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Variational Inference

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TitleStatusHype
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
A Generalization Bound for Online Variational Inference—0
A Generative Parser with a Discriminative Recognition Algorithm—0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference—0
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms—0
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series—0
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference—0
Algorithms for item categorization based on ordinal ranking data—0
Algorithms of the LDA model [REPORT]—0
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework—0
All in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference—0
Alpha-Beta Divergence For Variational Inference—0
Alpha-Divergences in Variational Dropout—0
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics—0
-VAEs : Optimising variational inference by learning data-dependent divergence skew—0
A Mathematical Walkthrough and Discussion of the Free Energy Principle—0
Variational inference of fractional Brownian motion with linear computational complexity—0
Amortised MAP Inference for Image Super-resolution—0
Amortized backward variational inference in nonlinear state-space models—0
Amortized Bayesian Meta-Learning—0
Amortized Context Vector Inference for Sequence-to-Sequence Networks—0
Amortized Inference of Variational Bounds for Learning Noisy-OR—0
Amortized Posterior Sampling with Diffusion Prior Distillation—0
Amortized Variational Inference for Deep Gaussian Processes—0
Amortized Variational Inference for Simple Hierarchical Models—0
Amortized Variational Inference: A Systematic Review—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Analog Bayesian neural networks are insensitive to the shape of the weight distribution—0
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks—0
Analytical Probability Distributions and EM-Learning for Deep Generative Networks—0
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations—0
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process—0
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm—0
A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation—0
An information-theoretic analysis of deep latent-variable models—0
An Information Theoretic Approach to Distributed Representation Learning—0
An Instability in Variational Inference for Topic Models—0
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications—0
An Introduction to Variational Inference—0
Anomaly Detection on Graph Time Series—0
A Non-negative VAE:the Generalized Gamma Belief Network—0
A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis—0
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models—0
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data—0
A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS—0
A Particle Algorithm for Mean-Field Variational Inference—0
Approximate Bayesian inference as a gauge theory—0
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