Anqi Wu
Anqi Wu
PhD of Neuroscience in Princeton University
Verified email at princeton.edu - Homepage
Title
Cited by
Cited by
Year
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
96*2018
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
882011
Gaussian process based nonlinear latent structure discovery in multivariate spike train data
A Wu, NA Roy, S Keeley, JW Pillow
Proceedings of the 31st International Conference on Neural Information …, 2017
682017
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
392016
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
A Wu, MC Aoi, JW Pillow
arXiv preprint arXiv:1704.00060, 2017
282017
Learning a latent manifold of odor representations from neural responses in piriform cortex.
A Wu, SL Pashkovski, SR Datta, JW Pillow
NeurIPS, 5383-5393, 2018
202018
Neural dynamics discovery via gaussian process recurrent neural networks
Q She, A Wu
Uncertainty in Artificial Intelligence, 454-464, 2020
182020
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
172014
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 & AAPG Pacific Section Meeting 2013 Joint Technical …, 2013
142013
Convolutional spike-triggered covariance analysis for neural subunit models.
A Wu, IM Park, JW Pillow
NIPS, 793-801, 2015
112015
Dependent relevance determination for smooth and structured sparse regression.
A Wu, O Koyejo, JW Pillow
J. Mach. Learn. Res. 20 (89), 1-43, 2019
92019
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, ...
bioRxiv, 2020
62020
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
52013
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
52011
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
42020
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
12021
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
MR Whiteway, D Biderman, Y Friedman, M Dipoppa, EK Buchanan, A Wu, ...
bioRxiv, 2021
12021
Domain Generalization via Domain-based Covariance Minimization
A Wu
arXiv preprint arXiv:2110.06298, 2021
2021
Semi-supervised sequence modeling for improved behavioral segmentation
MR Whiteway, ES Schaffer, A Wu, EK Buchanan, OF Onder, N Mishra, ...
bioRxiv, 2021
2021
Brain kernel: a new spatial covariance function for fMRI data
A Wu, SA Nastase, CA Baldassano, NB Turk-Browne, KA Norman, ...
bioRxiv, 2021
2021
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