arxiv
PublishedJuly 21, 2026 at 4:00 AM
Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization
Publisher summary· verbatim
arXiv:2605.29547v2 Announce Type: replace-cross 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-s
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning12harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation12harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making12harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection12hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