Publications related to Gibbs sampling

Gibbs sampling the posterior of neural networks

Lenka Zdeborová, Giovanni Piccioli, Emanuele Troiani

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Bristol2024

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

Axel Guy Marie Laureau, Elsa Merle

This article outlines the advancements made in broadening the application scope of the OpenMC neutron transport code to include thermohydraulic coupling and nuclear data uncertainty propagation. These developments primarily involve the incorporation of the ...

Taylor & Francis Inc2024

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann, Zhiye Wang

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

Bimanual dynamic grabbing and tossing of objects onto a moving target

Aude Billard, Michael Bosongo Bombile

Bimanual grabbing and tossing of packages onto trays or conveyor belts remains a human activity in the industry. For robots, such a dynamic task requires coordination between two arms and fast adaptation abilities when the tossing target is moving and subj ...

2023

Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings

Fabio Nobile, Juan Pablo Madrigal Cianci

In this work, we present, analyze, and implement a class of multilevel Markov chain Monte Carlo(ML-MCMC) algorithms based on independent Metropolis--Hastings proposals for Bayesian inverse problems. In this context, the likelihood function involves solving ...

2023

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Nicolas Henri Bernard Flammarion

We consider the problem of sampling from a density of the form p(x) ? exp(-f (x) - g(x)), where f : Rd-+ R is a smooth function and g : R-d-+ R is a convex and Lipschitz function. We propose a new algorithm based on the Metropolis-Hastings framework. Under ...

MICROTOME PUBL2022

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Alexandre Massoud Alahi, Megh Hiren Shukla

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

BMVA Press2022

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Gibbs sampling

Gibbs sampling the posterior of neural networks

Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Center-aware Adversarial Augmentation for Single Domain Generalization

Bimanual dynamic grabbing and tossing of objects onto a moving target

Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Graph Chatbot

Chat with Graph Search

Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Bimanual dynamic grabbing and tossing of objects onto a moving target

Gibbs sampling the posterior of neural networks

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Center-aware Adversarial Augmentation for Single Domain Generalization

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings