dataset stringclasses 1
value | domain stringclasses 1
value | example_id stringlengths 17 28 | group_id stringlengths 17 28 | input_text stringlengths 8.51k 461k | language stringclasses 1
value | measurement_descriptions dict | measurements dict | metadata dict | prompt_components dict | reference_outputs listlengths 0 0 | source_document_id stringlengths 1 6 | source_text stringclasses 1
value | split stringclasses 1
value | target_descriptions dict | targets dict | variant stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GraphArch | architecture_regression | GraphArch:Hiaml_506 | GraphArch:Hiaml:506 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_394[FLOAT, 64x3x3x3]
%onnx::Conv_395[FLOAT, 64]
%onnx::Conv_397[FLOAT, 64x64x1x1]
%onnx::Conv_400[FLOAT, 64x64x3x3]
%onnx::Conv_403[FLOAT, 64x64x3x3]
%onnx::Conv_406[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": 214873600,
"params": 2060362,
"val_accuracy": 92.29
} | {
"arch_str": "506",
"identifier": "Hiaml_506",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "506"
} | {
"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"
} | [] | 506 | 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": 214873600,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_241 | GraphArch:Hiaml:241 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_349[FLOAT, 64x3x3x3]
%onnx::Conv_350[FLOAT, 64]
%onnx::Conv_352[FLOAT, 64x64x1x1]
%onnx::Conv_355[FLOAT, 64x64x1x3]
%onnx::Conv_358[FLOAT, 64x64x3x1]
%onnx::Conv_361[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": 203240960,
"params": 2657610,
"val_accuracy": 92.43
} | {
"arch_str": "241",
"identifier": "Hiaml_241",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "241"
} | {
"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"
} | [] | 241 | 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": 203240960,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2136 | GraphArch:Hiaml:2136 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_333[FLOAT, 64x3x3x3]
%onnx::Conv_334[FLOAT, 64]
%onnx::Conv_336[FLOAT, 64x64x1x1]
%onnx::Conv_339[FLOAT, 64x64x1x3]
%onnx::Conv_342[FLOAT, 64x64x3x1]
%onnx::Conv_345[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": 151762432,
"params": 1763914,
"val_accuracy": 92.68
} | {
"arch_str": "2136",
"identifier": "Hiaml_2136",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2136"
} | {
"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"
} | [] | 2136 | 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": 151762432,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1746 | GraphArch:Hiaml:1746 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x3x3]
%onnx::Conv_411[FLOAT, 64x64x1x3]
%onnx::Conv_414[FLOAT, 64x64x3x1]
%onnx::Conv_417[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": 233780736,
"params": 2565066,
"val_accuracy": 92.71
} | {
"arch_str": "1746",
"identifier": "Hiaml_1746",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1746"
} | {
"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"
} | [] | 1746 | 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": 233780736,
"val_accuracy": 92.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_344 | GraphArch:Hiaml:344 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x1x1]
%onnx::Conv_443[FLOAT, 64x64x1x3]
%onnx::Conv_446[FLOAT, 64x64x3x1]
%onnx::Conv_449[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": 172996096,
"params": 2423370,
"val_accuracy": 92.56
} | {
"arch_str": "344",
"identifier": "Hiaml_344",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "344"
} | {
"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"
} | [] | 344 | 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": 172996096,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4341 | GraphArch:Hiaml:4341 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_495[FLOAT, 64x3x3x3]
%onnx::Conv_496[FLOAT, 64]
%onnx::Conv_498[FLOAT, 64x64x1x3]
%onnx::Conv_501[FLOAT, 64x64x3x1]
%onnx::Conv_504[FLOAT, 64x64x1x3]
%onnx::Conv_507[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": 190395904,
"params": 2382922,
"val_accuracy": 92.24
} | {
"arch_str": "4341",
"identifier": "Hiaml_4341",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4341"
} | {
"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"
} | [] | 4341 | 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": 190395904,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_384 | GraphArch:Hiaml:384 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_333[FLOAT, 64x3x3x3]
%onnx::Conv_334[FLOAT, 64]
%onnx::Conv_336[FLOAT, 64x64x1x1]
%onnx::Conv_339[FLOAT, 64x64x1x3]
%onnx::Conv_342[FLOAT, 64x64x3x1]
%onnx::Conv_345[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": 133838336,
"params": 1748298,
"val_accuracy": 91.93
} | {
"arch_str": "384",
"identifier": "Hiaml_384",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "384"
} | {
"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"
} | [] | 384 | 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": 133838336,
"val_accuracy": 91.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4198 | GraphArch:Hiaml:4198 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_457[FLOAT, 64x3x3x3]
%onnx::Conv_458[FLOAT, 64]
%onnx::Conv_460[FLOAT, 64x64x3x3]
%onnx::Conv_463[FLOAT, 64x64x1x1]
%onnx::Conv_466[FLOAT, 64x64x1x1]
%onnx::Conv_469[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": 185710080,
"params": 2423882,
"val_accuracy": 91.82
} | {
"arch_str": "4198",
"identifier": "Hiaml_4198",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4198"
} | {
"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"
} | [] | 4198 | 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": 185710080,
"val_accuracy": 91.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_524 | GraphArch:Hiaml:524 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_446[FLOAT, 64x3x3x3]
%onnx::Conv_447[FLOAT, 64]
%onnx::Conv_449[FLOAT, 64x64x1x1]
%onnx::Conv_452[FLOAT, 64x64x3x3]
%onnx::Conv_455[FLOAT, 64x64x3x3]
%onnx::Conv_458[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": 265467392,
"params": 3437642,
"val_accuracy": 92.67
} | {
"arch_str": "524",
"identifier": "Hiaml_524",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "524"
} | {
"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"
} | [] | 524 | 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": 265467392,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2322 | GraphArch:Hiaml:2322 | 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": 235451904,
"params": 3427658,
"val_accuracy": 91.69
} | {
"arch_str": "2322",
"identifier": "Hiaml_2322",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2322"
} | {
"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"
} | [] | 2322 | 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": 235451904,
"val_accuracy": 91.69
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4496 | GraphArch:Hiaml:4496 | 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, 64x64x1x3]
%onnx::Conv_435[FLOAT, 64x64x3x1]
%onnx::Conv_438[FLOAT, 64x64x1x3]
%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": 215299584,
"params": 2232906,
"val_accuracy": 92.72
} | {
"arch_str": "4496",
"identifier": "Hiaml_4496",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4496"
} | {
"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"
} | [] | 4496 | 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": 215299584,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3515 | GraphArch:Hiaml:3515 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_361[FLOAT, 64x3x3x3]
%onnx::Conv_362[FLOAT, 64]
%onnx::Conv_364[FLOAT, 64x64x1x3]
%onnx::Conv_367[FLOAT, 64x64x3x1]
%onnx::Conv_370[FLOAT, 64x64x1x3]
%onnx::Conv_373[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": 156153344,
"params": 1772874,
"val_accuracy": 92.43
} | {
"arch_str": "3515",
"identifier": "Hiaml_3515",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3515"
