Long short-term memory S Hochreiter, J Schmidhuber Neural computation 9 (8), 1735-1780, 1997 | 105814 | 1997 |

Gans trained by a two time-scale update rule converge to a local nash equilibrium M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter Advances in neural information processing systems 30, 2017 | 11897 | 2017 |

Fast and accurate deep network learning by exponential linear units (elus) DA Clevert, T Unterthiner, S Hochreiter arXiv preprint arXiv:1511.07289, 2015 | 6904 | 2015 |

The vanishing gradient problem during learning recurrent neural nets and problem solutions S Hochreiter INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE BASED SYSTEMS 6 …, 1998 | 3599 | 1998 |

Self-normalizing neural networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in neural information processing systems 30, 2017 | 3115 | 2017 |

Gradient flow in recurrent nets: the difficulty of learning long-term dependencies S Hochreiter, Y Bengio, P Frasconi, J Schmidhuber A field guide to dynamical recurrent neural networks. IEEE Press, 2001 | 3008* | 2001 |

Untersuchungen zu dynamischen neuronalen Netzen S Hochreiter Master's thesis, Institut fur Informatik, Technische Universitat, Munchen, 1991 | 1474 | 1991 |

LSTM can solve hard long time lag problems S Hochreiter, J Schmidhuber Advances in Neural Information Processing Systems 9: Proceedings of The 1996 …, 1997 | 1331 | 1997 |

DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 914 | 2016 |

Flat minima S Hochreiter, J Schmidhuber Neural Computation 9 (1), 1-42, 1997 | 912 | 1997 |

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium Nature biotechnology 32 (9), 903-914, 2014 | 845 | 2014 |

Learning to learn using gradient descent S Hochreiter, A Younger, P Conwell Artificial Neural Networks—ICANN 2001, 87-94, 2001 | 790 | 2001 |

msa: an R package for multiple sequence alignment U Bodenhofer, E Bonatesta, C Horejš-Kainrath, S Hochreiter Bioinformatics 31 (24), 3997-3999, 2015 | 512 | 2015 |

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL A Mayr, G Klambauer, T Unterthiner, M Steijaert, JK Wegner, ... Chemical science 9 (24), 5441-5451, 2018 | 502 | 2018 |

APCluster: an R package for affinity propagation clustering U Bodenhofer, A Kothmeier, S Hochreiter Bioinformatics 27 (17), 2463-2464, 2011 | 501 | 2011 |

cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate G Klambauer, K Schwarzbauer, A Mayr, DA Clevert, A Mitterecker, ... Nucleic Acids Research 40 (9), e69-e69, 2012 | 482 | 2012 |

Hopfield networks is all you need H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ... arXiv preprint arXiv:2008.02217, 2020 | 442 | 2020 |

Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F Kratzert, D Klotz, G Shalev, G Klambauer, S Hochreiter, G Nearing Hydrology and Earth System Sciences 23 (12), 5089-5110, 2019 | 442 | 2019 |

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning K Preuer, RPI Lewis, S Hochreiter, A Bender, KC Bulusu, G Klambauer Bioinformatics 34 (9), 1538-1546, 2018 | 431 | 2018 |

Toward improved predictions in ungauged basins: Exploiting the power of machine learning F Kratzert, D Klotz, M Herrnegger, AK Sampson, S Hochreiter, GS Nearing Water Resources Research 55 (12), 11344-11354, 2019 | 414 | 2019 |