Name
Abstract | ||
|---|---|---|
•New scalable Gaussian process (GP) paradigms to introduce additional modulation variables for learning rich statistical representation, e.g., het- eroscedastic noise, multi-modality and non-stationarity, from massive data.•Different variational inference strategies to arrive at analytical or tight evidence lower bounds (ELBOs) for effcient and effective model training.•Comprehensive comparison against state-of-the-art GP and neural net- work counterparts to showcase the superiority of scalable modulated GPs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.108121 | Pattern Recognition |
Keywords | DocType | Volume |
Gaussian process,Modulation,Scalability,Heteroscedastic noise,Multi-modality,Non-stationarity | Journal | 120 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Liu | 1 | 2 | 0.72 |
| Yew-Soon Ong | 2 | 263 | 23.35 |
| Xiaomo Jiang | 3 | 73 | 8.78 |
| Xiaofang Wang | 4 | 36 | 7.83 |