deep-linear-networks

This paper shows that discrete Gradient Descent with large step sizes restores symmetry in multi-pathway Deep Linear Networks, countering the symmetry-breaking predicted by Gradient Flow, and leads to signal re-balancing across pathways. The authors theoretically prove that balanced solutions are flatter (less sharp) than sparse ones, and large learning rates drive the network toward stable, balanced configurations.