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Variational Inference

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Showing 1901–1950 of 2274 papers

TitleStatusHype
Gamma Processes, Stick-Breaking, and Variational Inference—0
Gauging Variational Inference—0
Gaussian Density Parametrization Flow: Particle and Stochastic Approaches—0
Gaussian Mean Field Regularizes by Limiting Learned Information—0
Gaussian Process Conditional Density Estimation—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaussian Process Meta-Representations Of Neural Networks—0
Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Generalized Transformation-based Gradient—0
Generalized Variational Continual Learning—0
Generating Diverse Translation from Model Distribution with Dropout—0
Generative Flow Networks: Theory and Applications to Structure Learning—0
Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations—0
Generative Models for Learning from Crowds—0
Generative Particle Variational Inference via Estimation of Functional Gradients—0
Generative Temporal Models with Memory—0
Generative Video Compression as Hierarchical Variational Inference—0
Geometric Dirichlet Means algorithm for topic inference—0
Geometric variational inference—0
GFlowOut: Dropout with Generative Flow Networks—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Good Initializations of Variational Bayes for Deep Models—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
GRADE: Graph Dynamic Embedding—0
Gradient-based inference of abstract task representations for generalization in neural networks—0
Variational Laplace for Bayesian neural networks—0
Gradient Regularisation as Approximate Variational Inference—0
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows—0
PGODE: Towards High-quality System Dynamics Modeling—0
Neural Graph Collaborative Filtering Using Variational Inference—0
Gray-box inference for structured Gaussian process models—0
Group Factor Analysis—0
Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis—0
Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mapping—0
Hamiltonian Policy Optimization—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Hierarchical Bayes Autoencoders—0
Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond—0
Hierarchical Implicit Models and Likelihood-Free Variational Inference—0
Hierarchical mixtures of Unigram models for short text clustering: The role of Beta-Liouville priors—0
Hierarchical models for neural population dynamics in the presence of non-stationarity—0
Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis—0
HierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer—0
Higher-Order Inference for Multi-Class Log-Supermodular Models—0
High Mutual Information in Representation Learning with Symmetric Variational Inference—0
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints—0
How particular is the physics of the free energy principle?—0
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