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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
On Privileged and Convergent Bases in Neural Network Representations0
Layer-wise Linear Mode ConnectivityCode0
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free TrainabilityCode1
Re-basin via implicit Sinkhorn differentiationCode1
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural NetworksCode0
Git Re-Basin: Merging Models modulo Permutation SymmetriesCode2
The Role of Permutation Invariance in Linear Mode Connectivity of Neural NetworksCode1
Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win0
Linear Mode Connectivity in Multitask and Continual LearningCode1
Linear Mode Connectivity and the Lottery Ticket HypothesisCode1
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