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_3486 | GraphArch:Hiaml:3486 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_439[FLOAT, 64x3x3x3]
%onnx::Conv_440[FLOAT, 64]
%onnx::Conv_442[FLOAT, 64x64x1x1]
%onnx::Conv_445[FLOAT, 64x64x1x3]
%onnx::Conv_448[FLOAT, 64x64x3x1]
%onnx::Conv_451[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180532736,
"params": 2456138,
"val_accuracy": 91.95
} | {
"arch_str": "3486",
"identifier": "Hiaml_3486",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3486"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3486 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180532736,
"val_accuracy": 91.95
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_448 | GraphArch:Hiaml:448 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_423[FLOAT, 64x3x3x3]
%onnx::Conv_424[FLOAT, 64]
%onnx::Conv_426[FLOAT, 64x64x1x3]
%onnx::Conv_429[FLOAT, 64x64x3x1]
%onnx::Conv_432[FLOAT, 64x64x1x3]
%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": 199570944,
"params": 2503242,
"val_accuracy": 92.79
} | {
"arch_str": "448",
"identifier": "Hiaml_448",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "448"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 448 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199570944,
"val_accuracy": 92.79
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3631 | GraphArch:Hiaml:3631 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_329[FLOAT, 64x3x3x3]
%onnx::Conv_330[FLOAT, 64]
%onnx::Conv_332[FLOAT, 64x64x1x1]
%onnx::Conv_335[FLOAT, 64x64x1x3]
%onnx::Conv_338[FLOAT, 64x64x3x1]
%onnx::Conv_341[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 123516416,
"params": 1346378,
"val_accuracy": 92.06
} | {
"arch_str": "3631",
"identifier": "Hiaml_3631",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3631"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3631 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 123516416,
"val_accuracy": 92.06
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1592 | GraphArch:Hiaml:1592 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_526[FLOAT, 64x3x3x3]
%onnx::Conv_527[FLOAT, 64]
%onnx::Conv_529[FLOAT, 64x64x1x3]
%onnx::Conv_532[FLOAT, 64x64x3x1]
%onnx::Conv_535[FLOAT, 64x64x1x3]
%onnx::Conv_538[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227194368,
"params": 2694986,
"val_accuracy": 92.48
} | {
"arch_str": "1592",
"identifier": "Hiaml_1592",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1592"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1592 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227194368,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1621 | GraphArch:Hiaml:1621 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_401[FLOAT, 64x3x3x3]
%onnx::Conv_402[FLOAT, 64]
%onnx::Conv_404[FLOAT, 64x64x1x1]
%onnx::Conv_407[FLOAT, 64x64x1x3]
%onnx::Conv_410[FLOAT, 64x64x3x1]
%onnx::Conv_413[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 179582464,
"params": 2359242,
"val_accuracy": 92.67
} | {
"arch_str": "1621",
"identifier": "Hiaml_1621",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1621"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1621 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 179582464,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1847 | GraphArch:Hiaml:1847 | 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": 175846912,
"params": 2370378,
"val_accuracy": 91.45
} | {
"arch_str": "1847",
"identifier": "Hiaml_1847",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1847"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1847 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 175846912,
"val_accuracy": 91.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1438 | GraphArch:Hiaml:1438 | 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, 64x64x1x3]
%onnx::Conv_448[FLOAT, 64x64x3x1]
%onnx::Conv_451[FLOAT, 64x64x1x3]
%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": 190166528,
"params": 1937738,
"val_accuracy": 92.68
} | {
"arch_str": "1438",
"identifier": "Hiaml_1438",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1438"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1438 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190166528,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2630 | GraphArch:Hiaml:2630 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_457[FLOAT, 64x3x3x3]
%onnx::Conv_458[FLOAT, 64]
%onnx::Conv_460[FLOAT, 64x64x1x1]
%onnx::Conv_463[FLOAT, 64x64x1x3]
%onnx::Conv_466[FLOAT, 64x64x3x1]
%onnx::Conv_469[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245839360,
"params": 2864202,
"val_accuracy": 92.35
} | {
"arch_str": "2630",
"identifier": "Hiaml_2630",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2630"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2630 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245839360,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2097 | GraphArch:Hiaml:2097 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_427[FLOAT, 64x3x3x3]
%onnx::Conv_428[FLOAT, 64]
%onnx::Conv_430[FLOAT, 64x64x1x3]
%onnx::Conv_433[FLOAT, 64x64x3x1]
%onnx::Conv_436[FLOAT, 64x64x1x3]
%onnx::Conv_439[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159823360,
"params": 1663946,
"val_accuracy": 91.95
} | {
"arch_str": "2097",
"identifier": "Hiaml_2097",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2097"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2097 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159823360,
"val_accuracy": 91.95
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2043 | GraphArch:Hiaml:2043 | 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": 214021632,
"params": 2040266,
"val_accuracy": 92.55
} | {
"arch_str": "2043",
