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Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models. [PDF]
IEEE Trans Med ImagingLai Y +6 more
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Modular and Parameter-efficient Fine-tuning of Language Models
2023Transfer learning has recently become the dominant paradigm of natural language processing. Models pre-trained on unlabeled data can be fine-tuned for downstream tasks based on only a handful of examples. A long-term goal is to develop models that acquire new information at scale without incurring negative transfer and that generalize systematically to
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Modular and Parameter-Efficient Fine-Tuning for NLP Models
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, 2022Sebastian Ruder +2 more
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SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
International Journal of Computer Vision, 2023Pichao Wang +2 more
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A Study of Parameter Efficient Fine-tuning by Learning to Efficiently Fine-Tune
Findings of the Association for Computational Linguistics: EMNLP 2024Taha Ceritli +5 more
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Parameter-efficient Fine-tuning for Vision Transformers.
CoRR, 2022Xuehai He +4 more
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Parameter-efficient fine-tuning for single image snow removal
Expert Systems With ApplicationsXinwei Dai, Yuanbo Zhou, Xintao Qiu
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A survey of efficient fine-tuning methods for Vision-Language Models — Prompt and Adapter
Computers and GraphicsXiaoliang Chen, Yingfei Wang
exaly
Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning
Neural NetworksEunbyung Park, Youngjoon Hong
exaly