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Galaxy Image Simulation Using Progressive GANs
In this work, we provide an efficient and realistic data-driven approach to simulate astronomical images using deep generative models from machine learning.
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method
We characterize the detectability phase transitions in a large set of estimation problems, where we show that there exists a gap between what currently known polynomial algorithms (in particular spectral methods and approximate message-passing) can do and what is expected information theoretically.
A Compressed Sensing Approach for Distribution Matching
In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem.
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
We also show that for a large set of parameters, an iterative algorithm called approximate message-passing is Bayes optimal.
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