Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Learning precise timing with lstm recurrent networks
The Journal of Machine Learning Research
Neural Networks - 2005 Special issue: IJCNN 2005
ICML '06 Proceedings of the 23rd international conference on Machine learning
Neural Computation
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Bidirectional recurrent neural networks
IEEE Transactions on Signal Processing
Scalable Neural Networks for Board Games
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Indirect encoding of neural networks for scalable go
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
On fast deep nets for AGI vision
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
A MDRNN-SVM hybrid model for cursive offline handwriting recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Low resolution Arabic recognition with multidimensional recurrent neural networks
Proceedings of the 4th International Workshop on Multilingual OCR
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Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition. Some of the properties that make RNNs suitable for such tasks, for example robustness to input warping, and the ability to access contextual information, are also desirable in multi-dimensional domains. However, there has so far been no direct way of applying RNNs to data with more than one spatio-temporal dimension. This paper introduces multi-dimensional recurrent neural networks, thereby extending the potential applicability of RNNs to vision, video processing, medical imaging and many other areas, while avoiding the scaling problems that have plagued other multi-dimensional models. Experimental results are provided for two image segmentation tasks.