Publications related to Binary classification

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jonathan Dan, Jérôme Paul Rémy Thevenot

Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for quantifying daily coughs, providing c ...

arXiv2024

Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

Donal Patrick Hill

Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain a ...

Pergamon-Elsevier Science Ltd2024

On double-descent in uncertainty quantification in overparametrized models

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

Learning curves for the multi-class teacher-student perceptron

Lenka Zdeborová, Elisabetta Cornacchia, Bruno Loureiro, Bruno Loureiro, Francesca Mignacco

One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) ...

IOP Publishing Ltd2023

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, Marco Pizzolato, Alonso Ramirez Manzanares, Juan Luis Villarreal Haro, Alessandro Daducci, Ying-Chia Lin, Sara Sedlar, Caio Seguin, Kenji Marshall, Yang Ji

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMR ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

End-to-end kernel learning via generative random Fourier features

Fanghui Liu, Jie Yang

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

ELSEVIER SCI LTD2023

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Christian Leinenbach, Sergey Shevchik, Rafal Wróbel

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

Lausanne2023

Improved marine debris detection in satellite imagery with automatic refinement of coarse hand annotations

Devis Tuia, Marc Conrad Russwurm, Elif Dilge Gül

Plastic litter is a major environmental hazard that endangers human, animal, and plant health on the planet. A substantial portion of plastic pollutants is washed from rivers and beaches into the oceans and aggregates at the surface as marine debris before ...

2023

Error scaling laws for kernel classification under source and capacity conditions

Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui, Bruno Loureiro

In this manuscript we consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curves of ...

2023

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Binary classification

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

On double-descent in uncertainty quantification in overparametrized models

Learning curves for the multi-class teacher-student perceptron

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

End-to-end kernel learning via generative random Fourier features

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Improved marine debris detection in satellite imagery with automatic refinement of coarse hand annotations

Error scaling laws for kernel classification under source and capacity conditions

Graph Chatbot

Chat with Graph Search

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

On double-descent in uncertainty quantification in overparametrized models

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

Learning curves for the multi-class teacher-student perceptron

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

End-to-end kernel learning via generative random Fourier features

Improved marine debris detection in satellite imagery with automatic refinement of coarse hand annotations

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Error scaling laws for kernel classification under source and capacity conditions