| Graph convolutional neural networks via scattering D Zou, G Lerman Applied and Computational Harmonic Analysis 49 (3), 1046-1074, 2020 | 114 | 2020 |
| Robust subspace recovery layer for unsupervised anomaly detection CH Lai, D Zou, G Lerman International Conference on Learning Representations (ICLR 2020), 1-28, 2020 | 75 | 2020 |
| On lipschitz bounds of general convolutional neural networks D Zou, R Balan, M Singh IEEE Transactions on Information Theory 66 (3), 1738-1759, 2019 | 61 | 2019 |
| Lipschitz properties for deep convolutional networks R Balan, M Singh, D Zou Contemporary Mathematics 706, 129-151, 2018 | 46 | 2018 |
| On Lipschitz analysis and Lipschitz synthesis for the phase retrieval problem R Balan, D Zou Linear Algebra and its Applications 496, 152-181, 2016 | 38 | 2016 |
| Encoding robust representation for graph generation D Zou, G Lerman 2019 International Joint Conference on Neural Networks (IJCNN), 1-9, 2019 | 29 | 2019 |
| On Lipschitz inversion of nonlinear redundant representations R Balan, D Zou Contemporary Mathematics 650, 15-22, 2015 | 19* | 2015 |
| Robust variational autoencoding with wasserstein penalty for novelty detection CH Lai, D Zou, G Lerman International Conference on Artificial Intelligence and Statistics, 2023 | 13* | 2023 |
| Regularized variational data assimilation for bias treatment using the Wasserstein metric SK Tamang, A Ebtehaj, D Zou, G Lerman Quarterly Journal of the Royal Meteorological Society 146 (730), 2332-2346, 2020 | 11 | 2020 |
| Ensemble Riemannian data assimilation over the Wasserstein space SK Tamang, A Ebtehaj, PJ Van Leeuwen, D Zou, G Lerman Nonlinear Processes in Geophysics Discussions 2021, 1-26, 2021 | 9 | 2021 |
| Interpretability-aware industrial anomaly detection using autoencoders R Jiang, Y Xue, D Zou IEEE Access 11, 60490-60500, 2023 | 8 | 2023 |
| Enhancing Node-Level Adversarial Defenses by Lipschitz Regularization of Graph Neural Networks Y Jia, D Zou, H Wang, H Jin KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge …, 2023 | 7 | 2023 |
| Robust vector quantized-variational autoencoder CH Lai, D Zou, G Lerman arXiv preprint arXiv:2202.01987, 2022 | 6 | 2022 |
| Graph neural network-based node deployment for throughput enhancement Y Yang, D Zou, X He IEEE Transactions on Neural Networks and Learning Systems, 2023 | 4 | 2023 |
| Lorentzian Fully Hyperbolic Generative Adversarial Network E Qu, D Zou arXiv preprint arXiv:2201.12825, 2022 | 4* | 2022 |
| Systems and methods for optimized computer vision using deep neural networks and Litpschitz analysis R Balan, MK Singh, D Zou US Patent 10,839,253, 2020 | 4 | 2020 |
| Detection and localization of image and document forgery: Survey and benchmarking A Ghosh, D Zou, M Singh, V Analytics Detection and localization of image and document forgery: Survey and …, 2016 | 3 | 2016 |
| Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks J Gu, D Zou Learning on Graphs Conference, 14: 1-14: 29, 2024 | 1 | 2024 |
| Interpretable Graph Anomaly Detection using Gradient Attention Maps Y Yang, P Wang, X He, D Zou arXiv preprint arXiv:2311.06153, 2023 | 1 | 2023 |
| Hyperbolic Convolution via Kernel Point Aggregation E Qu, D Zou arXiv preprint arXiv:2306.08862, 2023 | 1 | 2023 |
