ADOPT: A Modified Adam Optimizer that Achieves Optimal Convergence Rate with Any β2
The ADOPT algorithm overcomes the convergence limitations of Adam and its variants in smooth nonconvex optimization by decorrelating the second moment estimate from the current gradient and changing the order of momentum update and normalization, achieving optimal convergence rate without relying on specific hyperparameter choices or bounded noise assumptions.
On Using Stochastic Differential Equations to Derive Square Root Scaling Rules for Adaptive Gradient Algorithms
This paper derives and validates square root scaling rules for the adaptive gradient optimization algorithms RMSprop and Adam, using stochastic differential equations (SDEs) to model the algorithms' behavior and analyze the impact of batch size on their performance.
The AdEMAMix Optimizer: Leveraging Old Gradients for Faster and Better Convergence in Deep Learning
AdEMAMix, a novel optimizer, can leverage very old gradients to reach better solutions faster compared to the widely used Adam optimizer. This is achieved by combining a fast-changing and a slow-changing exponential moving average of gradients.
Analyzing Adaptive Gradient Methods Without Square-Root
Removing the square root from adaptive gradient methods can close the generalization gap on convolutional architectures while maintaining performance on transformers, highlighting the overlooked role of adaptivity in their success.
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