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Semantic ScholarMax Welling
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- affiliation: University of California, Irvine, USA
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2010 – today
- 2018
[c94]- [i68]
- [i67]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. CoRR abs/1802.04687 (2018) - [i66]Maximilian Ilse, Jakub M. Tomczak, Max Welling:
Attention-based Deep Multiple Instance Learning. CoRR abs/1802.04712 (2018) - [i65]Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling:
HexaConv. CoRR abs/1803.02108 (2018) - [i64]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. CoRR abs/1803.05649 (2018) - 2017
- [j26]A. Eck, Luisa M. Zintgraf, E. F. J. de Groot, Tim G. J. de Meij, T. S. Cohen, P. H. M. Savelkoul, Max Welling, A. E. Budding:
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinformatics 18(1): 441:1-441:13 (2017) - [c93]Mijung Park, James R. Foulds, Kamalika Choudhary, Max Welling:
DP-EM: Differentially Private Expectation Maximization. AISTATS 2017: 896-904 - [c92]Christos Louizos, Max Welling:
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. ICML 2017: 2218-2227 - [c91]Christos Louizos, Karen Ullrich, Max Welling:
Bayesian Compression for Deep Learning. NIPS 2017: 3290-3300 - [c90]Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. NIPS 2017: 6449-6459 - [i63]Karen Ullrich, Edward Meeds, Max Welling:
Soft Weight-Sharing for Neural Network Compression. CoRR abs/1702.04008 (2017) - [i62]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. CoRR abs/1702.04595 (2017) - [i61]Christos Louizos, Max Welling:
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. CoRR abs/1703.01961 (2017) - [i60]Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling:
Modeling Relational Data with Graph Convolutional Networks. CoRR abs/1703.06103 (2017) - [i59]
- [i58]Christos Louizos, Karen Ullrich, Max Welling:
Bayesian Compression for Deep Learning. CoRR abs/1705.08665 (2017) - [i57]Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. CoRR abs/1705.08821 (2017) - [i56]Rianne van den Berg, Thomas N. Kipf, Max Welling:
Graph Convolutional Matrix Completion. CoRR abs/1706.02263 (2017) - [i55]Patrick Putzky, Max Welling:
Recurrent Inference Machines for Solving Inverse Problems. CoRR abs/1706.04008 (2017) - [i54]Peter O'Connor, Efstratios Gavves, Max Welling:
Temporally Efficient Deep Learning with Spikes. CoRR abs/1706.04159 (2017) - [i53]Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Convolutional Networks for Spherical Signals. CoRR abs/1709.04893 (2017) - [i52]Jakub M. Tomczak, Maximilian Ilse, Max Welling:
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification. CoRR abs/1712.00310 (2017) - [i51]Christos Louizos, Max Welling, Diederik P. Kingma:
Learning Sparse Neural Networks through L0 Regularization. CoRR abs/1712.01312 (2017) - 2016
- [j25]Yutian Chen, Luke Bornn, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. Journal of Machine Learning Research 17: 10:1-10:29 (2016) - [j24]Anoop Korattikara, Yutian Chen, Max Welling:
Sequential Tests for Large-Scale Learning. Neural Computation 28(1): 45-70 (2016) - [c89]Wenzhe Li, Sungjin Ahn, Max Welling:
Scalable MCMC for Mixed Membership Stochastic Blockmodels. AISTATS 2016: 723-731 - [c88]Christos Louizos, Max Welling:
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. ICML 2016: 1708-1716 - [c87]
- [c86]Ismail El-Helw, Rutger F. H. Hofman, Wenzhe Li, Sungjin Ahn, Max Welling, Henri E. Bal:
Scalable Overlapping Community Detection. IPDPS Workshops 2016: 1463-1472 - [c85]Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling:
