Max Welling's Publications in Machine Learning



Working Papers

M.Park, J.Foulds, K.Chaudhuri, M.Welling
Practical Privacy for Expectation Maximization,
2016, Under Review [pdf]

M.Park, J.Foulds, K.Chaudhuri, M.Welling
Variational Bayes in Private Settings (VIPS),
2016, Under Review [pdf]
 

T.Cohen, M.Welling,
Steerable CNNs,
2016, Under Review [pdf]

G.Bertone, M.P.Deisenroth, J.S.Kim, S.Liem, R.Ruiz de Austri, M.Welling
Accelerating the BSM interpretation of LHC data with machine learning,
2016, Under Review [pdf]

P.O'Connor, M.Welling,
Sigma Delta Quantized Networks,

2016, Under Review [pdf]

P.O'Connor, M.Welling,
Deep Spiking Networks,
2016, Under Review [pdf]

T.Kipf, M.Welling
Semisupervised Classification with Graph Convolutional Networks,
2016, Under Review [pdf]

A. Moreno, T.Adel, T.Meeds, J.M.Rehg, M.Welling
Automatic Variational ABC,
2016, Under Review [pdf]

L.Zintgraf, T.Cohen, T.Adel, M.Welling,
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis,
2016, Under Review [pdf]

K.Ullrich, T.Meeds, M.Welling,
Soft Weight-sharing For Neural Network Compression,
2016, Under Review
[pdf]

P.Putzky, M.Welling,
Recurrent Inference Machines for Solving Inverse Problems,
2016, Under Review [pdf]




Published Papers

J.M.Tomczak, M.Welling
Improving Variational Auto-Encoders using Householder Flow
NIPS Workshops 2016 [pdf]

T. Kipf, M.Welling
Variational Graph Auto-Encoders,
NIPS Workshops 2016 [pdf]

M. Park, J. Foulds, K. Chaudhuri, M.Welling
Private Topic Modeling,
NIPS Workshops 2016 [pdf]

D. Kingma, T. Salimans, R. Josefowicz, X. Chen, I. Sutskever, M.Welling
Improving Variational Inference with Inverse Autoregressive Flow,
NIPS 2016 [pdf]

M. Park, M.Welling
A note on Privacy Preseving Iteratively Reweighted Least Squares,
ICML Workshops 2016 [pdf]

T. Cohen, M.Welling,
Group Equivariant Convolutional Networks,
ICML2016 [pdf, suppl.mat. github-Experiments, github-software]

C. Louizos, M. Welling,
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors,
ICML2016 [pdf]

Interview NRC Bennie Mols
En Toen Ging de Computer Zelf Leren [pdf]

I. El-Helw, R. Hofman, W. Li, S. Ahn, M. Welling, H. Bal
Scalable Overlapping Community Detection
ParLearning 2016 [pdf]
Best Paper Award

J. Foulds, J. Geumlek, M. Welling, K. Chaudhuri
On the Theory and Practice of Privacy Preserving Data Analysis
UAI 2016 [pdf,suppl.mat.]

Y. Chen, M. Welling
Herding as a Learning System with Edge-of-Chaos Dynamics
Special Issue on "Perturbations, Optimization and Statistics"
Eds. T. Hazan, G. Papandreou, D. Tarlow; 2016 [pdf]

C. Louizos, K. Swersky, Y. Li, M. Welling, R. Zemel
The Variational Fair Auto-Encoder
ICLR 2016 [pdf]

W. Li, S. Ahn, M. Welling
Scalable Markov Chain Monte Carlo for Bayesian Network Models

AISTATS 2015 [pdf]

M. Welling
Are Machine Learning and Statistics Complementary?
Contribution to the Rountable Discussion at the 6th IMSISBA meeting on "Data Science in the next 50 years".
2015 [pdf]

M. Welling
Deep Learning maakt ons leven gemakkelijker - maar ook kwetsbaarder
Financieel Dagblad, 26/9/2015 [pdf]

A. Korattikara, Y. Chen, M. Welling
Sequential Tests for Large Scale Learning

Neural Computation 24, p.1-26, 2015 [pdf]

T. Meeds, M. Welling
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
NIPS 2015 [pdf]

D. Kingma, T. Salimans, M. Welling
Variational Dropout and the Local Reparameterization Trick

NIPS 2015 [pdf]

