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Understanding, Interpreting and Designing Neural Network Models Through Tensor Representations
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- 1 Introduction
- 2 Challenges in Neural Networks
- 3 Robustness of Neural Networks
- 4 Outline
- 5 Conceptual Challenge
- 6 Computational Challenge
- 7 Goal
- 8 Background Knowledge
- 9 Compression Techniques
- 10 Compression Methods
- 11 CP Layer
- 12 Low Rankedness
- 13 Reshaping
- 14 Generalization Error Bound
- 15 Performance
- 16 Evaluation
- 17 Interpreting transformers
- 18 Operations in tensor diagrams
- 19 Benefits of tensor diagrams
- 20 Single Hat SelfAttention
- 21 Multi Hat SelfAttention
- 22 Multi Hat Modes
- 23 Recap
- 24 Improved expressive power
- 25 Tensor representation for robust learning
- 26 Results
- 27 Summary
