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_3832 | GraphArch:Hiaml:3832 | 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, 64x64x1x3]
%onnx::Conv_351[FLOAT, 64x64x3x1]
%onnx::Conv_354[FLOAT, 64x64x1x3]
%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": 233715200,
"params": 2587082,
"val_accuracy": 92.6
} | {
"arch_str": "3832",
"identifier": "Hiaml_3832",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3832"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3832 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 233715200,
"val_accuracy": 92.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_914 | GraphArch:Hiaml:914 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_323[FLOAT, 64x3x3x3]
%onnx::Conv_324[FLOAT, 64]
%onnx::Conv_326[FLOAT, 64x64x3x3]
%onnx::Conv_329[FLOAT, 64x64x1x3]
%onnx::Conv_332[FLOAT, 64x64x3x1]
%onnx::Conv_335[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": 255440384,
"params": 3348938,
"val_accuracy": 93.1
} | {
"arch_str": "914",
"identifier": "Hiaml_914",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "914"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 914 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 255440384,
"val_accuracy": 93.1
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4006 | GraphArch:Hiaml:4006 | 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, 64x64x1x1]
%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": 177649152,
"params": 2306634,
"val_accuracy": 92.15
} | {
"arch_str": "4006",
"identifier": "Hiaml_4006",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4006"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4006 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177649152,
"val_accuracy": 92.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4127 | GraphArch:Hiaml:4127 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_425[FLOAT, 64x3x3x3]
%onnx::Conv_426[FLOAT, 64]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[FLOAT, 64x64x1x3]
%onnx::Conv_434[FLOAT, 64x64x3x1]
%onnx::Conv_437[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185775616,
"params": 2496330,
"val_accuracy": 91.43
} | {
"arch_str": "4127",
"identifier": "Hiaml_4127",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4127"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4127 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185775616,
"val_accuracy": 91.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3986 | GraphArch:Hiaml:3986 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_379[FLOAT, 64x3x3x3]
%onnx::Conv_380[FLOAT, 64]
%onnx::Conv_382[FLOAT, 64x64x1x1]
%onnx::Conv_385[FLOAT, 64x64x1x3]
%onnx::Conv_388[FLOAT, 64x64x3x1]
%onnx::Conv_391[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189675008,
"params": 2564042,
"val_accuracy": 92.53
} | {
"arch_str": "3986",
"identifier": "Hiaml_3986",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3986"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3986 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189675008,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1169 | GraphArch:Hiaml:1169 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_468[FLOAT, 64x3x3x3]
%onnx::Conv_469[FLOAT, 64]
%onnx::Conv_471[FLOAT, 64x64x1x1]
%onnx::Conv_474[FLOAT, 64x64x1x3]
%onnx::Conv_477[FLOAT, 64x64x3x1]
%onnx::Conv_480[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 253081088,
"params": 2898506,
"val_accuracy": 92.18
} | {
"arch_str": "1169",
"identifier": "Hiaml_1169",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1169"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1169 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 253081088,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3329 | GraphArch:Hiaml:3329 | 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, 64x64x1x1]
%onnx::Conv_420[FLOAT, 64x64x1x1]
%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": 191837696,
"params": 2348362,
"val_accuracy": 91.99
} | {
"arch_str": "3329",
"identifier": "Hiaml_3329",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3329"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3329 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 191837696,
"val_accuracy": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1736 | GraphArch:Hiaml:1736 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_414[FLOAT, 64x3x3x3]
%onnx::Conv_415[FLOAT, 64]
%onnx::Conv_417[FLOAT, 64x64x1x1]
%onnx::Conv_420[FLOAT, 64x64x1x3]
%onnx::Conv_423[FLOAT, 64x64x3x1]
%onnx::Conv_426[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": 190690816,
"params": 2562122,
"val_accuracy": 92.14
} | {
"arch_str": "1736",
"identifier": "Hiaml_1736",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1736"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1736 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190690816,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1335 | GraphArch:Hiaml:1335 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_543[FLOAT, 64x3x3x3]
%onnx::Conv_544[FLOAT, 64]
%onnx::Conv_546[FLOAT, 64x64x3x3]
%onnx::Conv_549[FLOAT, 64x64x1x1]
%onnx::Conv_552[FLOAT, 64x64x1x1]
%onnx::Conv_555[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211269120,
"params": 2622026,
"val_accuracy": 92.3
} | {
"arch_str": "1335",
"identifier": "Hiaml_1335",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1335"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1335 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211269120,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_622 | GraphArch:Hiaml:622 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_485[FLOAT, 64x3x3x3]
%onnx::Conv_486[FLOAT, 64]
%onnx::Conv_488[FLOAT, 64x64x1x1]
%onnx::Conv_491[FLOAT, 64x64x1x3]
%onnx::Conv_494[FLOAT, 64x64x3x1]
%onnx::Conv_497[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172307968,
"params": 2391626,
"val_accuracy": 91.49
} | {
"arch_str": "622",
"identifier": "Hiaml_622",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "622"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 622 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172307968,
"val_accuracy": 91.49
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4009 | GraphArch:Hiaml:4009 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_408[FLOAT, 64x3x3x3]
%onnx::Conv_409[FLOAT, 64]
%onnx::Conv_411[FLOAT, 64x64x3x3]
%onnx::Conv_414[FLOAT, 64x64x1x3]
%onnx::Conv_417[FLOAT, 64x64x3x1]
%onnx::Conv_420[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": 215954944,
"params": 2585162,
"val_accuracy": 92.63
} | {
"arch_str": "4009",
"identifier": "Hiaml_4009",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4009"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4009 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215954944,
"val_accuracy": 92.63
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2919 | GraphArch:Hiaml:2919 | 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, 64x64x3x3]
%onnx::Conv_400[FLOAT, 64x64x3x3]
%onnx::Conv_403[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231716352,
"params": 2092618,
"val_accuracy": 93.08
} | {
"arch_str": "2919",
"identifier": "Hiaml_2919",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2919"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2919 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231716352,
"val_accuracy": 93.08
