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cs.LG2025
Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT
Da Chang, Peng Xue, Yu Li +3
Parameter-Efficient Fine-Tuning (PEFT) methods are crucial for adapting large pre-trained models. Among these, LoRA is considered a foundational approach. Building on this, the inf…
cs.CV2025
Encoding Structural Constraints into Segment Anything Models via Probabilistic Graphical Models
Yu Li, Da Chang, Xi Xiao
While the Segment Anything Model (SAM) has achieved remarkable success in image segmentation, its direct application to medical imaging remains hindered by fundamental challenges,…
cs.LG2025
AlphaAdam:Asynchronous Masked Optimization with Dynamic Alpha for Selective Updates
Da Chang, Yu Li, Ganzhao Yuan
In the training of large language models (LLMs), updating parameters more efficiently and stably has always been an important challenge. To achieve efficient parameter updates, exi…