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Jinglai Li

On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design

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Aug 19, 2023
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Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

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Aug 16, 2023
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NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems

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Apr 17, 2023
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VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems

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Feb 22, 2023
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ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics

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Oct 18, 2022
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Linear-Mapping based Variational Ensemble Kalman Filter

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Mar 25, 2021
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An approximate {KLD} based experimental design for models with intractable likelihoods

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Apr 01, 2020
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Bayesian optimization with local search

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Nov 20, 2019
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Bayesian inverse regression for supervised dimension reduction with small datasets

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Jun 19, 2019
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Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions

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Mar 14, 2018
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