Adamlike

Adamlike, sometimes written Adam-like, is a term used in machine learning to describe optimization algorithms that resemble Adam, the adaptive moment estimation optimizer introduced by Kingma and Ba in 2014. These algorithms share a common aim of improving training efficiency by adjusting learning rates at a per-parameter level.

These optimizers typically maintain running estimates of the gradient’s first moment (mean) and second moment (uncentered variance), use these estimates to compute adaptive step sizes, and often apply bias correction to reduce initialization effects. In practice, Adam-like methods blend ideas from momentum and RMSProp to stabilize and accelerate training, especially for deep neural networks.

Commonly described as Adam-like are variants such as AdamW, which decouples weight decay from the gradient step; RAdam, which adds rectification to the variance term; AdaBelief, which updates its variance estimation based on a belief in the gradient’s reliability; and AMSGrad, which proposes a variant with improved convergence properties. These examples illustrate the range of adaptations that fall under the umbrella of “Adam-like.”

Adam-like methods are widely used due to fast convergence and resilience to noisy gradients, but they can pose generalization challenges and may be sensitive to hyperparameter choices such as learning rate and decay rates. The term remains informal, serving to categorize a family of optimizers rather than define a single standard.

See also: Adam (optimizer); stochastic gradient descent; adaptive optimization.