{"total":18,"rows":[{"id":"2402.18477","title":"Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes","created":"2024-02-28","cited_by_count":18,"area":null},{"id":"2404.06583","title":"Lecture notes on rough paths and applications to machine learning","created":"2024-04-09","cited_by_count":14,"area":null},{"id":"2308.03812","title":"Noncompact uniform universal approximation","created":"2023-08-07","cited_by_count":7,"area":null},{"id":"2410.14759","title":"Universal approximation results for neural networks with non-polynomial activation function over non-compact domains","created":"2024-10-18","cited_by_count":7,"area":null},{"id":"2508.14995","title":"Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs","created":"2025-08-20","cited_by_count":7,"area":null},{"id":"2411.03384","title":"Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion","created":"2024-11-05","cited_by_count":6,"area":null},{"id":"2511.01125","title":"One model to solve them all: 2BSDE families via neural operators","created":"2025-11-03","cited_by_count":4,"area":null},{"id":"2405.20094","title":"Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2410.14788","title":"Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2503.16696","title":"Universal approximation property of neural stochastic differential equations","created":"2025-03-20","cited_by_count":2,"area":null},{"id":"2504.02321","title":"On shallow feedforward neural networks with inputs from a topological space","created":"2025-04-03","cited_by_count":2,"area":null},{"id":"2512.16396","title":"Global universal approximation with Brownian signatures","created":"2025-12-18","cited_by_count":2,"area":null},{"id":"2510.09177","title":"Distributionally robust approximation property of neural networks","created":"2025-10-10","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":"2603.09773","title":"Global universality via discrete-time signatures","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2605.15285","title":"Universal Approximation of Nonlinear Operators and Their Derivatives","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2607.14361","title":"NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2609.02727","title":"Neural operators approximate strongly continuous convex monotone semigroups","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}