} | {
"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"
} | [] | 3515 | 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": 156153344,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2065 | GraphArch:Hiaml:2065 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x1x3]
%onnx::Conv_379[FLOAT, 64x64x3x1]
%onnx::Conv_382[FLOAT, 64x64x1x3]
%onnx::Conv_385[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": 290371072,
"params": 3804490,
"val_accuracy": 92.05
} | {
"arch_str": "2065",
"identifier": "Hiaml_2065",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2065"
} | {
"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"
} | [] | 2065 | 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": 290371072,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2834 | GraphArch:Hiaml:2834 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_480[FLOAT, 64x3x3x3]
%onnx::Conv_481[FLOAT, 64]
%onnx::Conv_483[FLOAT, 64x64x1x3]
%onnx::Conv_486[FLOAT, 64x64x3x1]
%onnx::Conv_489[FLOAT, 64x64x1x3]
%onnx::Conv_492[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": 173520384,
"params": 2276426,
"val_accuracy": 92.03
} | {
"arch_str": "2834",
"identifier": "Hiaml_2834",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2834"
} | {
"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"
} | [] | 2834 | 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": 173520384,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1108 | GraphArch:Hiaml:1108 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_403[FLOAT, 64x3x3x3]
%onnx::Conv_404[FLOAT, 64]
%onnx::Conv_406[FLOAT, 64x64x1x1]
%onnx::Conv_409[FLOAT, 64x64x1x3]
%onnx::Conv_412[FLOAT, 64x64x3x1]
%onnx::Conv_415[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": 184530432,
"params": 2117962,
"val_accuracy": 91.81
} | {
"arch_str": "1108",
"identifier": "Hiaml_1108",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1108"
} | {
"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"
} | [] | 1108 | 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": 184530432,
"val_accuracy": 91.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2923 | GraphArch:Hiaml:2923 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_325[FLOAT, 64x3x3x3]
%onnx::Conv_326[FLOAT, 64]
%onnx::Conv_328[FLOAT, 64x64x3x3]
%onnx::Conv_331[FLOAT, 64x64x1x1]
%onnx::Conv_334[FLOAT, 64x64x3x3]
%onnx::Conv_337[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": 143242752,
"params": 1624906,
"val_accuracy": 91.8
} | {
"arch_str": "2923",
"identifier": "Hiaml_2923",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2923"
} | {
"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"
} | [] | 2923 | 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": 143242752,
"val_accuracy": 91.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_276 | GraphArch:Hiaml:276 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_514[FLOAT, 64x3x3x3]
%onnx::Conv_515[FLOAT, 64]
%onnx::Conv_517[FLOAT, 64x64x1x1]
%onnx::Conv_520[FLOAT, 64x64x1x3]
%onnx::Conv_523[FLOAT, 64x64x3x1]
%onnx::Conv_526[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": 244954624,
"params": 2833994,
"val_accuracy": 92.03
} | {
"arch_str": "276",
"identifier": "Hiaml_276",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "276"
} | {
"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"
} | [] | 276 | 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": 244954624,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3017 | GraphArch:Hiaml:3017 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x3]
%onnx::Conv_427[FLOAT, 64x64x3x1]
%onnx::Conv_430[FLOAT, 64x64x1x1]
%onnx::Conv_433[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": 214251008,
"params": 2518858,
"val_accuracy": 92.97
} | {
"arch_str": "3017",
"identifier": "Hiaml_3017",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3017"
} | {
"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"
} | [] | 3017 | 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": 214251008,
"val_accuracy": 92.97
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3993 | GraphArch:Hiaml:3993 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_395[FLOAT, 64x3x3x3]
%onnx::Conv_396[FLOAT, 64]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[FLOAT, 64x64x1x1]
%onnx::Conv_404[FLOAT, 64x64x3x3]
%onnx::Conv_407[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": 188397056,
"params": 2472010,
"val_accuracy": 91.89
} | {
"arch_str": "3993",
"identifier": "Hiaml_3993",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3993"
} | {
"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"
} | [] | 3993 | 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": 188397056,
"val_accuracy": 91.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_603 | GraphArch:Hiaml:603 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x3x3]
%onnx::Conv_451[FLOAT, 64x64x1x1]
%onnx::Conv_454[FLOAT, 64x64x1x1]
%onnx::Conv_457[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": 194033152,
"params": 2488394,
"val_accuracy": 92.32
} | {
"arch_str": "603",
"identifier": "Hiaml_603",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "603"
} | {
"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"
} | [] | 603 | 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": 194033152,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2533 | GraphArch:Hiaml:2533 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_509[FLOAT, 64x3x3x3]
%onnx::Conv_510[FLOAT, 64]
%onnx::Conv_512[FLOAT, 64x64x1x3]
%onnx::Conv_515[FLOAT, 64x64x3x1]
%onnx::Conv_518[FLOAT, 64x64x1x3]
%onnx::Conv_521[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": 209401344,
"params": 2431818,
"val_accuracy": 92.13
} | {
"arch_str": "2533",
"identifier": "Hiaml_2533",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2533"
} | {
"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"
} | [] | 2533 | 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": 209401344,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3460 | GraphArch:Hiaml:3460 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_468[FLOAT, 64x3x3x3]
%onnx::Conv_469[FLOAT, 64]
%onnx::Conv_471[FLOAT, 64x64x1x3]
%onnx::Conv_474[FLOAT, 64x64x3x1]
%onnx::Conv_477[FLOAT, 64x64x1x3]
%onnx::Conv_480[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": 260650496,
"params": 2880330,
"val_accuracy": 92.54
} | {
"arch_str": "3460",
"identifier": "Hiaml_3460",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3460"
} | {
"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"
} | [] | 3460 | 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": 260650496,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4387 | GraphArch:Hiaml:4387 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_451[FLOAT, 64x3x3x3]
%onnx::Conv_452[FLOAT, 64]
%onnx::Conv_454[FLOAT, 64x64x1x3]
%onnx::Conv_457[FLOAT, 64x64x3x1]
%onnx::Conv_460[FLOAT, 64x64x1x3]
%onnx::Conv_463[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": 177845760,
"params": 1815114,
"val_accuracy": 92.6
} | {
"arch_str": "4387",
"identifier": "Hiaml_4387",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4387"
} | {
"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"
} | [] | 4387 | 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": 177845760,
"val_accuracy": 92.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2395 | GraphArch:Hiaml:2395 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_490[FLOAT, 64x3x3x3]
%onnx::Conv_491[FLOAT, 64]
%onnx::Conv_493[FLOAT, 64x64x1x1]
%onnx::Conv_496[FLOAT, 64x64x3x3]
%onnx::Conv_499[FLOAT, 64x64x3x3]
%onnx::Conv_502[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": 240367104,
"params": 2653770,
"val_accuracy": 92.27
} | {
"arch_str": "2395",
"identifier": "Hiaml_2395",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2395"
} | {
"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"
