19 citations · 69 across the 25 of their papers we have counts for
3 papers · 2 filters
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not
Chelsea Murray, James U. Allingham, Javier Antorán +1
Farquhar et al. [2021] show that correcting for active learning bias with underparameterised models leads to improved downstream performance. For overparameterised models such as N…
Depth Uncertainty Networks for Active Learning
Chelsea Murray, James U. Allingham, Javier Antorán +1
In active learning, the size and complexity of the training dataset changes over time. Simple models that are well specified by the amount of data available at the start of active…
Bootstrap Your Flow
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +1
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current fl…