Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
March 19, 2024Open Access

AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information

View Full Paper
Ask AI
Bookmark
Share

Authors

JHJiang HuJoslin Diabetes CenterQLQuanzheng LiNorthwestern Polytechnical University

Discussion

Loading...

Member takes

Implication

Key Points

Key points are not available for this paper at this time.

Cite This Study

Hu et al. (2024) studied this question.

synapsesocial.com/papers/68e73752b6db6435876b040chttps://doi.org/10.48550/arxiv.2403.13128
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AdaFisher: Adaptive Second Order Optimization via Fisher Information2024
  2. 2Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection2024
  3. 3LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning2026 · 1 citations
  4. 4FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts2025
  5. 5See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition2024