Phase Retrieval using Lipschitz Continuous Maps. - Radu V. Balan; Dongmian Zou (CoRR, 2014)

Lipschitz Properties for Deep Convolutional Networks. - Radu V. Balan; Maneesh Singh 001; Dongmian Zou (CoRR, 2017)

Nonlinear Analysis of Phase Retrieval and Deep Learning. - Dongmian Zou (2017)

Graph Convolutional Neural Networks via Scattering. - Dongmian Zou; Gilad Lerman (CoRR, 2018)

Graph Generation via Scattering. - Dongmian Zou; Gilad Lerman (CoRR, 2018)

Encoding robust representation for graph generation. - Dongmian Zou; Gilad Lerman (IJCNN, 2019)

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection. - Chieh-Hsin Lai; Dongmian Zou; Gilad Lerman (ICLR, 2020)

Novelty Detection via Robust Variational Autoencoding. - Chieh-Hsin Lai; Dongmian Zou; Gilad Lerman (CoRR, 2020)

On Lipschitz Bounds of General Convolutional Neural Networks. - Dongmian Zou; Radu V. Balan; Maneesh Singh 001 (IEEE Trans. Inf. Theory, 2020)

Hyperbolic Neural Networks for Molecular Generation. - Eric Qu; Dongmian Zou (CoRR, 2022)

Graph Neural Network Based Node Deployment for Throughput Enhancement. - Yifei Yang; Dongmian Zou; Xiaofan He (CoRR, 2022)

An Unpooling Layer for Graph Generation. - Yinglong Guo; Dongmian Zou; Gilad Lerman (AISTATS, 2023)

Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection. - Chieh-Hsin Lai; Dongmian Zou; Gilad Lerman (AISTATS, 2023)

Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks. - Yaning Jia; Dongmian Zou; Hongfei Wang; Hai Jin 001 (KDD, 2023)

Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. - Jing Gu; Dongmian Zou (LoG, 2023)

Interpretability-Aware Industrial Anomaly Detection Using Autoencoders. - Rui Jiang; Yijia Xue; Dongmian Zou (IEEE Access, 2023)

Hyperbolic Convolution via Kernel Point Aggregation. - Eric Qu; Dongmian Zou (CoRR, 2023)

Monotone Generative Modeling via a Gromov-Monge Embedding. - Wonjun Lee 004; Yifei Yang 001; Dongmian Zou; Gilad Lerman (CoRR, 2023)

Interpretable Graph Anomaly Detection using Gradient Attention Maps. - Yifei Yang; Peng Wang; Xiaofan He; Dongmian Zou (CoRR, 2023)

Improving Hyperbolic Representations via Gromov-Wasserstein Regularization. - Yifei Yang 001; Wonjun Lee 004; Dongmian Zou; Gilad Lerman (ECCV (82), 2024)

TopoUT: Enhancing Cell Segmentation Through Efficient Topological Regularization. - Shanru Lin; Temirlan Sabyrbayev; Zhexu Jin; Gaoyang Li; Huansheng Cao; Dongmian Zou (ISBI, 2024)

Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis. - Yuekang Li; Yidan Mao; Yifei Yang 001; Dongmian Zou (KDD, 2024)

Hyperbolic Kernel Convolution: A Generic Framework. - Eric Qu; Lige Zhang; Habib Debaya; Yue Wu; Dongmian Zou (LoG, 2024)

Klein Model for Hyperbolic Neural Networks. - Yidan Mao; Jing Gu; Marcus C. Werner; Dongmian Zou (CoRR, 2024)

GRAM: An interpretable approach for graph anomaly detection using gradient attention maps. - Yifei Yang 001; Peng Wang; Xiaofan He; Dongmian Zou (Neural Networks, 2024)

Autoencoding Hyperbolic Representation for Adversarial Generation. - Eric Qu; Dongmian Zou (Trans. Mach. Learn. Res., 2024)

Translating Test Responses to Images for Test-termination Prediction via Multiple Machine Learning Strategies. - Hongfei Wang; Jingyao Li; Jiayi Wang; Zijun Ping; Hongcan Xiong; Wei Liu 186; Dongmian Zou (ACM Trans. Design Autom. Electr. Syst., 2024)

GRAND: A Graph Neural Network Framework for Improved Diagnosis. - Hongfei Wang; Ziqiang Zhang; Hongcan Xiong; Dongmian Zou; Yu Chen; Hai Jin 001 (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2024)

Graph Neural Network-Based Node Deployment for Throughput Enhancement. - Yifei Yang 001; Dongmian Zou; Xiaofan He (IEEE Trans. Neural Networks Learn. Syst., 2024)

Geometry-Preserving Encoder/Decoder in Latent Generative Models. - Wonjun Lee 004; Riley C. W. O'Neill; Dongmian Zou; Jeff Calder; Gilad Lerman (CoRR, 2025)

Stein Discrepancy for Unsupervised Domain Adaptation. - Anneke von Seeger; Dongmian Zou; Gilad Lerman (CoRR, 2025)

Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection. - Shouju Wang; Yuchen Song; Sheng'en Li; Dongmian Zou (CoRR, 2025)