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1294 | GraphArch:Hiaml:1294 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x1x1]
%onnx::Conv_395[FLOAT, 64x64x1x1]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202356224,
"params": 2036042,
"val_accuracy": 92.77
} | {
"arch_str": "1294",
"identifier": "Hiaml_1294",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1294"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1294 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202356224,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_567 | GraphArch:Hiaml:567 | 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, 64x64x3x3]
%onnx::Conv_423[FLOAT, 64x64x1x1]
%onnx::Conv_426[FLOAT, 64x64x3x3]
%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": 192755200,
"params": 2480458,
"val_accuracy": 91.83
} | {
"arch_str": "567",
"identifier": "Hiaml_567",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "567"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 567 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 192755200,
"val_accuracy": 91.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2376 | GraphArch:Hiaml:2376 | 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, 64x64x1x3]
%onnx::Conv_403[FLOAT, 64x64x3x1]
%onnx::Conv_406[FLOAT, 64x64x1x3]
%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": 242234880,
"params": 2863178,
"val_accuracy": 92.74
} | {
"arch_str": "2376",
"identifier": "Hiaml_2376",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2376"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2376 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 242234880,
"val_accuracy": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3270 | GraphArch:Hiaml:3270 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x1x3]
%onnx::Conv_419[FLOAT, 64x64x3x1]
%onnx::Conv_422[FLOAT, 64x64x1x1]
%onnx::Conv_425[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239384064,
"params": 2187722,
"val_accuracy": 92.62
} | {
"arch_str": "3270",
"identifier": "Hiaml_3270",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3270"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3270 | Predict neural architecture validation accuracy and compute cost from 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": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3856 | GraphArch:Hiaml:3856 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_473[FLOAT, 64x3x3x3]
%onnx::Conv_474[FLOAT, 64]
%onnx::Conv_476[FLOAT, 64x64x1x3]
%onnx::Conv_479[FLOAT, 64x64x3x1]
%onnx::Conv_482[FLOAT, 64x64x1x1]
%onnx::Conv_485[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": 196621824,
"params": 2419146,
"val_accuracy": 91.93
} | {
"arch_str": "3856",
"identifier": "Hiaml_3856",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3856"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3856 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196621824,
"val_accuracy": 91.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4059 | GraphArch:Hiaml:4059 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_365[FLOAT, 64x3x3x3]
%onnx::Conv_366[FLOAT, 64]
%onnx::Conv_368[FLOAT, 64x64x1x1]
%onnx::Conv_371[FLOAT, 64x64x1x3]
%onnx::Conv_374[FLOAT, 64x64x3x1]
%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": 230536704,
"params": 3448266,
"val_accuracy": 92.4
} | {
"arch_str": "4059",
"identifier": "Hiaml_4059",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4059"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4059 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230536704,
"val_accuracy": 92.4
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3699 | GraphArch:Hiaml:3699 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_486[FLOAT, 64x3x3x3]
%onnx::Conv_487[FLOAT, 64]
%onnx::Conv_489[FLOAT, 64x64x1x1]
%onnx::Conv_492[FLOAT, 64x64x1x1]
%onnx::Conv_495[FLOAT, 64x64x3x3]
%onnx::Conv_498[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": 187020800,
"params": 2407754,
"val_accuracy": 91.92
} | {
"arch_str": "3699",
"identifier": "Hiaml_3699",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3699"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3699 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 187020800,
"val_accuracy": 91.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_55 | GraphArch:Hiaml:55 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_377[FLOAT, 64x3x3x3]
%onnx::Conv_378[FLOAT, 64]
%onnx::Conv_380[FLOAT, 64x64x3x3]
%onnx::Conv_383[FLOAT, 64x64x1x1]
%onnx::Conv_386[FLOAT, 64x64x3x3]
%onnx::Conv_389[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": 212481536,
"params": 2524362,
"val_accuracy": 92.42
} | {
"arch_str": "55",
"identifier": "Hiaml_55",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "55"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 55 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 212481536,
"val_accuracy": 92.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1470 | GraphArch:Hiaml:1470 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_497[FLOAT, 64x3x3x3]
%onnx::Conv_498[FLOAT, 64]
%onnx::Conv_500[FLOAT, 64x64x1x1]
%onnx::Conv_503[FLOAT, 64x64x1x3]
%onnx::Conv_506[FLOAT, 64x64x3x1]
%onnx::Conv_509[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": 222868992,
"params": 2661706,
"val_accuracy": 92.53
} | {
"arch_str": "1470",
"identifier": "Hiaml_1470",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1470"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1470 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222868992,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1159 | GraphArch:Hiaml:1159 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_309[FLOAT, 64x3x3x3]
%onnx::Conv_310[FLOAT, 64]
%onnx::Conv_312[FLOAT, 64x64x1x1]
%onnx::Conv_315[FLOAT, 64x64x3x3]
%onnx::Conv_318[FLOAT, 64x64x3x3]
%onnx::Conv_321[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": 180991488,
"params": 1697866,
"val_accuracy": 92.8
} | {
"arch_str": "1159",
"identifier": "Hiaml_1159",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1159"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1159 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180991488,
"val_accuracy": 92.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1894 | GraphArch:Hiaml:1894 | 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, 64x64x1x1]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%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": 205239808,
"params": 2096842,
"val_accuracy": 92.83
} | {
"arch_str": "1894",
"identifier": "Hiaml_1894",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1894"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1894 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205239808,
"val_accuracy": 92.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1502 | GraphArch:Hiaml:1502 | 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": 231618048,
"params": 2707786,
"val_accuracy": 91.95
} | {
"arch_str": "1502",
"identifier": "Hiaml_1502",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1502"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1502 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231618048,
"val_accuracy": 91.95
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_254 | GraphArch:Hiaml:254 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_459[FLOAT, 64x3x3x3]
%onnx::Conv_460[FLOAT, 64]
%onnx::Conv_462[FLOAT, 64x64x1x3]
%onnx::Conv_465[FLOAT, 64x64x3x1]
%onnx::Conv_468[FLOAT, 64x64x1x1]
