32 citations · 122 across the 21 of their papers we have counts for
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cs.LG2019
Learning to Generalize to Unseen Tasks with Bilevel Optimization
Hayeon Lee, Donghyun Na, Hae Beom Lee +1
Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distr…
cs.LG2019★ 19 cited
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
Hae Beom Lee, Hayeon Lee, Donghyun Na +4
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the num…
cs.LG2019★ 4 cited
Meta Dropout: Learning to Perturb Features for Generalization
Hae Beom Lee, Taewook Nam, Eunho Yang +1
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test exa…