Publications

Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, Max Welling (2021). E(n) Equivariant Normalizing Flows.

Emiel Hoogeboom, Victor Garcia Satorras, Max Welling (2021). E(n) Equivariant Graph Neural Networks. ICML 2021.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling (2021). Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions.

Simon Passenheim, Emiel Hoogeboom (2021). Variational Determinant Estimation with Spherical Normalizing Flows. AABI 2021.

Thomas Anderson Keller, Jorn W.T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling (2021). Self Normalizing Flows. ICML 2021.

Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling (2020). The Convolution Exponential and Generalized Sylvester Flows. NeurIPS 2020.

Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling (2020). SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS 2020.

Auke Wiggers, Emiel Hoogeboom (2020). Predictive Sampling with Forecasting Autoregressive Models. ICML 2020.

Emiel Hoogeboom, Taco S. Cohen, Jakub Tomczak (2020). Learning Discrete Distributions by Dequantization.

Christina Winkler, Daniel Worrall, Emiel Hoogeboom, Max Welling (2020). Learning Likelihoods with Conditional Normalizing Flows.

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

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

Emiel Hoogeboom, Jorn W.T. Peters, Taco S. Cohen, Max Welling (2018). HexaConv. ICLR 2018.