7.1k citations · 8.9k across the 43 of their papers we have counts for
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Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses
Mucong Ding, Kai Yang, Dit-Yan Yeung +1
The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictiv…
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
Xingjian Shi, Dit-Yan Yeung
Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence F…
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie +3
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all at…
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-w…
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to…
Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis
Zhourong Chen, Nevin L. Zhang, Dit-Yan Yeung +1
We are interested in exploring the possibility and benefits of structure learning for deep models. As the first step, this paper investigates the matter for Restricted Boltzmann Ma…
Collaborative Deep Learning for Recommender Systems
Hao Wang, Naiyan Wang, Dit-Yan Yeung
Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole s…
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
Yu Zhang, Dit-Yan Yeung
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we pro…