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Euclid: Identification of asteroid streaks in simulated images using deep learning
Publications associées (36)
Transport of dust across the Solar System: Constraints on the spatial origin of individual micrometeorites from cosmic-ray exposure
Reto Georg Trappitsch, Xuan Li
The origin of micrometeorites (MMs) from asteroids and comets is well-established, but the relative contribution from these two classes remains poorly resolved. Likewise, determining the precise origin of individual MMs is an open challenge. Here, cosmic-r ...
Royal Soc2024Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination
Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco
The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...
Oxford Univ Press2024Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...
EPFL2023Deep Learning Generalization with Limited and Noisy Labels
Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...
EPFL2023From Kernel Methods to Neural Networks: A Unifying Variational Formulation
The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...
New York2023Estimating and Improving the Robustness of Attributions in Text
End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...
EPFL2023Robust Training and Verification of Deep Neural Networks
According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...
EPFL2023Gaia Focused Product Release: Asteroid orbital solution: Properties and assessment
Stephan Morgenthaler, Shuangqing Liao
Context. We report the exploitation of a sample of Solar System observations based on data from the third Gaia Data Release (Gaia DR3) of nearly 157 000 asteroids. It extends the epoch astrometric solution over the time coverage planned for the Gaia DR4, w ...
Les Ulis Cedex A2023Graph Representation Learning in Computational Pathology
Advances in scanning systems have enabled the digitization of pathology slides into Whole-Slide Images (WSIs), opening up opportunities to develop Computational Pathology (CompPath) methods for computer-aided cancer diagnosis and prognosis. CompPath has be ...
EPFL2022Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
EPFL2022