1 citations · 1 across the 7 of their papers we have counts for
8 papers
Next Generation Active Learning: Mixture of LLMs in the Loop
Yuanyuan Qi, Xiaohao Yang, Jueqing Lu +4
With the rapid advancement and strong generalization capabilities of large language models (LLMs), they have been increasingly incorporated into the active learning pipelines as an…
ALScope: A Unified Toolkit for Deep Active Learning
Chenkai Wu, Yuanyuan Qi, Xiaohao Yang +4
Deep Active Learning (DAL) reduces annotation costs by selecting the most informative unlabeled samples during training. As real-world applications become more complex, challenges…
DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities
Jueqing Lu, Yuanyuan Qi, Xiaohao Yang +8
The performance of Visio-Language Transformers drops sharply when an input modality (e.g., image) is missing, because the model is forced to make predictions using incomplete infor…
CGMatch: A Different Perspective of Semi-supervised Learning
Bo Cheng, Jueqing Lu, Yuan Tian +3
Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generali…
Multi-Label Bayesian Active Learning with Inter-Label Relationships
Yuanyuan Qi, Jueqing Lu, Xiaohao Yang +2
The primary challenge of multi-label active learning, differing it from multi-class active learning, lies in assessing the informativeness of an indefinite number of labels while a…
Neural Topic Modeling with Large Language Models in the Loop
Xiaohao Yang, He Zhao, Weijie Xu +4
Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have d…
Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu, Wray Buntine, Yuanyuan Qi +3
Resolving conflicts is critical for improving the reliability of multi-view classification. While prior work focuses on learning consistent and informative representations across v…
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Jueqing Lu, Lan Du, Ming Liu +1
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training sam…