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The Laplace approximation for neural networks is a Bayesian technique that approximates the posterior distribution of network parameters using a Gaussian centered at the maximum a posteriori estimate. It provides a computationally efficient way to quantify uncertainty in neural network predictions.
Stochastic weight averaging–Gaussian (SWAG) is a technique in machine learning that improves model generalization and uncertainty estimation by approximating the posterior distribution of neural network weights using Gaussian distributions derived from stochastic weight averaging. It enhances predictive performance and reliability in deep learning models.
Uncertainty quantification in deep learning involves methods to measure and manage the confidence of predictions made by neural networks. It aims to identify the reliability of model outputs, especially in critical applications where decision-making depends on understanding the likelihood of errors.