Publications related to Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Entropy stable essentially non-oscillatory methods based on RBF reconstructions

Jan Sickmann Hesthaven, Fabian Mönkeberg

To solve hyperbolic conservation laws on general grids we propose to use high-order essentially nonoscillatory methods based on radial basis functions. We introduce an entropy stable arbitrary high-order finite dierence method (RBF-TeCNOp) and an entropy s ...

2018

Novel Algorithms For Clustering

James Peter Newling

Clustering is a method for discovering structure in data, widely used across many scientific disciplines. The two main clustering problems this dissertation considers are K-means and K-medoids. These are NP-hard problems in the number of samples and cluste ...

EPFL2018

Low-rank tensor methods for large Markov chains and forward feature selection methods

Francisco Santos Paredes Quartin de Macedo

In the first part of this thesis, we present and compare several approaches for the determination of the steady-state of large-scale Markov chains with an underlying low-rank tensor structure. Such structure is, in our context of interest, associated with ...

EPFL2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Mirrored Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis

We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...

2018

A Novel Method for Sampling Bandlimited Graph Signals

Pascal Frossard, Pinar Akyazi, Dion Eustathios Olivier Tzamarias

In this paper we propose a novel vertex based sampling method for k-bandlimited signals lying on arbitrary graphs, that has a reasonable computational complexity and results in low reconstruction error. Our goal is to find the smallest set of vertices that ...

IEEE COMPUTER SOC2018

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Volkan Cevher, Paul Thierry Yves Rolland, Jonathan Mark Scarlett, Ilija Bogunovic

Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of a ...

2018

CASSIS: Characterization with Adaptive Sample-Size Inferential Statistics Applied to Inexact Circuits

Vincent Frédéric Camus

To design faster and more energy-efficient systems, numerous inexact arithmetic operators have been proposed, generally obtained by modifying the logic structure of conventional circuits. However, as the quality of service of an application has to be ensur ...

2018

2D ultrasonic flow mapping of the secondary flow field in free-surface vortices

Giovanni De Cesare

In this study, an application of two dimensional ultrasonic doppler flow mapping was undertaken on a strong full air core free-surface vortex. The vortex was generated in a scroll type vortex chamber with a subcritical approach flow which have common appli ...

2018

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

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Entropy stable essentially non-oscillatory methods based on RBF reconstructions

Novel Algorithms For Clustering

Low-rank tensor methods for large Markov chains and forward feature selection methods

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

A Novel Method for Sampling Bandlimited Graph Signals

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

CASSIS: Characterization with Adaptive Sample-Size Inferential Statistics Applied to Inexact Circuits

2D ultrasonic flow mapping of the secondary flow field in free-surface vortices

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Low-rank tensor methods for large Markov chains and forward feature selection methods

High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups

Novel Algorithms For Clustering

Entropy stable essentially non-oscillatory methods based on RBF reconstructions

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

A Novel Method for Sampling Bandlimited Graph Signals

CASSIS: Characterization with Adaptive Sample-Size Inferential Statistics Applied to Inexact Circuits

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

2D ultrasonic flow mapping of the secondary flow field in free-surface vortices