Stochastic Deep

Stochastic deep learning focuses on incorporating randomness into deep neural networks to improve reliability, uncertainty quantification, and efficiency. Current research emphasizes developing novel architectures like Bayesian neural networks and ensemble methods, along with exploring techniques such as patch-based approaches for image registration and debate protocols for AI safety. This field is crucial for addressing limitations of deterministic deep learning, particularly in high-stakes applications like medical decision support, where reliable uncertainty estimation and robustness to data shifts are paramount.