Web: http://arxiv.org/abs/2209.11208

Sept. 23, 2022, 1:13 a.m. | James Harrison, Luke Metz, Jascha Sohl-Dickstein

stat.ML updates on arXiv.org arxiv.org

Learned optimizers -- neural networks that are trained to act as optimizers
-- have the potential to dramatically accelerate training of machine learning
models. However, even when meta-trained across thousands of tasks at huge
computational expense, blackbox learned optimizers often struggle with
stability and generalization when applied to tasks unlike those in their
meta-training set. In this paper, we use tools from dynamical systems to
investigate the inductive biases and stability properties of optimization
algorithms, and apply the resulting insights …

arxiv biases inductive optimization robustness

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