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Kawaguchi et al., 2020 - Google Patents

Ordered sgd: A new stochastic optimization framework for empirical risk minimization

Kawaguchi et al., 2020

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Document ID
15308788340793165433
Author
Kawaguchi K
Lu H
Publication year
Publication venue
International Conference on Artificial Intelligence and Statistics

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Snippet

We propose a new stochastic optimization framework for empirical risk minimization problems such as those that arise in machine learning. The traditional approaches, such as (mini-batch) stochastic gradient descent (SGD), utilize an unbiased gradient estimator of the …
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Classifications

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    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
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