{"total":22,"rows":[{"id":"2106.11905","title":"Dangers of Bayesian Model Averaging under Covariate Shift","created":"2021-06-22","cited_by_count":31,"area":null},{"id":"2107.04619","title":"Diverse Video Generation using a Gaussian Process Trigger","created":"2021-07-09","cited_by_count":17,"area":null},{"id":"2407.21176","title":"DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks","created":"2024-07-30","cited_by_count":7,"area":null},{"id":"2103.10134","title":"Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey","created":"2021-03-18","cited_by_count":5,"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":"2103.02926","title":"Calibrated simplex-mapping classification","created":"2021-03-04","cited_by_count":3,"area":null},{"id":"2201.12724","title":"Stochastic Neural Networks with Infinite Width are Deterministic","created":"2022-01-30","cited_by_count":3,"area":null},{"id":"2212.10306","title":"Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting","created":"2022-12-20","cited_by_count":3,"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":"2102.05208","title":"Attentive Gaussian processes for probabilistic time-series generation","created":"2021-02-10","cited_by_count":1,"area":null},{"id":"2105.09695","title":"Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$-Regularization","created":"2021-05-20","cited_by_count":1,"area":null},{"id":"2106.05848","title":"Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems","created":"2021-06-03","cited_by_count":1,"area":null},{"id":"2107.11042","title":"Deep Learning Based Reconstruction of Total Solar Irradiance","created":"2021-07-23","cited_by_count":1,"area":null},{"id":"2109.08965","title":"PCNN: A physics-constrained neural network for multiphase flows","created":"2021-09-18","cited_by_count":1,"area":null},{"id":"2205.10879","title":"Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation","created":"2022-05-22","cited_by_count":1,"area":null},{"id":"2112.06410","title":"How Good are Low-Rank Approximations in Gaussian Process Regression?","created":"2021-12-13","cited_by_count":0,"area":null},{"id":"2305.14543","title":"Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization","created":"2023-05-23","cited_by_count":0,"area":null},{"id":"2505.18526","title":"Scalable Deep Basis Kernel Gaussian Processes","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2509.01090","title":"A Class of Random-Kernel Network Models","created":"2025-09-01","cited_by_count":0,"area":null},{"id":"2509.21228","title":"Response to Promises and Pitfalls of Deep Kernel Learning","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2605.00708","title":"Deep Kernel Learning for Stratifying Glaucoma Trajectories","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2606.05264","title":"REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting","created":"2026-06-03","cited_by_count":0,"area":null}],"next_cursor":null}