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[LG] LoRA-Pro: Are Low-Rank Adapters Properly Optimized? Z Wang, J Liang [University of Science and Technology of China & Chinese Academy of Sciences] (2024) https://t.co/9zx1J3p3Ot - The paper proposes a method called LoRA-Pro to bridge the performance gap between LoRA and full… https://t.co/Zp5C6Ac0WQ
LoRA-Pro: A Groundbreaking Machine Learning Approach to Bridging the Performance Gap Between Low-Rank Adaptation and Full Fine-Tuning https://t.co/w96YehzUxF #MachineLearning #LoRAPro #AIimplementation #EfficientFineTuning #AIinnovation #ai #news #llm #ml #research #ainews #i… https://t.co/YGnZEAx6xf
LoRA-Pro: A Groundbreaking Machine Learning Approach to Bridging the Performance Gap Between Low-Rank Adaptation and Full Fine-Tuning https://t.co/31gel4SSWX
A recent study presented at the International Conference on Machine Learning (ICML) 2024 introduces LoRA-Pro, a novel approach designed to optimize low-rank adapters in machine learning. Researchers Z Wang and J Liang from the University of Science and Technology of China and the Chinese Academy of Sciences developed this method to address the performance gap between low-rank adaptation and full fine-tuning. The study highlights how optimizing algorithms can enhance AI capabilities by improving convergence in distributed systems, potentially leading to significant advancements in machine learning performance.