Variational Inference

Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals MLHC 2020 Deep Structural Causal Models for Tractable Counterfactual Inference NeurIPS 2020 DGE: Deep Generative Network Embedding Based on Commonality and Individuality AAAI 2020 Diffusion Variational Autoencoders IJCAI 2020 Dirichlet Graph Variational Autoencoder NeurIPS 2020 Discrete Latent Variable Representations for Low-Resource Text Classification ACL 2020 Disentangled Representations for Sequence Data using Information Bottleneck Principle ACML 2020 Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model IJCAI 2020 Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation ICML 2020 DIVA: Domain Invariant Variational Autoencoders MIDL 2020 Diverse Plausible Shape Completions from Ambiguous Depth Images CoRL 2020 Diversify Question Generation with Continuous Content Selectors and Question Type Modeling EMNLP 2020 DoLFIn: Distributions over Latent Features for Interpretability COLING 2020 Do sequence-to-sequence VAEs learn global features of sentences? EMNLP 2020 Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables ICML 2020 DualSDF: Semantic Shape Manipulation Using a Two-Level Representation CVPR 2020 Dynamic Prosody Generation for Speech Synthesis Using Linguistics-Driven Acoustic Embedding Selection Interspeech 2020 Effective Decoding in Graph Auto-Encoder Using Triadic Closure AAAI 2020 Effective Estimation of Deep Generative Language Models ACL 2020 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors ICML 2020 Efficient Low Rank Gaussian Variational Inference for Neural Networks NeurIPS 2020 Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity NeurIPS 2020 Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models AAAI 2020 Efficient Second-Order TreeCRF for Neural Dependency Parsing ACL 2020 Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee NeurIPS 2020