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_2188 | GraphArch:Hiaml:2188 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_477[FLOAT, 64x3x3x3]
%onnx::Conv_478[FLOAT, 64]
%onnx::Conv_480[FLOAT, 64x64x1x1]
%onnx::Conv_483[FLOAT, 64x64x1x3]
%onnx::Conv_486[FLOAT, 64x64x3x1]
%onnx::Conv_489[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": 177714688,
"params": 1939786,
"val_accuracy": 91.64
} | {
"arch_str": "2188",
"identifier": "Hiaml_2188",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2188"
} | {
"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"
} | [] | 2188 | 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": 177714688,
"val_accuracy": 91.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2693 | GraphArch:Hiaml:2693 | 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, 64x64x3x3]
%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": 272381440,
"params": 2880074,
"val_accuracy": 92.46
} | {
"arch_str": "2693",
"identifier": "Hiaml_2693",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2693"
} | {
"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"
} | [] | 2693 | 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": 272381440,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_216 | GraphArch:Hiaml:216 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x1x1]
%onnx::Conv_369[FLOAT, 64x64x3x3]
%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": 270218752,
"params": 2911818,
"val_accuracy": 92.95
} | {
"arch_str": "216",
"identifier": "Hiaml_216",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "216"
} | {
"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"
} | [] | 216 | 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": 270218752,
"val_accuracy": 92.95
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3301 | GraphArch:Hiaml:3301 | 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, 64x64x3x3]
%onnx::Conv_446[FLOAT, 64x64x3x3]
%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": 240137728,
"params": 2627914,
"val_accuracy": 92.65
} | {
"arch_str": "3301",
"identifier": "Hiaml_3301",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3301"
} | {
"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"
} | [] | 3301 | 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": 240137728,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1281 | GraphArch:Hiaml:1281 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x3x3]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[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": 234862080,
"params": 2609482,
"val_accuracy": 92.88
} | {
"arch_str": "1281",
"identifier": "Hiaml_1281",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1281"
} | {
"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"
} | [] | 1281 | 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": 234862080,
"val_accuracy": 92.88
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1384 | GraphArch:Hiaml:1384 | 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, 64x64x1x1]
%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": 152811008,
"params": 1773642,
"val_accuracy": 91.8
} | {
"arch_str": "1384",
"identifier": "Hiaml_1384",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1384"
} | {
"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"
} | [] | 1384 | 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": 152811008,
"val_accuracy": 91.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1831 | GraphArch:Hiaml:1831 | 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, 64x64x1x3]
%onnx::Conv_443[FLOAT, 64x64x3x1]
%onnx::Conv_446[FLOAT, 64x64x1x3]
%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": 191280640,
"params": 1749578,
"val_accuracy": 93.15
} | {
"arch_str": "1831",
"identifier": "Hiaml_1831",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1831"
} | {
"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"
} | [] | 1831 | 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": 191280640,
"val_accuracy": 93.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1276 | GraphArch:Hiaml:1276 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_471[FLOAT, 64x3x3x3]
%onnx::Conv_472[FLOAT, 64]
%onnx::Conv_474[FLOAT, 64x64x3x3]
%onnx::Conv_477[FLOAT, 64x64x1x3]
%onnx::Conv_480[FLOAT, 64x64x3x1]
%onnx::Conv_483[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222508544,
"params": 2587722,
"val_accuracy": 91.75
} | {
"arch_str": "1276",
"identifier": "Hiaml_1276",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1276"
} | {
"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"
} | [] | 1276 | 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": 222508544,
"val_accuracy": 91.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4624 | GraphArch:Hiaml:4624 | 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": 165590528,
"params": 1699914,
"val_accuracy": 92.3
} | {
"arch_str": "4624",
"identifier": "Hiaml_4624",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4624"
} | {
"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"
} | [] | 4624 | 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": 165590528,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1102 | GraphArch:Hiaml:1102 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_488[FLOAT, 64x3x3x3]
%onnx::Conv_489[FLOAT, 64]
%onnx::Conv_491[FLOAT, 64x64x1x3]
%onnx::Conv_494[FLOAT, 64x64x3x1]
%onnx::Conv_497[FLOAT, 64x64x1x1]
%onnx::Conv_500[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": 207074816,
"params": 2538570,
"val_accuracy": 91.92
} | {
"arch_str": "1102",
"identifier": "Hiaml_1102",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1102"
} | {
"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"
} | [] | 1102 | 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": 207074816,
"val_accuracy": 91.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3092 | GraphArch:Hiaml:3092 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_443[FLOAT, 64x3x3x3]
%onnx::Conv_444[FLOAT, 64]
%onnx::Conv_446[FLOAT, 64x64x1x3]
%onnx::Conv_449[FLOAT, 64x64x3x1]
%onnx::Conv_452[FLOAT, 64x64x1x1]
%onnx::Conv_455[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218576384,
"params": 2552138,
"val_accuracy": 92.33
} | {
"arch_str": "3092",
"identifier": "Hiaml_3092",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3092"
} | {
"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"
} | [] | 3092 | 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": 218576384,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1747 | GraphArch:Hiaml:1747 | 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, 64x64x1x3]
%onnx::Conv_439[FLOAT, 64x64x3x1]
%onnx::Conv_442[FLOAT, 64x64x1x3]
%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": 193377792,
"params": 1926090,
"val_accuracy": 92.5
} | {
"arch_str": "1747",
"identifier": "Hiaml_1747",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1747"
} | {
"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"
} | [] | 1747 | 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": 193377792,
"val_accuracy": 92.5
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_59 | GraphArch:Hiaml:59 | 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, 64x64x1x1]
%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": 198522368,
"params": 2447434,
"val_accuracy": 92.25
} | {
"arch_str": "59",
"identifier": "Hiaml_59",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "59"
} | {
"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"
