Linear Mode Connectivity and the Lottery Ticket Hypothesis

This research area focuses on understanding how neural networks optimize to the same linearly connected minimum under different conditions, such as SGD noise, and how sparse subnetworks (winning tickets) can achieve full model performance.

It involves concepts like iterative magnitude pruning and error landscape geometry.