Datasets:
Samsoup
/
GraphArch

Dataset card Data Studio Files
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measurement_descriptions
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GraphArch
architecture_regression
GraphArch:Hiaml_1020
GraphArch:Hiaml:1020
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_423[FLOAT, 64x3x3x3] %onnx::Conv_424[FLOAT, 64] %onnx::Conv_426[FLOAT, 64x64x3x3] %onnx::Conv_429[FLOAT, 64x64x1x3] %onnx::Conv_432[FLOAT, 64x64x3x1] %onnx::Conv_435[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 230635008, "params": 2652234, "val_accuracy": 92.22 }
{ "arch_str": "1020", "identifier": "Hiaml_1020", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1020" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1020
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 230635008, "val_accuracy": 92.22 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_4243
GraphArch:Hiaml:4243
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_430[FLOAT, 64x3x3x3] %onnx::Conv_431[FLOAT, 64] %onnx::Conv_433[FLOAT, 64x64x3x3] %onnx::Conv_436[FLOAT, 64x64x1x1] %onnx::Conv_439[FLOAT, 64x64x3x3] %onnx::Conv_442[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 209597952, "params": 2512714, "val_accuracy": 92.1 }
{ "arch_str": "4243", "identifier": "Hiaml_4243", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "4243" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
4243
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 209597952, "val_accuracy": 92.1 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3070
GraphArch:Hiaml:3070
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_458[FLOAT, 64x3x3x3] %onnx::Conv_459[FLOAT, 64] %onnx::Conv_461[FLOAT, 64x64x1x1] %onnx::Conv_464[FLOAT, 64x64x3x3] %onnx::Conv_467[FLOAT, 64x64x3x3] %onnx::Conv_470[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 247608832, "params": 2586698, "val_accuracy": 92.78 }
{ "arch_str": "3070", "identifier": "Hiaml_3070", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3070" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3070
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 247608832, "val_accuracy": 92.78 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3409
GraphArch:Hiaml:3409
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_489[FLOAT, 64x3x3x3] %onnx::Conv_490[FLOAT, 64] %onnx::Conv_492[FLOAT, 64x64x3x3] %onnx::Conv_495[FLOAT, 64x64x1x1] %onnx::Conv_498[FLOAT, 64x64x1x1] %onnx::Conv_501[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 211072512, "params": 2620490, "val_accuracy": 91.56 }
{ "arch_str": "3409", "identifier": "Hiaml_3409", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3409" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3409
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 211072512, "val_accuracy": 91.56 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_464
GraphArch:Hiaml:464
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_339[FLOAT, 64x3x3x3] %onnx::Conv_340[FLOAT, 64] %onnx::Conv_342[FLOAT, 64x64x3x3] %onnx::Conv_345[FLOAT, 64x64x1x3] %onnx::Conv_348[FLOAT, 64x64x3x1] %onnx::Conv_351[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 209368576, "params": 1908170, "val_accuracy": 92.55 }
{ "arch_str": "464", "identifier": "Hiaml_464", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "464" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
464
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 209368576, "val_accuracy": 92.55 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_245
GraphArch:Hiaml:245
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_389[FLOAT, 64x3x3x3] %onnx::Conv_390[FLOAT, 64] %onnx::Conv_392[FLOAT, 64x64x1x1] %onnx::Conv_395[FLOAT, 64x64x3x3] %onnx::Conv_398[FLOAT, 64x64x3x3] %onnx::Conv_401[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 244233728, "params": 3272778, "val_accuracy": 92.36 }
{ "arch_str": "245", "identifier": "Hiaml_245", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "245" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
245
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 244233728, "val_accuracy": 92.36 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_4401
GraphArch:Hiaml:4401
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_485[FLOAT, 64x3x3x3] %onnx::Conv_486[FLOAT, 64] %onnx::Conv_488[FLOAT, 64x64x1x1] %onnx::Conv_491[FLOAT, 64x64x1x1] %onnx::Conv_494[FLOAT, 64x64x3x3] %onnx::Conv_497[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 240694784, "params": 3413834, "val_accuracy": 91.67 }
{ "arch_str": "4401", "identifier": "Hiaml_4401", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "4401" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
4401
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 240694784, "val_accuracy": 91.67 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2805
GraphArch:Hiaml:2805
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_499[FLOAT, 64x3x3x3] %onnx::Conv_500[FLOAT, 64] %onnx::Conv_502[FLOAT, 64x64x1x1] %onnx::Conv_505[FLOAT, 64x64x1x1] %onnx::Conv_508[FLOAT, 64x64x3x3] %onnx::Conv_511[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 210220544, "params": 2587722, "val_accuracy": 91.78 }
{ "arch_str": "2805", "identifier": "Hiaml_2805", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2805" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2805
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 210220544, "val_accuracy": 91.78 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_527
GraphArch:Hiaml:527
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_452[FLOAT, 64x3x3x3] %onnx::Conv_453[FLOAT, 64] %onnx::Conv_455[FLOAT, 64x64x1x1] %onnx::Conv_458[FLOAT, 64x64x3x3] %onnx::Conv_461[FLOAT, 64x64x3x3] %onnx::Conv_464[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 214054400, "params": 2324554, "val_accuracy": 92.71 }
{ "arch_str": "527", "identifier": "Hiaml_527", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "527" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
527
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 214054400, "val_accuracy": 92.71 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2580
GraphArch:Hiaml:2580
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_471[FLOAT, 64x3x3x3] %onnx::Conv_472[FLOAT, 64] %onnx::Conv_474[FLOAT, 64x64x3x3] %onnx::Conv_477[FLOAT, 64x64x1x3] %onnx::Conv_480[FLOAT, 64x64x3x1] %onnx::Conv_483[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 243611136, "params": 2700618, "val_accuracy": 92.56 }
{ "arch_str": "2580", "identifier": "Hiaml_2580", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2580" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2580
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 243611136, "val_accuracy": 92.56 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2231
GraphArch:Hiaml:2231
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_524[FLOAT, 64x3x3x3] %onnx::Conv_525[FLOAT, 64] %onnx::Conv_527[FLOAT, 64x64x1x3] %onnx::Conv_530[FLOAT, 64x64x3x1] %onnx::Conv_533[FLOAT, 64x64x1x3] %onnx::Conv_536[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 210286080, "params": 2588746, "val_accuracy": 92.1 }
{ "arch_str": "2231", "identifier": "Hiaml_2231", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2231" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2231
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 210286080, "val_accuracy": 92.1 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2117
GraphArch:Hiaml:2117
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_543[FLOAT, 64x3x3x3] %onnx::Conv_544[FLOAT, 64] %onnx::Conv_546[FLOAT, 64x64x1x3] %onnx::Conv_549[FLOAT, 64x64x3x1] %onnx::Conv_552[FLOAT, 64x64x1x1] %onnx::Conv_555[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 214840832, "params": 2473546, "val_accuracy": 91.95 }
{ "arch_str": "2117", "identifier": "Hiaml_2117", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2117" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2117
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 214840832, "val_accuracy": 91.95 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3165
GraphArch:Hiaml:3165