} | [] | 59 | 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": 198522368,
"val_accuracy": 92.25
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2812 | GraphArch:Hiaml:2812 | 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": 223524352,
"params": 2789706,
"val_accuracy": 91.93
} | {
"arch_str": "2812",
"identifier": "Hiaml_2812",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2812"
} | {
"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"
} | [] | 2812 | 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": 223524352,
"val_accuracy": 91.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3387 | GraphArch:Hiaml:3387 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_510[FLOAT, 64x3x3x3]
%onnx::Conv_511[FLOAT, 64]
%onnx::Conv_513[FLOAT, 64x64x1x3]
%onnx::Conv_516[FLOAT, 64x64x3x1]
%onnx::Conv_519[FLOAT, 64x64x1x3]
%onnx::Conv_522[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": 191477248,
"params": 2416202,
"val_accuracy": 92.02
} | {
"arch_str": "3387",
"identifier": "Hiaml_3387",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3387"
} | {
"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"
} | [] | 3387 | 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": 191477248,
"val_accuracy": 92.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1381 | GraphArch:Hiaml:1381 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_507[FLOAT, 64x3x3x3]
%onnx::Conv_508[FLOAT, 64]
%onnx::Conv_510[FLOAT, 64x64x1x1]
%onnx::Conv_513[FLOAT, 64x64x1x3]
%onnx::Conv_516[FLOAT, 64x64x3x1]
%onnx::Conv_519[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": 228242944,
"params": 2702410,
"val_accuracy": 92.35
} | {
"arch_str": "1381",
"identifier": "Hiaml_1381",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1381"
} | {
"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"
} | [] | 1381 | 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": 228242944,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1157 | GraphArch:Hiaml:1157 | 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, 64x64x3x3]
%onnx::Conv_462[FLOAT, 64x64x3x3]
%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": 247608832,
"params": 2586698,
"val_accuracy": 92.48
} | {
"arch_str": "1157",
"identifier": "Hiaml_1157",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1157"
} | {
"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"
} | [] | 1157 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 247608832,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3864 | GraphArch:Hiaml:3864 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_335[FLOAT, 64x3x3x3]
%onnx::Conv_336[FLOAT, 64]
%onnx::Conv_338[FLOAT, 64x64x3x3]
%onnx::Conv_341[FLOAT, 64x64x1x3]
%onnx::Conv_344[FLOAT, 64x64x3x1]
%onnx::Conv_347[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": 198784512,
"params": 2452554,
"val_accuracy": 92.72
} | {
"arch_str": "3864",
"identifier": "Hiaml_3864",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3864"
} | {
"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"
} | [] | 3864 | 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": 198784512,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2265 | GraphArch:Hiaml:2265 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_483[FLOAT, 64x3x3x3]
%onnx::Conv_484[FLOAT, 64]
%onnx::Conv_486[FLOAT, 64x64x1x1]
%onnx::Conv_489[FLOAT, 64x64x1x1]
%onnx::Conv_492[FLOAT, 64x64x3x3]
%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": 198555136,
"params": 2497866,
"val_accuracy": 92.41
} | {
"arch_str": "2265",
"identifier": "Hiaml_2265",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2265"
} | {
"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"
} | [] | 2265 | 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": 198555136,
"val_accuracy": 92.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_978 | GraphArch:Hiaml:978 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x1x1]
%onnx::Conv_467[FLOAT, 64x64x3x3]
%onnx::Conv_470[FLOAT, 64x64x3x3]
%onnx::Conv_473[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": 260224512,
"params": 2698186,
"val_accuracy": 92.82
} | {
"arch_str": "978",
"identifier": "Hiaml_978",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "978"
} | {
"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"
} | [] | 978 | 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": 260224512,
"val_accuracy": 92.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1660 | GraphArch:Hiaml:1660 | 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, 64x64x1x3]
%onnx::Conv_492[FLOAT, 64x64x3x1]
%onnx::Conv_495[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223589888,
"params": 2621002,
"val_accuracy": 91.84
} | {
"arch_str": "1660",
"identifier": "Hiaml_1660",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1660"
} | {
"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"
} | [] | 1660 | 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": 91.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3321 | GraphArch:Hiaml:3321 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_407[FLOAT, 64x3x3x3]
%onnx::Conv_408[FLOAT, 64]
%onnx::Conv_410[FLOAT, 64x64x1x1]
%onnx::Conv_413[FLOAT, 64x64x1x3]
%onnx::Conv_416[FLOAT, 64x64x3x1]
%onnx::Conv_419[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": 181319168,
"params": 2487882,
"val_accuracy": 91.85
} | {
"arch_str": "3321",
"identifier": "Hiaml_3321",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3321"
} | {
"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"
} | [] | 3321 | 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": 181319168,
"val_accuracy": 91.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1743 | GraphArch:Hiaml:1743 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_357[FLOAT, 64x3x3x3]
%onnx::Conv_358[FLOAT, 64]
%onnx::Conv_360[FLOAT, 64x64x1x1]
%onnx::Conv_363[FLOAT, 64x64x3x3]
%onnx::Conv_366[FLOAT, 64x64x3x3]
%onnx::Conv_369[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": 208352768,
"params": 2428490,
"val_accuracy": 93.04
} | {
"arch_str": "1743",
"identifier": "Hiaml_1743",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1743"
} | {
"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"
} | [] | 1743 | 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": 208352768,
"val_accuracy": 93.04
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_733 | GraphArch:Hiaml:733 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_449[FLOAT, 64x3x3x3]
%onnx::Conv_450[FLOAT, 64]
%onnx::Conv_452[FLOAT, 64x64x1x1]
%onnx::Conv_455[FLOAT, 64x64x3x3]
%onnx::Conv_458[FLOAT, 64x64x3x3]
%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": 268547584,
"params": 2873674,
"val_accuracy": 92.29
} | {
"arch_str": "733",
"identifier": "Hiaml_733",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "733"
} | {
"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"
} | [] | 733 | 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": 268547584,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_274 | GraphArch:Hiaml:274 | 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, 64x64x1x3]
%onnx::Conv_334[FLOAT, 64x64x3x1]
%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": 231323136,
"params": 2189386,
"val_accuracy": 92.58
} | {
"arch_str": "274",
"identifier": "Hiaml_274",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "274"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 274 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231323136,
