6 citations · 21 across the 12 of their papers we have counts for
12 papers
Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective
Rui Yang, Boming Yang, Aosong Feng +7
Knowledge Graphs (KGs) are crucial in the field of artificial intelligence and are widely used in downstream tasks, such as question-answering (QA). The construction of KGs typical…
Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education
Rui Yang, Boming Yang, Sixun Ouyang +6
Knowledge graphs (KGs) are crucial in the field of artificial intelligence and are widely applied in downstream tasks, such as enhancing Question Answering (QA) systems. The constr…
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
Rui Yang, Boming Yang, Sixun Ouyang +6
In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, partic…
XDLM: Cross-lingual Diffusion Language Model for Machine Translation
Linyao Chen, Aosong Feng, Boming Yang +1
Recently, diffusion models have excelled in image generation tasks and have also been applied to neural language processing (NLP) for controllable text generation. However, the app…
HiPool: Modeling Long Documents Using Graph Neural Networks
Irene Li, Aosong Feng, Dragomir Radev +1
Encoding long sequences in Natural Language Processing (NLP) is a challenging problem. Though recent pretraining language models achieve satisfying performances in many NLP tasks,…
A Quantitative Theory of Bottleneck Structures for Data Networks
Jordi Ros-Giralt, Noah Amsel, Sruthi Yellamraju +5
The conventional view of the congestion control problem in data networks is based on the principle that a flow's performance is uniquely determined by the state of its bottleneck l…
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
Aosong Feng, Irene Li, Yuang Jiang +1
Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity. Sparse Transformer is a popular approach to improving t…
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
Aosong Feng, Leandros Tassiulas
Traffic flow forecasting on graphs has real-world applications in many fields, such as transportation system and computer networks. Traffic forecasting can be highly challenging du…
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Aosong Feng, Chenyu You, Shiqiang Wang +1
Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted com…
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Chenyu You, Lianyi Han, Aosong Feng +3
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence. Inspir…
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Irene Li, Aosong Feng, Hao Wu +3
Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider…
Energy-efficient and Robust Cumulative Training with Net2Net Transformation
Aosong Feng, Priyadarshini Panda
Deep learning has achieved state-of-the-art accuracies on several computer vision tasks. However, the computational and energy requirements associated with training such deep neura…