Deep kernel learning combines neural networks with Gaussian processes to make better predictions. It was thought to prevent overfitting, but it can still happen in some cases. By testing on different datasets, researchers found that a fully Bayesian approach can fix this issue and improve performance over regular neural networks and Gaussian processes.