{"total":21,"rows":[{"id":"2410.01405","title":"On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding","created":"2024-10-02","cited_by_count":36,"area":null},{"id":"2502.19002","title":"The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training","created":"2025-02-26","cited_by_count":30,"area":null},{"id":"2411.04551","title":"Measure-to-measure interpolation using Transformers","created":"2024-11-07","cited_by_count":29,"area":null},{"id":"2501.08537","title":"Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers","created":"2025-01-15","cited_by_count":20,"area":null},{"id":"2405.05409","title":"Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing","created":"2024-05-08","cited_by_count":17,"area":null},{"id":"2410.03011","title":"Towards Understanding the Universality of Transformers for Next-Token Prediction","created":"2024-10-03","cited_by_count":9,"area":null},{"id":"2410.11474","title":"How Transformers Get Rich: Approximation and Dynamics Analysis","created":"2024-10-15","cited_by_count":8,"area":null},{"id":"2502.15801","title":"An explainable transformer circuit for compositional generalization","created":"2025-02-19","cited_by_count":8,"area":null},{"id":"2509.25611","title":"Transformers through the lens of support-preserving maps between measures","created":"2025-09-30","cited_by_count":6,"area":null},{"id":"2504.13558","title":"Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective","created":"2025-04-18","cited_by_count":5,"area":null},{"id":"2504.12175","title":"Approximation Bounds for Transformer Networks with Application to Regression","created":"2025-04-16","cited_by_count":4,"area":null},{"id":"2505.06633","title":"Attention Is Not All You Need: The Importance of Feedforward Networks in Transformer Models","created":"2025-05-10","cited_by_count":4,"area":null},{"id":"2510.06662","title":"The Effect of Attention Head Count on Transformer Approximation","created":"2025-10-08","cited_by_count":4,"area":null},{"id":"2602.23057","title":"Affine-Scaled Attention: Towards Flexible and Stable Transformer Attention","created":"2026-02-26","cited_by_count":3,"area":null},{"id":"2605.26647","title":"More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations","created":"2026-05-26","cited_by_count":3,"area":null},{"id":"2506.23551","title":"A unified framework for establishing the universal approximation of transformer-type architectures","created":"2025-06-30","cited_by_count":2,"area":null},{"id":"2605.28075","title":"Measure-to-measure Regression with Transformers","created":"2026-05-27","cited_by_count":2,"area":null},{"id":"2603.03084","title":"On the Expressive Power of Transformers for Maxout Networks and Continuous Piecewise Linear Functions","created":"2026-03-03","cited_by_count":1,"area":null},{"id":"2604.14727","title":"Geometric Capacity of Transformers: A Tropical Geometry Perspective","created":"2026-04-16","cited_by_count":1,"area":null},{"id":"2609.35483","title":"Universal Approximation of Measure-to-Measure Operators by Pushforwards","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2510.03784","title":"Performance-Efficiency Tradeoffs in Transformers: An Approximation Theory Perspective","created":"2025-10-04","cited_by_count":0,"area":null}],"next_cursor":null}