66 citations · 114 across the 17 of their papers we have counts for
Showing 2020Show all
2 papers · 1 filter
cs.LG2020★ 11 cited
Bayesian Deep Learning via Subnetwork Inference
Erik Daxberger, Eric Nalisnick, James Urquhart Allingham +2
The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency. Alas, scaling Bayesian inference to large weigh…
stat.ML2020
Predictive Complexity Priors
Eric Nalisnick, Jonathan Gordon, José Miguel Hernández-Lobato
Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Reasoning about parameters is made challenging by the high-dimensionality and over-…