1997 BidirectionalRecurrentNeuralNet

• (Schuster & Paliwal, 1997) ⇒ Mike Schuster, and Kuldip K. Paliwal. (1997). “Bidirectional Recurrent Neural Networks.” In: IEEE Transactions on Signal Processing Journal, 45(11). doi:10.1109/78.650093

Subject Headings: Bidirectional LSTM.

Notes

2017

• (Schmidhuber, 2017) ⇒ Schmidhuber, J. (2017) "Deep Learning". In: Sammut, C., Webb, G.I. (eds) "Encyclopedia of Machine Learning and Data Mining". Springer, Boston, MA
  • QUOTE: … Bidirectional RNNs (BRNNs) (Schuster and Paliwal 1997) are designed for input sequences whose starts and ends are known in advance, such as spoken sentences to be labeled by their phonemes. DAG-RNNs (Baldi and Pollastri 2003) generalize BRNNs to multiple dimensions. Recursive NNs, BRNNs, and DAG-RNNs unfold their full potential when combined with LSTM (Graves et al. 2009).

Cited By

• http://scholar.google.com/scholar?q=%221997%22+Bidirectional+Recurrent+Neural+Networks
• http://dl.acm.org/citation.cfm?id=2198065.2205129&preflayout=flat#citedby

Quotes

Abstract

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1997 BidirectionalRecurrentNeuralNet	Mike Schuster Kuldip K. Paliwal			Bidirectional Recurrent Neural Networks				10.1109/78.650093		1997