Publications related to Role of sampling in evaluating classical time autocorrelation functions

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

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

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, Marco Pizzolato, Alonso Ramirez Manzanares, Juan Luis Villarreal Haro, Alessandro Daducci, Ying-Chia Lin, Sara Sedlar, Caio Seguin, Kenji Marshall, Yang Ji

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMR ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Shortest-support multi-spline bases for generalized sampling

Michaël Unser, Alexis Marie Frederic Goujon, Shayan Aziznejad, Alireza Naderi

Generalized sampling consists in the recovery of a function f, from the samples of the responses of a collection of linear shift-invariant systems to the input f . The reconstructed function is typically a member of a finitely generated integer-shift invar ...

2021

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Uncertainty propagation based on correlated sampling technique for nuclear data applications

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau

A correlated sampling technique has been implemented to estimate the impact of cross section modifications on the neutron transport and in Monte Carlo simulations in one single calculation. This implementation has been coupled to a Total Monte Carlo approa ...

2020

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...

2019

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...

Elsevier2018

Role of sampling in evaluating classical time autocorrelation functions

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Gibbs sampling the posterior of neural networks

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

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Shortest-support multi-spline bases for generalized sampling

Collaborative Sampling in Generative Adversarial Networks

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Uncertainty propagation based on correlated sampling technique for nuclear data applications

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Random sampling of bandlimited signals on graphs

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Shortest-support multi-spline bases for generalized sampling

Gibbs sampling the posterior of neural networks

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

Uncertainty propagation based on correlated sampling technique for nuclear data applications

Collaborative Sampling in Generative Adversarial Networks

Random sampling of bandlimited signals on graphs