arXiv:2606.15702v1 Announce Type: cross Abstract: Modern deep learning optimization features heterogeneous parameter structures, noisy gradients, and highly nonconvex landscapes, posing significant challenges for both algorithm design and theoretical analysis. Motivated by the limitations of SGD and the success of adaptive optimizers, we propose {\it Schattor}, a family of adaptive first-order methods based on Schatten norms. Schattor unifies SGD and the recently proposed matrix-variate adaptive optimizer Muon within a single Schatten-norm-based framework. We establish dimension-free stationar
The continuous evolution of deep learning optimization methods is driven by the increasing complexity of AI models and the need for more efficient training algorithms.
Improved optimization techniques can significantly enhance the training efficiency, stability, and performance of large-scale AI models, impacting the pace of AI development and deployment.
This research introduces a unifying framework for adaptive optimizers, potentially leading to more robust and powerful methods for training deep learning models beyond current state-of-the-art techniques.
- · AI researchers
- · Deep learning practitioners
- · Companies developing large AI models
- · Cloud computing providers
- · Developers reliant solely on older optimization techniques
More efficient training of advanced AI models across various applications.
Accelerated development of more complex and capable AI systems due to reduced computational burden.
Increased accessibility of advanced AI model development to a broader range of organizations due to optimization efficiencies.
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