# Scalable Bayesian neural networks by layer-wise input augmentation Source: [Scalable Bayesian neural networks by layer-wise input augmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:40:59.400Z Authors: Trung Trinh, Samuel Kaski, Markus Heinonen Published: 2020-10-26T11:45:19.000Z ## Abstract We introduce implicit Bayesian neural networks, a simple and scalable approach for uncertainty representation in deep learning. Standard Bayesian approach to deep learning requires the impractical inference of the posterior distribution over millions of parameters. Instead, we propose to induce a distribution that captures the uncertainty over neural networks by augmenting each layer's inputs with latent variables. We present appropriate input distributions and demonstrate state-of-the-art performance in terms of calibration, robustness and uncertainty characterisation over large-scale, multi-million parameter image classification tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()