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About A Simple Baseline For Bayesian Uncertainty in Deep Learning
This document proposes SWAG (Stochastic Weight Averaging Gaussian), a scalable approach for representing and calibrating uncertainty in deep learning models. SWAG builds on Stochastic Weight Averaging (SWA) by computing the first moment (mean) of SGD iterates using SWA and additionally fitting a Gaussian distribution using the SWA solution as the mean and a low-rank plus diagonal covariance derived from the SGD iterates, forming an approximate posterior distribution over neural network weights. The paper empirically finds that SWAG approximates the true posterior distribution and performs well on tasks like out-of-sample detection, calibration, and transfer learning compared to other popular Bayesian deep learning methods.
Detailed Information
| Author: | ['Leon'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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