Klaus Greff is a machine learning researcher whose work includes controlled comparisons of long short-term memory architectures. His author homepage identifies the researcher and links his publications; the original byline of LSTM: A Search Space Odyssey by Klaus Greff and others confirms his participation in that study.[ref]
The study compared eight architectural variants across three task families, optimizing hyperparameters separately for each variant. This matters because a poorly tuned variant is not a fair test of its architecture. None significantly improved on the standard architecture in the reported comparisons, while removing particular components exposed the importance of the LSTM forget gate and output activation.[ref]
LSTM: A Search Space Odyssey by Klaus Greff and others. Abstract: variant comparisons, separate tuning and component importance
For an illustrative ablation study, compare a complete recurrent neural network with a version lacking one gate, while keeping the training task and evaluation procedure comparable. A worse result after removal is evidence about that component under the tested conditions; it does not establish that no alternative architecture could ever replace it. This is the scientific value of a controlled comparison beyond displaying one successful network.
Klaus Greff, identified on his author homepage
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