565 citations · 696 across the 19 of their papers we have counts for
7 papers · 1 filter
Parallel Training of Deep Networks with Local Updates
Michael Laskin, Luke Metz, Seth Nabarro +5
Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to g…
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Jack Parker-Holder, Luke Metz, Cinjon Resnick +6
Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various…
Reverse engineering learned optimizers reveals known and novel mechanisms
Niru Maheswaranathan, David Sussillo, Luke Metz +2
Learned optimizers are algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple updat…
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.…
On Linear Identifiability of Learned Representations
Geoffrey Roeder, Luke Metz, Diederik P. Kingma
Identifiability is a desirable property of a statistical model: it implies that the true model parameters may be estimated to any desired precision, given sufficient computational…
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…