HIGHLIGHTS
- who: Nikola Simidjievski from the Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom, Department of have published the research: Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice, in the Journal: (JOURNAL)
- what: The authors design and systematically analyze several deeplearning approaches for data integration based on Variational Autoencoders (VAEs) (Kingma and Welling, 2014). In contrast, in this paper the authors investigate approaches that build upon probabilistic autoencoders which implement Variational Bayesian inference for unsupervised learning of latent data representations. The authors perform a systematic evaluation ( the authors evaluate . . .
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