Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis

Nov-20-2025, 02:42:58 GMT–Neural Information Processing Systems

The remarkable success of transformers in sequence modeling tasks, spanning various applications in natural language processing and computer vision, is attributed to the critical role of self-attention.

artificial intelligence, machine learning, natural language, (18 more...)

Neural Information Processing Systems

Nov-20-2025, 02:42:58 GMT

Conferences PDF

Country:
- Asia
  - China > Hong Kong (0.04)
  - Singapore (0.04)
- Europe > Italy
  - Tuscany > Florence (0.04)
- North America
  - Dominican Republic (0.04)
  - United States
    - Minnesota > Hennepin County
      - Minneapolis (0.14)
    - Texas > Travis County
      - Austin (0.04)

Genre:
- Research Report > Experimental Study (0.93)

Industry:
- Government (0.93)
- Information Technology > Security & Privacy (0.68)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning
    - Neural Networks > Deep Learning (0.46)
    - Statistical Learning > Principal Component Analysis (0.41)
  - Natural Language (1.00)
  - Representation & Reasoning (1.00)
  - Vision (1.00)

Duplicate Docs Excel Report

Title
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis

Similar Docs Excel Report more

Title	Similarity	Source
None found