- Robust Higher Order Statistics Max Welling
- Flexible Priors for Infinite Mixture Models Max Welling WELLING@ICS.UCI.EDU
- Journal of Machine Learning Research x (2009) x-xx Submitted 6/08; Published xx/xx Distributed Algorithms for Topic Models
- Positive Tensor Factorization Max Welling 1 and Markus Weber 2
- Independent Component Analysis of Incomplete Data
- On the Choice of Regions for Generalized Belief Propagation Max Welling (welling@ics.uci.edu)
- Gatsby Computational Neuroscience Unit 17 Queen Square, London University College London WC1N 3AR, United Kingdom
- Collapsed Variational Dirichlet Process Mixture Models Kenichi Kurihara
- Fast Collapsed Gibbs Sampling For Latent Dirichlet Ian Porteous
- Hybrid Variational/Gibbs Collapsed Inference in Topic Models Max Welling
- Correction to Recurrence Proof for Herding Yutian Chen & Max Welling
- THIS IS A DRAFT VERSION. FINAL VERSION TO BE PUBLISHED AT NIPS '06 Structure Learning in Markov Random Fields
- Exponential Family Harmoniums with an Application to Information Retrieval
- On the Choice of Regions for Generalized Belief Propagation Max Welling (welling@ics.uci.edu)
- Accelerated Variational Dirichlet Process Mixtures Kenichi Kurihara
- THIS IS A DRAFT VERSION. FINAL VERSION TO BE PUBLISHED AT NIPS '06 A Collapsed Variational Bayesian Inference
- A New Learning Algorithm for Mean Field Boltzmann Machines
- Unsupervised Learning of Models for Recognition M. Weber 1 M. Welling 2 P. Perona 1;2;3
- Probabilistic Sequential Independent Components Analysis Max Welling
- Collapsed Variational Dirichlet Process Mixture Models # Kenichi Kurihara
- Unsupervised Learning of Models for Visual Object Class Recognition
- Wormholes Improve Contrastive Divergence Geoffrey Hinton, Max Welling and Andriy Mnih
- ViewpointInvariant Learning and Detection of Human Heads M. Weber y W. Einhauser x
- Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
- Labelling with Loopy Belief Revision Max Welling
- Collapsed Variational Inference for HDP Yee Whye Teh
- A Constrained EM Algorithm for Independent Component Analysis
- On Improving the Eciency of the Iterative Proportional Fitting Procedure
- Approximate Inference in Boltzmann Max Welling Yee Whye Teh
- An Expectation Maximization Algorithm for Inferring OffsetNormal Shape Distributions
- Deterministic Latent Variable Models and their Pitfalls Max Welling
- Approximate Inference by Markov Chains on Union Spaces Max Welling welling@ics.uci.edu
- Journal of Machine Learning Research 0 (2003) 00 Submitted 0/0; Published 0/0 EnergyBased Models for Sparse Overcomplete
- Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization Ian Porteous and Evgeniy Bart and Max Welling
- Linear Response for Approximate Inference Max Welling
- Learning in Markov Random Fields An Empirical Study
- Super-Samples from Kernel Herding Yutian Chen
- Statistical Optimization of Non-Negative Matrix Factorization Anoop Korattikara, Levi Boyles, Max Welling Jingu Kim, Haesun Park
- Hidden-Unit Conditional Random Fields Laurens van der Maaten Max Welling Lawrence K. Saul
- On Herding and the Perceptron Cycling Theorem Andrew E. Gelfand, Yutian Chen, Max Welling
- Dynamical Products of Experts for Modeling Financial Time Series
- Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures
- Proceedings of the International Workshop on Statistical-Mechanical Informatics March 710, 2010, Kyoto, Japan
- Parametric Herding Yutian Chen Max Welling
- Bayesian Extreme Components Analysis Yutian Chen Max Welling
- THIS IS A DRAFT VERSION. FINAL VERSION TO BE PUBLISHED AT NIPS '06 Structure Learning in Markov Random Fields
- Bayesian K-Means as a "Maximization-Expectation" Algorithm Max Welling
- Structured Region Graphs: Morphing EP into GBP Max Welling
- An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
- Linear Response Algorithms for Approximate Inference in Graphical Models
- Exploiting Unlabelled Data for Hybrid Object Classification
- Modelling the Statistics of Natural Images with Topographic Product of Student-t Models
- Bayesian Random Fields: The Bethe-Laplace Approximation Max Welling
- Inferring O set Normal Shape Distributions with EM Max Welling
- Bayesian K-Means as a "Maximization-Expectation" Kenichi Kurihara
- The Unified Propagation and Scaling Algorithm Yee Whye Teh
- Probabilistic Sequential Independent Components Analysis Max Welling
- Products of Experts Max Welling
- Self Supervised Boosting Max Welling, Richard S. Zemel and Geoffrey E. Hinton
- Bayesian K-Means as a "Maximization-Expectation" October 18, 2007
- Wormholes Improve Contrastive Divergence Geoffrey Hinton, Max Welling and Andriy Mnih
- Products of "Edge-perts" Peter Gehler
- Learning in Markov Random Fields with Contrastive Free Energies Max Welling
- Gatsby Computational Neuroscience Unit 17 Queen Square, London University College London WC1N 3AR, United Kingdom
- Generalized Darting Monte Carlo Cristian Sminchisescu
- Structured Region Graphs: Morphing EP into GBP Max Welling
- Robust Higher Order Statistics Max Welling
- Robust Higher Order Statistics Max Welling
- Towards Automatic Discovery of Object Categories M. Weber y M. Welling z
- Exponential Family Harmoniums with an Application to Information Retrieval
- Learning in Markov Random Fields with Contrastive Free Energies Max Welling
- Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
- Scalable Inference on Kingman's Coalescent using Pair Similarity Dilan Gorur, Levi Boyles and Max Welling
- Predicting Simulation Parameters of Biological Systems using a Gaussian Process Model
- Exchangeable Inconsistent Priors for Bayesian Posterior Inference
