Name
Abstract | ||
|---|---|---|
Recently there has been a dramatic increase in the performance of recognition systems due to the introduction of deep architectures for representation learning and classification. However, the mathematical reasons for this success remain elusive. This tutorial will review recent work that aims to provide a mathematical justification for several properties of deep networks, such as global optimality, geometric stability, and invariance of the learned representations. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Learning | Invariant (physics),Artificial intelligence,Deep learning,Global optimality,Mathematics,Machine learning,Feature learning |
DocType | Volume | Citations |
Journal | abs/1712.04741 | 13 |
PageRank | References | Authors |
0.54 | 36 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| rene victor valqui vidal | 1 | 5331 | 260.14 |
| J. Bruna | 2 | 1697 | 82.95 |
| Raja Giryes | 3 | 340 | 38.89 |
| Stefano Soatto | 4 | 4967 | 350.34 |