306 citations
- The University of SydneyAU10 papers
- University of Science and Technology of ChinaCN8 papers
- Chinese Academy of SciencesCN6 papers
- JDSU (United States)US6 papers
- University of Chinese Academy of SciencesCN5 papers
- Beihang UniversityCN4 papers
- Tsinghua UniversityCN4 papers
- Association for Computing MachineryUS3 papers
- Baidu (China)CN3 papers
- City University of Hong KongHK3 papers
- Institute of Computing TechnologyCN3 papers
- National University of SingaporeSG3 papers
17 papers · 1 filter
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
Yongcheng Jing, Yiding Yang, Xinchao Wang +2
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both th…
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder
Zhenyu Zhang, Tao Guo, Meng Chen
With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service t…
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
Lei Shen, Haolan Zhan, Xin Shen +3
Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better re…
ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones
Shan An, Guangfu Che, Jinghao Guo +7
Virtual try-on technology enables users to try various fashion items using augmented reality and provides a convenient online shopping experience. However, most previous works focu…
DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
Li Gao, Jing Zhang, Lefei Zhang +1
Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a segmentation model trained on the labeled source domain to the unlabeled target domain. Existing meth…