405 citations · 2.3k across the 68 of their papers we have counts for
4 papers · 1 filter
A DNN Framework for Learning Lagrangian Drift With Uncertainty
Joseph Jenkins, Adeline Paiement, Yann Ourmières +4
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…
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion
Randall Balestriero, Herve Glotin, Richard Baraniuk
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 o…
Physeter catodon localization by sparse coding
Sébastien Paris, Yann Doh, Hervé Glotin +2
This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in comp…
Stochastic Low-Rank Kernel Learning for Regression
Pierre Machart, Thomas Peel, Liva Ralaivola +2
We present a novel approach to learn a kernel-based regression function. It is based on the useof conical combinations of data-based parameterized kernels and on a new stochastic c…