Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover

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Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at √sNN=5.02 TeV

Jian Wang, Matthias Finger, Qian Wang, Yiming Li, João Miguel das Neves Duarte, Matthias Wolf, Varun Sharma, Yi Zhang, Tian Cheng, Yixing Chen, Alexis Kalogeropoulos, Ioannis Papadopoulos, Hua Zhang, Siyuan Wang, Xin Chen, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Davide Di Croce, Jian Zhao, Rakesh Chawla, Jan Steggemann, Konstantin Androsov, Anna Mascellani, Federica Legger, Matteo Galli, Gabriele Grosso

Two -particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead -lead collisions at a center -of -mass energy per nucleon pair of .'/sNN = 5.02 TeV. The data sample, containing 4.27 x 109 minimum bias events correspo ...

Amer Physical Soc2024

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

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

Why is the winner the best?

Jian Wang, Ho Ling Li, Gabriel Girard, Adrien Raphaël Depeursinge

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Ieee Computer Soc2023

Meteor: Meta-learning connecting earth problems observed from space

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

2023

New dwarf galaxy candidates in the sphere of influence of the Sombrero galaxy

Oliver Müller

We report the discovery of 40 new satellite dwarf galaxy candidates in the sphere of influence of the Sombrero Galaxy (M104), the most luminous galaxy in the Local Volume. Using the Subaru Hyper Suprime-Cam, we surveyed 14.4 deg(2) of its surroundings, ext ...

Oxford Univ Press2023

A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks

Berend Smit

Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space owing to their tunable molecular building blocks with diverse topologies. An unlimited number of MOFs can, in principle, be synthesized. Machine ...

NATURE PORTFOLIO2023