Random Perturbation

Random perturbation, the intentional introduction of noise into data or models, is a burgeoning research area with applications across machine learning and scientific computing. Current research focuses on leveraging perturbations to improve model robustness against adversarial attacks, enhance the efficiency of sampling and optimization algorithms (including those for recurrent neural networks and diffusion models), and address challenges in domain adaptation and fairness. These techniques are proving valuable for improving the reliability and performance of machine learning models in various applications, from speech enhancement and image generation to power system stability analysis and robustness verification.