SOTAVerified

Linear Mode Connectivity

Linear Mode Connectivity refers to the relationship between input and output variables in a linear regression model. In a linear regression model, input variables are combined with weights to predict output variables. Understanding the linear model connectivity can help interpret model results and identify which input variables are most important for predicting output variables.

Papers

Showing 2130 of 35 papers

TitleStatusHype
Finding Stable Subnetworks at Initialization with Dataset Distillation0
High-dimensional manifold of solutions in neural networks: insights from statistical physics0
The Empirical Impact of Reducing Symmetries on the Performance of Deep Ensembles and MoE0
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse0
Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion0
Understanding Mode Connectivity via Parameter Space Symmetry0
Unveiling the Dynamics of Information Interplay in Supervised Learning0
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural NetworksCode0
Proving Linear Mode Connectivity of Neural Networks via Optimal TransportCode0
Show:102550
← PrevPage 3 of 4Next →

No leaderboard results yet.