Harmony: Collection and analysis of parallel block vectors M Kambadur, K Tang, MA Kim ACM SIGARCH Computer Architecture News 40 (3), 452-463, 2012 | 45 | 2012 |
Understanding the Bethe approximation: When and how can it go wrong? A Weller, K Tang, T Jebara, DA Sontag UAI, 868-877, 2014 | 40 | 2014 |
Adaptive Anonymity via -Matching KM Choromanski, T Jebara, K Tang Advances in Neural Information Processing Systems 26, 2013 | 31 | 2013 |
Bethe learning of graphical models via MAP decoding K Tang, N Ruozzi, D Belanger, T Jebara Artificial Intelligence and Statistics, 1096-1104, 2016 | 16 | 2016 |
Parashares: Finding the important basic blocks in multithreaded programs M Kambadur, K Tang, MA Kim Euro-Par 2014 Parallel Processing: 20th International Conference, Porto …, 2014 | 16 | 2014 |
Bethe learning of conditional random fields via map decoding K Tang, N Ruozzi, D Belanger, T Jebara arXiv preprint arXiv:1503.01228, 2015 | 6 | 2015 |
Parallel scaling properties from a basic block view M Kambadur, K Tang, J Lopez, MA Kim Proceedings of the ACM SIGMETRICS/international conference on Measurement …, 2013 | 3 | 2013 |
Network ranking with Bethe pseudomarginals K Tang, A Weller, T Jebara NIPS Workshop on Discrete Optimization in Machine Learning, 2013 | 2 | 2013 |
Collection, analysis, and uses of parallel block vectors M Kambadur, K Tang IEEE Micro 99 (1), 1, 2013 | 2 | 2013 |
Parallel Block Vectors: Collection, Analysis, and Uses M Kambadur, K Tang, MA Kim IEEE Micro 33 (3), 86-94, 2013 | 1 | 2013 |
Quasi Monte Carlo Inference for Log Gaussian Cox Processes K Tang, J Forde, L Paninski | | 2013 |
Imperfect b-matching K Tang | | 2013 |
Inferring Direct and Indirect Functional Connectivity Between Neurons From Multiple Neural Spike Train Data B Shababo, K Tang, F Wood | | |
Personalized Emotion Classification with Latent Dirichlet Allocation K Tang | | |