Permutation-Invariant Representation of Neural Networks with Neuron Embeddings
Created by W.Langdon from
gp-bibliography.bib Revision:1.6704
- @InProceedings{Zhou:2022:EuroGP,
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author = "Ryan Zhou and Christian Muise and Ting Hu",
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title = "Permutation-Invariant Representation of Neural
Networks with Neuron Embeddings",
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booktitle = "EuroGP 2022: Proceedings of the 25th European
Conference on Genetic Programming",
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year = "2022",
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editor = "Eric Medvet and Gisele Pappa and Bing Xue",
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series = "LNCS",
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volume = "13223",
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publisher = "Springer Verlag",
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address = "Madrid, Spain",
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pages = "294--308",
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month = "20-22 " # apr,
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, ANN,
Neuroevolution, Indirect Encoding, Neural Networks,
Convolutional Neural Networks, Crossover, Permutation
Invariance: Poster",
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isbn13 = "978-3-031-02055-1",
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DOI = "
doi:10.1007/978-3-031-02056-8_19",
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abstract = "Neural networks are traditionally represented in terms
of their weights. A key property of this representation
is that there are multiple representations of a network
which can be obtained by permuting the order of the
neurons. These representations are generally not
compatible between networks, making recombination a
challenge for two arbitrary neural networks - an issue
known as the “permutation problem” in
neuroevolution. This paper proposes an indirect
encoding in which a neural network is represented in
terms of interactions between neurons rather than
explicit weights, and which works for both fully
connected and convolutional networks. In addition to
reducing the number of free parameters, this encoding
is agnostic to the ordering of neurons, bypassing a key
problem for direct weight-based representation. This
allows us to transplant individual neurons and layers
into another network without accounting for the
specific ordering of neurons. We show through
experiments on the MNIST and CIFAR-10 datasets that
this method is capable of representing networks which
achieve comparable performance to direct weight
representation, and that combining networks this way
preserves a larger degree of performance than through
direct weight transfer.",
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notes = "http://www.evostar.org/2022/eurogp/ Part of
\cite{Medvet:2022:GP} EuroGP'2022 held inconjunction
with EvoApplications2022 EvoCOP2022 EvoMusArt2022",
- }
Genetic Programming entries for
Ryan Zhou
Christian Muise
Ting Hu
Citations