A. Korattikara, V. Rathod, K.Murphy, M. Welling
Bayesian Dark Knowledge

NIPS 2015 [pdf]

M. Welling
Flexible Werken en voor Ieder een Basisinkomen
Financieel Dagblad, 22/8/2015 [pdf]

T. Meeds, R. Hendriks, S. al Faraby, M. Bruntink, M. Welling
MLitB: Machine Learning in the Browser
PeerJ Computer Science; 2015 [pdf

T. Meeds, M. Chiang, M. Lee, O. Cinquin, J. Lowengrub, M. Welling
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models

BMC Bioinformatics; 2015 (16:264)[pdf]

M. Chiang, A. Cinquin, A.Paz, E. Meeds, M.Welling, O. Cinquin
Control of C.Elegans Stem Cell Cycling Speed Meets Requirements of Design to Minimize Mutation Accumulation
BMC Bioinformatics; 2015 (13:51)[pdf]

M.Welling, Y.W. Teh, C. Andrieu, J. Kominiarczuk, T. Meeds
Bayesian Inference and Big Data: A Snapshot from a Conference
The ISBA Bulletin, V.21, Nr.4, Dec. 2015 [pdf]

T. S. Ahn, A. Korattikara, N. Liu, S. Rajan, M. Welling
Large Scale Distributed Bayesian Matrix Factorization
using Stochastic Gradient MCMC
KDD 2015 [pdf]

Meeds, R. Leenders, M. Welling
Hamiltonian ABC

UAI 2015 [pdf]

T. Cohen, M. Welling
Harmonic Exponential Families
on Manifolds
ICML 2015, [pdf, suppl. mat.]

T. Salimans, D. Kingma, M. Welling
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

ICML 2015 [pdf]

T. Cohen, M. Welling
Tranformation Properties of Learned
Visual Representations
ICLR 2015 [pdf]

M. Welling and Y.W. Teh
Bayesian Inference with Big Data: A Snapshot from a Conference
BIBiD 2014 [pdf]

D. Kingma, S. Mohamed, D. Rezende and M. Welling(2014)
Semi-supervised Learning with Deep Generative Models

NIPS 2014 [arXiv, software]

T. Meeds and M. Welling(2014)
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

UAI 2014 [pdf]

T. Cohen and M. Welling(2014)
Learning the Irreducible Representations of Commutative Lie Groups

ICML 2014 [pdf, supp.mat.]

D. Kingma and M. Welling(2014)
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
,
ICML 2014 [pdf]

S. Ahn, B. Shahbaba and M. Welling(2014)
Distributed Stochastic Gradient MCMC

ICML 2014 [pdf]

D. Kingma and M. Welling(2014)
Auto-Encoding Variational Bayes
,
ICLR 2014 [pdf]

A. Korattikara, Y. Chen and M. Welling(2014)
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
ICML 2014 [pdf]

M. Welling(2014)
Exploiting the Statistics of Learning and Inference

Proceedings of the NIPS 2014 Workshop on "Probabilistic Models for Big Data" [pdf]

M. Welling(2014)
Inaugural Speech (in Dutch)

University of Amsterdam 2014 [slides ppt][slides pdf][written text][spoken text][video]

Y. Chen, A. Gelfand and M. Welling(2014)
Herding for Structured Prediction

In: Advanced Structured Prediction, S.Nowozin, P.Gehler, J.Jancsary, C. Lampert (Eds) 2014 [pdf]

C. Dubois, A. Korattikara and M. Welling(2014)
Approximate Slice Sampling for Bayesian Posterior Inference

AISTATS 2014 [pdf]

J, Foulds, L. Boyles, C. Dubois, P, Smyth and M. Welling (2013)
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation

KDD 2013 [pdf arXiv]

L. Bornn, Y. Chen, N. de Freitas, M. Eskelin, J. Fang and M. Welling (2013)
Herded Gibbs Sampling

ICLR 2013 [pdf], JMLR [pdf]

P. Welinder, M. Welling and P. Perona (2013)
Semisupervised Classifier Evaluation and Recalibration

CVPR 2013 [pdf]

S. Ahn, Y. Chen and M. Welling (2013)
Distributed and Adaptive Darting Monte Carlo through Regenerations

AISTATS 2013 [pdf]

