Logo des Repositoriums

Auflistung nach Schlagwort "Representation learning"

1 - 2 von 2
  • Zeitschriftenartikel
    Autonomous Learning of Representations
    (KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Walter, Oliver; Haeb-Umbach, Reinhold; Mokbel, Bassam; Paassen, Benjamin; Hammer, Barbara
    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.
  • Zeitschriftenartikel
    Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations
    (KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Böhmer, Wendelin; Springenberg, Jost Tobias; Boedecker, Joschka; Riedmiller, Martin; Obermayer, Klaus
    This article reviews an emerging field that aims for autonomous reinforcement learning (RL) directly on sensor-observations. Straightforward end-to-end RL has recently shown remarkable success, but relies on large amounts of samples. As this is not feasible in robotics, we review two approaches to learn intermediate state representations from previous experiences: deep auto-encoders and slow-feature analysis. We analyze theoretical properties of the representations and point to potential improvements.