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Variational Inference

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TitleStatusHype
Stein Variational Gradient Descent for Approximate Bayesian Computation—0
Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models—0
Parametric Gaussian Process Regressors—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Rapid Model Comparison by Amortizing Across ModelsCode0
Sequential Learning for Dirichlet Process Mixtures—0
Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences—0
Bijectors.jl: Flexible transformations for probability distributionsCode0
Langevin Dynamics as Nonparametric Variational Inference—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Regularized Sparse Gaussian Processes—0
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization—0
Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects—0
High Mutual Information in Representation Learning with Symmetric Variational Inference—0
A Deep Factorization of Style and Structure in FontsCode0
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem—0
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications—0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Robust Variational Bayesian Point Set Registration—0
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference—0
Variational Few-Shot Learning—0
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory—0
Semi-supervised voice conversion with amortized variational inference—0
On PAC-Bayes Bounds for Deep Neural Networks using the Loss Curvature—0
Meta-Learning for Variational Inference—0
Gaussian Process Meta-Representations Of Neural Networks—0
An Information Theoretic Approach to Distributed Representation Learning—0
Refining the variational posterior through iterative optimization—0
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach—0
Hierarchical Bayes Autoencoders—0
Shallow VAEs with RealNVP Prior Can Perform as Well as Deep Hierarchical VAEs—0
Latent Variables on Spheres for Sampling and Inference—0
LAVAE: Disentangling Location and Appearance—0
BasisVAE: Orthogonal Latent Space for Deep Disentangled Representation—0
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models—0
On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
LIA: Latently Invertible Autoencoder with Adversarial Learning—0
Deep Bayesian Structure NetworksCode0
A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics—0
Variational Conditional GAN for Fine-grained Controllable Image Generation—0
Refined α-Divergence Variational Inference via Rejection Sampling—0
Variational Bayesian Context-aware Representation for Grocery RecommendationCode0
Compositional uncertainty in deep Gaussian processesCode0
Scalable Gaussian Process Classification with Additive Noise for Various LikelihoodsCode0
Reconstructing continuous distributions of 3D protein structure from cryo-EM imagesCode0
Curve Fitting from Probabilistic Emissions and Applications to Dynamic Item Response TheoryCode0
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation—0
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations—0
Latent Gaussian process with composite likelihoods and numerical quadrature—0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
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