{"total":26,"rows":[{"id":"2209.04836","title":"Git Re-Basin: Merging Models modulo Permutation Symmetries","created":"2022-09-11","cited_by_count":471,"area":null},{"id":"2211.08403","title":"REPAIR: REnormalizing Permuted Activations for Interpolation Repair","created":"2022-11-15","cited_by_count":120,"area":null},{"id":"2309.15698","title":"Deep Model Fusion: A Survey","created":"2023-09-27","cited_by_count":91,"area":null},{"id":"2211.08422","title":"Mechanistic Mode Connectivity","created":"2022-11-15","cited_by_count":50,"area":null},{"id":"2506.13018","title":"Symmetry in Neural Network Parameter Spaces","created":"2025-06-16","cited_by_count":36,"area":null},{"id":"2210.17216","title":"Symmetries, flat minima, and the conserved quantities of gradient flow","created":"2022-10-31","cited_by_count":35,"area":null},{"id":"2304.02902","title":"Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry","created":"2023-04-06","cited_by_count":21,"area":null},{"id":"2405.20231","title":"The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof","created":"2024-05-30","cited_by_count":21,"area":null},{"id":"2304.13871","title":"Typical and atypical solutions in non-convex neural networks with discrete and continuous weights","created":"2023-04-26","cited_by_count":13,"area":null},{"id":"2505.23681","title":"Understanding Mode Connectivity via Parameter Space Symmetry","created":"2025-05-29","cited_by_count":10,"area":null},{"id":"2206.03927","title":"Boundary between noise and information applied to filtering neural network weight matrices","created":"2022-06-08","cited_by_count":9,"area":null},{"id":"2410.14837","title":"Topological obstruction to the training of shallow ReLU neural networks","created":"2024-10-18","cited_by_count":6,"area":null},{"id":"2503.03382","title":"Paths and Ambient Spaces in Neural Loss Landscapes","created":"2025-03-05","cited_by_count":6,"area":null},{"id":"2402.05966","title":"Vanishing Feature: Diagnosing Model Merging and Beyond","created":"2024-02-05","cited_by_count":5,"area":null},{"id":"2402.19102","title":"FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness","created":"2024-02-29","cited_by_count":5,"area":null},{"id":"2309.09240","title":"High-dimensional manifold of solutions in neural networks: insights from statistical physics","created":"2023-09-17","cited_by_count":4,"area":null},{"id":"2310.10171","title":"On permutation symmetries in Bayesian neural network posteriors: a variational perspective","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2408.12237","title":"Weight Scope Alignment: A Frustratingly Easy Method for Model Merging","created":"2024-08-22","cited_by_count":2,"area":null},{"id":"2503.10404","title":"Architecture-Aware Minimization (A$^2$M): How to Find Flat Minima in Neural Architecture Search","created":"2025-03-13","cited_by_count":2,"area":null},{"id":"2605.25234","title":"On the Epistemic Uncertainty of Overparametrized Neural Networks","created":"2026-05-24","cited_by_count":2,"area":null},{"id":"2412.00119","title":"Training Multi-Layer Binary Neural Networks With Local Binary Error Signals","created":"2024-11-28","cited_by_count":1,"area":null},{"id":"2511.12601","title":"Symmetry-Aware Graph Metanetwork Autoencoders: Model Merging through Parameter Canonicalization","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2605.04103","title":"HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2606.04754","title":"Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2609.31634","title":"Symmetry-quotient Flatness and Generalization","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.38540","title":"When a Flatness Proxy Is Not a Function: Robustness Certificates and Training Interventions","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}