"val_accuracy": 92.58
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1024 | GraphArch:Hiaml:1024 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x1x1]
%onnx::Conv_369[FLOAT, 64x64x3x3]
%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": 203142656,
"params": 1871946,
"val_accuracy": 92.62
} | {
"arch_str": "1024",
"identifier": "Hiaml_1024",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1024"
} | {
"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"
} | [] | 1024 | 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": 203142656,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3476 | GraphArch:Hiaml:3476 | 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, 64x64x1x1]
%onnx::Conv_407[FLOAT, 64x64x3x3]
%onnx::Conv_410[FLOAT, 64x64x3x3]
%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": 226375168,
"params": 2421834,
"val_accuracy": 92.34
} | {
"arch_str": "3476",
"identifier": "Hiaml_3476",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3476"
} | {
"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"
} | [] | 3476 | 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": 226375168,
"val_accuracy": 92.34
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2864 | GraphArch:Hiaml:2864 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_499[FLOAT, 64x3x3x3]
%onnx::Conv_500[FLOAT, 64]
%onnx::Conv_502[FLOAT, 64x64x1x3]
%onnx::Conv_505[FLOAT, 64x64x3x1]
%onnx::Conv_508[FLOAT, 64x64x1x3]
%onnx::Conv_511[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198882816,
"params": 2473034,
"val_accuracy": 92.26
} | {
"arch_str": "2864",
"identifier": "Hiaml_2864",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2864"
} | {
"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"
} | [] | 2864 | 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": 92.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2379 | GraphArch:Hiaml:2379 | 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, 64x64x1x1]
%onnx::Conv_351[FLOAT, 64x64x1x3]
%onnx::Conv_354[FLOAT, 64x64x3x1]
%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": 217789952,
"params": 2698058,
"val_accuracy": 92.47
} | {
"arch_str": "2379",
"identifier": "Hiaml_2379",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2379"
} | {
"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"
} | [] | 2379 | 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": 217789952,
"val_accuracy": 92.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4406 | GraphArch:Hiaml:4406 | 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, 64x64x1x3]
%onnx::Conv_401[FLOAT, 64x64x3x1]
%onnx::Conv_404[FLOAT, 64x64x1x3]
%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": 193050112,
"params": 2047690,
"val_accuracy": 92.76
} | {
"arch_str": "4406",
"identifier": "Hiaml_4406",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4406"
} | {
"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"
} | [] | 4406 | 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": 193050112,
"val_accuracy": 92.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2691 | GraphArch:Hiaml:2691 | 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": 181614080,
"params": 2264394,
"val_accuracy": 92.5
} | {
"arch_str": "2691",
"identifier": "Hiaml_2691",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2691"
} | {
"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"
} | [] | 2691 | 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": 181614080,
"val_accuracy": 92.5
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2681 | GraphArch:Hiaml:2681 | 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, 64x64x1x1]
%onnx::Conv_462[FLOAT, 64x64x3x3]
%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": 210187776,
"params": 2511946,
"val_accuracy": 92.02
} | {
"arch_str": "2681",
"identifier": "Hiaml_2681",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2681"
} | {
"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"
} | [] | 2681 | 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": 210187776,
"val_accuracy": 92.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4111 | GraphArch:Hiaml:4111 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_541[FLOAT, 64x3x3x3]
%onnx::Conv_542[FLOAT, 64]
%onnx::Conv_544[FLOAT, 64x64x1x1]
%onnx::Conv_547[FLOAT, 64x64x1x3]
%onnx::Conv_550[FLOAT, 64x64x3x1]
%onnx::Conv_553[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": 228308480,
"params": 2703434,
"val_accuracy": 92.37
} | {
"arch_str": "4111",
"identifier": "Hiaml_4111",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4111"
} | {
"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"
} | [] | 4111 | 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": 228308480,
"val_accuracy": 92.37
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1544 | GraphArch:Hiaml:1544 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_417[FLOAT, 64x3x3x3]
%onnx::Conv_418[FLOAT, 64]
%onnx::Conv_420[FLOAT, 64x64x1x1]
%onnx::Conv_423[FLOAT, 64x64x1x3]
%onnx::Conv_426[FLOAT, 64x64x3x1]
%onnx::Conv_429[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": 172275200,
"params": 1896522,
"val_accuracy": 91.78
} | {
"arch_str": "1544",
"identifier": "Hiaml_1544",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1544"
} | {
"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"
} | [] | 1544 | 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": 172275200,
"val_accuracy": 91.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1235 | GraphArch:Hiaml:1235 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_371[FLOAT, 64x3x3x3]
%onnx::Conv_372[FLOAT, 64]
%onnx::Conv_374[FLOAT, 64x64x3x3]
%onnx::Conv_377[FLOAT, 64x64x1x3]
%onnx::Conv_380[FLOAT, 64x64x3x1]
%onnx::Conv_383[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": 264091136,
"params": 3513802,
"val_accuracy": 92.49
} | {
"arch_str": "1235",
"identifier": "Hiaml_1235",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1235"
} | {
"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"
} | [] | 1235 | 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": 264091136,
"val_accuracy": 92.49
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_365 | GraphArch:Hiaml:365 | 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, 64x64x3x3]
%onnx::Conv_379[FLOAT, 64x64x1x3]
%onnx::Conv_382[FLOAT, 64x64x3x1]
%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": 247215616,
"params": 2436938,
"val_accuracy": 92.3
} | {
"arch_str": "365",
"identifier": "Hiaml_365",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "365"
} | {
"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"
} | [] | 365 | 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": 247215616,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_417 | GraphArch:Hiaml:417 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x3x3]
%onnx::Conv_375[FLOAT, 64x64x1x1]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[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": 231388672,
"params": 3395402,
"val_accuracy": 92.44
} | {
"arch_str": "417",
"identifier": "Hiaml_417",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "417"
} | {
"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"
} | [] | 417 | 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": 231388672,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1572 | GraphArch:Hiaml:1572 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x1x3]
%onnx::Conv_467[FLOAT, 64x64x3x1]
%onnx::Conv_470[FLOAT, 64x64x1x1]
