Juergen Schmidhuber
Juergen Schmidhuber
King Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of Lugano
Zweryfikowany adres z kaust.edu.sa - Strona główna
Cytowane przez
Cytowane przez
Long short-term memory
S Hochreiter, J Schmidhuber
Neural computation 9 (8), 1735-1780, 1997
Deep learning in neural networks: An overview
J Schmidhuber
Neural networks 61, 85-117, 2015
Learning to forget: Continual prediction with LSTM
FA Gers, J Schmidhuber, F Cummins
Neural computation 12 (10), 2451-2471, 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
Multi-column deep neural network for traffic sign classification
D CireşAn, U Meier, J Masci, J Schmidhuber
Neural networks 32, 333-338, 2012
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
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
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
A Graves, J Schmidhuber
Neural networks 18 (5-6), 602-610, 2005
Long short-term memory
A Graves, A Graves
Supervised sequence labelling with recurrent neural networks, 37-45, 2012
Training very deep networks
RK Srivastava, K Greff, J Schmidhuber
Advances in neural information processing systems 28, 2015
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
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
Stacked convolutional auto-encoders for hierarchical feature extraction
J Masci, U Meier, D Cireşan, J Schmidhuber
Artificial Neural Networks and Machine Learning–ICANN 2011: 21st …, 2011
Highway networks
RK Srivastava, K Greff, J Schmidhuber
arXiv preprint arXiv:1505.00387, 2015
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
Learning precise timing with LSTM recurrent networks
FA Gers, NN Schraudolph, J Schmidhuber
Journal of machine learning research 3 (Aug), 115-143, 2002
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
Mitosis detection in breast cancer histology images with deep neural networks
DC Cireşan, A Giusti, LM Gambardella, J Schmidhuber
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013
Deep, big, simple neural nets for handwritten digit recognition
DC Cireşan, U Meier, LM Gambardella, J Schmidhuber
Neural computation 22 (12), 3207-3220, 2010
Offline handwriting recognition with multidimensional recurrent neural networks
A Graves, J Schmidhuber
Advances in neural information processing systems 21, 2008
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