} | [] | 2395 | 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": 240367104,
"val_accuracy": 92.27
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_329 | GraphArch:Hiaml:329 | 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, 64x64x1x3]
%onnx::Conv_489[FLOAT, 64x64x3x1]
%onnx::Conv_492[FLOAT, 64x64x1x3]
%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": 231257600,
"params": 2714570,
"val_accuracy": 92.67
} | {
"arch_str": "329",
"identifier": "Hiaml_329",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "329"
} | {
"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"
} | [] | 329 | 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": 231257600,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4186 | GraphArch:Hiaml:4186 | 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, 64x64x1x1]
%onnx::Conv_487[FLOAT, 64x64x1x3]
%onnx::Conv_490[FLOAT, 64x64x3x1]
%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": 235615744,
"params": 2684234,
"val_accuracy": 92.08
} | {
"arch_str": "4186",
"identifier": "Hiaml_4186",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4186"
} | {
"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"
} | [] | 4186 | 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": 235615744,
"val_accuracy": 92.08
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4015 | GraphArch:Hiaml:4015 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_441[FLOAT, 64x3x3x3]
%onnx::Conv_442[FLOAT, 64]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[FLOAT, 64x64x3x3]
%onnx::Conv_450[FLOAT, 64x64x3x3]
%onnx::Conv_453[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": 223589888,
"params": 2028106,
"val_accuracy": 92.96
} | {
"arch_str": "4015",
"identifier": "Hiaml_4015",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4015"
} | {
"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"
} | [] | 4015 | 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": 223589888,
"val_accuracy": 92.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4430 | GraphArch:Hiaml:4430 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_532[FLOAT, 64x3x3x3]
%onnx::Conv_533[FLOAT, 64]
%onnx::Conv_535[FLOAT, 64x64x3x3]
%onnx::Conv_538[FLOAT, 64x64x1x3]
%onnx::Conv_541[FLOAT, 64x64x3x1]
%onnx::Conv_544[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": 92.36
} | {
"arch_str": "4430",
"identifier": "Hiaml_4430",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4430"
} | {
"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"
} | [] | 4430 | 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": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_454 | GraphArch:Hiaml:454 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x3]
%onnx::Conv_451[FLOAT, 64x64x3x1]
%onnx::Conv_454[FLOAT, 64x64x1x3]
%onnx::Conv_457[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": 217396736,
"params": 2163658,
"val_accuracy": 92.57
} | {
"arch_str": "454",
"identifier": "Hiaml_454",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "454"
} | {
"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"
} | [] | 454 | 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": 217396736,
"val_accuracy": 92.57
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3982 | GraphArch:Hiaml:3982 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[FLOAT, 64x64x1x3]
%onnx::Conv_430[FLOAT, 64x64x3x1]
%onnx::Conv_433[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": 224605696,
"params": 2822986,
"val_accuracy": 92.05
} | {
"arch_str": "3982",
"identifier": "Hiaml_3982",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3982"
} | {
"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"
} | [] | 3982 | 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": 224605696,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4378 | GraphArch:Hiaml:4378 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_434[FLOAT, 64x3x3x3]
%onnx::Conv_435[FLOAT, 64]
%onnx::Conv_437[FLOAT, 64x64x1x1]
%onnx::Conv_440[FLOAT, 64x64x1x3]
%onnx::Conv_443[FLOAT, 64x64x3x1]
%onnx::Conv_446[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": 156415488,
"params": 1897546,
"val_accuracy": 91.6
} | {
"arch_str": "4378",
"identifier": "Hiaml_4378",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4378"
} | {
"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"
} | [] | 4378 | 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": 156415488,
"val_accuracy": 91.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3304 | GraphArch:Hiaml:3304 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[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": 178238976,
"params": 1774666,
"val_accuracy": 92.28
} | {
"arch_str": "3304",
"identifier": "Hiaml_3304",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3304"
} | {
"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"
} | [] | 3304 | 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": 178238976,
"val_accuracy": 92.28
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2795 | GraphArch:Hiaml:2795 | 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": 207468032,
"params": 2101066,
"val_accuracy": 92.39
} | {
"arch_str": "2795",
"identifier": "Hiaml_2795",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2795"
} | {
"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"
} | [] | 2795 | 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": 207468032,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1378 | GraphArch:Hiaml:1378 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_446[FLOAT, 64x3x3x3]
%onnx::Conv_447[FLOAT, 64]
%onnx::Conv_449[FLOAT, 64x64x3x3]
%onnx::Conv_452[FLOAT, 64x64x1x1]
%onnx::Conv_455[FLOAT, 64x64x1x1]
%onnx::Conv_458[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": 222344704,
"params": 2599370,
"val_accuracy": 92.76
} | {
"arch_str": "1378",
"identifier": "Hiaml_1378",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1378"
} | {
"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"
} | [] | 1378 | 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": 222344704,
"val_accuracy": 92.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3566 | GraphArch:Hiaml:3566 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_467[FLOAT, 64x3x3x3]
%onnx::Conv_468[FLOAT, 64]
%onnx::Conv_470[FLOAT, 64x64x1x3]
%onnx::Conv_473[FLOAT, 64x64x3x1]
%onnx::Conv_476[FLOAT, 64x64x1x1]
%onnx::Conv_479[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": 198686208,
"params": 1979978,
"val_accuracy": 92.86
} | {
"arch_str": "3566",
"identifier": "Hiaml_3566",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3566"
} | {
"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"
} | [] | 3566 | 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": 198686208,
"val_accuracy": 92.86
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1482 | GraphArch:Hiaml:1482 | 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, 64x64x3x3]
%onnx::Conv_369[FLOAT, 64x64x1x1]
%onnx::Conv_372[FLOAT, 64x64x3x3]
%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": 193672704,
"params": 2436682,
"val_accuracy": 92.15
} | {
"arch_str": "1482",
"identifier": "Hiaml_1482",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1482"
} | {
"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"
} | [] | 1482 | 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": 193672704,
"val_accuracy": 92.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1833 | GraphArch:Hiaml:1833 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_317[FLOAT, 64x3x3x3]
%onnx::Conv_318[FLOAT, 64]
%onnx::Conv_320[FLOAT, 64x64x3x3]
%onnx::Conv_323[FLOAT, 64x64x1x1]
%onnx::Conv_326[FLOAT, 64x64x3x3]
%onnx::Conv_329[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": 194426368,
"params": 1854282,
"val_accuracy": 91.37
} | {
"arch_str": "1833",
"identifier": "Hiaml_1833",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1833"
} | {
"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"
