Max Welling

According to our database1, Max Welling authored at least 207 papers between 2000 and 2018.

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2018
The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models.
CoRR, 2018

Predictive Uncertainty through Quantization.
CoRR, 2018

Relaxed Quantization for Discretized Neural Networks.
CoRR, 2018

Sinkhorn AutoEncoders.
CoRR, 2018

Probabilistic Binary Neural Networks.
CoRR, 2018

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data.
CoRR, 2018

Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks.
CoRR, 2018

Rotation Equivariant CNNs for Digital Pathology.
CoRR, 2018

BOCK : Bayesian Optimization with Cylindrical Kernels.
CoRR, 2018

Primal-Dual Wasserstein GAN.
CoRR, 2018

Extraction of Airways using Graph Neural Networks.
CoRR, 2018

Graphical Generative Adversarial Networks.
CoRR, 2018

Mean Field Network based Graph Refinement with application to Airway Tree Extraction.
CoRR, 2018

Sylvester Normalizing Flows for Variational Inference.
CoRR, 2018

HexaConv.
CoRR, 2018

Attention-based Deep Multiple Instance Learning.
CoRR, 2018

Neural Relational Inference for Interacting Systems.
CoRR, 2018

Spherical CNNs.
CoRR, 2018

Rotation Equivariant CNNs for Digital Pathology.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Mean Field Network Based Graph Refinement with Application to Airway Tree Extraction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

BOCK : Bayesian Optimization with Cylindrical Kernels.
Proceedings of the 35th International Conference on Machine Learning, 2018

Neural Relational Inference for Interacting Systems.
Proceedings of the 35th International Conference on Machine Learning, 2018

Attention-based Deep Multiple Instance Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Modeling Relational Data with Graph Convolutional Networks.
Proceedings of the Semantic Web - 15th International Conference, 2018

VAE with a VampPrior.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Learning Sparse Neural Networks through L0 Regularization.
CoRR, 2017

Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification.
CoRR, 2017

Convolutional Networks for Spherical Signals.
CoRR, 2017

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis.
CoRR, 2017

Soft Weight-Sharing for Neural Network Compression.
CoRR, 2017

VAE with a VampPrior.
CoRR, 2017

Modeling Relational Data with Graph Convolutional Networks.
CoRR, 2017

Recurrent Inference Machines for Solving Inverse Problems.
CoRR, 2017

Temporally Efficient Deep Learning with Spikes.
CoRR, 2017

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks.
CoRR, 2017

Bayesian Compression for Deep Learning.
CoRR, 2017

Causal Effect Inference with Deep Latent-Variable Models.
CoRR, 2017

Graph Convolutional Matrix Completion.
CoRR, 2017

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.
BMC Bioinformatics, 2017

Bayesian Compression for Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Causal Effect Inference with Deep Latent-Variable Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

DP-EM: Differentially Private Expectation Maximization.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Sequential Tests for Large-Scale Learning.
Neural Computation, 2016

Herded Gibbs Sampling.
Journal of Machine Learning Research, 2016

Marrying Graphical Models with Deep Learning.
ERCIM News, 2016

A New Method to Visualize Deep Neural Networks.
CoRR, 2016

Improving Variational Auto-Encoders using Householder Flow.
CoRR, 2016

A note on privacy preserving iteratively reweighted least squares.
CoRR, 2016

Variational Bayes In Private Settings (VIPS).
CoRR, 2016

Private Topic Modeling.
CoRR, 2016

Practical Privacy For Expectation Maximization.
CoRR, 2016

Sigma Delta Quantized Networks.
CoRR, 2016

Deep Spiking Networks.
CoRR, 2016

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors.
CoRR, 2016

Variational Graph Auto-Encoders.
CoRR, 2016

Semi-Supervised Classification with Graph Convolutional Networks.
CoRR, 2016

Improving Variational Inference with Inverse Autoregressive Flow.
CoRR, 2016

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis.
CoRR, 2016

Steerable CNNs.
CoRR, 2016

Group Equivariant Convolutional Networks.
CoRR, 2016

Herding as a Learning System with Edge-of-Chaos Dynamics.
CoRR, 2016

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Improving Variational Autoencoders with Inverse Autoregressive Flow.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Scalable Overlapping Community Detection.
Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops, 2016

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Group Equivariant Convolutional Networks.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Scalable MCMC for Mixed Membership Stochastic Blockmodels.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
MLitB: machine learning in the browser.
PeerJ Computer Science, 2015

