Autonomous Learning of Representations
| dc.contributor.author | Walter, Oliver | |
| dc.contributor.author | Haeb-Umbach, Reinhold | |
| dc.contributor.author | Mokbel, Bassam | |
| dc.contributor.author | Paassen, Benjamin | |
| dc.contributor.author | Hammer, Barbara | |
| dc.date.accessioned | 2018-01-08T09:18:05Z | |
| dc.date.available | 2018-01-08T09:18:05Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | Besides the core learning algorithm itself, one major question in machine learning is how to best encode given training data such that the learning technology can efficiently learn based thereon and generalize to novel data. While classical approaches often rely on a hand coded data representation, the topic of autonomous representation or feature learning plays a major role in modern learning architectures. The goal of this contribution is to give an overview about different principles of autonomous feature learning, and to exemplify two principles based on two recent examples: autonomous metric learning for sequences, and autonomous learning of a deep representation for spoken language, respectively. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11485 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 29, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Deep representation | |
| dc.subject | Metric learning | |
| dc.subject | Representation learning | |
| dc.subject | Spoken language | |
| dc.title | Autonomous Learning of Representations | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 351 | |
| gi.citation.startPage | 339 |