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Variational Inference

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TitleStatusHype
Deep Probabilistic Video Compression—0
Deep Probabilistic Programming—0
To Relieve Your Headache of Training an MRF, Take AdVIL—0
Deep Probabilistic Models to Detect Data Poisoning Attacks—0
Fully probabilistic deep models for forward and inverse problems in parametric PDEs—0
A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation—0
A Causal Ordering Prior for Unsupervised Representation Learning—0
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap—0
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization—0
Deep Poisson Factorization Machines: factor analysis for mapping behaviors in journalist ecosystem—0
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models—0
Learning Robot Skills with Temporal Variational Inference—0
Bayesian calibration of differentiable agent-based models—0
Machine Learning and the Future of Bayesian Computation—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Bayesian brains and the Rényi divergence—0
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm—0
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel—0
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models—0
Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models—0
Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization—0
MAGI: Multi-Annotated Explanation-Guided Learning—0
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems—0
Maximizing submodular functions using probabilistic graphical models—0
Logit Disagreement: OoD Detection with Bayesian Neural Networks—0
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds—0
BayesFormer: Transformer with Uncertainty Estimation—0
Longitudinal Deep Kernel Gaussian Process Regression—0
Learning Invariances using the Marginal Likelihood—0
Learning Hard Alignments with Variational Inference—0
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process—0
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution—0
Learning From Unpaired Data: A Variational Bayes Approach—0
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function—0
Deep kernel processes—0
Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions—0
Learning from demonstration using products of experts: applications to manipulation and task prioritization—0
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future—0
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation—0
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning—0
Learning Distributions via Monte-Carlo Marginalization—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Learning Optimal Filters Using Variational Inference—0
Learning proposals for sequential importance samplers using reinforced variational inference—0
Local-HDP: Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios—0
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems—0
Learning Set Functions with Implicit Differentiation—0
Deep Operator Networks for Bayesian Parameter Estimation in PDEs—0
Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization—0
Local Expectation Gradients for Black Box Variational Inference—0
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