Publications associées à Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

Andreas Pautz, Mathieu Hursin, Dimitri Rochman, Daniel Jerôme Siefman

Developments in data assimilation theory allow to adjust integral parameters and cross sections with stochastic sampling. This work investigates how two stochastic methods, MOCABA and BMC, perform relative to a sensitivity-based methodology called GLLS. St ...

SPRINGER HEIDELBERG2018

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

2018

Impedance-based real-time position sensor for lab-on-a-chip devices

Philippe Renaud, Harald Van Lintel, Jonathan Bastien Cottet

This paper presents the theoretical and experimental development of an integrated position sensor for lab-on-a-chip devices. The interest for single cell analysis is growing. However, this requires monitoring and controlling cell displacements in real time ...

2018

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

On the performance of random reshuffling in stochastic learning

Ali H. Sayed, Stefan Vlaski, Bicheng Ying, Kun Yuan

In empirical risk optimization, it has been observed that gradient descent implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data randomly and independently of each other. ...

2017

Sampling and Exact Reconstruction of Pulses with Variable Width

Martin Vetterli, Adam James Scholefield, Loïc Arnaud Baboulaz, Gilles Baechler

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, ...

Institute of Electrical and Electronics Engineers2017

Learning-based subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

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Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Impedance-based real-time position sensor for lab-on-a-chip devices

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

Safe Adaptive Importance Sampling

Sampling at unknown locations, with an application in surface retrieval

On the performance of random reshuffling in stochastic learning

Sampling and Exact Reconstruction of Pulses with Variable Width

Learning-based subsampling

Gauging the highs: design and realization of a water sample campaign during high and low streamflow

Graph Chatbot

Chattez avec Graph Search

Gauging the highs: design and realization of a water sample campaign during high and low streamflow

Sampling at unknown locations, with an application in surface retrieval

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

Impedance-based real-time position sensor for lab-on-a-chip devices

Stochastic vs. sensitivity-based integral parameter and nuclear data adjustments

Learning-based subsampling

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Safe Adaptive Importance Sampling

On the performance of random reshuffling in stochastic learning

Sampling and Exact Reconstruction of Pulses with Variable Width