| 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 |
| A Clockwork RNN J Koutnik, K Greff, F Gomez, J Schimdhuber International Conference on Machine Learning (ICML), 2014 | 785 | 2014 |
| Incremental evolution of complex general behavior F Gomez, R Miikkulainen Adaptive Behavior 5 (3-4), 317-342, 1997 | 627 | 1997 |
| Accelerated Neural Evolution through Cooperatively Coevolved Synapses. F Gomez, J Schmidhuber, R Miikkulainen Journal of Machine Learning Research 9 (5), 2008 | 425 | 2008 |
| Solving non-Markovian control tasks with neuroevolution FJ Gomez, R Miikkulainen IJCAI 99, 1356-1361, 1999 | 365 | 1999 |
| Training recurrent networks by evolino J Schmidhuber, D Wierstra, M Gagliolo, F Gomez Neural Computation 19 (3), 757-779, 2007 | 349 | 2007 |
| Deep networks with internal selective attention through feedback connections MF Stollenga, J Masci, F Gomez, J Schmidhuber Advances in neural information processing systems 27, 2014 | 344 | 2014 |
| A system for robotic heart surgery that learns to tie knots using recurrent neural networks H Mayer, F Gomez, D Wierstra, I Nagy, A Knoll, J Schmidhuber Advanced Robotics 22 (13-14), 1521-1537, 2008 | 314 | 2008 |
| Compete to compute RK Srivastava, J Masci, S Kazerounian, F Gomez, J Schmidhuber Advances in neural information processing systems 26, 2013 | 263 | 2013 |
| Model-based active exploration P Shyam, W Jaśkowski, F Gomez International conference on machine learning, 5779-5788, 2019 | 262 | 2019 |
| Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning J Koutník, G Cuccu, J Schmidhuber, F Gomez Genetic and Evolutionary Computation Conference (GECCO), 2013 | 258 | 2013 |
| Robust non-linear control through neuroevolution FJ Gomez Computer Science Department, University of Texas, 2003 | 217 | 2003 |
| Planning to be surprised: Optimal bayesian exploration in dynamic environments Y Sun, F Gomez, J Schmidhuber International conference on artificial general intelligence, 41-51, 2011 | 203 | 2011 |
| Efficient non-linear control through neuroevolution F Gomez, J Schmidhuber, R Miikkulainen European Conference on Machine Learning, 654-662, 2006 | 187 | 2006 |
| Active guidance for a finless rocket using neuroevolution F Gomez, R Miikkulainen International Conference on Genetic and Evolutionary Computation—GECCO 2003 …, 2003 | 181 | 2003 |
| Evolving deep unsupervised convolutional networks for vision-based reinforcement learning J Koutník, J Schmidhuber, F Gomez Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014 | 168 | 2014 |
| Evolino: Hybrid neuroevolution/optimal linear search for sequence prediction J Schmidhuber, D Wierstra, FJ Gomez Proceedings of the 19th International Joint Conferenceon Artificial …, 2005 | 154 | 2005 |
| When novelty is not enough G Cuccu, F Gomez Evostar 2011 (Turin), 234-243, 2011 | 134 | 2011 |
| Evolving neural networks in compressed weight space J Koutník, F Gomez, J Schmidhuber Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO-10), 2010 | 117 | 2010 |
| Co-evolving recurrent neurons learn deep memory POMDPs FJ Gomez, J Schmidhuber Proceedings of the 2005 Conference on Genetic and Evolutionary Computation …, 2005 | 115 | 2005 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoVerified email at kaust.edu.sa
