Jul 17, 2019 · In this paper, we construct low-dimensional subspaces of parameter space, such as the first principal components of the stochastic gradient ...

In this paper, we propose a different approach to ap- proximate Bayesian inference in deep learning models: we design a low-dimensional subspace S of the weight.

This repository contains a PyTorch code for the subspace inference method introduced in the paper Subspace Inference for Bayesian Deep Learning.

Bayesian inference was once a gold standard for learning with neural networks, provid- ing accurate full predictive distributions and.

We can approximate posterior of 36 million dimensional WideResNet in 5D subspace and get state-of-the-art results! 5. Page 6. SUBSPACE INFERENCE FOR BAYESIAN ...

This paper develops a practical and scalable Bayesian deep learning method that first trains a point estimate, and then infers a full covariance Gaussian ...

SUBSPACE INFERENCE FOR BAYESIAN DEEP LEARNING. INFERENCE. In the subspace, we are able to apply inference methods which struggle in the full parameter space.

Sep 12, 2023 · Subspace inference focus to generate low dimensional subspace from the DNN parameters and perform Bayesian inference on that subspace to generate uncertainties.

Jul 17, 2019 · Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well ...

Bayesian inference provides full predictive distributions and well calibrated uncertainty estimates, but is challenging to perform for modern deep neural.