| Auto-encoding variational bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013 | 10015 | 2013 |
| Semi-supervised classification with graph convolutional networks TN Kipf, M Welling arXiv preprint arXiv:1609.02907, 2016 | 4448 | 2016 |
| Semi-supervised learning with deep generative models DP Kingma, S Mohamed, DJ Rezende, M Welling Advances in neural information processing systems, 3581-3589, 2014 | 1584 | 2014 |
| Bayesian learning via stochastic gradient Langevin dynamics M Welling, YW Teh Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 1025 | 2011 |
| Unsupervised learning of models for recognition M Weber, M Welling, P Perona European conference on computer vision, 18-32, 2000 | 886 | 2000 |
| Improved variational inference with inverse autoregressive flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in neural information processing systems, 4743-4751, 2016 | 795 | 2016 |
| On smoothing and inference for topic models A Asuncion, M Welling, P Smyth, YW Teh arXiv preprint arXiv:1205.2662, 2012 | 663 | 2012 |
| A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation YW Teh, D Newman, M Welling Advances in neural information processing systems, 1353-1360, 2007 | 657 | 2007 |
| Modeling relational data with graph convolutional networks M Schlichtkrull, TN Kipf, P Bloem, R Van Den Berg, I Titov, M Welling European Semantic Web Conference, 593-607, 2018 | 648 | 2018 |
| Fast collapsed gibbs sampling for latent dirichlet allocation I Porteous, D Newman, A Ihler, A Asuncion, P Smyth, M Welling Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 606 | 2008 |
| Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling Advances in neural information processing systems, 2575-2583, 2015 | 558 | 2015 |
| Variational graph auto-encoders TN Kipf, M Welling arXiv preprint arXiv:1611.07308, 2016 | 534 | 2016 |
| Exponential family harmoniums with an application to information retrieval M Welling, M Rosen-Zvi, GE Hinton Advances in neural information processing systems, 1481-1488, 2005 | 523 | 2005 |
| Group equivariant convolutional networks T Cohen, M Welling International conference on machine learning, 2990-2999, 2016 | 487 | 2016 |
| Distributed algorithms for topic models. D Newman, A Asuncion, P Smyth, M Welling Journal of Machine Learning Research 10 (8), 2009 | 422 | 2009 |
| Markov chain monte carlo and variational inference: Bridging the gap T Salimans, D Kingma, M Welling International Conference on Machine Learning, 1218-1226, 2015 | 347 | 2015 |
| Towards automatic discovery of object categories M Weber, M Welling, P Perona Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR …, 2000 | 316 | 2000 |
| Distributed inference for latent dirichlet allocation D Newman, P Smyth, M Welling, AU Asuncion Advances in neural information processing systems, 1081-1088, 2008 | 292 | 2008 |
| Visualizing deep neural network decisions: Prediction difference analysis LM Zintgraf, TS Cohen, T Adel, M Welling arXiv preprint arXiv:1702.04595, 2017 | 269 | 2017 |
| Spherical cnns TS Cohen, M Geiger, J Köhler, M Welling arXiv preprint arXiv:1801.10130, 2018 | 258 | 2018 |
Paul NewmanProfessor University of OxfordVerified email at robots.ox.ac.uk
Paul NewmanProfessor University of OxfordVerified email at robots.ox.ac.uk