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Title: Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
We propose a Multiscale Invertible Generative Network (MsIGN) and associated training algorithm that leverages multiscale structure to solve high-dimensional Bayesian inference. To address the curse of dimensionality, MsIGN exploits the low-dimensional nature of the posterior, and generates samples from coarse to fine scale (low to high dimension) by iteratively upsampling and refining samples. MsIGN is trained in a multistage manner to minimize the Jeffreys divergence, which avoids mode dropping in high-dimensional cases. On two high-dimensional Bayesian inverse problems, we show superior performance of MsIGN over previous approaches in posterior approximation and multiple mode capture. On the natural image synthesis task, MsIGN achieves superior performance in bits-per-dimension over baseline models and yields great interpret-ability of its neurons in intermediate layers.  more » « less
Award ID(s):
1912654 1907977
NSF-PAR ID:
10286837
Author(s) / Creator(s):
; ;
Editor(s):
Meila, Marina and
Date Published:
Journal Name:
Proceedings of the 38 th International Conference on Machine Learning, PMLR
Volume:
139
Page Range / eLocation ID:
12632--12641
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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