Publications related to Enhanced change detection using nonlinear feature extraction

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

3D-Printed Beam Splitter for Polar Neutral Molecules

Andreas Osterwalder, Sean Dennis Steven Gordon

We describe a macroscopic beam splitter for polar neutral molecules. A complex electrode structure is required for the beam splitter which would be very difficult to produce with traditional manufacturing methods. Instead, we make use of a nascent manufact ...

Amer Physical Soc2017

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

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

In this paper, a new formulation in the time domain of the Cooray-Rubinstein expression is presented. This new formulation overcomes the problems related to the previous formulation: The Dirac pulse contained in the kernel of the previous formulation is ex ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Frank Grégoire Jean de Morsier

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Laplacian Support Vector Analysis for Subspace Discriminative Learning

Nikolaos Arvanitopoulos Darginis

In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which captu ...

2014

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

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

With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling cha ...

2014

Supervised change detection in VHR images using contextual information and support vector machines

Devis Tuia

In this paper we study an effective solution to deal with supervised change detection in very high geometrical resolution (VHR) images. High within-class variance as well as low between-class variance that characterize this kind of imagery make the detecti ...

Elsevier2013

Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

On The Optimality Of Operator-Like Wavelets For Sparse Ar(1) Processes

Michaël Unser, Pedram Pad

Sinusoidal transforms such as the DCT are known to be optimal-that is, asymptotically equivalent to the Karhunen-Loeve transform (KLT)-for the representation of Gaussian stationary processes, including the classical AR(1) processes. While the KLT remains a ...

Ieee2013

Enhanced change detection using nonlinear feature extraction

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Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

3D-Printed Beam Splitter for Polar Neutral Molecules

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

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Laplacian Support Vector Analysis for Subspace Discriminative Learning

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Supervised change detection in VHR images using contextual information and support vector machines

Sparse Binary Features for Image Classification

On The Optimality Of Operator-Like Wavelets For Sparse Ar(1) Processes

Sparse Binary Features for Image Classification

Supervised change detection in VHR images using contextual information and support vector machines

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

3D-Printed Beam Splitter for Polar Neutral Molecules

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

On The Optimality Of Operator-Like Wavelets For Sparse Ar(1) Processes

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

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

Laplacian Support Vector Analysis for Subspace Discriminative Learning

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection