Integrating Local Classifiers through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation Yutian Chen Andrew Gelfand Charless C. Fowlkes Max Welling Bren School of Information and Computer Scienc

Source URL: www.ics.uci.edu

• Applied mathematics
• Probability theory
• Image processing
• Artificial intelligence
• Conditional random field
• Segmentation
• Entailment
• Constructible universe
• Markov random field
• Logic
• Graphical models
• Theoretical computer science

Incremental Learning of Nonparametric Bayesian Mixture Models Ryan Gomes∗ Max Welling† ∗

Source URL: www.ics.uci.edu

• Cluster analysis
• Data analysis
• Estimation theory
• Expectation–maximization algorithm
• Dirichlet process
• Variational Bayesian methods
• Mixture model
• Principal component analysis
• Kernel principal component analysis
• Statistics
• Multivariate statistics
• Machine learning

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets Diederik P. Kingma Max Welling Machine Learning Group, University of Amsterdam

Source URL: www.ics.uci.edu

• Statistical models
• Statistical theory
• Statistical inference
• Expectation–maximization algorithm
• Bayesian network
• Gibbs sampling
• Normal distribution
• Maximum likelihood
• Latent variable
• Statistics
• Bayesian statistics
• Estimation theory

Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization Ian Porteous and Evgeniy Bart and Max Welling Dept. of Computer Science UC Irvine Irvine, CA 92697 {iporteou,welling}@ics.uci.edu,

Source URL: www.ics.uci.edu

Proceedings of the International Workshop on Statistical-Mechanical Informatics March 7–10, 2010, Kyoto, Japan Statistical Inference using Weak Chaos and Infinite Memory Max Welling and Yutian Chen

Source URL: www.ics.uci.edu

PDF Document

Source URL: www.philchang.com

• Optics
• James Welling
• John Szarkowski
• Mirror
• Reflection
• Chromogenic color print
• Black
• Chromogenic
• Photographic processes
• Photography
• Vision

Infinite State Bayesian Networks Max Welling∗, Ian Porteous, Evgeniy Bart† Donald Bren School of Information and Computer Sciences University of California Irvine Irvine, CAUSA {welling,iporteou}@ics.uci.

Source URL: www.ics.uci.edu

• Statistical natural language processing
• Markov models
• Graphical models
• Variational Bayesian methods
• Dirichlet process
• Gibbs sampling
• Bayesian network
• Latent Dirichlet allocation
• Mixture model
• Statistics
• Bayesian statistics
• Statistical models

On Smoothing and Inference for Topic Models Arthur Asuncion, Max Welling, Padhraic Smyth Department of Computer Science University of California, Irvine Irvine, CA, USA

Source URL: www.ics.uci.edu

Predicting Simulation Parameters of Biological Systems using a Gaussian Process Model Xiangxin Zhu, Max Welling Fang Jin, John Lowengrub

Source URL: www.ics.uci.edu

• Statistical theory
• Statistical models
• Normal distribution
• Parametric model
• Regression analysis
• Gaussian process
• Expectation–maximization algorithm
• Maximum likelihood
• Statistics
• Estimation theory
• Econometrics

Distributed Inference for Latent Dirichlet Allocation David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling Department of Computer Science University of California, Irvine newman,asuncion,smyth,welling
@ics.uci.ed

Source URL: www.ics.uci.edu

• Latent Dirichlet allocation
• Linear discriminant analysis
• Machine learning
• Parallel computing
• Algorithm
• Gibbs sampling
• Mixture model
• Speedup
• Expectation–maximization algorithm
• Statistics
• Statistical natural language processing
• Statistical classification