%onnx::Conv_471[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": 228963840,
"params": 2709322,
"val_accuracy": 92.3
} | {
"arch_str": "254",
"identifier": "Hiaml_254",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "254"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 254 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 228963840,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4522 | GraphArch:Hiaml:4522 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_463[FLOAT, 64x3x3x3]
%onnx::Conv_464[FLOAT, 64]
%onnx::Conv_466[FLOAT, 64x64x1x1]
%onnx::Conv_469[FLOAT, 64x64x1x3]
%onnx::Conv_472[FLOAT, 64x64x3x1]
%onnx::Conv_475[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": 253277696,
"params": 3486794,
"val_accuracy": 92.18
} | {
"arch_str": "4522",
"identifier": "Hiaml_4522",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4522"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4522 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 253277696,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2323 | GraphArch:Hiaml:2323 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_430[FLOAT, 64x3x3x3]
%onnx::Conv_431[FLOAT, 64]
%onnx::Conv_433[FLOAT, 64x64x3x3]
%onnx::Conv_436[FLOAT, 64x64x1x1]
%onnx::Conv_439[FLOAT, 64x64x3x3]
%onnx::Conv_442[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181384704,
"params": 1922890,
"val_accuracy": 91.43
} | {
"arch_str": "2323",
"identifier": "Hiaml_2323",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2323"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2323 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181384704,
"val_accuracy": 91.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_514 | GraphArch:Hiaml:514 | 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, 64x64x1x1]
%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": 209696256,
"params": 3297610,
"val_accuracy": 91.61
} | {
"arch_str": "514",
"identifier": "Hiaml_514",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "514"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 514 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209696256,
"val_accuracy": 91.61
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1875 | GraphArch:Hiaml:1875 | 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, 64x64x3x3]
%onnx::Conv_406[FLOAT, 64x64x3x3]
%onnx::Conv_409[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189609472,
"params": 2233930,
"val_accuracy": 92.2
} | {
"arch_str": "1875",
"identifier": "Hiaml_1875",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1875"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1875 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189609472,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_498 | GraphArch:Hiaml:498 | 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, 64x64x1x1]
%onnx::Conv_377[FLOAT, 64x64x1x1]
%onnx::Conv_380[FLOAT, 64x64x3x3]
%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": 265205248,
"params": 3018058,
"val_accuracy": 91.76
} | {
"arch_str": "498",
"identifier": "Hiaml_498",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "498"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 498 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 265205248,
"val_accuracy": 91.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3992 | GraphArch:Hiaml:3992 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_401[FLOAT, 64x3x3x3]
%onnx::Conv_402[FLOAT, 64]
%onnx::Conv_404[FLOAT, 64x64x1x3]
%onnx::Conv_407[FLOAT, 64x64x3x1]
%onnx::Conv_410[FLOAT, 64x64x1x1]
%onnx::Conv_413[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209925632,
"params": 2485578,
"val_accuracy": 92.8
} | {
"arch_str": "3992",
"identifier": "Hiaml_3992",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3992"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3992 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209925632,
"val_accuracy": 92.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_325 | GraphArch:Hiaml:325 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_393[FLOAT, 64x3x3x3]
%onnx::Conv_394[FLOAT, 64]
%onnx::Conv_396[FLOAT, 64x64x1x1]
%onnx::Conv_399[FLOAT, 64x64x1x3]
%onnx::Conv_402[FLOAT, 64x64x3x1]
%onnx::Conv_405[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 139277824,
"params": 2135626,
"val_accuracy": 91.8
} | {
"arch_str": "325",
"identifier": "Hiaml_325",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "325"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 325 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 139277824,
"val_accuracy": 91.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3125 | GraphArch:Hiaml:3125 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_349[FLOAT, 64x3x3x3]
%onnx::Conv_350[FLOAT, 64]
%onnx::Conv_352[FLOAT, 64x64x1x1]
%onnx::Conv_355[FLOAT, 64x64x3x3]
%onnx::Conv_358[FLOAT, 64x64x3x3]
%onnx::Conv_361[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 276542976,
"params": 3414986,
"val_accuracy": 92.72
} | {
"arch_str": "3125",
"identifier": "Hiaml_3125",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3125"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3125 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 276542976,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2589 | GraphArch:Hiaml:2589 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_438[FLOAT, 64x3x3x3]
%onnx::Conv_439[FLOAT, 64]
%onnx::Conv_441[FLOAT, 64x64x1x1]
%onnx::Conv_444[FLOAT, 64x64x1x3]
%onnx::Conv_447[FLOAT, 64x64x3x1]
%onnx::Conv_450[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223852032,
"params": 2175050,
"val_accuracy": 92.89
} | {
"arch_str": "2589",
"identifier": "Hiaml_2589",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2589"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2589 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223852032,
"val_accuracy": 92.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2896 | GraphArch:Hiaml:2896 | 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": 215266816,
"params": 2601034,
"val_accuracy": 92.36
} | {
"arch_str": "2896",
"identifier": "Hiaml_2896",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2896"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2896 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215266816,
"val_accuracy": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4188 | GraphArch:Hiaml:4188 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_447[FLOAT, 64x3x3x3]
%onnx::Conv_448[FLOAT, 64]
%onnx::Conv_450[FLOAT, 64x64x1x1]
%onnx::Conv_453[FLOAT, 64x64x1x3]
%onnx::Conv_456[FLOAT, 64x64x3x1]
%onnx::Conv_459[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225916416,
"params": 2549194,
"val_accuracy": 92.44
} | {
"arch_str": "4188",
"identifier": "Hiaml_4188",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4188"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4188 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225916416,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2991 | GraphArch:Hiaml:2991 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x3]
%onnx::Conv_451[FLOAT, 64x64x3x1]
%onnx::Conv_454[FLOAT, 64x64x1x3]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235353600,
"params": 2683210,
"val_accuracy": 92.8
} | {
"arch_str": "2991",
"identifier": "Hiaml_2991",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2991"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2991 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235353600,
"val_accuracy": 92.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2608 | GraphArch:Hiaml:2608 | 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, 64x64x1x1]
%onnx::Conv_345[FLOAT, 64x64x1x1]
%onnx::Conv_348[FLOAT, 64x64x3x3]
%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": 173913600,
"params": 1812810,
"val_accuracy": 92.13
} | {
"arch_str": "2608",
"identifier": "Hiaml_2608",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2608"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2608 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 173913600,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_679 | GraphArch:Hiaml:679 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_361[FLOAT, 64x3x3x3]
%onnx::Conv_362[FLOAT, 64]
%onnx::Conv_364[FLOAT, 64x64x1x1]
%onnx::Conv_367[FLOAT, 64x64x3x3]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 286012928,
"params": 2935882,
"val_accuracy": 92.83
} | {
"arch_str": "679",
"identifier": "Hiaml_679",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "679"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 679 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 286012928,
"val_accuracy": 92.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4013 | GraphArch:Hiaml:4013 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[FLOAT, 64x64x3x3]
%onnx::Conv_414[FLOAT, 64x64x3x3]
%onnx::Conv_417[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196163072,
"params": 1925834,
"val_accuracy": 92.53
} | {
"arch_str": "4013",
"identifier": "Hiaml_4013",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4013"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4013 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196163072,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3883 | GraphArch:Hiaml:3883 | 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, 64x64x1x3]
%onnx::Conv_513[FLOAT, 64x64x3x1]
%onnx::Conv_516[FLOAT, 64x64x1x3]
%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": 211465728,
"params": 2571338,
"val_accuracy": 92.25
} | {
"arch_str": "3883",
"identifier": "Hiaml_3883",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3883"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3883 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211465728,
"val_accuracy": 92.25
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_75 | GraphArch:Hiaml:75 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_502[FLOAT, 64x3x3x3]
%onnx::Conv_503[FLOAT, 64]
%onnx::Conv_505[FLOAT, 64x64x3x3]
%onnx::Conv_508[FLOAT, 64x64x1x1]
%onnx::Conv_511[FLOAT, 64x64x1x1]
%onnx::Conv_514[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205829632,
"params": 2580298,
"val_accuracy": 91.75
} | {
"arch_str": "75",
"identifier": "Hiaml_75",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "75"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 75 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205829632,
"val_accuracy": 91.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2885 | GraphArch:Hiaml:2885 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_509[FLOAT, 64x3x3x3]
%onnx::Conv_510[FLOAT, 64]
%onnx::Conv_512[FLOAT, 64x64x1x3]
%onnx::Conv_515[FLOAT, 64x64x3x1]
%onnx::Conv_518[FLOAT, 64x64x1x3]
%onnx::Conv_521[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226113024,
"params": 2661706,
"val_accuracy": 92.4
} | {
"arch_str": "2885",
"identifier": "Hiaml_2885",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2885"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2885 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226113024,
"val_accuracy": 92.4
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2898 | GraphArch:Hiaml:2898 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_463[FLOAT, 64x3x3x3]
%onnx::Conv_464[FLOAT, 64]
%onnx::Conv_466[FLOAT, 64x64x1x1]
%onnx::Conv_469[FLOAT, 64x64x1x3]
%onnx::Conv_472[FLOAT, 64x64x3x1]
%onnx::Conv_475[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": 194328064,
"params": 2439242,
"val_accuracy": 92.73
} | {
"arch_str": "2898",
"identifier": "Hiaml_2898",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2898"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2898 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194328064,
"val_accuracy": 92.73
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3749 | GraphArch:Hiaml:3749 | 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, 64x64x1x3]
%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": 206878208,
"params": 2167626,
"val_accuracy": 92.3
} | {
"arch_str": "3749",
"identifier": "Hiaml_3749",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3749"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3749 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206878208,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3348 | GraphArch:Hiaml:3348 | 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": 188495360,
"params": 1978186,
"val_accuracy": 92.44
} | {
"arch_str": "3348",
"identifier": "Hiaml_3348",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3348"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3348 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188495360,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2002 | GraphArch:Hiaml:2002 | 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": 200685056,
"params": 2464074,
"val_accuracy": 91.99
} | {
"arch_str": "2002",
"identifier": "Hiaml_2002",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2002"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2002 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 200685056,
"val_accuracy": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3711 | GraphArch:Hiaml:3711 | 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, 64x64x1x3]
%onnx::Conv_431[FLOAT, 64x64x3x1]
%onnx::Conv_434[FLOAT, 64x64x1x3]
%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": 159659520,
"params": 1823818,
"val_accuracy": 91.76
} | {
"arch_str": "3711",
"identifier": "Hiaml_3711",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3711"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3711 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159659520,
"val_accuracy": 91.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1876 | GraphArch:Hiaml:1876 | 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, 64x64x1x1]
%onnx::Conv_494[FLOAT, 64x64x1x3]
%onnx::Conv_497[FLOAT, 64x64x3x1]
%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": 172406272,
"params": 2391626,
"val_accuracy": 92.24
} | {
"arch_str": "1876",
"identifier": "Hiaml_1876",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1876"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1876 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172406272,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4563 | GraphArch:Hiaml:4563 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_393[FLOAT, 64x3x3x3]
%onnx::Conv_394[FLOAT, 64]
%onnx::Conv_396[FLOAT, 64x64x3x3]
%onnx::Conv_399[FLOAT, 64x64x1x3]
%onnx::Conv_402[FLOAT, 64x64x3x1]
%onnx::Conv_405[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214939136,
"params": 2059850,
"val_accuracy": 92.78
} | {
"arch_str": "4563",
"identifier": "Hiaml_4563",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4563"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4563 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214939136,
