59 citations · 134 across the 21 of their papers we have counts for
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cs.LG2019★ 59 cited
Improving Generalization in Meta Reinforcement Learning using Learned Objectives
Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Our novel meta reinforcement learning algorithm MetaGenRL is ins…
cs.LG2019★ 1 cited
Gaussian Mean Field Regularizes by Limiting Learned Information
Julius Kunze, Louis Kirsch, Hippolyt Ritter +1
Variational inference with a factorized Gaussian posterior estimate is a widely used approach for learning parameters and hidden variables. Empirically, a regularizing effect can b…