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_2001 | GraphArch:Hiaml:2001 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_467[FLOAT, 64x3x3x3]
%onnx::Conv_468[FLOAT, 64]
%onnx::Conv_470[FLOAT, 64x64x1x3]
%onnx::Conv_473[FLOAT, 64x64x3x1]
%onnx::Conv_476[FLOAT, 64x64x1x3]
%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": 199734784,
"params": 2480458,
"val_accuracy": 92.72
} | {
"arch_str": "2001",
"identifier": "Hiaml_2001",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2001"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2001 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199734784,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1692 | GraphArch:Hiaml:1692 | 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": 170178048,
"params": 1585226,
"val_accuracy": 92.74
} | {
"arch_str": "1692",
"identifier": "Hiaml_1692",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1692"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1692 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170178048,
"val_accuracy": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1209 | GraphArch:Hiaml:1209 | 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": 225326592,
"params": 2658634,
"val_accuracy": 93.02
} | {
"arch_str": "1209",
"identifier": "Hiaml_1209",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1209"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1209 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225326592,
"val_accuracy": 93.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4349 | GraphArch:Hiaml:4349 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_339[FLOAT, 64x3x3x3]
%onnx::Conv_340[FLOAT, 64]
%onnx::Conv_342[FLOAT, 64x64x3x3]
%onnx::Conv_345[FLOAT, 64x64x1x3]
%onnx::Conv_348[FLOAT, 64x64x3x1]
%onnx::Conv_351[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201111040,
"params": 1841610,
"val_accuracy": 93.06
} | {
"arch_str": "4349",
"identifier": "Hiaml_4349",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4349"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4349 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201111040,
"val_accuracy": 93.06
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3014 | GraphArch:Hiaml:3014 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x3x3]
%onnx::Conv_425[FLOAT, 64x64x1x3]
%onnx::Conv_428[FLOAT, 64x64x3x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239056384,
"params": 2631114,
"val_accuracy": 92.94
} | {
"arch_str": "3014",
"identifier": "Hiaml_3014",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3014"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3014 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239056384,
"val_accuracy": 92.94
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4038 | GraphArch:Hiaml:4038 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x3x3]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 270513664,
"params": 3477834,
"val_accuracy": 92.7
} | {
"arch_str": "4038",
"identifier": "Hiaml_4038",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4038"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4038 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 270513664,
"val_accuracy": 92.7
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_432 | GraphArch:Hiaml:432 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_418[FLOAT, 64x3x3x3]
%onnx::Conv_419[FLOAT, 64]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[FLOAT, 64x64x1x3]
%onnx::Conv_430[F... | graph | {
"flops": "Floating-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": 1904458,
"val_accuracy": 92.6
} | {
"arch_str": "432",
"identifier": "Hiaml_432",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "432"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 432 | Predict neural architecture validation accuracy and compute cost from 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": 92.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1196 | GraphArch:Hiaml:1196 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_475[FLOAT, 64x3x3x3]
%onnx::Conv_476[FLOAT, 64]
%onnx::Conv_478[FLOAT, 64x64x1x3]
%onnx::Conv_481[FLOAT, 64x64x3x1]
%onnx::Conv_484[FLOAT, 64x64x1x3]
%onnx::Conv_487[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235288064,
"params": 2660682,
"val_accuracy": 92.48
} | {
"arch_str": "1196",
"identifier": "Hiaml_1196",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1196"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1196 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235288064,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1264 | GraphArch:Hiaml:1264 | 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": 206059008,
"params": 2431818,
"val_accuracy": 92.48
} | {
"arch_str": "1264",
"identifier": "Hiaml_1264",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1264"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1264 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206059008,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3746 | GraphArch:Hiaml:3746 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[FLOAT, 64x64x1x3]
%onnx::Conv_430[FLOAT, 64x64x3x1]
%onnx::Conv_433[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181745152,
"params": 1970762,
"val_accuracy": 92.34
} | {
"arch_str": "3746",
"identifier": "Hiaml_3746",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3746"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3746 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181745152,
"val_accuracy": 92.34
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1954 | GraphArch:Hiaml:1954 | 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, 64x64x1x3]
%onnx::Conv_348[FLOAT, 64x64x3x1]
%onnx::Conv_351[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210548224,
"params": 3279690,
"val_accuracy": 92.18
} | {
"arch_str": "1954",
"identifier": "Hiaml_1954",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1954"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1954 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210548224,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_132 | GraphArch:Hiaml:132 | 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, 64x64x3x3]
%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": 225195520,
"params": 2734154,
"val_accuracy": 91.98
} | {
"arch_str": "132",
"identifier": "Hiaml_132",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "132"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 132 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225195520,
"val_accuracy": 91.98
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4499 | GraphArch:Hiaml:4499 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_479[FLOAT, 64x3x3x3]
%onnx::Conv_480[FLOAT, 64]
%onnx::Conv_482[FLOAT, 64x64x3x3]
%onnx::Conv_485[FLOAT, 64x64x1x3]
%onnx::Conv_488[FLOAT, 64x64x3x1]
%onnx::Conv_491[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206812672,
"params": 2489930,
"val_accuracy": 92.47
} | {
"arch_str": "4499",
