Long short-term memory S Hochreiter, J Schmidhuber Neural computation 9 (8), 1735-1780, 1997 | 21990 | 1997 |

Fast and accurate deep network learning by exponential linear units (elus) DA Clevert, T Unterthiner, S Hochreiter arXiv preprint arXiv:1511.07289, 2015 | 1696 | 2015 |

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 | 940 | 2001 |

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 | 665 | 1998 |

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, 6626-6637, 2017 | 634 | 2017 |

Self-normalizing neural networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in neural information processing systems, 971-980, 2017 | 579 | 2017 |

Untersuchungen zu dynamischen neuronalen Netzen S Hochreiter Master's thesis, Institut fur Informatik, Technische Universitat, Munchen, 1991 | 520 | 1991 |

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium Z Su, PP Łabaj, S Li, J Thierry-Mieg, D Thierry-Mieg, W Shi, C Wang, ... Nature biotechnology 32 (9), 903, 2014 | 449 | 2014 |

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 | 265 | 2012 |

A new summarization method for Affymetrix probe level data S Hochreiter, DA Clevert, K Obermayer Bioinformatics 22 (8), 943-949, 2006 | 263 | 2006 |

Flat minima S Hochreiter, J Schmidhuber Neural Computation 9 (1), 1-42, 1997 | 251 | 1997 |

FABIA: factor analysis for bicluster acquisition S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ... Bioinformatics 26 (12), 1520-1527, 2010 | 241 | 2010 |

APCluster: an R package for affinity propagation clustering U Bodenhofer, A Kothmeier, S Hochreiter Bioinformatics 27 (17), 2463-2464, 2011 | 240 | 2011 |

LSTM can solve hard long time lag problems S Hochreiter, J Schmidhuber Advances in Neural Information Processing Systems 9: Proceedings of The 1996 …, 1997 | 237 | 1997 |

Learning to learn using gradient descent S Hochreiter, A Younger, P Conwell Artificial Neural Networks—ICANN 2001, 87-94, 2001 | 221 | 2001 |

DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 211 | 2016 |

Reinforcement driven information acquisition in non-deterministic environments J Storck, S Hochreiter, J Schmidhuber Proceedings of the international conference on artificial neural networks …, 1995 | 148 | 1995 |

I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data W Talloen, DA Clevert, S Hochreiter, D Amaratunga, L Bijnens, S Kass, ... Bioinformatics 23 (21), 2897-2902, 2007 | 123 | 2007 |

Support vector machines for dyadic data S Hochreiter, K Obermayer Neural Computation 18 (6), 1472-1510, 2006 | 106 | 2006 |

Deep learning as an opportunity in virtual screening T Unterthiner, A Mayr, G Klambauer, M Steijaert, JK Wegner, ... Proceedings of the deep learning workshop at NIPS 27, 1-9, 2014 | 97 | 2014 |