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» Variational methods for Reinforcement Learning
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NN
2010
Springer
125views Neural Networks» more  NN 2010»
15 years 25 days ago
Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Frank Sehnke, Christian Osendorfer, Thomas Rü...
HRI
2006
ACM
15 years 8 months ago
FOCUS: a generalized method for object discovery for robots that observe and interact with humans
The essence of the signal-to-symbol problem consists of associating a symbolic description of an object (e.g., a chair) to a signal (e.g., an image) that captures the real object....
Manuela M. Veloso, Paul E. Rybski, Felix von Hunde...
189
Voted
CVPR
2011
IEEE
14 years 11 months ago
Learning Effective Human Pose Estimation from Inaccurate Annotation
The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from di...
Sam Johnson, Mark Everingham
204
Voted

Book
519views
17 years 1 months ago
Information Theory, Inference, and Learning Algorithms
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and...
David J. C. MacKay
116
Voted
CVPR
2007
IEEE
16 years 4 months ago
Learning Dynamic Event Descriptions in Image Sequences
Automatic detection of dynamic events in video sequences has a variety of applications including visual surveillance and monitoring, video highlight extraction, intelligent transp...
Harini Veeraraghavan, Nikolaos Papanikolopoulos, P...