15.1k citations · 36.9k across the 152 of their papers we have counts for
8 papers · 2 filters
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…
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…
Bayesian Random Fields: The Bethe-Laplace Approximation
Max Welling, Sridevi Parise
While learning the maximum likelihood value of parameters of an undirected graphical model is hard, modelling the posterior distribution over parameters given data is harder. Yet,…
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
Sungjin Ahn, Anoop Korattikara, Max Welling
In this paper we address the following question: Can we approximately sample from a Bayesian posterior distribution if we are only allowed to touch a small mini-batch of data-items…
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Max Welling, Yee Whye Teh, Hilbert Kappen
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvanta…
On Smoothing and Inference for Topic Models
Arthur Asuncion, Max Welling, Padhraic Smyth +1
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been develop…