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Bayesian & Probabilistic

4,335 papers (2 classified directly here)

Subtopics

Probabilistic Modeling (2,599) Bayesian Learning (1,663) Bayesian Inference (523) Variational Inference (107) Gaussian Processes (6) Bayesian Networks (5)

Papers per year

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Papers (including subtopics)

Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging UAI 2019
Locally Private Bayesian Inference for Count Models ICML 2019
U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps ICCV 2019
Alleviating Label Switching with Optimal Transport NIPS 2019
Multiple Futures Prediction NIPS 2019
Amortized Bethe Free Energy Minimization for Learning MRFs NIPS 2019
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models ACL 2019
AutoSense Model for Word Sense Induction AAAI 2019
Approximate Bayesian Inference in Spatial Environments RSS 2019
Variational Convolutional Neural Network Pruning CVPR 2019
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters NIPS 2019
Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data MIDL 2019
Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables AAAI 2019
Learning Reward Machines for Partially Observable Reinforcement Learning NIPS 2019
Conditional Independence in Testing Bayesian Networks ICML 2019
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model NAACL 2019
Structured Variational Inference in Continuous Cox Process Models NIPS 2019
Towards Zero-shot Language Modeling EMNLP 2019
Gaussian Process Latent Variable Alignment Learning AISTATS 2019
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models IJCAI 2019
Bayesian Optimized 1-Bit CNNs ICCV 2019
Variational Temporal Abstraction NIPS 2019
Learning Diverse Bayesian Networks AAAI 2019
Neural Gaussian Copula for Variational Autoencoder IJCNLP 2019
Polynomial-Time Probabilistic Reasoning with Partial Observations via Implicit Learning in Probability Logics AAAI 2019