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of 237
pro vyhledávání: '"GLOTIN, Hervé"'
Autor:
Jenkins, Joseph, Paiement, Adeline, Ourmières, Yann, Sommer, Julien Le, Verron, Jacques, Ubelmann, Clément, Glotin, Hervé
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach require
Externí odkaz:
http://arxiv.org/abs/2204.05891
Publikováno v:
Scientific Reports of the Port-Cros National Park, Parc National de Port-Cros, 2021, pp.411-425
Assuming that the anthropogenic impact of visitors to a natural park can be reduced by communication actions, we have drawn up a bioacoustical protocol combined with a protocol to measure the effectiveness of the communication actions. A simple eco-a
Externí odkaz:
http://arxiv.org/abs/2203.16899
We develop an interpretable and learnable Wigner-Ville distribution that produces a super-resolved quadratic signal representation for time-series analysis. Our approach has two main hallmarks. First, it interpolates between known time-frequency repr
Externí odkaz:
http://arxiv.org/abs/2006.07713
Akademický článek
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Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings c
Externí odkaz:
http://arxiv.org/abs/1811.02275
Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly appropriate. Yet acou
Externí odkaz:
http://arxiv.org/abs/1807.05812
Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks. However, their performance relies on the availability of a large set of labeled training data, which limits the breadth of their applicabili
Externí odkaz:
http://arxiv.org/abs/1802.10172
We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach outperforms current state-of-the-art methods on M
Externí odkaz:
http://arxiv.org/abs/1711.04313
Autor:
Balestriero, Randall, Glotin, Herve
In this paper we propose a scalable version of a state-of-the-art deterministic time-invariant feature extraction approach based on consecutive changes of basis and nonlinearities, namely, the scattering network. The first focus of the paper is to ex
Externí odkaz:
http://arxiv.org/abs/1707.05841
The scattering framework offers an optimal hierarchical convolutional decomposition according to its kernels. Convolutional Neural Net (CNN) can be seen as an optimal kernel decomposition, nevertheless it requires large amount of training data to lea
Externí odkaz:
http://arxiv.org/abs/1611.08749