Autonomous Learning of Representations

Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B (2015)
KI - Künstliche Intelligenz 29(4): 339–351.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
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.
Erscheinungsjahr
Zeitschriftentitel
KI - Künstliche Intelligenz
Band
29
Zeitschriftennummer
4
Seite
339–351
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Walter O, Häb-Umbach R, Mokbel B, Paaßen B, Hammer B. Autonomous Learning of Representations. KI - Künstliche Intelligenz. 2015;29(4):339–351.
Walter, O., Häb-Umbach, R., Mokbel, B., Paaßen, B., & Hammer, B. (2015). Autonomous Learning of Representations. KI - Künstliche Intelligenz, 29(4), 339–351. doi:10.1007/s13218-015-0372-1
Walter, O., Häb-Umbach, R., Mokbel, B., Paaßen, B., and Hammer, B. (2015). Autonomous Learning of Representations. KI - Künstliche Intelligenz 29, 339–351.
Walter, O., et al., 2015. Autonomous Learning of Representations. KI - Künstliche Intelligenz, 29(4), p 339–351.
O. Walter, et al., “Autonomous Learning of Representations”, KI - Künstliche Intelligenz, vol. 29, 2015, pp. 339–351.
Walter, O., Häb-Umbach, R., Mokbel, B., Paaßen, B., Hammer, B.: Autonomous Learning of Representations. KI - Künstliche Intelligenz. 29, 339–351 (2015).
Walter, Oliver, Häb-Umbach, Reinhold, Mokbel, Bassam, Paaßen, Benjamin, and Hammer, Barbara. “Autonomous Learning of Representations”. KI - Künstliche Intelligenz 29.4 (2015): 339–351.
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