Norm Bounded

"Norm-bounded" research explores the limitations and capabilities of systems with constrained parameters, primarily focusing on the impact of these bounds on approximation capacity and generalization performance. Current research investigates this in various contexts, including neural networks (analyzing the effects of bounded weights and biases on approximation power and employing techniques like Bayesian optimization for hyperparameter tuning), robotic control (developing algorithms for swarm robotics and motion planning with guaranteed safety under bounded resources), and machine learning (designing robust loss functions and analyzing generalization bounds for models with bounded parameters). This work is significant for improving the reliability, efficiency, and safety of machine learning models and robotic systems in real-world applications where resource constraints are inherent.