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Variational Inference

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TitleStatusHype
MaxEntropy Pursuit Variational Inference—0
Maximizing submodular functions using probabilistic graphical models—0
Max-Margin Nonparametric Latent Feature Models for Link Prediction—0
MCMC-driven learning—0
MCMC-Interactive Variational Inference—0
MCMC Variational Inference via Uncorrected Hamiltonian Annealing—0
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes—0
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models—0
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations—0
Measurement error models: from nonparametric methods to deep neural networks—0
Measuring Systematic Risk with Neural Network Factor Model—0
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning—0
Memoized Online Variational Inference for Dirichlet Process Mixture Models—0
Message Passing Stein Variational Gradient Descent—0
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory—0
Meta-Learning for Variational Inference—0
Meta-Learning Divergences of Variational Inference—0
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference—0
Metropolis-Hastings view on variational inference and adversarial training—0
MISSO: Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex and Nonsmooth Problems—0
Mixed Membership Stochastic Blockmodels—0
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
Mixture Representation Learning with Coupled Autoencoding Agents—0
Mixture weights optimisation for Alpha-Divergence Variational Inference—0
Model Informed Flows for Bayesian Inference of Probabilistic Programs—0
Modeling Item Response Theory with Stochastic Variational Inference—0
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field—0
Modeling Overlapping Communities with Node Popularities—0
Modeling Randomly Observed Spatiotemporal Dynamical Systems—0
Modeling Randomly Walking Volatility with Chained Gamma Distributions—0
Modeling the Long Term Future in Model-Based Reinforcement Learning—0
Modeling uncertainty for Gaussian Splatting—0
Modified Frank Wolfe in Probability Space—0
Moment-Based Variational Inference for Stochastic Differential Equations—0
Moment-Based Variational Inference for Markov Jump Processes—0
Monotonic Alpha-divergence Minimisation for Variational Inference—0
Monte Carlo Structured SVI for Two-Level Non-Conjugate Models—0
MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity—0
MudiNet: Task-guided Disentangled Representation Learning for 5G Indoor Multipath-assisted Positioning—0
Multi-Adversarial Variational Autoencoder Networks—0
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks—0
Multilabel Classification using Bayesian Compressed Sensing—0
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability—0
Multilevel Monte Carlo Variational Inference—0
Multi-Loss Sub-Ensembles for Accurate Classification with Uncertainty Estimation—0
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation—0
Multi-output Gaussian Process Modulated Poisson Processes for Event Prediction—0
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference—0
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