Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Véry, Philippe"'
The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising te
Externí odkaz:
http://arxiv.org/abs/2410.07727
Accurately estimating aircraft fuel flow is essential for evaluating new procedures, designing next-generation aircraft, and monitoring the environmental impact of current aviation practices. This paper investigates the generalization capabilities of
Externí odkaz:
http://arxiv.org/abs/2410.07717
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data. If domain invariant representations have dramatically improved the adap
Externí odkaz:
http://arxiv.org/abs/2012.01843
Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence of Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new
Externí odkaz:
http://arxiv.org/abs/2006.13629
Unsupervised Domain Adaptation aims to learn a model on a source domain with labeled data in order to perform well on unlabeled data of a target domain. Current approaches focus on learning \textit{Domain Invariant Representations}. It relies on the
Externí odkaz:
http://arxiv.org/abs/1907.12299
Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such representations becomes highly challenging in NLP tasks since the nuisance fact
Externí odkaz:
http://arxiv.org/abs/1907.12305
We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of
Externí odkaz:
http://arxiv.org/abs/1509.05475
We describe a new visualization tool, dubbed HCMapper, that visually helps to compare a pair of dendrograms computed on the same dataset by displaying multiscale partition-based layered structures. The dendrograms are obtained by hierarchical cluster
Externí odkaz:
http://arxiv.org/abs/1507.08137
We present in this paper a novel non-parametric approach useful for clustering Markov processes. We introduce a pre-processing step consisting in mapping multivariate independent and identically distributed samples from random variables to a generic
Externí odkaz:
http://arxiv.org/abs/1506.09163