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_363[FLOAT, 64x3x3x3] %onnx::Conv_364[FLOAT, 64] %onnx::Conv_366[FLOAT, 64x64x1x1] %onnx::Conv_369[FLOAT, 64x64x1x3] %onnx::Conv_372[FLOAT, 64x64x3x1] %onnx::Conv_375[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 160183808, "params": 1953610, "val_accuracy": 92.25 }
{ "arch_str": "3165", "identifier": "Hiaml_3165", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3165" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3165
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 160183808, "val_accuracy": 92.25 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3776
GraphArch:Hiaml:3776
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_458[FLOAT, 64x3x3x3] %onnx::Conv_459[FLOAT, 64] %onnx::Conv_461[FLOAT, 64x64x1x1] %onnx::Conv_464[FLOAT, 64x64x1x1] %onnx::Conv_467[FLOAT, 64x64x3x3] %onnx::Conv_470[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 219526656, "params": 2168650, "val_accuracy": 92.26 }
{ "arch_str": "3776", "identifier": "Hiaml_3776", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3776" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3776
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 219526656, "val_accuracy": 92.26 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2661
GraphArch:Hiaml:2661
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_475[FLOAT, 64x3x3x3] %onnx::Conv_476[FLOAT, 64] %onnx::Conv_478[FLOAT, 64x64x1x3] %onnx::Conv_481[FLOAT, 64x64x3x1] %onnx::Conv_484[FLOAT, 64x64x1x3] %onnx::Conv_487[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 212252160, "params": 2579274, "val_accuracy": 92.49 }
{ "arch_str": "2661", "identifier": "Hiaml_2661", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2661" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2661
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 212252160, "val_accuracy": 92.49 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3420
GraphArch:Hiaml:3420
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_465[FLOAT, 64x3x3x3] %onnx::Conv_466[FLOAT, 64] %onnx::Conv_468[FLOAT, 64x64x3x3] %onnx::Conv_471[FLOAT, 64x64x1x1] %onnx::Conv_474[FLOAT, 64x64x1x1] %onnx::Conv_477[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 194295296, "params": 2094666, "val_accuracy": 92.1 }
{ "arch_str": "3420", "identifier": "Hiaml_3420", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3420" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3420
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 194295296, "val_accuracy": 92.1 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_27
GraphArch:Hiaml:27
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_481[FLOAT, 64x3x3x3] %onnx::Conv_482[FLOAT, 64] %onnx::Conv_484[FLOAT, 64x64x1x3] %onnx::Conv_487[FLOAT, 64x64x3x1] %onnx::Conv_490[FLOAT, 64x64x1x1] %onnx::Conv_493[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 214545920, "params": 2594122, "val_accuracy": 92.24 }
{ "arch_str": "27", "identifier": "Hiaml_27", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "27" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
27
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 214545920, "val_accuracy": 92.24 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3910
GraphArch:Hiaml:3910
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_337[FLOAT, 64x3x3x3] %onnx::Conv_338[FLOAT, 64] %onnx::Conv_340[FLOAT, 64x64x3x3] %onnx::Conv_343[FLOAT, 64x64x1x1] %onnx::Conv_346[FLOAT, 64x64x3x3] %onnx::Conv_349[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 168343040, "params": 1920330, "val_accuracy": 92.84 }
{ "arch_str": "3910", "identifier": "Hiaml_3910", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3910" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3910
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 168343040, "val_accuracy": 92.84 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_4199
GraphArch:Hiaml:4199
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_393[FLOAT, 64x3x3x3] %onnx::Conv_394[FLOAT, 64] %onnx::Conv_396[FLOAT, 64x64x1x1] %onnx::Conv_399[FLOAT, 64x64x3x3] %onnx::Conv_402[FLOAT, 64x64x3x3] %onnx::Conv_405[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 285160960, "params": 2928458, "val_accuracy": 92.55 }
{ "arch_str": "4199", "identifier": "Hiaml_4199", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "4199" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
4199
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 285160960, "val_accuracy": 92.55 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1179
GraphArch:Hiaml:1179
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_483[FLOAT, 64x3x3x3] %onnx::Conv_484[FLOAT, 64] %onnx::Conv_486[FLOAT, 64x64x1x1] %onnx::Conv_489[FLOAT, 64x64x1x3] %onnx::Conv_492[FLOAT, 64x64x3x1] %onnx::Conv_495[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 202618368, "params": 2653770, "val_accuracy": 91.99 }
{ "arch_str": "1179", "identifier": "Hiaml_1179", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1179" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1179
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 202618368, "val_accuracy": 91.99 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2052
GraphArch:Hiaml:2052
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_455[FLOAT, 64x3x3x3] %onnx::Conv_456[FLOAT, 64] %onnx::Conv_458[FLOAT, 64x64x3x3] %onnx::Conv_461[FLOAT, 64x64x1x3] %onnx::Conv_464[FLOAT, 64x64x3x1] %onnx::Conv_467[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 198260224, "params": 2398794, "val_accuracy": 92.36 }
{ "arch_str": "2052", "identifier": "Hiaml_2052", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2052" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2052
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 198260224, "val_accuracy": 92.36 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_4537
GraphArch:Hiaml:4537
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_375[FLOAT, 64x3x3x3] %onnx::Conv_376[FLOAT, 64] %onnx::Conv_378[FLOAT, 64x64x3x3] %onnx::Conv_381[FLOAT, 64x64x1x3] %onnx::Conv_384[FLOAT, 64x64x3x1] %onnx::Conv_387[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 189642240, "params": 1862730, "val_accuracy": 92.95 }
{ "arch_str": "4537", "identifier": "Hiaml_4537", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "4537" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
4537
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 189642240, "val_accuracy": 92.95 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_884
GraphArch:Hiaml:884
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_429[FLOAT, 64x3x3x3] %onnx::Conv_430[FLOAT, 64] %onnx::Conv_432[FLOAT, 64x64x1x1] %onnx::Conv_435[FLOAT, 64x64x3x3] %onnx::Conv_438[FLOAT, 64x64x3x3] %onnx::Conv_441[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 222410240, "params": 2118474, "val_accuracy": 92.5 }
{ "arch_str": "884", "identifier": "Hiaml_884", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "884" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
884
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 222410240, "val_accuracy": 92.5 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1493
GraphArch:Hiaml:1493
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_389[FLOAT, 64x3x3x3] %onnx::Conv_390[FLOAT, 64] %onnx::Conv_392[FLOAT, 64x64x1x1] %onnx::Conv_395[FLOAT, 64x64x1x1] %onnx::Conv_398[FLOAT, 64x64x3x3] %onnx::Conv_401[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 242300416, "params": 2359242, "val_accuracy": 92.77 }
{ "arch_str": "1493", "identifier": "Hiaml_1493", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1493" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1493
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 242300416, "val_accuracy": 92.77 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3555
GraphArch:Hiaml:3555
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_420[FLOAT, 64x3x3x3] %onnx::Conv_421[FLOAT, 64] %onnx::Conv_423[FLOAT, 64x64x3x3] %onnx::Conv_426[FLOAT, 64x64x1x3] %onnx::Conv_429[FLOAT, 64x64x3x1] %onnx::Conv_432[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 224441856, "params": 1962570, "val_accuracy": 92.4 }
