| Modeling Relational Data with Graph Convolutional Networks M Schlichtkrull, TN Kipf, P Bloem, R van den Berg, I Titov, M Welling arXiv preprint arXiv:1703.06103, 2017 | 1605 | 2017 |
| Graph Convolutional Matrix Completion R van den Berg, TN Kipf, M Welling arXiv preprint arXiv:1706.02263, 2017 | 512 | 2017 |
| Sylvester normalizing flows for variational inference R Berg, L Hasenclever, JM Tomczak, M Welling arXiv preprint arXiv:1803.05649, 2018 | 143 | 2018 |
| Atomic spin-chain realization of a model for quantum criticality R Toskovic, R van den Berg, A Spinelli, IS Eliens, B van den Toorn, ... Nature Physics 12 (7), 656-660, 2016 | 91 | 2016 |
| Emerging convolutions for generative normalizing flows E Hoogeboom, R Van Den Berg, M Welling International Conference on Machine Learning, 2771-2780, 2019 | 62 | 2019 |
| Integer discrete flows and lossless compression E Hoogeboom, J Peters, R van den Berg, M Welling Advances in Neural Information Processing Systems, 12134-12144, 2019 | 53 | 2019 |
| Sinkhorn autoencoders G Patrini, R Berg, P Forré, M Carioni, S Bhargav, M Welling, T Genewein, ... arXiv preprint arXiv:1810.01118, 2018 | 43 | 2018 |
| Separation of Time Scales in a Quantum Newton’s Cradle R van den Berg, B Wouters, S Eliëns, J De Nardis, RM Konik, JS Caux Physical Review Letters 116 (22), 225302, 2016 | 39 | 2016 |
| A spectral energy distance for parallel speech synthesis A Gritsenko, T Salimans, R van den Berg, J Snoek, N Kalchbrenner Advances in Neural Information Processing Systems 33, 2020 | 13 | 2020 |
| Competing interactions in semiconductor quantum dots R van den Berg, GP Brandino, O El Araby, RM Konik, V Gritsev, JS Caux Physical Review B 90 (15), 155117, 2014 | 13 | 2014 |
| Idf++: Analyzing and improving integer discrete flows for lossless compression R van den Berg, AA Gritsenko, M Dehghani, CK Sønderby, T Salimans International Conference on Learning Representations, 2020 | 8* | 2020 |
| Variational inference with orthogonal normalizing flows L Hasenclever, J Tomczak, R van den Berg, M Welling Bayesian Deep Learning, NIPS 2017 workshop, 2017 | 5 | 2017 |
| Differentiable probabilistic models of scientific imaging with the Fourier slice theorem K Ullrich, R Berg, M Brubaker, D Fleet, M Welling arXiv preprint arXiv:1906.07582, 2019 | 4 | 2019 |
| Probing pairing correlations in Sn isotopes using Richardson-Gaudin integrability S De Baerdemacker, V Hellemans, R van den Berg, JS Caux, K Heyde, ... Journal of Physics: Conference Series 533 (1), 012058, 2014 | 3 | 2014 |
| Structured Denoising Diffusion Models in Discrete State-Spaces J Austin, D Johnson, J Ho, D Tarlow, R Berg arXiv preprint arXiv:2107.03006, 2021 | 2 | 2021 |
| Predictive Uncertainty through Quantization BS Veeling, R Berg, M Welling arXiv preprint arXiv:1810.05500, 2018 | 2 | 2018 |
| Integrable spin chains with random interactions FHL Essler, R van den Berg, V Gritsev Physical Review B 98 (2), 024203, 2018 | 2 | 2018 |
| Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent S Abnar, R Berg, G Ghiasi, M Dehghani, N Kalchbrenner, H Sedghi arXiv preprint arXiv:2106.06080, 2021 | 1 | 2021 |
| Autoregressive Diffusion Models E Hoogeboom, AA Gritsenko, J Bastings, B Poole, R Berg, T Salimans arXiv preprint arXiv:2110.02037, 2021 | | 2021 |
| Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models DD Johnson, J Austin, R van den Berg, D Tarlow ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021 | | 2021 |
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