%onnx::Conv_473[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": 203929088,
"params": 2118474,
"val_accuracy": 92.46
} | {
"arch_str": "1572",
"identifier": "Hiaml_1572",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1572"
} | {
"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"
} | [] | 1572 | 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": 203929088,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1398 | GraphArch:Hiaml:1398 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_447[FLOAT, 64x3x3x3]
%onnx::Conv_448[FLOAT, 64]
%onnx::Conv_450[FLOAT, 64x64x1x3]
%onnx::Conv_453[FLOAT, 64x64x3x1]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 175584768,
"params": 2155978,
"val_accuracy": 92.21
} | {
"arch_str": "1398",
"identifier": "Hiaml_1398",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1398"
} | {
"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"
} | [] | 1398 | 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": 175584768,
"val_accuracy": 92.21
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1915 | GraphArch:Hiaml:1915 | 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, 64x64x3x3]
%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": 197178880,
"params": 1922378,
"val_accuracy": 92.32
} | {
"arch_str": "1915",
"identifier": "Hiaml_1915",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1915"
} | {
"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"
} | [] | 1915 | 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": 197178880,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2046 | GraphArch:Hiaml:2046 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_511[FLOAT, 64x3x3x3]
%onnx::Conv_512[FLOAT, 64]
%onnx::Conv_514[FLOAT, 64x64x1x1]
%onnx::Conv_517[FLOAT, 64x64x1x3]
%onnx::Conv_520[FLOAT, 64x64x3x1]
%onnx::Conv_523[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": 207205888,
"params": 2539082,
"val_accuracy": 92.2
} | {
"arch_str": "2046",
"identifier": "Hiaml_2046",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2046"
} | {
"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"
} | [] | 2046 | 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": 207205888,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1578 | GraphArch:Hiaml:1578 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x1x3]
%onnx::Conv_418[FLOAT, 64x64x3x1]
%onnx::Conv_421[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": 156218880,
"params": 1799242,
"val_accuracy": 92.36
} | {
"arch_str": "1578",
"identifier": "Hiaml_1578",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1578"
} | {
"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"
} | [] | 1578 | 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": 156218880,
"val_accuracy": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3673 | GraphArch:Hiaml:3673 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_343[FLOAT, 64x3x3x3]
%onnx::Conv_344[FLOAT, 64]
%onnx::Conv_346[FLOAT, 64x64x3x3]
%onnx::Conv_349[FLOAT, 64x64x1x1]
%onnx::Conv_352[FLOAT, 64x64x3x3]
%onnx::Conv_355[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": 216675840,
"params": 2603978,
"val_accuracy": 92.61
} | {
"arch_str": "3673",
"identifier": "Hiaml_3673",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3673"
} | {
"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"
} | [] | 3673 | 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": 216675840,
"val_accuracy": 92.61
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2903 | GraphArch:Hiaml:2903 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_407[FLOAT, 64x3x3x3]
%onnx::Conv_408[FLOAT, 64]
%onnx::Conv_410[FLOAT, 64x64x1x1]
%onnx::Conv_413[FLOAT, 64x64x3x3]
%onnx::Conv_416[FLOAT, 64x64x3x3]
%onnx::Conv_419[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": 228570624,
"params": 2167114,
"val_accuracy": 92.39
} | {
"arch_str": "2903",
"identifier": "Hiaml_2903",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2903"
} | {
"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"
} | [] | 2903 | 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": 228570624,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3822 | GraphArch:Hiaml:3822 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_407[FLOAT, 64x3x3x3]
%onnx::Conv_408[FLOAT, 64]
%onnx::Conv_410[FLOAT, 64x64x1x1]
%onnx::Conv_413[FLOAT, 64x64x3x3]
%onnx::Conv_416[FLOAT, 64x64x3x3]
%onnx::Conv_419[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": 276641280,
"params": 2913866,
"val_accuracy": 92.38
} | {
"arch_str": "3822",
"identifier": "Hiaml_3822",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3822"
} | {
"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"
} | [] | 3822 | 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": 276641280,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2585 | GraphArch:Hiaml:2585 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[FLOAT, 64x64x3x1]
%onnx::Conv_428[FLOAT, 64x64x1x3]
%onnx::Conv_431[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": 160577024,
"params": 2078026,
"val_accuracy": 92.17
} | {
"arch_str": "2585",
"identifier": "Hiaml_2585",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2585"
} | {
"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"
} | [] | 2585 | 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": 160577024,
"val_accuracy": 92.17
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3363 | GraphArch:Hiaml:3363 | 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": 216184320,
"params": 1945162,
"val_accuracy": 92.22
} | {
"arch_str": "3363",
"identifier": "Hiaml_3363",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3363"
} | {
"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"
} | [] | 3363 | 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": 216184320,
"val_accuracy": 92.22
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4151 | GraphArch:Hiaml:4151 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_542[FLOAT, 64x3x3x3]
%onnx::Conv_543[FLOAT, 64]
%onnx::Conv_545[FLOAT, 64x64x1x3]
%onnx::Conv_548[FLOAT, 64x64x3x1]
%onnx::Conv_551[FLOAT, 64x64x1x1]
%onnx::Conv_554[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": 206452224,
"params": 2161482,
"val_accuracy": 92.12
} | {
"arch_str": "4151",
"identifier": "Hiaml_4151",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4151"
} | {
"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"
} | [] | 4151 | 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": 206452224,
"val_accuracy": 92.12
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_230 | GraphArch:Hiaml:230 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_462[FLOAT, 64x3x3x3]
%onnx::Conv_463[FLOAT, 64]
%onnx::Conv_465[FLOAT, 64x64x1x1]
%onnx::Conv_468[FLOAT, 64x64x1x3]
%onnx::Conv_471[FLOAT, 64x64x3x1]
%onnx::Conv_474[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": 177321472,
"params": 2456650,
"val_accuracy": 92.45
} | {
"arch_str": "230",
"identifier": "Hiaml_230",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "230"
} | {
"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"
} | [] | 230 | 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": 177321472,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_486 | GraphArch:Hiaml:486 | 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": 210941440,
"params": 2174538,
"val_accuracy": 92.67
} | {
"arch_str": "486",
"identifier": "Hiaml_486",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "486"