{ "arch_str": "3555", "identifier": "Hiaml_3555", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3555" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3555
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 224441856, "val_accuracy": 92.4 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1486
GraphArch:Hiaml:1486
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_427[FLOAT, 64x3x3x3] %onnx::Conv_428[FLOAT, 64] %onnx::Conv_430[FLOAT, 64x64x1x1] %onnx::Conv_433[FLOAT, 64x64x1x3] %onnx::Conv_436[FLOAT, 64x64x3x1] %onnx::Conv_439[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 264517120, "params": 2864202, "val_accuracy": 92.08 }
{ "arch_str": "1486", "identifier": "Hiaml_1486", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1486" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1486
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 264517120, "val_accuracy": 92.08 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_4165
GraphArch:Hiaml:4165
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_447[FLOAT, 64x3x3x3] %onnx::Conv_448[FLOAT, 64] %onnx::Conv_450[FLOAT, 64x64x1x1] %onnx::Conv_453[FLOAT, 64x64x1x3] %onnx::Conv_456[FLOAT, 64x64x3x1] %onnx::Conv_459[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 244758016, "params": 3420746, "val_accuracy": 92.93 }
{ "arch_str": "4165", "identifier": "Hiaml_4165", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "4165" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
4165
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 244758016, "val_accuracy": 92.93 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_4297
GraphArch:Hiaml:4297
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_383[FLOAT, 64x3x3x3] %onnx::Conv_384[FLOAT, 64] %onnx::Conv_386[FLOAT, 64x64x3x3] %onnx::Conv_389[FLOAT, 64x64x1x1] %onnx::Conv_392[FLOAT, 64x64x1x1] %onnx::Conv_395[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 212678144, "params": 2400714, "val_accuracy": 92.67 }
{ "arch_str": "4297", "identifier": "Hiaml_4297", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "4297" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
4297
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 212678144, "val_accuracy": 92.67 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2067
GraphArch:Hiaml:2067
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_353[FLOAT, 64x3x3x3] %onnx::Conv_354[FLOAT, 64] %onnx::Conv_356[FLOAT, 64x64x1x1] %onnx::Conv_359[FLOAT, 64x64x3x3] %onnx::Conv_362[FLOAT, 64x64x3x3] %onnx::Conv_365[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 235582976, "params": 2616394, "val_accuracy": 92.68 }
{ "arch_str": "2067", "identifier": "Hiaml_2067", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2067" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2067
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 235582976, "val_accuracy": 92.68 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2119
GraphArch:Hiaml:2119
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_397[FLOAT, 64x3x3x3] %onnx::Conv_398[FLOAT, 64] %onnx::Conv_400[FLOAT, 64x64x1x1] %onnx::Conv_403[FLOAT, 64x64x1x1] %onnx::Conv_406[FLOAT, 64x64x3x3] %onnx::Conv_409[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 210908672, "params": 2518858, "val_accuracy": 92.41 }
{ "arch_str": "2119", "identifier": "Hiaml_2119", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2119" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2119
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 210908672, "val_accuracy": 92.41 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2415
GraphArch:Hiaml:2415
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_327[FLOAT, 64x3x3x3] %onnx::Conv_328[FLOAT, 64] %onnx::Conv_330[FLOAT, 64x64x3x3] %onnx::Conv_333[FLOAT, 64x64x1x1] %onnx::Conv_336[FLOAT, 64x64x3x3] %onnx::Conv_339[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 204879360, "params": 2477898, "val_accuracy": 92.51 }
{ "arch_str": "2415", "identifier": "Hiaml_2415", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2415" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2415
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 204879360, "val_accuracy": 92.51 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3153
GraphArch:Hiaml:3153
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_443[FLOAT, 64x3x3x3] %onnx::Conv_444[FLOAT, 64] %onnx::Conv_446[FLOAT, 64x64x1x1] %onnx::Conv_449[FLOAT, 64x64x1x1] %onnx::Conv_452[FLOAT, 64x64x3x3] %onnx::Conv_455[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 193312256, "params": 2454602, "val_accuracy": 92 }
{ "arch_str": "3153", "identifier": "Hiaml_3153", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3153" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3153
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 193312256, "val_accuracy": 92 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2647
GraphArch:Hiaml:2647
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_363[FLOAT, 64x3x3x3] %onnx::Conv_364[FLOAT, 64] %onnx::Conv_366[FLOAT, 64x64x1x1] %onnx::Conv_369[FLOAT, 64x64x1x3] %onnx::Conv_372[FLOAT, 64x64x3x1] %onnx::Conv_375[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 196916736, "params": 2006986, "val_accuracy": 92.92 }
{ "arch_str": "2647", "identifier": "Hiaml_2647", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2647" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2647
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 196916736, "val_accuracy": 92.92 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3471
GraphArch:Hiaml:3471
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_399[FLOAT, 64x3x3x3] %onnx::Conv_400[FLOAT, 64] %onnx::Conv_402[FLOAT, 64x64x1x3] %onnx::Conv_405[FLOAT, 64x64x3x1] %onnx::Conv_408[FLOAT, 64x64x1x1] %onnx::Conv_411[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 182629888, "params": 1683018, "val_accuracy": 92.32 }
{ "arch_str": "3471", "identifier": "Hiaml_3471", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3471" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3471
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 182629888, "val_accuracy": 92.32 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1577
GraphArch:Hiaml:1577
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_331[FLOAT, 64x3x3x3] %onnx::Conv_332[FLOAT, 64] %onnx::Conv_334[FLOAT, 64x64x1x1] %onnx::Conv_337[FLOAT, 64x64x1x3] %onnx::Conv_340[FLOAT, 64x64x3x1] %onnx::Conv_343[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 182105600, "params": 2001226, "val_accuracy": 92.03 }
{ "arch_str": "1577", "identifier": "Hiaml_1577", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1577" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1577
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 182105600, "val_accuracy": 92.03 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_23
GraphArch:Hiaml:23
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_387[FLOAT, 64x3x3x3] %onnx::Conv_388[FLOAT, 64] %onnx::Conv_390[FLOAT, 64x64x3x3] %onnx::Conv_393[FLOAT, 64x64x1x3] %onnx::Conv_396[FLOAT, 64x64x3x1] %onnx::Conv_399[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 173979136, "params": 1568842, "val_accuracy": 92.28 }
{ "arch_str": "23", "identifier": "Hiaml_23", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "23" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
23
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 173979136, "val_accuracy": 92.28 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_3759
GraphArch:Hiaml:3759
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_526[FLOAT, 64x3x3x3] %onnx::Conv_527[FLOAT, 64] %onnx::Conv_529[FLOAT, 64x64x3x3] %onnx::Conv_532[FLOAT, 64x64x1x3] %onnx::Conv_535[FLOAT, 64x64x3x1] %onnx::Conv_538[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 226932224, "params": 2646346, "val_accuracy": 92.25 }
{ "arch_str": "3759", "identifier": "Hiaml_3759", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "3759" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
3759
