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Variational Inference

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TitleStatusHype
How to deal with missing data in supervised deep learning?—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hybrid Variational Autoencoder for Time Series Forecasting—0
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework—0
Variational Hyper-Encoding Networks—0
Identifiable Feature Learning for Spatial Data with Nonlinear ICA—0
Identifying signal and noise structure in neural population activity with Gaussian process factor models—0
Idiographic Personality Gaussian Process for Psychological Assessment—0
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
Importance Sampled Stochastic Optimization for Variational Inference—0
Importance Weighted Structure Learning for Scene Graph Generation—0
Importance Weighting and Variational Inference—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Gradient-Based Optimization Over Discrete Distributions—0
Improved Gradient Estimators for Stochastic Discrete Variables—0
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Improving Graph Out-of-distribution Generalization on Real-world Data—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction—0
Incorporating Word Correlation Knowledge into Topic Modeling—0
Incremental Variational Inference for Latent Dirichlet Allocation—0
Independent projections of diffusions: Gradient flows for variational inference and optimal mean field approximations—0
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations—0
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning—0
Indian Buffet Process Deep Generative Models for Semi-Supervised Classification—0
Inducing Interpretable Representations with Variational Autoencoders—0
Inference for determinantal point processes without spectral knowledge—0
Inferring the quantum density matrix with machine learning—0
Infinite Mixture of Inverted Dirichlet Distributions—0
InfoNCE is variational inference in a recognition parameterised model—0
Information Field Theory and Artificial Intelligence—0
Information Theory with Kernel Methods—0
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification—0
Injective flows for star-like manifolds—0
Input Dependent Sparse Gaussian Processes—0
Instance-Aware Graph Convolutional Network for Multi-Label Classification—0
Integrated Non-Factorized Variational Inference—0
Integrating Document Clustering and Topic Modeling—0
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
Interactive Graph Convolutional Filtering—0
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning—0
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
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