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Variational Inference

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TitleStatusHype
Uncertainty Quantification in Alzheimer's Disease Progression Modeling—0
Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks—0
A Non-negative VAE:the Generalized Gamma Belief Network—0
Distance-Preserving Spatial Representations in Genomic Data—0
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification—0
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastographyCode0
Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network—0
Variational Inference Using Material Point Method—0
Amortized Posterior Sampling with Diffusion Prior Distillation—0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes—0
Gradient-based inference of abstract task representations for generalization in neural networks—0
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
Neural information field filter—0
SoftCVI: Contrastive variational inference with self-generated soft labelsCode0
Revisiting Score Function Estimators for k-Subset Sampling—0
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework—0
Improving Graph Out-of-distribution Generalization on Real-world Data—0
Variational Inference via Smoothed Particle Hydrodynamics—0
Autoregressive Speech Synthesis without Vector Quantization—0
Variational Best-of-N Alignment—0
Scalable Variational Causal Discovery Unconstrained by AcyclicityCode0
Idiographic Personality Gaussian Process for Psychological Assessment—0
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-ShiftsCode0
Neural Probabilistic Logic Learning for Knowledge Graph Reasoning—0
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference—0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Particle Semi-Implicit Variational InferenceCode0
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models—0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Learning Optimal Filters Using Variational Inference—0
What type of inference is planning?Code0
Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference—0
Tree-based variational inference for Poisson log-normal modelsCode0
CausalMMM: Learning Causal Structure for Marketing Mix Modeling—0
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical dataCode0
Flexible Tails for Normalizing FlowsCode0
Probabilistic Programming with Programmable Variational Inference—0
Video Generation with Learned Action Prior—0
Variational Distillation of Diffusion Policies into Mixture of Experts—0
Period Singer: Integrating Periodic and Aperiodic Variational Autoencoders for Natural-Sounding End-to-End Singing Voice Synthesis—0
Injective flows for star-like manifolds—0
Assessment of Uncertainty Quantification in Universal Differential Equations—0
Convergence rate of random scan Coordinate Ascent Variational Inference under log-concavity—0
Efficient Mixture Learning in Black-Box Variational InferenceCode0
Central Limit Theorem for Bayesian Neural Network trained with Variational Inference—0
Variational Flow Matching for Graph Generation—0
Stochastic full waveform inversion with deep generative prior for uncertainty quantification—0
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte CarloCode0
Concurrent Training and Layer Pruning of Deep Neural Networks—0
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