deep gaussian process

Distributional Deep Gaussian Processes UAI 2026 Exact Uncertainty Propagation via Gaussian Process Neurons UAI 2026 Adaptive RKHS Fourier Features for Compositional Gaussian Process Models AISTATS 2025 A theory of representation learning gives a deep generalisation of kernel methods ICML 2023 Posterior Contraction for Deep Gaussian Process Priors JMLR 2023 Thin and deep Gaussian processes NeurIPS 2023 Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems AAAI 2021 Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features NeurIPS 2021 Deep kernel processes ICML 2021 Deep Neural Networks as Point Estimates for Deep Gaussian Processes NeurIPS 2021 Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning ICCV 2021 Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes ICML 2021 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021 Probabilistic selection of inducing points in sparse Gaussian processes UAI 2021 Sequence-to-Sequence Learning for Deep Gaussian Process Based Speech Synthesis Using Self-Attention GP Layer Interspeech 2021 Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations AISTATS 2021 Subset-of-data variational inference for deep Gaussian-processes regression UAI 2021 The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective NeurIPS 2021 Amortized variance reduction for doubly stochastic objective UAI 2020 Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties NeurIPS 2020 Compositional uncertainty in deep Gaussian processes UAI 2020 Deep Sigma Point Processes UAI 2020 Inter-domain Deep Gaussian Processes ICML 2020 Interpretable Deep Gaussian Processes with Moments AISTATS 2020 Multi-Speaker Text-to-Speech Synthesis Using Deep Gaussian Processes Interspeech 2020