"val_accuracy": 92.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3799 | GraphArch:Hiaml:3799 | 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": 234698240,
"params": 2684490,
"val_accuracy": 92.58
} | {
"arch_str": "3799",
"identifier": "Hiaml_3799",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3799"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3799 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234698240,
"val_accuracy": 92.58
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4614 | GraphArch:Hiaml:4614 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_395[FLOAT, 64x3x3x3]
%onnx::Conv_396[FLOAT, 64]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[FLOAT, 64x64x1x3]
%onnx::Conv_404[FLOAT, 64x64x3x1]
%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": 268383744,
"params": 2895690,
"val_accuracy": 92.52
} | {
"arch_str": "4614",
"identifier": "Hiaml_4614",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4614"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4614 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 268383744,
"val_accuracy": 92.52
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_421 | GraphArch:Hiaml:421 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_467[FLOAT, 64x3x3x3]
%onnx::Conv_468[FLOAT, 64]
%onnx::Conv_470[FLOAT, 64x64x1x1]
%onnx::Conv_473[FLOAT, 64x64x1x3]
%onnx::Conv_476[FLOAT, 64x64x3x1]
%onnx::Conv_479[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213399040,
"params": 2635082,
"val_accuracy": 91.83
} | {
"arch_str": "421",
"identifier": "Hiaml_421",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "421"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 421 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213399040,
"val_accuracy": 91.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3521 | GraphArch:Hiaml:3521 | 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": 241022464,
"params": 2658122,
"val_accuracy": 93.02
} | {
"arch_str": "3521",
"identifier": "Hiaml_3521",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3521"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3521 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 241022464,
"val_accuracy": 93.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2484 | GraphArch:Hiaml:2484 | 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, 64x64x3x3]
%onnx::Conv_449[FLOAT, 64x64x1x1]
%onnx::Conv_452[FLOAT, 64x64x3x3]
%onnx::Conv_455[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 191968768,
"params": 2127690,
"val_accuracy": 92.15
} | {
"arch_str": "2484",
"identifier": "Hiaml_2484",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2484"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2484 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 191968768,
"val_accuracy": 92.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3379 | GraphArch:Hiaml:3379 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_359[FLOAT, 64x3x3x3]
%onnx::Conv_360[FLOAT, 64]
%onnx::Conv_362[FLOAT, 64x64x3x3]
%onnx::Conv_365[FLOAT, 64x64x1x3]
%onnx::Conv_368[FLOAT, 64x64x3x1]
%onnx::Conv_371[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209499648,
"params": 2018122,
"val_accuracy": 92.34
} | {
"arch_str": "3379",
"identifier": "Hiaml_3379",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3379"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3379 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209499648,
"val_accuracy": 92.34
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3139 | GraphArch:Hiaml:3139 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[FLOAT, 64x64x3x3]
%onnx::Conv_454[FLOAT, 64x64x3x3]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260257280,
"params": 2732874,
"val_accuracy": 92.77
} | {
"arch_str": "3139",
"identifier": "Hiaml_3139",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3139"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3139 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260257280,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1798 | GraphArch:Hiaml:1798 | 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, 64x64x3x3]
%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": 227194368,
"params": 2157642,
"val_accuracy": 92.34
} | {
"arch_str": "1798",
"identifier": "Hiaml_1798",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1798"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1798 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227194368,
"val_accuracy": 92.34
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_683 | GraphArch:Hiaml:683 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_347[FLOAT, 64x3x3x3]
%onnx::Conv_348[FLOAT, 64]
%onnx::Conv_350[FLOAT, 64x64x3x3]
%onnx::Conv_353[FLOAT, 64x64x1x1]
%onnx::Conv_356[FLOAT, 64x64x3x3]
%onnx::Conv_359[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": 185120256,
"params": 2051402,
"val_accuracy": 92.44
} | {
"arch_str": "683",
"identifier": "Hiaml_683",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "683"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 683 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185120256,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2926 | GraphArch:Hiaml:2926 | 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, 64x64x1x1]
%onnx::Conv_344[FLOAT, 64x64x1x1]
%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": 2550858,
"val_accuracy": 92.75
} | {
"arch_str": "2926",
"identifier": "Hiaml_2926",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2926"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2926 | Predict neural architecture validation accuracy and compute cost from 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": 92.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4159 | GraphArch:Hiaml:4159 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_333[FLOAT, 64x3x3x3]
%onnx::Conv_334[FLOAT, 64]
%onnx::Conv_336[FLOAT, 64x64x3x3]
%onnx::Conv_339[FLOAT, 64x64x1x1]
%onnx::Conv_342[FLOAT, 64x64x1x1]
%onnx::Conv_345[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215660032,
"params": 2165066,
"val_accuracy": 92.27
} | {
"arch_str": "4159",
"identifier": "Hiaml_4159",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4159"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4159 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215660032,
"val_accuracy": 92.27
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3067 | GraphArch:Hiaml:3067 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_469[FLOAT, 64x3x3x3]
%onnx::Conv_470[FLOAT, 64]
%onnx::Conv_472[FLOAT, 64x64x1x3]
%onnx::Conv_475[FLOAT, 64x64x3x1]
%onnx::Conv_478[FLOAT, 64x64x1x3]
%onnx::Conv_481[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": 201766400,
"params": 2497354,
"val_accuracy": 92.38
} | {
"arch_str": "3067",
"identifier": "Hiaml_3067",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3067"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3067 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201766400,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3862 | GraphArch:Hiaml:3862 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_441[FLOAT, 64x3x3x3]
%onnx::Conv_442[FLOAT, 64]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[FLOAT, 64x64x1x1]
%onnx::Conv_450[FLOAT, 64x64x3x3]
%onnx::Conv_453[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 175453696,
"params": 1946954,
"val_accuracy": 91.85
} | {
"arch_str": "3862",
