
DeepSphere: a graphbased spherical CNN
Designing a convolution for a spherical neural network requires a delica...
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Scalable Graph Networks for Particle Simulations
Learning system dynamics directly from observations is a promising direc...
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GACELA – A generative adversarial context encoder for long audio inpainting
We introduce GACELA, a generative adversarial network (GAN) designed to ...
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Emulation of cosmological mass maps with conditional generative adversarial networks
Mass maps created using weak gravitational lensing techniques play a cru...
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Cosmological Nbody simulations: a challenge for scalable generative models
Deep generative models, such as Generative Adversarial Networks (GANs) o...
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DeepSphere: towards an equivariant graphbased spherical CNN
Spherical data is found in many applications. By modeling the discretize...
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Adversarial Generation of TimeFrequency Features with application in audio synthesis
Timefrequency (TF) representations provide powerful and intuitive featu...
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Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accur...
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DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learn...
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A context encoder for audio inpainting
We studied the ability of deep neural networks (DNNs) to restore missing...
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Improving DNNbased Music Source Separation using Phase Features
Music source separation with deep neural networks typically relies only ...
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Large Scale Graph Learning from Smooth Signals
Graphs are a prevalent tool in data science, as they model the inherent ...
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Compressive Embedding and Visualization using Graphs
Visualizing highdimensional data has been a focus in data analysis comm...
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Stationary timevertex signal processing
The goal of this paper is to improve learning for multivariate processes...
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Similarity graphs for the concealment of long duration data loss in music
We present a novel method for the compensation of long duration data gap...
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Predicting the evolution of stationary graph signals
An emerging way of tackling the dimensionality issues arising in the mod...
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Towards stationary timevertex signal processing
Graphbased methods for signal processing have shown promise for the ana...
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Global and Local Uncertainty Principles for Signals on Graphs
Uncertainty principles such as Heisenberg's provide limits on the timef...
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Stationary signal processing on graphs
Graphs are a central tool in machine learning and information processing...
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Fast Robust PCA on Graphs
Mining useful clusters from high dimensional data has received significa...
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UNLocBoX: A MATLAB convex optimization toolbox for proximalsplitting methods
Convex optimization is an essential tool for machine learning, as many o...
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Nathanaël Perraudin
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