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Create Multi-Objectives Optimization Benchmarks for Deep Neural Networks. #6

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Deathn0t opened this issue Mar 10, 2023 · 2 comments
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@Deathn0t
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Similarly to ECP-Candle and PINNBench benchmarks some new benchmarks compatible with Multi-Objective Optimization (MOO) should be defined. They could be "extensions" of the already existing benchmark but compatible with MOO.

@Deathn0t
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I discovered 2 benchmarks related to MOO for Deep Neural Networks (AutoML):

  • YAHPO
  • JAHS-Bench-201
    I created the corresponding folders in the lib/ directory with a link to the Github repository of these benchmarks in the README.md.

@thchang
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thchang commented Mar 17, 2023

Thanks, I'll check this out when I'm done with the analytical test funcitons for #5

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