7 citations · 11 across the 7 of their papers we have counts for
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cs.LG2026
AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
Junru Zhang, Lang Feng, Haoran Shi +4
Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heurist…
cs.LG2024★ 2 cited
A Note on LoRA
Vlad Fomenko, Han Yu, Jongho Lee +2
LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the or…
cs.LG2023★ 2 cited
Efficient RLHF: Reducing the Memory Usage of PPO
Michael Santacroce, Yadong Lu, Han Yu +2
Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimizatio…