Faustino Gomez

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Indice H	31	26
i10-index	48	33

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Coautori

Juergen SchmidhuberThe Swiss AI Lab IDSIA / USI & SUPSIEmail verificata su idsia.ch
Risto MiikkulainenProfessor of Computer Science, the University of Texas at AustinEmail verificata su cs.utexas.edu
Jan KoutníkDirector of Intelligent Automation, NNAISENSEEmail verificata su idsia.ch
Jonathan MasciNNAISENSE SAEmail verificata su nnaisense.com
Giuseppe CuccuUniversity of FribourgEmail verificata su unifr.ch
Alex GravesUniversity of TorontoEmail verificata su cs.toronto.edu
Santiago FernandezIK4-TeknikerEmail verificata su tekniker.es
Yi SunGoogleEmail verificata su idsia.ch
Matteo GaglioloAssociate Professor. GERME, Institute of Sociology, Université libre de Bruxelles, Brussels, BelgiumEmail verificata su ulb.ac.be
Rupesh Kumar SrivastavaNNAISENSEEmail verificata su nnaisense.com
Tom SchaulSenior Staff Research Scientist, DeepMindEmail verificata su nyu.edu
Marijn StollengaIDSIA/Supsi, USIEmail verificata su idsia.ch
Julian TogeliusAssociate Professor of Computer Science and Engineering, New York University; co-founder, modl.aiEmail verificata su nyu.edu
Klaus GreffMachine Learning PhD Student, Università della Svizzera ItalianaEmail verificata su usi.ch
Wojciech JaśkowskiNNAISENSEEmail verificata su nnaisense.com
Marco GallieriSenior Researcher, NNAISENSE SA. Former F1 Engineer, Cambridge PhD.Email verificata su nnaisense.com
Doug BurgerTechnical Fellow, Microsoft AzureEmail verificata su microsoft.com
Mark RingIDSIAEmail verificata su markring.com
Matthew LuciwPrincipal Scientist, NeuralaEmail verificata su idsia.ch
Christian IgelDepartment of Computer Science, University of CopenhagenEmail verificata su diku.dk

Faustino Gomez

CEO NNAISENSE SA. Former Senior Researcher, IDSIA, Switzerland

Email verificata su idsia.ch

AI Neural Networks Evolutionary Reinforcement Learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	2853	2006
Incremental evolution of complex general behavior F Gomez, R Miikkulainen Adaptive Behavior 5 (3-4), 317-342, 1997	565	1997
A Clockwork RNN J Koutnik, K Greff, F Gomez, J Schimdhuber International Conference on Machine Learning (ICML), 2014	405	2014
Accelerated Neural Evolution through Cooperatively Coevolved Synapses. F Gomez, J Schmidhuber, R Miikkulainen, M Mitchell Journal of Machine Learning Research 9 (5), 2008	330	2008
Solving non-Markovian control tasks with neuroevolution FJ Gomez, R Miikkulainen IJCAI 99, 1356-1361, 1999	313	1999
Training recurrent networks by evolino J Schmidhuber, D Wierstra, M Gagliolo, F Gomez Neural Computation 19 (3), 757-779, 2007	272	2007
Deep networks with internal selective attention through feedback connections M Stollenga, J Masci, F Gomez, J Schmidhuber arXiv preprint arXiv:1407.3068, 2014	234	2014
Robust non-linear control through neuroevolution FJ Gomez Computer Science Department, University of Texas, 2003	206	2003
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	205	2008
Efficient non-linear control through neuroevolution F Gomez, J Schmidhuber, R Miikkulainen European Conference on Machine Learning, 654-662, 2006	169	2006
Compete to Compute. RK Srivastava, J Masci, S Kazerounian, FJ Gomez, J Schmidhuber NIPS, 2310-2318, 2013	168	2013
Active guidance for a finless rocket using neuroevolution F Gomez, R Miikkulainen International Conference on Genetic and Evolutionary Computation—GECCO 2003 …, 2003	157	2003
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	144	2013
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	114	2014
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	111	2011
Evolino: Hybrid neuroevolution/optimal linear search for sequence prediction J Schmidhuber, D Wierstra, FJ Gomez Proceedings of the 19th International Joint Conferenceon Artificial …, 2005	104	2005
When novelty is not enough G Cuccu, F Gomez Evostar 2011 (Turin), 234-243, 2011	97	2011
Sustaining diversity using behavioral information distance FJ Gomez Proceedings of the 11th Annual Conference on Genetic and Evolutionary …, 2009	79	2009
Co-evolving recurrent neurons learn deep memory POMDPs FJ Gomez, J Schmidhuber Proceedings of the 2005 Conference on Genetic and Evolutionary Computation …, 2005	75	2005
2-D pole-balancing with recurrent evolutionary networks F Gomez, R Miikkulainen Proceedings of the International Conference on Artificial Neural Networks …, 1998	73	1998

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