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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 110 of 35 papers

TitleStatusHype
Git Re-Basin: Merging Models modulo Permutation SymmetriesCode2
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
Linear Mode Connectivity in Multitask and Continual LearningCode1
Re-basin via implicit Sinkhorn differentiationCode1
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 and the Lottery Ticket HypothesisCode1
Disentangling Linear Mode-Connectivity0
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching0
High-dimensional manifold of solutions in neural networks: insights from statistical physics0
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