} | [] | 1833 | 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": 194426368,
"val_accuracy": 91.37
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3193 | GraphArch:Hiaml:3193 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_385[FLOAT, 64x3x3x3]
%onnx::Conv_386[FLOAT, 64]
%onnx::Conv_388[FLOAT, 64x64x1x1]
%onnx::Conv_391[FLOAT, 64x64x1x3]
%onnx::Conv_394[FLOAT, 64x64x3x1]
%onnx::Conv_397[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": 225588736,
"params": 2620874,
"val_accuracy": 92.68
} | {
"arch_str": "3193",
"identifier": "Hiaml_3193",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3193"
} | {
"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"
} | [] | 3193 | 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": 225588736,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1815 | GraphArch:Hiaml:1815 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_361[FLOAT, 64x3x3x3]
%onnx::Conv_362[FLOAT, 64]
%onnx::Conv_364[FLOAT, 64x64x1x1]
%onnx::Conv_367[FLOAT, 64x64x3x3]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[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": 223131136,
"params": 3108426,
"val_accuracy": 92.13
} | {
"arch_str": "1815",
"identifier": "Hiaml_1815",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1815"
} | {
"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"
} | [] | 1815 | 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": 223131136,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3660 | GraphArch:Hiaml:3660 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_509[FLOAT, 64x3x3x3]
%onnx::Conv_510[FLOAT, 64]
%onnx::Conv_512[FLOAT, 64x64x3x3]
%onnx::Conv_515[FLOAT, 64x64x1x3]
%onnx::Conv_518[FLOAT, 64x64x3x1]
%onnx::Conv_521[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": 2654282,
"val_accuracy": 92.37
} | {
"arch_str": "3660",
"identifier": "Hiaml_3660",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3660"
} | {
"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"
} | [] | 3660 | 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": 92.37
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2268 | GraphArch:Hiaml:2268 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_502[FLOAT, 64x3x3x3]
%onnx::Conv_503[FLOAT, 64]
%onnx::Conv_505[FLOAT, 64x64x1x1]
%onnx::Conv_508[FLOAT, 64x64x3x3]
%onnx::Conv_511[FLOAT, 64x64x3x3]
%onnx::Conv_514[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": 239482368,
"params": 2522186,
"val_accuracy": 92.01
} | {
"arch_str": "2268",
"identifier": "Hiaml_2268",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2268"
} | {
"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"
} | [] | 2268 | 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": 239482368,
"val_accuracy": 92.01
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1948 | GraphArch:Hiaml:1948 | 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, 64x64x1x3]
%onnx::Conv_455[FLOAT, 64x64x3x1]
%onnx::Conv_458[FLOAT, 64x64x1x3]
%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": 192361984,
"params": 2053194,
"val_accuracy": 92.41
} | {
"arch_str": "1948",
"identifier": "Hiaml_1948",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1948"
} | {
"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"
} | [] | 1948 | 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": 192361984,
"val_accuracy": 92.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3362 | GraphArch:Hiaml:3362 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_476[FLOAT, 64x3x3x3]
%onnx::Conv_477[FLOAT, 64]
%onnx::Conv_479[FLOAT, 64x64x3x3]
%onnx::Conv_482[FLOAT, 64x64x1x3]
%onnx::Conv_485[FLOAT, 64x64x3x1]
%onnx::Conv_488[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": 201504256,
"params": 2448714,
"val_accuracy": 92.41
} | {
"arch_str": "3362",
"identifier": "Hiaml_3362",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3362"
} | {
"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"
} | [] | 3362 | 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": 201504256,
"val_accuracy": 92.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1048 | GraphArch:Hiaml:1048 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_385[FLOAT, 64x3x3x3]
%onnx::Conv_386[FLOAT, 64]
%onnx::Conv_388[FLOAT, 64x64x1x1]
%onnx::Conv_391[FLOAT, 64x64x1x3]
%onnx::Conv_394[FLOAT, 64x64x3x1]
%onnx::Conv_397[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": 234960384,
"params": 2805322,
"val_accuracy": 91.47
} | {
"arch_str": "1048",
"identifier": "Hiaml_1048",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1048"
} | {
"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"
} | [] | 1048 | 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": 234960384,
"val_accuracy": 91.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3027 | GraphArch:Hiaml:3027 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_395[FLOAT, 64x3x3x3]
%onnx::Conv_396[FLOAT, 64]
%onnx::Conv_398[FLOAT, 64x64x1x1]
%onnx::Conv_401[FLOAT, 64x64x3x3]
%onnx::Conv_404[FLOAT, 64x64x3x3]
%onnx::Conv_407[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": 215922176,
"params": 2461770,
"val_accuracy": 92.87
} | {
"arch_str": "3027",
"identifier": "Hiaml_3027",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3027"
} | {
"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"
} | [] | 3027 | 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": 215922176,
"val_accuracy": 92.87
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_999 | GraphArch:Hiaml:999 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[FLOAT, 64x64x3x3]
%onnx::Conv_414[FLOAT, 64x64x3x3]
%onnx::Conv_417[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": 260027904,
"params": 3395914,
"val_accuracy": 92.55
} | {
"arch_str": "999",
"identifier": "Hiaml_999",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "999"
} | {
"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"
} | [] | 999 | 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": 260027904,
"val_accuracy": 92.55
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2899 | GraphArch:Hiaml:2899 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_521[FLOAT, 64x3x3x3]
%onnx::Conv_522[FLOAT, 64]
%onnx::Conv_524[FLOAT, 64x64x1x3]
%onnx::Conv_527[FLOAT, 64x64x3x1]
%onnx::Conv_530[FLOAT, 64x64x1x1]
%onnx::Conv_533[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": 214644224,
"params": 2571850,
"val_accuracy": 91.91
} | {
"arch_str": "2899",
"identifier": "Hiaml_2899",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2899"
} | {
"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"
} | [] | 2899 | 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": 214644224,
"val_accuracy": 91.91
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2045 | GraphArch:Hiaml:2045 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_329[FLOAT, 64x3x3x3]
%onnx::Conv_330[FLOAT, 64]
%onnx::Conv_332[FLOAT, 64x64x3x3]
%onnx::Conv_335[FLOAT, 64x64x1x1]
%onnx::Conv_338[FLOAT, 64x64x3x3]
%onnx::Conv_341[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": 170341888,
"params": 2354506,
"val_accuracy": 92.12
} | {
"arch_str": "2045",
"identifier": "Hiaml_2045",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2045"
} | {
"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"
} | [] | 2045 | 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": 170341888,
"val_accuracy": 92.12
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3399 | GraphArch:Hiaml:3399 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_515[FLOAT, 64x3x3x3]
%onnx::Conv_516[FLOAT, 64]
%onnx::Conv_518[FLOAT, 64x64x1x3]
%onnx::Conv_521[FLOAT, 64x64x3x1]
%onnx::Conv_524[FLOAT, 64x64x1x3]
%onnx::Conv_527[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": 209270272,
"params": 2456650,
"val_accuracy": 92.11
} | {
"arch_str": "3399",
"identifier": "Hiaml_3399",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3399"
} | {
"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"
