Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity

Documenti analoghi

• Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
  di: Havrilla, Alex, et al.
  Pubblicazione: (2024)
• Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation
  di: Lin, Max Qiushi, et al.
  Pubblicazione: (2025)
• Theory of Decentralized Robust Kernel-Based Learning
  di: Yu, Zhan, et al.
  Pubblicazione: (2025)
• Vertex-Softmax: Tight Transformer Verification via Exact Softmax Optimization
  di: Rezazadeh, Navid, et al.
  Pubblicazione: (2026)
• Universal Approximation with Softmax Attention
  di: Hu, Jerry Yao-Chieh, et al.
  Pubblicazione: (2025)