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Computer Science
Artificial Intelligence
Deep Learning
Transformer deep learning architecture
1. Foundational Concepts and Predecessors
2. The Original Transformer Architecture
3. Transformer Encoder
4. Transformer Decoder
5. Output Generation and Decoding
6. Training Methodology
7. Mathematical Foundations
8. Architectural Analysis
9. Interpretability and Analysis
10. Transformer Variants and Evolution
11. Advanced Attention Mechanisms
12. Applications and Adaptations
13. Implementation Considerations
9.
Interpretability and Analysis
9.1.
Attention Visualization
9.1.1.
Attention Weight Matrices
9.1.2.
Head-specific Patterns
9.1.3.
Layer-wise Analysis
9.1.4.
Attention Rollout
9.2.
Representation Analysis
9.2.1.
Embedding Space Structure
9.2.2.
Layer-wise Representations
9.2.3.
Probing Tasks
9.2.4.
Geometric Properties
9.3.
Learned Patterns
9.3.1.
Syntactic Patterns
9.3.2.
Semantic Patterns
9.3.3.
Positional Patterns
9.3.4.
Multi-Head Specialization
9.4.
Diagnostic Techniques
9.4.1.
Attention Entropy
9.4.2.
Attention Distance
9.4.3.
Head Importance Scoring
9.4.4.
Layer Importance Analysis
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8. Architectural Analysis
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10. Transformer Variants and Evolution