Dongmian Zou

ORCID

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 26

Top Venues

CoRR

AISTATS

KDD

IEEE Trans. Inf. Theory

University of Maryland, College Park, MD, USA (PHD)

Education History

University of Maryland, College Park, MD, USA

PHD

Publications

Yifei Yang, Wonjun Lee, Dongmian Zou, Gilad Lerman
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization. CoRR (2024)
Yuekang Li, Yidan Mao, Yifei Yang, Dongmian Zou
Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis. KDD (2024)
Yifei Yang, Peng Wang, Xiaofan He, Dongmian Zou
GRAM: An interpretable approach for graph anomaly detection using gradient attention maps. Neural Networks 178 (2024)
Jing Gu, Dongmian Zou
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. CoRR (2024)
Hongfei Wang, Ziqiang Zhang, Hongcan Xiong, Dongmian Zou, Yu Chen, Hai Jin
GRAND: A Graph Neural Network Framework for Improved Diagnosis. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43 (4) (2024)
Yaning Jia, Dongmian Zou, Hongfei Wang, Hai Jin
Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks. KDD (2023)
Wonjun Lee, Yifei Yang, Dongmian Zou, Gilad Lerman
Monotone Generative Modeling via a Gromov-Monge Embedding. CoRR (2023)
Yinglong Guo, Dongmian Zou, Gilad Lerman
An Unpooling Layer for Graph Generation. AISTATS (2023)
Eric Qu, Dongmian Zou
Hyperbolic Convolution via Kernel Point Aggregation. CoRR (2023)
Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection. AISTATS (2023)
Jing Gu, Dongmian Zou
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. LoG (2023)
Rui Jiang, Yijia Xue, Dongmian Zou
Interpretability-Aware Industrial Anomaly Detection Using Autoencoders. IEEE Access 11 (2023)
Yifei Yang, Peng Wang, Xiaofan He, Dongmian Zou
Interpretable Graph Anomaly Detection using Gradient Attention Maps. CoRR (2023)
Yifei Yang, Dongmian Zou, Xiaofan He
Graph Neural Network Based Node Deployment for Throughput Enhancement. CoRR (2022)
Eric Qu, Dongmian Zou
Hyperbolic Neural Networks for Molecular Generation. CoRR (2022)
Yinglong Guo, Dongmian Zou, Gilad Lerman
An Unpooling Layer for Graph Generation. CoRR (2022)
Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Robust Vector Quantized-Variational Autoencoder. CoRR (2022)
Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Novelty Detection via Robust Variational Autoencoding. CoRR (2020)
Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection. ICLR (2020)
Dongmian Zou, Radu V. Balan, Maneesh Singh
On Lipschitz Bounds of General Convolutional Neural Networks. IEEE Trans. Inf. Theory 66 (3) (2020)
Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection. CoRR (2019)
Dongmian Zou, Gilad Lerman
Encoding robust representation for graph generation. IJCNN (2019)
Dongmian Zou, Gilad Lerman
Graph Generation via Scattering. CoRR (2018)
Dongmian Zou, Radu V. Balan, Maneesh Singh
On Lipschitz Bounds of General Convolutional Neural Networks. CoRR (2018)
Dongmian Zou, Gilad Lerman
Graph Convolutional Neural Networks via Scattering. CoRR (2018)
Radu V. Balan, Maneesh Singh, Dongmian Zou
Lipschitz Properties for Deep Convolutional Networks. CoRR (2017)
Radu V. Balan, Dongmian Zou
Phase Retrieval using Lipschitz Continuous Maps. CoRR (2014)

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Publication Activity (10 Years)

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Dongmian Zou