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Efficient Link Prediction via GNN Layers Induced by Negative Sampling
Yuxin Wang, Xiannian Hu, Quan Gan +3
Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each…
From Hypergraph Energy Functions to Hypergraph Neural Networks
Yuxin Wang, Quan Gan, Xipeng Qiu +2
Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a v…
What Dense Graph Do You Need for Self-Attention?
Yuxin Wang, Chu-Tak Lee, Qipeng Guo +4
Transformers have made progress in miscellaneous tasks, but suffer from quadratic computational and memory complexities. Recent works propose sparse Transformers with attention on…
Learning to Teach with Student Feedback
Yitao Liu, Tianxiang Sun, Xipeng Qiu +1
Knowledge distillation (KD) has gained much attention due to its effectiveness in compressing large-scale pre-trained models. In typical KD methods, the small student model is trai…
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis
Minlong Peng, Qi Zhang, Xuanjing Huang
Cross-domain sentiment analysis is currently a hot topic in the research and engineering areas. One of the most popular frameworks in this field is the domain-invariant representat…