{"total":23,"rows":[{"id":"2102.06571","title":"Bayesian Neural Network Priors Revisited","created":"2021-02-12","cited_by_count":121,"area":null},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2106.03795","title":"Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks","created":"2021-06-07","cited_by_count":44,"area":null},{"id":"2309.16314","title":"A Primer on Bayesian Neural Networks: Review and Debates","created":"2023-09-28","cited_by_count":27,"area":null},{"id":"2203.07379","title":"Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks","created":"2022-03-14","cited_by_count":25,"area":null},{"id":"2205.08187","title":"Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility","created":"2022-05-17","cited_by_count":13,"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":"2206.00944","title":"Feature Space Particle Inference for Neural Network Ensembles","created":"2022-06-02","cited_by_count":9,"area":null},{"id":"2108.02316","title":"Deep Stable neural networks: large-width asymptotics and convergence rates","created":"2021-08-02","cited_by_count":8,"area":null},{"id":"2106.11064","title":"$\\alpha$-Stable convergence of heavy-tailed infinitely-wide neural networks","created":"2021-06-18","cited_by_count":5,"area":null},{"id":"2107.01408","title":"Scale Mixtures of Neural Network Gaussian Processes","created":"2021-07-03","cited_by_count":5,"area":null},{"id":"2110.02885","title":"Bayesian neural network unit priors and generalized Weibull-tail property","created":"2021-10-06","cited_by_count":5,"area":null},{"id":"2304.04008","title":"Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions","created":"2023-04-08","cited_by_count":5,"area":null},{"id":"2111.14397","title":"Dependence between Bayesian neural network units","created":"2021-11-29","cited_by_count":3,"area":null},{"id":"2302.01002","title":"Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning","created":"2023-02-02","cited_by_count":3,"area":null},{"id":"2305.10664","title":"Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance","created":"2023-05-18","cited_by_count":3,"area":null},{"id":"2507.09093","title":"Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization","created":"2025-07-12","cited_by_count":3,"area":null},{"id":"2410.13954","title":"Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees","created":"2024-10-17","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":"2410.01284","title":"Deep Kernel Posterior Learning under Infinite Variance Prior Weights","created":"2024-10-02","cited_by_count":0,"area":null},{"id":"2411.11132","title":"Variational Bayesian Bow tie Neural Networks with Shrinkage","created":"2024-11-17","cited_by_count":0,"area":null},{"id":"2605.07634","title":"Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.08218","title":"How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs","created":"2026-06-06","cited_by_count":0,"area":null}],"next_cursor":null}