Emiel Hoogeboom

PhD Candidate (advised by Max Welling)
Delta Lab
Institute of Informatics
University of Amsterdam

 

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Emiel Hoogeboom and is a PhD Candidate at the University of Amsterdam under the supervision of Max Welling, in the UvA-Bosch Delta Lab. Research interests include deep generative models and molecular generation.


Selected Publications

  1. ICLR
    Autoregressive Diffusion Models
    Hoogeboom, Emiel, Gritsenko, Alexey A., Bastings, Jasmijn, Poole, Ben, Berg, Rianne, and Salimans, Tim
    In International Conference on Learning Representations, ICLR Apr 2022
  2. arXiv
    Equivariant Diffusion for Molecule Generation in 3D
    Hoogeboom, Emiel, Satorras, Victor Garcia, Vignac, Clément, and Welling, Max
    CoRR Apr 2022
  3. AABI
    Learning Discrete Distributions by Dequantization
    Hoogeboom, Emiel, Cohen, Taco S., and Tomczak, Jakub M.
    In 3rd Symposium on Advances in Approximate Bayesian Inference, AABI Apr 2021
  4. NeurIPS
    Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
    Hoogeboom, Emiel, Nielsen, Didrik, Jaini, Priyank, Forré, Patrick, and Welling, Max
    In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems, NeurIPS Apr 2021
  5. NeurIPS
    E(n) Equivariant Normalizing Flows
    Satorras, Victor Garcia, Hoogeboom, Emiel, Fuchs, Fabian Bernd, Posner, Ingmar, and Welling, Max
    In Advances in Neural Information Processing Systems, NeurIPS Apr 2021
  6. ICML
    Self Normalizing Flows
    Keller, T. Anderson, Peters, Jorn W. T., Jaini, Priyank, Hoogeboom, Emiel, Forré, Patrick, and Welling, Max
    In Proceedings of the 38th International Conference on Machine Learning, ICML Apr 2021
  7. INNF+
    Discrete Denoising Flows
    Lindt, Alexandra, and Hoogeboom, Emiel
    In ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models Apr 2021
  8. AABI
    Variational Determinant Estimation with Spherical Normalizing Flows
    Passenheim, Simon Arthur, and Hoogeboom, Emiel
    In Third Symposium on Advances in Approximate Bayesian Inference, AABI Apr 2021
  9. ICML
    E(n) Equivariant Graph Neural Networks
    Satorras, Victor Garcia, Hoogeboom, Emiel, and Welling, Max
    In Proceedings of the 38th International Conference on Machine Learning, ICML Apr 2021
  10. NeurIPS
    The Convolution Exponential and Generalized Sylvester Flows
    Hoogeboom, Emiel, Satorras, Victor Garcia, Tomczak, Jakub M., and Welling, Max
    In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS Apr 2020
  11. NeurIPS
    SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
    Nielsen, Didrik, Jaini, Priyank, Hoogeboom, Emiel, Winther, Ole, and Welling, Max
    In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS Apr 2020
  12. ICML
    Predictive Sampling with Forecasting Autoregressive Models
    Wiggers, Auke J., and Hoogeboom, Emiel
    In Proceedings of the 37th International Conference on Machine Learning, ICML Apr 2020
  13. ICML
    Emerging Convolutions for Generative Normalizing Flows
    Hoogeboom, Emiel, Berg, Rianne van den, and Welling, Max
    In Proceedings of the 36th International Conference on Machine Learning, ICML Apr 2019
  14. NeurIPS
    Integer Discrete Flows and Lossless Compression
    Hoogeboom, Emiel, Peters, Jorn W. T., Berg, Rianne, and Welling, Max
    In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS Apr 2019
  15. arXiv
    Learning Likelihoods with Conditional Normalizing Flows
    Winkler, Christina, Worrall, Daniel E., Hoogeboom, Emiel, and Welling, Max
    CoRR Apr 2019
  16. ICLR
    HexaConv
    Hoogeboom, Emiel, Peters, Jorn W. T., Cohen, Taco S., and Welling, Max
    In 6th International Conference on Learning Representations, ICLR Apr 2018