104 citations · 153 across the 11 of their papers we have counts for
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cs.LG2021★ 25 cited
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango +2
Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty…
cs.LG2021
In-Loop Meta-Learning with Gradient-Alignment Reward
Samuel Müller, André Biedenkapp, Frank Hutter
At the heart of the standard deep learning training loop is a greedy gradient step minimizing a given loss. We propose to add a second step to maximize training generalization. To…