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ICML
2006
IEEE
12 years 6 months ago
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is...
Alex Graves, Faustino J. Gomez, Jürgen Schmid...
NIPS
2008
11 years 7 months ago
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks
Offline handwriting recognition--the transcription of images of handwritten text--is an interesting task, in that it combines computer vision with sequence learning. In most syste...
Alex Graves, Jürgen Schmidhuber
TNN
1998
123views more  TNN 1998»
11 years 5 months ago
A general framework for adaptive processing of data structures
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...
Paolo Frasconi, Marco Gori, Alessandro Sperduti
IDA
1998
Springer
11 years 5 months ago
Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences
We investigate a form of modular neural network for classification with (a) pre-separated input vectors entering its specialist (expert) networks, (b) specialist networks which ar...
Sylvian R. Ray, William H. Hsu
ECAI
2000
Springer
11 years 9 months ago
Learning Efficiently with Neural Networks: A Theoretical Comparison between Structured and Flat Representations
Abstract. We are interested in the relationship between learning efficiency and representation in the case of supervised neural networks for pattern classification trained by conti...
Marco Gori, Paolo Frasconi, Alessandro Sperduti
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