6.9k citations · 9k across the 31 of their papers we have counts for
4 papers · 1 filter
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Oscar Li, James Harrison, Jascha Sohl-Dickstein +2
Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions…
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
C. Daniel Freeman, Luke Metz, David Ha
Much of model-based reinforcement learning involves learning a model of an agent's world, and training an agent to leverage this model to perform a task more efficiently. While the…
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…