4 citations · 6 across the 13 of their papers we have counts for
14 papers
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
Xiaosong Han, Ke Chen, Xindi Dai +7
In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. Ho…
Advancing Graph Few-Shot Learning via In-Context Learning
Renchu Guan, Yajun Wang, Chunli Guo +5
Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning. However, existing methods…
Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion
Yonghao Liu, Jialu Sun, Wei Pang +4
Graph few-shot learning, which focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks, has garnered significant research attention.…
Learning from Label Proportions with Dual-proportion Constraints
Tianhao Ma, Ximing Li, Changchun Li +1
Learning from Label Proportions (LLP) is a weakly supervised problem in which the training data comprise bags, that is, groups of instances, each annotated only with bag-level clas…
Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios
Bing Wang, Ximing Li, Changchun Li +4
Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automa…
Hypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs
Renchu Guan, Xuyang Li, Yachao Zhang +5
Hypergraphs, as a generalization of traditional graphs, naturally capture high-order relationships. In recent years, hypergraph neural networks (HNNs) have been widely used to capt…
Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration
Yonghao Liu, Yajun Wang, Chunli Guo +5
Graph few-shot learning has attracted increasing attention due to its ability to rapidly adapt models to new tasks with only limited labeled nodes. Despite the remarkable progress…
Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations
Bing Wang, Ximing Li, Yiming Wang +4
The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effec…
Robust Misinformation Detection by Visiting Potential Commonsense Conflict
Bing Wang, Ximing Li, Changchun Li +4
The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Mis…
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
Yonghao Liu, Mengyu Li, Fausto Giunchiglia +4
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph model…
A Simple Graph Contrastive Learning Framework for Short Text Classification
Yonghao Liu, Fausto Giunchiglia, Lan Huang +3
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined…
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Yonghao Liu, Mengyu Li, Wei Pang +4
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical sce…
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
Yonghao Liu, Fausto Giunchiglia, Ximing Li +3
Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among rese…
Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
Bing Wang, Shengsheng Wang, Changchun Li +2
Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying m…