Publication

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Publications associées (28)

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Sébastien Marcel, Hatef Otroshi Shahreza

This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft- ...

IEEE COMPUTER SOC2023

On double-descent in uncertainty quantification in overparametrized models

Florent Gérard Krzakala, Lenka Zdeborová, Bruno Loureiro, Lucas Andry Clarte, Bruno Loureiro

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

PMLR Proceedings of Machine Learning Research2023

Real-time Personalized Monitoring of Neurological Disorders on Wearable Systems

Renato Zanetti

The terminology of neurological disorders encompasses a range of serious illnesses (e.g., epilepsy, Alzheimer's disease) leading to morbidity, disability, and stigma. Epilepsy alone affects over 50 million people worldwide, and these figures are rising as ...

EPFL2023

Exploring neural networks and their potential for species distribution modeling

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Benjamin Alexander Kellenberger, Lloyd Haydn Hughes

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

EPFL2023

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

Sahand Kashani

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

EPFL2023

The impact of human expert visual inspection on the discovery of strong gravitational lenses

Frédéric Courbin, Elodie Marie Charlène Savary, Martin Raoul Robert Millon, Eric Gérard Guy Paic, Rémy Elie Joseph, Hung-Hsu Chan, Karina Alexandra Rojas Olate, Benjamin Yvan Alexandre Clement, Yue Pan

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 per cent of the project. During the classification t ...

OXFORD UNIV PRESS2023