Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Anil Yaman"'
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 2, p e1009882 (2022)
Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the
Externí odkaz:
https://doaj.org/article/2bcc431db99840bb8af560441a3e0721
Publikováno v:
SSRN Electronic Journal.
With the advent of cheap, miniaturized electronics, ubiquitous networking has reached an unprecedented level of complexity, scale and heterogeneity, becoming the core of several modern applications such as smart industry, smart buildings and smart ci
Publikováno v:
2021 IEEE Symposium Series on Computational Intelligence (SSCI).
Autor:
Matt Coler, Anil Yaman, Decebal Constantin Mocanu, George H. L. Fletcher, Giovanni Iacca, Mykola Pechenizkiy
Publikováno v:
ArXiv. Cornell University Press
arXiv, 2019:1904.01709v1. Cornell University Library
Evolutionary Computation, 29(3), 391-414. MIT Press Journals
Evolutionary Computation. MIT Press
Pure TUe
arXiv, 2019:1904.01709v1. Cornell University Library
Evolutionary Computation, 29(3), 391-414. MIT Press Journals
Evolutionary Computation. MIT Press
Pure TUe
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity prope
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ddcf712d7f263f2ded5b1df510ca76c
https://direct.mit.edu/evco/article-abstract/29/3/391/97351/Evolving-Plasticity-for-Autonomous-Learning-under?redirectedFrom=fulltext
https://direct.mit.edu/evco/article-abstract/29/3/391/97351/Evolving-Plasticity-for-Autonomous-Learning-under?redirectedFrom=fulltext
Autor:
George H. L. Fletcher, Giovanni Iacca, Mykola Pechenizkiy, Decebal Constantin Mocanu, Anil Yaman
Publikováno v:
GECCO Companion
GECCO'20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 93-94
STARTPAGE=93;ENDPAGE=94;TITLE=GECCO'20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
GECCO'20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 93-94
STARTPAGE=93;ENDPAGE=94;TITLE=GECCO'20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
A learning process with the plasticity property often requires reinforcement signals to guide the process. However, in some tasks (e.g. maze-navigation), it is very difficult (or impossible) to measure the performance of an agent (i.e. a fitness valu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53fb0da65fd78eb6c99d589c29fad594
http://arxiv.org/abs/2002.03620
http://arxiv.org/abs/2002.03620
Autor:
Giovanni Iacca, Decebal Constantin Mocanu, Anil Yaman, Mykola Pechenizkiy, George H. L. Fletcher
Publikováno v:
GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 152-160
STARTPAGE=152;ENDPAGE=160;TITLE=GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference
STARTPAGE=152;ENDPAGE=160;TITLE=GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference
The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76528cd1df32b169c9793cbbd949283b
https://doi.org/10.1145/3321707.3321723
https://doi.org/10.1145/3321707.3321723
Publikováno v:
Journal of Chemical Theory and Computation, 15(3), 1777-1784. American Chemical Society
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation
[Image: see text] We present a general framework for the construction of a deep feedforward neural network (FFNN) to predict distance and orientation dependent electronic coupling elements in disordered molecular materials. An evolutionary algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5c4c769f6a2f8913486c781d943f41f
https://research.tue.nl/nl/publications/7423b880-c038-4e69-a14f-885c8808caf8
https://research.tue.nl/nl/publications/7423b880-c038-4e69-a14f-885c8808caf8
Autor:
Anil Yaman, Giovanni Iacca
Publikováno v:
Applied Soft Computing. 101:106993
In several network problems the optimal behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the agents might be required to adapt, i.e. change their behavior based on the environment conditions. In the
Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution
Autor:
Decebal Constantin Mocanu, George H. L. Fletcher, Mykola Pechenizkiy, Giovanni Iacca, Anil Yaman
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference on-GECCO 18
Proceedings of the Genetic and Evolutionary Computation Conference on -GECCO '18
Proceedings of the Genetic and Evolutionary Computation Conference
GECCO
GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference, 569-576
STARTPAGE=569;ENDPAGE=576;TITLE=GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Proceedings of the Genetic and Evolutionary Computation Conference on -GECCO '18
Proceedings of the Genetic and Evolutionary Computation Conference
GECCO
GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference, 569-576
STARTPAGE=569;ENDPAGE=576;TITLE=GECCO 2018-Proceedings of the 2018 Genetic and Evolutionary Computation Conference
Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architectures tend to be large. Neuroevolution is an effective approach for opt
Autor:
Andrew Goldstein, Shreya Chakrabarti, Tian Kang, Anil Yaman, Anando Sen, Patrick B. Ryan, Chunhua Weng, Ning Shang
Publikováno v:
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association, 25(3), 239-247. Oxford University Press
Journal of the American Medical Informatics Association, 25(3), 239-247. Oxford University Press
Objective The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between re