"identifier": "Hiaml_2043",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2043"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2043 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214021632,
"val_accuracy": 92.55
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1664 | GraphArch:Hiaml:1664 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_353[FLOAT, 64x3x3x3]
%onnx::Conv_354[FLOAT, 64]
%onnx::Conv_356[FLOAT, 64x64x3x3]
%onnx::Conv_359[FLOAT, 64x64x1x1]
%onnx::Conv_362[FLOAT, 64x64x1x1]
%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": 202028544,
"params": 2157642,
"val_accuracy": 92.38
} | {
"arch_str": "1664",
"identifier": "Hiaml_1664",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1664"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1664 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202028544,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2373 | GraphArch:Hiaml:2373 | 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, 64x64x1x1]
%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": 222410240,
"params": 3285450,
"val_accuracy": 92.29
} | {
"arch_str": "2373",
"identifier": "Hiaml_2373",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2373"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2373 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222410240,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1043 | GraphArch:Hiaml:1043 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_521[FLOAT, 64x3x3x3]
%onnx::Conv_522[FLOAT, 64]
%onnx::Conv_524[FLOAT, 64x64x1x3]
%onnx::Conv_527[FLOAT, 64x64x3x1]
%onnx::Conv_530[FLOAT, 64x64x1x1]
%onnx::Conv_533[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205338112,
"params": 2153034,
"val_accuracy": 92.14
} | {
"arch_str": "1043",
"identifier": "Hiaml_1043",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1043"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1043 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205338112,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4322 | GraphArch:Hiaml:4322 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x1x1]
%onnx::Conv_467[FLOAT, 64x64x1x3]
%onnx::Conv_470[FLOAT, 64x64x3x1]
%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": 234305024,
"params": 2627402,
"val_accuracy": 91.98
} | {
"arch_str": "4322",
"identifier": "Hiaml_4322",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4322"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4322 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234305024,
"val_accuracy": 91.98
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_323 | GraphArch:Hiaml:323 | 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": 222770688,
"params": 2636362,
"val_accuracy": 92.14
} | {
"arch_str": "323",
"identifier": "Hiaml_323",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "323"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 323 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222770688,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1622 | GraphArch:Hiaml:1622 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_438[FLOAT, 64x3x3x3]
%onnx::Conv_439[FLOAT, 64]
%onnx::Conv_441[FLOAT, 64x64x1x1]
%onnx::Conv_444[FLOAT, 64x64x1x3]
%onnx::Conv_447[FLOAT, 64x64x3x1]
%onnx::Conv_450[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172996096,
"params": 2423370,
"val_accuracy": 92.2
} | {
"arch_str": "1622",
"identifier": "Hiaml_1622",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1622"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1622 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172996096,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_755 | GraphArch:Hiaml:755 | 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, 64x64x1x3]
%onnx::Conv_417[FLOAT, 64x64x3x1]
%onnx::Conv_420[FLOAT, 64x64x1x3]
%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": 218215936,
"params": 2650186,
"val_accuracy": 92.62
} | {
"arch_str": "755",
"identifier": "Hiaml_755",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "755"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 755 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218215936,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_211 | GraphArch:Hiaml:211 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_481[FLOAT, 64x3x3x3]
%onnx::Conv_482[FLOAT, 64]
%onnx::Conv_484[FLOAT, 64x64x1x1]
%onnx::Conv_487[FLOAT, 64x64x1x3]
%onnx::Conv_490[FLOAT, 64x64x3x1]
%onnx::Conv_493[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240825856,
"params": 3389002,
"val_accuracy": 91.98
} | {
"arch_str": "211",
"identifier": "Hiaml_211",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "211"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 211 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240825856,
"val_accuracy": 91.98
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2348 | GraphArch:Hiaml:2348 | 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": 240498176,
"params": 2825546,
"val_accuracy": 91.87
} | {
"arch_str": "2348",
"identifier": "Hiaml_2348",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2348"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2348 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240498176,
"val_accuracy": 91.87
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2574 | GraphArch:Hiaml:2574 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_529[FLOAT, 64x3x3x3]
%onnx::Conv_530[FLOAT, 64]
%onnx::Conv_532[FLOAT, 64x64x1x3]
%onnx::Conv_535[FLOAT, 64x64x3x1]
%onnx::Conv_538[FLOAT, 64x64x1x1]
%onnx::Conv_541[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206353920,
"params": 2518986,
"val_accuracy": 92.63
} | {
"arch_str": "2574",
"identifier": "Hiaml_2574",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2574"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2574 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206353920,
