Publications associées à Periodogram

The spectral bias of polynomial neural networks

Volkan Cevher, Grigorios Chrysos, Leello Tadesse Dadi, Moulik Choraria

Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

2022

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu, Yongtao Wu

Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural network architectures, but it is incomplete for neural networks with Hadamar ...

2022

Measurement of the Gas Velocity in a Water-Air Mixture in CROCUS Using Neutron Noise Techniques

Andreas Pautz, Vincent Pierre Lamirand, Mathieu Hursin, Oskari Ville Pakari, Pavel Frajtag

The possibility of measuring the gas-phase velocity in a two-phase mixture through the use of neutron noise techniques is demonstrated in the zero-power reactor CROCUS of the Ecole Polytechnique Federale de Lausanne. It is the first step toward the experim ...

TAYLOR & FRANCIS INC2020

Multipliers-Driven Perturbation of Coefficients for Low-Power Operation in Reconfigurable FIR Filters

Andreas Peter Burg, Adam Shmuel Teman, Andrea Bonetti

Reconfigurable finite-impulse response (FIR) filters are one of the most widely implemented components in Internet of Things systems that require flexibility to support several target applications while consuming the minimum amount of power to comply with ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Data-driven spatio-temporal discretization for pedestrian flow characterization

Michel Bierlaire, Marija Nikolic

We propose a novel approach to pedestrian flow characterization. The definitions of density, flow and velocity existing in the literature are extended through a data-driven spatio-temporal discretization framework. The framework is based on three-dimension ...

Elsevier Science Bv2017

Plasmonic Nanohole Arrays on a Robust Hybrid Substrate for Highly Sensitive Label-Free Biosensing

Hatice Altug, Arif Engin Cetin, Dordaneh Etezadi, Mickaël Pierre Busson, Yasa Eksioglu Özok

Plasmonic nanohole arrays have received significant attention, as they have highly advantageous optical properties for ultrasensitive and label-free biosensing applications. Currently, most of these subwavelength periodic apertures are mainly implemented o ...

Amer Chemical Soc2015

Fast live cell imaging at nanometer scale using annihilating filter based low rank Hankel matrix approach

Michaël Unser, Suliana Manley, Lina Carlini

Localization microscopy such as STORM/PALM can achieve a nanometer scale spatial resolution by iteratively localizing fluorescence molecules. It was shown that imaging of densely activated molecules can accelerate temporal resolution which was considered a ...

Spie-Int Soc Optical Engineering2015

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Periodogram

The spectral bias of polynomial neural networks

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Measurement of the Gas Velocity in a Water-Air Mixture in CROCUS Using Neutron Noise Techniques

Multipliers-Driven Perturbation of Coefficients for Low-Power Operation in Reconfigurable FIR Filters

Data-driven spatio-temporal discretization for pedestrian flow characterization

Data-driven fundamental models for pedestrian movements

Compact multifunctional system for imaging spectroscopy

Plasmonic Nanohole Arrays on a Robust Hybrid Substrate for Highly Sensitive Label-Free Biosensing

Fast live cell imaging at nanometer scale using annihilating filter based low rank Hankel matrix approach

Graph Chatbot

Chattez avec Graph Search

Data-driven fundamental models for pedestrian movements

The spectral bias of polynomial neural networks

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Measurement of the Gas Velocity in a Water-Air Mixture in CROCUS Using Neutron Noise Techniques

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Multipliers-Driven Perturbation of Coefficients for Low-Power Operation in Reconfigurable FIR Filters

Data-driven spatio-temporal discretization for pedestrian flow characterization

Fast live cell imaging at nanometer scale using annihilating filter based low rank Hankel matrix approach

Compact multifunctional system for imaging spectroscopy

Plasmonic Nanohole Arrays on a Robust Hybrid Substrate for Highly Sensitive Label-Free Biosensing