Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

Documenti analoghi

• Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
  di: Hu, Jerry Yao-Chieh, et al.
  Pubblicazione: (2024)
• On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
  di: Hu, Jerry Yao-Chieh, et al.
  Pubblicazione: (2024)
• Attention Mechanism, Max-Affine Partition, and Universal Approximation
  di: Liu, Hude, et al.
  Pubblicazione: (2025)
• Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes
  di: Hu, Jerry Yao-Chieh, et al.
  Pubblicazione: (2024)
• On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
  di: Hu, Jerry Yao-Chieh, et al.
  Pubblicazione: (2024)