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Countering Poisonous Inputs with Memetic Neuroevolution.

J. Togelius, T. Schaul, J. Schmidhuber, and F. Gomez. PPSN, volume 5199 of Lecture Notes in Computer Science, page 610-619. Springer, (2008)

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Tom Ott University of Stuttgart

Tom Tenzer University of Stuttgart

Katharine Tomsich University of Stuttgart

Sandra Toms University of Stuttgart

Dr. phil. André Tomalka University of Stuttgart

Other publications of authors with the same name

AI for the Social Good (Dagstuhl Seminar 19082).C. Clopath, R. Winne, M. Khan, and T. Schaul. Dagstuhl Reports, 9 (2): 111-122 (2019)A Linear Time Natural Evolution Strategy for Non-Separable FunctionsY. Sun, F. Gomez, T. Schaul, and J. Schmidhuber. CoRR, (2011)Deep Q-learning From Demonstrations.T. Hester, M. Vecerík, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband and 4 other author(s). AAAI, page 3223-3230. AAAI Press, (2018)Episodic Reinforcement Learning by Logistic Reward-Weighted Regression.D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber. ICANN (1), volume 5163 of Lecture Notes in Computer Science, page 407-416. Springer, (2008)Stochastic search using the natural gradient.Y. Sun, D. Wierstra, T. Schaul, and J. Schmidhuber. ICML, volume 382 of ACM International Conference Proceeding Series, page 1161-1168. ACM, (2009)No more pesky learning rates.T. Schaul, S. Zhang, and Y. LeCun. ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 343-351. JMLR.org, (2013)Efficient natural evolution strategies.Y. Sun, D. Wierstra, T. Schaul, and J. Schmidhuber. GECCO, page 539-546. ACM, (2009)Benchmarking separable natural evolution strategies on the noiseless and noisy black-box optimization testbeds.T. Schaul. GECCO (Companion), page 205-212. ACM, (2012)Exponential natural evolution strategies.T. Glasmachers, T. Schaul, Y. Sun, D. Wierstra, and J. Schmidhuber. GECCO, page 393-400. ACM, (2010)Adapting Behaviour for Learning Progress.T. Schaul, D. Borsa, D. Ding, D. Szepesvari, G. Ostrovski, W. Dabney, and S. Osindero. CoRR, (2019)