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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
Unveiling the Dynamics of Information Interplay in Supervised Learning0
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching0
The Empirical Impact of Reducing Symmetries on the Performance of Deep Ensembles and MoE0
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse0
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity0
High-dimensional manifold of solutions in neural networks: insights from statistical physics0
Improving Group Connectivity for Generalization of Federated Deep Learning0
Landscaping Linear Mode Connectivity0
Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win0
Finding Stable Subnetworks at Initialization with Dataset Distillation0
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