Stochastic Layer

Stochastic layers in neural networks introduce randomness during computation, primarily to improve model robustness, generalization, and privacy. Current research focuses on integrating stochasticity into various architectures, including vision transformers and binary neural networks, often employing techniques like stochastic gradient descent and Markov chain-based approaches to manage the added complexity. This research aims to better understand the impact of stochasticity on training dynamics and representational learning, leading to more efficient and reliable deep learning models with enhanced properties like improved calibration and adversarial robustness. The resulting advancements have implications for both theoretical understanding of neural networks and practical applications across diverse fields.