← Bayesian & Probabilistic
Artificial Intelligence › Bayesian & Probabilistic ›

Probabilistic Modeling

2591 directly classified papers

Papers per year

Papers

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA NIPS 2020
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NIPS 2020
Modeling Shared responses in Neuroimaging Studies through MultiView ICA NIPS 2020
RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection SEMEVAL 2020
A Bayesian Nonparametrics View into Deep Representations NIPS 2020
Multi-task Causal Learning with Gaussian Processes NIPS 2020
Stochastic Deep Gaussian Processes over Graphs NIPS 2020
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes UAI 2020
Graphical continuous Lyapunov models UAI 2020
Ordering Variables for Weighted Model Integration UAI 2020
Layering-MCMC for Structure Learning in Bayesian Networks UAI 2020
Deep Sigma Point Processes UAI 2020
Slice Sampling for General Completely Random Measures UAI 2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks NIPS 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties NIPS 2020
Approximation Based Variance Reduction for Reparameterization Gradients NIPS 2020
Generating Narrative Text in a Switching Dynamical System CONLL 2020
Domain adaptation model for retinopathy detection from cross-domain OCT images MIDL 2020
PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python PGM 2020
A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital PGM 2020
CREMA: A Java Library for Credal Network Inference PGM 2020
Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty COLING 2020
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes NIPS 2020
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery AISTATS 2020
Enriched mixtures of generalised Gaussian process experts AISTATS 2020