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stat.ML2021
Improving Classification Accuracy with Graph Filtering
Mounia Hamidouche, Carlos Lassance, Yuqing Hu +3
In machine learning, classifiers are typically susceptible to noise in the training data. In this work, we aim at reducing intra-class noise with the help of graph filtering to imp…
stat.ML2019
Learning Latent Dynamics for Partially-Observed Chaotic Systems
Said Ouala, Duong Nguyen, Lucas Drumetz +5
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i.e., dynamical systems for which some components are never o…