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 1120 of 35 papers

TitleStatusHype
Landscaping Linear Mode Connectivity0
Unveiling the Dynamics of Information Interplay in Supervised Learning0
The Empirical Impact of Neural Parameter Symmetries, or Lack ThereofCode0
Linear Mode Connectivity in Differentiable Tree Ensembles0
Federated Learning over Connected ModesCode0
Improving Group Connectivity for Generalization of Federated Deep Learning0
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching0
Vanishing Feature: Diagnosing Model Merging and BeyondCode0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion0
Disentangling Linear Mode-Connectivity0
Show:102550
← PrevPage 2 of 4Next →

No leaderboard results yet.