59 citations · 115 across the 12 of their papers we have counts for
13 papers
Cross-Resolution Semantic Learning for Graph Domain Adaptation
Yingxu Wang, Haoze Huang, Zhongkai Zheng +1
Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing methods align representations…
Improving the Expressiveness of -hop Message-Passing GNNs by Injecting Contextualized Substructure Information
Tianjun Yao, Yiongxu Wang, Kun Zhang +1
Graph neural networks (GNNs) have become the \textit{de facto} standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-relate…
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Jinpeng Wang, Bin Chen, Qiang Zhang +3
Deep quantization methods have shown high efficiency on large-scale image retrieval. However, current models heavily rely on ground-truth information, hindering the application of…
CLEX: Continuous Length Extrapolation for Large Language Models
Guanzheng Chen, Xin Li, Zaiqiao Meng +2
Transformer-based Large Language Models (LLMs) are pioneering advances in many natural language processing tasks, however, their exceptional capabilities are restricted within the…
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
Muhammad Arslan Manzoor, Sarah Albarri, Ziting Xian +3
Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variet…
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Jiahang Cao, Jinyuan Fang, Zaiqiao Meng +1
Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wi…
Revisiting Parameter-Efficient Tuning: Are We Really There Yet?
Guanzheng Chen, Fangyu Liu, Zaiqiao Meng +1
Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs). By tuning just a fraction amount of paramete…
Learning to Detect Few-Shot-Few-Clue Misinformation
Qiang Zhang, Hongbin Huang, Shangsong Liang +2
The quality of digital information on the web has been disquieting due to the lack of careful manual review. Consequently, a large volume of false textual information has been diss…
Addressing Class-Imbalance Problem in Personalized Ranking
Lu Yu, Shichao Pei, Chuxu Zhang +4
Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into \emph{…
Semi-supervisedly Co-embedding Attributed Networks
Zaiqiao Meng, Shangsong Liang, Jinyuan Fang +1
Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised variational auto-encoders (SVAE) as a classical DGM offer a principled framework to effectively gen…
Variational Self-attention Model for Sentence Representation
Qiang Zhang, Shangsong Liang, Emine Yilmaz
This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention. We model the self-attention vector as random variables by…
Neural Variational Hybrid Collaborative Filtering
Teng Xiao, Shangsong Liang, Hong Shen +1
Collaborative Filtering (CF) is one of the most used methods for Recommender System. Because of the Bayesian nature and nonlinearity, deep generative models, e.g. Variational Autoe…
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
Xisen Jin, Wenqiang Lei, Zhaochun Ren +4
The task of dialogue generation aims to automatically provide responses given previous utterances. Tracking dialogue states is an important ingredient in dialogue generation for es…