} | {
"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"
} | [] | 486 | 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": 210941440,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1507 | GraphArch:Hiaml:1507 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x1x1]
%onnx::Conv_369[FLOAT, 64x64x1x3]
%onnx::Conv_372[FLOAT, 64x64x3x1]
%onnx::Conv_375[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193967616,
"params": 1785034,
"val_accuracy": 93.07
} | {
"arch_str": "1507",
"identifier": "Hiaml_1507",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1507"
} | {
"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"
} | [] | 1507 | 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": 193967616,
"val_accuracy": 93.07
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2840 | GraphArch:Hiaml:2840 | 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": 255899136,
"params": 2664906,
"val_accuracy": 93.26
} | {
"arch_str": "2840",
"identifier": "Hiaml_2840",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2840"
} | {
"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"
} | [] | 2840 | 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": 255899136,
"val_accuracy": 93.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_916 | GraphArch:Hiaml:916 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x3x3]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[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": 219002368,
"params": 2488394,
"val_accuracy": 92.65
} | {
"arch_str": "916",
"identifier": "Hiaml_916",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "916"
} | {
"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"
} | [] | 916 | 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": 219002368,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4570 | GraphArch:Hiaml:4570 | 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, 64x64x1x3]
%onnx::Conv_421[FLOAT, 64x64x3x1]
%onnx::Conv_424[FLOAT, 64x64x1x3]
%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": 246756864,
"params": 2367690,
"val_accuracy": 92.66
} | {
"arch_str": "4570",
"identifier": "Hiaml_4570",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4570"
} | {
"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"
} | [] | 4570 | 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": 246756864,
"val_accuracy": 92.66
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_249 | GraphArch:Hiaml:249 | 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, 64x64x1x1]
%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": 157136384,
"params": 1681738,
"val_accuracy": 92
} | {
"arch_str": "249",
"identifier": "Hiaml_249",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "249"
} | {
"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"
} | [] | 249 | 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": 157136384,
"val_accuracy": 92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1076 | GraphArch:Hiaml:1076 | 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": 168801792,
"params": 1797706,
"val_accuracy": 91.85
} | {
"arch_str": "1076",
"identifier": "Hiaml_1076",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1076"
} | {
"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"
} | [] | 1076 | 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": 168801792,
"val_accuracy": 91.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2451 | GraphArch:Hiaml:2451 | 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, 64x64x3x3]
%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": 172013056,
"params": 1713098,
"val_accuracy": 92.35
} | {
"arch_str": "2451",
"identifier": "Hiaml_2451",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2451"
} | {
"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"
} | [] | 2451 | 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": 172013056,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_779 | GraphArch:Hiaml:779 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_477[FLOAT, 64x3x3x3]
%onnx::Conv_478[FLOAT, 64]
%onnx::Conv_480[FLOAT, 64x64x1x3]
%onnx::Conv_483[FLOAT, 64x64x3x1]
%onnx::Conv_486[FLOAT, 64x64x1x3]
%onnx::Conv_489[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": 92.48
} | {
"arch_str": "779",
"identifier": "Hiaml_779",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "779"
} | {
"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"
} | [] | 779 | 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": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_665 | GraphArch:Hiaml:665 | 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": 242824704,
"params": 2821194,
"val_accuracy": 92.07
} | {
"arch_str": "665",
"identifier": "Hiaml_665",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "665"
} | {
"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"
} | [] | 665 | 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": 242824704,
"val_accuracy": 92.07
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2897 | GraphArch:Hiaml:2897 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_477[FLOAT, 64x3x3x3]
%onnx::Conv_478[FLOAT, 64]
%onnx::Conv_480[FLOAT, 64x64x1x1]
%onnx::Conv_483[FLOAT, 64x64x1x3]
%onnx::Conv_486[FLOAT, 64x64x3x1]
%onnx::Conv_489[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": 188102144,
"params": 2489418,
"val_accuracy": 92.16
} | {
"arch_str": "2897",
"identifier": "Hiaml_2897",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2897"
} | {
"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"
} | [] | 2897 | 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": 188102144,
"val_accuracy": 92.16
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2676 | GraphArch:Hiaml:2676 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_343[FLOAT, 64x3x3x3]
%onnx::Conv_344[FLOAT, 64]
%onnx::Conv_346[FLOAT, 64x64x1x3]
%onnx::Conv_349[FLOAT, 64x64x3x1]
%onnx::Conv_352[FLOAT, 64x64x1x3]
%onnx::Conv_355[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": 173848064,
"params": 1641290,
"val_accuracy": 92.2
} | {
"arch_str": "2676",
"identifier": "Hiaml_2676",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2676"
} | {
"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"
} | [] | 2676 | 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": 173848064,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2165 | GraphArch:Hiaml:2165 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_431[FLOAT, 64x3x3x3]
%onnx::Conv_432[FLOAT, 64]
%onnx::Conv_434[FLOAT, 64x64x1x3]
%onnx::Conv_437[FLOAT, 64x64x3x1]
%onnx::Conv_440[FLOAT, 64x64x1x3]
%onnx::Conv_443[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": 238302720,
"params": 2732362,
"val_accuracy": 92.29
} | {
"arch_str": "2165",
"identifier": "Hiaml_2165",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2165"
} | {
"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"
} | [] | 2165 | 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": 238302720,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1837 | GraphArch:Hiaml:1837 | 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, 64x64x3x3]
%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": 196097536,
"params": 2382154,
"val_accuracy": 92.14
} | {
"arch_str": "1837",
"identifier": "Hiaml_1837",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1837"
} | {
"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"