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 226932224, "val_accuracy": 92.25 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1397
GraphArch:Hiaml:1397
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_423[FLOAT, 64x3x3x3] %onnx::Conv_424[FLOAT, 64] %onnx::Conv_426[FLOAT, 64x64x1x1] %onnx::Conv_429[FLOAT, 64x64x1x3] %onnx::Conv_432[FLOAT, 64x64x3x1] %onnx::Conv_435[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 205731328, "params": 2504266, "val_accuracy": 92.3 }
{ "arch_str": "1397", "identifier": "Hiaml_1397", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1397" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1397
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 205731328, "val_accuracy": 92.3 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1606
GraphArch:Hiaml:1606
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_379[FLOAT, 64x3x3x3] %onnx::Conv_380[FLOAT, 64] %onnx::Conv_382[FLOAT, 64x64x1x3] %onnx::Conv_385[FLOAT, 64x64x3x1] %onnx::Conv_388[FLOAT, 64x64x1x3] %onnx::Conv_391[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 224474624, "params": 2698826, "val_accuracy": 92.71 }
{ "arch_str": "1606", "identifier": "Hiaml_1606", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1606" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1606
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 224474624, "val_accuracy": 92.71 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_1779
GraphArch:Hiaml:1779
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_449[FLOAT, 64x3x3x3] %onnx::Conv_450[FLOAT, 64] %onnx::Conv_452[FLOAT, 64x64x1x1] %onnx::Conv_455[FLOAT, 64x64x1x3] %onnx::Conv_458[FLOAT, 64x64x3x1] %onnx::Conv_461[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 167065088, "params": 2226250, "val_accuracy": 91.45 }
{ "arch_str": "1779", "identifier": "Hiaml_1779", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "1779" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
1779
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 167065088, "val_accuracy": 91.45 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_2414
GraphArch:Hiaml:2414
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_474[FLOAT, 64x3x3x3] %onnx::Conv_475[FLOAT, 64] %onnx::Conv_477[FLOAT, 64x64x1x3] %onnx::Conv_480[FLOAT, 64x64x3x1] %onnx::Conv_483[FLOAT, 64x64x1x1] %onnx::Conv_486[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 227915264, "params": 2701898, "val_accuracy": 92.69 }
{ "arch_str": "2414", "identifier": "Hiaml_2414", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2414" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
2414
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 227915264, "val_accuracy": 92.69 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_333
GraphArch:Hiaml:333
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_355[FLOAT, 64x3x3x3] %onnx::Conv_356[FLOAT, 64] %onnx::Conv_358[FLOAT, 64x64x3x3] %onnx::Conv_361[FLOAT, 64x64x1x3] %onnx::Conv_364[FLOAT, 64x64x3x1] %onnx::Conv_367[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 203208192, "params": 1968970, "val_accuracy": 92.65 }
{ "arch_str": "333", "identifier": "Hiaml_333", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "333" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
333
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 203208192, "val_accuracy": 92.65 }
full
GraphArch
architecture_regression
GraphArch:Hiaml_126
GraphArch:Hiaml:126
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %out_net.classifier.weight[FLOAT, 10x256] %out_net.classifier.bias[FLOAT, 10] %onnx::Conv_427[FLOAT, 64x3x3x3] %onnx::Conv_428[FLOAT, 64] %onnx::Conv_430[FLOAT, 64x64x3x3] %onnx::Conv_433[FLOAT, 64x64x1x3] %onnx::Conv_436[FLOAT, 64x64x3x1] %onnx::Conv_439[F...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 247412224, "params": 2783306, "val_accuracy": 91.83 }
{ "arch_str": "126", "identifier": "Hiaml_126", "params_retained_not_default_target": true, "search_space": "Hiaml", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "126" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Hiaml" }
[]
126
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 247412224, "val_accuracy": 91.83 }
full
GraphArch
architecture_regression
GraphArch:Inception_512
GraphArch:Inception:512
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.0.op.1.weight[FLOAT, 32] %blocks.0.paths.1.0.op.1.bias[FLOAT, 32] %blocks.2.paths.0.1.op.1.weight[FLOAT, 16] %blocks.2.paths.0.1.op.1.bias[FLOAT, 16] %blocks.5.paths.2.4.op.0.weight[FLOAT, 32x64x1x1] %blocks.6.paths.2.4.op.0.weight[FLOAT, 32x6...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 406207488, "params": 3906650, "val_accuracy": 91.04 }
{ "arch_str": "512", "identifier": "Inception_512", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "512" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
512
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 406207488, "val_accuracy": 91.04 }
full
GraphArch
architecture_regression
GraphArch:Inception_186
GraphArch:Inception:186
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.1.weight[FLOAT, 32] %blocks.0.paths.0.0.op.1.bias[FLOAT, 32] %blocks.3.paths.2.0.op.1.weight[FLOAT, 64] %blocks.3.paths.2.0.op.1.bias[FLOAT, 64] %blocks.6.paths.0.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.7.paths.0.0.op.0.weight[FLOAT, 32x1...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 541899776, "params": 7469770, "val_accuracy": 91.1 }
{ "arch_str": "186", "identifier": "Inception_186", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "186" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
186
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 541899776, "val_accuracy": 91.1 }
full
GraphArch
architecture_regression
GraphArch:Inception_554
GraphArch:Inception:554
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.1.weight[FLOAT, 32] %blocks.0.paths.0.0.op.1.bias[FLOAT, 32] %blocks.11.paths.0.0.op.0.weight[FLOAT, 32x128x1x1] %blocks.11.paths.2.0.op.1.weight[FLOAT, 128] %blocks.11.paths.2.0.op.1.bias[FLOAT, 128] %blocks.11.paths.2.1.op.0.weight[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 357856256, "params": 3102714, "val_accuracy": 91.38 }
{ "arch_str": "554", "identifier": "Inception_554", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "554" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
554
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 357856256, "val_accuracy": 91.38 }
full
GraphArch
architecture_regression
GraphArch:Inception_159
GraphArch:Inception:159
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.5.paths.0.0.op.1.weight[FLOAT, 32] %blocks.5.paths.0.0.op.1.bias[FLOAT, 32] %blocks.5.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.paths.3.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.6.paths.0.0.op.1.weight[FLOAT, 64] %blocks.6.paths.0.0.op.1.bias[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 612191232, "params": 7326218, "val_accuracy": 91.55 }
{ "arch_str": "159", "identifier": "Inception_159", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "159" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
159
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 612191232, "val_accuracy": 91.55 }
full
GraphArch
architecture_regression
GraphArch:Inception_393
GraphArch:Inception:393
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.4.paths.0.1.op.1.weight[FLOAT, 32] %blocks.4.paths.0.1.op.1.bias[FLOAT, 32] %blocks.5.paths.0.0.op.0.weight[FLOAT, 32x64x1x1] %classifier.weight[FLOAT, 10x512] %classifier.bias[FLOAT, 10] %onnx::Conv_2631[FLOAT, 32x3x3x3] %onnx::Conv_2632[FLOAT, 32] ...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 307764224, "params": 4737642, "val_accuracy": 91.38 }
{ "arch_str": "393", "identifier": "Inception_393", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "393" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
393
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 307764224, "val_accuracy": 91.38 }
full
GraphArch
architecture_regression
GraphArch:Inception_528
GraphArch:Inception:528
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.1.weight[FLOAT, 32] %blocks.0.paths.0.0.op.1.bias[FLOAT, 32] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 387222528, "params": 2561530, "val_accuracy": 91.42 }
