6.9k citations · 9k across the 31 of their papers we have counts for
4 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…
Adversarial Spheres
Justin Gilmer, Luke Metz, Fartash Faghri +4
State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are b…