Publications related to Rejection sampling

Customized Neural Network training to predict the highly imbalanced data of domestic hot water usage

Despite space heating and cooling, the energy use for hot water production has not changed significantly over time and accounts for a big share in modern, well-insulated buildings. The main challenge of hot water generation lies in the highly stochastic na ...

CLIMA 2022 conference2022

Equivariant Diffusion for Molecule Generation in 3D

Clément Arthur Yvon Vignac

This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on bot ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Distributionally Robust Optimization with Markovian Data

Daniel Kuhn, Mengmeng Li, Tobias Sutter

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

2021

Raytraverse: Navigating the Lightfield to Enhance Climate-Based Daylight Modeling

Marilyne Andersen, Jan Wienold, Stephen William Wasilewski

We propose a new method for climate-based daylight modeling (CBDM) based on simulating and evaluating only the most important features. By adaptively sampling the temporal lightfield that describes daylight in buildings, our method escapes the curse of dim ...

2021

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

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

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

The PETALE experimental programme in the CROCUS reactor intends to provide integral measurements to constrain stainless steel nuclear data. This article presents the tools and the methodology developed to design and optimize the experiments, and its operat ...

2020

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Martin Vetterli, Paul Hurley, Matthieu Martin Jean-André Simeoni, Adrien Georges Jean Besson

Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for han ...

2020

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

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Rejection sampling

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Customized Neural Network training to predict the highly imbalanced data of domestic hot water usage

Equivariant Diffusion for Molecule Generation in 3D

Uncertain Sampling with Certain Priors

Distributionally Robust Optimization with Markovian Data

Raytraverse: Navigating the Lightfield to Enhance Climate-Based Daylight Modeling

Uncertainty propagation based on correlated sampling technique for nuclear data applications

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Collaborative Sampling in Generative Adversarial Networks

Graph Chatbot

Chat with Graph Search

Raytraverse: Navigating the Lightfield to Enhance Climate-Based Daylight Modeling

Distributionally Robust Optimization with Markovian Data

Uncertain Sampling with Certain Priors

Equivariant Diffusion for Molecule Generation in 3D

Customized Neural Network training to predict the highly imbalanced data of domestic hot water usage

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Uncertainty propagation based on correlated sampling technique for nuclear data applications

Collaborative Sampling in Generative Adversarial Networks

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI