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Sepp Hochreiter
Sepp Hochreiter
Institute for Machine Learning, Johannes Kepler University Linz
在 ml.jku.at 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Long short-term memory
S Hochreiter, J Schmidhuber
Neural computation 9 (8), 1735-1780, 1997
1177691997
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
144932017
Fast and accurate deep network learning by exponential linear units (elus)
DA Clevert
arXiv preprint arXiv:1511.07289, 2015
75942015
Long short-term memory
A Graves, A Graves
Supervised sequence labelling with recurrent neural networks, 37-45, 2012
41252012
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
40001998
Self-normalizing neural networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in neural information processing systems 30, 2017
34892017
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
3140*2001
Untersuchungen zu dynamischen neuronalen Netzen
S Hochreiter
Master's thesis, Institut fur Informatik, Technische Universitat, Munchen, 1991
15591991
LSTM can solve hard long time lag problems
S Hochreiter, J Schmidhuber
Advances in Neural Information Processing Systems 9: Proceedings of The 1996 …, 1997
14421997
Flat minima
S Hochreiter, J Schmidhuber
Neural Computation 9 (1), 1-42, 1997
10251997
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
10042016
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
Nature biotechnology 32 (9), 903-914, 2014
8792014
Learning to learn using gradient descent
S Hochreiter, A Younger, P Conwell
Artificial Neural Networks—ICANN 2001, 87-94, 2001
8592001
msa: an R package for multiple sequence alignment
U Bodenhofer, E Bonatesta, C Horejš-Kainrath, S Hochreiter
Bioinformatics 31 (24), 3997-3999, 2015
5872015
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
5622019
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
5522020
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
5512018
APCluster: an R package for affinity propagation clustering
U Bodenhofer, A Kothmeier, S Hochreiter
Bioinformatics 27 (17), 2463-2464, 2011
5232011
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
5132018
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
5102019
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