Y. Chen and M. Welling (2013)
Evidence Estimation for Partially Observed MRFs

AISTATS 2013 [pdf]

L. Boyles and M. Welling (2012)
The Time-Marginalized Coalescent Prior for Hierarchical Clustering

NIPS 2012 [paper:pdf][suppl.mat.:pdf]

M. Welling, A. Gelfand and A. Ihler (2012)
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation

UAI 2012 [pdf]

A. Gelfand and M. Welling (2012)
Generalized Belief Propagation on Tree Robust Structured Region Graphs

UAI 2012 [pdf]

Y. Chen and M. Welling (2012)
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior

UAI 2012 [pdf]

S. Ahn, A. Korattikara and M. Welling (2012)
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

ICML 2012 [pdf][Google Talk]
Winner of the ICML 2012 Best Paper Award.

M. Welling, I. Porteous and K. Kurihara (2012)
Exchangeable Inconsistent Priors for Bayesian Posterior Inference

Workshop on Information Theory and Applications (ITA) 2012 [pdf]

X. Zhu, J. Lowengrub and M. Welling (2012)
Predicting Simulation Parameters of Biological Systems using a Gaussian Process Model

JSM & Special Issue of the Stat. Analysis and data Mining Journal 2012 [pdf]
Winner of the ASA SDLM Student Paper Competition,2012

D. Gorur, L. Boyles and M. Welling (2011)
Scalable Inference on Kingman?s Coalescent using Pair Similarity

AISTATS 2012 [pdf]

L. Boyles, A. Korattikara, D. Ramanan and M. Welling (2011)
Statistical Tests for Optimization Efficiency

NIPS 2011 [pdf][software]

Y. Chen, A. Gelfand, C. Fowlkes and M. Welling (2011)
Integrating Local Classifiers through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation
ICCV 2011 [pdf]

M. Welling and Y.W. Teh (2011)
Bayesian Learning via Stochastic Gradient Langevin Dynamics

ICML 2011 [pdf]

A. Korattikara, L. Boyles, J. Kim, H. Park and M. Welling (2011)
Statistical Optimization for Nonnegative Matrix Factorization

AISTATS 2011 [pdf]

L. Van Der Maaten, M. Welling and L.K. Saul (2011)
Hidden-Unit Conditional Random Fields

AISTATS 2011 [pdf] (software)

A. Asuncion, D. Newman, I. Porteous, S. Triglia, P. Smyth and M. Welling (2010)
Distributed Gibbs Sampling for Latent Variable Models
Bookchapter in: Scaling Up Machine Learning, Cambridge University Press (to appear)

E. Bart, M. Welling and P. Perona (2010)
Unsupervised Organization of Image Collections: Taxonomies and Beyond
Transactions on Pattern Analysis and Machine Intelligence [pdf] (TPAMI - to appear)

Alfred Kume and Max Welling (2010)
Maximum-Likelihood Estimation for the Offset Normal Shape Distributions using EM
Journal of Computational and Graphical Statistics, Vol. 19, No. 3: 702?723 [url][pdf]

A. Gelfand, L. Van Der Maaten, Y. Chen, M. Welling(2010)
On Herding and the Perceptron Cycling Theorem
NIPS 2010 [pdf]

Y. Chen, M. Welling and A. Smola(2010)
Supersamples from Kernel-Herding
UAI 2010 [pdf]

Y. Chen and M. Welling(2010)
Dynamical Products of Experts for Modeling Financial Time Series
ICML 2010 [pdf]

A. Asuncion, P. Smyth, M. Welling (2010)
Asynchronous Distributed Estimation of Topic Models for Document Analysis
Statistical Methodology 2010 [url]

I. Porteous, A. Asuncion, M. Welling (2010)
Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures
AAAI 2010 [pdf]

M. Welling and Y. Chen(2010)
Statistical Inference Using Weak Chaos and Infinite Memory
Proceedings of the Int'l Workshop on Statistical-Mechanical Informatics
(IW-SMI
2010)[pdf][url]

Y. Chen and M. Welling(2010)
Parametric Herding
AISTATS 2010 [pdf]

Y. Zhang, L. Bao, S.H. Yang, M. Welling, Di Wu(2010)
Localization Algorithms for Wireless Sensor Retrieval
The Computer Journal 2010 [url]