"identifier": "Hiaml_3862",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3862"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3862 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 175453696,
"val_accuracy": 91.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4157 | GraphArch:Hiaml:4157 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x3x3]
%onnx::Conv_419[FLOAT, 64x64x1x1]
%onnx::Conv_422[FLOAT, 64x64x1x1]
%onnx::Conv_425[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189838848,
"params": 2060874,
"val_accuracy": 91.86
} | {
"arch_str": "4157",
"identifier": "Hiaml_4157",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4157"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4157 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189838848,
"val_accuracy": 91.86
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2035 | GraphArch:Hiaml:2035 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_456[FLOAT, 64x3x3x3]
%onnx::Conv_457[FLOAT, 64]
%onnx::Conv_459[FLOAT, 64x64x3x3]
%onnx::Conv_462[FLOAT, 64x64x1x3]
%onnx::Conv_465[FLOAT, 64x64x3x1]
%onnx::Conv_468[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": 185841152,
"params": 1833034,
"val_accuracy": 92.31
} | {
"arch_str": "2035",
"identifier": "Hiaml_2035",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2035"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2035 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185841152,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_154 | GraphArch:Hiaml:154 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_525[FLOAT, 64x3x3x3]
%onnx::Conv_526[FLOAT, 64]
%onnx::Conv_528[FLOAT, 64x64x1x1]
%onnx::Conv_531[FLOAT, 64x64x1x3]
%onnx::Conv_534[FLOAT, 64x64x3x1]
%onnx::Conv_537[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": 2564426,
"val_accuracy": 92.03
} | {
"arch_str": "154",
"identifier": "Hiaml_154",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "154"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 154 | Predict neural architecture validation accuracy and compute cost from 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": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4192 | GraphArch:Hiaml:4192 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_522[FLOAT, 64x3x3x3]
%onnx::Conv_523[FLOAT, 64]
%onnx::Conv_525[FLOAT, 64x64x1x3]
%onnx::Conv_528[FLOAT, 64x64x3x1]
%onnx::Conv_531[FLOAT, 64x64x1x1]
%onnx::Conv_534[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244954624,
"params": 2833994,
"val_accuracy": 92.3
} | {
"arch_str": "4192",
"identifier": "Hiaml_4192",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4192"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4192 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244954624,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3221 | GraphArch:Hiaml:3221 | 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": 229520896,
"params": 2592330,
"val_accuracy": 92.32
} | {
"arch_str": "3221",
"identifier": "Hiaml_3221",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3221"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3221 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 229520896,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_95 | GraphArch:Hiaml:95 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_475[FLOAT, 64x3x3x3]
%onnx::Conv_476[FLOAT, 64]
%onnx::Conv_478[FLOAT, 64x64x3x3]
%onnx::Conv_481[FLOAT, 64x64x1x3]
%onnx::Conv_484[FLOAT, 64x64x3x1]
%onnx::Conv_487[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218281472,
"params": 2579786,
"val_accuracy": 92.05
} | {
"arch_str": "95",
"identifier": "Hiaml_95",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "95"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 95 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218281472,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4048 | GraphArch:Hiaml:4048 | 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, 64x64x1x1]
%onnx::Conv_420[FLOAT, 64x64x1x1]
%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": 215004672,
"params": 3240522,
"val_accuracy": 91.41
} | {
"arch_str": "4048",
"identifier": "Hiaml_4048",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4048"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4048 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215004672,
"val_accuracy": 91.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3453 | GraphArch:Hiaml:3453 | 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": 206976512,
"params": 2560842,
"val_accuracy": 92.49
} | {
"arch_str": "3453",
"identifier": "Hiaml_3453",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3453"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3453 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206976512,
"val_accuracy": 92.49
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4311 | GraphArch:Hiaml:4311 | 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, 64x64x3x3]
%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": 238302720,
"params": 2612554,
"val_accuracy": 92.38
} | {
"arch_str": "4311",
"identifier": "Hiaml_4311",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4311"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4311 | Predict neural architecture validation accuracy and compute cost from 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": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2583 | GraphArch:Hiaml:2583 | 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, 64x64x3x3]
%onnx::Conv_449[FLOAT, 64x64x1x3]
%onnx::Conv_452[FLOAT, 64x64x3x1]
%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": 210941440,
"params": 2126922,
"val_accuracy": 92.09
} | {
"arch_str": "2583",
"identifier": "Hiaml_2583",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2583"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2583 | Predict neural architecture validation accuracy and compute cost from 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": 92.09
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_726 | GraphArch:Hiaml:726 | 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, 64x64x3x3]
%onnx::Conv_453[FLOAT, 64x64x1x3]
%onnx::Conv_456[FLOAT, 64x64x3x1]
%onnx::Conv_459[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198424064,
"params": 1758538,
"val_accuracy": 92.56
} | {
"arch_str": "726",
"identifier": "Hiaml_726",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "726"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 726 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198424064,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_813 | GraphArch:Hiaml:813 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_472[FLOAT, 64x3x3x3]
%onnx::Conv_473[FLOAT, 64]
%onnx::Conv_475[FLOAT, 64x64x1x1]
%onnx::Conv_478[FLOAT, 64x64x1x3]
%onnx::Conv_481[FLOAT, 64x64x3x1]
%onnx::Conv_484[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194295296,
"params": 2586186,
"val_accuracy": 92.24
} | {
"arch_str": "813",
"identifier": "Hiaml_813",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "813"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 813 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194295296,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2753 | GraphArch:Hiaml:2753 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x1x1]
%onnx::Conv_419[FLOAT, 64x64x1x3]
%onnx::Conv_422[FLOAT, 64x64x3x1]
%onnx::Conv_425[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170243584,
