SOTAVerified

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 110 of 35 papers

TitleStatusHype
Git Re-Basin: Merging Models modulo Permutation SymmetriesCode2
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
The Role of Permutation Invariance in Linear Mode Connectivity of Neural NetworksCode1
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free TrainabilityCode1
Linear Mode Connectivity in Multitask and Continual LearningCode1
Re-basin via implicit Sinkhorn differentiationCode1
Linear Mode Connectivity and the Lottery Ticket HypothesisCode1
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & BeyondCode0
Layer-wise Linear Mode ConnectivityCode0
Proving Linear Mode Connectivity of Neural Networks via Optimal TransportCode0
Show:102550
← PrevPage 1 of 4Next →

No leaderboard results yet.