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Publications associées (71)
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Yi Wu
In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...
2024MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning
Alexandre Massoud Alahi, Mohamed Ossama Ahmed Abdelfattah, Mariam Ahmed Mahmoud Hegazy Hassan
Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...
2024Topics in statistical physics of high-dimensional machine learning
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 ...
EPFL2024Transfer learning application of self-supervised learning in ARPES
There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...
IOP Publishing Ltd2023Why is the winner the best?
Jian Wang, Gabriel Girard, Ho Ling Li, Adrien Raphaël Depeursinge, Yong Yang, Fan Xia, Xiao Wang, Jing Li, Hui Wang
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 ...
Los Alamitos2023Improving Generalization of Pretrained Language Models
In this dissertation, we propose multiple methods to improve transfer learning for pretrained language models (PLMs). Broadly, transfer learning is a powerful technique in natural language processing, where a language model is first pre-trained on a data-r ...
EPFL2023Meteor: Meta-learning connecting earth problems observed from space
Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm
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 ...
2023MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA
Jiancheng Yang, Stanislav Lukyanenko
Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...
New York2023Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Christian Leinenbach, Sergey Shevchik, Rafal Wróbel, Marc Leparoux
This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...
London2023Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection
Florent Evariste Forest, Yunhong Che
Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide t ...
London2023