most citedBayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

111 citations · 296 across the 13 of their papers we have counts for

collaborators

13 papers

cs.AI2013103 cited

Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation

Max Welling, Yee Whye Teh

We present a novel inference algorithm for arbitrary, binary, undirected graphs. Unlike loopy belief propagation, which iterates fixed point equations, we directly descend on the B…

cs.LG20131 cited

Herded Gibbs Sampling

Luke Bornn, Yutian Chen, Nando de Freitas +3

The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, t…

cs.LG20125 cited

Efficient Parametric Projection Pursuit Density Estimation

Max Welling, Richard S. Zemel, Geoffrey E. Hinton

Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert mod…

cs.AI20122 cited

A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation

Max Welling, Andrew E. Gelfand, Alexander T. Ihler

We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) rec…

cs.AI20124 cited

Generalized Belief Propagation on Tree Robust Structured Region Graphs

Andrew E. Gelfand, Max Welling

This paper provides some new guidance in the construction of region graphs for Generalized Belief Propagation (GBP). We connect the problem of choosing the outer regions of a LoopS…

cs.LG20121 cited

Semisupervised Classifier Evaluation and Recalibration

Peter Welinder, Max Welling, Pietro Perona

How many labeled examples are needed to estimate a classifier's performance on a new dataset? We study the case where data is plentiful, but labels are expensive. We show that by m…