| Long short-term memory S Hochreiter, J Schmidhuber Neural computation 9 (8), 1735-1780, 1997 | 33832 | 1997 |
| Deep learning in neural networks: An overview J Schmidhuber Neural networks 61, 85-117, 2015 | 9378 | 2015 |
| Multi-column deep neural networks for image classification D Ciregan, U Meier, J Schmidhuber 2012 IEEE conference on computer vision and pattern recognition, 3642-3649, 2012 | 4071* | 2012 |
| Learning to forget: Continual prediction with LSTM FA Gers, J Schmidhuber, F Cummins IET Digital Library, 1999 | 3075 | 1999 |
| Framewise phoneme classification with bidirectional LSTM and other neural network architectures A Graves, J Schmidhuber Neural networks 18 (5-6), 602-610, 2005 | 2368 | 2005 |
| LSTM: A search space odyssey K Greff, RK Srivastava, J Koutník, BR Steunebrink, J Schmidhuber IEEE transactions on neural networks and learning systems 28 (10), 2222-2232, 2016 | 2353 | 2016 |
| Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks A Graves, S Fernández, F Gomez, J Schmidhuber Proceedings of the 23rd international conference on Machine learning, 369-376, 2006 | 2269 | 2006 |
| A novel connectionist system for unconstrained handwriting recognition A Graves, M Liwicki, S Fernández, R Bertolami, H Bunke, J Schmidhuber IEEE transactions on pattern analysis and machine intelligence 31 (5), 855-868, 2008 | 1515 | 2008 |
| 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 | 1222 | 2001 |
| Highway networks RK Srivastava, K Greff, J Schmidhuber arXiv preprint arXiv:1505.00387, 2015 | 1215 | 2015 |
| Stacked convolutional auto-encoders for hierarchical feature extraction J Masci, U Meier, D Cireşan, J Schmidhuber International conference on artificial neural networks, 52-59, 2011 | 1180 | 2011 |
| Deep neural networks segment neuronal membranes in electron microscopy images D Ciresan, A Giusti, LM Gambardella, J Schmidhuber Advances in neural information processing systems, 2843-2851, 2012 | 1172 | 2012 |
| Flexible, high performance convolutional neural networks for image classification DC Ciresan, U Meier, J Masci, LM Gambardella, J Schmidhuber Twenty-Second International Joint Conference on Artificial Intelligence, 2011 | 1161 | 2011 |
| Mitosis detection in breast cancer histology images with deep neural networks DC Cireşan, A Giusti, LM Gambardella, J Schmidhuber International conference on medical image computing and computer-assisted …, 2013 | 1108 | 2013 |
| Learning precise timing with LSTM recurrent networks FA Gers, NN Schraudolph, J Schmidhuber Journal of machine learning research 3 (Aug), 115-143, 2002 | 1103 | 2002 |
| Training very deep networks RK Srivastava, K Greff, J Schmidhuber Advances in neural information processing systems, 2377-2385, 2015 | 1073 | 2015 |
| Deep, big, simple neural nets for handwritten digit recognition DC Cireşan, U Meier, LM Gambardella, J Schmidhuber Neural computation 22 (12), 3207-3220, 2010 | 999 | 2010 |
| f-gan: Training generative neural samplers using variational divergence minimization S Nowozin, B Cseke, R Tomioka Advances in neural information processing systems, 271-279, 2016 | 960* | 2016 |
| Offline handwriting recognition with multidimensional recurrent neural networks A Graves, J Schmidhuber Advances in neural information processing systems, 545-552, 2009 | 892 | 2009 |
| Modeling attacks on physical unclonable functions U Rührmair, F Sehnke, J Sölter, G Dror, S Devadas, J Schmidhuber Proceedings of the 17th ACM conference on Computer and communications …, 2010 | 697 | 2010 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
