{"total":25,"rows":[{"id":"2307.06092","title":"Quantitative CLTs in Deep Neural Networks","created":"2023-07-12","cited_by_count":20,"area":null},{"id":"2312.11737","title":"Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes","created":"2023-12-18","cited_by_count":13,"area":null},{"id":"2304.04010","title":"Non-asymptotic approximations of Gaussian neural networks via second-order Poincar\\'e inequalities","created":"2023-04-08","cited_by_count":11,"area":null},{"id":"2306.16308","title":"Gaussian random field approximation via Stein's method with applications to wide random neural networks","created":"2023-06-28","cited_by_count":11,"area":null},{"id":"2205.12379","title":"Gaussian Pre-Activations in Neural Networks: Myth or Reality?","created":"2022-05-24","cited_by_count":9,"area":null},{"id":"2306.16932","title":"A Quantitative Functional Central Limit Theorem for Shallow Neural Networks","created":"2023-06-29","cited_by_count":8,"area":null},{"id":"2507.12686","title":"Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights","created":"2025-07-16","cited_by_count":5,"area":null},{"id":"2405.09541","title":"Spectral complexity of deep neural networks","created":"2024-05-15","cited_by_count":3,"area":null},{"id":"2405.16958","title":"Large Deviations of Gaussian Neural Networks with ReLU activation","created":"2024-05-27","cited_by_count":3,"area":null},{"id":"2407.18707","title":"Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection","created":"2024-07-26","cited_by_count":3,"area":null},{"id":"2502.04247","title":"Student-t processes as infinite-width limits of posterior Bayesian neural networks","created":"2025-02-06","cited_by_count":3,"area":null},{"id":"2312.13259","title":"A note on regularised NTK dynamics with an application to PAC-Bayesian training","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2504.06250","title":"Fractal and Regular Geometry of Deep Neural Networks","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2509.24544","title":"Quantitative convergence of trained single layer neural networks to Gaussian processes","created":"2025-09-29","cited_by_count":1,"area":null},{"id":"2607.06290","title":"Quantitative Gaussian-Process limits of Tensor Programs","created":"2026-07-07","cited_by_count":1,"area":null},{"id":"2206.08065","title":"Large-width asymptotics for ReLU neural networks with $\\alpha$-Stable initializations","created":"2022-06-16","cited_by_count":0,"area":null},{"id":"2406.01708","title":"SnatchML: Hijacking ML models without Training Access","created":"2024-06-03","cited_by_count":0,"area":null},{"id":"2505.17000","title":"Critical Points of Random Neural Networks","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2506.00486","title":"It Takes a Good Model to Train a Good Model: Generalized Gaussian Priors for Optimized LLMs","created":"2025-05-31","cited_by_count":0,"area":null},{"id":"2507.22095","title":"Posterior Bayesian Neural Networks with Dependent Weights","created":"2025-07-29","cited_by_count":0,"area":null},{"id":"2603.29612","title":"Central limit theorems for the outputs of fully convolutional neural networks with time series input","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2605.24072","title":"Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.30831","title":"Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2608.26526","title":"High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2610.18166","title":"Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}