5.2k citations · 6k across the 10 of their papers we have counts for
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cs.LG2018★ 71 cited
Learning Latent Subspaces in Variational Autoencoders
Jack Klys, Jake Snell, Richard Zemel
Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to…
cs.LG2018★ 725 cited
Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi +5
In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has…