Smoothing Factor

Smoothing techniques aim to reduce noise and improve the robustness and efficiency of various models and algorithms across diverse fields. Current research focuses on applying smoothing within machine learning, particularly to enhance the robustness of deep learning models against adversarial attacks and improve the accuracy of predictions, often employing randomized smoothing, denoising diffusion models, and graph convolutional networks. These advancements have significant implications for improving the reliability and trustworthiness of machine learning systems in critical applications like medical image analysis and autonomous driving, as well as accelerating optimization in probabilistic programming.