136 citations · 150 across the 8 of their papers we have counts for
8 papers
Interpretable time series neural representation for classification purposes
Etienne Le Naour, Ghislain Agoua, Nicolas Baskiotis +1
Deep learning has made significant advances in creating efficient representations of time series data by automatically identifying complex patterns. However, these approaches lack…
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
Etienne Le Naour, Louis Serrano, Léon Migus +5
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular sample…
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà +3
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different…
LEADS: Learning Dynamical Systems that Generalize Across Environments
Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac +2
When modeling dynamical systems from real-world data samples, the distribution of data often changes according to the environment in which they are captured, and the dynamics of th…
From Node Embedding To Community Embedding : A Hyperbolic Approach
Thomas Gerald, Hadi Zaatiti, Hatem Hajri +2
Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art community embedding approach called \textit{ComE} tackles this prob…
Binary Stochastic Representations for Large Multi-class Classification
Thomas Gerald, Aurélia Léon, Nicolas Baskiotis +1
Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in so…