@inproceedings{
varre2024sgd,
title={{SGD} vs {GD}: Rank Deficiency in Linear Networks},
author={Aditya Varre and Margarita Sagitova and Nicolas Flammarion},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=TSaieShX3j}
}
In this article, we study the behaviour of continuous-time gradient methods on a two-layer linear network with square loss. A dichotomy between SGD and GD is revealed: GD preserves the rank at initialization while (label noise) SGD diminishes the rank regardless of the initialization. We demonstrate this rank deficiency by studying the time evolution of the *determinant* of a matrix of parameters. To further understand this phenomenon, we derive the stochastic differential equation (SDE) governing the eigenvalues of the parameter matrix. This SDE unveils a *replusive force* between the eigenvalues: a key regularization mechanism which induces rank deficiency. Our results are well supported by experiments illustrating the phenomenon beyond linear networks and regression tasks.
SGD vs GD: Rank Deficiency in Linear Networks
Aditya Varre Margarita Sagitova Nicolas Flammarion