It shows that Adam minimizes a specific form of sharpness —specifically the trace of the square root of the Hessian—which is fundamentally different from how SGD behaves. 4. Better Embeddings with Coupled Adam
Published in 2025, this paper "splits" the problem of in LLM embeddings. Splitting Adam
It's often applied to power grid reliability or particle transport. 3. Adam Reduces a Unique Form of Sharpness It shows that Adam minimizes a specific form
It isolates the stochastic direction (the sign of the gradient) from the adaptive step size (the relative variance). It's often applied to power grid reliability or
This version of ADAM is used for "splitting" an elite population of particles to better sample rare events or solve multi-objective optimization problems.
This paper effectively "splits" the Adam algorithm into two distinct components to study them:
Based on your interest in "Splitting Adam," you are likely referring to research surrounding the widely used in machine learning. There isn't one single paper with that exact title, but several "interesting" papers analyze splitting the algorithm's components or its behavior in complex ways: 1. The Sign, Magnitude and Variance of Stochastic Gradients