Publications associées à Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Publications associées (21)

Reverse engineering primate sensorimotor control with machine learning

The ability of the brain to produce adaptive and dexterous movements relies on the seamless integration of sensory feedback into motor commands. Despite significant advances in motor neuroscience, critical gaps persist in understanding how proprioceptive a ...

EPFL2025

Mathematical Analysis and Optimization of Features for Atomistic Machine Learning

Kevin Kazuki Huguenin-Dumittan

Computational methods at the atomic scale based on machine learning have been a fruitful research field in the past decade by merging the advantages of classical and quantum mechanical approaches. While parts of the field are already reaching a mature stat ...

EPFL2025

Advancing Automated Identification of Airborne Fungal Spores: Guidelines for Cultivation and Reference Dataset Creation

Sophie Catherine Erb

Airborne bioparticles, including fungal spores, are of major concern for human and plant health, necessitating precise monitoring systems. While a European norm exists for manual volumetric monitoring, there's a growing interest in automated real-time meth ...

SPRINGER2025

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis

Mary-Anne Hartley

Background: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve i ...

2024

Uncertainty quantification in groundwater volume predictions from seismic data using neural networks

Jan Sickmann Hesthaven

Optimum groundwater management is important for addressing the global challenges of water scarcity, especially in arid regions. This study demonstrates the application of synthetic data combined with seismic methods to quantify water volumes in a controlle ...

European Association of Geoscientists and Engineers, EAGE2024

A 2.46-mm2 Miniaturized Brain-Machine Interface (MiBMI) Enabling 31-Class Brain-to-Text Decoding

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri, Amitabh Yadav

Recent advancements in brain-machine interface (BMI) technology offer groundbreaking solutions for individuals with motor impairments, potentially extending to speech synthesis and handwriting assistance. However, current BMIs rely on cumbersome benchtop s ...

2024

Partial annotations in active learning for semantic segmentation

Katrin Beyer, Radhakrishna Achanta

Semantic segmentation with deep learning plays a crucial role in various fields, including civil engineering, particularly in tasks such as damage assessment and urban planning. This paper addresses the challenge of efficiently training deep learning model ...

2024

Learning Time-Scales in Two-Layers Neural Networks

Raphaël Jean Berthier

Gradient-based learning in multi-layer neural networks displays a number of striking features. In particular, the decrease rate of empirical risk is non-monotone even after averaging over large batches. Long plateaus in which one observes barely any progre ...

2024

Neurostimulation and Wireless Data Transmission Circuits for Implantable High-Density Neural Interfaces

Cong Ding

Over the past years, implantable closed-loop neural interfaces have received increasing attention for their potential to enhance the understanding of the brain, treat neurological disorders, and provide unprecedented opportunities for individuals with para ...

EPFL2024

Geometry-Driven Stock-Constrained Truss Design via Equilibrium-Based Structural Models

Corentin Jean Dominique Fivet

This paper presents a geometry-driven approach to form-finding with reused stock elements. Our proposed workflow uses a K-mean algorithm to cluster stock elements and incorporate their geometrical values early in the form-finding process. A feedback loop i ...

Springer2024

Universality laws for Gaussian mixtures in generalized linear models

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi

A recent line of work in high-dimensional statistics working under the Gaussian mixture hypothesis has led to a number of results in the context of empirical risk minimization, Bayesian uncertainty quantification, separation of kernel methods and neural ne ...

2024

Reduced order models to address temporal complexity and geometrical variability in hemodynamics

Riccardo Tenderini

In recent years, numerical simulations of hemodynamics have gained significant attention within the medical community, thanks to their ability of non-invasively estimating the blood flow conditions. However, high-fidelity simulations require extensive comp ...

EPFL2024

Cellpose training data and scripts from "Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments"

Johan Auwerx, Olivier Burri, Martin Rainer Wohlwend

This Workflow contains all the material necessary to reproduce the results of the QuPath analysis performed in the paper "Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments" Inside this workflow and dataset, you will f ...

Zenodo2024

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Tanja Christina Käser Jacober, Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

Corinne Louise Jones

Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods different in essence from kernel-based methods. In this work we translate several classical convolutional neural networks into kernel-based counterparts. Ea ...

2023

End-to-end learned early classification of time series for in-season crop type mapping

Devis Tuia

Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to p ...

2023

A Wearable Smart Glove and Its Application of Pose and Gesture Detection to Sign Language Classification

Josephine Anna Eleanor Hughes

Advances in soft sensors coupled with machine learning are enabling increasingly capable wearable systems. Since hand motion in particular can convey useful information for developing intuitive interfaces, glove-based systems can have a significant impact ...

Institute of Electrical and Electronics Engineers Inc.2022

Teaching machines how to find strongly lensed galaxies in cosmological sky surveys

Elodie Marie Charlène Savary

Analyzing gravitationally lensed objects enables a wide range of physical and cosmological applications, such as probing the dark matter content in galaxies and clusters or measuring the Universe's expansion rate. The precision of these applications can be ...

EPFL2022

Event-driven acquisition for content-enriched microscopy

Suliana Manley, Dora Mahecic, Martin Weigert, Juliette Griffie, Chen Zhang, Willi Leopold Stepp

Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution. A common goal ...

Nature Publishing Group2022

Complex Analysis meets Statistical Mechanics: Applications to Kernel Methods and Conformal Field Theory

Francesco Spadaro

This thesis is centered on questions coming from Machine Learning (ML) and Statistical Field Theory (SFT). In Machine Learning, we consider the subfield of Supervised Learning (SL), and in particular regression tasks where one tries to find a regressor tha ...

EPFL2021