Deterministic Neural Network

Deterministic neural networks (DNNs), while achieving state-of-the-art performance in many tasks, face challenges in uncertainty quantification and robustness to out-of-distribution data. Current research focuses on improving DNNs through techniques like incorporating probabilistic elements (e.g., using Bayesian frameworks or stochastic neuron models), leveraging symmetries for improved efficiency and accuracy, and developing novel loss functions to enhance calibration and out-of-distribution detection. These advancements aim to address limitations of traditional DNNs, leading to more reliable and trustworthy predictions across diverse applications, including image synthesis, physical system modeling, and cosmological simulations.