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Variational Inference

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Showing 1426–1450 of 2274 papers

TitleStatusHype
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
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