14 citations · 23 across the 3 of their papers we have counts for
4 papers
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Luke Metz, Niru Maheswaranathan, C. Daniel Freeman +2
Much as replacing hand-designed features with learned functions has revolutionized how we solve perceptual tasks, we believe learned algorithms will transform how we train models.…
Using a thousand optimization tasks to learn hyperparameter search strategies
Luke Metz, Niru Maheswaranathan, Ruoxi Sun +3
We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from ima…
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…
Monte Carlo Tensor Network Renormalization
William Huggins, C. Daniel Freeman, Miles Stoudenmire +2
Techniques for approximately contracting tensor networks are limited in how efficiently they can make use of parallel computing resources. In this work we demonstrate and character…