167 citations · 257 across the 7 of their papers we have counts for
3 papers · 1 filter
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.…
How recurrent networks implement contextual processing in sentiment analysis
Niru Maheswaranathan, David Sussillo
Neural networks have a remarkable capacity for contextual processing--using recent or nearby inputs to modify processing of current input. For example, in natural language, context…
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…