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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
Proving Linear Mode Connectivity of Neural Networks via Optimal TransportCode0
Vanishing Feature: Diagnosing Model Merging and BeyondCode0
Federated Learning over Connected ModesCode0
The Empirical Impact of Neural Parameter Symmetries, or Lack ThereofCode0
Linear Mode Connectivity in Differentiable Tree Ensembles0
Disentangling Linear Mode-Connectivity0
Linear Mode Connectivity in Sparse Neural Networks0
CopRA: A Progressive LoRA Training Strategy0
Mode Combinability: Exploring Convex Combinations of Permutation Aligned Models0
Mode Connectivity and Data Heterogeneity of Federated Learning0
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