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Models/NASGEP

NASGEP

Reported on 8 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology8 results

  • Neural Architecture SearchonCIFAR-100
    Percentage Error· 2020-05-15
    18.83
    best: 11.7 (DNA-c)
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • Neural Architecture SearchonCIFAR-10
    Search Time (GPU days)· 2020-05-15
    1
    best: 224 (AlphaX-1 (cutout NASNet))
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • Neural Architecture SearchonImageNet
    Accuracy· 2020-05-15
    70.49
    best: 83.9 (DeepMAD-50M)
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • Neural Architecture SearchonImageNet
    Top-1 Error Rate· 2020-05-15
    29.51
    best: 16.1 (DeepMAD-50M)
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • AutoMLonCIFAR-100
    Percentage Error· 2020-05-15
    18.83
    best: 11.7 (DNA-c)
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • AutoMLonCIFAR-10
    Search Time (GPU days)· 2020-05-15
    1
    best: 224 (AlphaX-1 (cutout NASNet))
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • AutoMLonImageNet
    Accuracy· 2020-05-15
    70.49
    best: 83.9 (DeepMAD-50M)
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669
  • AutoMLonImageNet
    Top-1 Error Rate· 2020-05-15
    29.51
    best: 16.1 (DeepMAD-50M)
    Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming ApproacharXiv:2005.07669