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

Learning Likelihoods with Conditional Normalizing Flows. - Christina Winkler; Daniel E. Worrall; Emiel Hoogeboom; Max Welling (CoRR, 2019)

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

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

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

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

Variational Determinant Estimation with Spherical Normalizing Flows. - Simon Passenheim; Emiel Hoogeboom (CoRR, 2020)

Self Normalizing Flows. - T. Anderson Keller 001; Jorn W. T. Peters; Priyank Jaini; Emiel Hoogeboom; Patrick Forré; Max Welling (ICML, 2021)

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

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

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

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. - Emiel Hoogeboom; Didrik Nielsen; Priyank Jaini; Patrick Forré; Max Welling (CoRR, 2021)

E(n) Equivariant Normalizing Flows for Molecule Generation in 3D. - Victor Garcia Satorras; Emiel Hoogeboom; Fabian B. Fuchs; Ingmar Posner; Max Welling (CoRR, 2021)

Discrete Denoising Flows. - Alexandra Lindt; Emiel Hoogeboom (CoRR, 2021)

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

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

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

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

High-Fidelity Image Compression with Score-based Generative Models. - Emiel Hoogeboom; Eirikur Agustsson; Fabian Mentzer; Luca Versari; George Toderici; Lucas Theis (CoRR, 2023)

DORSal: Diffusion for Object-centric Representations of Scenes et al. - Allan Jabri; Sjoerd van Steenkiste; Emiel Hoogeboom; Mehdi S. M. Sajjadi; Thomas Kipf (ICLR, 2024)

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

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

Multistep Consistency Models. - Jonathan Heek; Emiel Hoogeboom; Tim Salimans (CoRR, 2024)

Semantica: An Adaptable Image-Conditioned Diffusion Model. - Manoj Kumar; Neil Houlsby; Emiel Hoogeboom (CoRR, 2024)

Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion. - Emiel Hoogeboom; Thomas Mensink; Jonathan Heek; Kay Lamerigts; Ruiqi Gao; Tim Salimans (CoRR, 2024)

Model Integrity when Unlearning with T2I Diffusion Models. - Andrea Schioppa; Emiel Hoogeboom; Jonathan Heek (CoRR, 2024)

The Latent Doctor Model for Modeling Inter-Observer Variability. - Jasper Linmans; Emiel Hoogeboom; Jeroen van der Laak; Geert Litjens 001 (IEEE J. Biomed. Health Informatics, 2024)

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