Learning To Optimize
Nando de Freitas
2016 Talk
in
Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice
in
Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice
Abstract
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this talk I describe how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. The learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure.
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