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cs.LG2025
AMS-QUANT: Adaptive Mantissa Sharing for Floating-point Quantization
Mengtao Lv, Ruiqi Zhu, Xinyu Wang +1
Large language models (LLMs) have demonstrated remarkable capabilities in various kinds of tasks, while the billion or even trillion parameters bring storage and efficiency bottlen…
cs.LG2025
Bi-CoG: Bi-Consistency-Guided Self-Training for Vision-Language Models
Rui Zhu, Song-Lin Lv, Zi-Kang Wang +1
Exploiting unlabeled data through semi-supervised learning (SSL) or leveraging pre-trained models via fine-tuning are two prevailing paradigms for addressing label-scarce scenarios…
cs.LG2025★ 1 cited
Unlabeled Data vs. Pre-trained Knowledge: Rethinking SSL in the Era of Large Models
Song-Lin Lv, Rui Zhu, Tong Wei +2
Semi-supervised learning (SSL) alleviates the cost of data labeling process by exploiting unlabeled data and has achieved promising results. Meanwhile, with the development of larg…