{ "arch_str": "528", "identifier": "Inception_528", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "528" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
528
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 387222528, "val_accuracy": 91.42 }
full
GraphArch
architecture_regression
GraphArch:Inception_479
GraphArch:Inception:479
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.0.1.op.1.weight[FLOAT, 16] %blocks.0.paths.0.1.op.1.bias[FLOAT, 16] %blocks.1.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.1.op.1.weight[FLOAT, 32] %blocks.2.paths.1.1.op.1.bias[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 603884544, "params": 4104314, "val_accuracy": 91.61 }
{ "arch_str": "479", "identifier": "Inception_479", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "479" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
479
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 603884544, "val_accuracy": 91.61 }
full
GraphArch
architecture_regression
GraphArch:Inception_579
GraphArch:Inception:579
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.1.weight[FLOAT, 32] %blocks.0.paths.0.0.op.1.bias[FLOAT, 32] %blocks.0.paths.0.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.0.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.0.1.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 540953600, "params": 3906346, "val_accuracy": 92.67 }
{ "arch_str": "579", "identifier": "Inception_579", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "579" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
579
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 540953600, "val_accuracy": 92.67 }
full
GraphArch
architecture_regression
GraphArch:Inception_313
GraphArch:Inception:313
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.1.op.1.weight[FLOAT, 16] %blocks.0.paths.0.1.op.1.bias[FLOAT, 16] %blocks.7.paths.0.1.op.1.weight[FLOAT, 64] %blocks.7.paths.0.1.op.1.bias[FLOAT, 64] %blocks.12.paths.0.0.op.0.weight[FLOAT, 64x128x1x1] %blocks.13.paths.0.0.op.0.weight[FLOAT, 6...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 413924352, "params": 3584554, "val_accuracy": 91.78 }
{ "arch_str": "313", "identifier": "Inception_313", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "313" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
313
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 413924352, "val_accuracy": 91.78 }
full
GraphArch
architecture_regression
GraphArch:Inception_240
GraphArch:Inception:240
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.5.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.paths.1.4.op.1.weight[FLOAT, 16] %blocks.5.paths.1.4.op.1.bias[FLOAT, 16] %blocks.6.paths.0.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.7.paths.0.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.8.paths.0.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 769555456, "params": 4359162, "val_accuracy": 92.16 }
{ "arch_str": "240", "identifier": "Inception_240", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "240" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
240
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 769555456, "val_accuracy": 92.16 }
full
GraphArch
architecture_regression
GraphArch:Inception_252
GraphArch:Inception:252
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.3.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.4.op.1.weight[FLOAT, 16] %blocks.3.paths.1.4.op.1.bias[FLOAT, 16] %blocks.4.paths.1.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.5.paths.1.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.6.paths.1.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 413645824, "params": 2816746, "val_accuracy": 91.34 }
{ "arch_str": "252", "identifier": "Inception_252", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "252" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
252
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 413645824, "val_accuracy": 91.34 }
full
GraphArch
architecture_regression
GraphArch:Inception_449
GraphArch:Inception:449
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.8.paths.0.1.op.1.weight[FLOAT, 32] %blocks.8.paths.0.1.op.1.bias[FLOAT, 32] %blocks.12.paths.0.2.op.0.weight[FLOAT, 64x32x1x1] %blocks.13.paths.0.2.op.0.weight[FLOAT, 64x32x1x1] %blocks.14.paths.0.2.op.0.weight[FLOAT, 64x32x1x1] %classifier.weight[FLOAT...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 381193216, "params": 3999098, "val_accuracy": 92.17 }
{ "arch_str": "449", "identifier": "Inception_449", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "449" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
449
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 381193216, "val_accuracy": 92.17 }
full
GraphArch
architecture_regression
GraphArch:Inception_158
GraphArch:Inception:158
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.0.5.op.1.weight[FLOAT, 16] %blocks.0.paths.0.5.op.1.bias[FLOAT, 16] %blocks.1.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.0.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 394646528, "params": 2626266, "val_accuracy": 91.05 }
{ "arch_str": "158", "identifier": "Inception_158", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "158" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
158
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 394646528, "val_accuracy": 91.05 }
full
GraphArch
architecture_regression
GraphArch:Inception_404
GraphArch:Inception:404
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.4.paths.0.3.op.1.weight[FLOAT, 16] %blocks.4.paths.0.3.op.1.bias[FLOAT, 16] %blocks.4.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.paths.1.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.6.paths.0.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.6.paths.0.7.op.1.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 337005568, "params": 5691386, "val_accuracy": 90.4 }
{ "arch_str": "404", "identifier": "Inception_404", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "404" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
404
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 337005568, "val_accuracy": 90.4 }
full
GraphArch
architecture_regression
GraphArch:Inception_155
GraphArch:Inception:155
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.2.0.op.1.weight[FLOAT, 32] %blocks.0.paths.2.0.op.1.bias[FLOAT, 32] %blocks.0.paths.2.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.3.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.2.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.3.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 662170624, "params": 7038090, "val_accuracy": 92.27 }
{ "arch_str": "155", "identifier": "Inception_155", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "155" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
155
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 662170624, "val_accuracy": 92.27 }
full
GraphArch
architecture_regression
GraphArch:Inception_456
GraphArch:Inception:456
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.6.paths.0.8.op.1.weight[FLOAT, 32] %blocks.6.paths.0.8.op.1.bias[FLOAT, 32] %blocks.6.paths.3.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.7.paths.3.0.op.0.weight[FLOAT, 32x128x1x1] %blocks.8.paths.3.0.op.0.weight[FLOAT, 32x128x1x1] %blocks.9.paths.3.0.op.0....
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 391359488, "params": 4426826, "val_accuracy": 92.2 }
{ "arch_str": "456", "identifier": "Inception_456", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "456" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
456
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 391359488, "val_accuracy": 92.2 }
full
GraphArch
architecture_regression
GraphArch:Inception_283
GraphArch:Inception:283
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.8.paths.0.2.op.0.weight[FLOAT, 64x32x1x1] %blocks.8.paths.1.2.op.1.weight[FLOAT, 64] %blocks.8.paths.1.2.op.1.bias[FLOAT, 64] %blocks.9.paths.0.2.op.0.weight[FLOAT, 64x32x1x1] %blocks.9.paths.1.0.op.0.weight[FLOAT, 64x128x1x1] %blocks.10.paths.0.2.op.0....
