Sepp Hochreiter
Title
Cited by
Cited by
Year
Long short-term memory
S Hochreiter, J Schmidhuber
Neural computation 9 (8), 1735-1780, 1997
403541997
Fast and accurate deep network learning by exponential linear units (elus)
DA Clevert, T Unterthiner, S Hochreiter
arXiv preprint arXiv:1511.07289, 2015
28762015
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
21522017
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
1599*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
12731998
Self-normalizing neural networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in neural information processing systems, 971-980, 2017
11952017
Untersuchungen zu dynamischen neuronalen Netzen
S Hochreiter
Master's thesis, Institut fur Informatik, Technische Universitat, Munchen, 1991
7601991
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), 903-914, 2014
5532014
LSTM can solve hard long time lag problems
S Hochreiter, J Schmidhuber
Advances in Neural Information Processing Systems 9: Proceedings of The 1996 …, 1997
4801997
Flat minima
S Hochreiter, J Schmidhuber
Neural Computation 9 (1), 1-42, 1997
3781997
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
3632016
Learning to learn using gradient descent
S Hochreiter, A Younger, P Conwell
Artificial Neural Networks—ICANN 2001, 87-94, 2001
3562001
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
3202012
APCluster: an R package for affinity propagation clustering
U Bodenhofer, A Kothmeier, S Hochreiter
Bioinformatics 27 (17), 2463-2464, 2011
3062011
FABIA: factor analysis for bicluster acquisition
S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ...
Bioinformatics 26 (12), 1520-1527, 2010
2912010
A new summarization method for Affymetrix probe level data
S Hochreiter, DA Clevert, K Obermayer
Bioinformatics 22 (8), 943-949, 2006
2912006
msa: an R package for multiple sequence alignment
U Bodenhofer, E Bonatesta, C Horejš-Kainrath, S Hochreiter
Bioinformatics 31 (24), 3997-3999, 2015
1682015
Reinforcement driven information acquisition in non-deterministic environments
J Storck, S Hochreiter, J Schmidhuber
Proceedings of the international conference on artificial neural networks …, 1995
1651995
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
1522018
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
1442014
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