337 citations · 823 across the 46 of their papers we have counts for
47 papers
Von Mises-Fisher Mixture Model with Dynamic Shrinkage for Realistic Test-Time Transduction
Jiazhen Huang, Zhiming Liu, Changhu Wang +3
A range of methods aim to enhance the performance of vision-language models (VLMs) at test time. Among them, transduction has emerged as a promising paradigm due to its strong comp…
Interests Burn-down Diffusion Process for Personalized Collaborative Filtering
Yifang Qin, Zhaobin Li, Arisa Watanabe +3
Generative methods have gained widespread attention in Collaborative Filtering (CF) tasks for their ability to produce high-quality personalized samples aligned with users' interes…
Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
Wei Ju, Wei Zhang, Siyu Yi +6
Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However…
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
Junyu Luo, Bohan Wu, Xiao Luo +8
Post-training of Large Language Models (LLMs) is crucial for unlocking their task generalization potential and domain-specific capabilities. However, the current LLM post-training…
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
Junyu Luo, Yuhao Tang, Yiwei Fu +6
Unsupervised Graph Domain Adaptation (UGDA) leverages labeled source domain graphs to achieve effective performance in unlabeled target domains despite distribution shifts. However…
MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning
Yusheng Zhao, Xiao Luo, Weizhi Zhang +4
The ability to reason is one of the most fundamental capabilities of large language models (LLMs), enabling a wide range of downstream tasks through sophisticated problem-solving.…
Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval
Junyu Luo, Yusheng Zhao, Xiao Luo +5
Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, while maintaining low storage cost and high…
Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs
Yusheng Zhao, Qixin Zhang, Xiao Luo +5
Large language models (LLMs) have been used in many zero-shot learning problems, with their strong generalization ability. Recently, adopting LLMs in text-attributed graphs (TAGs)…
Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness
Yusheng Zhao, Junyu Luo, Xiao Luo +5
Multi-modal large language models (MLLMs) have recently achieved great success in processing and understanding information from diverse modalities (e.g., text, audio, and visual si…
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
Junyu Luo, Weizhi Zhang, Ye Yuan +23
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic a…
ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models
Kangjie Zheng, Junwei Yang, Siyue Liang +5
Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input seq…
Cluster-guided Contrastive Class-imbalanced Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi +6
This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions…
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation
Hourun Li, Yifan Wang, Zhiping Xiao +4
Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendatio…
Embracing Large Language Models in Traffic Flow Forecasting
Yusheng Zhao, Xiao Luo, Haomin Wen +3
Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportat…
SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision
Kangjie Zheng, Siyue Liang, Junwei Yang +5
SMILES, a crucial textual representation of molecular structures, has garnered significant attention as a foundation for pre-trained language models (LMs). However, most existing p…
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Junyu Luo, Yiyang Gu, Xiao Luo +5
Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing appro…
Semi-supervised Fine-tuning for Large Language Models
Junyu Luo, Xiao Luo, Xiusi Chen +3
Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practic…
Rank and Align: Towards Effective Source-free Graph Domain Adaptation
Junyu Luo, Zhiping Xiao, Yifan Wang +5
Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to p…
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
Yifan Wang, Xiao Luo, Chong Chen +3
Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and…
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
Jinsheng Huang, Liang Chen, Taian Guo +13
Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image,…
Hypergraph-enhanced Dual Semi-supervised Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi +6
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unl…
Towards Graph Contrastive Learning: A Survey and Beyond
Wei Ju, Yifan Wang, Yifang Qin +10
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to i…
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Wei Ju, Yusheng Zhao, Yifang Qin +6
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attent…
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Wei Ju, Siyu Yi, Yifan Wang +10
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and netwo…