The G aussian Process Latent Variable Model

By Neil D. Lawrence. 2006

48 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Graham W. Taylor
3. Geoffrey E. Hinton
4. Ruslan Salakhutdinov
5. Dacheng Tao
6. Kyunghyun Cho
7. Quoc V. Le
8. Sergey Levine

Citations in the corpus

• Deep Gaussian Processes (2012, background): “Lawrence (2006) “The Gaussian process latent variable model” Technical Report no CS-06-03, The University of Sheffield, Department of Computer Science • N. D. Lawrence (2006) “Probabilistic dimensional reduction with the Gaussian process latent variable model” (talk) • C.”
• Two Distributed-State Models For Generating High-Dimensional Time Series (2011, methodology): “Again, we copy the real-valued probabilities of the first layer hidden units toinitialize Gibbs sampling for the next frame, and repeat steps 3 and 4 above for as many frames as desired.”
• Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria (2017, methodology): “To more explicitly model the temporal dynamics of this differentiation process, we developed GPfates, a temporal mixture model that builds on the Gaussian Process Latent Variable Model (GPLVM) (16) and Overlapping Mixtures of Gaussian Processes (OMGP) (17).”