"params": 1756746,
"val_accuracy": 92.48
} | {
"arch_str": "2753",
"identifier": "Hiaml_2753",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2753"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2753 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170243584,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2675 | GraphArch:Hiaml:2675 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_482[FLOAT, 64x3x3x3]
%onnx::Conv_483[FLOAT, 64]
%onnx::Conv_485[FLOAT, 64x64x1x1]
%onnx::Conv_488[FLOAT, 64x64x1x1]
%onnx::Conv_491[FLOAT, 64x64x3x3]
%onnx::Conv_494[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": 197670400,
"params": 2119754,
"val_accuracy": 91.55
} | {
"arch_str": "2675",
"identifier": "Hiaml_2675",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2675"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2675 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197670400,
"val_accuracy": 91.55
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1003 | GraphArch:Hiaml:1003 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_467[FLOAT, 64x3x3x3]
%onnx::Conv_468[FLOAT, 64]
%onnx::Conv_470[FLOAT, 64x64x3x3]
%onnx::Conv_473[FLOAT, 64x64x1x1]
%onnx::Conv_476[FLOAT, 64x64x3x3]
%onnx::Conv_479[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205600256,
"params": 2579786,
"val_accuracy": 91.51
} | {
"arch_str": "1003",
"identifier": "Hiaml_1003",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1003"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1003 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205600256,
"val_accuracy": 91.51
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3200 | GraphArch:Hiaml:3200 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_457[FLOAT, 64x3x3x3]
%onnx::Conv_458[FLOAT, 64]
%onnx::Conv_460[FLOAT, 64x64x1x3]
%onnx::Conv_463[FLOAT, 64x64x3x1]
%onnx::Conv_466[FLOAT, 64x64x1x1]
%onnx::Conv_469[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226997760,
"params": 2691914,
"val_accuracy": 92.74
} | {
"arch_str": "3200",
"identifier": "Hiaml_3200",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3200"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3200 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226997760,
"val_accuracy": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_264 | GraphArch:Hiaml:264 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_440[FLOAT, 64x3x3x3]
%onnx::Conv_441[FLOAT, 64]
%onnx::Conv_443[FLOAT, 64x64x1x1]
%onnx::Conv_446[FLOAT, 64x64x3x3]
%onnx::Conv_449[FLOAT, 64x64x3x3]
%onnx::Conv_452[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": 231716352,
"params": 2587210,
"val_accuracy": 92.74
} | {
"arch_str": "264",
"identifier": "Hiaml_264",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "264"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 264 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231716352,
"val_accuracy": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4309 | GraphArch:Hiaml:4309 | 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": 92.45
} | {
"arch_str": "4309",
"identifier": "Hiaml_4309",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4309"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4309 | Predict neural architecture validation accuracy and compute cost from 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": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3171 | GraphArch:Hiaml:3171 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_483[FLOAT, 64x3x3x3]
%onnx::Conv_484[FLOAT, 64]
%onnx::Conv_486[FLOAT, 64x64x1x3]
%onnx::Conv_489[FLOAT, 64x64x3x1]
%onnx::Conv_492[FLOAT, 64x64x1x1]
%onnx::Conv_495[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185251328,
"params": 1861578,
"val_accuracy": 92.3
} | {
"arch_str": "3171",
"identifier": "Hiaml_3171",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3171"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3171 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185251328,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4488 | GraphArch:Hiaml:4488 | 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, 64x64x1x3]
%onnx::Conv_387[FLOAT, 64x64x3x1]
%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": 174142976,
"params": 2305610,
"val_accuracy": 92.4
} | {
"arch_str": "4488",
"identifier": "Hiaml_4488",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4488"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4488 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 174142976,
"val_accuracy": 92.4
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2750 | GraphArch:Hiaml:2750 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x1x1]
%onnx::Conv_395[FLOAT, 64x64x3x3]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207402496,
"params": 2397258,
"val_accuracy": 92.53
} | {
"arch_str": "2750",
"identifier": "Hiaml_2750",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2750"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2750 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207402496,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2727 | GraphArch:Hiaml:2727 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x3x3]
%onnx::Conv_427[FLOAT, 64x64x1x1]
%onnx::Conv_430[FLOAT, 64x64x3x3]
%onnx::Conv_433[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230536704,
"params": 2799690,
"val_accuracy": 92.41
} | {
"arch_str": "2727",
"identifier": "Hiaml_2727",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2727"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2727 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230536704,
"val_accuracy": 92.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_891 | GraphArch:Hiaml:891 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_526[FLOAT, 64x3x3x3]
%onnx::Conv_527[FLOAT, 64]
%onnx::Conv_529[FLOAT, 64x64x3x3]
%onnx::Conv_532[FLOAT, 64x64x1x1]
%onnx::Conv_535[FLOAT, 64x64x1x1]
%onnx::Conv_538[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210155008,
"params": 2613578,
"val_accuracy": 92.1
} | {
"arch_str": "891",
"identifier": "Hiaml_891",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "891"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 891 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210155008,
"val_accuracy": 92.1
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2672 | GraphArch:Hiaml:2672 | 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, 64x64x3x3]
%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": 180368896,
"params": 2160202,
"val_accuracy": 92.25
} | {
"arch_str": "2672",
"identifier": "Hiaml_2672",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2672"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2672 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180368896,
"val_accuracy": 92.25
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2129 | GraphArch:Hiaml:2129 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_465[FLOAT, 64x3x3x3]
%onnx::Conv_466[FLOAT, 64]
%onnx::Conv_468[FLOAT, 64x64x1x1]
%onnx::Conv_471[FLOAT, 64x64x3x3]
%onnx::Conv_474[FLOAT, 64x64x3x3]
%onnx::Conv_477[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 216315392,
"params": 1799754,
"val_accuracy": 92.66
} | {
"arch_str": "2129",
"identifier": "Hiaml_2129",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2129"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2129 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 216315392,
