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
The paper addresses the ongoing challenge of making practical improvements in neural network training, specifically in estimating weight uncertainty, which is crucial for model reliability and efficiency.
Improving the efficiency and implementability of structured weight uncertainty could lead to more robust, auditable, and less resource-intensive AI models, impacting AI development across various applications.
This research offers a method to incorporate structured weight uncertainty into neural networks without significant cost or major changes to existing training pipelines, potentially broadening its adoption.
- · AI researchers
- · Deep learning practitioners
- · Developers of resource-constrained AI systems
- · Industries requiring reliable AI (e.g., healthcare, autonomous driving)
More efficient and reliable deep learning models become feasible for broader applications.
Reduced computational costs for developing and deploying advanced AI could accelerate innovation in several domains.
Increased interpretability and robustness of AI systems foster greater public trust and accelerate regulatory acceptance.
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Read at arXiv cs.LG