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Sepp Hochreiter
Sepp Hochreiter
Institute for Machine Learning, Johannes Kepler University Linz
Dirección de correo verificada de ml.jku.at - Página principal
Título
Citado por
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Año
Long short-term memory
S Hochreiter, J Schmidhuber
Neural computation 9 (8), 1735-1780, 1997
1084011997
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
124232017
Fast and accurate deep network learning by exponential linear units (elus)
DA Clevert, T Unterthiner, S Hochreiter
arXiv preprint arXiv:1511.07289, 2015
70842015
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
36831998
Long short-term memory
A Graves, A Graves
Supervised sequence labelling with recurrent neural networks, 37-45, 2012
36072012
Self-normalizing neural networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in neural information processing systems 30, 2017
32342017
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
3014*2001
Untersuchungen zu dynamischen neuronalen Netzen
S Hochreiter
Master's thesis, Institut fur Informatik, Technische Universitat, Munchen, 1991
14931991
LSTM can solve hard long time lag problems
S Hochreiter, J Schmidhuber
Advances in Neural Information Processing Systems 9: Proceedings of The 1996 …, 1997
13651997
Flat minima
S Hochreiter, J Schmidhuber
Neural Computation 9 (1), 1-42, 1997
9461997
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
9282016
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
Nature biotechnology 32 (9), 903-914, 2014
8492014
Learning to learn using gradient descent
S Hochreiter, A Younger, P Conwell
Artificial Neural Networks—ICANN 2001, 87-94, 2001
8052001
msa: an R package for multiple sequence alignment
U Bodenhofer, E Bonatesta, C Horejš-Kainrath, S Hochreiter
Bioinformatics 31 (24), 3997-3999, 2015
5342015
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
5132018
APCluster: an R package for affinity propagation clustering
U Bodenhofer, A Kothmeier, S Hochreiter
Bioinformatics 27 (17), 2463-2464, 2011
5042011
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
4892012
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
4722019
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
4652020
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
4522018
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Artículos 1–20