} | [] | 3399 | 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": 209270272,
"val_accuracy": 92.11
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3091 | GraphArch:Hiaml:3091 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_479[FLOAT, 64x3x3x3]
%onnx::Conv_480[FLOAT, 64]
%onnx::Conv_482[FLOAT, 64x64x1x3]
%onnx::Conv_485[FLOAT, 64x64x3x1]
%onnx::Conv_488[FLOAT, 64x64x1x1]
%onnx::Conv_491[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": 186136064,
"params": 2250826,
"val_accuracy": 92.25
} | {
"arch_str": "3091",
"identifier": "Hiaml_3091",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3091"
} | {
"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"
} | [] | 3091 | 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": 186136064,
"val_accuracy": 92.25
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_660 | GraphArch:Hiaml:660 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_439[FLOAT, 64x3x3x3]
%onnx::Conv_440[FLOAT, 64]
%onnx::Conv_442[FLOAT, 64x64x1x3]
%onnx::Conv_445[FLOAT, 64x64x3x1]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[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": 194426368,
"params": 2437706,
"val_accuracy": 92.01
} | {
"arch_str": "660",
"identifier": "Hiaml_660",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "660"
} | {
"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"
} | [] | 660 | 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": 194426368,
"val_accuracy": 92.01
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2728 | GraphArch:Hiaml:2728 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_401[FLOAT, 64x3x3x3]
%onnx::Conv_402[FLOAT, 64]
%onnx::Conv_404[FLOAT, 64x64x1x3]
%onnx::Conv_407[FLOAT, 64x64x3x1]
%onnx::Conv_410[FLOAT, 64x64x1x1]
%onnx::Conv_413[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": 252884480,
"params": 2895434,
"val_accuracy": 92.2
} | {
"arch_str": "2728",
"identifier": "Hiaml_2728",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2728"
} | {
"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"
} | [] | 2728 | 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": 252884480,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2525 | GraphArch:Hiaml:2525 | 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, 64x64x1x3]
%onnx::Conv_395[FLOAT, 64x64x3x1]
%onnx::Conv_398[FLOAT, 64x64x1x3]
%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": 171095552,
"params": 1592906,
"val_accuracy": 92.45
} | {
"arch_str": "2525",
"identifier": "Hiaml_2525",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2525"
} | {
"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"
} | [] | 2525 | 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": 171095552,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_193 | GraphArch:Hiaml:193 | 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": 202618368,
"params": 2135370,
"val_accuracy": 91.79
} | {
"arch_str": "193",
"identifier": "Hiaml_193",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "193"
} | {
"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"
} | [] | 193 | 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": 91.79
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_793 | GraphArch:Hiaml:793 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x1x1]
%onnx::Conv_421[FLOAT, 64x64x1x1]
%onnx::Conv_424[FLOAT, 64x64x3x3]
%onnx::Conv_427[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": 214119936,
"params": 2543690,
"val_accuracy": 92.05
} | {
"arch_str": "793",
"identifier": "Hiaml_793",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "793"
} | {
"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"
} | [] | 793 | 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": 214119936,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2443 | GraphArch:Hiaml:2443 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_434[FLOAT, 64x3x3x3]
%onnx::Conv_435[FLOAT, 64]
%onnx::Conv_437[FLOAT, 64x64x3x3]
%onnx::Conv_440[FLOAT, 64x64x1x1]
%onnx::Conv_443[FLOAT, 64x64x1x1]
%onnx::Conv_446[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": 164607488,
"params": 2259530,
"val_accuracy": 91.56
} | {
"arch_str": "2443",
"identifier": "Hiaml_2443",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2443"
} | {
"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"
} | [] | 2443 | 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": 164607488,
"val_accuracy": 91.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3542 | GraphArch:Hiaml:3542 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_341[FLOAT, 64x3x3x3]
%onnx::Conv_342[FLOAT, 64]
%onnx::Conv_344[FLOAT, 64x64x3x3]
%onnx::Conv_347[FLOAT, 64x64x1x1]
%onnx::Conv_350[FLOAT, 64x64x1x1]
%onnx::Conv_353[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": 209335808,
"params": 2239818,
"val_accuracy": 91.62
} | {
"arch_str": "3542",
"identifier": "Hiaml_3542",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3542"
} | {
"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"
} | [] | 3542 | 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": 209335808,
"val_accuracy": 91.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2150 | GraphArch:Hiaml:2150 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_359[FLOAT, 64x3x3x3]
%onnx::Conv_360[FLOAT, 64]
%onnx::Conv_362[FLOAT, 64x64x3x3]
%onnx::Conv_365[FLOAT, 64x64x1x3]
%onnx::Conv_368[FLOAT, 64x64x3x1]
%onnx::Conv_371[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": 203109888,
"params": 2485834,
"val_accuracy": 92.75
} | {
"arch_str": "2150",
"identifier": "Hiaml_2150",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2150"
} | {
"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"
} | [] | 2150 | 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": 203109888,
"val_accuracy": 92.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1539 | GraphArch:Hiaml:1539 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[FLOAT, 64x64x1x3]
%onnx::Conv_454[FLOAT, 64x64x3x1]
%onnx::Conv_457[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": 176371200,
"params": 2398538,
"val_accuracy": 92.42
} | {
"arch_str": "1539",
"identifier": "Hiaml_1539",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1539"
} | {
"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"
} | [] | 1539 | 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": 176371200,
"val_accuracy": 92.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1442 | GraphArch:Hiaml:1442 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[FLOAT, 64x64x1x3]
%onnx::Conv_462[FLOAT, 64x64x3x1]
%onnx::Conv_465[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": 170374656,
"params": 1758794,
"val_accuracy": 92.05
} | {
"arch_str": "1442",
"identifier": "Hiaml_1442",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1442"
} | {
"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"
} | [] | 1442 | 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": 170374656,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3217 | GraphArch:Hiaml:3217 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_391[FLOAT, 64x3x3x3]
%onnx::Conv_392[FLOAT, 64]
%onnx::Conv_394[FLOAT, 64x64x1x1]
%onnx::Conv_397[FLOAT, 64x64x1x3]
%onnx::Conv_400[FLOAT, 64x64x3x1]
%onnx::Conv_403[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": 209663488,
"params": 2633546,
"val_accuracy": 92.51
} | {
"arch_str": "3217",
"identifier": "Hiaml_3217",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3217"
} | {
"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"
} | [] | 3217 | 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": 209663488,
"val_accuracy": 92.51
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3212 | GraphArch:Hiaml:3212 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x1x1]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[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": 197375488,
"params": 3174986,
"val_accuracy": 91.93
} | {
"arch_str": "3212",
