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Linear Mode Connectivity
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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.
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CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
Model Assembly Learning with Heterogeneous Layer Weight Merging
Finding Stable Subnetworks at Initialization with Dataset Distillation
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity
The Empirical Impact of Reducing Symmetries on the Performance of Deep Ensembles and MoE
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
CopRA: A Progressive LoRA Training Strategy
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse
Approaching Deep Learning through the Spectral Dynamics of Weights
Landscaping Linear Mode Connectivity
Unveiling the Dynamics of Information Interplay in Supervised Learning
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
Linear Mode Connectivity in Differentiable Tree Ensembles
Federated Learning over Connected Modes
Improving Group Connectivity for Generalization of Federated Deep Learning
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching
Vanishing Feature: Diagnosing Model Merging and Beyond
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion
Disentangling Linear Mode-Connectivity
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Linear Mode Connectivity in Sparse Neural Networks
Mode Connectivity and Data Heterogeneity of Federated Learning
High-dimensional manifold of solutions in neural networks: insights from statistical physics
Mode Combinability: Exploring Convex Combinations of Permutation Aligned Models
On Privileged and Convergent Bases in Neural Network Representations
Layer-wise Linear Mode Connectivity
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Re-basin via implicit Sinkhorn differentiation
Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks
Git Re-Basin: Merging Models modulo Permutation Symmetries
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Towards Understanding Iterative Magnitude Pruning: Why Lottery Tickets Win
Linear Mode Connectivity in Multitask and Continual Learning
Linear Mode Connectivity and the Lottery Ticket Hypothesis
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