T. Anderson Keller

PhD candidate (advised by Max Welling)
AMLab, Delta Lab
Institute of Informatics
University of Amsterdam
Science Park 904, Room C3.201


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My first name is Thomas, but most people call me by my middle name – Andy. I’m currently a third-year PhD student supervised by Max Welling at the University of Amsterdam. My work is focused on unsupervised structured representation learning, inspired and supported by observations from neuroscience. In pursuit of this goal, during my PhD I have developed novel methods for probabilistic generative modeling which make use of biologically plausible mechanisms such as learned feedback connections and topographic organization to approximate otherwise analytically intractible solutions. In the long term, I hope to be able to answer the question of how transformations and invariances are learned and encoded in the brain, and further understand how the 2-dimensional structure of the cortical surface shapes how learning proceeds. More immediately, my current interests broadly include: developing unsupervised methods for learning approximately equivariant & invariant representations, exploring the computational benefits of topographically organized representations, and improving techniques for efficiently training deep latent variable models.

Selected Publications

  1. NeurIPS
    Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders
    Keller, T. Anderson, Gao, Qinghe, and Welling, Max
    In SVRHM 2021 Workshop at NeurIPS Dec 2021
  2. ICCV
    Predictive Coding With Topographic Variational Autoencoders
    Keller, T. Anderson, and Welling, Max
    In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Oct 2021
  3. NeurIPS
    Topographic VAEs learn Equivariant Capsules
    Keller, T. Anderson, and Welling, Max
    In Advances in Neural Information Processing Systems Oct 2021
  4. 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 18–24 jul 2021
  5. NeurIPS
    As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance
    Wever, Fiorella, Keller, T. Anderson, Garcia, Victor, and Symul, Laura
    In Self-Supervised Learning Workshop at NeurIPS 18–24 jul 2021