515 citations · 1.2k across the 19 of their papers we have counts for
11 papers · 1 filter
On the optimization and generalization of overparameterized implicit neural networks
Tianxiang Gao, Hongyang Gao
Implicit neural networks have become increasingly attractive in the machine learning community since they can achieve competitive performance but use much less computational resour…
Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths
Tianxiang Gao, Hongyang Gao
Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep netw…
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
Zhaoning Yu, Hongyang Gao
We consider feature representation learning problem of molecular graphs. Graph Neural Networks have been widely used in feature representation learning of molecular graphs. However…
MotifExplainer: a Motif-based Graph Neural Network Explainer
Zhaoning Yu, Hongyang Gao
We consider the explanation problem of Graph Neural Networks (GNNs). Most existing GNN explanation methods identify the most important edges or nodes but fail to consider substruct…
A global convergence theory for deep ReLU implicit networks via over-parameterization
Tianxiang Gao, Hailiang Liu, Jia Liu +2
Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectu…
Topology-Aware Graph Pooling Networks
Hongyang Gao, Yi Liu, Shuiwang Ji
Pooling operations have shown to be effective on computer vision and natural language processing tasks. One challenge of performing pooling operations on graph data is the lack of…
Towards Deeper Graph Neural Networks
Meng Liu, Hongyang Gao, Shuiwang Ji
Graph neural networks have shown significant success in the field of graph representation learning. Graph convolutions perform neighborhood aggregation and represent one of the mos…
Graph Representation Learning via Hard and Channel-Wise Attention Networks
Hongyang Gao, Shuiwang Ji
Attention operators have been widely applied in various fields, including computer vision, natural language processing, and network embedding learning. Attention operators on graph…
Graph U-Nets
Hongyang Gao, Shuiwang Ji
We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with gr…
Large-Scale Learnable Graph Convolutional Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs.…
Pixel Deconvolutional Networks
Hongyang Gao, Hao Yuan, Zhengyang Wang +1
Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models fo…