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02/20/2023
An evaluation of deep learning models for predicting water depth evolution in urban floods
In this technical report we compare different deep learning models for p...
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10/04/2022
Diffusion Models for Graphs Benefit From Discrete State Spaces
Denoising diffusion probabilistic models and score matching models have ...
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05/23/2022
What You See is What You Classify: Black Box Attributions
An important step towards explaining deep image classifiers lies in the ...
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04/04/2022
SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
We approach the graph generation problem from a spectral perspective by ...
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09/24/2021
A data acquisition setup for data driven acoustic design
In this paper, we present a novel interdisciplinary approach to study th...
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12/30/2020
DeepSphere: a graph-based spherical CNN
Designing a convolution for a spherical neural network requires a delica...
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10/14/2020
Scalable Graph Networks for Particle Simulations
Learning system dynamics directly from observations is a promising direc...
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05/11/2020
GACELA – A generative adversarial context encoder for long audio inpainting
We introduce GACELA, a generative adversarial network (GAN) designed to ...
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04/17/2020
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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08/15/2019
Cosmological N-body simulations: a challenge for scalable generative models
Deep generative models, such as Generative Adversarial Networks (GANs) o...
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04/08/2019
DeepSphere: towards an equivariant graph-based spherical CNN
Spherical data is found in many applications. By modeling the discretize...
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02/11/2019
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Time-frequency (TF) representations provide powerful and intuitive featu...
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11/13/2018
Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accur...
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10/29/2018
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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10/29/2018
A context encoder for audio inpainting
We studied the ability of deep neural networks (DNNs) to restore missing...
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07/07/2018
Improving DNN-based Music Source Separation using Phase Features
Music source separation with deep neural networks typically relies only ...
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10/16/2017
Large Scale Graph Learning from Smooth Signals
Graphs are a prevalent tool in data science, as they model the inherent ...
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02/19/2017
Compressive Embedding and Visualization using Graphs
Visualizing high-dimensional data has been a focus in data analysis comm...
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11/01/2016
Stationary time-vertex signal processing
The goal of this paper is to improve learning for multivariate processes...
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07/22/2016
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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07/12/2016
Predicting the evolution of stationary graph signals
An emerging way of tackling the dimensionality issues arising in the mod...
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06/22/2016
Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the ana...
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03/10/2016
Global and Local Uncertainty Principles for Signals on Graphs
Uncertainty principles such as Heisenberg's provide limits on the time-f...
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01/11/2016
Stationary signal processing on graphs
Graphs are a central tool in machine learning and information processing...
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07/29/2015
Fast Robust PCA on Graphs
Mining useful clusters from high dimensional data has received significa...
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02/04/2014