A slightly modified version of the machine learning strategy Momentum with stronger theoretical convergence guarantees for convex functions.

Edits on 7 Mar 2019

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A slightly modified version of the machine learning strategy Momentum with stronger theoretical convergence guarantees for convex functions.

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A slightly modified version of Momentum with stronger theoretical convergence guarantees for convex functions.

Nesterov momentum, or Nesterov Accelerated Gradient (NAG), is a slightly modified version of Momentum with stronger theoretical convergence guarantees for convex functions. In practice, it has produced slightly better results than classical Momentum.

In the standard Momentum method, the gradient is computed using current parameters (* θt*). Nesterov momentum achieves stronger convergence by applying the velocity (

The reason this is sometimes referred to as a "lookahead" gradient is that computing the gradient based on interim parameters allow NAG to change velocity in a faster and more responsive way, resulting in more stable behavior than classical Momentum in many situations, particularly for higher values of *μ. *NAG is the correction factor for classical Momentum method.

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Author

Link

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CS231n Convolutional Neural Networks for Visual Recognition

Stanford Computer Science

Web

Momentum Method and Nesterov Accelerated Gradient - Konvergen - Medium

Roan Gylberth

Web

On the importance of initialization and momentum in deep learning

Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton

Edits on 7 Mar 2019

Edits made to:

A slightly modified version of the machine learning strategy Momentum with stronger theoretical convergence guarantees for convex functions.

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