167 citations · 257 across the 7 of their papers we have counts for
3 papers · 1 filter
Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon +2
Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may simil…
Guided evolutionary strategies: Augmenting random search with surrogate gradients
Niru Maheswaranathan, Luke Metz, George Tucker +2
Many applications in machine learning require optimizing a function whose true gradient is unknown, but where surrogate gradient information (directions that may be correlated with…
Meta-Learning Update Rules for Unsupervised Representation Learning
Luke Metz, Niru Maheswaranathan, Brian Cheung +1
A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this…