13 citations · 29 across the 6 of their papers we have counts for
6 papers
On the Equivalence between Neural Network and Support Vector Machine
Yilan Chen, Wei Huang, Lam M. Nguyen +1
Recent research shows that the dynamics of an infinitely wide neural network (NN) trained by gradient descent can be characterized by Neural Tangent Kernel (NTK) \citep{jacot2018ne…
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Wei Huang, Yayong Li, Weitao Du +4
Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from…
Implicit bias of deep linear networks in the large learning rate phase
Wei Huang, Weitao Du, Richard Yi Da Xu +1
Most theoretical studies explaining the regularization effect in deep learning have only focused on gradient descent with a sufficient small learning rate or even gradient flow (in…
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
Wei Huang, Weitao Du, Richard Yi Da Xu
The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training. The increase in learning speed that results from orthogonal…
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
Wei Huang, Richard Yi Da Xu
Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information. Compared to traditional recommender systems, CARS…
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply
Wei Huang, Richard Yi Da Xu, Weitao Du +2
In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural netw…