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Publications associées (110)
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 ...
Bristol2024Tool 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 Inc2024Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots
Alcherio Martinoli, Wanting Jin
Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurement ...
2024Center-aware Adversarial Augmentation for Single Domain Generalization
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 SOC2023Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
EPFL2022