"identifier": "Hiaml_4499",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4499"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4499 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206812672,
"val_accuracy": 92.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1300 | GraphArch:Hiaml:1300 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x1x3]
%onnx::Conv_467[FLOAT, 64x64x3x1]
%onnx::Conv_470[FLOAT, 64x64x1x1]
%onnx::Conv_473[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214513152,
"params": 2519882,
"val_accuracy": 92.29
} | {
"arch_str": "1300",
"identifier": "Hiaml_1300",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1300"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1300 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214513152,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2936 | GraphArch:Hiaml:2936 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x3x3]
%onnx::Conv_425[FLOAT, 64x64x1x1]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219035136,
"params": 2660170,
"val_accuracy": 92.43
} | {
"arch_str": "2936",
"identifier": "Hiaml_2936",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2936"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2936 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219035136,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2775 | GraphArch:Hiaml:2775 | 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": 176961024,
"params": 1789770,
"val_accuracy": 92.73
} | {
"arch_str": "2775",
"identifier": "Hiaml_2775",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2775"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2775 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176961024,
"val_accuracy": 92.73
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_570 | GraphArch:Hiaml:570 | 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, 64x64x1x1]
%onnx::Conv_475[FLOAT, 64x64x3x3]
%onnx::Conv_478[FLOAT, 64x64x3x3]
%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": 260388352,
"params": 2733386,
"val_accuracy": 92.67
} | {
"arch_str": "570",
"identifier": "Hiaml_570",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "570"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 570 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260388352,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1034 | GraphArch:Hiaml:1034 | 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, 64x64x1x3]
%onnx::Conv_423[FLOAT, 64x64x3x1]
%onnx::Conv_426[FLOAT, 64x64x1x1]
%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": 173323776,
"params": 1880138,
"val_accuracy": 91.98
} | {
"arch_str": "1034",
"identifier": "Hiaml_1034",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1034"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1034 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 173323776,
"val_accuracy": 91.98
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_162 | GraphArch:Hiaml:162 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x1x1]
%onnx::Conv_425[FLOAT, 64x64x1x3]
%onnx::Conv_428[FLOAT, 64x64x3x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180401664,
"params": 1987914,
"val_accuracy": 91.85
} | {
"arch_str": "162",
"identifier": "Hiaml_162",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "162"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 162 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180401664,
"val_accuracy": 91.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4064 | GraphArch:Hiaml:4064 | 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": 201242112,
"params": 1915082,
"val_accuracy": 92.38
} | {
"arch_str": "4064",
"identifier": "Hiaml_4064",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4064"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4064 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201242112,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3775 | GraphArch:Hiaml:3775 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x1x1]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223589888,
"params": 2175562,
"val_accuracy": 92.78
} | {
"arch_str": "3775",
"identifier": "Hiaml_3775",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3775"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3775 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223589888,
"val_accuracy": 92.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1691 | GraphArch:Hiaml:1691 | 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": 214939136,
"params": 1777610,
"val_accuracy": 92.74
} | {
"arch_str": "1691",
"identifier": "Hiaml_1691",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1691"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1691 | Predict neural architecture validation accuracy and compute cost from 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": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4438 | GraphArch:Hiaml:4438 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_489[FLOAT, 64x3x3x3]
%onnx::Conv_490[FLOAT, 64]
%onnx::Conv_492[FLOAT, 64x64x1x1]
%onnx::Conv_495[FLOAT, 64x64x1x1]
%onnx::Conv_498[FLOAT, 64x64x3x3]
%onnx::Conv_501[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210220544,
"params": 2587722,
"val_accuracy": 91.71
} | {
"arch_str": "4438",
"identifier": "Hiaml_4438",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4438"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4438 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210220544,
"val_accuracy": 91.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1653 | GraphArch:Hiaml:1653 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_442[FLOAT, 64x3x3x3]
%onnx::Conv_443[FLOAT, 64]
%onnx::Conv_445[FLOAT, 64x64x3x3]
%onnx::Conv_448[FLOAT, 64x64x1x3]
%onnx::Conv_451[FLOAT, 64x64x3x1]
%onnx::Conv_454[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214939136,
"params": 2554442,
"val_accuracy": 92.49
} | {
"arch_str": "1653",
"identifier": "Hiaml_1653",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1653"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1653 | Predict neural architecture validation accuracy and compute cost from 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": 92.49
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2076 | GraphArch:Hiaml:2076 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_489[FLOAT, 64x3x3x3]
%onnx::Conv_490[FLOAT, 64]
%onnx::Conv_492[FLOAT, 64x64x1x3]
%onnx::Conv_495[FLOAT, 64x64x3x1]
%onnx::Conv_498[FLOAT, 64x64x1x1]
%onnx::Conv_501[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184169984,
"params": 2321354,
"val_accuracy": 92.03
} | {
"arch_str": "2076",
"identifier": "Hiaml_2076",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2076"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2076 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184169984,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3529 | GraphArch:Hiaml:3529 | 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": 232371712,
"params": 2815050,
"val_accuracy": 92.42
} | {
"arch_str": "3529",
"identifier": "Hiaml_3529",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3529"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3529 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 232371712,
