convergence behavior
This term describes the manner in which an iterative algorithm approaches its final solution. Analyzing convergence behavior is vital for understanding the efficiency and stability of training processes in machine learning.
- Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
- Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers
- Asymptotic theory of SGD with a general learning-rate
- CAMO: Convergence-Aware Multi-Fidelity Bayesian Optimization
- Dynamical Properties of Tokens in Self-Attention and Effects of Positional Encoding
- Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
- On the $O(\frac{\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\ell_1$ Norm