Presentation	2014-11-18 Denoising High-dimensional Sequences with the Bidirectional Recurrent Restricted Boltzmann Machine Shoken KANEKO,
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Abstract(in English)	We propose a probabilistic neural network for modeling high-dimensional sequences with complex non-linearities. Our model is an extension of the previously introduced Recurrent Neural Network-Restricted Boltzmann Machine. We extend the model by adding a backward recurrent chain, which makes bidirectional propagation of information possible and allowing our model to incorporate knowledge of future observations. Our model can be readily applied for tasks such as denoising of high-dimensional sequences. We show that our model outperforms the unidirectional model in the task of denoising moving pictures of balls bouncing in a box, reconstructing much smoother sequences.
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Keyword(in English)	Machine Learning / Neural Networks / Recurrent Neural Network-Restricted Boltzmann Machines
Paper #	IBISML2014-62
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Committee	IBISML
Conference Date	2014/11/10(1days)
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Paper Information
Registration To	Information-Based Induction Sciences and Machine Learning (IBISML)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Denoising High-dimensional Sequences with the Bidirectional Recurrent Restricted Boltzmann Machine
Sub Title (in English)
Keyword(1)	Machine Learning
Keyword(2)	Neural Networks
Keyword(3)	Recurrent Neural Network-Restricted Boltzmann Machines
1st Author's Name	Shoken KANEKO
1st Author's Affiliation	Research and Development Division, Yamaha Corporation()
Date	2014-11-18
Paper #	IBISML2014-62
Volume (vol)	vol.114
Number (no)	306
Page	pp.pp.-
#Pages	6
Date of Issue