Fetching the paper…
Reading the bibliography…
Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from the experimental data in a Bayesian framework.
On a measure of the information provided by an experiment
Dennis V Lindley · 1956
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Image formation in diffusion mri: a review of recent technical developments
Wenchuan Wu and Karla L Miller · 2017
Earlier work this paper cites.
fastmri: An open dataset and benchmarks for accelerated mri
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, et al · 2018
Earlier work this paper cites.
Variational bayesian optimal experimental design
Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, and Noah Goodman · 2019
Earlier work this paper cites.
Guided image generation with conditional invertible neural networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 2019
Earlier work this paper cites.
Learning-based optimization of the under-sampling pattern in mri
Cagla Deniz Bahadir, Adrian V Dalca, and Mert R Sabuncu · 2019
Cited alongside, same era.
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and Gilles Louppe · 2020
Cited alongside, same era.
A unified stochastic gradient approach to designing bayesian-optimal experiments
Adam Foster, Martin Jankowiak, Matthew O’Meara, Yee Whye Teh, and Tom Rainforth · 2020
Cited alongside, same era.
Bayesflow: Learning complex stochastic models with invertible neural networks
Stefan T Radev, Ulf K Mertens, Andreas Voss, Lynton Ardizzone, and Ullrich Köthe · 2020
Cited alongside, same era.
Extending loupe for k-space under-sampling pattern optimization in multi-coil mri
Jinwei Zhang, Hang Zhang, Alan Wang, Qihao Zhang, Mert Sabuncu, Pascal Spincemaille, Thanh D Nguyen, and Yi Wang · 2020
Cited alongside, same era.
End-to-end sequential sampling and reconstruction for mr imaging
Edge-promoting adaptive bayesian experimental design for x-ray imaging
Tapio Helin, Nuutti Hyvonen, and Juha-Pekka Puska · 2022
Later among the works it cites.
Invertiblenetworks. jl: A julia package for scalable normalizing flows
Rafael Orozco, Philipp Witte, Mathias Louboutin, Ali Siahkoohi, Gabrio Rizzuti, Bas Peters, and Felix J Herrmann · 2023
Later among the works it cites.
A fast and scalable computational framework for large-scale high-dimensional bayesian optimal experimental design
Keyi Wu, Peng Chen, and Omar Ghattas · 2023
Later among the works it cites.
Design amortization for bayesian optimal experimental design
Noble Kennamer, Steven Walton, and Alexander Ihler · 2023
Later among the works it cites.
Sequential experimental design for x-ray ct using deep reinforcement learning
Tianyuan Wang, Felix Lucka, and Tristan van Leeuwen · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tianwei Yin, Zihui Wu, He Sun, Adrian V Dalca, Yisong Yue, and Katherine L Bouman · 2021
Cited alongside, same era.
Minimizing the expected posterior entropy yields optimal summary statistics
Till Hoffmann and Jukka-Pekka Onnela · 2022
Cited alongside, same era.
Later among the works it cites.
On the universality of coupling-based normalizing flows
Felix Draxler, Stefan Wahl, Christoph Schnörr, and Ullrich Köthe · 2024
Closest in time.