7.1k citations · 10.4k across the 36 of their papers we have counts for
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Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Jelena Luketina, Mathias Berglund, Klaus Greff +1
Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance. We propose a gradient-based approach for loc…
Binding via Reconstruction Clustering
Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber
Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples. However, for complex dat…
Training Very Deep Networks
Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber
Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success. However, training becomes more difficult as depth increases, and traini…
Highway Networks
Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber
There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with…
LSTM: A Search Space Odyssey
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník +2
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks hav…