Bayesian & Probabilistic

Deep kernel processes ICML 2021 Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data NeurIPS 2021 Deep Neural Networks as Point Estimates for Deep Gaussian Processes NeurIPS 2021 Deep Probabilistic Canonical Correlation Analysis AAAI 2021 Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging AAAI 2021 Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model AAAI 2021 De-randomizing MCMC dynamics with the diffusion Stein operator NeurIPS 2021 Diagnostics for conditional density models and Bayesian inference algorithms UAI 2021 Diffusion Probabilistic Models for 3D Point Cloud Generation CVPR 2021 Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling NeurIPS 2021 Directed Spectrum Measures Improve Latent Network Models Of Neural Populations NeurIPS 2021 Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect NeurIPS 2021 Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution CoNLL 2021 Dual Parameterization of Sparse Variational Gaussian Processes NeurIPS 2021 Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit NeurIPS 2021 Dynamic Cutset Networks AISTATS 2021 Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series AAAI 2021 Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation CVPR 2021 Efficient Bayesian network structure learning via local Markov boundary search NeurIPS 2021 Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings AAAI 2021 Efficient debiased evidence estimation by multilevel Monte Carlo sampling UAI 2021 Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model ICML 2021 Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging NeurIPS 2021 End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series ICML 2021 Entropy-based adaptive Hamiltonian Monte Carlo NeurIPS 2021