"identifier": "Hiaml_3212",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3212"
} | {
"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"
} | [] | 3212 | 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": 197375488,
"val_accuracy": 91.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4089 | GraphArch:Hiaml:4089 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_361[FLOAT, 64x3x3x3]
%onnx::Conv_362[FLOAT, 64]
%onnx::Conv_364[FLOAT, 64x64x3x3]
%onnx::Conv_367[FLOAT, 64x64x1x3]
%onnx::Conv_370[FLOAT, 64x64x3x1]
%onnx::Conv_373[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": 203109888,
"params": 2485834,
"val_accuracy": 93
} | {
"arch_str": "4089",
"identifier": "Hiaml_4089",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4089"
} | {
"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"
} | [] | 4089 | 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": 203109888,
"val_accuracy": 93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_286 | GraphArch:Hiaml:286 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x1x1]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[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": 206943744,
"params": 2043466,
"val_accuracy": 92.67
} | {
"arch_str": "286",
"identifier": "Hiaml_286",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "286"
} | {
"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"
} | [] | 286 | 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": 206943744,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1308 | GraphArch:Hiaml:1308 | 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, 64x64x3x3]
%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": 201111040,
"params": 1966794,
"val_accuracy": 92.67
} | {
"arch_str": "1308",
"identifier": "Hiaml_1308",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1308"
} | {
"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"
} | [] | 1308 | 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": 201111040,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2922 | GraphArch:Hiaml:2922 | 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, 64x64x1x3]
%onnx::Conv_498[FLOAT, 64x64x3x1]
%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": 236172800,
"params": 2719306,
"val_accuracy": 92.83
} | {
"arch_str": "2922",
"identifier": "Hiaml_2922",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2922"
} | {
"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"
} | [] | 2922 | 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": 236172800,
"val_accuracy": 92.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4060 | GraphArch:Hiaml:4060 | 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, 64x64x3x3]
%onnx::Conv_359[FLOAT, 64x64x1x3]
%onnx::Conv_362[FLOAT, 64x64x3x1]
%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": 2714698,
"val_accuracy": 92.57
} | {
"arch_str": "4060",
"identifier": "Hiaml_4060",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4060"
} | {
"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"
} | [] | 4060 | 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": 92.57
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3345 | GraphArch:Hiaml:3345 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_391[FLOAT, 64x3x3x3]
%onnx::Conv_392[FLOAT, 64]
%onnx::Conv_394[FLOAT, 64x64x1x1]
%onnx::Conv_397[FLOAT, 64x64x1x1]
%onnx::Conv_400[FLOAT, 64x64x3x3]
%onnx::Conv_403[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": 225523200,
"params": 2228170,
"val_accuracy": 92.81
} | {
"arch_str": "3345",
"identifier": "Hiaml_3345",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3345"
} | {
"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"
} | [] | 3345 | 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": 225523200,
"val_accuracy": 92.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4222 | GraphArch:Hiaml:4222 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[FLOAT, 64x64x1x3]
%onnx::Conv_414[FLOAT, 64x64x3x1]
%onnx::Conv_417[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": 205665792,
"params": 2282058,
"val_accuracy": 91.54
} | {
"arch_str": "4222",
"identifier": "Hiaml_4222",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4222"
} | {
"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"
} | [] | 4222 | 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": 205665792,
"val_accuracy": 91.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1967 | GraphArch:Hiaml:1967 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_410[FLOAT, 64x3x3x3]
%onnx::Conv_411[FLOAT, 64]
%onnx::Conv_413[FLOAT, 64x64x1x1]
%onnx::Conv_416[FLOAT, 64x64x3x3]
%onnx::Conv_419[FLOAT, 64x64x3x3]
%onnx::Conv_422[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": 202225152,
"params": 2357322,
"val_accuracy": 92.82
} | {
"arch_str": "1967",
"identifier": "Hiaml_1967",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1967"
} | {
"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"
} | [] | 1967 | 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": 202225152,
"val_accuracy": 92.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1446 | GraphArch:Hiaml:1446 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_495[FLOAT, 64x3x3x3]
%onnx::Conv_496[FLOAT, 64]
%onnx::Conv_498[FLOAT, 64x64x1x1]
%onnx::Conv_501[FLOAT, 64x64x1x1]
%onnx::Conv_504[FLOAT, 64x64x3x3]
%onnx::Conv_507[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": 216446464,
"params": 2637386,
"val_accuracy": 92.27
} | {
"arch_str": "1446",
"identifier": "Hiaml_1446",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1446"
} | {
"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"
} | [] | 1446 | 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": 216446464,
"val_accuracy": 92.27
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_189 | GraphArch:Hiaml:189 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x3x3]
%onnx::Conv_411[FLOAT, 64x64x1x1]
%onnx::Conv_414[FLOAT, 64x64x1x1]
%onnx::Conv_417[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": 236828160,
"params": 2823498,
"val_accuracy": 92.17
} | {
"arch_str": "189",
"identifier": "Hiaml_189",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "189"
} | {
"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"
} | [] | 189 | 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": 236828160,
"val_accuracy": 92.17
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2931 | GraphArch:Hiaml:2931 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_438[FLOAT, 64x3x3x3]
%onnx::Conv_439[FLOAT, 64]
%onnx::Conv_441[FLOAT, 64x64x1x1]
%onnx::Conv_444[FLOAT, 64x64x1x3]
%onnx::Conv_447[FLOAT, 64x64x3x1]
%onnx::Conv_450[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": 205665792,
"params": 2307402,
"val_accuracy": 92.26
} | {
"arch_str": "2931",
"identifier": "Hiaml_2931",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2931"
} | {
"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"
} | [] | 2931 | 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": 205665792,
"val_accuracy": 92.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_543 | GraphArch:Hiaml:543 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_345[FLOAT, 64x3x3x3]
%onnx::Conv_346[FLOAT, 64]
%onnx::Conv_348[FLOAT, 64x64x3x3]
%onnx::Conv_351[FLOAT, 64x64x1x1]
%onnx::Conv_354[FLOAT, 64x64x3x3]
%onnx::Conv_357[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": 2248010,
"val_accuracy": 91.99
} | {
"arch_str": "543",
"identifier": "Hiaml_543",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "543"
} | {
"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"
} | [] | 543 | 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": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4602 | GraphArch:Hiaml:4602 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_394[FLOAT, 64x3x3x3]
%onnx::Conv_395[FLOAT, 64]
%onnx::Conv_397[FLOAT, 64x64x3x3]
%onnx::Conv_400[FLOAT, 64x64x1x1]