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 283675648, "params": 3080426, "val_accuracy": 91.29 }
{ "arch_str": "283", "identifier": "Inception_283", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "283" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
283
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 283675648, "val_accuracy": 91.29 }
full
GraphArch
architecture_regression
GraphArch:Inception_517
GraphArch:Inception:517
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.4.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.p...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 845261824, "params": 8426922, "val_accuracy": 92.2 }
{ "arch_str": "517", "identifier": "Inception_517", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "517" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
517
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 845261824, "val_accuracy": 92.2 }
full
GraphArch
architecture_regression
GraphArch:Inception_299
GraphArch:Inception:299
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.7.op.1.weight[FLOAT, 16] %blocks.0.paths.0.7.op.1.bias[FLOAT, 16] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.3.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.3.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 530611200, "params": 2797882, "val_accuracy": 93.19 }
{ "arch_str": "299", "identifier": "Inception_299", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "299" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
299
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 530611200, "val_accuracy": 93.19 }
full
GraphArch
architecture_regression
GraphArch:Inception_568
GraphArch:Inception:568
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.3.op.1.weight[FLOAT, 16] %blocks.0.paths.0.3.op.1.bias[FLOAT, 16] %blocks.6.paths.1.1.op.0.weight[FLOAT, 32x16x1x1] %blocks.7.paths.1.1.op.0.weight[FLOAT, 32x16x1x1] %blocks.13.paths.3.0.op.0.weight[FLOAT, 64x128x1x1] %blocks.14.paths.3.0.op.0...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 395604992, "params": 3830346, "val_accuracy": 92.38 }
{ "arch_str": "568", "identifier": "Inception_568", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "568" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
568
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 395604992, "val_accuracy": 92.38 }
full
GraphArch
architecture_regression
GraphArch:Inception_373
GraphArch:Inception:373
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.7.paths.0.0.op.1.weight[FLOAT, 64] %blocks.7.paths.0.0.op.1.bias[FLOAT, 64] %blocks.7.paths.3.3.op.0.weight[FLOAT, 32x64x1x1] %blocks.8.paths.0.0.op.1.weight[FLOAT, 128] %blocks.8.paths.0.0.op.1.bias[FLOAT, 128] %blocks.8.paths.3.3.op.0.weight[FLOAT, 32...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 515435520, "params": 8637594, "val_accuracy": 91.34 }
{ "arch_str": "373", "identifier": "Inception_373", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "373" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
373
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 515435520, "val_accuracy": 91.34 }
full
GraphArch
architecture_regression
GraphArch:Inception_140
GraphArch:Inception:140
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.7.paths.0.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.7.paths.0.6.op.1.weight[FLOAT, 32] %blocks.7.paths.0.6.op.1.bias[FLOAT, 32] %blocks.7.paths.3.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.8.paths.0.0.op.0.weight[FLOAT, 32x128x1x1] %blocks.8.paths.3.0.op.0.w...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 489040896, "params": 8343546, "val_accuracy": 91.01 }
{ "arch_str": "140", "identifier": "Inception_140", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "140" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
140
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 489040896, "val_accuracy": 91.01 }
full
GraphArch
architecture_regression
GraphArch:Inception_509
GraphArch:Inception:509
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.0.4.op.1.weight[FLOAT, 16] %blocks.0.paths.0.4.op.1.bias[FLOAT, 16] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 339397632, "params": 4438170, "val_accuracy": 91.88 }
{ "arch_str": "509", "identifier": "Inception_509", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "509" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
509
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 339397632, "val_accuracy": 91.88 }
full
GraphArch
architecture_regression
GraphArch:Inception_209
GraphArch:Inception:209
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.3.paths.0.0.op.1.weight[FLOAT, 32] %blocks.3.paths.0.0.op.1.bias[FLOAT, 32] %blocks.3.paths.0.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.2.op.1.weight[FLOAT, 16] %blocks.3.paths.1.2.op.1.bias[FLOAT, 16] %blocks.4.paths.0.0.op.1.weight[FLOAT, 64] ...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 369400832, "params": 5979274, "val_accuracy": 90.68 }
{ "arch_str": "209", "identifier": "Inception_209", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "209" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
209
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 369400832, "val_accuracy": 90.68 }
full
GraphArch
architecture_regression
GraphArch:Inception_569
GraphArch:Inception:569
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.2.op.1.weight[FLOAT, 32] %blocks.0.paths.0.2.op.1.bias[FLOAT, 32] %blocks.14.paths.2.1.op.1.weight[FLOAT, 64] %blocks.14.paths.2.1.op.1.bias[FLOAT, 64] %classifier.weight[FLOAT, 10x512] %classifier.bias[FLOAT, 10] %onnx::Conv_2437[FLOAT, 32x...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 379481088, "params": 3942858, "val_accuracy": 92.89 }
{ "arch_str": "569", "identifier": "Inception_569", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "569" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
569
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 379481088, "val_accuracy": 92.89 }
full
GraphArch
architecture_regression
GraphArch:Inception_539
GraphArch:Inception:539
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.1.op.1.weight[FLOAT, 32] %blocks.0.paths.0.1.op.1.bias[FLOAT, 32] %classifier.weight[FLOAT, 10x512] %classifier.bias[FLOAT, 10] %onnx::Conv_3377[FLOAT, 32x3x3x3] %onnx::Conv_3378[FLOAT, 32] %onnx::Conv_3380[FLOAT, 16x32x1x3] %onnx::Conv_33...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 724003840, "params": 10162058, "val_accuracy": 91.85 }
{ "arch_str": "539", "identifier": "Inception_539", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "539" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
539
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 724003840, "val_accuracy": 91.85 }
full
GraphArch
architecture_regression
GraphArch:Inception_398
GraphArch:Inception:398
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.3.paths.0.4.op.1.weight[FLOAT, 16] %blocks.3.paths.0.4.op.1.bias[FLOAT, 16] %blocks.4.paths.2.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.5.paths.2.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.6.paths.2.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.7.paths.2.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 439454720, "params": 4256154, "val_accuracy": 90.79 }
{ "arch_str": "398", "identifier": "Inception_398", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "398" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
398
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 439454720, "val_accuracy": 90.79 }
full
GraphArch
architecture_regression
GraphArch:Inception_108
GraphArch:Inception:108
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.0.1.op.1.weight[FLOAT, 16] %blocks.0.paths.0.1.op.1.bias[FLOAT, 16] %blocks.1.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.0.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 351028224, "params": 5051386, "val_accuracy": 92.02 }
{ "arch_str": "108", "identifier": "Inception_108", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "108" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
108
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 351028224, "val_accuracy": 92.02 }
full
GraphArch
architecture_regression
GraphArch:Inception_429
GraphArch:Inception:429
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.3.op.1.weight[FLOAT, 16] %blocks.0.paths.1.3.op.1.bias[FLOAT, 16] %blocks.12.paths.0.0.op.1.weight[FLOAT, 128] %blocks.12.paths.0.0.op.1.bias[FLOAT, 128] %blocks.13.paths.0.0.op.1.weight[FLOAT, 256] %blocks.13.paths.0.0.op.1.bias[FLOAT, 256] ...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 479632384, "params": 5280186, "val_accuracy": 92.12 }
{ "arch_str": "429", "identifier": "Inception_429", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "429" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
429
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 479632384, "val_accuracy": 92.12 }
full
GraphArch
architecture_regression
GraphArch:Inception_421
GraphArch:Inception:421
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.2.op.1.weight[FLOAT, 16] %blocks.0.paths.0.2.op.1.bias[FLOAT, 16] %blocks.0.paths.0.3.op.0.weight[FLOAT, 32x16x1x1] %blocks.1.paths.0.3.op.0.weight[FLOAT, 32x16x1x1] %blocks.2.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 295918592, "params": 3087882, "val_accuracy": 91.08 }
{ "arch_str": "421", "identifier": "Inception_421", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "421" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
421
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 295918592, "val_accuracy": 91.08 }
full
GraphArch
architecture_regression
GraphArch:Inception_427
GraphArch:Inception:427
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.4.op.1.weight[FLOAT, 16] %blocks.0.paths.0.4.op.1.bias[FLOAT, 16] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.2.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 458771456, "params": 3665466, "val_accuracy": 92.38 }
