Linear Projections and Gaussian Process Reconstructions
author:
Joaquin Quiñonero Candela,
Max Planck Institute for Biological Cybernetics
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| Slides | |
| 0:00 | Linear Projections and Gaussian Process |
| 1:38 | Acknowledgements |
| 1:59 | Linear Dimensionality Reduction |
| 5:28 | Linear Reconstructions |
| 6:40 | A Poor Reconstruction vs a Cool Reconstruction |
| 8:43 | Reconstruction as a Regression Problem |
| 9:28 | Bayesian Regression with Gaussian Process Priors |
| 14:10 | Gaussian Processes as Smooth Priors Over Functions |
| 16:07 | Evidence and predictive distribution |
| 17:06 | Gaussian Process Latent Variable Model (GP-LVM) pt 1 |
| 17:40 | Gaussian Process Latent Variable Model (GP-LVM) pt 2 |
| 18:47 | The GP-LVM in action |
| 21:44 | Limitations of the GP-LVM |
| 25:41 | Symbiosis |
| 30:29 | Digits Revisited pt 1 |
| 31:02 | Digits Revisited pt 2 |
| 31:37 | Swiss Roll |
| 33:07 | Discussion |
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