{"total":21,"rows":[{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2106.06529","title":"The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective","created":"2021-06-11","cited_by_count":21,"area":null},{"id":"2104.05674","title":"GPflux: A Library for Deep Gaussian Processes","created":"2021-04-12","cited_by_count":13,"area":null},{"id":"2106.05992","title":"Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition","created":"2021-06-10","cited_by_count":8,"area":null},{"id":"2309.10068","title":"A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes","created":"2023-09-18","cited_by_count":8,"area":null},{"id":"2312.05568","title":"Sparse Variational Student-t Processes","created":"2023-12-09","cited_by_count":8,"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":"2104.13756","title":"Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation","created":"2021-04-28","cited_by_count":4,"area":null},{"id":"2309.09814","title":"Convolutional Deep Kernel Machines","created":"2023-09-18","cited_by_count":3,"area":null},{"id":"2502.04750","title":"Tighter sparse variational Gaussian processes","created":"2025-02-07","cited_by_count":3,"area":null},{"id":"2408.06699","title":"Sparse Variational Student-t Processes for Heavy-tailed Modeling","created":"2024-08-13","cited_by_count":2,"area":null},{"id":"2111.00184","title":"Geometry-Aware Hierarchical Bayesian Learning on Manifolds","created":"2021-10-30","cited_by_count":1,"area":null},{"id":"2206.08972","title":"Shallow and Deep Nonparametric Convolutions for Gaussian Processes","created":"2022-06-17","cited_by_count":1,"area":null},{"id":"2206.13346","title":"Distributional Gaussian Processes Layers for Out-of-Distribution Detection","created":"2022-06-27","cited_by_count":1,"area":null},{"id":"2305.04871","title":"Gaussian process deconvolution","created":"2023-05-08","cited_by_count":1,"area":null},{"id":"2311.14828","title":"Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning","created":"2023-11-24","cited_by_count":1,"area":null},{"id":"2107.12797","title":"Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference","created":"2021-07-26","cited_by_count":0,"area":null},{"id":"2407.15877","title":"Gaussian Process Model with Tensorial Inputs and Its Application to the Design of 3D Printed Antennas","created":"2024-07-19","cited_by_count":0,"area":null},{"id":"2410.22119","title":"Deep Q-Exponential Processes","created":"2024-10-29","cited_by_count":0,"area":null},{"id":"2603.16621","title":"Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classification","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2605.10285","title":"Scalable Gaussian process inference via neural feature maps","created":"2026-05-11","cited_by_count":0,"area":null}],"next_cursor":null}