Name
Abstract | ||
|---|---|---|
In this work, we exploit a novel algorithm for capturing the Lie group manifold structure of the visual impression. By developing the single-layer Lie group model, we show how the representation learning algorithm can be stacked to yield a deep architecture. In addition, we design a Lie group based gradient descent algorithm to solve the learning problem of network weights. We show that our proposed technique yields representations that significantly better suited for training deep network and is also computationally efficient. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ipl.2018.03.006 | Information Processing Letters |
Keywords | Field | DocType |
Visual impression,Deep learning,Lie group,Algorithms | Manifold structure,Lie group,Discrete mathematics,Architecture,Gradient descent,Impression,Theoretical computer science,Exploit,Artificial intelligence,Deep learning,Mathematics,Feature learning | Journal |
Volume | ISSN | Citations |
136 | 0020-0190 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengduo Yang | 1 | 0 | 0.68 |
| fanzhang li | 2 | 75 | 8.73 |
| li zhang | 3 | 49 | 8.10 |
| Zhao Zhang | 4 | 938 | 65.99 |