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Dongmian Zou

Assistant Professor of Data Science at Duke Kunshan University
DKU Faculty

Selected Publications


Recognition of Yuan blue and white porcelain produced in Jingdezhen based on graph anomaly detection combining portable X-ray fluorescence spectrometry

Journal Article Heritage Science · December 1, 2024 The blue and white porcelain produced in Jingdezhen during China’s Yuan Dynasty is an outstanding cultural heritage of ceramic art that has attracted wide attention for its identification. However, the traditional visual identification method is susceptibl ... Full text Cite

GRAND: A Graph Neural Network Framework for Improved Diagnosis

Journal Article IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · April 1, 2024 The pursuit of accurate diagnosis with good resolution is driven by yield learning during both early bring-up and production excursions. Unfortunately, fault callouts from diagnosis tools often render poor resolution that hinders the follow-up failure anal ... Full text Cite

Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks

Conference Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · August 6, 2023 Graph neural networks (GNNs) have shown considerable promise for graph-structured data. However, they are also known to be unstable and vulnerable to perturbations and attacks. Recently, the Lipschitz constant has been adopted as a control on the stability ... Full text Cite

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

Journal Article IEEE transactions on neural networks and learning systems · June 2023 The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, ... Full text Cite

An Unpooling Layer for Graph Generation

Conference Proceedings of Machine Learning Research · April 25, 2023 Link to item Cite

Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection

Conference Proceedings of Machine Learning Research · April 25, 2023 Link to item Cite

Interpretability-Aware Industrial Anomaly Detection Using Autoencoders

Journal Article IEEE Access · January 1, 2023 The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily r ... Full text Cite

Ensemble Riemannian data assimilation over the Wasserstein space

Journal Article Nonlinear Processes in Geophysics · July 6, 2021 In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the translat ... Full text Cite

Graph convolutional neural networks via scattering

Journal Article Applied and Computational Harmonic Analysis · November 1, 2020 We generalize the scattering transform to graphs and consequently construct a convolutional neural network on graphs. We show that under certain conditions, any feature generated by such a network is approximately invariant to permutations and stable to si ... Full text Cite

Regularized variational data assimilation for bias treatment using the Wasserstein metric

Journal Article Quarterly Journal of the Royal Meteorological Society · July 1, 2020 This article presents a new variational data assimilation (VDA) approach for the formal treatment of bias in both model outputs and observations. This approach relies on the Wasserstein metric, stemming from the theory of optimal mass transport, to penaliz ... Full text Open Access Cite

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

Conference · May 1, 2020 We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away fr ... Link to item Cite

Recognition of Yuan blue and white porcelain produced in Jingdezhen based on graph anomaly detection combining portable X-ray fluorescence spectrometry

Journal Article Heritage Science · December 1, 2024 The blue and white porcelain produced in Jingdezhen during China’s Yuan Dynasty is an outstanding cultural heritage of ceramic art that has attracted wide attention for its identification. However, the traditional visual identification method is susceptibl ... Full text Cite

GRAND: A Graph Neural Network Framework for Improved Diagnosis

Journal Article IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · April 1, 2024 The pursuit of accurate diagnosis with good resolution is driven by yield learning during both early bring-up and production excursions. Unfortunately, fault callouts from diagnosis tools often render poor resolution that hinders the follow-up failure anal ... Full text Cite

Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks

Conference Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · August 6, 2023 Graph neural networks (GNNs) have shown considerable promise for graph-structured data. However, they are also known to be unstable and vulnerable to perturbations and attacks. Recently, the Lipschitz constant has been adopted as a control on the stability ... Full text Cite

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

Journal Article IEEE transactions on neural networks and learning systems · June 2023 The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, ... Full text Cite

An Unpooling Layer for Graph Generation

Conference Proceedings of Machine Learning Research · April 25, 2023 Link to item Cite

Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection

Conference Proceedings of Machine Learning Research · April 25, 2023 Link to item Cite

Interpretability-Aware Industrial Anomaly Detection Using Autoencoders

Journal Article IEEE Access · January 1, 2023 The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily r ... Full text Cite

Ensemble Riemannian data assimilation over the Wasserstein space

Journal Article Nonlinear Processes in Geophysics · July 6, 2021 In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the translat ... Full text Cite

Graph convolutional neural networks via scattering

Journal Article Applied and Computational Harmonic Analysis · November 1, 2020 We generalize the scattering transform to graphs and consequently construct a convolutional neural network on graphs. We show that under certain conditions, any feature generated by such a network is approximately invariant to permutations and stable to si ... Full text Cite

Regularized variational data assimilation for bias treatment using the Wasserstein metric

Journal Article Quarterly Journal of the Royal Meteorological Society · July 1, 2020 This article presents a new variational data assimilation (VDA) approach for the formal treatment of bias in both model outputs and observations. This approach relies on the Wasserstein metric, stemming from the theory of optimal mass transport, to penaliz ... Full text Open Access Cite

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

Conference · May 1, 2020 We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away fr ... Link to item Cite

On Lipschitz Bounds of General Convolutional Neural Networks

Journal Article IEEE Transactions on Information Theory · March 1, 2020 Many convolutional neural networks (CNN's) have a feed-forward structure. In this paper, we model a general framework for analyzing the Lipschitz bounds of CNN's and propose a linear program that estimates these bounds. Several CNN's, including the scatter ... Full text Cite

Encoding robust representation for graph generation

Conference Proceedings of the International Joint Conference on Neural Networks · July 1, 2019 Generative networks have made it possible to generate meaningful signals such as images and texts from simple noise. Recently, generative methods based on GAN and VAE were developed for graphs and graph signals. However, the mathematical properties of thes ... Full text Cite

Lipschitz properties for deep convolutional networks

Chapter · January 1, 2018 In this paper we discuss the stability properties of convolutional neural networks. Convolutional neural networks are widely used in machine learning. In classification they are mainly used as feature extractors. Ideally, we expect similar features when th ... Full text Cite

On Lipschitz analysis and Lipschitz synthesis for the phase retrieval problem

Journal Article Linear Algebra and Its Applications · May 1, 2016 We prove two results with regard to reconstruction from magnitudes of frame coefficients (the so called "phase retrieval problem"). First we show that phase retrievable nonlinear maps are bi-Lipschitz with respect to appropriate metrics on the quotient spa ... Full text Open Access Cite

On Lipschitz inversion of nonlinear redundant representations

Chapter · October 27, 2015 This volume contains the proceedings of the AMS Special Session on Harmonic Analysis and Its Applications held March 29-30, 2014, at the University of Maryland, Baltimore County, Baltimore, MD. It provides an in depth look at the many ... ... Link to item Cite

Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks

Conference Proceedings of Machine Learning Research Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks. Despite advancements of methodology based on deep learning in recent years, existing benchmarking approaches exhibit limitations that hinder a comprehensive ... Link to item Cite