{"total":21,"rows":[{"id":"2410.17878","title":"Relaxed Equivariance via Multitask Learning","created":"2024-10-23","cited_by_count":13,"area":null},{"id":"2501.01999","title":"Probing Equivariance and Symmetry Breaking in Convolutional Networks","created":"2025-01-01","cited_by_count":12,"area":null},{"id":"2503.07596","title":"Denoising Hamiltonian Network for Physical Reasoning","created":"2025-03-10","cited_by_count":10,"area":null},{"id":"2505.13631","title":"Learning (Approximately) Equivariant Networks via Constrained Optimization","created":"2025-05-19","cited_by_count":10,"area":null},{"id":"2410.01452","title":"Ensembles provably learn equivariance through data augmentation","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2502.05169","title":"Flopping for FLOPs: Leveraging equivariance for computational efficiency","created":"2025-02-07","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":"2512.11855","title":"Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry","created":"2025-12-05","cited_by_count":7,"area":null},{"id":"2509.24886","title":"Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks","created":"2025-09-29","cited_by_count":3,"area":null},{"id":"2512.10723","title":"Generalized Spherical Neural Operators: Green's Function Formulation","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2601.05028","title":"Approximate Equivariance via Projection-based Regularisation","created":"2026-01-08","cited_by_count":2,"area":null},{"id":"2605.27662","title":"How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks","created":"2026-05-26","cited_by_count":2,"area":null},{"id":"2501.01136","title":"Symmetries-enhanced Multi-Agent Reinforcement Learning","created":"2025-01-02","cited_by_count":1,"area":null},{"id":"2512.02303","title":"Training Dynamics of Learning 3D-Rotational Equivariance","created":"2025-12-02","cited_by_count":1,"area":null},{"id":"2604.23720","title":"Quasi-Equivariant Metanetworks","created":"2026-04-26","cited_by_count":1,"area":null},{"id":"2512.09673","title":"Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power","created":"2025-12-10","cited_by_count":0,"area":null},{"id":"2601.03561","title":"Neural Operators for Biomedical Spherical Heterogeneity","created":"2026-01-07","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":"2603.02843","title":"Scale-invariant Gaussian derivative residual networks","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2608.31045","title":"Rotational Equivariance in Machine Learning: A Comprehensive Tutorial","created":"2026-08-31","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}