12 citations · 27 across the 5 of their papers we have counts for
6 papers
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding, Kezhi Kong, Jingling Li +4
Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To sca…
Understanding Overparameterization in Generative Adversarial Networks
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat +4
A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of th…
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences
Mucong Ding, Constantinos Daskalakis, Soheil Feizi
Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs have been widely used in sample synthesis, de-noising, domain tra…
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…
Transfer Learning using Representation Learning in Massive Open Online Courses
Mucong Ding, Yanbang Wang, Erik Hemberg +1
In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoi…
First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation
Mucong Ding, Kwok Yip Szeto
We obtain an exact formula for the first-passage time probability distribution for random walks on complex networks using inverse Laplace transform. We write the formula as the sum…