Publications related to Variational autoencoder

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

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

San Diego2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Lausanne2024

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

Nicola Marzari, Marnik Bercx, Elena Gazzarrini, Carl Simon Adorf

Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight he ...

Nature Portfolio2024

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Mathieu Salzmann, Zheng Dang

In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...

Ieee Computer Soc2024

Neural ADMIXTURE for rapid genomic clustering

Albert Dominguez Mantes

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...

SPRINGERNATURE2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

IEEE2023

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Sébastien Marcel, Parsa Rahimi Noshanagh, Christophe René Joseph Ecabert

Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin. The main reason are the biases in ...

IEEE2023

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Olga Fink, Gaëtan Michel Frusque, Tianfu Li, Qi Li, Baorui Dai

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

2023

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Variational autoencoder

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

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Residual-based attention in physics-informed neural networks

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Neural ADMIXTURE for rapid genomic clustering

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Graph Chatbot

Chat with Graph Search

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Neural ADMIXTURE for rapid genomic clustering

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

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

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

Residual-based attention in physics-informed neural networks