My contribution during GSoC 2021 is two case studies for using Turing.jl with Latent Variable Models. The models I have implemented are probabilistic PCA and Gaussian Process Latent Variable Models and a few extensions of these models. Each one is documented in a separate Github pull request (see links below).
- Basic tutorial and model (https://www.robots.ox.ac.uk/~cvrg/hilary2006/ppca.pdf)
- Automatic Relevance Determination (Bishop, C.M., 2006. Machine learning and pattern recognition. Information science and statistics. Springer, Heidelberg; Chapter 12.2.3 (and Chapter 7.2.2))
- Residual PCA (demonstrating removal of batch effects) (https://arxiv.org/abs/1106.4333)