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Mur anti-bruit

Publications associées (102)

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Removing Structured Noise With Self-Supervised Blind-Spot Networks

Martin Weigert

Removal of noise from fluorescence microscopy images is an important first step in many biological analysis pipelines. Current state-of-the-art supervised methods employ convolutional neural networks that are trained with clean (ground-truth) images. Recen ...

IEEE2020

Spatial associations of mood disorders and cognitive performance with neighborhood environmental attributes and brain plasticity

Marco André Vieira Ruas

Objectives : This study aimed to examine associations between mood disorders and cognitive impairments with environmental neighborhood attributes and evaluate their effects on brain plasticity. Setting: Lausanne and Outskirts. Methods: Multiscale and simpl ...

2020

Development of a plasma electroacoustic actuator for active noise control applications

Ivo Furno, Alan Howling, Hervé Lissek, Pénélope Leyland, Stanislav Sergeev, Gennady Plyushchev

Conventional loudspeakers generate sound through the vibration of a diaphragm, attached to a rigid frame through elastic suspensions. Although such construction is satisfactory for sound diffusion in steady environments, it is likely to fail in harsh condi ...

2020

Good Vibrations and Unknown Excitations

Dalia Salem Hassan Fahmy El Badawy

Inspired by the human ability to localize sounds, even with only one ear, as well as to recognize objects using active echolocation, we investigate the role of sound scattering and prior knowledge in regularizing ill-posed inverse problems in acoustics. In ...

EPFL2020

Improving sound absorption through nonlinear active electroacoustic resonators

Romain Christophe Rémy Fleury, Hervé Lissek, Xinxin Guo

Absorbing airborne noise at frequencies below 300 Hz is a particularly vexing problem due to the absence of natural sound absorbing materials at these frequencies. The prevailing solution for low-frequency sound absorption is the use of passive narrow-band ...

2020

Extrapolation for Large-batch Training in Deep Learning

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Lingjing Kong

Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data. A major roadblock faced when increasing the batc ...

2020

Reducing Noise in GAN Training with Variance Reduced Extragradient

François Fleuret, Tatjana Chavdarova

We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with ...

2019

Geometry of adversarial robustness of deep networks: methods and applications

Seyed Mohsen Moosavi Dezfooli

We are witnessing a rise in the popularity of using artificial neural networks in many fields of science and technology. Deep neural networks in particular have shown impressive classification performance on a number of challenging benchmarks, generally in ...

EPFL2019

POD-Kriging reduced method’s application in Tandem Cylinders’ flow

Jan Sickmann Hesthaven, Han Xiao

The configuration of Tandem Cylinders is a typical one in researching noise and interactions between different components, where the Distance-diameter ratio is a very significant parameter with which the flow form varies a lot. To clearly research the rati ...

2018

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