| Semi-supervised classification with graph convolutional networks TN Kipf, M Welling International Conference on Learning Representations (ICLR), 2016 | 52338 | 2016 |
| Auto-encoding variational bayes DP Kingma, M Welling International Conference on Learning Representations (ICLR), 2013 | 51856 | 2013 |
| 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 | 7576 | 2018 |
| An introduction to variational autoencoders DP Kingma, M Welling arXiv preprint arXiv:1906.02691, 2019 | 4409 | 2019 |
| Variational graph auto-encoders TN Kipf, M Welling NeurIPS Workshop on Bayesian Deep Learning (NeurIPS BDL), 2016 | 4269 | 2016 |
| Semi-supervised learning with deep generative models DP Kingma, DJ Rezende, S Mohamed, M Welling Advances in neural information processing systems 27, 2014 | 4076 | 2014 |
| Bayesian learning via stochastic gradient Langevin dynamics M Welling, YW Teh Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 3745 | 2011 |
| Attention-based deep multiple instance learning M Ilse, J Tomczak, M Welling International conference on machine learning, 2127-2136, 2018 | 3084 | 2018 |
| Group equivariant convolutional networks T Cohen, M Welling International conference on machine learning, 2990-2999, 2016 | 2913 | 2016 |
| 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 | 2516 | 2016 |
| Attention, learn to solve routing problems! W Kool, H Van Hoof, M Welling International Conference on Learning Representations (ICLR), 2018 | 2352 | 2018 |
| Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling Advances in neural information processing systems 28, 2015 | 2105 | 2015 |
| Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 1898 | 2023 |
| E (n) equivariant graph neural networks VG Satorras, E Hoogeboom, M Welling International conference on machine learning, 9323-9332, 2021 | 1606 | 2021 |
| Learning sparse neural networks through regularization C Louizos, M Welling, DP Kingma International Conference on Learning Representations (ICLR), 2017 | 1586 | 2017 |
| Spherical cnns TS Cohen, M Geiger, J Köhler, M Welling International Conference on Learning Representations (ICLR), 2018 | 1316 | 2018 |
| Neural relational inference for interacting systems T Kipf, E Fetaya, KC Wang, M Welling, R Zemel International conference on machine learning, 2688-2697, 2018 | 1212 | 2018 |
| Causal effect inference with deep latent-variable models C Louizos, U Shalit, JM Mooij, D Sontag, R Zemel, M Welling Advances in neural information processing systems 30, 2017 | 1112 | 2017 |
| Graph convolutional matrix completion R Van Den Berg, NK Thomas, M Welling KDD Deep Learning Day (KDD DLD), 2017 | 1097 | 2017 |
| Equivariant diffusion for molecule generation in 3d E Hoogeboom, VG Satorras, C Vignac, M Welling International conference on machine learning, 8867-8887, 2022 | 1052 | 2022 |
Paul NewmanProfessor University of OxfordVerified email at robots.ox.ac.uk
