33 citations · 52 across the 12 of their papers we have counts for
13 papers
Self-Conditioning Enables Deep Sequential Reasoning in Flow Matching
Zeyang Zhang, Linxiang Gao, Meiqi Gu +4
Recent work has explored visual reasoning through image generation, where models solve visual-reasoning problems by directly generating solution images. Intuitively, iterative samp…
FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
Xingyan Chen, Tian Du, Changqiao Xu +4
Federated Learning (FL) faces challenges from client data heterogeneity and resource-constrained mobile devices, which can degrade model accuracy. Personalized Federated Learning (…
RPS: Information Elicitation with Reinforcement Prompt Selection
Tao Wang, Jingyao Lu, Xibo Wang +5
Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known but concealed information in o…
Differences in Text Generated by Diffusion and Autoregressive Language Models
Zeyang Zhang, Chengwei Liang, Xingyan Chen +4
Diffusion language models (DLMs) are promising alternatives to autoregressive language models (ARMs), yet the intrinsic differences in their generated text remain underexplored. We…
Identifying Evidence Subgraphs for Financial Risk Detection via Graph Counterfactual and Factual Reasoning
Huaming Du, Lei Yuan, Qing Yang +6
Company financial risks pose a significant threat to personal wealth and national economic stability, stimulating increasing attention towards the development of efficient andtimel…
MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models
Zhongpu Chen, Yinfeng Liu, Long Shi +3
Large language models (LLMs) are expected to offer structured Markdown responses for the sake of readability in web chatbots (e.g., ChatGPT). Although there are a myriad of metrics…
Graph Dimension Attention Networks for Enterprise Credit Assessment
Shaopeng Wei, Beni Egressy, Xingyan Chen +4
Enterprise credit assessment is critical for evaluating financial risk, and Graph Neural Networks (GNNs), with their advanced capability to model inter-entity relationships, are a…
Taming Gradient Variance in Federated Learning with Networked Control Variates
Xingyan Chen, Yaling Liu, Huaming Du +2
Federated learning, a decentralized approach to machine learning, faces significant challenges such as extensive communication overheads, slow convergence, and unstable improvement…
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey
Shaopeng Wei, Jun Wang, Yu Zhao +6
Graph learning is a prevalent domain that endeavors to learn the intricate relationships among nodes and the topological structure of graphs. Over the years, graph learning has tra…
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling
Yu Guo, Zhilong Xie, Xingyan Chen +7
Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough…
Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks
Yu Zhao, Shaopeng Wei, Yu Guo +6
Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is an important step for financial institutions when making decisions about loans. Existing studies in b…
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks
Yu Zhao, Shaopeng Wei, Huaming Du +5
Bi-type multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise know…
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Yu Zhao, Huaming Du, Ying Liu +6
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent f…