Local Feature Based Online Mode Detection with Recurrent Neural Networks

By Sebastian Otte, Dirk Krechel, Marcus Liwicki, Andreas R. Dengel. 2012

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Urgen Schmidhuber
2. David R Ha
3. Alex Graves
4. Sergey Levine
5. Yoshua Bengio
6. Quoc V. Le
7. Faustino J. Gomez
8. Mohit Bansal

In Electrical and Electronic Engineering

1. J. Urgen Schmidhuber
2. Rupesh Kumar Srivastava

In Signal Processing

1. J. Urgen Schmidhuber
2. Alex Graves
3. Yoshua Bengio
4. Hasim Sak
5. Franccoise Beaufays
6. Santiago Fernández
7. Zachary Chase Lipton
8. Jinyu Li

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “12) outperformed all other methods (HMMs, SVMs) on online mode detection (Otte et al., 2012; Indermuhle et al., 2012) and keyword spotting (Indermuhle et al.”
• Understanding LSTM - a tutorial into Long Short-Term Memory Recurrent Neural Networks (2019, unknown): “In 2012 BSLTM was applied to keyword spotting and mode detection distinguishing different types of content in handwritten documents, such as text, formulas, diagrams and figures, outperforming HMMs and SVMs [44, 45, 59].”