5 citations · 6 across the 5 of their papers we have counts for
6 papers
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models
Ying Nie, Binwei Yan, Tianyu Guo +9
Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging and domain-specific task, such as finance, has not b…
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Lecheng Kong, Jiarui Feng, Hao Liu +3
While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A pa…
One for All: Towards Training One Graph Model for All Classification Tasks
Hao Liu, Jiarui Feng, Lecheng Kong +4
Designing a single model to address multiple tasks has been a long-standing objective in artificial intelligence. Recently, large language models have demonstrated exceptional capa…
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
Feng Yan, Xiaoheng Jiang, Yang Lu +5
Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection…
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Xiaoheng Jiang, Kaiyi Guo, Yang Lu +5
Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress,…
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
Hao Liu, Jiarui Feng, Lecheng Kong +3
Graph Neural Networks (GNNs) have become popular in Graph Representation Learning (GRL). One fundamental application is few-shot node classification. Most existing methods follow t…