"val_accuracy": 92.63
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4142 | GraphArch:Hiaml:4142 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_505[FLOAT, 64x3x3x3]
%onnx::Conv_506[FLOAT, 64]
%onnx::Conv_508[FLOAT, 64x64x3x3]
%onnx::Conv_511[FLOAT, 64x64x1x3]
%onnx::Conv_514[FLOAT, 64x64x3x1]
%onnx::Conv_517[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222606848,
"params": 2613066,
"val_accuracy": 92.21
} | {
"arch_str": "4142",
"identifier": "Hiaml_4142",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4142"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4142 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222606848,
"val_accuracy": 92.21
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2175 | GraphArch:Hiaml:2175 | 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, 64x64x1x1]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176043520,
"params": 2225226,
"val_accuracy": 91.68
} | {
"arch_str": "2175",
"identifier": "Hiaml_2175",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2175"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2175 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176043520,
"val_accuracy": 91.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_222 | GraphArch:Hiaml:222 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_387[FLOAT, 64x3x3x3]
%onnx::Conv_388[FLOAT, 64]
%onnx::Conv_390[FLOAT, 64x64x3x3]
%onnx::Conv_393[FLOAT, 64x64x1x3]
%onnx::Conv_396[FLOAT, 64x64x3x1]
%onnx::Conv_399[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176043520,
"params": 1880394,
"val_accuracy": 92.48
} | {
"arch_str": "222",
"identifier": "Hiaml_222",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "222"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 222 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176043520,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1013 | GraphArch:Hiaml:1013 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_387[FLOAT, 64x3x3x3]
%onnx::Conv_388[FLOAT, 64]
%onnx::Conv_390[FLOAT, 64x64x3x3]
%onnx::Conv_393[FLOAT, 64x64x1x3]
%onnx::Conv_396[FLOAT, 64x64x3x1]
%onnx::Conv_399[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 249411072,
"params": 3411786,
"val_accuracy": 92.53
} | {
"arch_str": "1013",
"identifier": "Hiaml_1013",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1013"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1013 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 249411072,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_170 | GraphArch:Hiaml:170 | 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": 218117632,
"params": 2110282,
"val_accuracy": 92.52
} | {
"arch_str": "170",
"identifier": "Hiaml_170",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "170"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 170 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 218117632,
"val_accuracy": 92.52
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3407 | GraphArch:Hiaml:3407 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_365[FLOAT, 64x3x3x3]
%onnx::Conv_366[FLOAT, 64]
%onnx::Conv_368[FLOAT, 64x64x3x3]
%onnx::Conv_371[FLOAT, 64x64x1x1]
%onnx::Conv_374[FLOAT, 64x64x1x1]
%onnx::Conv_377[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 155039232,
"params": 1777098,
"val_accuracy": 92.65
} | {
"arch_str": "3407",
"identifier": "Hiaml_3407",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3407"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3407 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 155039232,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4456 | GraphArch:Hiaml:4456 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_407[FLOAT, 64x3x3x3]
%onnx::Conv_408[FLOAT, 64]
%onnx::Conv_410[FLOAT, 64x64x3x3]
%onnx::Conv_413[FLOAT, 64x64x1x3]
%onnx::Conv_416[FLOAT, 64x64x3x1]
%onnx::Conv_419[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 267400704,
"params": 2887754,
"val_accuracy": 92.01
} | {
"arch_str": "4456",
"identifier": "Hiaml_4456",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4456"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4456 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 267400704,
"val_accuracy": 92.01
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_466 | GraphArch:Hiaml:466 | 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": 234501632,
"params": 2616266,
"val_accuracy": 92.77
} | {
"arch_str": "466",
"identifier": "Hiaml_466",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "466"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 466 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234501632,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1678 | GraphArch:Hiaml:1678 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_347[FLOAT, 64x3x3x3]
%onnx::Conv_348[FLOAT, 64]
%onnx::Conv_350[FLOAT, 64x64x1x1]
%onnx::Conv_353[FLOAT, 64x64x1x3]
%onnx::Conv_356[FLOAT, 64x64x3x1]
%onnx::Conv_359[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 147568128,
"params": 1559882,
"val_accuracy": 91.82
} | {
"arch_str": "1678",
"identifier": "Hiaml_1678",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1678"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1678 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 147568128,
"val_accuracy": 91.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4373 | GraphArch:Hiaml:4373 | 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": 239121920,
"params": 2040778,
"val_accuracy": 93.26
} | {
"arch_str": "4373",
"identifier": "Hiaml_4373",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4373"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4373 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239121920,
"val_accuracy": 93.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_104 | GraphArch:Hiaml:104 | 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": 230700544,
