Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization
arXiv:2605.29547v1 Announce Type: new Abstract: Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due to non-smooth components such as ReLU activations and quantization operators. In such non-smooth regimes, adaptive optimizers such as Adam suffer from gradient chattering, violent oscillations caused by conflicting signals within the Clarke subdifferential, leading to poor convergence and suboptimal generalization. To address this, we introduce Singularity-aware Adam (S-Adam), a novel optimizer tha
The increasing complexity and adoption of deep learning models, particularly those with non-smooth components, necessitate more robust optimization techniques than currently exist.
Improved optimization algorithms for non-smooth AI models will unlock greater stability, convergence, and generalization, accelerating advancements in various AI applications.
Optimizers like Adam can be made demonstrably more effective in challenging non-smooth loss landscapes, leading to more reliable and deployable AI systems.
- · AI developers
- · Deep learning researchers
- · Industries deploying AI with non-smooth architectures
- · Inefficient AI development cycles
- · Current suboptimal non-smooth optimization methods
More stable and faster training of advanced AI models.
Accelerated development and deployment of robust AI systems across various sectors.
Potentially enables new AI architectures and applications previously limited by optimization challenges.
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Read at arXiv cs.LG