21 citations · 61 across the 7 of their papers we have counts for
4 papers · 1 filter
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the sam…
Training Learned Optimizers with Randomly Initialized Learned Optimizers
Luke Metz, C. Daniel Freeman, Niru Maheswaranathan +1
Learned optimizers are increasingly effective, with performance exceeding that of hand designed optimizers such as Adam~\citep{kingma2014adam} on specific tasks \citep{metz2019unde…
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…