The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Training Recurrent Networks by Evolino
Abstract
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for training RNNs, due to numerous local minima. For such cases, we present a novel method: EVOlution of systems with LINear Outputs (Evolino). Evolino evolves weights to the nonlinear, hidden nodes of RNNs while computing optimal linear mappings from hidden state to output, using methods such as pseudo-inverse-based linear regression. If we instead use quadratic programming to maximize the margin, we obtain the first evolutionary recurrent support vector machines. We show that Evolino-based LSTM can solve tasks that Echo State nets (Jaeger, 2004a) cannot and achieves higher accuracy in certain continuous function generation tasks than conventional gradient descent RNNs, including gradient-based LSTM.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Self-citationHarnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Optimization and applications of echo state networks with leaky- integrator neurons
Jaeger, H., Lukoševičius, M., Popovici, D., & Siewert, U. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.016 · Cited by 948
Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Self-citationGreff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
Self-citationSupervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1703.03864 · Cited by 1,027
“, 2008; 2014), as well as for the training of the recurrent weights in recurrent neural networks (the hidden-to-output weights have been trained with supervised learning; Schmidhuber et al., 2007).”
Background
Metadata
- Authors
- Schmidhuber, J., Wierstra, D., Gagliolo, M., & Gomez, F.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2007-03
- Volume
- 19
- Issue
- 3
- Pages
- 757-779
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2147107577
- Cited by
- 248
- References
- 3
- FWCI
- 15.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Schmidhuber, J., Wierstra, D., Gagliolo, M., & Gomez, F. (2007). Training Recurrent Networks by Evolino. Neural Computation. https://doi.org/10.1162/neco.2007.19.3.757
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