Y. Zhang, L. Bao, M. Welling, S.H. Yang(2009)
Base Station Localization in Search of Empty Spectrum Spaces for Cognitive Radio Networks
Mobile Ad-hoc and Sensor Networks (MSN) 2009 [pdf]

D. Newman, A. Asuncion, P. Smyth, M. Welling(2009)
Distributed Algorithm for Topic Models
Journal Machine Learning Research 2009 [pdf]

M. Welling(2009)
Herding Dynamic Weights for Partially Observed Random Field Models
UAI 2009 [pdf] [Correction to proof of recurrence, thanks to Olivier Delalleau for pointing out the issue]

M. Welling(2009)
Herding Dynamic Weights to Learn
ICML 2009 [pdf] [Correction to proof of recurrence, thanks to Olivier Delalleau for pointing out the issue]

A. Asuncion, P. Smyth, M. Welling, Y.W. Teh(2009)
On Smoothing and Inference for Topic Models
UAI 2009 [pdf]

Y. Chen and M. Welling(2009)
Bayesian Extreme Components Analysis
IJCAI 2009 [pdf]

S.A. Cole, M. Welling, R.Dioso-Villa, R. Carpenter(2008)
Beyond the Individuality of Fingerprints:
A Measure of Simulated Computer Latent Print Source Attribution Accuracy
Law, Probability and Risk 2008 [pdf]

A. Ascuncion, P. Smyth and M. Welling(2008)
Asynchronous Distributed Learning of Topic Models
NIPS 2008 [pdf]

I. Porteous, A. Ascuncion, D. Newman, A. Ihler, P. Smyth and M. Welling(2008)
Fast Collapsed Gibbs Sampling For Latent Dirichlet Allocation
KDD 2008 [pdf] [software]

Max Welling, Y.W. Teh and B. Kappen(2008)
Hybrid Variational-MCMC Inference in Bayesian Networks
UAI 2008 [pdf]

R. Gomes, M. Welling and P. Perona(2008)
Memory Bounded Inference in Topic Models
ICML 2008 [pdf]

Ian Porteous, Evgeniy Bart and Max Welling (2008)
Multi-HDP: A Nonparametric Bayesian Model for Tensor Factorization
AAAI 2008 [pdf]

Ryan Gomes, Max Welling and Pietro Perona(2008)
Incremental Learning of Nonparametric Bayesian Mixture Models
CVPR 2008 [pdf]

Evgeniy Bart, Ian Porteous, Pietro Perona and Max Welling (2008)
Unsupervised Learning of Visual Taxonomies
CVPR 2008 [pdf]

Max Welling, Chaitanya Chemudugunta and Nathan Sutter (2008)
Deterministic Latent Variable Models and Their Pitfalls
SIAM Conference on Data Mining SDM 2008 [pdf]

Kenichi Kurihara and Max Welling (2008)
Bayesian K-Means as a ?Maximization-Expectation? Algorithm
Neural Computation, accepted [pdf]

Max Welling, Ian Porteous and Evgeniy Bart (2007)
Infinite State Bayesian Networks For Structured Domains
NIPS 2007 [pdf]

Dave Newman, Arthur Ascuncion, Padhriac Smyth and Max Welling (2007)
Distributed Inference for Latent Dirichlet Allocation
NIPS 2007 [pdf]

Yee Whye Teh, Kenichi Kurihara and Max Welling (2007)
Collapsed Variational Inference for HDP
NIPS 2007 [pdf]

Max Welling (2007)
Products of Experts
ScholarPedia 2007 [pdf,url]

Alex Holub, Max Welling and Pietro Perona (2007)
Hybrid Generative-Discriminative Object Recognition
International Journal Computer Vision (IJCV) [pdf]

Max Welling and Joseph Lim (2007)
SLEEP: Sensor Location Estimation with Expectation Propagation
ICANN 2007[pdf,June'08, mistake corrected in Alg. Box 3I relative to published version
]

Christian Sminchisescu and Max Welling (2007)
Generalized Darting Monte Carlo
AISTATS 2007 [pdf]

Kenichi Kurihara, Max Welling and Yee Whye Teh (2007)
Collapsed Variational Dirichlet Process Mixture Models
IJCAI 2007 [ps,pdf]

