Stochastic Neural Network

Stochastic neural networks (SNNs) introduce randomness into neural network architectures, aiming to improve model robustness, uncertainty quantification, and efficiency. Current research focuses on developing and analyzing SNNs using various techniques, including Bayesian neural networks, ensemble methods, and spiking neural networks with novel coding schemes, often applied to architectures like transformers and convolutional neural networks. This work is significant for enhancing the reliability and trustworthiness of AI, particularly in critical applications like clinical decision support, where accurate uncertainty estimation is paramount, and for providing insights into the brain's probabilistic computation.