1.4k citations · 2k across the 18 of their papers we have counts for
19 papers
Practical tradeoffs between memory, compute, and performance in learned optimizers
Luke Metz, C. Daniel Freeman, James Harrison +2
Optimization plays a costly and crucial role in developing machine learning systems. In learned optimizers, the few hyperparameters of commonly used hand-designed optimizers, e.g.…
Understanding How Encoder-Decoder Architectures Attend
Kyle Aitken, Vinay V Ramasesh, Yuan Cao +1
Encoder-decoder networks with attention have proven to be a powerful way to solve many sequence-to-sequence tasks. In these networks, attention aligns encoder and decoder states an…
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…
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…
The geometry of integration in text classification RNNs
Kyle Aitken, Vinay V. Ramasesh, Ankush Garg +3
Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a unified understanding of how RNNs solve these tasks remains elusive. In particul…
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.…