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An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages

Publications associées (34)

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Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Referencing in YouTube Knowledge Communication Videos

Daniel Gatica-Perez, Haeeun Kim

In recent years, there has been widespread concern about misinformation and hateful content on social media that are damaging societies. Being one of the most influential social media that practically serves as a newsearch engine, YouTube has accepted crit ...

New York2023

Robustness and invariance properties of image classifiers

Apostolos Modas

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

EPFL2022

Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of acade ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Dif-MAML: Decentralized Multi-Agent Meta-Learning

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

IEEE2021

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Chang-Hyun Park

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

OXFORD UNIV PRESS INC2021

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

The quantification of population-level health behaviors is crucial for guiding public health policy. However, traditional methods for measuring such health behaviors have several short- comings. In recent years social media data has been successfully used ...

EPFL2021