96,336 research outputs found
Noisy Independent Factor Analysis Model for Density Estimation and Classification
We consider the problem of multivariate density estimation when the unknown
density is assumed to follow a particular form of dimensionality reduction, a
noisy independent factor analysis (IFA) model. In this model the data are
generated by a number of latent independent components having unknown
distributions and are observed in Gaussian noise. We do not assume that either
the number of components or the matrix mixing the components are known. We show
that the densities of this form can be estimated with a fast rate. Using the
mirror averaging aggregation algorithm, we construct a density estimator which
achieves a nearly parametric rate log^(1/4)n/sqrt(n), independent of the
dimensionality of the data, as the sample size tends to infinity. This
estimator is adaptive to the number of components, their distributions and the
mixing matrix. We then apply this density estimator to construct nonparametric
plug-in classifiers and show that they achieve the best obtainable rate of the
excess Bayes risk, to within a logarithmic factor independent of the dimension
of the data. Applications of this classifier to simulated data sets and to real
data from a remote sensing experiment show promising results
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
Electricity load forecasting is a necessary capability for power system
operators and electricity market participants. The proliferation of local
generation, demand response, and electrification of heat and transport are
changing the fundamental drivers of electricity load and increasing the
complexity of load modelling and forecasting. We address this challenge in two
ways. First, our setting is adaptive; our models take into account the most
recent observations available, yielding a forecasting strategy able to
automatically respond to changes in the underlying process. Second, we consider
probabilistic rather than point forecasting; indeed, uncertainty quantification
is required to operate electricity systems efficiently and reliably. Our
methodology relies on the Kalman filter, previously used successfully for
adaptive point load forecasting. The probabilistic forecasts are obtained by
quantile regressions on the residuals of the point forecasting model. We
achieve adaptive quantile regressions using the online gradient descent; we
avoid the choice of the gradient step size considering multiple learning rates
and aggregation of experts. We apply the method to two data sets: the regional
net-load in Great Britain and the demand of seven large cities in the United
States. Adaptive procedures improve forecast performance substantially in both
use cases for both point and probabilistic forecasting
Learning a Mixture of Deep Networks for Single Image Super-Resolution
Single image super-resolution (SR) is an ill-posed problem which aims to
recover high-resolution (HR) images from their low-resolution (LR)
observations. The crux of this problem lies in learning the complex mapping
between low-resolution patches and the corresponding high-resolution patches.
Prior arts have used either a mixture of simple regression models or a single
non-linear neural network for this propose. This paper proposes the method of
learning a mixture of SR inference modules in a unified framework to tackle
this problem. Specifically, a number of SR inference modules specialized in
different image local patterns are first independently applied on the LR image
to obtain various HR estimates, and the resultant HR estimates are adaptively
aggregated to form the final HR image. By selecting neural networks as the SR
inference module, the whole procedure can be incorporated into a unified
network and be optimized jointly. Extensive experiments are conducted to
investigate the relation between restoration performance and different network
architectures. Compared with other current image SR approaches, our proposed
method achieves state-of-the-arts restoration results on a wide range of images
consistently while allowing more flexible design choices. The source codes are
available in http://www.ifp.illinois.edu/~dingliu2/accv2016
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
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