%onnx::Conv_403[FLOAT, 64x64x1x1]
%onnx::Conv_406[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": 181384704,
"params": 1896010,
"val_accuracy": 92.12
} | {
"arch_str": "4602",
"identifier": "Hiaml_4602",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4602"
} | {
"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"
} | [] | 4602 | 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": 181384704,
"val_accuracy": 92.12
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2030 | GraphArch:Hiaml:2030 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_468[FLOAT, 64x3x3x3]
%onnx::Conv_469[FLOAT, 64]
%onnx::Conv_471[FLOAT, 64x64x1x1]
%onnx::Conv_474[FLOAT, 64x64x1x3]
%onnx::Conv_477[FLOAT, 64x64x3x1]
%onnx::Conv_480[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": 211138048,
"params": 2570826,
"val_accuracy": 92.85
} | {
"arch_str": "2030",
"identifier": "Hiaml_2030",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2030"
} | {
"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"
} | [] | 2030 | 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": 211138048,
"val_accuracy": 92.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2674 | GraphArch:Hiaml:2674 | 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, 64x64x1x3]
%onnx::Conv_433[FLOAT, 64x64x3x1]
%onnx::Conv_436[FLOAT, 64x64x1x1]
%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": 188036608,
"params": 2019914,
"val_accuracy": 91.89
} | {
"arch_str": "2674",
"identifier": "Hiaml_2674",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2674"
} | {
"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"
} | [] | 2674 | 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": 188036608,
"val_accuracy": 91.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_218 | GraphArch:Hiaml:218 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x1x1]
%onnx::Conv_379[FLOAT, 64x64x1x1]
%onnx::Conv_382[FLOAT, 64x64x3x3]
%onnx::Conv_385[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": 162477568,
"params": 2240330,
"val_accuracy": 91.99
} | {
"arch_str": "218",
"identifier": "Hiaml_218",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "218"
} | {
"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"
} | [] | 218 | 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": 162477568,
"val_accuracy": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3059 | GraphArch:Hiaml:3059 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x3x3]
%onnx::Conv_427[FLOAT, 64x64x1x1]
%onnx::Conv_430[FLOAT, 64x64x1x1]
%onnx::Conv_433[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": 190821888,
"params": 2463562,
"val_accuracy": 92.3
} | {
"arch_str": "3059",
"identifier": "Hiaml_3059",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3059"
} | {
"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"
} | [] | 3059 | 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": 190821888,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2299 | GraphArch:Hiaml:2299 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_444[FLOAT, 64x3x3x3]
%onnx::Conv_445[FLOAT, 64]
%onnx::Conv_447[FLOAT, 64x64x1x3]
%onnx::Conv_450[FLOAT, 64x64x3x1]
%onnx::Conv_453[FLOAT, 64x64x1x3]
%onnx::Conv_456[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": 195147264,
"params": 2472266,
"val_accuracy": 92.15
} | {
"arch_str": "2299",
"identifier": "Hiaml_2299",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2299"
} | {
"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"
} | [] | 2299 | 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": 195147264,
"val_accuracy": 92.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1230 | GraphArch:Hiaml:1230 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[FLOAT, 64x64x3x3]
%onnx::Conv_430[FLOAT, 64x64x3x3]
%onnx::Conv_433[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": 246560256,
"params": 2613450,
"val_accuracy": 92.91
} | {
"arch_str": "1230",
"identifier": "Hiaml_1230",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1230"
} | {
"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"
} | [] | 1230 | 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": 246560256,
"val_accuracy": 92.91
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3958 | GraphArch:Hiaml:3958 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_381[FLOAT, 64x3x3x3]
%onnx::Conv_382[FLOAT, 64]
%onnx::Conv_384[FLOAT, 64x64x3x3]
%onnx::Conv_387[FLOAT, 64x64x1x1]
%onnx::Conv_390[FLOAT, 64x64x1x1]
%onnx::Conv_393[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": 176993792,
"params": 2356298,
"val_accuracy": 91.72
} | {
"arch_str": "3958",
"identifier": "Hiaml_3958",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3958"
} | {
"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"
} | [] | 3958 | 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": 176993792,
"val_accuracy": 91.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1682 | GraphArch:Hiaml:1682 | 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, 64x64x1x3]
%onnx::Conv_429[FLOAT, 64x64x3x1]
%onnx::Conv_432[FLOAT, 64x64x1x1]
%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": 229914112,
"params": 2740554,
"val_accuracy": 92.47
} | {
"arch_str": "1682",
"identifier": "Hiaml_1682",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1682"
} | {
"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"
} | [] | 1682 | 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": 229914112,
"val_accuracy": 92.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2657 | GraphArch:Hiaml:2657 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_479[FLOAT, 64x3x3x3]
%onnx::Conv_480[FLOAT, 64]
%onnx::Conv_482[FLOAT, 64x64x1x3]
%onnx::Conv_485[FLOAT, 64x64x3x1]
%onnx::Conv_488[FLOAT, 64x64x1x1]
%onnx::Conv_491[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": 197768704,
"params": 2069834,
"val_accuracy": 92.39
} | {
"arch_str": "2657",
"identifier": "Hiaml_2657",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2657"
} | {
"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"
} | [] | 2657 | 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": 197768704,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_639 | GraphArch:Hiaml:639 | 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, 64x64x1x3]
%onnx::Conv_406[FLOAT, 64x64x3x1]
%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": 167884288,
"params": 2355274,
"val_accuracy": 92.07
} | {
"arch_str": "639",
"identifier": "Hiaml_639",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "639"
} | {
"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"
} | [] | 639 | 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": 167884288,
"val_accuracy": 92.07
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1916 | GraphArch:Hiaml:1916 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_435[FLOAT, 64x3x3x3]
%onnx::Conv_436[FLOAT, 64]
%onnx::Conv_438[FLOAT, 64x64x1x3]
%onnx::Conv_441[FLOAT, 64x64x3x1]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[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": 222672384,
"params": 2265418,
"val_accuracy": 92.11
} | {
"arch_str": "1916",
"identifier": "Hiaml_1916",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1916"
} | {
"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"
} | [] | 1916 | 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": 222672384,
"val_accuracy": 92.11
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3752 | GraphArch:Hiaml:3752 | 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": 192624128,
"params": 2041546,
"val_accuracy": 92.64
} | {
"arch_str": "3752",
"identifier": "Hiaml_3752",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3752"
} | {
"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"
} | [] | 3752 | 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": 192624128,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_686 | GraphArch:Hiaml:686 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[FLOAT, 64x64x1x3]
