Publications associées à Analysis of classifiers' robustness to adversarial perturbations

Publications associées (25)

Domain shift, domain adaptation, and generalization: A focus on MRI

Tobias Kober, Meritxell Bach Cuadra, Veronica Lily Ravano, Jonas Richiardi

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Academic Press2025

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Zaffar Ahmed Shaikh

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

SAGE PUBLICATIONS LTD2025

Distribution Testing with a Confused Collector

Nathaniel Idein Harms

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learni ...

Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing2024

Robust Machine Learning - Distributed Methods for Safe AI

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot

Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may mis ...

Springer2024

Exploring crystallographic texture manipulation in stainless steels via laser powder bed fusion: insights from neutron diffraction and machine learning

Roland Logé

Laser powder bed fusion of metals (PBF-LB/M) is a pivotal additive manufacturing technique that enables the production of intricate components. In addition to enabling the production of complex shapes, it allows for a high degree of freedom in manipulating ...

Taylor & Francis2024

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

Model Tuning and Performance Evaluation in Machine Learning Models for PV Power Forecasting: Case Study of a BIPV System in Switzerland

Hugo James André Quest

High PV penetration into the electricity grid can lead to issues such as congestion if PV systems are not integrated effectively. Accurate PV power forecasting helps to address this issue. In recent years machine learning approaches have gotten a lot of at ...

Institute of Electrical and Electronics Engineers Inc.2024

Robust and Private Federated Learning on LLMs

Rachid Guerraoui, Nirupam Gupta

Large Language Models (LLMs) have gained significant attention in recent years due to their potential to revolutionize various industries and sectors. However, scaling LLMs further requires access to substantial linguistic resources that are being rapidly ...

Springer Nature2024

Brain microstructural and functional MRI: developments and application to a rat model of Alzheimer's disease

Yujian Diao

Magnetic resonance imaging (MRI) has been a valuable tool in investigating the pathological cascade of Alzheimer's disease (AD) and its progression, which are still open questions. Although some MRI-derived hallmarks in terms of functional connectivity and ...

EPFL2023

Building typological classification in Switzerland using deep learning methods for seismic assessment

Annalisa Casciato, Pierino Lestuzzi, Alireza Khodaverdian

Natural disasters, such as earthquakes, have always represented a danger to human life. Seismic risk assessment consists of the evaluation of existing buildings and their expected response in case of an earthquake; the exposure model of buildings plays a k ...

Elsevier2023

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Danya Li

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Elsevier2023

Brain dynamics uncovered using a machine-learning algorithm

Mackenzie Mathis

CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...

NATURE PORTFOLIO2023

Hyperdimensional computing for biosignal monitoring: Applications for epilepsy detection

Una Pale

Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...

EPFL2023

Fluctuations, bias, variance and ensemble of learners: exact asymptotics for convex losses in high-dimension

Florent Gérard Krzakala, Bruno Loureiro

From the sampling of data to the initialisation of parameters, randomness is ubiquitous in modern Machine Learning practice. Understanding the statistical fluctuations engendered by the different sources of randomness in prediction is therefore key to unde ...

Iop Publishing Ltd2023

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

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

David Atienza Alonso, Dario Floreano, Ricardo Andres Chavarriaga Lozano, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Ping-Keng Jao

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers’ performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learnin ...

2022

Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning

Tanja Christina Käser Jacober

Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions cou ...

Association for Computing Machinery2022

Self-Exciting Point Processes: Identification and Control

Michael Mark

This thesis addresses theoretical and practical aspects of identification and subsequent control of self-exciting point processes. The main contributions correspond to four separate scientific papers. In the first paper, we address the challenge of robust ...

EPFL2022

Dynamic Range and Complexity Optimization of Mixed-Signal Machine Learning Systems

Andreas Peter Burg, Catherine Dehollain

Audio processing had been in demand throughout the electronic era. Recent advances in neural networks increased the demand on audio processing for speech recognition applications. In this work, a rigorous study on the dynamic range and system complexity op ...

IEEE2021

Adapting Virtual Embodiment through Reinforcement Learning

Olaf Blanke, Ronan Boulic, Bruno Herbelin, Thibault Serge Mario Porssut

In Virtual Reality, having a virtual body opens a wide range of possibilities as the participant's avatar can appear to be quite different from oneself for the sake of the targeted application (e.g. for perspective-taking). In addition, the system can part ...

2021