"val_accuracy": 92.66
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4561 | GraphArch:Hiaml:4561 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_395[FLOAT, 64x3x3x3]
%onnx::Conv_396[FLOAT, 64]
%onnx::Conv_398[FLOAT, 64x64x1x1]
%onnx::Conv_401[FLOAT, 64x64x3x3]
%onnx::Conv_404[FLOAT, 64x64x3x3]
%onnx::Conv_407[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231618048,
"params": 2609482,
"val_accuracy": 92.64
} | {
"arch_str": "4561",
"identifier": "Hiaml_4561",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4561"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4561 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231618048,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3805 | GraphArch:Hiaml:3805 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_476[FLOAT, 64x3x3x3]
%onnx::Conv_477[FLOAT, 64]
%onnx::Conv_479[FLOAT, 64x64x3x3]
%onnx::Conv_482[FLOAT, 64x64x1x1]
%onnx::Conv_485[FLOAT, 64x64x1x1]
%onnx::Conv_488[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210875904,
"params": 2620490,
"val_accuracy": 92.25
} | {
"arch_str": "3805",
"identifier": "Hiaml_3805",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3805"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3805 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210875904,
"val_accuracy": 92.25
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_876 | GraphArch:Hiaml:876 | 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": 220935680,
"params": 2668618,
"val_accuracy": 92.39
} | {
"arch_str": "876",
"identifier": "Hiaml_876",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "876"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 876 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 220935680,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3241 | GraphArch:Hiaml:3241 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_439[FLOAT, 64x3x3x3]
%onnx::Conv_440[FLOAT, 64]
%onnx::Conv_442[FLOAT, 64x64x3x3]
%onnx::Conv_445[FLOAT, 64x64x1x3]
%onnx::Conv_448[FLOAT, 64x64x3x1]
%onnx::Conv_451[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 200488448,
"params": 2057418,
"val_accuracy": 92.39
} | {
"arch_str": "3241",
"identifier": "Hiaml_3241",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3241"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3241 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 200488448,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1295 | GraphArch:Hiaml:1295 | 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, 64x64x1x3]
%onnx::Conv_410[FLOAT, 64x64x3x1]
%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": 220313088,
"params": 3354698,
"val_accuracy": 92.11
} | {
"arch_str": "1295",
"identifier": "Hiaml_1295",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1295"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1295 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 220313088,
"val_accuracy": 92.11
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4495 | GraphArch:Hiaml:4495 | 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, 64x64x1x3]
%onnx::Conv_415[FLOAT, 64x64x3x1]
%onnx::Conv_418[FLOAT, 64x64x1x3]
%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": 195147264,
"params": 2076746,
"val_accuracy": 92.96
} | {
"arch_str": "4495",
"identifier": "Hiaml_4495",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4495"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4495 | Predict neural architecture validation accuracy and compute cost from 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": 92.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4285 | GraphArch:Hiaml:4285 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_474[FLOAT, 64x3x3x3]
%onnx::Conv_475[FLOAT, 64]
%onnx::Conv_477[FLOAT, 64x64x1x3]
%onnx::Conv_480[FLOAT, 64x64x3x1]
%onnx::Conv_483[FLOAT, 64x64x1x1]
%onnx::Conv_486[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 270054912,
"params": 3617866,
"val_accuracy": 92.36
} | {
"arch_str": "4285",
"identifier": "Hiaml_4285",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4285"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4285 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 270054912,
"val_accuracy": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1115 | GraphArch:Hiaml:1115 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_321[FLOAT, 64x3x3x3]
%onnx::Conv_322[FLOAT, 64]
%onnx::Conv_324[FLOAT, 64x64x1x1]
%onnx::Conv_327[FLOAT, 64x64x3x3]
%onnx::Conv_330[FLOAT, 64x64x3x3]
%onnx::Conv_333[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197768704,
"params": 1828938,
"val_accuracy": 92.76
} | {
"arch_str": "1115",
"identifier": "Hiaml_1115",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1115"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1115 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197768704,
"val_accuracy": 92.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1830 | GraphArch:Hiaml:1830 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_468[FLOAT, 64x3x3x3]
%onnx::Conv_469[FLOAT, 64]
%onnx::Conv_471[FLOAT, 64x64x3x3]
%onnx::Conv_474[FLOAT, 64x64x1x1]
%onnx::Conv_477[FLOAT, 64x64x1x1]
%onnx::Conv_480[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210875904,
"params": 2620490,
"val_accuracy": 92.61
} | {
"arch_str": "1830",
"identifier": "Hiaml_1830",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1830"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 1830 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210875904,
"val_accuracy": 92.61
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3030 | GraphArch:Hiaml:3030 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_486[FLOAT, 64x3x3x3]
%onnx::Conv_487[FLOAT, 64]
%onnx::Conv_489[FLOAT, 64x64x1x1]
%onnx::Conv_492[FLOAT, 64x64x3x3]
%onnx::Conv_495[FLOAT, 64x64x3x3]
%onnx::Conv_498[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": 230995456,
"params": 2567626,
"val_accuracy": 92.72
} | {
"arch_str": "3030",
"identifier": "Hiaml_3030",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3030"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 3030 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230995456,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4294 | GraphArch:Hiaml:4294 | 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, 64x64x3x3]
%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": 226244096,
"params": 2643274,
"val_accuracy": 91.69
} | {
"arch_str": "4294",
"identifier": "Hiaml_4294",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4294"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 4294 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226244096,
"val_accuracy": 91.69
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2431 | GraphArch:Hiaml:2431 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_399[FLOAT, 64x3x3x3]
%onnx::Conv_400[FLOAT, 64]
%onnx::Conv_402[FLOAT, 64x64x1x3]
%onnx::Conv_405[FLOAT, 64x64x3x1]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 278050304,
"params": 3681866,
"val_accuracy": 92.18
} | {
"arch_str": "2431",
"identifier": "Hiaml_2431",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2431"
} | {
"input_format": "serialized neural-network graph text",
"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"
} | [] | 2431 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 278050304,
"val_accuracy": 92.18
} | full |
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