Publications associées à Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Olaf Blanke, Andrea Serino, Roberta Ronchi

The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

Wiley2024

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

The inductive bias of deep learning: Connecting weights and functions

Guillermo Ortiz Jimenez

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

EPFL2023

Differential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits

Dimitri Nestor Alice Van De Ville, Philip Egger, Nathalie Heidi Meyer, Elena Beanato, Gabriel Girard, Julia Brügger, Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Philipp Johannes Koch, Giorgia Giulia Evangelista, Andéol Geoffroy Cadic-Melchior, Lisa Aïcha Mireille Julie Fleury

Background:Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretic ...

2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

EPFL2023

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study

Jonathan Dönz, Mary-Anne Hartley

BackgroundInterstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prom ...

BMC2023

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Mu ...

2023

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

EPFL2023

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Cristina Ramona Cudalbu

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

FRONTIERS MEDIA SA2023

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke

Friedhelm Christoph Hummel, Philipp Johannes Koch

BACKGROUND:The indication for mechanical thrombectomy (MT) in stroke patients with large vessel occlusion has been constantly expanded over the past years. Despite remarkable treatment effects at the group level in clinical trials, many patients remain sev ...

Lippincott Williams & Wilkins2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Chao Cui, Florent Gérard Krzakala, Lenka Zdeborová

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Hidden-nucleons neural-network quantum states for the nuclear many-body problem

Giuseppe Carleo

We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrodinger equation in a systematically improvable fashion. We demonstrate that adding hi ...

AMER PHYSICAL SOC2022

Maintenance scheduling of manufacturing systems based on optimal price of the network

Olga Fink

Goods can exhibit positive externalities impacting decisions of customers in social networks. Suppliers can integrate these externalities in their pricing strategies to increase their revenue. Besides optimizing the prize, suppliers also have to consider t ...

2022

Simulating the progression of brain structural alterations in Parkinson's disease

Chang-Hyun Park

Considering brain structural alterations as neurodegenerative consequences of Parkinson's disease (PD), we sought to infer the progression of PD via the ordering of brain structural alterations from cross-sectional MRI observations. Having measured cortica ...

NATURE PORTFOLIO2022

Hidden Markov Modeling Over Graphs

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Mert Kayaalp

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

IEEE2022

Opinion Formation over Adaptive Networks

Virginia Bordignon

An adaptive network consists of multiple communicating agents, equipped with sensing and learning abilities that allow them to extract meaningful information from measurements. The objective of the network is to solve a global inference problem in a decent ...

EPFL2022

Mechanical intelligence for learning embodied sensor-object relationships

Ahalya Prabhakar

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...

Nature Portfolio2022

Customized Neural Network training to predict the highly imbalanced data of domestic hot water usage

Dolaana Khovalyg, Amirreza Heidari

Despite space heating and cooling, the energy use for hot water production has not changed significantly over time and accounts for a big share in modern, well-insulated buildings. The main challenge of hot water generation lies in the highly stochastic na ...

CLIMA 2022 conference2022