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tl;dr: You should project the gradients before feeding them to Adam. You should also do the clipping afterwards in case of non-linear constraints and to avoid accumulation of numerical errors. Background: The problem is not the exponential moving average but Adam's gradient normalisation based on the second moment. Essentially, the average gradient is ...


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Using momentum is a noise reduction (noisy gradients) and signal amplification strategy. Imagine a large hill with a rough terrain with lots of ups-and-downs. We are trying to navigate to the bottom of the hill by using purely local information. A bad strategy is course correct frequently every time we see a potential new direction with steeper descent. The ...


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There is not necessarily a difference between the two, but the most important reason we minimize a cost function instead of maximizing a profit function is because of the optimization method. Neural networks are optimized using gradient descent, in which we use the derivative of the cost function to calculate the gradients and use the gradient to adjust the ...


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