Efficient and Invariant Convolutional Neural Networks for Dense Prediction. - Hongyang Gao; Shuiwang Ji (ICDM, 2017)

Pixel Deconvolutional Networks. - Hongyang Gao; Hao Yuan 001; Zhengyang Wang; Shuiwang Ji (CoRR, 2017)

Deep Adversarial Learning for Multi-Modality Missing Data Completion. - Lei Cai; Zhengyang Wang; Hongyang Gao; Dinggang Shen; Shuiwang Ji (KDD, 2018)

Voxel Deconvolutional Networks for 3D Brain Image Labeling. - Yongjun Chen; Hongyang Gao; Lei Cai; Min Shi; Dinggang Shen; Shuiwang Ji (KDD, 2018)

Large-Scale Learnable Graph Convolutional Networks. - Hongyang Gao; Zhengyang Wang; Shuiwang Ji (KDD, 2018)

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. - Hongyang Gao; Zhengyang Wang; Shuiwang Ji (NeurIPS, 2018)

Graph Representation Learning via Hard and Channel-Wise Attention Networks. - Hongyang Gao; Shuiwang Ji (KDD, 2019)

Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation. - Lei Cai; Hongyang Gao; Shuiwang Ji (SDM, 2019)

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. - Hongyang Gao; Yongjun Chen; Shuiwang Ji (WWW, 2019)

Dynamic Multi-Objective Dispatch Considering Wind Power and Electric Vehicles With Probabilistic Characteristics. - Yongsheng Zhu; Hongyang Gao; Junming Xiao; Boyang Qu; Fanbing Zhu; Lu Yang (IEEE Access, 2019)

Adaptive Convolutional ReLUs. - Hongyang Gao; Lei Cai; Shuiwang Ji (AAAI, 2020)

Circuit parameter optimization of class E power amplifier for wireless power transmission applications. - Kun Li; Junyou Liu; Shijie Zhang; Yang Liu; Peng Wang; Hongyang Gao (EITCE, 2020)

Design of Wireless Power Transmission System with Double D-coil. - Kun Li; Shijie Zhang; Qing Liu; Junyou Liu; Peng Wang; Hongyang Gao; Heming Wang; Hongzheng Wang (EITCE, 2020)

A low-power and low-cost battery equalizing circuit topology. - Yang Liu; Daolian Jiang; Kun Li; Changsong Wang; Yongjian Liu; Lei Wang; Peng Wang; Hongyang Gao (EITCE, 2020)

Kronecker Attention Networks. - Hongyang Gao; Zhengyang Wang; Shuiwang Ji (KDD, 2020)

Towards Deeper Graph Neural Networks. - Meng Liu 015; Hongyang Gao; Shuiwang Ji (KDD, 2020)

Pixel Transposed Convolutional Networks. - Hongyang Gao; Hao Yuan 001; Zhengyang Wang; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence. - Hongyang Gao; Yi Liu 059; Xuan Zhang; Shuiwang Ji (CoRR, 2021)

Topology-Aware Graph Pooling Networks. - Hongyang Gao; Yi Liu 059; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. - Hongyang Gao; Zhengyang Wang; Lei Cai; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

A global convergence theory for deep ReLU implicit networks via over-parameterization. - Tianxiang Gao; Hailiang Liu; Jia Liu 002; Hridesh Rajan; Hongyang Gao (ICLR, 2022)

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks. - Zhaoning Yu; Hongyang Gao (ICML, 2022)

MotifExplainer: a Motif-based Graph Neural Network Explainer. - Zhaoning Yu; Hongyang Gao (CoRR, 2022)

Molecular Graph Representation Learning via Heterogeneous Motif Graph Construction. - Zhaoning Yu; Hongyang Gao (CoRR, 2022)

Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths. - Tianxiang Gao; Hongyang Gao (CoRR, 2022)

On the optimization and generalization of overparameterized implicit neural networks. - Tianxiang Gao; Hongyang Gao (CoRR, 2022)

DeepDFA: Dataflow Analysis-Guided Efficient Graph Learning for Vulnerability Detection. - Benjamin Steenhoek; Wei Le; Hongyang Gao (CoRR, 2022)

Graph U-Nets. - Hongyang Gao; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

HSELDA: Heterogeneous Sub-Graph Learning for lncRNA-Disease Associations Prediction. - Tiancheng Zhou; Zachary Glanz; Mei Liu; Jiang Bian 001; Rui Yin; Hongyang Gao (BIBM, 2023)

Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models. - Tianxiang Gao; Xiaokai Huo; Hailiang Liu; Hongyang Gao (NeurIPS, 2023)

Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning. - Siyuan Sun; Hongyang Gao (NeurIPS, 2023)

MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning. - Zhaoning Yu; Hongyang Gao (CoRR, 2023)

High-Frequency Normalizing Flow for Image Rescaling. - Yiming Zhu; Cairong Wang; Chenyu Dong; Ke Zhang; Hongyang Gao; Chun Yuan (IEEE Trans. Image Process., 2023)

Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment. - Shibbir Ahmed; Hongyang Gao; Hridesh Rajan (ICSE, 2024)

Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection. - Benjamin Steenhoek; Hongyang Gao; Wei Le (ICSE, 2024)

G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models. - Zhaoning Yu; Xiangyang Xu; Hongyang Gao (CoRR, 2024)

Global Convergence in Neural ODEs: Impact of Activation Functions. - Tianxiang Gao; Siyuan Sun; Hailiang Liu; Hongyang Gao (ICLR, 2025)

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation. - Zhaoning Yu; Hongyang Gao (ICLR, 2025)