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Variational Inference

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Showing 1651–1700 of 2274 papers

TitleStatusHype
Neural Topic Model with Reinforcement Learning—0
Neural Variational Dropout Processes—0
Neural Variational Inference and Learning in Undirected Graphical Models—0
Neural Variational Inference and Learning in Belief Networks—0
Neural Variational Inference For Embedding Knowledge Graphs—0
Neuron ranking -- an informed way to condense convolutional neural networks architecture—0
Neuro-Symbolic Entity Alignment via Variational Inference—0
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport—0
Newtonian Monte Carlo: a second-order gradient method for speeding up MCMC—0
Noise-Aware Differentially Private Variational Inference—0
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization—0
Non-Factorised Variational Inference in Dynamical Systems—0
Nonlinear Hawkes Processes in Time-Varying System—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control—0
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation—0
Non-parametric Power-law Data Clustering—0
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning—0
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes—0
No-Regret Generative Modeling via Parabolic Monge-Ampère PDE—0
Distributional Bayesian optimisation for variational inference on black-box simulatorsCode0
Multi-Object Representation Learning with Iterative Variational InferenceCode0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
Distributed Variational Inference for Online Supervised LearningCode0
Barrier Frank-Wolfe for Marginal InferenceCode0
Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural NetworksCode0
The VampPrior Mixture ModelCode0
Approximation Based Variance Reduction for Reparameterization GradientsCode0
Disentangling Latent Space for VAE by Label Relevant/Irrelevant DimensionsCode0
Multiple Importance Sampling ELBO and Deep Ensembles of Variational ApproximationsCode0
Variational Bayesian Neural Networks via Resolution of SingularitiesCode0
The Variational Predictive Natural GradientCode0
Variational Inference with Normalizing FlowsCode0
Doubly Reparameterized Gradient Estimators for Monte Carlo ObjectivesCode0
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
Variational inference for Monte Carlo objectivesCode0
Thompson Sampling via Local UncertaintyCode0
A Hierarchical Bayesian Model for Deep Few-Shot Meta LearningCode0
Variational Inference with Numerical Derivatives: variance reduction through couplingCode0
Time Series Clustering with General State Space Models via Stochastic Variational InferenceCode0
Narrative Text Generation with a Latent Discrete PlanCode0
DropMax: Adaptive Variational SoftmaxCode0
Dropout Inference in Bayesian Neural Networks with Alpha-divergencesCode0
NASH: Toward End-to-End Neural Architecture for Generative Semantic HashingCode0
Time Series Domain Adaptation via Latent Invariant Causal MechanismCode0
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed ModelsCode0
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-IdentificationCode0
VICatMix: variational Bayesian clustering and variable selection for discrete biomedical dataCode0
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