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS 2016: 4736-4744 - [c84]James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. UAI 2016 - [e10]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I. Lecture Notes in Computer Science 9905, Springer 2016, ISBN 978-3-319-46447-3 [contents] - [e9]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II. Lecture Notes in Computer Science 9906, Springer 2016, ISBN 978-3-319-46474-9 [contents] - [e8]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III. Lecture Notes in Computer Science 9907, Springer 2016, ISBN 978-3-319-46486-2 [contents] - [e7]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV. Lecture Notes in Computer Science 9908, Springer 2016, ISBN 978-3-319-46492-3 [contents] - [e6]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V. Lecture Notes in Computer Science 9909, Springer 2016, ISBN 978-3-319-46453-4 [contents] - [e5]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI. Lecture Notes in Computer Science 9910, Springer 2016, ISBN 978-3-319-46465-7 [contents] - [e4]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VII. Lecture Notes in Computer Science 9911, Springer 2016, ISBN 978-3-319-46477-0 [contents] - [e3]Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling:
Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII. Lecture Notes in Computer Science 9912, Springer 2016, ISBN 978-3-319-46483-1 [contents] - [i50]Yutian Chen, Max Welling:
Herding as a Learning System with Edge-of-Chaos Dynamics. CoRR abs/1602.03014 (2016) - [i49]
- [i48]
- [i47]Luisa M. Zintgraf, Taco S. Cohen, Max Welling:
A New Method to Visualize Deep Neural Networks. CoRR abs/1603.02518 (2016) - [i46]Christos Louizos, Max Welling:
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. CoRR abs/1603.04733 (2016) - [i45]James R. Foulds, Joseph Geumlek, Max Welling, Kamalika Chaudhuri:
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. CoRR abs/1603.07294 (2016) - [i44]Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling:
Practical Privacy For Expectation Maximization. CoRR abs/1605.06995 (2016) - [i43]Mijung Park, Max Welling:
A note on privacy preserving iteratively reweighted least squares. CoRR abs/1605.07511 (2016) - [i42]Diederik P. Kingma, Tim Salimans, Max Welling:
Improving Variational Inference with Inverse Autoregressive Flow. CoRR abs/1606.04934 (2016) - [i41]Thomas N. Kipf, Max Welling:
Semi-Supervised Classification with Graph Convolutional Networks. CoRR abs/1609.02907 (2016) - [i40]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Private Topic Modeling. CoRR abs/1609.04120 (2016) - [i39]Mijung Park, James R. Foulds, Kamalika Chaudhuri, Max Welling:
Variational Bayes In Private Settings (VIPS). CoRR abs/1611.00340 (2016) - [i38]
- [i37]
- [i36]Jakub M. Tomczak, Max Welling:
Improving Variational Auto-Encoders using Householder Flow. CoRR abs/1611.09630 (2016) - [i35]
- [i34]
- 2015
- [j23]Edward Meeds, Michael Chiang, Mary Lee, Olivier Cinquin, John S. Lowengrub, Max Welling:
POPE: post optimization posterior evaluation of likelihood free models. BMC Bioinformatics 16: 264:1-264:20 (2015) - [j22]Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling:
MLitB: machine learning in the browser. PeerJ Computer Science 1: e11 (2015) - [c83]Tim Salimans, Diederik P. Kingma, Max Welling:
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. ICML 2015: 1218-1226 - [c82]
- [c81]Sungjin Ahn, Anoop Korattikara, Nathan Liu, Suju Rajan, Max Welling:
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. KDD 2015: 9-18 - [c80]Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. NIPS 2015: 2080-2088 - [c79]Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling:
Bayesian dark knowledge. NIPS 2015: 3438-3446 - [c78]
- [i33]Sungjin Ahn, Anoop Korattikara Balan, Nathan Liu, Suju Rajan, Max Welling:
