Table of Links
Abstract and 1. Introduction
2. Background
2.1 Amortized Stochastic Variational Bayesian GPLVM
2.2 Encoding Domain Knowledge through Kernels
3. Our Model and Pre-Processing and Likelihood
3.2 Encoder
4. Results and Discussion and 4.1 Each Component is Crucial to Modifies Model Performance
4.2 Modified Model achieves Significant Improvements over Standard Bayesian GPLVM and is Comparable to SCVI
4.3 Consistency of Latent Space with Biological Factors
4. Conclusion, Acknowledgement, and References
A. Baseline Models
B. Experiment Details
C. Latent Space Metrics
D. Detailed Metrics
2 BACKGROUND
This section provides a concise introduction to existing BGPLVM models from the literature.
2.1 AMORTIZED STOCHASTIC VARIATIONAL BAYESIAN GPLVM
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where the variational distributions are:
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This paper is available on arxiv under CC BY-SA 4.0 DEED license.
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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).
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