Deep Learning, Quiz 2

May 2023 term, 6 Aug 2023, Set QPE3

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Question 1

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Suppose that a neural network has millions of parameters (weights and biases). A team decides to use an optimization algorithm with a learning rate scheme that is local to each individual parameters in the network. Moreover, the learning rate changes in each iteration based on the magnitude of gradients pertaining to a parameter in the past. Which of the following optimization algorithms satisfy the team’s requirements?

Select all that apply

Select all that apply.

• A.

  GD with an exponentially decaying learning rate scheduler

• B.

  AdaGrad

• C.

  AdaM

• D.

  NADAM

• E.

  RMSProp

• F.

  SGD with line search

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Suppose that a neural network has millions of parameters (weights and biases). A team decides to use an optimization algorithm with a learning rate scheme that is local to each individual parameters in the network. Moreover, the learning rate changes in each iteration based on the magnitude of gradients pertaining to a parameter in the past. Which of the following optimization algorithms satisfy the team’s requirements? Figure from the original question paper Figure from the original question paper