Variational Inference

Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts ICML 2023 Bayesian Posterior Approximation With Stochastic Ensembles CVPR 2023 Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders JMLR 2023 Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation NeurIPS 2023 Bit Allocation using Optimization ICML 2023 Blind Video Deflickering by Neural Filtering With a Flawed Atlas CVPR 2023 Boosting Summarization with Normalizing Flows and Aggressive Training EMNLP 2023 Bounding Evidence and Estimating Log-Likelihood in VAE AISTATS 2023 Bridge the Inference Gaps of Neural Processes via Expectation Maximization ICLR 2023 BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning AAAI 2023 Catch Missing Details: Image Reconstruction With Frequency Augmented Variational Autoencoder CVPR 2023 Causal Recurrent Variational Autoencoder for Medical Time Series Generation AAAI 2023 Cell Variational Information Bottleneck Network ACML 2023 CMVAE: Causal Meta VAE for Unsupervised Meta-Learning AAAI 2023 C-NTPP: Learning Cluster-Aware Neural Temporal Point Process AAAI 2023 Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency EMNLP 2023 Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates ICML 2023 Collaborative Score Distillation for Consistent Visual Editing NeurIPS 2023 Collapsed Inference for Bayesian Deep Learning NeurIPS 2023 Complementary Intrinsics From Neural Radiance Fields and CNNs for Outdoor Scene Relighting CVPR 2023 Compression with Bayesian Implicit Neural Representations NeurIPS 2023 Conditional Graph Information Bottleneck for Molecular Relational Learning ICML 2023 Continual Unsupervised Disentangling of Self-Organizing Representations ICLR 2023 Continual Variational Autoencoder via Continual Generative Knowledge Distillation AAAI 2023 Continuous Mixtures of Tractable Probabilistic Models AAAI 2023