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09/11/2023
Neural Koopman prior for data assimilation
With the increasing availability of large scale datasets, computational ...
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05/05/2023
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting
Over the last few years, massive amounts of satellite multispectral and ...
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03/13/2023
Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data
Over the last few years, several works have proposed deep learning archi...
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03/10/2023
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many superv...
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01/16/2023
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
The field of visual few-shot classification aims at transferring the sta...
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11/18/2022
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
It has been shown beneficial for many types of data which present an und...
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09/23/2022
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings
We consider a novel formulation of the problem of Active Few-Shot Classi...
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09/22/2022
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Normalizing Flows (NF) are powerful likelihood-based generative models t...
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08/07/2022
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
Labeling a classification dataset implies to define classes and associat...
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06/17/2022
Spherical Sliced-Wasserstein
Many variants of the Wasserstein distance have been introduced to reduce...
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01/12/2022
Preventing Manifold Intrusion with Locality: Local Mixup
Mixup is a data-dependent regularization technique that consists in line...
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10/21/2021
Sliced-Wasserstein Gradient Flows
Minimizing functionals in the space of probability distributions can be ...
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10/21/2021
Subspace Detours Meet Gromov-Wasserstein
In the context of optimal transport methods, the subspace detour approac...
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10/08/2021
Graphs as Tools to Improve Deep Learning Methods
In recent years, deep neural networks (DNNs) have known an important ris...
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05/18/2021
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
Stochastic differential equations (SDEs) are one of the most important r...
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02/18/2021
Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks
The field of Graph Signal Processing (GSP) has proposed tools to general...
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01/12/2021
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in t...
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09/04/2020
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
The data-driven recovery of the unknown governing equations of dynamical...
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07/25/2020
Learning Variational Data Assimilation Models and Solvers
This paper addresses variational data assimilation from a learning point...
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06/05/2020
Joint learning of variational representations and solvers for inverse problems with partially-observed data
Designing appropriate variational regularization schemes is a crucial pa...
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05/03/2020
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry m...
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10/01/2019
End-to-end learning of energy-based representations for irregularly-sampled signals and images
For numerous domains, including for instance earth observation, medical ...
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07/04/2019
Learning Latent Dynamics for Partially-Observed Chaotic Systems
This paper addresses the data-driven identification of latent dynamical ...
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03/25/2019
