| Lanczosnet: Multi-scale deep graph convolutional networks R Liao, Z Zhao, R Urtasun, RS Zemel arXiv preprint arXiv:1901.01484, 2019 | 299 | 2019 |
| Max-sliced wasserstein distance and its use for gans I Deshpande, YT Hu, R Sun, A Pyrros, N Siddiqui, S Koyejo, Z Zhao, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 233 | 2019 |
| Rotationally invariant image representation for viewing direction classification in cryo-EM Z Zhao, A Singer Journal of structural biology 186 (1), 153-166, 2014 | 128 | 2014 |
| Denoising gravitational waves with enhanced deep recurrent denoising auto-encoders H Shen, D George, EA Huerta, Z Zhao ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 122 | 2019 |
| Viewing angle classification of cryo-electron microscopy images using eigenvectors A Singer, Z Zhao, Y Shkolnisky, R Hadani SIAM Journal on Imaging Sciences 4 (2), 723-759, 2011 | 108 | 2011 |
| Bispectrum inversion with application to multireference alignment T Bendory, N Boumal, C Ma, Z Zhao, A Singer IEEE Transactions on signal processing 66 (4), 1037-1050, 2017 | 104 | 2017 |
| Analog forecasting with dynamics-adapted kernels Z Zhao, D Giannakis Nonlinearity 29 (9), 2888, 2016 | 103 | 2016 |
| Enabling real-time multi-messenger astrophysics discoveries with deep learning EA Huerta, G Allen, I Andreoni, JM Antelis, E Bachelet, GB Berriman, ... Nature Reviews Physics 1 (10), 600-608, 2019 | 88 | 2019 |
| Spatiotemporal feature extraction with data-driven Koopman operators D Giannakis, J Slawinska, Z Zhao Feature Extraction: Modern Questions and Challenges, 103-115, 2015 | 80 | 2015 |
| Fast steerable principal component analysis Z Zhao, Y Shkolnisky, A Singer IEEE transactions on computational imaging 2 (1), 1-12, 2016 | 76 | 2016 |
| Fourier–Bessel rotational invariant eigenimages Z Zhao, A Singer JOSA A 30 (5), 871-877, 2013 | 71 | 2013 |
| Gauge Invariant Autoregressive Neural Networks for Quantum Lattice Models D Luo, Z Chen, K Hu, Z Zhao, VM Hur, BK Clark arXiv preprint arXiv:2101.07243, 2021 | 63* | 2021 |
| Statistically-informed deep learning for gravitational wave parameter estimation H Shen, EA Huerta, E O’Shea, P Kumar, Z Zhao Machine Learning: Science and Technology 3 (1), 015007, 2021 | 41 | 2021 |
| FAIR for AI: An interdisciplinary and international community building perspective EA Huerta, B Blaiszik, LC Brinson, KE Bouchard, D Diaz, C Doglioni, ... Scientific data 10 (1), 487, 2023 | 40 | 2023 |
| Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms A Gupta, E Huerta, Z Zhao, I Moussa 8th European Medical and Biological Engineering Conference: Proceedings of …, 2021 | 40 | 2021 |
| Kernel analog forecasting of tropical intraseasonal oscillations R Alexander, Z Zhao, E Székely, D Giannakis Journal of the Atmospheric Sciences 74 (4), 1321-1342, 2017 | 33 | 2017 |
| Advances in machine and deep learning for modeling and real-time detection of multi-messenger sources EA Huerta, Z Zhao Handbook of Gravitational Wave Astronomy, 1793-1819, 2022 | 31 | 2022 |
| Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability D Comeau, Z Zhao, D Giannakis, AJ Majda Climate Dynamics 48, 1855-1872, 2017 | 29 | 2017 |
| A FAIR and AI-ready Higgs boson decay dataset Y Chen, EA Huerta, J Duarte, P Harris, DS Katz, MS Neubauer, D Diaz, ... Scientific Data 9 (1), 31, 2022 | 28 | 2022 |
| Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting D Comeau, D Giannakis, Z Zhao, AJ Majda Climate Dynamics 52, 5507-5525, 2019 | 28 | 2019 |
Amit SingerPrinceton UniversityBestätigte E-Mail-Adresse bei math.princeton.edu
