Dive into a comprehensive 50-minute video explanation of the Linformer model, which addresses the resource-intensive nature of Transformers' self-attention mechanism. Explore how this innovative approach reduces computational complexity from O(n^2) to O(n) in both time and space. Learn about the low-rank approximation of the attention matrix, empirical investigations into RoBERTa, and the theoretical foundations of linear self-attention. Discover the model's performance in language modeling and NLP benchmarks, as well as its significant compute time and memory gains. Gain insights into the broader impact of this advancement in transformer technology, making it more accessible for long sequence processing in natural language applications.
Linformer - Self-Attention with Linear Complexity
Overview
Syllabus
- Intro & Overview
- The Complexity of Self-Attention
- Embedding Dimension & Multiple Heads
- Formal Attention
- Empirical Investigation into RoBERTa
- Theorem: Self-Attention is Low Rank
- Linear Self-Attention Method
- Theorem: Linear Self-Attention
- Language Modeling
- NLP Benchmarks
- Compute Time & Memory Gains
- Broader Impact Statement
- Conclusion
Taught by
Yannic Kilcher
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