} | [] | 1837 | 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": 196097536,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1131 | GraphArch:Hiaml:1131 | 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, 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": 188659200,
"params": 1940426,
"val_accuracy": 92.85
} | {
"arch_str": "1131",
"identifier": "Hiaml_1131",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1131"
} | {
"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"
} | [] | 1131 | 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": 188659200,
"val_accuracy": 92.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3929 | GraphArch:Hiaml:3929 | 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, 64x64x1x1]
%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": 228275712,
"params": 2879050,
"val_accuracy": 92.01
} | {
"arch_str": "3929",
"identifier": "Hiaml_3929",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3929"
} | {
"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"
} | [] | 3929 | 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": 228275712,
"val_accuracy": 92.01
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1366 | GraphArch:Hiaml:1366 | 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, 64x64x3x3]
%onnx::Conv_371[FLOAT, 64x64x1x1]
%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": 256554496,
"params": 3592010,
"val_accuracy": 92.16
} | {
"arch_str": "1366",
"identifier": "Hiaml_1366",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1366"
} | {
"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"
} | [] | 1366 | 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": 256554496,
"val_accuracy": 92.16
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3188 | GraphArch:Hiaml:3188 | 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": 202388992,
"params": 2478922,
"val_accuracy": 92.17
} | {
"arch_str": "3188",
"identifier": "Hiaml_3188",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3188"
} | {
"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"
} | [] | 3188 | 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": 202388992,
"val_accuracy": 92.17
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_753 | GraphArch:Hiaml:753 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_339[FLOAT, 64x3x3x3]
%onnx::Conv_340[FLOAT, 64]
%onnx::Conv_342[FLOAT, 64x64x3x3]
%onnx::Conv_345[FLOAT, 64x64x1x3]
%onnx::Conv_348[FLOAT, 64x64x3x1]
%onnx::Conv_351[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 167556608,
"params": 1677770,
"val_accuracy": 92.75
} | {
"arch_str": "753",
"identifier": "Hiaml_753",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "753"
} | {
"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"
} | [] | 753 | 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": 167556608,
"val_accuracy": 92.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1966 | GraphArch:Hiaml:1966 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_442[FLOAT, 64x3x3x3]
%onnx::Conv_443[FLOAT, 64]
%onnx::Conv_445[FLOAT, 64x64x1x1]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[FLOAT, 64x64x3x3]
%onnx::Conv_454[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": 265401856,
"params": 3021130,
"val_accuracy": 91.71
} | {
"arch_str": "1966",
"identifier": "Hiaml_1966",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1966"
} | {
"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"
} | [] | 1966 | 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": 265401856,
"val_accuracy": 91.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1957 | GraphArch:Hiaml:1957 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x1x1]
%onnx::Conv_375[FLOAT, 64x64x3x3]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[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": 205370880,
"params": 1973706,
"val_accuracy": 92.59
} | {
"arch_str": "1957",
"identifier": "Hiaml_1957",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1957"
} | {
"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"
} | [] | 1957 | 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": 205370880,
"val_accuracy": 92.59
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2489 | GraphArch:Hiaml:2489 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_505[FLOAT, 64x3x3x3]
%onnx::Conv_506[FLOAT, 64]
%onnx::Conv_508[FLOAT, 64x64x1x3]
%onnx::Conv_511[FLOAT, 64x64x3x1]
%onnx::Conv_514[FLOAT, 64x64x1x3]
%onnx::Conv_517[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": 194622976,
"params": 1973066,
"val_accuracy": 92.38
} | {
"arch_str": "2489",
"identifier": "Hiaml_2489",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2489"
} | {
"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"
} | [] | 2489 | 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": 194622976,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2965 | GraphArch:Hiaml:2965 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_343[FLOAT, 64x3x3x3]
%onnx::Conv_344[FLOAT, 64]
%onnx::Conv_346[FLOAT, 64x64x3x3]
%onnx::Conv_349[FLOAT, 64x64x1x3]
%onnx::Conv_352[FLOAT, 64x64x3x1]
%onnx::Conv_355[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": 195737088,
"params": 1739850,
"val_accuracy": 92.32
} | {
"arch_str": "2965",
"identifier": "Hiaml_2965",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2965"
} | {
"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"
} | [] | 2965 | 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": 195737088,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3531 | GraphArch:Hiaml:3531 | 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, 64x64x3x3]
%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": 201242112,
"params": 2435018,
"val_accuracy": 92.54
} | {
"arch_str": "3531",
"identifier": "Hiaml_3531",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3531"
} | {
"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"
} | [] | 3531 | 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": 201242112,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1106 | GraphArch:Hiaml:1106 | 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, 64x64x3x3]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[FLOAT, 64x64x1x1]
%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": 215070208,
"params": 2160202,
"val_accuracy": 91.9
} | {
"arch_str": "1106",
"identifier": "Hiaml_1106",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1106"
} | {
"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"
} | [] | 1106 | 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": 215070208,
"val_accuracy": 91.9
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2058 | GraphArch:Hiaml:2058 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_517[FLOAT, 64x3x3x3]
%onnx::Conv_518[FLOAT, 64]
%onnx::Conv_520[FLOAT, 64x64x1x1]
%onnx::Conv_523[FLOAT, 64x64x1x3]
%onnx::Conv_526[FLOAT, 64x64x3x1]
%onnx::Conv_529[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": 214676992,
"params": 2596170,
"val_accuracy": 92.01
} | {
"arch_str": "2058",
"identifier": "Hiaml_2058",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2058"
} | {
"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"
} | [] | 2058 | 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": 214676992,
"val_accuracy": 92.01
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1593 | GraphArch:Hiaml:1593 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_399[FLOAT, 64x3x3x3]
%onnx::Conv_400[FLOAT, 64]
%onnx::Conv_402[FLOAT, 64x64x3x3]
%onnx::Conv_405[FLOAT, 64x64x1x3]
%onnx::Conv_408[FLOAT, 64x64x3x1]