%onnx::Conv_430[FLOAT, 64x64x3x1]
%onnx::Conv_433[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": 160544256,
"params": 1659722,
"val_accuracy": 92.2
} | {
"arch_str": "686",
"identifier": "Hiaml_686",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "686"
} | {
"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"
} | [] | 686 | 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": 160544256,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4251 | GraphArch:Hiaml:4251 | 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, 64x64x3x3]
%onnx::Conv_489[FLOAT, 64x64x1x1]
%onnx::Conv_492[FLOAT, 64x64x1x1]
%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": 204748288,
"params": 2547018,
"val_accuracy": 91.9
} | {
"arch_str": "4251",
"identifier": "Hiaml_4251",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4251"
} | {
"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"
} | [] | 4251 | 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": 204748288,
"val_accuracy": 91.9
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_588 | GraphArch:Hiaml:588 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x3x3]
%onnx::Conv_451[FLOAT, 64x64x1x1]
%onnx::Conv_454[FLOAT, 64x64x1x1]
%onnx::Conv_457[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": 181646848,
"params": 1725770,
"val_accuracy": 92.15
} | {
"arch_str": "588",
"identifier": "Hiaml_588",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "588"
} | {
"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"
} | [] | 588 | 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": 181646848,
"val_accuracy": 92.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2695 | GraphArch:Hiaml:2695 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x1x3]
%onnx::Conv_419[FLOAT, 64x64x3x1]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[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": 173127168,
"params": 1807178,
"val_accuracy": 92.31
} | {
"arch_str": "2695",
"identifier": "Hiaml_2695",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2695"
} | {
"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"
} | [] | 2695 | 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": 173127168,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1529 | GraphArch:Hiaml:1529 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x3x3]
%onnx::Conv_411[FLOAT, 64x64x1x1]
%onnx::Conv_414[FLOAT, 64x64x1x1]
%onnx::Conv_417[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": 183449088,
"params": 2429514,
"val_accuracy": 92.44
} | {
"arch_str": "1529",
"identifier": "Hiaml_1529",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1529"
} | {
"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"
} | [] | 1529 | 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": 183449088,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1203 | GraphArch:Hiaml:1203 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_531[FLOAT, 64x3x3x3]
%onnx::Conv_532[FLOAT, 64]
%onnx::Conv_534[FLOAT, 64x64x1x3]
%onnx::Conv_537[FLOAT, 64x64x3x1]
%onnx::Conv_540[FLOAT, 64x64x1x3]
%onnx::Conv_543[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": 219985408,
"params": 2637386,
"val_accuracy": 91.99
} | {
"arch_str": "1203",
"identifier": "Hiaml_1203",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1203"
} | {
"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"
} | [] | 1203 | 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": 219985408,
"val_accuracy": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3532 | GraphArch:Hiaml:3532 | 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, 64x64x3x3]
%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": 206288384,
"params": 1994826,
"val_accuracy": 92.39
} | {
"arch_str": "3532",
"identifier": "Hiaml_3532",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3532"
} | {
"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"
} | [] | 3532 | 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": 206288384,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4306 | GraphArch:Hiaml:4306 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_425[FLOAT, 64x3x3x3]
%onnx::Conv_426[FLOAT, 64]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[FLOAT, 64x64x1x3]
%onnx::Conv_434[FLOAT, 64x64x3x1]
%onnx::Conv_437[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": 152155648,
"params": 1766986,
"val_accuracy": 91.63
} | {
"arch_str": "4306",
"identifier": "Hiaml_4306",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4306"
} | {
"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"
} | [] | 4306 | 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": 152155648,
"val_accuracy": 91.63
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2449 | GraphArch:Hiaml:2449 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_530[FLOAT, 64x3x3x3]
%onnx::Conv_531[FLOAT, 64]
%onnx::Conv_533[FLOAT, 64x64x1x1]
%onnx::Conv_536[FLOAT, 64x64x1x3]
%onnx::Conv_539[FLOAT, 64x64x3x1]
%onnx::Conv_542[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": 198882816,
"params": 2596682,
"val_accuracy": 91.86
} | {
"arch_str": "2449",
"identifier": "Hiaml_2449",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2449"
} | {
"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"
} | [] | 2449 | 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": 198882816,
"val_accuracy": 91.86
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4325 | GraphArch:Hiaml:4325 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_365[FLOAT, 64x3x3x3]
%onnx::Conv_366[FLOAT, 64]
%onnx::Conv_368[FLOAT, 64x64x1x1]
%onnx::Conv_371[FLOAT, 64x64x3x3]
%onnx::Conv_374[FLOAT, 64x64x3x3]
%onnx::Conv_377[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": 239908352,
"params": 3239498,
"val_accuracy": 92.43
} | {
"arch_str": "4325",
"identifier": "Hiaml_4325",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4325"
} | {
"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"
} | [] | 4325 | 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": 239908352,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_6 | GraphArch:Hiaml:6 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_433[FLOAT, 64x3x3x3]
%onnx::Conv_434[FLOAT, 64]
%onnx::Conv_436[FLOAT, 64x64x1x1]
%onnx::Conv_439[FLOAT, 64x64x1x3]
%onnx::Conv_442[FLOAT, 64x64x3x1]
%onnx::Conv_445[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": 239252992,
"params": 2803914,
"val_accuracy": 92.92
} | {
"arch_str": "6",
"identifier": "Hiaml_6",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "6"
} | {
"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"
} | [] | 6 | 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": 239252992,
"val_accuracy": 92.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_130 | GraphArch:Hiaml:130 | 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, 64x64x1x3]
%onnx::Conv_402[FLOAT, 64x64x3x1]
%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": 148813312,
"params": 1839178,
"val_accuracy": 92.28
} | {
"arch_str": "130",
"identifier": "Hiaml_130",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "130"
} | {
"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"
} | [] | 130 | 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": 148813312,
"val_accuracy": 92.28
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4591 | GraphArch:Hiaml:4591 | 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, 64x64x3x3]
%onnx::Conv_359[FLOAT, 64x64x1x3]
%onnx::Conv_362[FLOAT, 64x64x3x1]
%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": 268154368,
"params": 2857930,
"val_accuracy": 92.79
} | {
"arch_str": "4591",
"identifier": "Hiaml_4591",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4591"
} | {
"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"
} | [] | 4591 | 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": 268154368,
"val_accuracy": 92.79
} | full |
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