{"total":13,"rows":[{"id":"2210.10749","title":"Transformers Learn Shortcuts to Automata","created":"2022-10-19","cited_by_count":268,"area":null},{"id":"2302.11055","title":"SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics","created":"2023-02-21","cited_by_count":143,"area":null},{"id":"2310.06110","title":"Grokking as the Transition from Lazy to Rich Training Dynamics","created":"2023-10-09","cited_by_count":108,"area":null},{"id":"2210.15651","title":"Learning Single-Index Models with Shallow Neural Networks","created":"2022-10-27","cited_by_count":92,"area":null},{"id":"2307.11353","title":"What can a Single Attention Layer Learn? A Study Through the Random Features Lens","created":"2023-07-21","cited_by_count":40,"area":null},{"id":"2309.15111","title":"SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem","created":"2023-09-26","cited_by_count":39,"area":null},{"id":"2305.06986","title":"Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks","created":"2023-05-11","cited_by_count":19,"area":null},{"id":"2307.02284","title":"Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks","created":"2023-07-05","cited_by_count":4,"area":null},{"id":"2305.13141","title":"Tight conditions for when the NTK approximation is valid","created":"2023-05-22","cited_by_count":1,"area":null},{"id":"2507.12780","title":"Compact Vision Transformer by Reduction of Kernel Complexity","created":"2025-07-17","cited_by_count":0,"area":null},{"id":"2512.20562","title":"Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention","created":"2025-12-23","cited_by_count":0,"area":null},{"id":"2603.21062","title":"Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate","created":"2026-03-22","cited_by_count":0,"area":null},{"id":"2609.03117","title":"Kernel Reboot: Breaking the Boundaries of Neural Tangent Kernels for Neural Fields","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}