| Auto-encoding variational bayes DP Kingma, M Welling arXiv preprint arXiv:1312.6114, 2013 | 21816 | 2013 |
| Semi-supervised classification with graph convolutional networks TN Kipf, M Welling arXiv preprint arXiv:1609.02907, 2016 | 11973 | 2016 |
| Semi-supervised classification with graph convolutional networks M Welling, TN Kipf J. International Conference on Learning Representations (ICLR 2017), 2016 | 5991 | 2016 |
| Semi-supervised learning with deep generative models DP Kingma, S Mohamed, D Jimenez Rezende, M Welling Advances in neural information processing systems 27, 2014 | 2627 | 2014 |
| Modeling relational data with graph convolutional networks M Schlichtkrull, TN Kipf, P Bloem, R Berg, I Titov, M Welling European semantic web conference, 593-607, 2018 | 2449 | 2018 |
| Variational graph auto-encoders TN Kipf, M Welling arXiv preprint arXiv:1611.07308, 2016 | 1875 | 2016 |
| Bayesian learning via stochastic gradient Langevin dynamics M Welling, YW Teh Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 1864 | 2011 |
| 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 29, 2016 | 1462 | 2016 |
| Group equivariant convolutional networks T Cohen, M Welling International conference on machine learning, 2990-2999, 2016 | 1181 | 2016 |
| Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling Advances in neural information processing systems 28, 2015 | 1151 | 2015 |
| An introduction to variational autoencoders DP Kingma, M Welling Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019 | 961 | 2019 |
| Unsupervised learning of models for recognition M Weber, M Welling, P Perona European conference on computer vision, 18-32, 2000 | 945 | 2000 |
| Graph convolutional matrix completion R Berg, TN Kipf, M Welling arXiv preprint arXiv:1706.02263, 2017 | 794 | 2017 |
| On smoothing and inference for topic models A Asuncion, M Welling, P Smyth, YW Teh arXiv preprint arXiv:1205.2662, 2012 | 765 | 2012 |
| A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation Y Teh, D Newman, M Welling Advances in neural information processing systems 19, 2006 | 738 | 2006 |
| Learning Sparse Neural Networks through Regularization C Louizos, M Welling, DP Kingma arXiv preprint arXiv:1712.01312, 2017 | 733 | 2017 |
| 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 | 725 | 2008 |
| Attention-based deep multiple instance learning M Ilse, J Tomczak, M Welling International conference on machine learning, 2127-2136, 2018 | 693 | 2018 |
| Spherical cnns TS Cohen, M Geiger, J Köhler, M Welling arXiv preprint arXiv:1801.10130, 2018 | 667 | 2018 |
| Visualizing deep neural network decisions: Prediction difference analysis LM Zintgraf, TS Cohen, T Adel, M Welling arXiv preprint arXiv:1702.04595, 2017 | 591 | 2017 |
Paul NewmanProfessor University of OxfordVerified email at robots.ox.ac.uk
