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Found 26 papers, 14 papers with code
Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds
On Euclidean spaces, a popular alternative is the Sliced-Wasserstein distance, which leverages a closed-form solution of the Wasserstein distance in one dimension, but which is not readily available on manifolds.
Koopman Ensembles for Probabilistic Time Series Forecasting
In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed.
On Transfer in Classification: How Well do Subsets of Classes Generalize?
In classification, it is usual to observe that models trained on a given set of classes can generalize to previously unseen ones, suggesting the ability to learn beyond the initial task.
Physics Informed and Data Driven Simulation of Underwater Images via Residual Learning
We propose a deep learning-based architecture to automatically simulate the underwater effects where only a dehazing-like image formation equation is known to the network, and the additional degradation due to the other unknown factors if inferred in a data-driven way.
Time-changed normalizing flows for accurate SDE modeling
The generative paradigm has become increasingly important in machine learning and deep learning models.
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework.
Neural Koopman prior for data assimilation
With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data.
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting
Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth's surface have been made publicly available for scientific purpose, for example through the European Copernicus project.
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 architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics.
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals.
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
Then, we introduce a downstream classifier meant to exploit the presence of multiple objects to improve the performance of few-shot classification, in the case of extreme settings where only one shot is given for its class.
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces.
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
BCI Motor Imagery datasets usually are small and have different electrodes setups.
Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices
The application of Euclidean methods to integrate differential equations lying on such objects does not respect the geometry of the manifold, which can cause many numerical issues.
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings
We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restrained labeling budget.
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities.
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
In this paper we are interested in the problem of solving fine-grain classification or regression, using a model trained on coarse-grain labels only.
Spherical Sliced-Wasserstein
Many variants of the Wasserstein distance have been introduced to reduce its original computational burden.
Preventing Manifold Intrusion with Locality: Local Mixup
Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs.
Ranked #9 on
Image Classification
on Fashion-MNIST
Efficient Gradient Flows in Sliced-Wasserstein Space
However, it requires solving a nested optimization problem at each iteration, and is known for its computational challenges, especially in high dimension.
Subspace Detours Meet Gromov-Wasserstein
In the context of optimal transport methods, the subspace detour approach was recently presented by Muzellec and Cuturi (2019).
Graphs as Tools to Improve Deep Learning Methods
This chapter is composed of four main parts: tools for visualizing intermediate layers in a DNN, denoising data representations, optimizing graph objective functions and regularizing the learning process.
Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme
They are notable for the ability to include a deterministic component of the system and a stochastic one to represent random unknown factors.
Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks
The field of Graph Signal Processing (GSP) has proposed tools to generalize harmonic analysis to complex domains represented through graphs.
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in the training data.
Learning Sentinel-2 Spectral Dynamics for Long-Run Predictions Using Residual Neural Networks
Making the most of multispectral image time-series is a promising but still relatively under-explored research direction because of the complexity of jointly analyzing spatial, spectral and temporal information.
