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Variational Inference

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Showing 526–550 of 2274 papers

TitleStatusHype
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis—0
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference—0
Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography—0
A stable deep adversarial learning approach for geological facies generation—0
Semi-Implicit Variational Inference via Kernelized Path Gradient Descent—0
Differentially private stochastic expectation propagation (DP-SEP)—0
Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion—0
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation—0
Bethe Projections for Non-Local Inference—0
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling—0
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference—0
Beta Process Multiple Kernel Learning—0
Brain-like variational inference—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search—0
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models—0
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise—0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM—0
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation—0
Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder—0
Diversity-Promoting Bayesian Learning of Latent Variable Models—0
β-CapsNet: Learning Disentangled Representation for CapsNet by Information Bottleneck—0
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