👉 NoS (Noise Sensitivity Optimization) engineering is a specialized field focused on enhancing the robustness of machine learning models against noisy or erroneous input data. It involves designing and implementing techniques to mitigate the impact of noise, such as outliers, corrupted data, or adversarial perturbations, ensuring that models maintain high performance and reliability even under suboptimal conditions. This can include methods like robust loss functions, data augmentation with noise, and model architectures that are inherently more resilient to noise. NoS engineering is crucial in applications where data quality is inconsistent or unpredictable, such as in real-world sensor networks, financial forecasting, or autonomous systems.
