开课单位--达姆施塔特工业大学
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Avoid playing learner and system off against each other[避免让学习者和系统互相对抗]
  Ji-Ung Lee(达姆施塔特工业大学) Avoid playing learner and system off against each other
热度:35

2
Opportunities and challenges of plant biotechnology for the production of active substances[植物生物技术生产活性物质的机遇与挑战]
  Heribert Warzecha(达姆施塔特工业大学) Plants have evolved an enormous variety of biosynthetic pathways affording formation of diverse chemical structures. These so-called specialized compo...
热度:36

4
Preference Learning[偏好学习]
  Johannes Fürnkranz, Eyke Hullermeier(达姆施塔特工业大学) The topic of "preferences" has recently attracted considerable attention in artificial intelligence in general and machine learning in parti...
热度:204

6
Monocular 3D Pose Estimation and Tracking by Detection[单眼三维姿态估计和跟踪检测]
  Mykhaylo Andriluka(达姆施塔特工业大学 ) Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although ...
热度:269

7
Robot skill Learning[机器人技能学习]
  Jan Peters(达姆施塔特工业大学) Autonomous robots that can assist humans in situations of daily life have been a long standing vision of artificial intelligence, robotics, and cognit...
热度:125

8
Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification[用于多类分类的三元纠错输出代码的有效解码]
  Sang-Hyeun Park(达姆施塔特工业大学) We present an adaptive decoding algorithm for ternary ECOC matrices which reduces the number of needed classifier evaluations for multiclass classific...
热度:62

9
Learning Concurrent Motor Skills in Versatile Solution Spaces[在多功能解决方案空间学习并行运动技能]
  Christian Daniel(达姆施塔特工业大学) Future intelligent robots will need to interact with uncertain and changing environments. One key aspect to allow robotic agents to adapt to such situ...
热度:55

10
Preference-based policy iteration: Leveraging preference learning for reinforcement learning[基于偏好的政策迭代:利用偏好进行强化学习]
  Johannes Fürnkranz(达姆施塔特工业大学) This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL)....
热度:88
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