Name
Abstract | ||
|---|---|---|
Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment-matching networks (CGMMN), which learn a conditional distribution given some input variables based on a conditional maximum mean discrepancy (CMMD) criterion. The learning is performed by stochastic gradient descent with the gradient calculated by back-propagation. We evaluate CGMMN on a wide range of tasks, including predictive modeling, contextual generation, and Bayesian dark knowledge, which distills knowledge from a Bayesian model by learning a relatively small CGMMN student network. Our results demonstrate competitive performance in all the tasks. |
Year | Venue | DocType |
|---|---|---|
2016 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | Conference |
Volume | ISSN | Citations |
29 | 1049-5258 | 3 |
PageRank | References | Authors |
0.39 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Ren | 1 | 4 | 2.09 |
| Jialian Li | 2 | 4 | 2.09 |
| Yucen Luo | 3 | 9 | 2.17 |
| Jun Zhu | 4 | 1926 | 154.82 |