Publications related to Log-logistic distribution

A story of two transitions: From adhesive to abrasive wear and from ductile to brittle regime

Jean-François Molinari, Sacha Zenon Wattel

Atomistic simulations performed with a family of model potential with tunable hardness have proven to be a great tool for advancing the understanding of wear processes at the asperity level. They have been instrumental in finding a critical length scale, w ...

2024

Federated Learning under Covariate Shifts with Generalization Guarantees

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Thomas Michaelsen Pethick

This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance. To handle covariate shifts, we formulate a new global model training paradigm and propose Federated Impor ...

2023

Making Computer Vision Models Robust and Adaptive

Shuqing Teresa Yeo

Visual perception is indispensable for many real-world applications. However, perception models deployed in the real world will encounter numerous and unpredictable distribution shifts, for example, changes in geographic locations, motion blur, and adverse ...

EPFL2023

Towards a multiscale point cloud structural similarity metric

Touradj Ebrahimi, Davi Nachtigall Lazzarotto

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

IEEE2023

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Yuejiang Liu

Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

EPFL2023

Towards Robust Vision Transformer

Shaokai Ye, Yuan He

Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and com ...

IEEE COMPUTER SOC2022

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar, Seyedbehzad Bozorgtabar

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despit ...

PMLR2022

Feature distribution learning by passive exposure

David Pascucci, Gizay Ceylan

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects ...

2022

3PAC: A Plug-and-Play System for Distributed Power Sharing and Communication in Modular Robots

Jamie Paik, Kevin Andrew Holdcroft, Christoph Heinrich Belke

In modular robotics, there is classically a compromise between coupling complexity and functionality. The mechanical and electrical designs are linked, such that adding electrical features often requires redesigning the mechanical components. However, incr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Constrained Language Models for Interactive Poem Generation

Andrei Popescu-Belis, Gabriel Luthier, Alejandro Ramírez Atrio, Aris Xanthos, Simon Mattei

This paper describes a system for interactive poem generation, which combines neural language models (LMs) for poem generation with explicit constraints that can be set by users on form, topic, emotion, and rhyming scheme. LMs cannot learn such constraints ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2022

Visualizing the multifractal wave functions of a disordered two-dimensional electron gas

Klaus Kern

The wave functions of a disordered two-dimensional electron gas at the quantum-critical Anderson transition are predicted to exhibit multifractal scaling in their real space amplitude. We experimentally investigate the appearance of these characteristics i ...

AMER PHYSICAL SOC2021

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Daniel Kuhn, Tobias Sutter, Andreas Krause

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribut ...

2021

Reconstructing the base field from imaginary multiplicative chaos

Juhan Aru, Janne Matias Junnila

We show that the imaginary multiplicative chaos exp(i beta Gamma) determines the gradient of the underlying field Gamma for all log-correlated Gaussian fields with covariance of the form -log|x-y|+g(x,y) with mild regularity conditions on g, for all d >= 2 ...

WILEY2021

Investigating Crosstalk Among PTMs Provides Novel Insight Into the Structural Basis Underlying the Differential Effects of Nt17 PTMs on Mutant Httex1 Aggregation

Hilal Lashuel, Matteo Dal Peraro, Luciano Andres Abriata, Lucien Fabrice Krapp, Anass Chiki, Driss Boudeffa, Iman Rostami, Rajasekhar Kolla, Zhidian Zhang

Post-translational modifications (PTMs) within the first 17 amino acids (Nt17) of the Huntingtin protein (Htt) have been shown to inhibit the aggregation and attenuate the toxicity of mutant Htt proteins in vitro and in various models of Huntington’s disea ...

2021

Effects of spanwise confinement on stratified shear instabilities

François Gallaire, Yves-Marie François Ducimetière

We consider the influence of transverse confinement on the instability properties of velocity and density distributions reminiscent of those pertaining to exchange flows in stratified inclined ducts, such as the recent experiment of Lefauve et al. [J. Flui ...

2021

Redundant features can hurt robustness to distribution shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Ship-based measurements of ice nuclei concentrations over the Arctic, Atlantic, Pacific and Southern oceans

Julia Schmale, André Welti

Ambient concentrations of ice-forming particles measured during ship expeditions are collected and summarised with the aim of determining the spatial distribution and variability in ice nuclei in oceanic regions. The presented data from literature and prev ...

Copernicus Publications2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

Multimodal water age distributions and the challenge of complex hydrological landscapes

Paolo Benettin

Travel time distributions (TTDs) are concise descriptions of transport processes in catchments based on water ages, and they are particularly efficient as lumped hydrological models to simulate tracers in outflows. Past studies have approximated catchment ...

2020