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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…
Dynamic Key-Value Memory Networks for Knowledge Tracing
Jiani Zhang, Xingjian Shi, Irwin King +1
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One impor…
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Hao Wang, Dit-Yan Yeung
While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning…
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…
A Survey on Bayesian Deep Learning
Hao Wang, Dit-Yan Yeung
A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional…