Publications related to Variational autoencoder

Learning in Volatile Environments With the Bayes Factor Surprise

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

MIT PRESS2021

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Prediction of the reaction forces of spiral-groove gas journal bearings by artificial neural network regression models

Jürg Alexander Schiffmann, Elia Iseli

This paper presents neural network regression models for predicting the nonlinear static and linearized dynamic reaction forces of spiral grooved gas journal bearings. The partial differential equations (PDEs) are sampled, based on a full factorial and ran ...

2021

A Shared Representation for Photorealistic Driving Simulators

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan, Siyuan Li

A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidel ...

2021

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Touradj Ebrahimi, Pinar Akyazi

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...

SPIE-INT SOC OPTICAL ENGINEERING2021

The Solution of the Deep Boltzmann Machine on the Nishimori Line

Emanuele Mingione, Diego Alberici

The deep Boltzmann machine on the Nishimori line with a finite number of layers is exactly solved by a theorem that expresses its pressure through a finite dimensional variational problem of min-max type. In the absence of magnetic fields the order paramet ...

SPRINGER2021

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

Jean-Philippe Thiran, Meritxell Bach Cuadra, Thomas Yu, Cristina Granziera, Francesco La Rosa

Background and objective: Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion con ...

2021

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

Petr Motlicek

This paper describes our participation in the shared evaluation campaign of MexA3T 2020. Our main goal wasto evaluate a Supervised Autoencoder (SAE) learning algorithm in text classification tasks. For our experiments,we used three different sets of featur ...

CEUR Workshop Proceedings2020

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Variational autoencoder

Learning in Volatile Environments With the Bayes Factor Surprise

Statistical limits of high-dimensional inference problems

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Prediction of the reaction forces of spiral-groove gas journal bearings by artificial neural network regression models

A Shared Representation for Photorealistic Driving Simulators

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

The Solution of the Deep Boltzmann Machine on the Nishimori Line

Conditional LSTM-GAN for Melody Generation from Lyrics

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

Graph Chatbot

Chat with Graph Search

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Prediction of the reaction forces of spiral-groove gas journal bearings by artificial neural network regression models

Idiap and UAM Participation at MEX-A3T Evaluation Campaign

Statistical limits of high-dimensional inference problems

The Solution of the Deep Boltzmann Machine on the Nishimori Line

Conditional LSTM-GAN for Melody Generation from Lyrics

A Shared Representation for Photorealistic Driving Simulators

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Learning in Volatile Environments With the Bayes Factor Surprise

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients