In Partial Label Learning (PLL), each instance is associated with a candidate label set, with exactly one label being true. While most studies implicitly assume balanced class distributions, real-world data often exhibit severe class imbalance distributions, leading to the Long-Tailed Partial Label Learning (LT-PLL) pr...
Xiangyu Ren, Mingxuan Xia, Guang-Cheng Zhu et al.· Proceedings of the Thirty-Fi...· 0 citations
This work derives a reformulated pixel-level contrastive learning objective by modeling feature distributions with the von Mises-Fisher distribution, and introduces a Reliability-aware Filter based on the vMF-derived metrics, which performs adaptive class-wise pixel reliability assessment to identify unreliable pixels...
Yi-Bo Wang, Rui-Kang Xu, Guang-Cheng Zhu et al.· Proceedings of the Thirty-Fi...· 0 citations
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-policy rollouts and the cost of obtaining reliable targets at scale. Existing methods address sample selection, incomplete supervision, or noisy labels separately, ofte...
Shen-Zhi Yang, Guang-Cheng Zhu, Kai Tang et al.· 0 citations
FCCA, which estimates the signed input--error cross-covariance, whitens it with diagonal Fisher moments, truncates it in the resulting local metric, maps the selected directions back, and applies thin QR to obtain stable core coordinates, shows that a carefully selected fixed span can recover most of the benefit of mov...
Wen-Song Ye, Zhan-Ming Shen, Zhiqing Xiao et al.· 1 citation· ⚡1
This work introduces TCR-Bench, a diagnostic benchmark for Table Content-level Answerability in RAG, and tests whether a lightweight two-stage pipeline, Answerability-Aware Reranking (AAR), applying direct query-table answerability judgment, can recover performance.
Jiaming Tian, Liyao Li, Wen-song Ye et al.· 0 citations
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