Table of Links
2. Background
2.1 Amortized Stochastic Variational Bayesian GPLVM
2.2 Encoding Domain Knowledge through Kernels
3. Our Model and Pre-Processing and Likelihood
4. Results and Discussion and 4.1 Each Component is Crucial to Modifies Model Performance
4.3 Consistency of Latent Space with Biological Factors
4. Conclusion, Acknowledgement, and References
2 BACKGROUND
This section provides a concise introduction to existing BGPLVM models from the literature.
2.1 AMORTIZED STOCHASTIC VARIATIONAL BAYESIAN GPLVM
where the variational distributions are:
This paper is available on arxiv under CC BY-SA 4.0 DEED license.
Authors:
(1) Sarah Zhao, Department of Statistics, Stanford University, (smxzhao@stanford.edu);
(2) Aditya Ravuri, Department of Computer Science, University of Cambridge (ar847@cam.ac.uk);
(3) Vidhi Lalchand, Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard (vidrl@mit.edu);
(4) Neil D. Lawrence, Department of Computer Science, University of Cambridge (ndl21@cam.ac.uk).