"val_accuracy": 92.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1889 | GraphArch:Hiaml:1889 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x1x1]
%onnx::Conv_375[FLOAT, 64x64x1x1]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 174110208,
"params": 1813578,
"val_accuracy": 92.88
} | {
"arch_str": "1889",
"identifier": "Hiaml_1889",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1889"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1889 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 174110208,
"val_accuracy": 92.88
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_357 | GraphArch:Hiaml:357 | 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": 231454208,
"params": 2586186,
"val_accuracy": 92.8
} | {
"arch_str": "357",
"identifier": "Hiaml_357",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "357"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 357 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231454208,
"val_accuracy": 92.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_895 | GraphArch:Hiaml:895 | 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, 64x64x1x1]
%onnx::Conv_402[FLOAT, 64x64x3x3]
%onnx::Conv_405[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209827328,
"params": 2571978,
"val_accuracy": 92.51
} | {
"arch_str": "895",
"identifier": "Hiaml_895",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "895"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 895 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209827328,
"val_accuracy": 92.51
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_374 | GraphArch:Hiaml:374 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[FLOAT, 64x64x3x1]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198489600,
"params": 2469962,
"val_accuracy": 92.67
} | {
"arch_str": "374",
"identifier": "Hiaml_374",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "374"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 374 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198489600,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_212 | GraphArch:Hiaml:212 | 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, 64x64x1x1]
%onnx::Conv_387[FLOAT, 64x64x1x3]
%onnx::Conv_390[FLOAT, 64x64x3x1]
%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": 185644544,
"params": 2101066,
"val_accuracy": 91.88
} | {
"arch_str": "212",
"identifier": "Hiaml_212",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "212"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 212 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185644544,
"val_accuracy": 91.88
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3960 | GraphArch:Hiaml:3960 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_315[FLOAT, 64x3x3x3]
%onnx::Conv_316[FLOAT, 64]
%onnx::Conv_318[FLOAT, 64x64x3x3]
%onnx::Conv_321[FLOAT, 64x64x1x1]
%onnx::Conv_324[FLOAT, 64x64x3x3]
%onnx::Conv_327[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 179713536,
"params": 2034762,
"val_accuracy": 92.14
} | {
"arch_str": "3960",
"identifier": "Hiaml_3960",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3960"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3960 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 179713536,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3826 | GraphArch:Hiaml:3826 | 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, 64x64x1x1]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194295296,
"params": 2438730,
"val_accuracy": 92.24
} | {
"arch_str": "3826",
"identifier": "Hiaml_3826",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3826"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3826 | Predict neural architecture validation accuracy and compute cost from 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_2613 | GraphArch:Hiaml:2613 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x1x3]
%onnx::Conv_418[FLOAT, 64x64x3x1]
%onnx::Conv_421[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239384064,
"params": 2187722,
"val_accuracy": 92.84
} | {
"arch_str": "2613",
"identifier": "Hiaml_2613",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2613"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2613 | Predict neural architecture validation accuracy and compute cost from 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": 92.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2137 | GraphArch:Hiaml:2137 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_385[FLOAT, 64x3x3x3]
%onnx::Conv_386[FLOAT, 64]
%onnx::Conv_388[FLOAT, 64x64x1x1]
%onnx::Conv_391[FLOAT, 64x64x1x3]
%onnx::Conv_394[FLOAT, 64x64x3x1]
%onnx::Conv_397[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 192951808,
"params": 2183242,
"val_accuracy": 92.64
} | {
"arch_str": "2137",
"identifier": "Hiaml_2137",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2137"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2137 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 192951808,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4156 | GraphArch:Hiaml:4156 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_385[FLOAT, 64x3x3x3]
%onnx::Conv_386[FLOAT, 64]
%onnx::Conv_388[FLOAT, 64x64x1x1]
%onnx::Conv_391[FLOAT, 64x64x1x1]
%onnx::Conv_394[FLOAT, 64x64x3x3]
%onnx::Conv_397[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 165787136,
"params": 1748298,
"val_accuracy": 92.41
} | {
"arch_str": "4156",
"identifier": "Hiaml_4156",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4156"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4156 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 165787136,
"val_accuracy": 92.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2261 | GraphArch:Hiaml:2261 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x3x3]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189838848,
"params": 1962570,
"val_accuracy": 92.36
} | {
"arch_str": "2261",
"identifier": "Hiaml_2261",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2261"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2261 | Predict neural architecture validation accuracy and compute cost from 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": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1139 | GraphArch:Hiaml:1139 | 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": 3607882,
"val_accuracy": 92.85
} | {
"arch_str": "1139",
"identifier": "Hiaml_1139",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1139"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1139 | Predict neural architecture validation accuracy and compute cost from 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": 92.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4169 | GraphArch:Hiaml:4169 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_441[FLOAT, 64x3x3x3]
%onnx::Conv_442[FLOAT, 64]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[FLOAT, 64x64x3x3]
%onnx::Conv_450[FLOAT, 64x64x3x3]
%onnx::Conv_453[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240268800,
"params": 2676042,
