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Test record #04-FR (validation email)

Matthias Wolfgang Bräuninger

Expected subject: [Infoscience] Veuillez vérifier l’affiliation d’une publication Expected body: Bonjour, Une nouvelle publication dans Infoscience a provisoirement été attribuée à votre laboratoire/unité. En tant que lab manager, il vous appartient de con ...

EPFL <> SISB2022

Test record #04-EN (validation email)

Matthias Wolfgang Bräuninger

Expected subject: [Infoscience] Please verify the affiliation of a publication Expected body: Hello, A new record in Infoscience has provisionally been assigned to your lab/unit. As lab manager, it is your responsibility to confirm or deny this affiliation ...

EPFL <> SISB2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sadegh Farhadkhani, Rafaël Benjamin Pinot

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

PMLR2022

Micro 3D printing of a functional MEMS accelerometer

Luis Guillermo Villanueva Torrijo, Damien Maillard, Göran Stemme, Simone Pagliano

Microelectromechanical system (MEMS) devices, such as accelerometers, are widely used across industries, including the automotive, consumer electronics, and medical industries. MEMS are efficiently produced at very high volumes using large-scale semiconduc ...

SPRINGERNATURE2022

Pretraining boosts out-of-domain robustness for pose estimation

Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Matthias Bethge, Thomas Ray Biasi, Mert Yüksekgönül

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

Template Induction over Unstructured Email Corpora

Julia Proskurnia

Unsupervised template induction over email data is a central component in applications such as information extraction, document classification, and auto-reply. The benefits of automatically generating such templates are known for structured data, e.g. mach ...

Adresse électronique

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