arXiv:2606.00442v1 Announce Type: new Abstract: Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep networks. Surprisingly, no previous work has exploited the curvature constraints that arise from well known weight-space symmetries in loss landscapes. By analytically averaging over group actions that leave the loss invariant, we construct structured Hessian approximations from single gradients that can be tractably estimated, stored, and inverted. The choice of user-specified symmetry group directly
The paper addresses a long-standing challenge in large-scale machine learning, offering a novel approach to approximating curvature by exploiting previously unconsidered weight-space symmetries.
Efficiently approximating curvature in deep learning models is crucial for advancing training stability, optimization, and uncertainty quantification, directly impacting the development of more robust and accurate AI systems.
The proposed method introduces a new paradigm for Hessian approximation that leverages inherent symmetries, potentially leading to more tractable and scalable solutions compared to existing techniques.
- · AI researchers and developers
- · Deep learning practitioners
- · Companies investing in large-scale AI
- · Academic institutions
- · Developers reliant on less efficient optimization methods
Improved efficiency and scalability of training various deep learning models, especially large ones.
Faster development cycles for new AI architectures and applications due to more effective optimization.
Acceleration in the pace of AI advancement, enabling more complex and reliable AI systems across various industries.
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