289 citations · 702 across the 8 of their papers we have counts for
9 papers
OCP: Orthogonal Constrained Projection for Sparse Scaling in Industrial Commodity Recommendation
Chen Sun, Beilin Xu, Boheng Tan +6
In industrial commodity recommendation systems, the representation quality of Item-Id vocabularies directly impacts the scalability and generalization ability of recommendation mod…
Constrained Reinforcement Learning for Short Video Recommendation
Qingpeng Cai, Ruohan Zhan, Chi Zhang +5
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and…
Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
Yucheng Lin, Huiting Hong, Xiaoqing Yang +3
Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/e…
A Survey on Machine Learning from Few Samples
Jiang Lu, Pinghua Gong, Jieping Ye +2
Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a notic…
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Huaxiu Yao, Fei Wu, Jintao Ke +6
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate re…
Linear Convergence of Variance-Reduced Stochastic Gradient without Strong Convexity
Pinghua Gong, Jieping Ye
Stochastic gradient algorithms estimate the gradient based on only one or a few samples and enjoy low computational cost per iteration. They have been widely used in large-scale op…
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
Pinghua Gong, Changshui Zhang, Zhaosong Lu +2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over…
Efficient Superimposition Recovering Algorithm
Han Li, Kun Gai, Pinghua Gong +1
In this article, we address the issue of recovering latent transparent layers from superimposition images. Here, we assume we have the estimated transformations and extracted gradi…
Multi-Stage Multi-Task Feature Learning
Pinghua Gong, Jieping Ye, Changshui Zhang
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applicatio…