arXiv (All)
open access
Low-Rank Friction for Memory-Efficient Transformer Pretraining
iKFAD is a recently proposed optimiser that replaces adaptive learning rates with adaptive friction in the momentum dynamics, yet performs as well as Adam. Its limitation is that the full friction tensor $ξ\in\mathbb{R}^{m\times n}$ carries the same $\mathcal{O}(mn)$ memory overhead per layer as Ada
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Runtime-Incremental Transformer for Reinforcement-Learning-Based Adaptive Control#1071325
- Training Trajectories Determine Circuit Removability in Annealable Soft-Prior Transformers#997182
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.#1091062
- Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution#1118294
- A pullback-corrected scalar auxiliary variable optimizer with momentum and adaptive mobility#1071348
Record · ID 1104541
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive