Publications

Emiel Hoogeboom, David Ruhe, Jonathan Heek, Thomas Mensink, Tim Salimans (2026). Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD. Preprint.

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Jonathan Heek, Emiel Hoogeboom, Thomas Mensink, Tim Salimans (2026). Unified Latents (UL): How to train your latents. Preprint.

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Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans (2025). Simpler Diffusion: 1.5 FID on ImageNet512 with Pixel-space Diffusion. CVPR 2025.

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Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom (2024). Multistep Distillation of Diffusion Models via Moment Matching. NeurIPS 2024.

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David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom (2024). Rolling Diffusion Models. ICML 2024.

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Emiel Hoogeboom, Jonathan Heek, Tim Salimans (2023). simple diffusion: End-to-end diffusion for high resolution images. ICML 2023.

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Emiel Hoogeboom, Tim Salimans (2023). Blurring Diffusion Models. ICLR 2023.

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Emiel Hoogeboom, Victor Garcia Satorras, Clement Vignac, Max Welling (2022). Equivariant Diffusion for Molecule Generation in 3D. ICML 2022.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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