%onnx::Conv_411[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193934848,
"params": 2019914,
"val_accuracy": 92.56
} | {
"arch_str": "1593",
"identifier": "Hiaml_1593",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1593"
} | {
"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"
} | [] | 1593 | 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": 193934848,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3622 | GraphArch:Hiaml:3622 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_407[FLOAT, 64x3x3x3]
%onnx::Conv_408[FLOAT, 64]
%onnx::Conv_410[FLOAT, 64x64x3x3]
%onnx::Conv_413[FLOAT, 64x64x1x3]
%onnx::Conv_416[FLOAT, 64x64x3x1]
%onnx::Conv_419[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": 253703680,
"params": 2880074,
"val_accuracy": 92.39
} | {
"arch_str": "3622",
"identifier": "Hiaml_3622",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3622"
} | {
"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"
} | [] | 3622 | 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": 253703680,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1001 | GraphArch:Hiaml:1001 | 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, 64x64x1x3]
%onnx::Conv_401[FLOAT, 64x64x3x1]
%onnx::Conv_404[FLOAT, 64x64x1x1]
%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": 193082880,
"params": 2060106,
"val_accuracy": 92.65
} | {
"arch_str": "1001",
"identifier": "Hiaml_1001",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1001"
} | {
"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"
} | [] | 1001 | 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": 193082880,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2662 | GraphArch:Hiaml:2662 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_351[FLOAT, 64x3x3x3]
%onnx::Conv_352[FLOAT, 64]
%onnx::Conv_354[FLOAT, 64x64x3x3]
%onnx::Conv_357[FLOAT, 64x64x1x3]
%onnx::Conv_360[FLOAT, 64x64x3x1]
%onnx::Conv_363[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": 194688512,
"params": 1904458,
"val_accuracy": 92.61
} | {
"arch_str": "2662",
"identifier": "Hiaml_2662",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2662"
} | {
"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"
} | [] | 2662 | 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": 194688512,
"val_accuracy": 92.61
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_804 | GraphArch:Hiaml:804 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_477[FLOAT, 64x3x3x3]
%onnx::Conv_478[FLOAT, 64]
%onnx::Conv_480[FLOAT, 64x64x3x3]
%onnx::Conv_483[FLOAT, 64x64x1x3]
%onnx::Conv_486[FLOAT, 64x64x3x1]
%onnx::Conv_489[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": 222541312,
"params": 2612042,
"val_accuracy": 92.32
} | {
"arch_str": "804",
"identifier": "Hiaml_804",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "804"
} | {
"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"
} | [] | 804 | 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": 222541312,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3208 | GraphArch:Hiaml:3208 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_383[FLOAT, 64x3x3x3]
%onnx::Conv_384[FLOAT, 64]
%onnx::Conv_386[FLOAT, 64x64x1x1]
%onnx::Conv_389[FLOAT, 64x64x3x3]
%onnx::Conv_392[FLOAT, 64x64x3x3]
%onnx::Conv_395[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239941120,
"params": 2281546,
"val_accuracy": 92.67
} | {
"arch_str": "3208",
"identifier": "Hiaml_3208",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3208"
} | {
"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"
} | [] | 3208 | 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": 239941120,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2347 | GraphArch:Hiaml:2347 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_538[FLOAT, 64x3x3x3]
%onnx::Conv_539[FLOAT, 64]
%onnx::Conv_541[FLOAT, 64x64x1x1]
%onnx::Conv_544[FLOAT, 64x64x3x3]
%onnx::Conv_547[FLOAT, 64x64x3x3]
%onnx::Conv_550[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": 244823552,
"params": 2687562,
"val_accuracy": 92.82
} | {
"arch_str": "2347",
"identifier": "Hiaml_2347",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2347"
} | {
"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"
} | [] | 2347 | 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": 244823552,
"val_accuracy": 92.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1998 | GraphArch:Hiaml:1998 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x3x3]
%onnx::Conv_425[FLOAT, 64x64x1x1]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[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": 208614912,
"params": 2479434,
"val_accuracy": 91.77
} | {
"arch_str": "1998",
"identifier": "Hiaml_1998",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1998"
} | {
"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"
} | [] | 1998 | 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": 208614912,
"val_accuracy": 91.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1722 | GraphArch:Hiaml:1722 | 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": 247707136,
"params": 3568202,
"val_accuracy": 91.86
} | {
"arch_str": "1722",
"identifier": "Hiaml_1722",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1722"
} | {
"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"
} | [] | 1722 | 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": 247707136,
"val_accuracy": 91.86
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2088 | GraphArch:Hiaml:2088 | 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": 177321472,
"params": 1838410,
"val_accuracy": 92.79
} | {
"arch_str": "2088",
"identifier": "Hiaml_2088",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2088"
} | {
"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"
} | [] | 2088 | 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": 177321472,
"val_accuracy": 92.79
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4155 | GraphArch:Hiaml:4155 | 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, 64x64x1x3]
%onnx::Conv_411[FLOAT, 64x64x3x1]
%onnx::Conv_414[FLOAT, 64x64x1x3]
%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": 195114496,
"params": 2494794,
"val_accuracy": 92.56
} | {
"arch_str": "4155",
"identifier": "Hiaml_4155",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4155"
} | {
"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"
} | [] | 4155 | 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": 195114496,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3 | GraphArch:Hiaml:3 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_383[FLOAT, 64x3x3x3]
%onnx::Conv_384[FLOAT, 64]
%onnx::Conv_386[FLOAT, 64x64x3x3]
%onnx::Conv_389[FLOAT, 64x64x1x3]
%onnx::Conv_392[FLOAT, 64x64x3x1]
%onnx::Conv_395[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 168670720,
"params": 1700426,
"val_accuracy": 92.27
} | {
"arch_str": "3",
"identifier": "Hiaml_3",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3"
} | {
"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"
} | [] | 3 | 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": 168670720,
"val_accuracy": 92.27
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_118 | GraphArch:Hiaml:118 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_506[FLOAT, 64x3x3x3]
%onnx::Conv_507[FLOAT, 64]
%onnx::Conv_509[FLOAT, 64x64x3x3]
%onnx::Conv_512[FLOAT, 64x64x1x1]
%onnx::Conv_515[FLOAT, 64x64x3x3]
%onnx::Conv_518[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": 206812672,
"params": 2613578,
"val_accuracy": 92.32
