Publications associées à Asymptotic learning curves of kernel methods: empirical data versus teacher-student paradigm

Publications associées (13)

Unlabeled Principal Component Analysis and Matrix Completion

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Microtome Publishing2024

Extending Capabilities of Attention-based Models

Arnaud Pannatier

Deep learning has brought incredible progress in areas where traditional algorithms struggle. It can be because they need rules that are sometimes hard to conceptualize, it can also be that the number and manner of combining these rules are too complex to ...

EPFL2024

Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows

Maria Laura Battagliola

This article focuses on the study of lactating sows, where the main interest is the influence of temperature, measured throughout the day, on the lower quantiles of the daily feed intake. We outline a model framework and estimation methodology for quantile ...

Springer2024

The Earthquake Risk Model of Switzerland, ERM-CH23

Pierino Lestuzzi, Alireza Khodaverdian

Understanding seismic risk at both the national and sub-national level is essential for devising effective strategies and interventions aimed at its mitigation. The Earthquake Risk Model of Switzerland (ERM-CH23), released in early 2023, is the culmination ...

COPERNICUS GESELLSCHAFT MBH2024

Uncertainty quantification for a deep learning models for image-based crack segmentation

Onaïa Joseph Savary, Katrin Beyer, Bryan German Pantoja Rosero, Ketson Roberto Maximiano Dos Santos

Recent advancements in deep learning have found compelling applications in the image-based inspection of infrastructure systems. However, the efficacy of these models hinges significantly on the quality and diversity of the input data used for training. In ...

2024

Early decoding of walking tasks with minimal set of EMG channels

Silvestro Micera

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decod ...

IOP Publishing Ltd2023

Dense Image-based Predictions for Comics Analysis

Deblina Bhattacharjee

Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...

EPFL2023

Improving Object Detection under Domain Shifts

Vidit Vidit

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

EPFL2023

Prediction of plasma volume and total hemoglobin mass with machine learning

Jean-Marc Vesin

Hemoglobin concentration ([Hb]) is used for the clinical diagnosis of anemia, and in sports as a marker of blood doping. [Hb] is however subject to significant variations mainly due to shifts in plasma volume (PV). This study proposes a newly developed mod ...

WILEY2023

Detecting Anomalies and Obstacles in Road Scenes

Krzysztof Maciej Lis

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

EPFL2023

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Bryan German Pantoja Rosero

Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

EPFL2023

Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

Romain Christophe Rémy Fleury, Ali Momeni

Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditiona ...

Nature Research2022

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

Diego Ghezzi, Eleonora Borda, Vivien Gaillet, Elodie Geneviève Zollinger

Objective. Optic nerve's intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural op ...

IOP PUBLISHING LTD2021