arXiv:2408.05560v2 Announce Type: replace Abstract: Stochastic gradient updates are widely used for their efficiency and scalability, but their effective step sizes can depend strongly on feature scaling and local model sensitivity. Gauss-Newton methods address such scale effects through curvature information, but in their standard mini-batch form they require matrix-vector products, linear solves, or structured approximations. This paper studies the special case of scalar-output losses evaluated one sample at a time. In this setting, the generalized Gauss-Newton matrix has rank at most one, a
The paper likely builds on recent advances in optimization for large-scale machine learning, as researchers continuously seek more efficient and scalable algorithms.
Improved optimization techniques can lead to faster training times, more robust AI models, and potentially lower computational resource requirements, impacting the efficiency of AI development.
This research introduces an incremental Gauss-Newton descent method for specific machine learning problems, potentially offering an alternative to stochastic gradient updates by better handling scale effects with curvature information.
- · Machine Learning Researchers
- · AI Development Platforms
- · Cloud Computing Providers (efficiency gains for users)
- · NA
Further research and implementation of incremental Gauss-Newton methods in specific AI applications requiring high optimization efficiency.
Potentially simpler tuning for large-scale models if effective step sizes become less dependent on feature scaling.
Reduced iteration costs for certain types of models, marginally lowering the barrier to entry for complex AI development.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG