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Concept

Stochastique

Publications associées (29)

Optimization methods for collaborative learning

Sai Praneeth Reddy Karimireddy

A traditional machine learning pipeline involves collecting massive amounts of data centrally on a server and training models to fit the data. However, increasing concerns about the privacy and security of user's data, combined with the sheer growth in the ...

EPFL2021

A Study on More Realistic Room Simulation for Far-Field Keyword Spotting

Robin Scheibler, Eric Bezzam

We investigate the impact of more realistic room simulation for training far-field keyword spotting systems without fine-tuning on in-domain data. To this end, we study the impact of incorporating the following factors in the room impulse response (RIR) ge ...

IEEE2020

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy

We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates. We show that the rate of convergence in all cases consists of two terms: (i) a stocha ...

Microtome Publishing2020

Assessment of representativity of the PETALE experiments for validation of Swiss LWRs ex-core dosimetry calculations

Andreas Pautz, Vincent Pierre Lamirand, Hakim Ferroukhi, Dimitri Rochman, Axel Guy Marie Laureau

The international experimental program PETALE will be carried out at the CROCUS research reactor of EPFL. The program aims at measuring neutron penetration in slabs made of materials composing typical LWR reactor pressure vessel. The measurements will be u ...

E D P SCIENCES2020

On the Influence of Bias-Correction on Distributed Stochastic Optimization

Ali H. Sayed, Bicheng Ying, Kun Yuan, Sulaiman A S A E Alghunaim

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

The bias of dark matter tracers: assessing the accuracy of mapping techniques

Cheng Zhao

We present a comparison between approximated methods for the construction of mock catalogues based on the halo-bias mapping technique. To this end, we use as reference a high-resolution N-body simulation of 38403 dark matter particles on a 400 h(-1) Mpc cu ...

OXFORD UNIV PRESS2020

Grid-aware distributed control of electric vehicle charging stations in active distribution grids

Mario Paolone, Rahul Kumar Gupta, Sherif Alaa Salaheldin Fahmy

The penetration of electric vehicle (EV) charging stations (CSs), along with the progressive connection of stochastic distributed generation, is increasing the probability of violating the power distribution grid operational constraints and deteriorate the ...

Elsevier2020

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Jan Skaloud, Stéphane Guerrier, Yannick Stebler

Stochastic modeling is a challenging task for low-cost sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. For example, first-order Gauss–Markov processes are ...

IOP Publishing2011

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Alexis Berne, Jussi Sakari Leinonen

Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling'' in the atmospheric sciences: improving the spatial resolution of low-resolution images. The abil ...

2020