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference.
CoRR, 2015

Hamiltonian ABC.
CoRR, 2015

The Variational Fair Autoencoder.
CoRR, 2015

Scalable MCMC for Mixed Membership Stochastic Blockmodels.
CoRR, 2015

Variational Dropout and the Local Reparameterization Trick.
CoRR, 2015

Bayesian Dark Knowledge.
CoRR, 2015

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC.
CoRR, 2015

POPE: post optimization posterior evaluation of likelihood free models.
BMC Bioinformatics, 2015

Hamiltonian ABC.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Bayesian dark knowledge.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Harmonic Exponential Families on Manifolds.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Exploiting the Statistics of Learning and Inference.
CoRR, 2014

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation.
CoRR, 2014

MLitB: Machine Learning in the Browser.
CoRR, 2014

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets.
CoRR, 2014

Semi-Supervised Learning with Deep Generative Models.
CoRR, 2014

Transformation Properties of Learned Visual Representations.
CoRR, 2014

Learning the Irreducible Representations of Commutative Lie Groups.
CoRR, 2014

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior.
CoRR, 2014

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Semi-supervised Learning with Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets.
Proceedings of the 31th International Conference on Machine Learning, 2014

Learning the Irreducible Representations of Commutative Lie Groups.
Proceedings of the 31th International Conference on Machine Learning, 2014

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget.
Proceedings of the 31th International Conference on Machine Learning, 2014

Distributed Stochastic Gradient MCMC.
Proceedings of the 31th International Conference on Machine Learning, 2014

Approximate Slice Sampling for Bayesian Posterior Inference.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
CoRR, 2013

Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
CoRR, 2013

Herded Gibbs Sampling
CoRR, 2013

Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
CoRR, 2013

Auto-Encoding Variational Bayes.
CoRR, 2013

Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

Evidence Estimation for Bayesian Partially Observed MRFs.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Distributed and Adaptive Darting Monte Carlo through Regenerations.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Predicting simulation parameters of biological systems using a Gaussian process model.
Statistical Analysis and Data Mining, 2012

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2012

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2012

State of the Journal.
IEEE Trans. Pattern Anal. Mach. Intell., 2012

Scalable Inference on Kingman's Coalescent using Pair Similarity .
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Efficient Parametric Projection Pursuit Density Estimation
CoRR, 2012

A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
CoRR, 2012

Generalized Belief Propagation on Tree Robust Structured Region Graphs
CoRR, 2012

Semisupervised Classifier Evaluation and Recalibration
CoRR, 2012

On the Choice of Regions for Generalized Belief Propagation
CoRR, 2012

Structured Region Graphs: Morphing EP into GBP
CoRR, 2012

Hybrid Variational/Gibbs Collapsed Inference in Topic Models
CoRR, 2012

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
CoRR, 2012

On Smoothing and Inference for Topic Models
CoRR, 2012

Herding Dynamic Weights for Partially Observed Random Field Models
CoRR, 2012

Super-Samples from Kernel Herding
CoRR, 2012

Bayesian Random Fields: The Bethe-Laplace Approximation.
CoRR, 2012

Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation.
CoRR, 2012

A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Generalized Belief Propagation on Tree Robust Structured Region Graphs.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

The Time-Marginalized Coalescent Prior for Hierarchical Clustering.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Exchangeable inconsistent priors for Bayesian posterior inference.
Proceedings of the 2012 Information Theory and Applications Workshop, 2012

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Generalized darting Monte Carlo.
Pattern Recognition, 2011

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Unsupervised Organization of Image Collections: Taxonomies and Beyond.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Hidden-Unit Conditional Random Fields.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Statistical Optimization of Non-Negative Matrix Factorization.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Statistical Tests for Optimization Efficiency.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Bayesian Learning via Stochastic Gradient Langevin Dynamics.
Proceedings of the 28th International Conference on Machine Learning, 2011

Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation.
Proceedings of the IEEE International Conference on Computer Vision, 2011

2010
Parametric Herding.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Localization Algorithms for Wireless Sensor Retrieval.
Comput. J., 2010

Super-Samples from Kernel Herding.
Proceedings of the UAI 2010, 2010

On Herding and the Perceptron Cycling Theorem.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Dynamical Products of Experts for Modeling Financial Time Series.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009
Bayesian k-Means as a "Maximization-Expectation" Algorithm.
Neural Computation, 2009

Distributed Algorithms for Topic Models.
Journal of Machine Learning Research, 2009

Preface.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Herding Dynamic Weights for Partially Observed Random Field Models.
Proceedings of the UAI 2009, 2009

