Probabilistic Neural Network

Probabilistic neural networks (PNNs) aim to enhance traditional neural networks by explicitly modeling uncertainty in predictions, providing not just point estimates but also probability distributions over possible outcomes. Current research focuses on improving PNN calibration, exploring various architectures like restricted Boltzmann machines and diffusion models, and developing efficient training methods such as Monte Carlo Dropout and alternatives to computationally expensive variational inference. This work is significant because accurately representing uncertainty is crucial for building robust and reliable AI systems across diverse applications, from scientific machine learning and weather forecasting to medical diagnosis and industrial process monitoring.

Papers