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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 2635 of 35 papers

TitleStatusHype
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
Layer-wise Linear Mode ConnectivityCode0
Vanishing Feature: Diagnosing Model Merging and BeyondCode0
Federated Learning over Connected ModesCode0
The Empirical Impact of Neural Parameter Symmetries, or Lack ThereofCode0
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & BeyondCode0
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