On Smoothing and Inference for Topic Models.
Proceedings of the UAI 2009, 2009

Base Station Localization in Search of Empty Spectrum Spaces in Cognitive Radio Networks.
Proceedings of the MSN 2009, 2009

Bayesian Extreme Components Analysis.
Proceedings of the IJCAI 2009, 2009

Herding dynamical weights to learn.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Hybrid Generative-Discriminative Visual Categorization.
International Journal of Computer Vision, 2008

Hybrid Variational/Gibbs Collapsed Inference in Topic Models.
Proceedings of the UAI 2008, 2008

Deterministic Latent Variable Models and Their Pitfalls.
Proceedings of the SIAM International Conference on Data Mining, 2008

Asynchronous Distributed Learning of Topic Models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Fast collapsed gibbs sampling for latent dirichlet allocation.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Memory bounded inference in topic models.
Proceedings of the Machine Learning, 2008

Incremental learning of nonparametric Bayesian mixture models.
Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

Unsupervised learning of visual taxonomies.
Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

Multi-HDP: A Non Parametric Bayesian Model for Tensor Factorization.
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007
Product of experts.
Scholarpedia, 2007

Generalized Darting Monte Carlo.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Infinite State Bayes-Nets for Structured Domains.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Collapsed Variational Inference for HDP.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Distributed Inference for Latent Dirichlet Allocation.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Collapsed Variational Dirichlet Process Mixture Models.
Proceedings of the IJCAI 2007, 2007

A Distributed Message Passing Algorithm for Sensor Localization.
Proceedings of the Artificial Neural Networks, 2007

2006
Topographic Product Models Applied to Natural Scene Statistics.
Neural Computation, 2006

Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation.
Cognitive Science, 2006

Bayesian Random Fields: The Bethe-Laplace Approximation.
Proceedings of the UAI '06, 2006

Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation.
Proceedings of the UAI '06, 2006

Bayesian K-Means as a "Maximization-Expectation" Algorithm.
Proceedings of the Sixth SIAM International Conference on Data Mining, 2006

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Bayesian Model Scoring in Markov Random Fields.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Accelerated Variational Dirichlet Process Mixtures.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

The rate adapting poisson model for information retrieval and object recognition.
Proceedings of the Machine Learning, 2006

2005
Structured Region Graphs: Morphing EP into GBP.
Proceedings of the UAI '05, 2005

Products of Edge-perts.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Combining Generative Models and Fisher Kernels for Object Recognition.
Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

Learning in Markov Random Fields with Contrastive Free Energies.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

Robust Higher Order Statistics.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

An Expectation Maximization Algorithm for Inferring Offset-Normal Shape Distributions.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004
Probabilistic sequential independent components analysis.
IEEE Trans. Neural Networks, 2004

Linear Response Algorithms for Approximate Inference in Graphical Models.
Neural Computation, 2004

On the Choice of Regions for Generalized Belief Propagation.
Proceedings of the UAI '04, 2004

Exponential Family Harmoniums with an Application to Information Retrieval.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Approximate inference by Markov chains on union spaces.
Proceedings of the Machine Learning, 2004

2003
Energy-Based Models for Sparse Overcomplete Representations.
Journal of Machine Learning Research, 2003

Approximate inference in Boltzmann machines.
Artif. Intell., 2003

Efficient Parametric Projection Pursuit Density Estimation.
Proceedings of the UAI '03, 2003

Linear Response for Approximate Inference.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Extreme Components Analysis.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Wormholes Improve Contrastive Divergence.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

On Improving the Efficiency of the Iterative Proportional Fitting Procedure.
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002
Self Supervised Boosting.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Learning Sparse Topographic Representations with Products of Student-t Distributions.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

A New Learning Algorithm for Mean Field Boltzmann Machines.
Proceedings of the Artificial Neural Networks, 2002

2001
Positive tensor factorization.
Pattern Recognition Letters, 2001

A Constrained EM Algorithm for Independent Component Analysis.
Neural Computation, 2001

Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation.
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

The Unified Propagation and Scaling Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000
Viewpoint-Invariant Learning and Detection of Human Heads.
Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2000), 2000

Unsupervised Learning of Models for Recognition.
Proceedings of the Computer Vision - ECCV 2000, 6th European Conference on Computer Vision, Dublin, Ireland, June 26, 2000

Towards Automatic Discovery of Object Categories.
Proceedings of the 2000 Conference on Computer Vision and Pattern Recognition (CVPR 2000), 2000


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