| Spatiotemporal attention for multivariate time series prediction and interpretation T Gangopadhyay, SY Tan, Z Jiang, R Meng, S Sarkar ICASSP 2021-2021 IEEE international conference on acoustics, speech and …, 2021 | 54 | 2021 |
| Growth curve registration for evaluating salinity tolerance in barley R Meng, S Saade, S Kurtek, B Berger, C Brien, K Pillen, M Tester, Y Sun Plant methods 13, 1-9, 2017 | 42 | 2017 |
| A hidden Markov model for population‐level cervical cancer screening data BC Soper, M Nygård, G Abdulla, R Meng, JF Nygård Statistics in Medicine 39 (25), 3569-3590, 2020 | 18 | 2020 |
| Disentangled sequential graph autoencoder for preclinical Alzheimer’s disease characterizations from ADNI study F Yang, R Meng, H Cho, G Wu, WH Kim Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 15 | 2021 |
| Nonstationary multivariate Gaussian processes for electronic health records R Meng, B Soper, HKH Lee, VX Liu, JD Greene, P Ray Journal of Biomedical Informatics 117, 103698, 2021 | 13 | 2021 |
| Sparse Gaussian processes for solving nonlinear PDEs R Meng, X Yang Journal of Computational Physics 490, 112340, 2023 | 10 | 2023 |
| Dynamic covariance estimation via predictive Wishart process with an application on brain connectivity estimation R Meng, F Yang, WH Kim Computational Statistics & Data Analysis 185, 107763, 2023 | 4 | 2023 |
| Interpretable research replication prediction via variational contextual consistency sentence masking T Luo, R Meng, XE Wang, Y Liu arXiv preprint arXiv:2203.14474, 2022 | 4 | 2022 |
| Hierarchical continuous-time inhomogeneous hidden Markov model for cancer screening with extensive followup data R Meng, B Soper, HKH Lee, JF Nygård, M Nygård Statistical Methods in Medical Research 31 (12), 2383-2399, 2022 | 3 | 2022 |
| Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks R Meng, HKH Lee, K Bouchard Conference of the International Federation of Classification Societies, 253-261, 2022 | 3 | 2022 |
| Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model R Meng, K Bouchard arXiv preprint arXiv:2106.13379, 2021 | 3 | 2021 |
| Hierarchical hidden markov jump processes for cancer screening modeling R Meng, S Braden, J Nygard, M Nygrad, H Lee arXiv preprint arXiv:1910.05847, 2019 | 2 | 2019 |
| Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations Y Song, D Lee, R Meng, WH Kim arXiv preprint arXiv:2406.06149, 2024 | 1 | 2024 |
| Collaborative nonstationary multivariate gaussian process model R Meng, H Lee, K Bouchard arXiv preprint arXiv:2106.00719 520, 2021 | 1 | 2021 |
| Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model R Meng, KE Bouchard PLOS Computational Biology 20 (4), e1011975, 2024 | | 2024 |
| Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells H Cheng, JS Medina, J Zhou, EM Pinho, R Meng, L Wang, Q He, ... Environmental Science & Technology 58 (5), 2360-2372, 2024 | | 2024 |
| Achieving Shrinkage and Sparsity in Bayesian Vector Autoregressions with Three-Parameter-Beta-Normal Prior R Meng, H Rangarajan, K Bouchard | | 2021 |
| Supplementary Material for: Growth curve registration for evaluating salinity tolerance in barley R Meng, S Saade, S Kurtek, B Berger, C Brien, K Pillen, MA Tester, Y Sun figshare, 2017 | | 2017 |
