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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 3135 of 35 papers

TitleStatusHype
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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