F. Gomez

发表

Measuring and Optimizing Behavioral Complexity

J. Schmidhuber, F. Gomez, J. Togelius, 2009 .

Evolving deep unsupervised convolutional networks for vision-based reinforcement learning

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2014, GECCO.

Intrinsically Motivated Evolutionary Search for Vision-Based Reinforcement Learning

Faustino J. Gomez, Giuseppe Cuccu, Faustino Gomez, 2011 .

Online Evolution of Deep Convolutional Network for Vision-Based Reinforcement Learning

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2014, SAB.

Evolving large-scale neural networks for vision-based reinforcement learning

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2013, GECCO '13.

Solving Non-Markovian Control Tasks with Neuro-Evolution

Risto Miikkulainen, Faustino J. Gomez, R. Miikkulainen, 1999, IJCAI.

Accelerated Neural Evolution through Cooperatively Coevolved Synapses

Risto Miikkulainen, Jürgen Schmidhuber, Faustino J. Gomez, 2008, J. Mach. Learn. Res..

Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2011, AGI.

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Jürgen Schmidhuber, Alois Knoll, Faustino J. Gomez, 2006 .

When Novelty Is Not Enough

Faustino J. Gomez, Giuseppe Cuccu, F. Gomez, 2011, EvoApplications.

Compete to Compute

Jürgen Schmidhuber, Jonathan Masci, Faustino J. Gomez, 2013, NIPS.

Intrinsically motivated neuroevolution for vision-based reinforcement learning

Jurgen Schmidhuber, Giuseppe Cuccu, Matthew Luciw, 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

Robust non-linear control through neuroevolution

Risto Miikkulainen, Faustino Gomez, R. Miikkulainen, 2003 .

2-D Pole Balancing with Recurrent Evolutionary Networks

Risto Miikkulainen, Faustino Gomez, R. Miikkulainen, 1998 .

The IM-CLeVeR Project : Intrinsically Motivated Cumulative Learning Versatile Robots

Julian Togelius, Francesco Mannella, Jürgen Schmidhuber, 2009 .

A Clockwork RNN

Jürgen Schmidhuber, Jan Koutník, Klaus Greff, 2014, ICML.

Training Recurrent Networks by Evolino

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2007, Neural Computation.

Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, IJCAI.

Modeling systems with internal state using evolino

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, GECCO '05.

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Jürgen Schmidhuber, Alois Knoll, Faustino J. Gomez, 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

Christian Osendorfer, Jonathan Masci, Faustino J. Gomez, 2018, NeurIPS.

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2006, ICML.

Incremental Evolution of Complex General Behavior

Risto Miikkulainen, Faustino J. Gomez, R. Miikkulainen, 1997, Adapt. Behav..

Evolino for recurrent support vector machines pdf

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, ESANN.

Evolino: Hybrid Neuroevolution / Optimal Linear Search for Sequence Prediction

Jürgen Schmidhuber, Daan Wierstra, Faustino Gomez, 2005, IJCAI 2005.

Deep Networks with Internal Selective Attention through Feedback Connections

Jürgen Schmidhuber, Marijn F. Stollenga, Jonathan Masci, 2014, NIPS.

Efficient Non-linear Control Through Neuroevolution

Risto Miikkulainen, Jürgen Schmidhuber, Faustino J. Gomez, 2006, ECML.

Understanding Locally Competitive Networks pdf

Jürgen Schmidhuber, Jonathan Masci, Faustino J. Gomez, 2014, ICLR.

Searching for Minimal Neural Networks in Fourier Space

Jürgen Schmidhuber, Jan Koutník, Faustino Gomez, 2010, AGI 2010.

Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning

Julian Togelius, Jürgen Schmidhuber, Faustino J. Gomez, 2009, ICANN.

Active Guidance for a Finless Rocket Using Neuroevolution

Risto Miikkulainen, Faustino J. Gomez, R. Miikkulainen, 2003, GECCO.

Novelty-based restarts for evolution strategies

Faustino J. Gomez, Giuseppe Cuccu, Tobias Glasmachers, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Nascence Project: Nanoscale Engineering for Novel Computation Using Evolution

Julian Francis Miller, Gunnar Tufte, Faustino J. Gomez, 2012, Int. J. Unconv. Comput..

On the Size of the Online Kernel Sparsification Dictionary pdf

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2012, ICML.

Recurrent Support Vector Machines

Faustino J. Gomez, J. Schmidhuber, Daan Wierstra, 2005 .

1 5 D ec 2 00 5 Evolino for Recurrent Support Vector Machines

Faustino J. Gomez, J. Schmidhuber, Daan Wierstra, 2022 .

Compressed Network Complexity Search

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2012, PPSN.

Automatic design of novel potential 3CLpro and PLpro inhibitors pdf

Saeed Saremi, Jonathan Masci, Faustino J. Gomez, 2021, ArXiv.

Sustaining diversity using behavioral information distance

Faustino J. Gomez, F. Gomez, 2009, GECCO.

Safe Interactive Model-Based Learning pdf

N. E. Toklu, Jonathan Masci, Nihat Engin Toklu, 2019, ArXiv.

Model-Based Active Exploration pdf

Wojciech Jaskowski, Faustino J. Gomez, Faustino Gomez, 2018, ICML.

Modular deep belief networks that do not forget

Jürgen Schmidhuber, Faustino J. Gomez, Leo Pape, 2011, The 2011 International Joint Conference on Neural Networks.

Curiosity-driven optimization

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Evolving neural networks in compressed weight space

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2010, GECCO '10.

Scalable Neuroevolution for Reinforcement Learning

Faustino J. Gomez, F. Gomez, 2012, TPNC.

A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients

Jürgen Schmidhuber, José David Martín-Guerrero, Emilio Soria-Olivas, 2009, Expert Syst. Appl..

Co-evolving recurrent neurons learn deep memory POMDPs

Jürgen Schmidhuber, Faustino J. Gomez, J. Schmidhuber, 2005, GECCO '05.

T-Learning pdf

Faustino J. Gomez, Vincent Graziano, Mark B. Ring, 2011, ArXiv.

A linear time natural evolution strategy for non-separable functions

Tom Schaul, Faustino Gomez, Yi Sun, 2011, GECCO.

Transfer of Neuroevolved Controllers in Unstable Domains

Risto Miikkulainen, Faustino J. Gomez, R. Miikkulainen, 2004, GECCO.

Automatic design of novel potential 3CLpro and PLpro inhibitors

Saeed Saremi, Jonathan Masci, Timothy Atkinson, 2021, ArXiv.

Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot pdf

Jürgen Schmidhuber, Marcus Hutter, Faustino J. Gomez, 2006, IAS.

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

J. Schmidhuber, F. Gomez, Santiago Fernández, 2006, ICML.

Incremental Basis Construction from Temporal Difference Error

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2011, ICML.

Ontogenetic and Phylogenetic Reinforcement Learning

Julian Togelius, Tom Schaul, Jürgen Schmidhuber, 2009, Künstliche Intell..

Evolving Modular Fast-Weight Networks for Control

Jürgen Schmidhuber, Faustino J. Gomez, J. Schmidhuber, 2005, ICANN.