output
20062025
most citedDynamical Variational Autoencoders: A Comprehensive Review

169 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cs.LGShow all

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cs.LG2025

How Foundational are Foundation Models for Time Series Forecasting?

Nouha Karaouli, Denis Coquenet, Elisa Fromont +2

Foundation Models are designed to serve as versatile embedding machines, with strong zero shot capabilities and superior generalization performance when fine-tuned on diverse downs…

cs.LG2025

Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting

Nouha Karaouli, Denis Coquenet, Elisa Fromont +2

While Time Series Foundation Models (TSFMs) excel in zero-shot tasks, their behavior under continual fine tuning is poorly understood. We present the first systematic study of cata…

cs.LG2020★ 169 cited

Dynamical Variational Autoencoders: A Comprehensive Review

Laurent Girin, Simon Leglaive, Xiaoyu Bie +3

Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsu…