"The future is already here — it's just not very evenly distributed"
- William Gibson -
I am interested in the topics of data fusion and transportability. Loosely, this is motivated by the observation that humans can:
(1) seamlessly fuse signals from various sources, then;
(2) quickly select the most relevant subsets for any given task.
My past research has predominantly focused on unsupervised (deep) machine learning with a special interest in methods exploring (non-trivial) manifold learning.
T.R. Davidson*, L. Falorsi*, N. De Cao*, T. Kipf, J.M. Tomczak, Hyperspherical Variational Autoencoders, Oral presentation at the 34th Conference on Uncertainty in Artificial Intelligence (UAI, 2018)
[arXiv] [code] [oral]
L. Falorsi*, P. de Haan*, T.R. Davidson*, N. De Cao, M. Weiler, P. Forré, T. Cohen, Explorations in Homeomorphic Variational Auto-Encoding, ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models (ICML, 2018)
L. Falorsi, P. de Haan, T.R. Davidson, P. Forré, Reparameterizing Distributions on Lie Groups, Oral presentation at the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS, 2019)
T.R. Davidson, J.M. Tomczak, E. Gavves, Increasing Expressivity of a Hyperspherical VAE, NeurIPS Workshop on Bayesian Deep Learning (NeurIPS, 2019)
T.R. Davidson, The Shape of a Black Box: A Closer Look at Structured Latent Spaces, Master's Thesis, University of Amsterdam, 2019