7.4k citations · 22.9k across the 86 of their papers we have counts for
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stat.ML2020★ 2 cited
Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Kameni +2
Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning…
stat.ML2020★ 32 cited
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen +2
We present a new method for evaluating and training unnormalized density models. Our approach only requires access to the gradient of the unnormalized model's log-density. We estim…