Publications associées à Positive-definite kernel

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particular, our algorithm can be specialized to invert positive definite matrices i ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Sparse approximation of multilinear problems with applications to kernel-based methods in UQ

Fabio Nobile

We provide a framework for the sparse approximation of multilinear problems and show that several problems in uncertainty quantification fit within this framework. In these problems, the value of a multilinear map has to be approximated using approximation ...

2018

A Sampling Theory for Non-Decaying Signals

Michaël Unser, Quy Ha Nguyen

The classical assumption in sampling and spline theories is that the input signal is square-integrable, which prevents us from applying such techniques to signals that do not decay or even grow at infinity. In this paper, we develop a sampling theory for m ...

2017

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Simultaneous and proportional control of a prosthetic hand and wrist is still a controversial issue, although giant steps have lately been made in this direction. In this paper, we study the application of a novel machine learning method to the problem, wi ...

2017

Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Despite recent advances in prosthetics and assistive robotics in general, robust simultaneous and proportional control of dexterous prosthetic devices remains an unsolved problem, mainly because of inadequate sensorization. In this paper, we study the appl ...

2017

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

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Rudolf Haraksim

This study presents a method for computing likelihood ratios (LRs) from multimodal score distributions, as the ones produced by some commercial off-the-shelf automated fingerprint identification systems (AFISs). The AFIS algorithms used to compare fingerma ...

Inst Engineering Technology-IET2017

Null Controllability and Finite Time Stabilization for the Heat Equations with Variable Coefficients in Space in One Dimension via Backstepping Approach

Hoài-Minh Nguyên

Using the backstepping approach we recover the null controllability for the heat equations with variable coefficients in space in one dimension and prove that these equations can be stabilized in finite time by means of periodic time-varying feedback laws. ...

Springer Verlag2017

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization

Yury Koush

Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on be ...

Ieee2016

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Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Sparse approximation of multilinear problems with applications to kernel-based methods in UQ

A Sampling Theory for Non-Decaying Signals

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Null Controllability and Finite Time Stabilization for the Heat Equations with Variable Coefficients in Space in One Dimension via Backstepping Approach

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization

Sparse approximation of multilinear problems with applications to kernel-based methods in UQ

A Sampling Theory for Non-Decaying Signals

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Null Controllability and Finite Time Stabilization for the Heat Equations with Variable Coefficients in Space in One Dimension via Backstepping Approach

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization