Scalable Bayesian Inference For Deep Neural Networks
Neural networks can't express model uncertainty, leading to overconfident predictions & poor decisions. Researchers develop scalable methods to equip neural nets with uncertainty estimates using Laplace approximation.
This is a Plain English Papers summary of a research paper called Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview Large neural networks trained on big datasets have become the dominant approach in machine learning. These systems rely on point estimates of their parameters, which means they cannot express model uncertainty. This can lead to overconfident predictions and prevents the use of deep learning mod...