Variational learning for latent Gaussian model of discrete data

16 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Approximate Inference Turns Deep Networks into Gaussian Processes (2019, methodology): “6 in [9] for details of this method.”
Assumed Density Filtering Methods for Learning Bayesian Neural Networks (2016, background): “Although more accurate alternate bounds have been proposed (Khan 2012 where { z Unfortunately, this estimator is known to have high variance and further variance reduction techniques are required (Paisley, Blei, and Jordan 2012).”
Practical Deep Learning with Bayesian Principles (2019, neutral): “6 in Khan [21] for details).”
Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence (2017, background): “For example, Khan [41] discusses a variety of strategies for bounding the binary logistic regression loss and indeed proves that other bounds are tighter than the Taylor expansion (“Bohning”) bound that we use.”