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Found 5 papers, 0 papers with code
The Generalized Reparameterization Gradient
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective.
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems.
Local Expectation Gradients for Black Box Variational Inference
This algorithm divides the problem of estimating the stochastic gradients over multiple variational parameters into smaller sub-tasks so that each sub-task explores intelligently the most relevant part of the variational distribution.
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models
We introduce a novel sampling algorithm for Markov chain Monte Carlo-based Bayesian inference for factorial hidden Markov models.
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression.
