{"total":24,"rows":[{"id":"2210.10749","title":"Transformers Learn Shortcuts to Automata","created":"2022-10-19","cited_by_count":268,"area":null},{"id":"2107.13163","title":"Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers","created":"2021-07-28","cited_by_count":102,"area":null},{"id":"2212.14424","title":"Normalizing flow neural networks by JKO scheme","created":"2022-12-29","cited_by_count":39,"area":null},{"id":"2105.13010","title":"An error analysis of generative adversarial networks for learning distributions","created":"2021-05-27","cited_by_count":36,"area":null},{"id":"2310.17582","title":"Convergence of flow-based generative models via proximal gradient descent in Wasserstein space","created":"2023-10-26","cited_by_count":34,"area":null},{"id":"2109.02832","title":"Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks","created":"2021-09-07","cited_by_count":32,"area":null},{"id":"2101.12353","title":"On the capacity of deep generative networks for approximating distributions","created":"2021-01-29","cited_by_count":19,"area":null},{"id":"2210.12101","title":"Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective","created":"2022-10-21","cited_by_count":15,"area":null},{"id":"2110.04227","title":"Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows","created":"2021-10-08","cited_by_count":13,"area":null},{"id":"2103.17059","title":"Reliable Detection of Compressed and Encrypted Data","created":"2021-03-31","cited_by_count":8,"area":null},{"id":"2103.05793","title":"Universal Approximation of Residual Flows in Maximum Mean Discrepancy","created":"2021-03-10","cited_by_count":7,"area":null},{"id":"2506.05200","title":"Transformers Meet In-Context Learning: A Universal Approximation Theory","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2107.12466","title":"High-Dimensional Distribution Generation Through Deep Neural Networks","created":"2021-07-26","cited_by_count":5,"area":null},{"id":"2307.01177","title":"Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2401.04778","title":"Generative neural networks for characteristic functions","created":"2024-01-09","cited_by_count":3,"area":null},{"id":"2110.10295","title":"Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem","created":"2021-10-19","cited_by_count":2,"area":null},{"id":"2209.00735","title":"Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms","created":"2022-09-01","cited_by_count":2,"area":null},{"id":"2404.17461","title":"Multi-layer random features and the approximation power of neural networks","created":"2024-04-26","cited_by_count":2,"area":null},{"id":"2405.03723","title":"Generative adversarial learning with optimal input dimension and its adaptive generator architecture","created":"2024-05-06","cited_by_count":1,"area":null},{"id":"2101.09973","title":"Approximating Probability Distributions by ReLU Networks","created":"2021-01-25","cited_by_count":0,"area":null},{"id":"2205.02162","title":"UnrealNAS: Can We Search Neural Architectures with Unreal Data?","created":"2022-05-04","cited_by_count":0,"area":null},{"id":"2405.03435","title":"A method for quantifying the generalization capabilities of generative models for solving Ising models","created":"2024-05-06","cited_by_count":0,"area":null},{"id":"2504.08964","title":"Bidirectional Linear Recurrent Models for Sequence-Level Multisource Fusion","created":"2025-04-11","cited_by_count":0,"area":null},{"id":"2504.18017","title":"Non-identifiability distinguishes Neural Networks among Parametric Models","created":"2025-04-25","cited_by_count":0,"area":null}],"next_cursor":null}