Graph Convolutional Matrix Completion. - Rianne van den Berg; Thomas N. Kipf; Max Welling (CoRR, 2017)

Modeling Relational Data with Graph Convolutional Networks. - Michael Sejr Schlichtkrull; Thomas N. Kipf; Peter Bloem; Rianne van den Berg; Ivan Titov 001; Max Welling (ESWC, 2018)

Sylvester Normalizing Flows for Variational Inference. - Rianne van den Berg; Leonard Hasenclever; Jakub M. Tomczak; Max Welling (UAI, 2018)

Predictive Uncertainty through Quantization. - Bastiaan S. Veeling; Rianne van den Berg; Max Welling (CoRR, 2018)

Emerging Convolutions for Generative Normalizing Flows. - Emiel Hoogeboom; Rianne van den Berg; Max Welling (ICML, 2019)

Integer Discrete Flows and Lossless Compression. - Emiel Hoogeboom; Jorn W. T. Peters; Rianne van den Berg; Max Welling (NeurIPS, 2019)

Sinkhorn AutoEncoders. - Giorgio Patrini; Rianne van den Berg; Patrick Forré; Marcello Carioni; Samarth Bhargav 001; Max Welling; Tim Genewein; Frank Nielsen (UAI, 2019)

Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem. - Karen Ullrich; Rianne van den Berg; Marcus A. Brubaker; David J. Fleet; Max Welling (UAI, 2019)

A Spectral Energy Distance for Parallel Speech Synthesis. - Alexey A. Gritsenko; Tim Salimans; Rianne van den Berg; Jasper Snoek; Nal Kalchbrenner (NeurIPS, 2020)

IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression. - Rianne van den Berg; Alexey A. Gritsenko; Mostafa Dehghani 001; Casper Kaae Sønderby; Tim Salimans (ICLR, 2021)

Structured Denoising Diffusion Models in Discrete State-Spaces. - Jacob Austin; Daniel D. Johnson 001; Jonathan Ho; Daniel Tarlow; Rianne van den Berg (NeurIPS, 2021)

Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent. - Samira Abnar; Rianne van den Berg; Golnaz Ghiasi; Mostafa Dehghani 001; Nal Kalchbrenner; Hanie Sedghi (CoRR, 2021)

Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models. - Daniel D. Johnson 001; Jacob Austin; Rianne van den Berg; Daniel Tarlow (CoRR, 2021)

Autoregressive Diffusion Models. - Emiel Hoogeboom; Alexey A. Gritsenko; Jasmijn Bastings; Ben Poole; Rianne van den Berg; Tim Salimans (ICLR, 2022)

Protein structure generation via folding diffusion. - Kevin E. Wu; Kevin K. Yang; Rianne van den Berg; James Y. Zou; Alex X. Lu; Ava P. Amini (CoRR, 2022)

Clifford Neural Layers for PDE Modeling. - Johannes Brandstetter; Rianne van den Berg; Max Welling; Jayesh K. Gupta (ICLR, 2023)

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. - Marloes Arts; Victor Garcia Satorras; Chin-Wei Huang; Daniel Zügner; Marco Federici; Cecilia Clementi; Frank Noé; Robert Pinsler; Rianne van den Berg (CoRR, 2023)

Accurate and scalable exchange-correlation with deep learning. - Giulia Luise; Chin-Wei Huang; Thijs Vogels; Derk P. Kooi; Sebastian Ehlert; Stephanie Lanius; Klaas J. H. Giesbertz; Amir Karton; Deniz Gunceler; Megan Stanley; Wessel P. Bruinsma; Lin Huang; et al. (CoRR, 2025)