Publications associées à Auto-encodeur variationnel

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

Complex-Valued-Autoencoder for Structural Health Monitoring with Frequency Modulated Continuous Wave Radar

Olga Fink, Gaëtan Michel Frusque

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequence ...

Research Publishing2023

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Jiancheng Yang, Zheng Dang, Zhen Wei, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Los Alamitos2023

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

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

Research Publishing2023

Phenomenological theory of variational quantum ground-state preparation

Ivano Tavernelli, Giuseppe Carleo

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting para ...

College Pk2023

Benign Overfitting in Deep Neural Networks under Lazy Training

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

2023

Learning Linearized Degradation of Health Indicators using Deep Koopman Operator Approach

Olga Fink, Sergei Garmaev

With the current trend of increasing complexity of industrial systems, the construction and monitoring of health indicators becomes even more challenging. Given that health indicators are commonly employed to predict the end of life, a crucial criterion fo ...

Research Publishing2023

Backpropagation-free training of deep physical neural networks

Romain Christophe Rémy Fleury, Ali Momeni, Babak Rahmani, Matthieu Francis Malléjac, Marc Philipp Del Hougne

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

2023

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Auto-encodeur variationnel

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

Complex-Valued-Autoencoder for Structural Health Monitoring with Frequency Modulated Continuous Wave Radar

Evaluating the effect of sparse convolutions on point cloud compression

Robust Outlier Rejection for 3D Registration with Variational Bayes

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Phenomenological theory of variational quantum ground-state preparation

Benign Overfitting in Deep Neural Networks under Lazy Training

Learning Linearized Degradation of Health Indicators using Deep Koopman Operator Approach

Backpropagation-free training of deep physical neural networks

Graph Chatbot

Chattez avec Graph Search

Evaluating the effect of sparse convolutions on point cloud compression

Benign Overfitting in Deep Neural Networks under Lazy Training

Learning Linearized Degradation of Health Indicators using Deep Koopman Operator Approach

Complex-Valued-Autoencoder for Structural Health Monitoring with Frequency Modulated Continuous Wave Radar

Phenomenological theory of variational quantum ground-state preparation

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Robust Outlier Rejection for 3D Registration with Variational Bayes

Backpropagation-free training of deep physical neural networks

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

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning