| Prevalence, awareness, treatment, and control of hypertension in China: data from 1· 7 million adults in a population-based screening study (China PEACE Million Persons Project) J Lu, Y Lu, X Wang, X Li, GC Linderman, C Wu, X Cheng, L Mu, H Zhang, ... The Lancet 390 (10112), 2549-2558, 2017 | 1082 | 2017 |
| Nucleation of ordered phases in block copolymers X Cheng, L Lin, W E, P Zhang, AC Shi Physical review letters 104 (14), 148301, 2010 | 135* | 2010 |
| The spectrum of random inner-product kernel matrices X Cheng, A Singer Random Matrices: Theory and Applications 2 (04), 1350010, 2013 | 111 | 2013 |
| Detection of differentially abundant cell subpopulations in scRNA-seq data J Zhao, A Jaffe, H Li, O Lindenbaum, E Sefik, R Jackson, X Cheng, ... Proceedings of the National Academy of Sciences 118 (22), e2100293118, 2021 | 110 | 2021 |
| DCFNet: Deep neural network with decomposed convolutional filters Q Qiu, X Cheng, R Calderbank, G Sapiro International Conference on Machine Learning, 4198-4207, 2018 | 79 | 2018 |
| Defending against adversarial images using basis functions transformations U Shaham, J Garritano, Y Yamada, E Weinberger, A Cloninger, X Cheng, ... arXiv preprint arXiv:1803.10840, 2018 | 75 | 2018 |
| Unsupervised deep haar scattering on graphs X Chen, X Cheng, S Mallat Advances in Neural Information Processing Systems 27, 2014 | 65 | 2014 |
| Marčenko–Pastur law for Tyler’s M-estimator T Zhang, X Cheng, A Singer Journal of Multivariate Analysis 149, 114-123, 2016 | 56 | 2016 |
| A deep learning approach to unsupervised ensemble learning U Shaham, X Cheng, O Dror, A Jaffe, B Nadler, J Chang, Y Kluger International conference on machine learning, 30-39, 2016 | 47 | 2016 |
| Rotdcf: Decomposition of convolutional filters for rotation-equivariant deep networks X Cheng, Q Qiu, R Calderbank, G Sapiro arXiv preprint arXiv:1805.06846, 2018 | 46 | 2018 |
| Scale-equivariant neural networks with decomposed convolutional filters W Zhu, Q Qiu, R Calderbank, G Sapiro, X Cheng | 45* | 2019 |
| Classification logit two-sample testing by neural networks for differentiating near manifold densities X Cheng, A Cloninger IEEE transactions on information theory 68 (10), 6631-6662, 2022 | 43 | 2022 |
| Provable estimation of the number of blocks in block models B Yan, P Sarkar, X Cheng Proceedings of the Twenty-First International Conference on Artificial …, 2018 | 43* | 2018 |
| Deep Haar scattering networks X Cheng, X Chen, S Mallat Information and Inference: A Journal of the IMA 5 (2), 105-133, 2016 | 42 | 2016 |
| Neural tangent kernel maximum mean discrepancy X Cheng, Y Xie Advances in Neural Information Processing Systems 34, 6658-6670, 2021 | 28 | 2021 |
| A numerical method for the study of nucleation of ordered phases L Lin, X Cheng, E Weinan, AC Shi, P Zhang Journal of Computational Physics 229 (5), 1797-1809, 2010 | 26 | 2010 |
| Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation X Cheng, N Wu Applied and Computational Harmonic Analysis 61, 132-190, 2022 | 23 | 2022 |
| On the diffusion geometry of graph Laplacians and applications X Cheng, M Rachh, S Steinerberger Applied and Computational Harmonic Analysis 46 (3), 674-688, 2019 | 23 | 2019 |
| Two-sample statistics based on anisotropic kernels X Cheng, A Cloninger, RR Coifman Information and Inference: A Journal of the IMA 9 (3), 677-719, 2020 | 22 | 2020 |
| Butterfly-Net: Optimal function representation based on convolutional neural networks Y Li, X Cheng, J Lu arXiv preprint arXiv:1805.07451, 2018 | 22 | 2018 |