"val_accuracy": 92.77
} | {
"arch_str": "4169",
"identifier": "Hiaml_4169",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4169"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4169 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240268800,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_105 | GraphArch:Hiaml:105 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x3x3]
%onnx::Conv_395[FLOAT, 64x64x1x1]
%onnx::Conv_398[FLOAT, 64x64x1x1]
%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": 214939136,
"params": 2158154,
"val_accuracy": 91.92
} | {
"arch_str": "105",
"identifier": "Hiaml_105",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "105"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 105 | Predict neural architecture validation accuracy and compute cost from 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": 91.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3715 | GraphArch:Hiaml:3715 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_438[FLOAT, 64x3x3x3]
%onnx::Conv_439[FLOAT, 64]
%onnx::Conv_441[FLOAT, 64x64x3x3]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[FLOAT, 64x64x3x3]
%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": 231585280,
"params": 2808650,
"val_accuracy": 91.59
} | {
"arch_str": "3715",
"identifier": "Hiaml_3715",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3715"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3715 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231585280,
"val_accuracy": 91.59
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3166 | GraphArch:Hiaml:3166 | 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, 64x64x1x1]
%onnx::Conv_464[FLOAT, 64x64x3x3]
%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": 193213952,
"params": 2332490,
"val_accuracy": 92.33
} | {
"arch_str": "3166",
"identifier": "Hiaml_3166",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3166"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3166 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193213952,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3370 | GraphArch:Hiaml:3370 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_345[FLOAT, 64x3x3x3]
%onnx::Conv_346[FLOAT, 64]
%onnx::Conv_348[FLOAT, 64x64x3x3]
%onnx::Conv_351[FLOAT, 64x64x1x1]
%onnx::Conv_354[FLOAT, 64x64x1x1]
%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": 189511168,
"params": 1678282,
"val_accuracy": 92.83
} | {
"arch_str": "3370",
"identifier": "Hiaml_3370",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3370"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3370 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189511168,
"val_accuracy": 92.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2289 | GraphArch:Hiaml:2289 | 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, 64x64x1x1]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207042048,
"params": 2462026,
"val_accuracy": 92.4
} | {
"arch_str": "2289",
"identifier": "Hiaml_2289",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2289"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2289 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207042048,
"val_accuracy": 92.4
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3757 | GraphArch:Hiaml:3757 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x1x1]
%onnx::Conv_375[FLOAT, 64x64x3x3]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 280770048,
"params": 2600778,
"val_accuracy": 92.09
} | {
"arch_str": "3757",
"identifier": "Hiaml_3757",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3757"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3757 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 280770048,
"val_accuracy": 92.09
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1978 | GraphArch:Hiaml:1978 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x1x3]
%onnx::Conv_421[FLOAT, 64x64x3x1]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190100992,
"params": 2404426,
"val_accuracy": 91.77
} | {
"arch_str": "1978",
"identifier": "Hiaml_1978",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1978"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1978 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190100992,
"val_accuracy": 91.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4233 | GraphArch:Hiaml:4233 | 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, 64x64x3x3]
%onnx::Conv_403[FLOAT, 64x64x1x3]
%onnx::Conv_406[FLOAT, 64x64x3x1]
%onnx::Conv_409[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176076288,
"params": 1758026,
"val_accuracy": 91.92
} | {
"arch_str": "4233",
"identifier": "Hiaml_4233",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4233"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4233 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176076288,
"val_accuracy": 91.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2954 | GraphArch:Hiaml:2954 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_495[FLOAT, 64x3x3x3]
%onnx::Conv_496[FLOAT, 64]
%onnx::Conv_498[FLOAT, 64x64x1x3]
%onnx::Conv_501[FLOAT, 64x64x3x1]
%onnx::Conv_504[FLOAT, 64x64x1x3]
%onnx::Conv_507[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234632704,
"params": 2676298,
"val_accuracy": 92.6
} | {
"arch_str": "2954",
"identifier": "Hiaml_2954",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2954"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2954 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234632704,
"val_accuracy": 92.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_299 | GraphArch:Hiaml:299 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_353[FLOAT, 64x3x3x3]
%onnx::Conv_354[FLOAT, 64]
%onnx::Conv_356[FLOAT, 64x64x1x1]
%onnx::Conv_359[FLOAT, 64x64x1x1]
%onnx::Conv_362[FLOAT, 64x64x3x3]
%onnx::Conv_365[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 147699200,
"params": 1609546,
"val_accuracy": 92.35
} | {
"arch_str": "299",
"identifier": "Hiaml_299",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "299"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 299 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 147699200,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3550 | GraphArch:Hiaml:3550 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_351[FLOAT, 64x3x3x3]
%onnx::Conv_352[FLOAT, 64]
%onnx::Conv_354[FLOAT, 64x64x1x1]
%onnx::Conv_357[FLOAT, 64x64x3x3]
%onnx::Conv_360[FLOAT, 64x64x3x3]
%onnx::Conv_363[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 302691840,
"params": 3730506,
"val_accuracy": 92.74
} | {
"arch_str": "3550",
"identifier": "Hiaml_3550",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3550"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3550 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 302691840,
"val_accuracy": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3126 | GraphArch:Hiaml:3126 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%onnx::Conv_379[FLOAT, 64x64x1x3]
%onnx::Conv_382[FLOAT, 64x64x3x1]
%onnx::Conv_385[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206419456,
"params": 1993802,
