Variational Inference

Contrastive Deterministic Autoencoders For Language Modeling EMNLP 2023 Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding ACL 2023 Controllable Text Generation via Probability Density Estimation in the Latent Space ACL 2023 Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network ICML 2023 Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders ICML 2023 Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking CVPR 2023 Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach AAAI 2023 Coupled Variational Autoencoder ICML 2023 Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE AISTATS 2023 Cross-Domain Facial Expression Recognition via Disentangling Identity Representation IJCAI 2023 DAVA: Disentangling Adversarial Variational Autoencoder ICLR 2023 Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction ICCV 2023 Deep equilibrium models as estimators for continuous latent variables AISTATS 2023 Deep Stochastic Processes via Functional Markov Transition Operators NeurIPS 2023 Deep Variational Implicit Processes ICLR 2023 Differentiable Random Partition Models NeurIPS 2023 Differentiable user models UAI 2023 Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation EMNLP 2023 Disentangled CVAEs with Contrastive Learning for Explainable Recommendation AAAI 2023 Disentangled Representation for Causal Mediation Analysis AAAI 2023 Disentanglement of Latent Representations via Causal Interventions IJCAI 2023 Disentanglement via Latent Quantization NeurIPS 2023 Disentanglement with Biological Constraints: A Theory of Functional Cell Types ICLR 2023 Disentangling Learning Representations with Density Estimation ICLR 2023 Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023