variational inference

Deep Variational Implicit Processes ICLR 2023 Design Amortization for Bayesian Optimal Experimental Design AAAI 2023 Differentiable Random Partition Models NeurIPS 2023 Differentiable user models UAI 2023 Diffusing Gaussian Mixtures for Generating Categorical Data AAAI 2023 Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation NeurIPS 2023 Diffusion Model as Representation Learner ICCV 2023 Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier NeurIPS 2023 Disentanglement via Latent Quantization NeurIPS 2023 Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference ICML 2023 Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023 Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation ICLR 2023 Dynamic Belief for Decentralized Multi-Agent Cooperative Learning IJCAI 2023 Dynamic Structured Neural Topic Model with Self-Attention Mechanism ACL 2023 Effective Neural Topic Modeling with Embedding Clustering Regularization ICML 2023 Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification ICML 2023 Embracing the chaos: analysis and diagnosis of numerical instability in variational flows NeurIPS 2023 End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics NeurIPS 2023 Energy-Based Test Sample Adaptation for Domain Generalization ICLR 2023 Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs NeurIPS 2023 Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design ICLR 2023 Euler-Lagrange Analysis of Generative Adversarial Networks JMLR 2023 Factorial SDE for Multi-Output Gaussian Process Regression AISTATS 2023 Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models AISTATS 2023 Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach ICLR 2023