Sridevi Parise and Max Welling (2006)
Structure Learning in Markov Random Fields
NIPS 2006 [ps,pdf]

Yee Whye Teh, Dave Newman and Max Welling (2006)
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
NIPS 2006 [ps,pdf]

Kenichi Kurihara, Max Welling and Nikos Vlassis (2006)
Accelerated Variational DP mixture Models
NIPS 2006 [ps,pdf]

Max Welling (2006)
Flexible Priors for Infinite Mixture Models
ICML workshop on Nonparametric Baysian methods 2006 [pdf]

Ian Porteous, Alex Ihler, Padhriac Smyth and Max Welling (2006)
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick-Breaking Representation
UAI 2006 [pdf]

Max Welling and Sridevi Parise (2006)
Bayesian Random Fields: The Bethe-Laplace Approximation
UAI 2006 [pdf]

Peter Gehler, Alex Holub and Max Welling (2006)
The Rate Adapting Poisson (RAP) model for Information Retrieval and Object Recognition.
ICML 2006 [pdf,software]

Max Welling and Kenichi Kurihara (2005)
Bayesian K-Means as a ?Maximization-Expectation? Algorithm
SIAM Conference on Data Mining SDM2006 [pdf,tech-report,software]

Sridevi Parise and Max Welling (2005)
Learning in Markov Random Fields: An Empirical Study
Joint Statistical Meeting JSM2005 [pdf,software]

Geoffrey Hinton, Simon Osindero, Max Welling and Yee Whye Teh (2005)
Unsupervised Discovery of Non-Linear Structure using Contrastive Back-Propagation
Accepted in Cognitive Science 30(4) 2006 [pdf]

Simon Osindero, Max Welling and Geoffrey Hinton (2005)
Topographic Product Models Applied to Natural Scene Statistics
Neural Computation (accepted) [pdf]
 

Alex Holub, Max Welling and Pietro Perona (2005)
Combining Generative Models and Fisher Kernels for Object Recognition
ICCV 2005 [pdf]

Peter Gehler and max Welling (2005)
Products of ?Edge-Perts?
NIPS 2005 [pdf] [software]

Max Welling, Tom Minka and Yee Whye Teh (2005)
Structured Region Graphs: Morphing EP into GBP.
UAI 2005 [ps,pdf] (extended version with proofs)

Max Welling (2005)
Robust Higher Order Statistics
AISTATS 2005 [ps,pdf]

Max Welling (2005)
An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions
AISTATS 2005 [ps,pdf]

Max Welling & Charles Sutton (2005)
Learning in Markov Random Fields with Contrastive Free Energies
AISTATS 2005 [ps,pdf]

Max Welling, Michal Rosen-Zvi & Geoffrey Hinton (2004)
Exponential Family Harmoniums with an Application to Information Retrieval
NIPS 2004 [ps pdf]

Max Welling, Richard Zemel and Geoffrey Hinton (2003)
Probabilistic Sequential Independent Components Analysis
IEEE Transactions on Neural Networks [ps pdf] 

Max Welling (2004)
On the Choice of Regions for Generalized Belief Propagation
UAI 2004 [ps pdf]

Max Welling, Michal Rosen-Zvi & Yee Whye Teh (2004)
Approximate Inference by Markov Chains on Union Spaces
ICML 2004 [ps pdf]

Max Welling & Yee Whye Teh (2002)
Linear Response Algorithms for Approximate Inference in Graphical Models
Neural Computation 16 [pdf,Feb.08 typo corrected-thanks to Vicenš Gˇmez]

Max Welling, Geoffrey Hinton and Andriy Mnih (2003)
Wormholes Improve Contrastive Divergence
NIPS 2003 [ps pdf]

Max Welling, Felix Agakov & Chris Williams (2003)
Extreme Components Analysis
NIPS 2003 [pdf]

Max Welling & Yee Whye Teh (2003)
Linear Response for Approximate Inference
NIPS 2003 [pdf,Feb.08 typo corrected-thanks to Vicenš Gˇmez]

Max Welling, Richard Zemel and Geoffrey Hinton (2003)
Efficient Parametric Projection Pursuit Density Estimation
UAI 2003 [ps]

Yee Whye Teh, Max Welling, Simon Osindero & Geoffrey Hinton (2003)
Energy-Based Models for Sparse Overcomplete Representations
JMLR [ps]

