15 citations · 28 across the 9 of their papers we have counts for
9 papers
DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation
Qiuyu Tian, Haoliang Sun, Yunshan Wang +2
Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit overconfidence or underconfidence, l…
Variational Rectification Inference for Learning with Noisy Labels
Haoliang Sun, Qi Wei, Lei Feng +4
Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re…
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
Bin Wang, Fan Wang, Pingping Wang +5
In this paper, we introduce \textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction.…
A Flow Model with Low-Rank Transformers for Incomplete Multimodal Survival Analysis
Yi Yin, Yuntao Shou, Zao Dai +4
In recent years, multimodal medical data-based survival analysis has attracted much attention. However, real-world datasets often suffer from the problem of incomplete modality, wh…
Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model
Rundong He, Yicong Dong, Lanzhe Guo +2
Semi-supervised learning (SSL) effectively leverages unlabeled data and has been proven successful across various fields. Current safe SSL methods believe that unseen classes in un…
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition
Yicong Dong, Rundong He, Guangyao Chen +4
Graph Neural Networks (GNNs) have achieved significant success in machine learning, with wide applications in social networks, bioinformatics, knowledge graphs, and other fields. M…
Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch
Qikai Wang, Rundong He, Yongshun Gong +4
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often…
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Zhongyi Han, Guanglin Zhou, Rundong He +7
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climat…
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen, Haoliang Sun, Yingjun Du +4
In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational rando…