Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Sjoerd van Steenkiste"'
Publikováno v:
Neural Networks. 130:309-325
Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9af1ef3bb037b2edda21d24b4fad5376
https://doi.org/10.1016/j.neunet.2020.07.007
https://doi.org/10.1016/j.neunet.2020.07.007
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics. The notion of an abstract object, however, encompasses a wide variety of physical objects that differ greatly in terms of the co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7e0e08249406397301c4e799458fd32
Publikováno v:
GECCO
Proceedings of the Genetic and Evolutionary Computation Conference, 517-524
STARTPAGE=517;ENDPAGE=524;TITLE=Proceedings of the Genetic and Evolutionary Computation Conference
Proceedings of the Genetic and Evolutionary Computation Conference, 517-524
STARTPAGE=517;ENDPAGE=524;TITLE=Proceedings of the Genetic and Evolutionary Computation Conference
A new indirect scheme for encoding neural network connection weights as sets of wavelet-domain coefficients is proposed in this paper. It exploits spatial regularities in the weight-space to reduce the gene-space dimension by considering the low-freq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f13add70f1e3167db1bcd0db914a062
https://doi.org/10.1145/2908812.2908905
https://doi.org/10.1145/2908812.2908905