Diederik Kingma and Jimmy Ba published a landmark paper presenting the Adam (Adaptive Moment Estimation) optimizer. By combining the principles of Momentum and RMSProp to compute adaptive learning rates for individual network parameters based on estimates of both the first and second moments of gradients, Adam rapidly became the universal standard training optimizer for deep learning.
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