bayesian inference

Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling MIDL 2023 Estimation of Counterfactual Interventions under Uncertainties ACML 2023 Event Temporal Relation Extraction with Bayesian Translational Model EACL 2023 Exploration via Epistemic Value Estimation AAAI 2023 Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms AISTATS 2023 Federated Learning with Uncertainty via Distilled Predictive Distributions ACML 2023 FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation AAAI 2023 Few-Shot Non-Line-of-Sight Imaging With Signal-Surface Collaborative Regularization CVPR 2023 Fine-Tuning Generative Models as an Inference Method for Robotic Tasks CoRL 2023 Folded Hamiltonian Monte Carlo for Bayesian Generative Adversarial Networks ACML 2023 ForecastPFN: Synthetically-Trained Zero-Shot Forecasting NeurIPS 2023 Free Energy of Bayesian Convolutional Neural Network with Skip Connection ACML 2023 Frequency Domain Gaussian Process Models for $H^∞$ Uncertainties L4DC 2023 Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes ICML 2023 Function Space Bayesian Pseudocoreset for Bayesian Neural Networks NeurIPS 2023 Function-Space Regularization in Neural Networks: A Probabilistic Perspective ICML 2023 Gaussian Processes with Errors in Variables: Theory and Computation JMLR 2023 Generalization in the Face of Adaptivity: A Bayesian Perspective NeurIPS 2023 Generalized Belief Transport NeurIPS 2023 GFlowOut: Dropout with Generative Flow Networks ICML 2023 Gradient-Free Kernel Stein Discrepancy NeurIPS 2023 Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution ICLR 2023 Guided Deep Kernel Learning UAI 2023 Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration NeurIPS 2023 Hierarchical VAEs provide a normative account of motion processing in the primate brain NeurIPS 2023