"val_accuracy": 92.75
} | {
"arch_str": "3126",
"identifier": "Hiaml_3126",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3126"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3126 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206419456,
"val_accuracy": 92.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2986 | GraphArch:Hiaml:2986 | 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, 64x64x1x1]
%onnx::Conv_387[FLOAT, 64x64x3x3]
%onnx::Conv_390[FLOAT, 64x64x3x3]
%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": 202356224,
"params": 1863242,
"val_accuracy": 92.64
} | {
"arch_str": "2986",
"identifier": "Hiaml_2986",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2986"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2986 | Predict neural architecture validation accuracy and compute cost from 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": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_797 | GraphArch:Hiaml:797 | 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, 64x64x3x3]
%onnx::Conv_403[FLOAT, 64x64x1x1]
%onnx::Conv_406[FLOAT, 64x64x3x3]
%onnx::Conv_409[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 208418304,
"params": 2650698,
"val_accuracy": 92.24
} | {
"arch_str": "797",
"identifier": "Hiaml_797",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "797"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 797 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 208418304,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1474 | GraphArch:Hiaml:1474 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x1x1]
%onnx::Conv_443[FLOAT, 64x64x3x3]
%onnx::Conv_446[FLOAT, 64x64x3x3]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 256062976,
"params": 2700106,
"val_accuracy": 92.7
} | {
"arch_str": "1474",
"identifier": "Hiaml_1474",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1474"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1474 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 256062976,
"val_accuracy": 92.7
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2122 | GraphArch:Hiaml:2122 | 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": 225392128,
"params": 2154698,
"val_accuracy": 92.78
} | {
"arch_str": "2122",
"identifier": "Hiaml_2122",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2122"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2122 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225392128,
"val_accuracy": 92.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3009 | GraphArch:Hiaml:3009 | 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, 64x64x3x3]
%onnx::Conv_388[FLOAT, 64x64x3x3]
%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": 213661184,
"params": 2323018,
"val_accuracy": 92.65
} | {
"arch_str": "3009",
"identifier": "Hiaml_3009",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3009"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3009 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213661184,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_28 | GraphArch:Hiaml:28 | 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": 177288704,
"params": 2307914,
"val_accuracy": 92.33
} | {
"arch_str": "28",
"identifier": "Hiaml_28",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "28"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 28 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177288704,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2003 | GraphArch:Hiaml:2003 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_317[FLOAT, 64x3x3x3]
%onnx::Conv_318[FLOAT, 64]
%onnx::Conv_320[FLOAT, 64x64x1x1]
%onnx::Conv_323[FLOAT, 64x64x1x3]
%onnx::Conv_326[FLOAT, 64x64x3x1]
%onnx::Conv_329[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222934528,
"params": 2320458,
"val_accuracy": 91.71
} | {
"arch_str": "2003",
"identifier": "Hiaml_2003",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2003"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2003 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222934528,
"val_accuracy": 91.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1956 | GraphArch:Hiaml:1956 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_339[FLOAT, 64x3x3x3]
%onnx::Conv_340[FLOAT, 64]
%onnx::Conv_342[FLOAT, 64x64x3x3]
%onnx::Conv_345[FLOAT, 64x64x1x3]
%onnx::Conv_348[FLOAT, 64x64x3x1]
%onnx::Conv_351[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172635648,
"params": 1854794,
"val_accuracy": 92.66
} | {
"arch_str": "1956",
"identifier": "Hiaml_1956",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1956"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1956 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172635648,
"val_accuracy": 92.66
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2074 | GraphArch:Hiaml:2074 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_431[FLOAT, 64x3x3x3]
%onnx::Conv_432[FLOAT, 64]
%onnx::Conv_434[FLOAT, 64x64x1x3]
%onnx::Conv_437[FLOAT, 64x64x3x1]
%onnx::Conv_440[FLOAT, 64x64x1x3]
%onnx::Conv_443[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 241382912,
"params": 2855754,
"val_accuracy": 92.35
} | {
"arch_str": "2074",
"identifier": "Hiaml_2074",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2074"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2074 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 241382912,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1461 | GraphArch:Hiaml:1461 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x3x3]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222279168,
"params": 2500042,
"val_accuracy": 92.33
} | {
"arch_str": "1461",
"identifier": "Hiaml_1461",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1461"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1461 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222279168,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3076 | GraphArch:Hiaml:3076 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%onnx::Conv_379[FLOAT, 64x64x1x3]
%onnx::Conv_382[FLOAT, 64x64x3x1]
%onnx::Conv_385[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 247313920,
"params": 3382730,
"val_accuracy": 92.68
} | {
"arch_str": "3076",
"identifier": "Hiaml_3076",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3076"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3076 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 247313920,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_777 | GraphArch:Hiaml:777 | 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, 64x64x1x1]
%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": 223131136,
"params": 3305034,
"val_accuracy": 91.74
} | {
"arch_str": "777",
"identifier": "Hiaml_777",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "777"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 777 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223131136,
"val_accuracy": 91.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4597 | GraphArch:Hiaml:4597 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_491[FLOAT, 64x3x3x3]
%onnx::Conv_492[FLOAT, 64]
