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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

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Showing 21–30 of 35 papers

TitleStatusHype
Federated Learning over Connected ModesCode0
Improving Group Connectivity for Generalization of Federated Deep Learning—0
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching—0
Vanishing Feature: Diagnosing Model Merging and BeyondCode0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion—0
Disentangling Linear Mode-Connectivity—0
Proving Linear Mode Connectivity of Neural Networks via Optimal TransportCode0
Linear Mode Connectivity in Sparse Neural Networks—0
Mode Connectivity and Data Heterogeneity of Federated Learning—0
High-dimensional manifold of solutions in neural networks: insights from statistical physics—0
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