{"total":38,"rows":[{"id":"2209.11395","title":"Achieve the Minimum Width of Neural Networks for Universal Approximation","created":"2022-09-23","cited_by_count":26,"area":null},{"id":"2403.11871","title":"The Real Tropical Geometry of Neural Networks","created":"2024-03-18","cited_by_count":24,"area":null},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2309.10402","title":"Minimum width for universal approximation using ReLU networks on compact domain","created":"2023-09-19","cited_by_count":18,"area":null},{"id":"2407.12895","title":"A Survey on Universal Approximation Theorems","created":"2024-07-17","cited_by_count":15,"area":null},{"id":"2305.18460","title":"Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation","created":"2023-05-29","cited_by_count":13,"area":null},{"id":"2211.13866","title":"Minimal Width for Universal Property of Deep RNN","created":"2022-11-25","cited_by_count":12,"area":null},{"id":"2105.12033","title":"TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems","created":"2021-05-25","cited_by_count":10,"area":null},{"id":"2202.03841","title":"Width is Less Important than Depth in ReLU Neural Networks","created":"2022-02-08","cited_by_count":10,"area":null},{"id":"2203.11355","title":"Origami in N dimensions: How feed-forward networks manufacture linear separability","created":"2022-03-21","cited_by_count":9,"area":null},{"id":"2408.16389","title":"Addressing common misinterpretations of KART and UAT in neural network literature","created":"2024-08-29","cited_by_count":9,"area":null},{"id":"2305.12205","title":"Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions","created":"2023-05-20","cited_by_count":6,"area":null},{"id":"2102.08354","title":"Topological Deep Learning: Classification Neural Networks","created":"2021-02-16","cited_by_count":5,"area":null},{"id":"2209.10909","title":"Vanilla Feedforward Neural Networks as a Discretization of Dynamical Systems","created":"2022-09-22","cited_by_count":4,"area":null},{"id":"2304.10552","title":"Approximation and interpolation of deep neural networks","created":"2023-04-20","cited_by_count":4,"area":null},{"id":"2208.04995","title":"A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems","created":"2022-08-09","cited_by_count":3,"area":null},{"id":"2504.07371","title":"Minimum width for universal approximation using squashable activation functions","created":"2025-04-10","cited_by_count":3,"area":null},{"id":"2203.13410","title":"Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation","created":"2022-03-25","cited_by_count":2,"area":null},{"id":"2409.02363","title":"Optimal Neural Network Approximation for High-Dimensional Continuous Functions","created":"2024-09-04","cited_by_count":2,"area":null},{"id":"2503.15696","title":"Approximation properties of neural ODEs","created":"2025-03-19","cited_by_count":2,"area":null},{"id":"2511.06837","title":"Minimum Width of Deep Narrow Networks for Universal Approximation","created":"2025-11-10","cited_by_count":2,"area":null},{"id":"2211.13935","title":"LU decomposition and Toeplitz decomposition of a neural network","created":"2022-11-25","cited_by_count":1,"area":null},{"id":"2410.00396","title":"Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis","created":"2024-10-01","cited_by_count":1,"area":null},{"id":"2504.11397","title":"MLPs and KANs for data-driven learning in physical problems: A performance comparison","created":"2025-04-15","cited_by_count":1,"area":null},{"id":"2506.01569","title":"Latent Space Topology Evolution in Multilayer Perceptrons","created":"2025-06-02","cited_by_count":1,"area":null},{"id":"2606.31856","title":"Low-dimensional topology of deep neural networks","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2112.15026","title":"Two Instances of Interpretable Neural Network for Universal Approximations","created":"2021-12-30","cited_by_count":0,"area":null},{"id":"2208.09033","title":"Quantitative Universal Approximation Bounds for Deep Belief Networks","created":"2022-08-18","cited_by_count":0,"area":null},{"id":"2301.09381","title":"A Structural Approach to the Design of Domain Specific Neural Network Architectures","created":"2023-01-23","cited_by_count":0,"area":null},{"id":"2305.13519","title":"Development of Non-Linear Equations for Predicting Electrical Conductivity in Silicates","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2501.05930","title":"Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks","created":"2025-01-10","cited_by_count":0,"area":null},{"id":"2504.18710","title":"Explicit neural network classifiers for non-separable data","created":"2025-04-25","cited_by_count":0,"area":null},{"id":"2601.14026","title":"Universal Approximation Theorem for Input-Connected Multilayer Perceptrons","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2602.23381","title":"Universality of shallow and deep neural networks on non-Euclidean spaces","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2605.21451","title":"Approximation Theory for Neural Networks: Old and New","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2607.04597","title":"Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2607.13574","title":"Approximation of solutions of parameter-dependent problems by residual neural networks","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2610.20229","title":"The minimal width of neural networks and immersions of real projective space","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}