Name
Abstract | ||
|---|---|---|
A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupervised and semi-supervised problems, including semi-supervised analysis of the ImageNet data, demonstrating the scalability of the model to large datasets. |
Year | Venue | Field |
|---|---|---|
2017 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | Importance sampling,Gradient descent,Mathematical optimization,Computer science,Parametric statistics,Encoder,Artificial intelligence,Machine learning,Scalability |
DocType | Volume | ISSN |
Conference | 30 | 1049-5258 |
Citations | PageRank | References |
12 | 0.64 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pu, Yunchen | 1 | 57 | 1.97 |
| Zhe Gan | 2 | 319 | 32.58 |
| Ricardo Henao | 3 | 286 | 23.85 |
| Chunyuan Li | 4 | 467 | 33.86 |
| Han, Shaobo | 5 | 16 | 1.06 |
| L. Carin | 6 | 4603 | 339.36 |