Peer reviewed publications

  • Z. Chen and W. Zhu, "On the implicit bias of linear equivariant steerable networks," Neural Information Processing Systems (NeurIPS). (2023) [link]
  • Z. Chen, M. Katsoulakis, L. Rey-Bellet, and W. Zhu, "Sample complexity of probability divergences under group symmetry," International Conference on Machine Learning (ICML). (2023) [link]
  • W. Zhu, H. Zhang, and P.G. Kevrekidis, "Machine learning of independent conservation laws through neural deflation," Physical Review E. (2023) [link]
  • S. Saqlain, W. Zhu, E.G.Charalampidis, and P.G. Kevrekidis, "Discovering governing equations in discrete systems using PINNs," Communications in Nonlinear Science and Numerical Simulation . (2023) [link]
  • Z. Gao, L. Harrington, W. Zhu, L. Barrientos, C. Alfonso-Parra, F. Avila, J. Clark, and L. He, "Accurate age-grading of field-collected mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks," Journal of Medical Entomology. (2023) [link]
  • J. Birrell, M. Katsoulakis, L. Rey-Bellet, and W. Zhu, "Structure-preserving GANs," International Conference on Machine Learning (ICML). (2022) [link]
  • L. Gao, G. Lin, and W. Zhu, "Deformation robust roto-scale-translation equivariant CNNs," Transactions on Machine Learning Research (TMLR), (2022) [link]
  • W. Zhu, W. Khademi, E.G. Charalampidis, and P.G. Kevrekidis, "Neural networks enforcing physical symmetries in nonlinear dynamical lattices: the case example of the Ablowitz-Ladik model," Physica D: Nonlinear Phenomena, (2022) [link]
  • W. Zhu, Q. Qiu, R. Calderbank, G. Sapiro, and X. Cheng, "Scaling-translation-equivariant networks with decomposed convolutional filters," Journal of Machine Learning Research (JMLR), (2022) [link]
  • B. Wang, AT. Lin, Z. Shi, W. Zhu, P. Yin, A. Bertozzi, and S. Osher, "Adversarial defense via data dependent activation function and total variation minimization," Inverse Problems and Imaging, (2020) [link]
  • W. Zhu, Z. Shi, and S. Osher, "Low dimensional manifold model in hyperspectral image reconstruction," Advances in Computer Vision and Pattern Recognition. Springer, Cham. (2020) [.pdf]
  • W. Zhu, Q. Qiu, B. Wang, J. Lu, G. Sapiro, and I. Daubechies, "Stop memorizing: a data-dependent regularization framework for intrinsic pattern learning," SIAM Journal on Mathematics of Data Science, (2019) [link]
  • Z. Wu, W. Zhu, J. Chanussot, Y. Xu, and S. Osher, "Hyperspectral anomaly detection via global and local joint modeling of background," IEEE Transactions on Signal Processing, (2019) [link]
  • B. Wang, X. Luo, Z. Li, W. Zhu, Z. Shi, and S. Osher, "Deep neural nets with interpolating function as output activation," 32nd Conference on Neural Information Processing Systems (NeurIPS), (2018) [link]
  • W. Zhu*, Z. Shi*, and S. Osher, "Scalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images," IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), (2018) [.pdf]
  • W. Zhu, Q. Qiu, J. Huang, R. Calderbank, G. Sapiro, and I. Daubechies, "LDMNet: Low dimensional manifold regularized neural networks," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2018) [link]
  • W. Zhu, B. Wang, R. Barnard, C. Hauck, F. Jenko, and S. Osher, "Scientific data interpolation with low dimensional manifold model," Journal of Computational Physics, (2018) [link]
  • Z. Shi, S. Osher, and W. Zhu, "Generalization of the weighted nonlocal laplacian in low dimensional manifold model," Journal of Scientific Computing, (2018) [link]
  • W. Zhu, V. Chayes, A. Tiard, S. Sanchez, D. Dahlberg, A. Bertozzi, S. Osher, D. Zosso, and D. Kuang, "Unsupervised classification in hyperspectral imagery with nonlocal total variation and primal-dual hybrid gradient algorithm," IEEE Transactions on Geoscience and Remote Sensing, (2017) [link]
  • Z. Shi, S. Osher, and W. Zhu, "Weighted nonlocal laplacian on interpolation from sparse data," Journal of Scientific Computing, (2017) [link]
  • V. Chayes, K. Miller, R. Bhalerao, J. Luo, W. Zhu, A. Bertozzi, W. Liao, and S. Osher, "Pre-processing and classification of hyperspectral imagery via selective inpainting presentation," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2017) [.pdf]
  • S Osher, Z. Shi, and W. Zhu, "Low dimensional manifold model for image processing," SIAM Journal on Imaging Sciences, (2017) [link]

Preprints

  • J. Birrell, M. Katsoulakis, L. Rey-Bellet, B. Zhang, and W. Zhu, "Nonlinear denoising score matching for enhanced learning of structured distributions." (2024) [link]
  • Z. Chen, H. Gu, M. Katsoulakis, L. Rey-Bellet, and W. Zhu, "Learning heavy-tailed distributions with Wasserstein-proximal-regularized -divergences." (2024) [link]
  • S. Yang, S. Chen, W. Zhu, and P.G. Kevrekidis, "Identification of moment equations via data-driven approaches in nonlinear Schrodinger models." (2024)
  • Z. Chen, M. Katsoulakis, L. Rey-Bellet, and W. Zhu, "Statistical guarantees of group-invariant GANs." (2023) [link]
  • W. Li, Y. Zhang, L. He, and W. Zhu, "Machine learning-assisted bacterial cell quantification in low-magnification microscopic imagery." (2023)
  • W. Zhu and I. Daubechies, "Constructing curvelet-like bases and low-redundancy frames." (2019) [link]