94,708 research outputs found
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling
This paper explores a fully unsupervised deep learning approach for computing
distance-preserving maps that generate low-dimensional embeddings for a certain
class of manifolds. We use the Siamese configuration to train a neural network
to solve the problem of least squares multidimensional scaling for generating
maps that approximately preserve geodesic distances. By training with only a
few landmarks, we show a significantly improved local and nonlocal
generalization of the isometric mapping as compared to analogous non-parametric
counterparts. Importantly, the combination of a deep-learning framework with a
multidimensional scaling objective enables a numerical analysis of network
architectures to aid in understanding their representation power. This provides
a geometric perspective to the generalizability of deep learning.Comment: 10 pages, 11 Figure
Sequential sampling strategy for the modeling of parameterized microwave and RF components
Accurate modeling of parameterized microwave and RF components often requires a large number of full-wave electromagnetic simulations. In order to reduce the overall simulation cost, a sequential sampling algorithm is proposed that selects a sparse set of data samples which characterize the overall response of the system. The resulting data samples can be fed into existing modeling techniques. The effectiveness of the approach is illustrated by a parameterized H-shaped microwave antenna
Importance Sampling and its Optimality for Stochastic Simulation Models
We consider the problem of estimating an expected outcome from a stochastic
simulation model. Our goal is to develop a theoretical framework on importance
sampling for such estimation. By investigating the variance of an importance
sampling estimator, we propose a two-stage procedure that involves a regression
stage and a sampling stage to construct the final estimator. We introduce a
parametric and a nonparametric regression estimator in the first stage and
study how the allocation between the two stages affects the performance of the
final estimator. We analyze the variance reduction rates and derive oracle
properties of both methods. We evaluate the empirical performances of the
methods using two numerical examples and a case study on wind turbine
reliability evaluation.Comment: 37 pages, 6 figures, 2 tables. Accepted to the Electronic Journal of
Statistic
Open TURNS: An industrial software for uncertainty quantification in simulation
The needs to assess robust performances for complex systems and to answer
tighter regulatory processes (security, safety, environmental control, and
health impacts, etc.) have led to the emergence of a new industrial simulation
challenge: to take uncertainties into account when dealing with complex
numerical simulation frameworks. Therefore, a generic methodology has emerged
from the joint effort of several industrial companies and academic
institutions. EDF R&D, Airbus Group and Phimeca Engineering started a
collaboration at the beginning of 2005, joined by IMACS in 2014, for the
development of an Open Source software platform dedicated to uncertainty
propagation by probabilistic methods, named OpenTURNS for Open source Treatment
of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial
challenges attached to uncertainties, which are transparency, genericity,
modularity and multi-accessibility. This paper focuses on OpenTURNS and
presents its main features: openTURNS is an open source software under the LGPL
license, that presents itself as a C++ library and a Python TUI, and which
works under Linux and Windows environment. All the methodological tools are
described in the different sections of this paper: uncertainty quantification,
uncertainty propagation, sensitivity analysis and metamodeling. A section also
explains the generic wrappers way to link openTURNS to any external code. The
paper illustrates as much as possible the methodological tools on an
educational example that simulates the height of a river and compares it to the
height of a dyke that protects industrial facilities. At last, it gives an
overview of the main developments planned for the next few years
Action classification using a discriminative non-parametric hidden Markov model
We classify human actions occurring in videos, using the skeletal joint positions extracted from a depth image sequence as features. Each action class is represented by a non-parametric Hidden Markov Model (NP-HMM) and the model parameters are learnt in a discriminative way. Specifically, we use a Bayesian framework based on Hierarchical Dirichlet Process (HDP) to automatically infer the cardinality of hidden states and formulate a discriminative function based on distance between Gaussian distributions to improve classification performance. We use elliptical slice sampling to efficiently sample parameters from the complex posterior distribution induced by our discriminative likelihood function. We illustrate our classification results for action class models trained using this technique
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