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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
Model Assembly Learning with Heterogeneous Layer Weight Merging—0
On Privileged and Convergent Bases in Neural Network Representations—0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion—0
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving—0
Understanding Mode Connectivity via Parameter Space Symmetry—0
Unveiling the Dynamics of Information Interplay in Supervised Learning—0
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching—0
The Empirical Impact of Reducing Symmetries on the Performance of Deep Ensembles and MoE—0
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse—0
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity—0
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