A Stochastic Momentum Accelerated Quasi-Newton Method for Neural Networks (Student Abstract)

Indrapriyadarsini Sendilkkumaar, Shahrzad Mahboubi, Hiroshi Ninomiya, Takeshi Kamio, Hideki Asai

[AAAI-22] Student Abstract and Poster Program

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Poster Session 2 @ Blue 2, Poster Session 9 @ Blue 2, Poster Session 2, Poster Session 9

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Abstract: Incorporating curvature information in stochastic methods has been a challenging task. This paper proposes a momentum accelerated BFGS quasi-Newton method in both its full and limited memory forms, for solving stochastic large scale non-convex optimization problems in neural networks (NN).

Sessions where this paper appears

  • Poster Session 2

    Fri, February 25 12:45 AM - 2:30 AM (+00:00)
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  • Poster Session 9

    Sun, February 27 8:45 AM - 10:30 AM (+00:00)
    Blue 2
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