Concept

Gumbel distribution

Related publications (29)

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When applied to new datasets, acquired at different time moments, with different sensors or under different acquisition conditions, deep learning models might fail spectacularly. This is because they have learned from the data distribution observed during ...

Wiley2021

Sub‐asymptotic motivation for new conditional multivariate extreme models

Anthony Christopher Davison, Thomas Lugrin

Statistical models for extreme values are generally derived from non-degenerate probabilistic limits that can be used to approximate the distribution of events that exceed a selected high threshold. If convergence to the limit distribution is slow, then th ...

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Revisiting the Calculation of the Early Time HEMP Conducted Environment

Farhad Rachidi-Haeri, Nicolas Mora Parra, Carlos Alberto Romero Romero

The early time high altitude electromagnetic pulse conducted environment calculation is revisited by using the transmission line (TL) theory and including high-frequency corrections that were not present in the earlier studies. Waveform salient parameters, ...

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Bayesian Methods for the Identification of Distribution Networks

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The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...

IEEE2021

Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis, Sadegh Farhadkhani

We study Byzantine collaborative learning, where n nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node is trusted, and f < n nodes can behave arbitrarily. We prove that collaborati ...

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A universal signature in the melting of metallic nanoparticles

Kevin Rossi

Predicting when phase changes occur in nanoparticles is fundamental for designing the next generation of devices suitable for catalysis, biomedicine, optics, chemical sensing and electronic circuits. The estimate of the temperature at which metallic nanopa ...

ROYAL SOC CHEMISTRY2021

The influence of trip length distribution on urban traffic in network-level models

Mikhail Murashkin

Modeling urban traffic on the level of network is a wide research area oriented to the development of ITS. In this thesis properties of models based on MFD (Macroscopic Fundamental Diagram) are studied. The idea behind MFD is to say that the state of the t ...

EPFL2021

Low Noise Clock Generation: Modeling, Analysis and Implementation

Duygu Kostak

The aim of this thesis is to implement, analyze and improve the selected low noise clock generation and distribution techniques for ADC implementations. The thesis is divided into two parts. The first part focuses on the sampling phase generation and distr ...

EPFL2021

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

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EPFL2021

Related publications (29)

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EPFL2021