| Deterministic variational inference for robust bayesian neural networks A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt arXiv preprint arXiv:1810.03958, 2018 | 217 | 2018 |
| Gaussian process based nonlinear latent structure discovery in multivariate spike train data A Wu, NA Roy, S Keeley, JW Pillow Advances in neural information processing systems 30, 2017 | 132 | 2017 |
| Dynamic time warping constraint learning for large margin nearest neighbor classification D Yu, X Yu, Q Hu, J Liu, A Wu Information Sciences 181 (13), 2787-2796, 2011 | 103 | 2011 |
| Neural latents benchmark'21: evaluating latent variable models of neural population activity F Pei, J Ye, D Zoltowski, A Wu, RH Chowdhury, H Sohn, JE O'Doherty, ... arXiv preprint arXiv:2109.04463, 2021 | 68 | 2021 |
| Global model for failure prediction for artificial lift systems Y Liu, KT Yao, CS Raghavendra, A Wu, D Guo, J Zheng, L Olabinjo, ... US Patent 9,292,799, 2016 | 68 | 2016 |
| Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature A Wu, MC Aoi, JW Pillow arXiv preprint arXiv:1704.00060, 2017 | 49 | 2017 |
| Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking A Wu, EK Buchanan, M Whiteway, M Schartner, G Meijer, JP Noel, ... Advances in Neural Information Processing Systems 33, 6040-6052, 2020 | 46 | 2020 |
| Neural dynamics discovery via gaussian process recurrent neural networks Q She, A Wu Uncertainty in Artificial Intelligence, 454-464, 2020 | 41 | 2020 |
| Learning a latent manifold of odor representations from neural responses in piriform cortex A Wu, S Pashkovski, SR Datta, JW Pillow Advances in Neural Information Processing Systems 31, 2018 | 37 | 2018 |
| Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders MR Whiteway, D Biderman, Y Friedman, M Dipoppa, EK Buchanan, A Wu, ... PLoS computational biology 17 (9), e1009439, 2021 | 28 | 2021 |
| Sparse Bayesian structure learning with dependent relevance determination priors A Wu, M Park, OO Koyejo, JW Pillow Advances in Neural Information Processing Systems, 1628-1636, 2014 | 22 | 2014 |
| Global model for failure prediction for rod pump artificial lift systems Y Liu, KT Yao, CS Raghavenda, A Wu, D Guo, J Zheng, L Olabinjo, ... SPE Western Regional Meeting, SPE-165374-MS, 2013 | 22 | 2013 |
| Convolutional spike-triggered covariance analysis for neural subunit models A Wu, IM Park, JW Pillow Advances in neural information processing systems 28, 2015 | 21 | 2015 |
| Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis MB Cai, M Shvartsman, A Wu, H Zhang, X Zhu Neuropsychologia 144, 107500, 2020 | 15 | 2020 |
| Dependent relevance determination for smooth and structured sparse regression. A Wu, O Koyejo, JW Pillow J. Mach. Learn. Res. 20 (89), 1-43, 2019 | 11 | 2019 |
| Weighted task regularization for multitask learning Y Liu, A Wu, D Guo, KT Yao, CS Raghavendra 2013 IEEE 13th International Conference on Data Mining Workshops, 399-406, 2013 | 6 | 2013 |
| Making the nearest neighbor meaningful for time series classification D Yu, X Yu, A Wu 2011 4th International Congress on Image and Signal Processing 5, 2481-2485, 2011 | 6 | 2011 |
| Extraction and recovery of spatio-temporal structure in latent dynamics alignment with diffusion model Y Wang, Z Wu, C Li, A Wu Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework A Blau, C Gebhardt, A Bendesky, L Paninski, A Wu arXiv preprint arXiv:2204.07072, 2022 | 5 | 2022 |
| Brain kernel: a new spatial covariance function for fMRI data A Wu, SA Nastase, CA Baldassano, NB Turk-Browne, KA Norman, ... NeuroImage 245, 118580, 2021 | 5 | 2021 |
Jonathan PillowProfessor of Neuroscience, Princeton University在 princeton.edu 的电子邮件经过验证