{ "arch_str": "427", "identifier": "Inception_427", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "427" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
427
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 458771456, "val_accuracy": 92.38 }
full
GraphArch
architecture_regression
GraphArch:Inception_242
GraphArch:Inception:242
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.5.op.1.weight[FLOAT, 32] %blocks.0.paths.0.5.op.1.bias[FLOAT, 32] %blocks.0.paths.0.8.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.2.1.op.1.weight[FLOAT, 16] %blocks.0.paths.2.1.op.1.bias[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 1045572608, "params": 4001930, "val_accuracy": 92.94 }
{ "arch_str": "242", "identifier": "Inception_242", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "242" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
242
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 1045572608, "val_accuracy": 92.94 }
full
GraphArch
architecture_regression
GraphArch:Inception_366
GraphArch:Inception:366
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.1.op.1.weight[FLOAT, 32] %blocks.0.paths.0.1.op.1.bias[FLOAT, 32] %blocks.12.paths.0.8.op.1.weight[FLOAT, 16] %blocks.12.paths.0.8.op.1.bias[FLOAT, 16] %blocks.12.paths.2.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.12.paths.3.0.op.0.weight[FLOAT, ...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 2482027520, "params": 6253930, "val_accuracy": 93.76 }
{ "arch_str": "366", "identifier": "Inception_366", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "366" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
366
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 2482027520, "val_accuracy": 93.76 }
full
GraphArch
architecture_regression
GraphArch:Inception_521
GraphArch:Inception:521
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.4.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.p...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 518482944, "params": 6577578, "val_accuracy": 92.01 }
{ "arch_str": "521", "identifier": "Inception_521", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "521" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
521
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 518482944, "val_accuracy": 92.01 }
full
GraphArch
architecture_regression
GraphArch:Inception_166
GraphArch:Inception:166
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.0.5.op.1.weight[FLOAT, 32] %blocks.3.paths.0.5.op.1.bias[FLOAT, 32] %blocks.4.paths.0.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 369136640, "params": 6444106, "val_accuracy": 91.13 }
{ "arch_str": "166", "identifier": "Inception_166", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "166" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
166
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 369136640, "val_accuracy": 91.13 }
full
GraphArch
architecture_regression
GraphArch:Inception_529
GraphArch:Inception:529
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.1.op.1.weight[FLOAT, 16] %blocks.0.paths.0.1.op.1.bias[FLOAT, 16] %blocks.0.paths.3.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.3.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.8.paths.0.0.op.1.weight[FLOAT, 64] %blocks.8.paths.0.0.op.1.bias[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 802663424, "params": 10738490, "val_accuracy": 92.29 }
{ "arch_str": "529", "identifier": "Inception_529", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "529" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
529
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 802663424, "val_accuracy": 92.29 }
full
GraphArch
architecture_regression
GraphArch:Inception_443
GraphArch:Inception:443
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.1.weight[FLOAT, 32] %blocks.0.paths.0.0.op.1.bias[FLOAT, 32] %blocks.4.paths.1.1.op.1.weight[FLOAT, 16] %blocks.4.paths.1.1.op.1.bias[FLOAT, 16] %classifier.weight[FLOAT, 10x512] %classifier.bias[FLOAT, 10] %onnx::Conv_2010[FLOAT, 32x3x...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 391441408, "params": 3731562, "val_accuracy": 92.36 }
{ "arch_str": "443", "identifier": "Inception_443", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "443" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
443
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 391441408, "val_accuracy": 92.36 }
full
GraphArch
architecture_regression
GraphArch:Inception_564
GraphArch:Inception:564
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.5.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.5.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.5.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.5.op.0.weight[FLOAT, 16x32x1x1] %blocks.7.paths.1.11.op.0.weight[FLOAT, 32x16x1x1] %blocks.8....
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 536878080, "params": 3356346, "val_accuracy": 91.47 }
{ "arch_str": "564", "identifier": "Inception_564", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "564" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
564
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 536878080, "val_accuracy": 91.47 }
full
GraphArch
architecture_regression
GraphArch:Inception_180
GraphArch:Inception:180
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.8.paths.0.0.op.1.weight[FLOAT, 64] %blocks.8.paths.0.0.op.1.bias[FLOAT, 64] %blocks.8.paths.2.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.9.paths.0.0.op.1.weight[FLOAT, 128] %blocks.9.paths.0.0.op.1.bias[FLOAT, 128] %blocks.9.paths.2.0.op.0.weight[FLOAT, 32...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 538684416, "params": 2873754, "val_accuracy": 92.28 }
{ "arch_str": "180", "identifier": "Inception_180", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "180" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
180
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 538684416, "val_accuracy": 92.28 }
full
GraphArch
architecture_regression
GraphArch:Inception_71
GraphArch:Inception:71
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.2.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.4.paths.1.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.5.paths.0.1.op.0.weight[FLOAT, 64x32x1x1] %blocks.5.paths.1.5.op.1.weight[FLOAT, 64] %blocks.5.paths.1....
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 545745920, "params": 9438186, "val_accuracy": 92.55 }
{ "arch_str": "71", "identifier": "Inception_71", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "71" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
71
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 545745920, "val_accuracy": 92.55 }
full
GraphArch
architecture_regression
GraphArch:Inception_19
GraphArch:Inception:19
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.1.op.1.weight[FLOAT, 16] %blocks.0.paths.0.1.op.1.bias[FLOAT, 16] %blocks.12.paths.0.0.op.1.weight[FLOAT, 64] %blocks.12.paths.0.0.op.1.bias[FLOAT, 64] %blocks.13.paths.0.0.op.1.weight[FLOAT, 128] %blocks.13.paths.0.0.op.1.bias[FLOAT, 128] %...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 682191872, "params": 4668570, "val_accuracy": 92.15 }
{ "arch_str": "19", "identifier": "Inception_19", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "19" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
19
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 682191872, "val_accuracy": 92.15 }
full
GraphArch
architecture_regression
GraphArch:Inception_274
GraphArch:Inception:274
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.8.paths.1.1.op.1.weight[FLOAT, 64] %blocks.8.paths.1.1.op.1.bias[FLOAT, 64] %blocks.11.paths.0.4.op.0.weight[FLOAT, 64x128x1x1] %blocks.11.paths.2.0.op.0.weight[FLOAT, 64x128x1x1] %blocks.12.paths.0.0.op.0.weight[FLOAT, 128x256x1x1] %blocks.12.paths.0.4...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 851686400, "params": 6460554, "val_accuracy": 93.15 }
{ "arch_str": "274", "identifier": "Inception_274", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "274" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
274
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 851686400, "val_accuracy": 93.15 }
full
GraphArch
architecture_regression
GraphArch:Inception_2
GraphArch:Inception:2
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.3.paths.0.0.op.1.weight[FLOAT, 32] %blocks.3.paths.0.0.op.1.bias[FLOAT, 32] %blocks.3.paths.0.1.op.0.weight[FLOAT, 16x32x1x1] %blocks.4.paths.0.0.op.1.weight[FLOAT, 64] %blocks.4.paths.0.0.op.1.bias[FLOAT, 64] %blocks.4.paths.0.1.op.0.weight[FLOAT, 16x6...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 766076928, "params": 5459018, "val_accuracy": 92.97 }
{ "arch_str": "2", "identifier": "Inception_2", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "2" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
2
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 766076928, "val_accuracy": 92.97 }
full
GraphArch
architecture_regression
GraphArch:Inception_538
GraphArch:Inception:538
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.0.3.op.1.weight[FLOAT, 16] %blocks.0.paths.0.3.op.1.bias[FLOAT, 16] %blocks.1.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.0.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 994176000, "params": 7133626, "val_accuracy": 92.94 }
{ "arch_str": "538", "identifier": "Inception_538", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "538" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
538
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 994176000, "val_accuracy": 92.94 }
full
GraphArch
architecture_regression
GraphArch:Inception_394
GraphArch:Inception:394
