Now showing items 1-5 of 5

    • Augment-and-conquer negative binomial processes 

      Zhou, M; Carin, L (Advances in Neural Information Processing Systems, 2012-12-01)
      By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework. We develop fundamental properties of the models ...
    • Beta-negative binomial process and poisson factor analysis 

      Zhou, M; Hannah, LA; Dunson, DB; Carin, L (Journal of Machine Learning Research, 2012-01-01)
      © Copyright 2012 by the authors. A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multiscoop" generalization of the beta-Bernoulli process. The BNB process ...
    • Lognormal and gamma mixed negative binomial regression 

      Zhou, M; Li, L; Dunson, D; Carin, L (Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012-10-10)
      In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative ...
    • Nested dictionary learning for hierarchical organization of imagery and text 

      Li, L; Zhang, XX; Zhou, M; Carin, L (Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012, 2012-12-01)
      A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted ...
    • Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach 

      Han, J; Lu, J; Zhou, M (Journal of Computational Physics, 2020-12-15)
      © 2020 Elsevier Inc. We propose a new method to solve eigenvalue problems for linear and semilinear second order differential operators in high dimensions based on deep neural networks. The eigenvalue problem is reformulated ...