{"total":38,"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":"2211.06291","title":"Do Bayesian Neural Networks Need To Be Fully Stochastic?","created":"2022-11-11","cited_by_count":63,"area":null},{"id":"2312.17199","title":"Tractable Function-Space Variational Inference in Bayesian Neural Networks","created":"2023-12-28","cited_by_count":41,"area":null},{"id":"2101.03606","title":"The Gaussian Neural Process","created":"2021-01-10","cited_by_count":34,"area":null},{"id":"2307.13917","title":"BayesDAG: Gradient-Based Posterior Inference for Causal Discovery","created":"2023-07-26","cited_by_count":33,"area":null},{"id":"2305.15027","title":"A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods","created":"2023-05-24","cited_by_count":32,"area":null},{"id":"2106.03195","title":"Meta-Learning Reliable Priors in the Function Space","created":"2021-06-06","cited_by_count":23,"area":null},{"id":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"area":null},{"id":"2209.00517","title":"The Neural Process Family: Survey, Applications and Perspectives","created":"2022-09-01","cited_by_count":23,"area":null},{"id":"2205.06342","title":"Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning","created":"2022-05-12","cited_by_count":21,"area":null},{"id":"2312.17162","title":"Function-Space Regularization in Neural Networks: A Probabilistic Perspective","created":"2023-12-28","cited_by_count":20,"area":null},{"id":"2105.14594","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","created":"2021-05-30","cited_by_count":16,"area":null},{"id":"2110.07618","title":"Function-space Inference with Sparse Implicit Processes","created":"2021-10-14","cited_by_count":13,"area":null},{"id":"2206.06720","title":"Deep Variational Implicit Processes","created":"2022-06-14","cited_by_count":8,"area":null},{"id":"2209.07157","title":"On the detrimental effect of invariances in the likelihood for variational inference","created":"2022-09-15","cited_by_count":8,"area":null},{"id":"2107.10125","title":"A variational approximate posterior for the deep Wishart process","created":"2021-07-21","cited_by_count":6,"area":null},{"id":"2309.12658","title":"Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes","created":"2023-09-22","cited_by_count":6,"area":null},{"id":"2202.01929","title":"Energy-Based Models for Functional Data using Path Measure Tilting","created":"2022-02-04","cited_by_count":5,"area":null},{"id":"2404.13182","title":"Spectral Convolutional Conditional Neural Processes","created":"2024-04-19","cited_by_count":4,"area":null},{"id":"2305.18777","title":"Adaptive Conditional Quantile Neural Processes","created":"2023-05-30","cited_by_count":3,"area":null},{"id":"2403.06302","title":"Nonparametric Automatic Differentiation Variational Inference with Spline Approximation","created":"2024-03-10","cited_by_count":3,"area":null},{"id":"2403.19381","title":"On Uncertainty Quantification for Near-Bayes Optimal Algorithms","created":"2024-03-28","cited_by_count":3,"area":null},{"id":"2406.04317","title":"Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks","created":"2024-06-06","cited_by_count":3,"area":null},{"id":"2310.17852","title":"Function Space Bayesian Pseudocoreset for Bayesian Neural Networks","created":"2023-10-27","cited_by_count":2,"area":null},{"id":"2503.18309","title":"Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems","created":"2025-03-24","cited_by_count":2,"area":null},{"id":"2607.12922","title":"LatentFlow: A General Framework for Conditioning Stochastic Processes","created":"2026-07-14","cited_by_count":2,"area":null},{"id":"2110.00568","title":"Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning","created":"2021-10-01","cited_by_count":1,"area":null},{"id":"2207.10673","title":"Correcting Model Bias with Sparse Implicit Processes","created":"2022-07-21","cited_by_count":1,"area":null},{"id":"2311.12566","title":"Variational Elliptical Processes","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2405.15991","title":"R\\'enyi Neural Processes","created":"2024-05-25","cited_by_count":1,"area":null},{"id":"2408.03746","title":"Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling","created":"2024-08-07","cited_by_count":1,"area":null},{"id":"2502.18300","title":"Bayesian Computation in Deep Learning","created":"2025-02-25","cited_by_count":1,"area":null},{"id":"2606.01954","title":"Flow-Transformed Implicit Processes for Function-Space Variational Inference","created":"2026-06-01","cited_by_count":1,"area":null},{"id":"2307.06055","title":"Function-Space Regularization for Deep Bayesian Classification","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2506.03839","title":"Revisiting Unbiased Implicit Variational Inference","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2506.20928","title":"Active Learning for Manifold Gaussian Process Regression","created":"2025-06-26","cited_by_count":0,"area":null},{"id":"2602.22015","title":"Function-Space Empirical Bayes Regularisation with Student's t Priors","created":"2026-02-25","cited_by_count":0,"area":null}],"next_cursor":null}