NDLI: Hash table based feed forward neural networks: A scalable approach towards think aloud imitation

Content Provider	IEEE Xplore Digital Library
Author	Iqbal, J. Yousaf, M.M.
Copyright Year	2009
Description	Author affiliation: Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan (Iqbal, J.; Yousaf, M.M.)
Abstract	In this paper, we deal with the problem of inefficient context modules of recurrent networks (RNs), which form the basis of think aloud: a strategy for imitation. Learning from observation provides a fine way for knowledge acquisition of demonstrated task. In order to learn complex tasks then simply learning action sequences, strategy of think aloud imitation learning applies recurrent network model (RNM) [1]. We propose dynamic task imitation architecture in time and storage efficient way. Inefficient recurrent nodes are replaced with updated feed forward network (FFN). Our modified architecture is based on hash table. Single hash store is used instead of multiple recurrent nodes. History for input usability is saved for experience based task learning. Performance evaluation of this approach makes success guarantee for robot training. It is best suitable approach for all applications based on recurrent neural network by replacing this inefficient network with our designed approach.
Starting Page	17
Ending Page	22
File Size	361136
Page Count	6
File Format	PDF
ISBN	9781424456307
DOI	10.1109/ICET.2009.5353210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-19
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks efficient neural network learning from demonstration Knowledge acquisition Educational institutions Feedforward neural networks History Information technology Robotics and automation Robot programming Neural networks modified feed forward neural netwrok think aloud imitation Feeds
Content Type	Text
Resource Type	Article

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