Sample-Complexity of Estimating Convolutional and Recurrent Neural Networks

Sample-Complexity of Estimating Convolutional and Recurrent Neural Networks

Simons Institute via YouTube Direct link

Intro

1 of 13

1 of 13

Intro

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Classroom Contents

Sample-Complexity of Estimating Convolutional and Recurrent Neural Networks

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  1. 1 Intro
  2. 2 FNN, CNN and RNN architectures
  3. 3 CNN generative models
  4. 4 Minimax analysis
  5. 5 Estimator and Assumptions
  6. 6 Main results (Informal)
  7. 7 Related work
  8. 8 Upper bounds (formal)
  9. 9 Proof sketch
  10. 10 Lower bounds (formal)
  11. 11 Experiments - CNN (average pooling) vs FNN
  12. 12 Experiments - CNN (weighted pooling) vs FNN
  13. 13 Open questions

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