%onnx::Conv_494[FLOAT, 64x64x1x3]
%onnx::Conv_497[FLOAT, 64x64x3x1]
%onnx::Conv_500[FLOAT, 64x64x1x3]
%onnx::Conv_503[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190395904,
"params": 2382922,
"val_accuracy": 92.16
} | {
"arch_str": "4597",
"identifier": "Hiaml_4597",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4597"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4597 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190395904,
"val_accuracy": 92.16
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2700 | GraphArch:Hiaml:2700 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_479[FLOAT, 64x3x3x3]
%onnx::Conv_480[FLOAT, 64]
%onnx::Conv_482[FLOAT, 64x64x1x1]
%onnx::Conv_485[FLOAT, 64x64x1x3]
%onnx::Conv_488[FLOAT, 64x64x3x1]
%onnx::Conv_491[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188233216,
"params": 2366282,
"val_accuracy": 92.14
} | {
"arch_str": "2700",
"identifier": "Hiaml_2700",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2700"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2700 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188233216,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3902 | GraphArch:Hiaml:3902 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_418[FLOAT, 64x3x3x3]
%onnx::Conv_419[FLOAT, 64]
%onnx::Conv_421[FLOAT, 64x64x1x1]
%onnx::Conv_424[FLOAT, 64x64x3x3]
%onnx::Conv_427[FLOAT, 64x64x3x3]
%onnx::Conv_430[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244168192,
"params": 2685002,
"val_accuracy": 92.35
} | {
"arch_str": "3902",
"identifier": "Hiaml_3902",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3902"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3902 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244168192,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4504 | GraphArch:Hiaml:4504 | 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, 64x64x1x3]
%onnx::Conv_423[FLOAT, 64x64x3x1]
%onnx::Conv_426[FLOAT, 64x64x1x1]
%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": 248755712,
"params": 2372170,
"val_accuracy": 92.13
} | {
"arch_str": "4504",
"identifier": "Hiaml_4504",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4504"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4504 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 248755712,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3433 | GraphArch:Hiaml:3433 | 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, 64x64x3x3]
%onnx::Conv_422[FLOAT, 64x64x3x3]
%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": 255866368,
"params": 2405194,
"val_accuracy": 92.64
} | {
"arch_str": "3433",
"identifier": "Hiaml_3433",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3433"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3433 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 255866368,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1784 | GraphArch:Hiaml:1784 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_490[FLOAT, 64x3x3x3]
%onnx::Conv_491[FLOAT, 64]
%onnx::Conv_493[FLOAT, 64x64x1x3]
%onnx::Conv_496[FLOAT, 64x64x3x1]
%onnx::Conv_499[FLOAT, 64x64x1x3]
%onnx::Conv_502[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214578688,
"params": 2225994,
"val_accuracy": 92.62
} | {
"arch_str": "1784",
"identifier": "Hiaml_1784",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1784"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1784 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214578688,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2644 | GraphArch:Hiaml:2644 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227390976,
"params": 2652234,
"val_accuracy": 92.08
} | {
"arch_str": "2644",
"identifier": "Hiaml_2644",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2644"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2644 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227390976,
"val_accuracy": 92.08
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2612 | GraphArch:Hiaml:2612 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x3x3]
%onnx::Conv_395[FLOAT, 64x64x1x1]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231486976,
"params": 2830922,
"val_accuracy": 91.84
} | {
"arch_str": "2612",
"identifier": "Hiaml_2612",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2612"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2612 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231486976,
"val_accuracy": 91.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2252 | GraphArch:Hiaml:2252 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_423[FLOAT, 64x3x3x3]
%onnx::Conv_424[FLOAT, 64]
%onnx::Conv_426[FLOAT, 64x64x1x1]
%onnx::Conv_429[FLOAT, 64x64x3x3]
%onnx::Conv_432[FLOAT, 64x64x3x3]
%onnx::Conv_435[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230733312,
"params": 2086218,
"val_accuracy": 92.71
} | {
"arch_str": "2252",
"identifier": "Hiaml_2252",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2252"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2252 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230733312,
"val_accuracy": 92.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3965 | GraphArch:Hiaml:3965 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x3]
%onnx::Conv_459[FLOAT, 64x64x3x1]
%onnx::Conv_462[FLOAT, 64x64x1x1]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170440192,
"params": 1586250,
"val_accuracy": 92.05
} | {
"arch_str": "3965",
"identifier": "Hiaml_3965",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3965"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3965 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170440192,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2120 | GraphArch:Hiaml:2120 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_480[FLOAT, 64x3x3x3]
%onnx::Conv_481[FLOAT, 64]
%onnx::Conv_483[FLOAT, 64x64x1x3]
%onnx::Conv_486[FLOAT, 64x64x3x1]
%onnx::Conv_489[FLOAT, 64x64x1x3]
%onnx::Conv_492[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245970432,
"params": 2864714,
"val_accuracy": 92.69
} | {
"arch_str": "2120",
"identifier": "Hiaml_2120",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2120"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2120 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245970432,
"val_accuracy": 92.69
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_277 | GraphArch:Hiaml:277 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_447[FLOAT, 64x3x3x3]
%onnx::Conv_448[FLOAT, 64]
%onnx::Conv_450[FLOAT, 64x64x1x3]
%onnx::Conv_453[FLOAT, 64x64x3x1]
%onnx::Conv_456[FLOAT, 64x64x1x3]
%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": 164148736,
"params": 1697226,
"val_accuracy": 92.59
} | {
"arch_str": "277",
"identifier": "Hiaml_277",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "277"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 277 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 164148736,
"val_accuracy": 92.59
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1618 | GraphArch:Hiaml:1618 | 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": 228570624,
"params": 2658634,
