| Long Short-Term Memory S Hochreiter, J Schmidhuber Neural Computation 9 (8), 1735–1780, 1997 | 141922 | 1997 |
| Deep learning in neural networks: An overview J Schmidhuber Neural networks 61, 85-117, 2015 | 26746 | 2015 |
| Learning to forget: Continual prediction with LSTM FA Gers, J Schmidhuber, F Cummins Neural computation 12 (10), 2451-2471, 2000 | 10223 | 2000 |
| 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 | 9104 | 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 | 8371 | 2006 |
| Multi-column deep neural network for traffic sign classification D CireşAn, U Meier, J Masci, J Schmidhuber Neural networks 32, 333-338, 2012 | 7647* | 2012 |
| Framewise phoneme classification with bidirectional LSTM and other neural network architectures A Graves, J Schmidhuber Neural networks 18 (5-6), 602-610, 2005 | 7508 | 2005 |
| 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 | 7464 | 2012 |
| Long short-term memory A Graves Supervised sequence labelling with recurrent neural networks, 37-45, 2012 | 5332 | 2012 |
| Training very deep networks RK Srivastava, K Greff, J Schmidhuber Advances in neural information processing systems 28, 2015 | 3625 | 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 | 3080 | 2001 |
| 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 | 3019 | 2011 |
| 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 | 2968 | 2008 |
| Highway networks RK Srivastava, K Greff, J Schmidhuber arXiv preprint arXiv:1505.00387, 2015 | 2933 | 2015 |
| Learning precise timing with LSTM recurrent networks FA Gers, NN Schraudolph, J Schmidhuber Journal of machine learning research 3 (Aug), 115-143, 2002 | 2441 | 2002 |
| Flexible, high performance convolutional neural networks for image classification DC Ciresan, U Meier, J Masci, L Maria Gambardella, J Schmidhuber IJCAI proceedings-international joint conference on artificial intelligence …, 2011 | 2349 | 2011 |
| Deep neural networks segment neuronal membranes in electron microscopy images D Ciresan, A Giusti, L Gambardella, J Schmidhuber Advances in neural information processing systems 25, 2012 | 2284 | 2012 |
| 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 | 2082 | 2013 |
| World models D Ha, J Schmidhuber arXiv preprint arXiv:1803.10122 2 (3), 2018 | 1886 | 2018 |
| MetaGPT: Meta programming for a multi-agent collaborative framework S Hong, M Zhuge, J Chen, X Zheng, Y Cheng, J Wang, C Zhang, Z Wang, ... The Twelfth International Conference on Learning Representations, 2023 | 1828 | 2023 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
