24 citations · 43 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 19 cited
Subspace Inference for Bayesian Deep Learning
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko +3
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Ba…
cs.LG2019★ 24 cited
SWALP : Stochastic Weight Averaging in Low-Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko +3
Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages…