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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
Disentangling Linear Mode-Connectivity0
Proving Linear Mode Connectivity of Neural Networks via Optimal TransportCode0
Linear Mode Connectivity in Sparse Neural Networks0
Mode Connectivity and Data Heterogeneity of Federated Learning0
High-dimensional manifold of solutions in neural networks: insights from statistical physics0
Mode Combinability: Exploring Convex Combinations of Permutation Aligned Models0
On Privileged and Convergent Bases in Neural Network Representations0
Layer-wise Linear Mode ConnectivityCode0
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural NetworksCode0
Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win0
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