Modeling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification Through Inferential Knowledge
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- @InProceedings{Olague:2022:GPTP,
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author = "Gustavo Olague and Matthieu Olague and
Gerardo Ibarra-Vazquez and Isnardo Reducindo and
Aaron Barrera and Axel Martinez and Jose Luis Briseno",
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title = "Modeling Hierarchical Architectures with Genetic
Programming and Neuroscience Knowledge for Image
Classification Through Inferential Knowledge",
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booktitle = "Genetic Programming Theory and Practice XIX",
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year = "2022",
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editor = "Leonardo Trujillo and Stephan M. Winkler and
Sara Silva and Wolfgang Banzhaf",
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series = "Genetic and Evolutionary Computation",
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pages = "141--166",
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address = "Ann Arbor, USA",
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month = jun # " 2-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-19-8459-4",
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URL = "http://link.springer.com/chapter/10.1007/978-981-19-8460-0_7",
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DOI = "doi:10.1007/978-981-19-8460-0_7",
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abstract = "Brain programming is a methodology based on the idea
that templates are necessary to describe artificial
dorsal and ventral streams and their combination into
an artificial visual cortex. We review the main
concerns by introducing some initial thoughts about the
status of genetic programming and other methodologies
related to our research work. This chapter proposes the
hierarchical integration of two architectures
(templates) to enhance the quality of acquiring
artificial visual percepts. We theoretically justified
the necessity for designing manual hierarchical
architectures. Planning complex structures through
inferential knowledge simplify the design while
adopting current technology. The methodology base its
analysis on providing domain knowledge (neuroscience)
at a higher level while looking for better
computational structures within a local (lower) level.
The efficiency of searching for optimal architectural
configurations proceeds from deductive and inductive
reasoning. This chapter brings a proposal of abductive
reasoning to enrich the brain programming paradigm by
taking advantage of computational re-use of dorsal
stream discoveries while enhancing the overall
complexity of the final proposal. We propose a Visual
Turing test to establish the quality of the proposal in
comparison with the state of the art. The results show
that our methodology can produce consistent outcomes
during training and testing, representing significant
progress toward thought representation.",
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notes = "Part of \cite{Banzhaf:2022:GPTP} published after the
workshop in 2023",
- }
Genetic Programming entries for
Gustavo Olague
Matthieu Olague
Gerardo Ibarra-Vazquez
Isnardo Reducindo Ruiz
Aaron Barrera
Axel Martinez
Jose Luis Briseno Cervantes
Citations