Implicit Bias of Per-sample Adam on Separable Data: Departure from the Full-batch Regime
NeutralArtificial Intelligence
A recent study explores the implicit bias of the Adam optimizer when applied incrementally to logistic regression on linearly separable data. While Adam is widely used in deep learning, its theoretical foundations are not fully understood. This research highlights how the optimizer's behavior can differ from traditional full-batch methods, which is significant for developers and researchers aiming to improve model performance and understanding of optimization techniques.
— Curated by the World Pulse Now AI Editorial System


