1,254 research outputs found
Estimating Local Function Complexity via Mixture of Gaussian Processes
Real world data often exhibit inhomogeneity, e.g., the noise level, the
sampling distribution or the complexity of the target function may change over
the input space. In this paper, we try to isolate local function complexity in
a practical, robust way. This is achieved by first estimating the locally
optimal kernel bandwidth as a functional relationship. Specifically, we propose
Spatially Adaptive Bandwidth Estimation in Regression (SABER), which employs
the mixture of experts consisting of multinomial kernel logistic regression as
a gate and Gaussian process regression models as experts. Using the locally
optimal kernel bandwidths, we deduce an estimate to the local function
complexity by drawing parallels to the theory of locally linear smoothing. We
demonstrate the usefulness of local function complexity for model
interpretation and active learning in quantum chemistry experiments and fluid
dynamics simulations.Comment: 19 pages, 16 figure
Compositional optimization of hard-magnetic phases with machine-learning models
Machine Learning (ML) plays an increasingly important role in the discovery
and design of new materials. In this paper, we demonstrate the potential of ML
for materials research using hard-magnetic phases as an illustrative case. We
build kernel-based ML models to predict optimal chemical compositions for new
permanent magnets, which are key components in many green-energy technologies.
The magnetic-property data used for training and testing the ML models are
obtained from a combinatorial high-throughput screening based on
density-functional theory calculations. Our straightforward choice of
describing the different configurations enables the subsequent use of the ML
models for compositional optimization and thereby the prediction of promising
substitutes of state-of-the-art magnetic materials like NdFeB with
similar intrinsic hard-magnetic properties but a lower amount of critical
rare-earth elements.Comment: 12 pages, 6 figure
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
A comparative study of calibration methods for low-cost ozone sensors in IoT platforms
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O 3 ). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal–oxide O 3 sensors, 25 electro-chemical O 3 sensors, 25 electro-chemical NO 2 sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.Peer ReviewedPostprint (author's final draft
Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing
Computation of document image quality metrics often depends upon the
availability of a ground truth image corresponding to the document. This limits
the applicability of quality metrics in applications such as hyperparameter
optimization of image processing algorithms that operate on-the-fly on unseen
documents. This work proposes the use of surrogate models to learn the behavior
of a given document quality metric on existing datasets where ground truth
images are available. The trained surrogate model can later be used to predict
the metric value on previously unseen document images without requiring access
to ground truth images. The surrogate model is empirically evaluated on the
Document Image Binarization Competition (DIBCO) and the Handwritten Document
Image Binarization Competition (H-DIBCO) datasets
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