Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small networks, small output spaces and small datasets. We address this limitation by introducing a scalable sample-based Bayesian inference method for conjugate Gaussian multi-output linear models, together with a matching method for hyperparameter (regularisation) selection. Furthermore, we use a classic feature normalisation method (the g-prior) to resolve a previously highlighted pathology of the linearised Laplace method. Together, these contributions allow us to perform linearised neural network inference with ResNet-18 on CIFAR100 (11M parameters, 100 output dimensions x 50k datapoints) and with a U-Net on a high-resolution tomographic reconstruction task (2M parameters, 251k output dimensions).

引入可扩展的基于样本的Bayesian推断方法、匹配的超参数选择方法和经典特征归一化方法，以解决线性化神经网络推断中的计算成本限制和先前突出的路径学问题，从而在CIFAR100、Imagenet和高分辨率层析成像重建任务中实现线性化神经网络推断。

基于采样的大型线性模型推断，应用于线性化拉普拉斯