图半监督蒸馏新方法被KDD2026录用，利用文本属性提升图表示学习效率。
arXiv:2607.20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph top…
利用未标注数据提升神经群体解码的泛化能力，打通稀疏标注与海量数据的桥梁。
arXiv:2607.14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interface…
类提示驱动的半监督脊柱分割方法，创新引入类别一致性约束提升分割精度。
arXiv:2606.15802v1 Announce Type: new Abstract: Vision Language Model (VLM) has great potential to enhance the quality of pseudo labels in semi-superv…
用极少量标注数据实现LLM推理能力扩展，半监督框架搭配轻量验证器新方法
arXiv:2606.16811v1 Announce Type: new Abstract: For the development of Large language models (LLMs), recent approaches to generating pseudo intermedia…
针对强化学习验证器数据需求难题，GeoMin用几何分布建模实现高效半监督学习，大幅降低标注成本
arXiv:2606.04516v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) significantly advances LLM reasoning, yet it f…
图节点分类新思路：利用未标记预测进行转导锐化，提升半监督性能。
arXiv:2605.20248v1 Announce Type: new Abstract: In the transductive setting, where the full graph is observed but node labels are only partially avail…
速览强化学习稀疏奖励的半监督解决方案，来自arXiv最新研究
arXiv:2501.19128v5 Announce Type: replace-cross Abstract: In many real-world scenarios, reward signal for agents are exceedingly sparse, making it cha…
医学指代图像分割遇上半监督学习，跨模态对齐降低标注成本，精准融合文本与视觉。
arXiv:2605.15720v1 Announce Type: cross Abstract: Medical referring image segmentation (MRIS) requires pixel-level masks aligned with textual descript…