SPIN Unprocessed July 10, 2026 ai_technology research
LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks
View original on arxiv.orgOverview
arXiv:2607.07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge. Lipschitz-constrained models guarantee robustness by design, yet the manual selection of the Lipschitz constraint L governs the resulting accuracy-robustness trade-off, and their calibration properties remain largely underexplored. In this
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- A law of robustness for two-layer neural networks with arbitrary weights
- Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure
- Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
- Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
- Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
- Scalable and Trustworthy Earth Observation Foundation Models
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO