Completed
Tensor representation for robust learning
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Understanding, Interpreting and Designing Neural Network Models Through Tensor Representations
Automatically move to the next video in the Classroom when playback concludes
- 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
