On Block Prediction For Learning-Based Point Cloud Compression

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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

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Gaston Jean Lenczner, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

New Particle Formation Events Can Reduce Cloud Droplets in Boundary Layer Clouds at the Continental Scale

Athanasios Nenes, Spyros Pandis

New particle formation (NPF) substantially contributes to global cloud condensation nuclei (CCN), and their climate impacts. Individual NPF events are also thought to increase local CCN, cloud droplet number (CDN), and cloud albedo. High resolution simulat ...

Amer Geophysical Union2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu, Chao Hu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Olga Fink

Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault d ...

Ieee-Inst Electrical Electronics Engineers Inc2024

The multimodality cell segmentation challenge: toward universal solutions

Sahand Jamal Rahi, Vojislav Gligorovski, Marco Labagnara, Jun Ma, Maxime Emmanuel Scheder, Xin Yang, Yao Zhang, Bo Wang, Yixin Wang, Lin Han

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Nature Portfolio2024

Highly Hygroscopic Aerosols Facilitate Summer and Early‐Autumn Cloud Formation at Extremely Low Concentrations Over the Central Arctic Ocean

Julia Schmale, Andrea Baccarini

Arctic clouds are sensitive to atmospheric particles since these are sometimes in such low concentrations that clouds cannot always form under supersaturated water vapor conditions. This is especially true in the late summer, when aerosol concentrations ar ...

2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Elsevier Science Sa2024

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Highly Hygroscopic Aerosols Facilitate Summer and Early‐Autumn Cloud Formation at Extremely Low Concentrations Over the Central Arctic Ocean

Julia Schmale, Andrea Baccarini

2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Elsevier Science Sa2024