{"total":32,"rows":[{"id":"2307.14023","title":"Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?","created":"2023-07-26","cited_by_count":47,"area":null},{"id":"2410.10807","title":"HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees","created":"2024-10-14","cited_by_count":31,"area":null},{"id":"2411.04551","title":"Measure-to-measure interpolation using Transformers","created":"2024-11-07","cited_by_count":29,"area":null},{"id":"2304.09179","title":"Pretrained Language Models as Visual Planners for Human Assistance","created":"2023-04-17","cited_by_count":28,"area":null},{"id":"2305.18475","title":"Approximation Rate of the Transformer Architecture for Sequence Modeling","created":"2023-05-29","cited_by_count":28,"area":null},{"id":"2201.13094","title":"Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer","created":"2022-01-31","cited_by_count":22,"area":null},{"id":"2508.10480","title":"Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers","created":"2025-08-14","cited_by_count":19,"area":null},{"id":"2111.14574","title":"On the rate of convergence of a classifier based on a Transformer encoder","created":"2021-11-29","cited_by_count":16,"area":null},{"id":"2302.13752","title":"A Brief Survey on the Approximation Theory for Sequence Modelling","created":"2023-02-27","cited_by_count":15,"area":null},{"id":"2209.06788","title":"Small Transformers Compute Universal Metric Embeddings","created":"2022-09-14","cited_by_count":12,"area":null},{"id":"2410.16295","title":"Universal Approximation of Mean-Field Models via Transformers","created":"2024-10-06","cited_by_count":7,"area":null},{"id":"2204.11231","title":"Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?","created":"2022-04-24","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":"2309.07579","title":"Structure-Preserving Transformers for Sequences of SPD Matrices","created":"2023-09-14","cited_by_count":4,"area":null},{"id":"2210.06741","title":"Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries","created":"2022-10-13","cited_by_count":3,"area":null},{"id":"2502.11467","title":"Approximation of Permutation Invariant Polynomials by Transformers: Efficient Construction in Column-Size","created":"2025-02-17","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":"2508.17391","title":"Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets","created":"2025-08-24","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":"2302.14346","title":"Sampled Transformer for Point Sets","created":"2023-02-28","cited_by_count":1,"area":null},{"id":"2406.12915","title":"How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability","created":"2024-06-13","cited_by_count":1,"area":null},{"id":"2506.24120","title":"Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime","created":"2025-06-30","cited_by_count":1,"area":null},{"id":"2601.14115","title":"Riemannian Liquid Spatio-Temporal Graph Network","created":"2026-01-20","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":"2407.00958","title":"Dynamic Universal Approximation Theory: The Basic Theory for Transformer-based Large Language Models","created":"2024-07-01","cited_by_count":0,"area":null},{"id":"2410.19819","title":"Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2602.03082","title":"Geometry-Preserving Neural Architectures on Manifolds with Boundary","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2603.29237","title":"Stochastic Dimension Implicit Functional Projections for Global Integral Conservation in High-Dimensional PINNs","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.24154","title":"Progressive Approximation in Deep Residual Networks: Theory and Validation","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.07463","title":"Approximation Error Upper and Lower Bounds for H\\\"{o}lder Class with Transformers","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.08559","title":"Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.22223","title":"How Many Different Outputs Can a Transformer Generate?","created":"2026-05-21","cited_by_count":0,"area":null}],"next_cursor":null}