34 citations · 53 across the 6 of their papers we have counts for
5 papers · 1 filter
Vision Transformer Finetuning Benefits from Non-Smooth Components
Ambroise Odonnat, Laetitia Chapel, Romain Tavenard +1
The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness. However, its role in t…
Differentiable Generalized Sliced Wasserstein Plans
Laetitia Chapel, Romain Tavenard, Samuel Vaiter
Optimal Transport (OT) has attracted significant interest in the machine learning community, not only for its ability to define meaningful distances between probability distributio…
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
François Painblanc, Laetitia Chapel, Nicolas Courty +3
While large volumes of unlabeled data are usually available, associated labels are often scarce. The unsupervised domain adaptation problem aims at exploiting labels from a source…
Early Classification for Agricultural Monitoring from Satellite Time Series
Marc Rußwurm, Romain Tavenard, Sébastien Lefèvre +1
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an addition…
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Yichang Wang, Rémi Emonet, Elisa Fromont +4
Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to thi…