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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Related publications (39)

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Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Pascal Fua, Wei Wang

Each smile is unique: one person surely smiles in different ways (e.g. closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video gen ...

IEEE2018

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Subspace Regularized Dynamic Time Warping for Spoken Query Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

Deep neural network posterior probabilities are the best features for query detection in speech archives. Dynamic time warping (DTW) is the state-of-the-art solution for this task. Posterior features live in low-dimensional subspaces whereas, the current D ...

2017

Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

We cast the query by example spoken term detection (QbE-STD) problem as subspace detection where query and background subspaces are modeled as union of low-dimensional subspaces. The speech exemplars used for subspace modeling are class-conditional posteri ...

Idiap2016

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

Structured Prediction of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Vincent Lepetit, Bugra Tekin

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...

2016

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000