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. CoRR abs/1503.01596 (2015) - [i32]
- [i31]Diederik P. Kingma, Tim Salimans, Max Welling:
Variational Dropout and the Local Reparameterization Trick. CoRR abs/1506.02557 (2015) - [i30]Edward Meeds, Max Welling:
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. CoRR abs/1506.03693 (2015) - [i29]Anoop Korattikara Balan, Vivek Rathod, Kevin Murphy, Max Welling:
Bayesian Dark Knowledge. CoRR abs/1506.04416 (2015) - [i28]Wenzhe Li, Sungjin Ahn, Max Welling:
Scalable MCMC for Mixed Membership Stochastic Blockmodels. CoRR abs/1510.04815 (2015) - [i27]Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel:
The Variational Fair Autoencoder. CoRR abs/1511.00830 (2015) - 2014
- [c77]Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth:
Approximate Slice Sampling for Bayesian Posterior Inference. AISTATS 2014: 185-193 - [c76]Anoop Korattikara Balan, Yutian Chen, Max Welling:
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. ICML 2014: 181-189 - [c75]Sungjin Ahn, Babak Shahbaba, Max Welling:
Distributed Stochastic Gradient MCMC. ICML 2014: 1044-1052 - [c74]Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. ICML 2014: 1755-1763 - [c73]Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. ICML 2014: 1782-1790 - [c72]Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling:
Semi-supervised Learning with Deep Generative Models. NIPS 2014: 3581-3589 - [c71]Edward Meeds, Max Welling:
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. UAI 2014: 593-602 - [e2]Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2014 [contents] - [i26]Edward Meeds, Max Welling:
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. CoRR abs/1401.2838 (2014) - [i25]Diederik P. Kingma, Max Welling:
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. CoRR abs/1402.0480 (2014) - [i24]Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. CoRR abs/1402.4437 (2014) - [i23]
- [i22]Diederik P. Kingma, Danilo Jimenez Rezende, Shakir Mohamed, Max Welling:
Semi-Supervised Learning with Deep Generative Models. CoRR abs/1406.5298 (2014) - [i21]Yutian Chen, Max Welling:
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. CoRR abs/1408.2047 (2014) - [i20]Edward Meeds, Remco Hendriks, Said al Faraby, Magiel Bruntink, Max Welling:
MLitB: Machine Learning in the Browser. CoRR abs/1412.2432 (2014) - [i19]Taco S. Cohen, Max Welling:
Transformation Properties of Learned Visual Representations. CoRR abs/1412.7659 (2014) - 2013
- [c70]Sungjin Ahn, Yutian Chen, Max Welling:
Distributed and Adaptive Darting Monte Carlo through Regenerations. AISTATS 2013: 108-116 - [c69]Yutian Chen, Max Welling:
Evidence Estimation for Bayesian Partially Observed MRFs. AISTATS 2013: 178-186 - [c68]Peter Welinder, Max Welling, Pietro Perona:
A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration. CVPR 2013: 3262-3269 - [c67]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. KDD 2013: 446-454 - [i18]Max Welling, Yee Whye Teh:
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation. CoRR abs/1301.2317 (2013) - [i17]Luke Bornn, Yutian Chen, Nando de Freitas, Mareija Eskelin, Jing Fang, Max Welling:
Herded Gibbs Sampling. CoRR abs/1301.4168 (2013) - [i16]Anoop Korattikara Balan, Yutian Chen, Max Welling:
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. CoRR abs/1304.5299 (2013) - [i15]James R. Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling:
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. CoRR abs/1305.2452 (2013) - [i14]
- 2012
- [j21]Sing Bing Kang, Jiri Matas, Max Welling, Ramin Zabih:
