59 citations · 240 across the 11 of their papers we have counts for
3 papers · 1 filter
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen +2
Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distributio…
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
Mohammad Emtiyaz Khan, Didrik Nielsen
Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to complex models such as deep neural networks. Variationa…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…