output
20022026
most citedMean-field backward stochastic differential equations: A limit approach

322 citations

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26 papers · 1 filter

cs.LG2025

Towards fully differentiable neural ocean model with Veros

Etienne Meunier, Said Ouala, Hugo Frezat +2

We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make th…

cs.LG2025

Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing

Clément Bonet, Elsa Cazelles, Lucas Drumetz +1

The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian man…

cs.LG2025

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory

Yi-Chun Liao, Chieh-Lin Tsai, Yuan-Hao Chang +3

Although deep learning has demonstrated remarkable capability in learning from unstructured data, modern tree-based ensemble models remain superior in extracting relevant informati…

cs.LG2025

Augmented Invertible Koopman Autoencoder for long-term time series forecasting

Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2

Following the introduction of Dynamic Mode Decomposition and its numerous extensions, many neural autoencoder-based implementations of the Koopman operator have recently been propo…

cs.LG2025

Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIS

Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurélie Michel +3

Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTempe…

cs.LG20241 cited

Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition

Damien Bouchabou, Sao Mai Nguyen

Within the evolving landscape of smart homes, the precise recognition of daily living activities using ambient sensor data stands paramount. This paper not only aims to bolster exi…