Beyond $\ell_2$-norm and $\ell_\infty$-norm: A Curvature-Inspired $\ell_p$-Norm Scheme for Deep Neural Networks
arXiv:2606.02078v1 Announce Type: new Abstract: The existing optimizers for deep neural networks (DNNs) typically rely on either the $\ell_2$ norm or the $\ell_\infty$ norm, resulting in optimizers that do not adapt well to substantial changes in curvature across parameter dimensions. Generally, the training process of DNNs often exhibits strong curvature anisotropy in the early period, whereas in the later period, the training process of DNNs tends to move toward flatter regions with weaker anisotropy. Particularly, optimizers based on the \(\ell_2\)-norm are usually dominated by high-curvatu
The continuous evolution of deep learning architectures and training complexities necessitates ongoing research into more efficient and robust optimization techniques. Researchers are pushing beyond established norms to address performance bottlenecks.
Improved optimization techniques can lead to more stable, faster training and potentially better generalization for Deep Neural Networks, impacting the efficiency and efficacy of AI systems across various applications.
This research introduces a novel optimization scheme that could lead to more adaptive and robust training of DNNs by moving beyond current standard norm-based approaches, potentially accelerating AI development cycles.
- · AI researchers
- · Deep learning practitioners
- · High-performance computing (HPC) providers
- · AI-driven industries
- · Developers reliant on suboptimal training
More efficient training allows for larger models or faster iteration on existing ones, benefiting AI development.
Reduced computational cost for training complex models could lower barriers to entry for some AI applications.
Improved model performance could lead to advancements in areas critical for broader AI deployment, such as autonomous systems.
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Read at arXiv cs.LG