Publications associées à Identifying uncertainty states during wayfinding in indoor environments: An EEG classification Study

Publications associées (44)

On the added value of sequential deep learning for the upscaling of evapotranspiration

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer Nature2024

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Zimin Xia

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

Springer Science and Business Media Deutschland GmbH2025

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Zaffar Ahmed Shaikh

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

PeerJ2025

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Jan Weinreich, Matthieu Nicolas Haeberle

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

IOP Publishing Ltd2025

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Pascal Fua

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Springer Science and Business Media Deutschland GmbH2025

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Lakmal Buddika Meegahapola

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

EPFL2024

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Seyedbehzad Bozorgtabar

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

IEEE2025

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Darong Huang, Luis Maria Costero Valero

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

2024

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Ramin Mohammadi, Nikolaos Stergiopoulos, Georgios Rovas, Lydia Aslanidou, Sokratis Anagnostopoulos

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

2025

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Amirhossein Shahbazinia, David Atienza Alonso, Giovanni Ansaloni, Flavio Ponzina, José Angel Miranda Calero, Jonathan Dan

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

IEEE2024

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Karl El Hajal

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Institute of Electrical and Electronics Engineers Inc.2025

On the Generalization of Stochastic Gradient Descent with Momentum

Volkan Cevher, Kimon Antonakopoulos

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Microtome Publishing2024

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Michael Lehning, Devis Tuia, Yael Frischholz

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

2025

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction

Olaf Blanke, José del Rocio Millán Ruiz, Ronan Boulic, Ricardo Andres Chavarriaga Lozano, Bruno Herbelin, Fumiaki Iwane, Thibault Serge Mario Porssut

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside th ...

2024

Automated Generation of Emotion-Conveying Sounds for Robots

Alaa Abboud

Sonic Human-Robot Interaction aims at equipping robots with the ability to convey emotions and intentions via sounds. Such sounds are typically handcrafted by human experts, which results in expensive, small sound sets with limited expressivity. To overcom ...

IEEE2025

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Springernature2024

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Juan Pablo Zuluaga Gomez

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition (ASR) on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are train ...

Institute of Electrical and Electronics Engineers2025