Solving Bayesian Inverse Problems via Variational Autoencoders

December 05, 2019 ยท Declared Dead ยท ๐Ÿ› Mathematical and Scientific Machine Learning

๐Ÿ‘ป CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Hwan Goh, Sheroze Sheriffdeen, Jonathan Wittmer, Tan Bui-Thanh arXiv ID 1912.04212 Category stat.ML: Machine Learning (Stat) Cross-listed cs.LG, eess.IV Citations 49 Venue Mathematical and Scientific Machine Learning Last Checked 3 months ago
Abstract
In recent years, the field of machine learning has made phenomenal progress in the pursuit of simulating real-world data generation processes. One notable example of such success is the variational autoencoder (VAE). In this work, with a small shift in perspective, we leverage and adapt VAEs for a different purpose: uncertainty quantification in scientific inverse problems. We introduce UQ-VAE: a flexible, adaptive, hybrid data/model-informed framework for training neural networks capable of rapid modelling of the posterior distribution representing the unknown parameter of interest. Specifically, from divergence-based variational inference, our framework is derived such that most of the information usually present in scientific inverse problems is fully utilized in the training procedure. Additionally, this framework includes an adjustable hyperparameter that allows selection of the notion of distance between the posterior model and the target distribution. This introduces more flexibility in controlling how optimization directs the learning of the posterior model. Further, this framework possesses an inherent adaptive optimization property that emerges through the learning of the posterior uncertainty.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Machine Learning (Stat)

๐Ÿ”ฎ ๐Ÿ”ฎ The Ethereal

Layer Normalization

Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton

stat.ML ๐Ÿ› arXiv ๐Ÿ“š 12.0K cites 9 years ago

Died the same way โ€” ๐Ÿ‘ป Ghosted