Bayesian & Probabilistic

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables NeurIPS 2020 Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization AAAI 2020 Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors EMNLP 2020 Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields EMNLP 2020 Tree-Structured Neural Topic Model ACL 2020 Tweedie-Hawkes Processes: Interpreting the Phenomena of Outbreaks AAAI 2020 Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks PGM 2020 Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning AAAI 2020 Uncertainty-Aware Score Distribution Learning for Action Quality Assessment CVPR 2020 Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization AAAI 2020 Uncertainty Quantification for Inferring Hawkes Networks NeurIPS 2020 Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification ACL 2020 Unsupervised Deep Shape Descriptor With Point Distribution Learning CVPR 2020 Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models NeurIPS 2020 Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020 Variational Autoencoders with Riemannian Brownian Motion Priors ICML 2020 Variational Bayesian Monte Carlo with Noisy Likelihoods NeurIPS 2020 Variational Bayesian Quantization ICML 2020 Variational Bayesian Unlearning NeurIPS 2020 Variational Inference for Sequential Data with Future Likelihood Estimates ICML 2020 Variational Inference for Sparse Gaussian Process Modulated Hawkes Process AAAI 2020 Variational Label Enhancement ICML 2020 Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge IJCAI 2020 Variational Metric Scaling for Metric-Based Meta-Learning AAAI 2020