Yee Whye Teh & Max Welling (2003)
On Improving the Efficiency of the Iterative Proportional Fitting Procedure
AISTATS 2003 [ps]

Max Welling, Richard Zemel and Geoffrey Hinton (2002)
Self-Supervised Boosting
NIPS 2002 [ps]

Max Welling, Geoffrey Hinton and Simon Osindero (2002)
Learning Sparse Topographic Representations with Products of Student-t Distributions
NIPS 2002 [ps]

Max Welling & Yee Whye Teh (2001)
Approximate Inference in Boltzmann Machines
AIJ [ps]

Max Welling & Geoffrey Hinton (2002)
A New Learning Algorithm for Mean Field Boltzmann Machines
ICANN2002, Madrid [ps]

Geoffrey E. Hinton, Max Welling, Yee Whye Teh & Simon K. Osindero (2001)
A New View of ICA
Int. Conf. on Independent Component Analysis and Blind Source Separation, ICA2001, San Diego [ps

Yee Whye Teh & Max Welling (2001)
The Unified Propagation and Scaling Algorithm
NIPS2001, Vancouver [ps]

Max Welling & Markus Weber(2001) 
Positive Tensor Factorization 
Pattern Recognition Letters 22 (12), pp. 1255-1261 [ps

Max Welling & Yee Whye Teh (2001)
Belief Optimization for Binary Networks: A stable Alternative to Loopy Belief Propagation
UAI2001, Seattle, Washington [ps]

Max Welling & Markus Weber(2001) 
A Constrained EM Algorithm for Independent Component Analysis 
neural computation 13 (3), pp. 677-689  [ps]

Markus Weber, Max Welling & Pietro Perona (2000)
Unsupervised Learning of Models for Recognition 
Proc. 6th Europ. Conf. Comp. Vis., ECCV2000, Dublin [ps]
Winner of the ECCV 2010 Koenderink Prize

Markus Weber, Max Welling & Pietro Perona (2000) 
Towards Automatic Discovery of Object Categories 
Proc. IEEE Comp. Soc. Conf. Comp. Vis. and Pat. Rec., CVPR20000, Hilton Head Island [ps]

Markus Weber, Wolfgang Einhauser, Max Welling & Pietro Perona (2000)
Viewpoint-Invariant Learning and Detection of Human Heads 
Proc. 4th Int. Conf. Autom. Face and Gesture Rec., FG2000, Grenoble [ps]

Max Welling & Markus Weber (1999) 
Independent Component Analysis of Incomplete Data 
Proceedings of the 6th Annual Joint Symposium on Neural Computation, JNSC99, Pasadena [ps]

M. Weber, M. Welling & P. Perona (1999)
Unsupervised learning of models for visual object class recognition
Proceedings of the 6th Annual Joint Symposium on Neural Computation, JNSC99, Pasadena [ps]


 

Max Welling's Technical Reports and Research Notes in Machine Learning

Max Welling (2008)
Hard Wall Stochastic Control with Hallucination EM and Power EP
Technical Report [pdf]

Alex Holub, Max Welling and Pietro Perona (2005)
Exploiting Unlabelled Data for Hybrid Object Classification
NIPS-2005 workshop in interclass transfer [pdf]

Simon Osindero, Max Welling and Geoffrey Hinton and (2004)
Modeling the Statistics of Natural Images with Topographic Product of Student-t Models
Technical Report [pdf]

Max Welling (2004)
EM Algorithms for Offset-Normal Shape Densities
Techical Report [ps]

Cristian Sminchisescu, Max Welling and Geoffrey Hinton (2003)
Generalized Darting Monte Carlo
Technical Report [pdf]

Max Welling (2001)
Labelling with Loopy Belief Revision
Research Note [ps]

Max Welling & Geoffrey Hinton (2001)
A New Learning Algorithm for Mean Field Boltzmann Machines
Technical Report GCNU TR 2001-002 [ps]

Yee Whye Teh & Max Welling (2001)
Passing and Bouncing Messages for Generalized Inference
Technical Report GCNU TR 2001-001 [ps]

Max Welling (1999)
Robust cumulant expansions for probability density estimation
Technical Report [ps]  


This material is based upon work supported by the National Science Foundation under Grant No. 0447903. and Grant No. 0535278. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

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