{"total":16,"rows":[{"id":"2403.03103","title":"Emergent Equivariance in Deep Ensembles","created":"2024-03-05","cited_by_count":18,"area":null},{"id":"2410.01452","title":"Ensembles provably learn equivariance through data augmentation","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2506.02269","title":"A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models","created":"2025-06-02","cited_by_count":8,"area":null},{"id":"2601.21645","title":"Identifiable Equivariant Networks are Layerwise Equivariant","created":"2026-01-29","cited_by_count":7,"area":null},{"id":"2502.06547","title":"Data Augmentation and Regularization for Learning Group Equivariance","created":"2025-02-10","cited_by_count":3,"area":null},{"id":"2506.13714","title":"Understanding Learning Invariance in Deep Linear Networks","created":"2025-06-16","cited_by_count":3,"area":null},{"id":"2405.18222","title":"From Learning to Optimize to Learning Optimization Algorithms","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2405.19995","title":"Symmetries in Overparametrized Neural Networks: A Mean-Field View","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2605.07067","title":"PolarAdamW: Disentangling Spectral Control and Schur Gauge-Equivariance in Matrix Optimisation","created":"2026-05-08","cited_by_count":2,"area":null},{"id":"2512.02303","title":"Training Dynamics of Learning 3D-Rotational Equivariance","created":"2025-12-02","cited_by_count":1,"area":null},{"id":"2606.10913","title":"Conservation Laws from Data Symmetry in Neural Networks","created":"2026-06-09","cited_by_count":1,"area":null},{"id":"2512.14338","title":"Implicit Bias and Invariance: How Hopfield Networks Efficiently Learn Graph Orbits","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2602.02853","title":"Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2606.26273","title":"Equivariance and Augmentation for Bayesian Neural Networks","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2608.14373","title":"Boosting Data Augmentation with Stochastic Weight Averaging","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2609.08381","title":"Equivariance Breaks the Learning Rate","created":"2026-09-08","cited_by_count":0,"area":null}],"next_cursor":null}