Publications related to Curse of dimensionality

A reduced basis finite element heterogeneous multiscale method for Stokes flow in porous media

A reduced basis Darcy-Stokes finite element heterogeneous multiscale method (RB-DS-FE-HMM) is proposed for the Stokes problem in porous media. The multiscale method is based on the Darcy-Stokes finite element heterogeneous multiscale method (DS-FE-HMM) int ...

Elsevier Science Sa2016

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Hassan Ghasemzadeh Mohammadi

With the growing number of process variation sources in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for chip design and verification. However, the high dimensionality of parameter space, for process ...

Ieee-Inst Electrical Electronics Engineers Inc2016

PCA using graph total variation

Pierre Vandergheynst, Nathanaël Perraudin, Benjamin Ricaud, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received sig- nificant attention of the signal processing and machine learning com- munity in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome th ...

Ieee2016

Eigenmaps Of Dynamic Functional Connectivity: Voxel-Level Dominant Patterns Through Eigenvector Centrality

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Dynamic functional connectivity (dFC) based on resting-state functional magnetic resonance imaging (fMRI) explores the ongoing temporal configuration of brain networks. To reduce the large dimensionality of the data, conventional dFC analysis usually fores ...

IEEE2016

Spectral based Discontinuous Galerkin Reduced Basis Element method for parametrized Stokes problems

Alfio Quarteroni, Paolo Pacciarini

In this work we extend to the Stokes problem the Discontinuous Galerkin Reduced Basis Element (DGRBE) method introduced in Antonietti et al. (2015). By this method we aim at reducing the computational cost for the approximation of a parametrized Stokes pro ...

Pergamon-Elsevier Science Ltd2016

MATHICSE Technical Report : Spectral based discontinuous Galerkin reduced basis element method for parametrized Stokes problems

Alfio Quarteroni, Paolo Pacciarini

In this work we extend to the Stokes problem the Discontinuous Galerkin Reduced Basis Element (DGRBE) method introduced in [1]. By this method we aim at reducing the computational cost for the approximation of a parametrized Stokes problem on a domain part ...

MATHICSE2016

MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

Alfio Quarteroni, Andrea Manzoni, Federico Negri

The numerical solution of partial differential equations (PDEs) depending on para- metrized or random input data is computationally intensive. Reduced order modeling techniques, such as the reduced basis methods, have been developed to alleviate this compu ...

MATHICSE2016

Low-Rank Representation of Nearest Neighbor Phone Posterior Probabilities to Enhance DNN Acoustic Modeling

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We hypothesize that optimal deep neural networks (DNN) class-conditional posterior probabilities live in a union of low-dimensional subspaces. In real test conditions, DNN posteriors encode uncertainties which can be regarded as a superposition of unstruct ...

Idiap2016

Incorporating advanced behavioral models in mixed linear optimization

Michel Bierlaire, Shadi Sharif Azadeh, Meritxell Pacheco Paneque

Discrete choice models are the state-of-the-art for the mathematical modelling of demand. Based on the concept of random utility, they are able to predict the choice behavior of individuals. However, these models are highly non linear and non convex in the ...

2016

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Curse of dimensionality

A reduced basis finite element heterogeneous multiscale method for Stokes flow in porous media

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

PCA using graph total variation

Eigenmaps Of Dynamic Functional Connectivity: Voxel-Level Dominant Patterns Through Eigenvector Centrality

Spectral based Discontinuous Galerkin Reduced Basis Element method for parametrized Stokes problems

MATHICSE Technical Report : Spectral based discontinuous Galerkin reduced basis element method for parametrized Stokes problems

MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

Robustness Analysis of Controllable-Polarity Silicon Nanowire Devices and Circuits

Low-Rank Representation of Nearest Neighbor Phone Posterior Probabilities to Enhance DNN Acoustic Modeling

Incorporating advanced behavioral models in mixed linear optimization

Graph Chatbot

Chat with Graph Search

PCA using graph total variation

Eigenmaps Of Dynamic Functional Connectivity: Voxel-Level Dominant Patterns Through Eigenvector Centrality

MATHICSE Technical Report : Spectral based discontinuous Galerkin reduced basis element method for parametrized Stokes problems

MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

A reduced basis finite element heterogeneous multiscale method for Stokes flow in porous media

Incorporating advanced behavioral models in mixed linear optimization

Spectral based Discontinuous Galerkin Reduced Basis Element method for parametrized Stokes problems

Low-Rank Representation of Nearest Neighbor Phone Posterior Probabilities to Enhance DNN Acoustic Modeling

Robustness Analysis of Controllable-Polarity Silicon Nanowire Devices and Circuits

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation