Auto-encoding variational bayes DP Kingma, M Welling
arXiv preprint arXiv:1312.6114, 2013
21859 2013 Semi-supervised classification with graph convolutional networks TN Kipf, M Welling
arXiv preprint arXiv:1609.02907, 2016
12042 2016 Semi-supervised classification with graph convolutional networks M Welling, TN Kipf
J. International Conference on Learning Representations (ICLR 2017), 2016
6007 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
2628 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
2461 2018 Variational graph auto-encoders TN Kipf, M Welling
arXiv preprint arXiv:1611.07308, 2016
1881 2016 Bayesian learning via stochastic gradient Langevin dynamics M Welling, YW Teh
Proceedings of the 28th international conference on machine learning (ICML …, 2011
1867 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
1464 2016 Group equivariant convolutional networks T Cohen, M Welling
International conference on machine learning, 2990-2999, 2016
1185 2016 Variational dropout and the local reparameterization trick DP Kingma, T Salimans, M Welling
Advances in neural information processing systems 28, 2015
1153 2015 An introduction to variational autoencoders DP Kingma, M Welling
Foundations and Trends® in Machine Learning 12 (4), 307-392, 2019
966 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
795 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
737 2006 Learning Sparse Neural Networks through C Louizos, M Welling, DP Kingma
arXiv preprint arXiv:1712.01312, 2017
734 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
697 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 OxfordZweryfikowany adres z robots.ox.ac.uk
