{"total":18,"rows":[{"id":"2405.02561","title":"Understanding the Difficulty of Solving Cauchy Problems with PINNs","created":"2024-05-04","cited_by_count":6,"area":null},{"id":"2409.00297","title":"On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic","created":"2024-08-30","cited_by_count":4,"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":"2405.13682","title":"Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines","created":"2024-05-22","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":"2409.06555","title":"Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks","created":"2024-09-10","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":"2408.06996","title":"Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2505.01995","title":"Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks","created":"2025-05-04","cited_by_count":1,"area":null},{"id":"2512.18059","title":"Approximation and learning with compositional tensor trains","created":"2025-12-19","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":"2601.00672","title":"Sparse FEONet: A Low-Cost, Memory-Efficient Operator Network via Finite-Element Local Sparsity for Parametric PDEs","created":"2026-01-02","cited_by_count":0,"area":null},{"id":"2602.12482","title":"Geometric separation and constructive universal approximation with two hidden layers","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2603.00064","title":"The minimal width of universal $p$-adic ReLU neural networks","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2605.07119","title":"Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples","created":"2026-05-08","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":"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}