (quantum) degrees of freedom
required for a given property
A scalable parallel Monte Carlo algorithm for atomistic simulations of precipitation in alloys
We present an extension of the semi-grandcanonical (SGC) ensemble that we
refer to as the variance-constrained semi-grandcanonical (VC-SGC) ensemble. It
allows for transmutation Monte Carlo simulations of multicomponent systems in
multiphase regions of the phase diagram and lends itself to scalable
simulations on massively parallel platforms. By combining transmutation moves
with molecular dynamics steps structural relaxations and thermal vibrations in
realistic alloys can be taken into account. In this way, we construct a robust
and efficient simulation technique that is ideally suited for large-scale
simulations of precipitation in multicomponent systems in the presence of
structural disorder. To illustrate the algorithm introduced in this work, we
study the precipitation of Cu in nanocrystalline Fe.Comment: 12 pages; 10 figure
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