State of the Journal. IEEE Trans. Pattern Anal. Mach. Intell. 34(1): 1-2 (2012) - [j20]Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 34(2): 209-210 (2012) - [j19]Ramin Zabih, Sing Bing Kang, Neil D. Lawrence, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 34(5): 833 (2012) - [j18]Xiangxin Zhu, Max Welling, Fang Jin, John S. Lowengrub:
Predicting simulation parameters of biological systems using a Gaussian process model. Statistical Analysis and Data Mining 5(6): 509-522 (2012) - [c66]Sungjin Ahn, Anoop Korattikara Balan, Max Welling:
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring. ICML 2012 - [c65]Max Welling, Ian Porteous, Kenichi Kurihara:
Exchangeable inconsistent priors for Bayesian posterior inference. ITA 2012: 407-414 - [c64]Levi Boyles, Max Welling:
The Time-Marginalized Coalescent Prior for Hierarchical Clustering. NIPS 2012: 2978-2986 - [c63]Yutian Chen, Max Welling:
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. UAI 2012: 174-184 - [c62]Andrew Gelfand, Max Welling:
Generalized Belief Propagation on Tree Robust Structured Region Graphs. UAI 2012: 296-305 - [c61]Max Welling, Andrew Gelfand, Alexander T. Ihler:
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation. UAI 2012: 883-892 - [c60]Dilan Görür, Levi Boyles, Max Welling:
Scalable Inference on Kingman's Coalescent using Pair Similarity . AISTATS 2012: 440-448 - [i13]Yutian Chen, Max Welling, Alexander J. Smola:
Super-Samples from Kernel Herding. CoRR abs/1203.3472 (2012) - [i12]Max Welling:
Herding Dynamic Weights for Partially Observed Random Field Models. CoRR abs/1205.2605 (2012) - [i11]Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh:
On Smoothing and Inference for Topic Models. CoRR abs/1205.2662 (2012) - [i10]Yutian Chen, Max Welling:
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior. CoRR abs/1206.1088 (2012) - [i9]Max Welling, Yee Whye Teh, Hilbert J. Kappen:
Hybrid Variational/Gibbs Collapsed Inference in Topic Models. CoRR abs/1206.3297 (2012) - [i8]Ian Porteous, Alexander T. Ihler, Padhraic Smyth, Max Welling:
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation. CoRR abs/1206.6845 (2012) - [i7]Max Welling, Sridevi Parise:
Bayesian Random Fields: The Bethe-Laplace Approximation. CoRR abs/1206.6868 (2012) - [i6]Max Welling, Thomas P. Minka, Yee Whye Teh:
Structured Region Graphs: Morphing EP into GBP. CoRR abs/1207.1426 (2012) - [i5]
- [i4]Peter Welinder, Max Welling, Pietro Perona:
Semisupervised Classifier Evaluation and Recalibration. CoRR abs/1210.2162 (2012) - [i3]Andrew Gelfand, Max Welling:
Generalized Belief Propagation on Tree Robust Structured Region Graphs. CoRR abs/1210.4857 (2012) - [i2]Max Welling, Andrew E. Gelfand, Alexander T. Ihler:
A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation. CoRR abs/1210.4916 (2012) - [i1]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. CoRR abs/1212.2513 (2012) - 2011
- [j17]Ramin Zabih, Sing Bing Kang, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 33(11): 2129-2130 (2011) - [j16]Evgeniy Bart, Max Welling, Pietro Perona:
Unsupervised Organization of Image Collections: Taxonomies and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 33(11): 2302-2315 (2011) - [j15]Ramin Zabih, Sing Bing Kang, Jiri Matas, Max Welling:
Editor's Note. IEEE Trans. Pattern Anal. Mach. Intell. 33(12): 2337-2340 (2011) - [j14]Cristian Sminchisescu, Max Welling:
Generalized darting Monte Carlo. Pattern Recognition 44(10-11): 2738-2748 (2011) - [c59]Yutian Chen, Andrew Gelfand, Charless C. Fowlkes, Max Welling:
Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation. ICCV 2011: 2635-2642 - [c58]Max Welling, Yee Whye Teh:
Bayesian Learning via Stochastic Gradient Langevin Dynamics. ICML 2011: 681-688 - [c57]Levi Boyles, Anoop Korattikara Balan, Deva Ramanan, Max Welling:
Statistical Tests for Optimization Efficiency. NIPS 2011: 2196-2204 - [c56]