On Bayesian Models with Networks for Reconstruction and Detection

On Bayesian Models with Networks for Reconstruction and Detection

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Intro

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1 of 18

Intro

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On Bayesian Models with Networks for Reconstruction and Detection

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  1. 1 Intro
  2. 2 Outline
  3. 3 Acknowledgements
  4. 4 Examples: Image enhancement
  5. 5 Posterior distribution
  6. 6 Cartoon representation
  7. 7 Why use generative models for analyzing images?
  8. 8 Principal component analysis
  9. 9 Variational auto-encoders
  10. 10 MRI acquisition
  11. 11 Bayesian model for image reconstruction
  12. 12 MAP estimation with network prior
  13. 13 Advantage of generative modeling: decoupling
  14. 14 A distinction in the concept of "prior"
  15. 15 Unsupervised outlier detection
  16. 16 Restoration for outlier detection
  17. 17 Experimental details
  18. 18 ROC curves

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