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.0.op.1.weight[FLOAT, 32] %blocks.0.paths.1.0.op.1.bias[FLOAT, 32] %blocks.0.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.2.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 925834240, "params": 6046058, "val_accuracy": 93.79 }
{ "arch_str": "394", "identifier": "Inception_394", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "394" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
394
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 925834240, "val_accuracy": 93.79 }
full
GraphArch
architecture_regression
GraphArch:Inception_535
GraphArch:Inception:535
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.7.op.1.weight[FLOAT, 32] %blocks.0.paths.0.7.op.1.bias[FLOAT, 32] %blocks.0.paths.0.8.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.0.8.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.2.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 1114392576, "params": 6049578, "val_accuracy": 93.71 }
{ "arch_str": "535", "identifier": "Inception_535", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "535" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
535
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 1114392576, "val_accuracy": 93.71 }
full
GraphArch
architecture_regression
GraphArch:Inception_211
GraphArch:Inception:211
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.2.op.1.weight[FLOAT, 16] %blocks.0.paths.0.2.op.1.bias[FLOAT, 16] %blocks.12.paths.0.3.op.1.weight[FLOAT, 64] %blocks.12.paths.0.3.op.1.bias[FLOAT, 64] %blocks.12.paths.1.2.op.0.weight[FLOAT, 64x32x1x1] %blocks.12.paths.2.0.op.1.weight[FLOAT, ...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 871271424, "params": 4163754, "val_accuracy": 92.83 }
{ "arch_str": "211", "identifier": "Inception_211", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "211" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
211
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 871271424, "val_accuracy": 92.83 }
full
GraphArch
architecture_regression
GraphArch:Inception_323
GraphArch:Inception:323
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.1.op.1.weight[FLOAT, 32] %blocks.2.paths.0.1.op.1.bias[FLOAT, 32] %blocks.5.paths.0.3.op.1.weight[FLOAT, 64] %blocks.5.paths.0.3.op.1.bias[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 539266048, "params": 2922122, "val_accuracy": 91.28 }
{ "arch_str": "323", "identifier": "Inception_323", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "323" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
323
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 539266048, "val_accuracy": 91.28 }
full
GraphArch
architecture_regression
GraphArch:Inception_134
GraphArch:Inception:134
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.2.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.1.2.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.1.op.1.weight[FLOAT, 32] %blocks.2.paths.0.1.op.1.bias[FLOAT, 32] %blocks.3.paths.0.0.op.0.weight[FLOAT, 32x64x1x1] %blocks.4.paths.0.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 654453760, "params": 12041098, "val_accuracy": 91.25 }
{ "arch_str": "134", "identifier": "Inception_134", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "134" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
134
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 654453760, "val_accuracy": 91.25 }
full
GraphArch
architecture_regression
GraphArch:Inception_387
GraphArch:Inception:387
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.2.paths.0.10.op.1.weight[FLOAT, 16] %blocks.2.paths.0.10.op.1.bias[FLOAT, 16] %blocks.2.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.2.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.4.paths.2.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.5.paths.2.0.op.0....
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 321391616, "params": 3638922, "val_accuracy": 90.81 }
{ "arch_str": "387", "identifier": "Inception_387", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "387" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
387
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 321391616, "val_accuracy": 90.81 }
full
GraphArch
architecture_regression
GraphArch:Inception_257
GraphArch:Inception:257
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.2.op.1.weight[FLOAT, 16] %blocks.0.paths.0.2.op.1.bias[FLOAT, 16] %blocks.3.paths.2.1.op.1.weight[FLOAT, 32] %blocks.3.paths.2.1.op.1.bias[FLOAT, 32] %blocks.3.paths.2.3.op.0.weight[FLOAT, 16x32x1x1] %blocks.4.paths.2.3.op.0.weight[FLOAT, 16x3...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 1705126912, "params": 34919850, "val_accuracy": 92.4 }
{ "arch_str": "257", "identifier": "Inception_257", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "257" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
257
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 1705126912, "val_accuracy": 92.4 }
full
GraphArch
architecture_regression
GraphArch:Inception_561
GraphArch:Inception:561
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.4.paths.0.1.op.1.weight[FLOAT, 16] %blocks.4.paths.0.1.op.1.bias[FLOAT, 16] %blocks.4.paths.3.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.paths.3.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.6.paths.3.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.7.paths.3.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 680389632, "params": 5174010, "val_accuracy": 91.84 }
{ "arch_str": "561", "identifier": "Inception_561", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "561" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
561
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 680389632, "val_accuracy": 91.84 }
full
GraphArch
architecture_regression
GraphArch:Inception_162
GraphArch:Inception:162
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.5.op.1.weight[FLOAT, 16] %blocks.0.paths.0.5.op.1.bias[FLOAT, 16] %blocks.13.paths.0.2.op.0.weight[FLOAT, 64x32x1x1] %blocks.13.paths.1.6.op.1.weight[FLOAT, 64] %blocks.13.paths.1.6.op.1.bias[FLOAT, 64] %blocks.14.paths.0.2.op.0.weight[FLOAT, ...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 587045888, "params": 3966090, "val_accuracy": 93.09 }
{ "arch_str": "162", "identifier": "Inception_162", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "162" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
162
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 587045888, "val_accuracy": 93.09 }
full
GraphArch
architecture_regression
GraphArch:Inception_357
GraphArch:Inception:357
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.0.7.op.1.weight[FLOAT, 32] %blocks.0.paths.0.7.op.1.bias[FLOAT, 32] %blocks.2.paths.0.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.0.11.op.1.weight[FLOAT, 16] %blocks.2.paths.0.11.op.1.bias[FLOAT, 16] %blocks.2.paths.2.0.op.0.weight[FLOAT, 16...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 586664960, "params": 2889850, "val_accuracy": 90.27 }
{ "arch_str": "357", "identifier": "Inception_357", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "357" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
357
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 586664960, "val_accuracy": 90.27 }
full
GraphArch
architecture_regression
GraphArch:Inception_389
GraphArch:Inception:389
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.4.paths.0.6.op.1.weight[FLOAT, 32] %blocks.4.paths.0.6.op.1.bias[FLOAT, 32] %blocks.4.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.paths.1.0.op.0.weight[FLOAT, 16x64x1x1] %blocks.6.paths.1.0.op.1.weight[FLOAT, 64] %blocks.6.paths.1.0.op.1.bias[FLOA...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 403110912, "params": 2567674, "val_accuracy": 92 }
{ "arch_str": "389", "identifier": "Inception_389", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "389" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
389
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 403110912, "val_accuracy": 92 }
full
GraphArch
architecture_regression
GraphArch:Inception_290
GraphArch:Inception:290
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.1.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.4.paths.2.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.5.p...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 806722560, "params": 3022346, "val_accuracy": 91.37 }
{ "arch_str": "290", "identifier": "Inception_290", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "290" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
290
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 806722560, "val_accuracy": 91.37 }
full
GraphArch
architecture_regression
GraphArch:Inception_254
GraphArch:Inception:254
graph torch_jit ( %input.1[FLOAT, 1x3x32x32] %blocks.0.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.0.paths.1.2.op.1.weight[FLOAT, 16] %blocks.0.paths.1.2.op.1.bias[FLOAT, 16] %blocks.1.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.2.paths.1.0.op.0.weight[FLOAT, 16x32x1x1] %blocks.3.paths.1.0.op.0.we...
graph
{ "flops": "Floating-point operation count for the architecture.", "params": "Number of trainable parameters in the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 290204672, "params": 2921658, "val_accuracy": 91.84 }
{ "arch_str": "254", "identifier": "Inception_254", "params_retained_not_default_target": true, "search_space": "Inception", "source_dataset": "grapharch_regression", "source_target_metric": "val_accuracy", "uid": "254" }
{ "input_format": "serialized neural-network graph text", "input_to_evaluate_column": "input_text", "problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.", "search_space": "Inception" }
[]
254
Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.
train
{ "flops": "Floating-point operation count for the architecture.", "val_accuracy": "Validation accuracy reported for the neural architecture." }
{ "flops": 290204672, "val_accuracy": 91.84 }
full