Noisy Computation

Noisy computation explores the challenges and opportunities of performing calculations in the presence of errors or uncertainties. Current research focuses on optimizing algorithms for tasks like function computation (e.g., OR, MAX) and image generation (e.g., Stable Diffusion), analyzing the impact of noise on neural network training and robustness, and developing tighter theoretical bounds on the query complexity of noisy algorithms. These investigations are crucial for advancing energy-efficient computing technologies, improving the reliability of machine learning models in real-world settings, and providing a deeper understanding of the fundamental limits of computation under uncertainty.