Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Stochastic optimization algorithms typically use learning rate schedules that behave asymptotically as µ(t) = µ0/t. The ensemble dynamics (Leen and Moody, 1993) for such algorithms provides an easy path to results on mean squared ight error and asymptotic normality.  apply this approach to stochastic gradient algorithms with momentum.  show that at late times, learning is governed by an effective learning rate µeff = µ0/(1 - β) where β is the momentum parameter.  describe the behavior of the asymptotic ight error and give conditions on µeff that insure optimal convergence speed. Finally,  use the results to develop an adaptive form of momentum that achieves optimal convergence speed independent of µ0.
['Todd K. Leen', 'Genevieve B. Orr']
I am writing a research paper on a idea Optimal Stochastic Search and Adaptive Momentum
[{"from": "human", "value": "I am writing a research paper on a idea Optimal Stochastic Search and Adaptive Momentum"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Stochastic optimization algorithms typically use learning rate schedules that behave asymptotically as \u00b5(t) = \u00b50/t. The ensemble dynamics (Leen and Moody, 1993) for such algorithms provides an easy path to results on mean squared ight error and asymptotic normality.  apply this approach to stochastic gradient algorithms with momentum.  show that at late times, learning is governed by an effective learning rate \u00b5eff = \u00b50/(1 - \u03b2) where \u03b2 is the momentum parameter.  describe the behavior of the asymptotic ight error and give conditions on \u00b5eff that insure optimal convergence speed. Finally,  use the results to develop an adaptive form of momentum that achieves optimal convergence speed independent of \u00b50."}]