} | {
"arch_str": "118",
"identifier": "Hiaml_118",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "118"
} | {
"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"
} | [] | 118 | 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": 206812672,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3705 | GraphArch:Hiaml:3705 | 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, 64x64x1x1]
%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": 181810688,
"params": 2217546,
"val_accuracy": 91.96
} | {
"arch_str": "3705",
"identifier": "Hiaml_3705",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3705"
} | {
"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"
} | [] | 3705 | 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": 181810688,
"val_accuracy": 91.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4024 | GraphArch:Hiaml:4024 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_455[FLOAT, 64x3x3x3]
%onnx::Conv_456[FLOAT, 64]
%onnx::Conv_458[FLOAT, 64x64x1x1]
%onnx::Conv_461[FLOAT, 64x64x1x3]
%onnx::Conv_464[FLOAT, 64x64x3x1]
%onnx::Conv_467[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198751744,
"params": 2470986,
"val_accuracy": 92.39
} | {
"arch_str": "4024",
"identifier": "Hiaml_4024",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4024"
} | {
"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"
} | [] | 4024 | 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": 198751744,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2930 | GraphArch:Hiaml:2930 | 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, 64x64x1x1]
%onnx::Conv_407[FLOAT, 64x64x1x1]
%onnx::Conv_410[FLOAT, 64x64x3x3]
%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": 195147264,
"params": 2076746,
"val_accuracy": 92.14
} | {
"arch_str": "2930",
"identifier": "Hiaml_2930",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2930"
} | {
"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"
} | [] | 2930 | 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": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_710 | GraphArch:Hiaml:710 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_293[FLOAT, 64x3x3x3]
%onnx::Conv_294[FLOAT, 64]
%onnx::Conv_296[FLOAT, 64x64x1x1]
%onnx::Conv_299[FLOAT, 64x64x3x3]
%onnx::Conv_302[FLOAT, 64x64x3x3]
%onnx::Conv_305[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": 226014720,
"params": 1938890,
"val_accuracy": 92.92
} | {
"arch_str": "710",
"identifier": "Hiaml_710",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "710"
} | {
"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"
} | [] | 710 | 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": 226014720,
"val_accuracy": 92.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_644 | GraphArch:Hiaml:644 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_367[FLOAT, 64x3x3x3]
%onnx::Conv_368[FLOAT, 64]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[FLOAT, 64x64x1x3]
%onnx::Conv_376[FLOAT, 64x64x3x1]
%onnx::Conv_379[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": 210515456,
"params": 2150218,
"val_accuracy": 92.39
} | {
"arch_str": "644",
"identifier": "Hiaml_644",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "644"
} | {
"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"
} | [] | 644 | 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": 210515456,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1070 | GraphArch:Hiaml:1070 | 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": 156284416,
"params": 2266442,
"val_accuracy": 92.18
} | {
"arch_str": "1070",
"identifier": "Hiaml_1070",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1070"
} | {
"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"
} | [] | 1070 | 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": 156284416,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_380 | GraphArch:Hiaml:380 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_498[FLOAT, 64x3x3x3]
%onnx::Conv_499[FLOAT, 64]
%onnx::Conv_501[FLOAT, 64x64x1x1]
%onnx::Conv_504[FLOAT, 64x64x3x3]
%onnx::Conv_507[FLOAT, 64x64x3x3]
%onnx::Conv_510[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": 239384064,
"params": 2645834,
"val_accuracy": 92.32
} | {
"arch_str": "380",
"identifier": "Hiaml_380",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "380"
} | {
"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"
} | [] | 380 | 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": 239384064,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4392 | GraphArch:Hiaml:4392 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_431[FLOAT, 64x3x3x3]
%onnx::Conv_432[FLOAT, 64]
%onnx::Conv_434[FLOAT, 64x64x3x3]
%onnx::Conv_437[FLOAT, 64x64x1x3]
%onnx::Conv_440[FLOAT, 64x64x3x1]
%onnx::Conv_443[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": 220280320,
"params": 2618442,
"val_accuracy": 92.29
} | {
"arch_str": "4392",
"identifier": "Hiaml_4392",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4392"
} | {
"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"
} | [] | 4392 | 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": 220280320,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4334 | GraphArch:Hiaml:4334 | 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, 64x64x3x3]
%onnx::Conv_455[FLOAT, 64x64x1x3]
%onnx::Conv_458[FLOAT, 64x64x3x1]
%onnx::Conv_461[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218215936,
"params": 2578762,
"val_accuracy": 92.36
} | {
"arch_str": "4334",
"identifier": "Hiaml_4334",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4334"
} | {
"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"
} | [] | 4334 | 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": 218215936,
"val_accuracy": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1212 | GraphArch:Hiaml:1212 | 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": 171521536,
"params": 1877194,
"val_accuracy": 92.54
} | {
"arch_str": "1212",
"identifier": "Hiaml_1212",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1212"
} | {
"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"
} | [] | 1212 | 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": 171521536,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3394 | GraphArch:Hiaml:3394 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_431[FLOAT, 64x3x3x3]
%onnx::Conv_432[FLOAT, 64]
%onnx::Conv_434[FLOAT, 64x64x3x3]
%onnx::Conv_437[FLOAT, 64x64x1x3]
%onnx::Conv_440[FLOAT, 64x64x3x1]
%onnx::Conv_443[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": 230700544,
"params": 2553418,
"val_accuracy": 92.31
} | {
"arch_str": "3394",
"identifier": "Hiaml_3394",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3394"
} | {
"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"
} | [] | 3394 | 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": 230700544,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_310 | GraphArch:Hiaml:310 | 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, 64x64x1x1]
%onnx::Conv_394[FLOAT, 64x64x3x3]
%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": 244299264,
"params": 2363722,
"val_accuracy": 91.83
} | {
"arch_str": "310",
"identifier": "Hiaml_310",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "310"
} | {
"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"
} | [] | 310 | 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": 244299264,
"val_accuracy": 91.83
} | full |
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