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
11 papers · 1 filter
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Shan An, Guangfu Che, Fangru Zhou +3
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently u…
CFS: A Distributed File System for Large Scale Container Platforms
Haifeng Liu, Wei Ding, Yuan Chen +7
We propose CFS, a distributed file system for large scale container platforms. CFS supports both sequential and random file accesses with optimized storage for both large files and…
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
Yikai Wang, Liang Zhang, Quanyu Dai +5
Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models…
Deep Social Collaborative Filtering
Wenqi Fan, Yao Ma, Dawei Yin +3
Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via t…
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
Zhibin Li, Jian Zhang, Qiang Wu +3
Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant…
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Pengcheng Li, Jinfeng Yi, Bowen Zhou +1
Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of…