{"total":25,"rows":[{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2205.10060","title":"The Unreasonable Effectiveness of Deep Evidential Regression","created":"2022-05-20","cited_by_count":40,"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":"2106.07512","title":"Last Layer Marginal Likelihood for Invariance Learning","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2202.12439","title":"Learning Invariant Weights in Neural Networks","created":"2022-02-25","cited_by_count":16,"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":"2307.01177","title":"Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2309.02048","title":"Probabilistic Self-supervised Learning via Scoring Rules Minimization","created":"2023-09-05","cited_by_count":4,"area":null},{"id":"2310.10171","title":"On permutation symmetries in Bayesian neural network posteriors: a variational perspective","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2405.13149","title":"Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation","created":"2024-05-21","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":"2303.08599","title":"Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval","created":"2023-03-15","cited_by_count":2,"area":null},{"id":"2303.15948","title":"Sparse Gaussian Processes with Spherical Harmonic Features Revisited","created":"2023-03-28","cited_by_count":2,"area":null},{"id":"2502.10540","title":"From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation","created":"2025-02-14","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":"2112.09820","title":"GPEX, A Framework For Interpreting Artificial Neural Networks","created":"2021-12-18","cited_by_count":1,"area":null},{"id":"2203.07411","title":"On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel","created":"2022-03-14","cited_by_count":1,"area":null},{"id":"2304.14034","title":"Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes","created":"2023-04-27","cited_by_count":1,"area":null},{"id":"2408.07588","title":"Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?","created":"2024-08-14","cited_by_count":1,"area":null},{"id":"2510.23745","title":"Bayesian neural networks with interpretable priors from Mercer kernels","created":"2025-10-27","cited_by_count":1,"area":null},{"id":"2606.28578","title":"Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design","created":"2026-06-26","cited_by_count":1,"area":null},{"id":"2205.08199","title":"Sharp asymptotics on the compression of two-layer neural networks","created":"2022-05-17","cited_by_count":0,"area":null},{"id":"2305.17583","title":"On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models","created":"2023-05-27","cited_by_count":0,"area":null},{"id":"2311.01646","title":"SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes","created":"2023-11-03","cited_by_count":0,"area":null},{"id":"2409.15393","title":"Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient","created":"2024-09-23","cited_by_count":0,"area":null}],"next_cursor":null}