515 citations · 1.2k across the 19 of their papers we have counts for
19 papers
A new direct detection electron scattering experiment to search for the X17 particle
D. Dutta, H. Gao, A. Gasparian +57
A new electron scattering experiment (E12-21-003) to verify and understand the nature of hidden sector particles, with particular emphasis on the so-called X17 particle, has been a…
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
Benjamin Steenhoek, Hongyang Gao, Wei Le
Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing approaches use tok…
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…
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence
Hongyang Gao, Yi Liu, Xuan Zhang +1
We study text representation methods using deep models. Current methods, such as word-level embedding and character-level embedding schemes, treat texts as either a sequence of ato…
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…
Kronecker Attention Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Attention operators have been applied on both 1-D data like texts and higher-order data such as images and videos. Use of attention operators on high-order data requires flattening…
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…
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
Hongyang Gao, Yongjun Chen, Shuiwang Ji
With the development of graph convolutional networks (GCN), deep learning methods have started to be used on graph data. In additional to convolutional layers, pooling layers are a…
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in emplo…
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.…
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Hongyang Gao, Shuiwang Ji
Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translation…
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation
Lei Cai, Hongyang Gao, Shuiwang Ji
Variational auto-encoder (VAE) is a powerful unsupervised learning framework for image generation. One drawback of VAE is that it generates blurry images due to its Gaussianity ass…
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…