4 citations · 9 across the 8 of their papers we have counts for
5 papers · 1 filter
Exploiting Field Dependencies for Learning on Categorical Data
Zhibin Li, Piotr Koniusz, Lu Zhang +2
Traditional approaches for learning on categorical data underexploit the dependencies between columns (\aka fields) in a dataset because they rely on the embedding of data points d…
Exploring Linear Feature Disentanglement For Neural Networks
Tiantian He, Zhibin Li, Yongshun Gong +3
Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs). Due to the complex non-linear characteristic of samples, the o…
Field-wise Learning for Multi-field Categorical Data
Zhibin Li, Jian Zhang, Yongshun Gong +2
We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect…
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
Yongshun Gong, Zhibin Li, Jian Zhang +2
Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world de…
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…