"val_accuracy": 92.62
} | {
"arch_str": "1618",
"identifier": "Hiaml_1618",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1618"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1618 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 228570624,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_858 | GraphArch:Hiaml:858 | 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": 195343872,
"params": 1781834,
"val_accuracy": 92.02
} | {
"arch_str": "858",
"identifier": "Hiaml_858",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "858"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 858 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 195343872,
"val_accuracy": 92.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2342 | GraphArch:Hiaml:2342 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_462[FLOAT, 64x3x3x3]
%onnx::Conv_463[FLOAT, 64]
%onnx::Conv_465[FLOAT, 64x64x1x1]
%onnx::Conv_468[FLOAT, 64x64x1x1]
%onnx::Conv_471[FLOAT, 64x64x3x3]
%onnx::Conv_474[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236172800,
"params": 2792266,
"val_accuracy": 91.83
} | {
"arch_str": "2342",
"identifier": "Hiaml_2342",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2342"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2342 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236172800,
"val_accuracy": 91.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3045 | GraphArch:Hiaml:3045 | 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, 64x64x1x3]
%onnx::Conv_474[FLOAT, 64x64x3x1]
%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": 231323136,
"params": 3302346,
"val_accuracy": 92.23
} | {
"arch_str": "3045",
"identifier": "Hiaml_3045",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3045"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3045 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231323136,
"val_accuracy": 92.23
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3682 | GraphArch:Hiaml:3682 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_490[FLOAT, 64x3x3x3]
%onnx::Conv_491[FLOAT, 64]
%onnx::Conv_493[FLOAT, 64x64x3x3]
%onnx::Conv_496[FLOAT, 64x64x1x1]
%onnx::Conv_499[FLOAT, 64x64x1x1]
%onnx::Conv_502[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206878208,
"params": 2587722,
"val_accuracy": 91.92
} | {
"arch_str": "3682",
"identifier": "Hiaml_3682",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3682"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3682 | Predict neural architecture validation accuracy and compute cost from 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": 91.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3169 | GraphArch:Hiaml:3169 | 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": 236107264,
"params": 3354186,
"val_accuracy": 92.48
} | {
"arch_str": "3169",
"identifier": "Hiaml_3169",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3169"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3169 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236107264,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_840 | GraphArch:Hiaml:840 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_333[FLOAT, 64x3x3x3]
%onnx::Conv_334[FLOAT, 64]
%onnx::Conv_336[FLOAT, 64x64x1x1]
%onnx::Conv_339[FLOAT, 64x64x1x3]
%onnx::Conv_342[FLOAT, 64x64x3x1]
%onnx::Conv_345[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 140293632,
"params": 1747786,
"val_accuracy": 92.1
} | {
"arch_str": "840",
"identifier": "Hiaml_840",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "840"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 840 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 140293632,
"val_accuracy": 92.1
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1440 | GraphArch:Hiaml:1440 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_345[FLOAT, 64x3x3x3]
%onnx::Conv_346[FLOAT, 64]
%onnx::Conv_348[FLOAT, 64x64x3x3]
%onnx::Conv_351[FLOAT, 64x64x1x1]
%onnx::Conv_354[FLOAT, 64x64x1x1]
%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": 223065600,
"params": 1940426,
"val_accuracy": 92.81
} | {
"arch_str": "1440",
"identifier": "Hiaml_1440",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1440"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1440 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223065600,
"val_accuracy": 92.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2634 | GraphArch:Hiaml:2634 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_454[FLOAT, 64x3x3x3]
%onnx::Conv_455[FLOAT, 64]
%onnx::Conv_457[FLOAT, 64x64x3x3]
%onnx::Conv_460[FLOAT, 64x64x1x3]
%onnx::Conv_463[FLOAT, 64x64x3x1]
%onnx::Conv_466[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197178880,
"params": 2415434,
"val_accuracy": 92.19
} | {
"arch_str": "2634",
"identifier": "Hiaml_2634",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2634"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2634 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197178880,
"val_accuracy": 92.19
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_34 | GraphArch:Hiaml:34 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x3x3]
%onnx::Conv_395[FLOAT, 64x64x1x1]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197801472,
"params": 2520650,
"val_accuracy": 92.2
} | {
"arch_str": "34",
"identifier": "Hiaml_34",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "34"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 34 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197801472,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3112 | GraphArch:Hiaml:3112 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_465[FLOAT, 64x3x3x3]
%onnx::Conv_466[FLOAT, 64]
%onnx::Conv_468[FLOAT, 64x64x3x3]
%onnx::Conv_471[FLOAT, 64x64x1x1]
%onnx::Conv_474[FLOAT, 64x64x1x1]
%onnx::Conv_477[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211072512,
"params": 2618954,
"val_accuracy": 92
} | {
"arch_str": "3112",
"identifier": "Hiaml_3112",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3112"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3112 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211072512,
"val_accuracy": 92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1296 | GraphArch:Hiaml:1296 | 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, 64x64x3x3]
%onnx::Conv_403[FLOAT, 64x64x1x3]
%onnx::Conv_406[FLOAT, 64x64x3x1]
%onnx::Conv_409[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218936832,
"params": 2585674,
"val_accuracy": 92.65
} | {
"arch_str": "1296",
"identifier": "Hiaml_1296",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1296"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1296 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218936832,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1240 | GraphArch:Hiaml:1240 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_433[FLOAT, 64x3x3x3]
%onnx::Conv_434[FLOAT, 64]
%onnx::Conv_436[FLOAT, 64x64x1x3]
%onnx::Conv_439[FLOAT, 64x64x3x1]
%onnx::Conv_442[FLOAT, 64x64x1x3]
%onnx::Conv_445[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 241415680,
"params": 3420746,
