| 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 | 5826 | 2017 |
| Graph Convolutional Matrix Completion R van den Berg, TN Kipf, M Welling arXiv preprint arXiv:1706.02263, 2017 | 1701 | 2017 |
| Structured denoising diffusion models in discrete state-spaces J Austin, DD Johnson, J Ho, D Tarlow, R van den Berg Advances in Neural Information Processing Systems 34, 17981-17993, 2021 | 733 | 2021 |
| Sylvester normalizing flows for variational inference R Berg, L Hasenclever, JM Tomczak, M Welling arXiv preprint arXiv:1803.05649, 2018 | 258 | 2018 |
| 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 | 172 | 2019 |
| Protein structure generation via folding diffusion KE Wu, KK Yang, R Berg, JY Zou, AX Lu, AP Amini arXiv preprint arXiv:2209.15611, 2022 | 152 | 2022 |
| 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 | 146 | 2016 |
| Autoregressive Diffusion Models E Hoogeboom, AA Gritsenko, J Bastings, B Poole, R Berg, T Salimans arXiv preprint arXiv:2110.02037, 2021 | 124 | 2021 |
| Sinkhorn autoencoders G Patrini, R Berg, P Forré, M Carioni, S Bhargav, M Welling, T Genewein, ... arXiv preprint arXiv:1810.01118, 2018 | 117 | 2018 |
| Emerging convolutions for generative normalizing flows E Hoogeboom, R Van Den Berg, M Welling International Conference on Machine Learning, 2771-2780, 2019 | 116 | 2019 |
| Clifford Neural Layers for PDE Modeling J Brandstetter, R Berg, M Welling, JK Gupta arXiv preprint arXiv:2209.04934, 2022 | 96 | 2022 |
| Two for one: Diffusion models and force fields for coarse-grained molecular dynamics M Arts, V Garcia Satorras, CW Huang, D Zügner, M Federici, C Clementi, ... Journal of Chemical Theory and Computation 19 (18), 6151-6159, 2023 | 76 | 2023 |
| 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 | 66 | 2020 |
| Protein generation with evolutionary diffusion: sequence is all you need S Alamdari, N Thakkar, R van den Berg, AX Lu, N Fusi, AP Amini, ... bioRxiv, 2023.09. 11.556673, 2023 | 60 | 2023 |
| 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 | 50* | 2020 |
| 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 | 49 | 2016 |
| 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 | 20 | 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 | 18 | 2021 |
| 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 | 17 | 2014 |
| 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 | 16 | 2019 |
Max WellingCAIO CuspAI & Professor Machine Learning, University of Amsterdamuva.nl의 이메일 확인됨