471 research outputs found
Quantum Machine Learning: A tutorial
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.Ministerio de Ciencia, Innovación y Universidades GC2018-095113-B-I00,PID2019-104002GB-C21, and PID2019-104002GB-C22 (MCIU/AEI/FEDER, UE
Forecasting the CATS benchmark with the Double Vector Quantization method
The Double Vector Quantization method, a long-term forecasting method based
on the SOM algorithm, has been used to predict the 100 missing values of the
CATS competition data set. An analysis of the proposed time series is provided
to estimate the dimension of the auto-regressive part of this nonlinear
auto-regressive forecasting method. Based on this analysis experimental results
using the Double Vector Quantization (DVQ) method are presented and discussed.
As one of the features of the DVQ method is its ability to predict scalars as
well as vectors of values, the number of iterative predictions needed to reach
the prediction horizon is further observed. The method stability for the long
term allows obtaining reliable values for a rather long-term forecasting
horizon.Comment: Accepted for publication in Neurocomputing, Elsevie
RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
Extreme learning machine (ELM) as an emerging branch of shallow networks has
shown its excellent generalization and fast learning speed. However, for
blended data, the robustness of ELM is weak because its weights and biases of
hidden nodes are set randomly. Moreover, the noisy data exert a negative
effect. To solve this problem, a new framework called RMSE-ELM is proposed in
this paper. It is a two-layer recursive model. In the first layer, the
framework trains lots of ELMs in different groups concurrently, then employs
selective ensemble to pick out an optimal set of ELMs in each group, which can
be merged into a large group of ELMs called candidate pool. In the second
layer, selective ensemble is recursively used on candidate pool to acquire the
final ensemble. In the experiments, we apply UCI blended datasets to confirm
the robustness of our new approach in two key aspects (mean square error and
standard deviation). The space complexity of our method is increased to some
degree, but the results have shown that RMSE-ELM significantly improves
robustness with slightly computational time compared with representative
methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential
framework to solve robustness issue of ELM for high-dimensional blended data in
the future.Comment: Accepted for publication in Mathematical Problems in Engineering,
09/22/201
Deep Randomized Neural Networks
Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains
Deep Randomized Neural Networks
Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic
fashion. Typical examples of such systems consist of multi-layered neural
network architectures where the connections to the hidden layer(s) are left
untrained after initialization. Limiting the training algorithms to operate on
a reduced set of weights inherently characterizes the class of Randomized
Neural Networks with a number of intriguing features. Among them, the extreme
efficiency of the resulting learning processes is undoubtedly a striking
advantage with respect to fully trained architectures. Besides, despite the
involved simplifications, randomized neural systems possess remarkable
properties both in practice, achieving state-of-the-art results in multiple
domains, and theoretically, allowing to analyze intrinsic properties of neural
architectures (e.g. before training of the hidden layers' connections). In
recent years, the study of Randomized Neural Networks has been extended towards
deep architectures, opening new research directions to the design of effective
yet extremely efficient deep learning models in vectorial as well as in more
complex data domains. This chapter surveys all the major aspects regarding the
design and analysis of Randomized Neural Networks, and some of the key results
with respect to their approximation capabilities. In particular, we first
introduce the fundamentals of randomized neural models in the context of
feed-forward networks (i.e., Random Vector Functional Link and equivalent
models) and convolutional filters, before moving to the case of recurrent
systems (i.e., Reservoir Computing networks). For both, we focus specifically
on recent results in the domain of deep randomized systems, and (for recurrent
models) their application to structured domains
Reservoir Topology in Deep Echo State Networks
Deep Echo State Networks (DeepESNs) recently extended the applicability of
Reservoir Computing (RC) methods towards the field of deep learning. In this
paper we study the impact of constrained reservoir topologies in the
architectural design of deep reservoirs, through numerical experiments on
several RC benchmarks. The major outcome of our investigation is to show the
remarkable effect, in terms of predictive performance gain, achieved by the
synergy between a deep reservoir construction and a structured organization of
the recurrent units in each layer. Our results also indicate that a
particularly advantageous architectural setting is obtained in correspondence
of DeepESNs where reservoir units are structured according to a permutation
recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201
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