4.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Peking UniversityCN85 papers
- Microsoft (United States)US82 papers
- Tsinghua UniversityCN81 papers
- University of Science and Technology of ChinaCN81 papers
- Hong Kong University of Science and TechnologyHK40 papers
- Beihang UniversityCN36 papers
- Chinese Academy of SciencesCN36 papers
- Shanghai Jiao Tong UniversityCN33 papers
- Microsoft Research (United Kingdom)GB28 papers
- Zhejiang UniversityCN28 papers
- Fudan UniversityCN24 papers
- Carnegie Mellon UniversityUS19 papers
14 papers · 2 filters
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Yujing Wang, Yaming Yang, Yiren Chen +7
Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how…
Transfer Learning with Dynamic Adversarial Adaptation Network
Chaohui Yu, Jindong Wang, Yiqiang Chen +1
The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution dis…
Transfer Learning with Dynamic Distribution Adaptation
Jindong Wang, Yiqiang Chen, Wenjie Feng +3
Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from diff…
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
Zhining Liu, Wei Cao, Zhifeng Gao +4
Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scal…
Factorization Bandits for Online Influence Maximization
Qingyun Wu, Zhige Li, Huazheng Wang +2
We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with…
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems
Xiao Zhou, Danyang Liu, Jianxun Lian +1
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback d…
Connectivity-Optimized Representation Learning via Persistent Homology
Christoph Hofer, Roland Kwitt, Mandar Dixit +1
We study the problem of learning representations with controllable connectivity properties. This is beneficial in situations when the imposed structure can be leveraged upstream. I…
Time-Series Anomaly Detection Service at Microsoft
Hansheng Ren, Bixiong Xu, Yujing Wang +7
Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomal…
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Yihong Chen, Bei Chen, Xiangnan He +4
Recommendation models mainly deal with categorical variables, such as user/item ID and attributes. Besides the high-cardinality issue, the interactions among such categorical varia…
Transcribing Content from Structural Images with Spotlight Mechanism
Yu Yin, Zhenya Huang, Enhong Chen +4
Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal str…
A Distributed Approach towards Discriminative Distance Metric Learning
Jun Li, Xun Lin, Xiaoguang Rui +2
Distance metric learning is successful in discovering intrinsic relations in data. However, most algorithms are computationally demanding when the problem size becomes large. In th…
Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization
Jingyuan Wang, Junjie Wu, Ze Wang +2
Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becom…
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
Shicong Cen, Huishuai Zhang, Yuejie Chi +2
Stochastic variance reduced methods have gained a lot of interest recently for empirical risk minimization due to its appealing run time complexity. When the data size is large and…
Stanza: Layer Separation for Distributed Training in Deep Learning
Xiaorui Wu, Hong Xu, Bo Li +1
The parameter server architecture is prevalently used for distributed deep learning. Each worker machine in a parameter server system trains the complete model, which leads to a he…