Tackling Covariate Shift with Node-Based Bayesian Neural Networks

Tackling Covariate Shift with Node-Based Bayesian Neural Networks

Finnish Center for Artificial Intelligence FCAI via YouTube Direct link

Conclusion

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18 of 18

Conclusion

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Classroom Contents

Tackling Covariate Shift with Node-Based Bayesian Neural Networks

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  1. 1 Intro
  2. 2 Covariate shift
  3. 3 Bayesian neural networks (BNNs)
  4. 4 BNNS perform worse than MAP models under corruption
  5. 5 Node-based Bayesian neural networks
  6. 6 Approximating the implicit corruption
  7. 7 Example of implicit corruptions
  8. 8 Entropy of latent variables and implicit corruptions
  9. 9 Is a model robust against its own corruptions?
  10. 10 How robust is a model against the other model's corruptio
  11. 11 Training a node-based BNN
  12. 12 Variational inference
  13. 13 Variational posterior
  14. 14 Training objective
  15. 15 Effects of on corruption robustness
  16. 16 Robust learning under label noise
  17. 17 Benchmark comparison
  18. 18 Conclusion

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