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Collapsed variational inference for Sum-Product Networks
Han Zhao
, Tameem Adel
, Geoff Gordon
, Brandon Amos
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Dive into the research topics of 'Collapsed variational inference for Sum-Product Networks'. Together they form a unique fingerprint.
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Keyphrases
Variational Inference
100%
Sum-product Networks
100%
Maximum Likelihood
16%
Computational Efficiency
16%
Latent Variables
16%
Formulation Optimization
16%
Linear Time
16%
Bayesian Approach
16%
Learning Approaches
16%
Posterior Inference
16%
Overfitting
16%
Bayesian Probability
16%
Approximation Techniques
16%
Parameter Learning
16%
Probabilistic Inference
16%
Exact Inference
16%
Inference Machine
16%
Likelihood-based Approach
16%
Easy-to-implement
16%
Posterior Sampling
16%
Inference Algorithms
16%
Maximum Likelihood Principle
16%
Computer Science
maximum-likelihood
100%
Learning Approach
50%
Optimization Formulation
50%
Bayesian Approach
50%
Approximation Technique
50%
Probabilistic Inference
50%
Likelihood Principle
50%
Mathematics
Product Sum
100%
Maximum Likelihood
16%
Bayesian
16%
Bayesian Approach
16%
Linear Time
16%
Approximation Technique
16%
Probabilistic Inference
16%
Maximum Likelihood Principle
16%