Concept

Variational Bayesian methods

Publications associées (121)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation

Jean-Philippe Thiran

Medical image segmentation is an important task forcomputer aided diagnosis. Pixelwise manual annotationsof large datasets require high expertise and is time consum-ing. Conventional data augmentations have limited benefitby not fully representing the unde ...

2020

Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems

Vincenzo Savona, Alexandra Nagy

The possibility to simulate the properties of many-body open quantum systems with a large number of degrees of freedom (d.o.f.) is the premise to the solution of several outstanding problems in quantum science and quantum information. The challenge posed b ...

AMER PHYSICAL SOC2019

Bayesian Gaussian mixture model for robotic policy imitation

Sylvain Calinon, Emmanuel Pignat

A common approach to learn robotic skills is to imitate a demonstrated policy. Due to the compounding of small errors and perturbations, this approach may let the robot leave the states in which the demonstrations were provided. This requires the considera ...

2019

Fast Mean Estimation with Sub-Gaussian Rates

Nicolas Henri Bernard Flammarion

We present an improved analysis of the Euler-Maruyama discretization of the Langevin diffusion. Our analysis does not require global contractivity, and yields polynomial dependence on the time horizon. Compared to existing approaches, we make an additional ...

2019

Sequential model identification of reaction systems-The missing path between the incremental and simultaneous approaches

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Modeling chemical reaction systems is an important but complex task. The identified kinetic model must be able to explain all the underlying rate processes such as chemical reactions and heat and mass transfers. Traditionally, the modeling task is carried ...

2019

Distributed Inference Over Networks Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that enforce the objectives across the network to lie in a low-dimensiona ...

IEEE2019

Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials

Mathieu Salzmann

Dense conditional random fields (CRFs) have become a popular framework for modeling several problems in computer vision such as stereo correspondence and multiclass semantic segmentation. By modeling long-range interactions, dense CRFs provide a labeling t ...

SIAM PUBLICATIONS2019

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

A novel approach is presented for constructing polynomial chaos representations of scalar quantities of interest (QoI) that extends previously developed methods for adaptation in Homogeneous Chaos spaces. In this work, we develop a Bayesian formulation of ...

ROYAL SOC2018

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approxi ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018