AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method | Zendy

Xiaoxia Wu | Zendy; Yuege Xie | Zendy; Simon S. Du | Zendy; Rachel Ward | Zendy

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AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method

Author(s) -

Xiaoxia Wu,

Yuege Xie,

Simon S. Du,

Rachel Ward

Publication year - 2022

Publication title -

proceedings of the aaai conference on artificial intelligence

Language(s) - English

Resource type - Journals

eISSN - 2374-3468

pISSN - 2159-5399

DOI - 10.1609/aaai.v36i8.20848

Subject(s) - parameterized complexity , convergence (economics) , gradient descent , schedule , computer science , context (archaeology) , polynomial , mathematical optimization , convex function , function (biology) , artificial neural network , rate of convergence , stochastic gradient descent , regular polygon , algorithm , mathematics , artificial intelligence , paleontology , mathematical analysis , channel (broadcasting) , computer network , geometry , evolutionary biology , economics , biology , economic growth , operating system

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