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Variational Inference

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TitleStatusHype
alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extractionCode1
Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures—0
Stochastic Autograd—0
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework—0
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease—0
Deep Causal Reasoning for RecommendationsCode1
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy GradientsCode0
Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy ClassificationCode0
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation—0
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization—0
Latent Time Neural Ordinary Differential Equations—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
Information Field Theory and Artificial Intelligence—0
Hierarchical Variational Memory for Few-shot Learning Across DomainsCode0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer—0
Neural Point Process for Learning Spatiotemporal Event DynamicsCode1
Fully Context-Aware Image Inpainting with a Learned Semantic PyramidCode1
A Continuous-time Stochastic Gradient Descent Method for Continuous Data—0
On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks—0
BCD Nets: Scalable Variational Approaches for Bayesian Causal DiscoveryCode1
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Latent Matters: Learning Deep State-Space Models—0
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels—0
Continuous-time edge modelling using non-parametric point processes—0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
Collapsed Variational Bounds for Bayesian Neural NetworksCode0
Modified Frank Wolfe in Probability Space—0
Functional Variational Inference based on Stochastic Process Generators—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Optimality of variational inference for stochasticblock model with missing links—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
EDGE: Explaining Deep Reinforcement Learning PoliciesCode1
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models—0
Topic Modeling Revisited: A Document Graph-based Neural Network PerspectiveCode1
Variational Continual Bayesian Meta-Learning—0
Probabilistic Tensor Decomposition of Neural Population Spiking ActivityCode0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
Variational Gibbs Inference for Statistical Model Estimation from Incomplete DataCode0
Differentially private stochastic expectation propagation (DP-SEP)—0
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule—0
Weight Pruning and Uncertainty in Radio Galaxy ClassificationCode0
Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach—0
Matrix Inversion free variational inference in Conditional Student's T Processes—0
Shooting Schrödinger’s Cat—0
Probabilistic Deep Learning with Generalised Variational Inference—0
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel—0
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