Understanding the limiting factors of topic modeling via posterior contraction analysis J Tang, Z Meng, X Nguyen, Q Mei, M Zhang International conference on machine learning, 190-198, 2014 | 361 | 2014 |
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs D George, W Lehrach, K Kansky, M Lázaro-Gredilla, C Laan, B Marthi, ... Science 358 (6368), eaag2612, 2017 | 304 | 2017 |
Multimodal sensing for pediatric obesity applications M Annavaram, N Medvidovic, U Mitra, S Narayanan, G Sukhatme, Z Meng, ... UrbanSense08, 21, 2008 | 53 | 2008 |
Learning latent variable Gaussian graphical models Z Meng, B Eriksson, A Hero International Conference on Machine Learning, 1269-1277, 2014 | 47 | 2014 |
Distributed learning of Gaussian graphical models via marginal likelihoods Z Meng, D Wei, A Wiesel, A Hero III Artificial Intelligence and Statistics, 39-47, 2013 | 35 | 2013 |
Distributed principal component analysis on networks via directed graphical models Z Meng, A Wiesel, AO Hero 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 30 | 2012 |
Marginal likelihoods for distributed parameter estimation of gaussian graphical models Z Meng, D Wei, A Wiesel, AO Hero IEEE Transactions on Signal Processing 62 (20), 5425-5438, 2014 | 27 | 2014 |
THU and ICRC at TRECVID 2008. Y Liang, X Liu, Z Wang, J Li, B Cao, Z Cao, Z Dai, Z Guo, W Li, L Luo, ... TRECVID, 2008 | 17 | 2008 |
Marginal likelihoods for distributed estimation of graphical model parameters Z Meng, D Wei, AO Hero, A Wiesel 2013 5th IEEE International Workshop on Computational Advances in Multi …, 2013 | 3 | 2013 |
Distributed Learning, Prediction and Detection in Probabilistic Graphs. Z Meng | | 2014 |