1 citations · 1 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
SubZero+: Efficient Zeroth-Order LLM Fine-Tuning via Large Learning Rates
Ziming Yu, Shuyao Xiao, Xingyu Zhao +6
Zeroth-order (ZO) optimization enables backpropagation-free fine-tuning of large language models, but existing ZO methods suffer from high-variance gradient estimators, making conv…
cs.LG2026
Topology enables learning-based hydrodynamic prediction of the global river system
Hancheng Ren, Gang Zhao, Shuo Wang +12
Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning has transformed Earth-system mode…
cs.LG2024★ 1 cited
Zeroth-Order Fine-Tuning of LLMs in Random Subspaces
Ziming Yu, Pan Zhou, Sike Wang +3
Fine-tuning Large Language Models (LLMs) has proven effective for a variety of downstream tasks. However, as LLMs grow in size, the memory demands for backpropagation become increa…