SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks

arXiv:2606.23357v2 Announce Type: replace Abstract: Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show that these issues can be addressed by adapting the SOAP optimizer. Our key idea is to run IVON, an existing diagonal-covariance variational method, in the eigenspace of SOAP's preconditioner and then use the preconditioner to transform the diagonal estimate into a non-diagonal covariance. The resulting method has costs similar to those of SOAP and requires no drastic changes to training pipelines

Source: arXiv cs.LG — read the full report at the original publisher.

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