"val_accuracy": 92.34
} | {
"arch_str": "1240",
"identifier": "Hiaml_1240",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1240"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1240 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 241415680,
"val_accuracy": 92.34
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2365 | GraphArch:Hiaml:2365 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x1x1]
%onnx::Conv_421[FLOAT, 64x64x1x1]
%onnx::Conv_424[FLOAT, 64x64x3x3]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236008960,
"params": 2667594,
"val_accuracy": 92.4
} | {
"arch_str": "2365",
"identifier": "Hiaml_2365",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2365"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2365 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236008960,
"val_accuracy": 92.4
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4530 | GraphArch:Hiaml:4530 | 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": 177419776,
"params": 2283082,
"val_accuracy": 92.18
} | {
"arch_str": "4530",
"identifier": "Hiaml_4530",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4530"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4530 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177419776,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2131 | GraphArch:Hiaml:2131 | 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": 208942592,
"params": 2443466,
"val_accuracy": 92.7
} | {
"arch_str": "2131",
"identifier": "Hiaml_2131",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2131"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2131 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 208942592,
"val_accuracy": 92.7
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3788 | GraphArch:Hiaml:3788 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[FLOAT, 64x64x1x3]
%onnx::Conv_414[FLOAT, 64x64x3x1]
%onnx::Conv_417[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185841152,
"params": 1720522,
"val_accuracy": 93.04
} | {
"arch_str": "3788",
"identifier": "Hiaml_3788",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3788"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3788 | Predict neural architecture validation accuracy and compute cost from 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": 93.04
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3783 | GraphArch:Hiaml:3783 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x1x3]
%onnx::Conv_443[FLOAT, 64x64x3x1]
%onnx::Conv_446[FLOAT, 64x64x1x1]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177649152,
"params": 1913418,
"val_accuracy": 92.07
} | {
"arch_str": "3783",
"identifier": "Hiaml_3783",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3783"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3783 | Predict neural architecture validation accuracy and compute cost from 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": 92.07
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3869 | GraphArch:Hiaml:3869 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_337[FLOAT, 64x3x3x3]
%onnx::Conv_338[FLOAT, 64]
%onnx::Conv_340[FLOAT, 64x64x1x1]
%onnx::Conv_343[FLOAT, 64x64x3x3]
%onnx::Conv_346[FLOAT, 64x64x3x3]
%onnx::Conv_349[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227259904,
"params": 2058826,
"val_accuracy": 92.77
} | {
"arch_str": "3869",
"identifier": "Hiaml_3869",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3869"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3869 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227259904,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2481 | GraphArch:Hiaml:2481 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[FLOAT, 64x64x1x3]
%onnx::Conv_454[FLOAT, 64x64x3x1]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218609152,
"params": 2122186,
"val_accuracy": 93.06
} | {
"arch_str": "2481",
"identifier": "Hiaml_2481",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2481"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2481 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218609152,
"val_accuracy": 93.06
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3238 | GraphArch:Hiaml:3238 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x3x3]
%onnx::Conv_395[FLOAT, 64x64x1x1]
%onnx::Conv_398[FLOAT, 64x64x3x3]
%onnx::Conv_401[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201143808,
"params": 2494538,
"val_accuracy": 91.89
} | {
"arch_str": "3238",
"identifier": "Hiaml_3238",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3238"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3238 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201143808,
"val_accuracy": 91.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1503 | GraphArch:Hiaml:1503 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_471[FLOAT, 64x3x3x3]
%onnx::Conv_472[FLOAT, 64]
%onnx::Conv_474[FLOAT, 64x64x1x1]
%onnx::Conv_477[FLOAT, 64x64x1x1]
%onnx::Conv_480[FLOAT, 64x64x3x3]
%onnx::Conv_483[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197473792,
"params": 2464586,
"val_accuracy": 92.3
} | {
"arch_str": "1503",
"identifier": "Hiaml_1503",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1503"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1503 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197473792,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2621 | GraphArch:Hiaml:2621 | 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, 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": 205895168,
"params": 2554442,
"val_accuracy": 91.6
} | {
"arch_str": "2621",
"identifier": "Hiaml_2621",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2621"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2621 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205895168,
"val_accuracy": 91.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4413 | GraphArch:Hiaml:4413 | 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, 64x64x1x3]
%onnx::Conv_503[FLOAT, 64x64x3x1]
%onnx::Conv_506[FLOAT, 64x64x1x3]
%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": 239547904,
"params": 2792778,
"val_accuracy": 92.53
} | {
"arch_str": "4413",
"identifier": "Hiaml_4413",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4413"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4413 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239547904,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1261 | GraphArch:Hiaml:1261 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_494[FLOAT, 64x3x3x3]
%onnx::Conv_495[FLOAT, 64]
%onnx::Conv_497[FLOAT, 64x64x1x3]
%onnx::Conv_500[FLOAT, 64x64x3x1]
%onnx::Conv_503[FLOAT, 64x64x1x3]
%onnx::Conv_506[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215332352,
"params": 2628938,
"val_accuracy": 92.57
} | {
"arch_str": "1261",
"identifier": "Hiaml_1261",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1261"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1261 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215332352,
"val_accuracy": 92.57
} | full |
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