"params": 2208586,
"val_accuracy": 92.76
} | {
"arch_str": "104",
"identifier": "Hiaml_104",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "104"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 104 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230700544,
"val_accuracy": 92.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3144 | GraphArch:Hiaml:3144 | 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, 64x64x1x3]
%onnx::Conv_477[FLOAT, 64x64x3x1]
%onnx::Conv_480[FLOAT, 64x64x1x3]
%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": 186136064,
"params": 2250826,
"val_accuracy": 92.09
} | {
"arch_str": "3144",
"identifier": "Hiaml_3144",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3144"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3144 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 186136064,
"val_accuracy": 92.09
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1278 | GraphArch:Hiaml:1278 | 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, 64x64x1x1]
%onnx::Conv_486[FLOAT, 64x64x3x3]
%onnx::Conv_489[FLOAT, 64x64x3x3]
%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": 251868672,
"params": 2718794,
"val_accuracy": 92.43
} | {
"arch_str": "1278",
"identifier": "Hiaml_1278",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1278"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1278 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 251868672,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1991 | GraphArch:Hiaml:1991 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_473[FLOAT, 64x3x3x3]
%onnx::Conv_474[FLOAT, 64]
%onnx::Conv_476[FLOAT, 64x64x3x3]
%onnx::Conv_479[FLOAT, 64x64x1x3]
%onnx::Conv_482[FLOAT, 64x64x3x1]
%onnx::Conv_485[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222541312,
"params": 2612042,
"val_accuracy": 92.3
} | {
"arch_str": "1991",
"identifier": "Hiaml_1991",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1991"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1991 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222541312,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_957 | GraphArch:Hiaml:957 | 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": 213759488,
"params": 2445130,
"val_accuracy": 92.67
} | {
"arch_str": "957",
"identifier": "Hiaml_957",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "957"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 957 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213759488,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1435 | GraphArch:Hiaml:1435 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_529[FLOAT, 64x3x3x3]
%onnx::Conv_530[FLOAT, 64]
%onnx::Conv_532[FLOAT, 64x64x1x1]
%onnx::Conv_535[FLOAT, 64x64x1x3]
%onnx::Conv_538[FLOAT, 64x64x3x1]
%onnx::Conv_541[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245085696,
"params": 2834506,
"val_accuracy": 92.33
} | {
"arch_str": "1435",
"identifier": "Hiaml_1435",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1435"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1435 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245085696,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4172 | GraphArch:Hiaml:4172 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_383[FLOAT, 64x3x3x3]
%onnx::Conv_384[FLOAT, 64]
%onnx::Conv_386[FLOAT, 64x64x3x3]
%onnx::Conv_389[FLOAT, 64x64x1x3]
%onnx::Conv_392[FLOAT, 64x64x3x1]
%onnx::Conv_395[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239941120,
"params": 2379850,
"val_accuracy": 92.56
} | {
"arch_str": "4172",
"identifier": "Hiaml_4172",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4172"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4172 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239941120,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4557 | GraphArch:Hiaml:4557 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_469[FLOAT, 64x3x3x3]
%onnx::Conv_470[FLOAT, 64]
%onnx::Conv_472[FLOAT, 64x64x1x3]
%onnx::Conv_475[FLOAT, 64x64x3x1]
%onnx::Conv_478[FLOAT, 64x64x1x1]
%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": 186070528,
"params": 2349642,
"val_accuracy": 92.11
} | {
"arch_str": "4557",
"identifier": "Hiaml_4557",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4557"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4557 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 186070528,
"val_accuracy": 92.11
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1920 | GraphArch:Hiaml:1920 | 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, 64x64x1x3]
%onnx::Conv_447[FLOAT, 64x64x3x1]
%onnx::Conv_450[FLOAT, 64x64x1x3]
%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": 202814976,
"params": 2110026,
"val_accuracy": 92.93
} | {
"arch_str": "1920",
"identifier": "Hiaml_1920",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1920"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1920 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202814976,
"val_accuracy": 92.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1496 | GraphArch:Hiaml:1496 | 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, 64x64x1x3]
%onnx::Conv_373[FLOAT, 64x64x3x1]
%onnx::Conv_376[FLOAT, 64x64x1x3]
%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": 205698560,
"params": 1923530,
"val_accuracy": 93.21
} | {
"arch_str": "1496",
"identifier": "Hiaml_1496",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1496"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1496 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205698560,
"val_accuracy": 93.21
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3322 | GraphArch:Hiaml:3322 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_574[FLOAT, 64x3x3x3]
%onnx::Conv_575[FLOAT, 64]
%onnx::Conv_577[FLOAT, 64x64x1x1]
%onnx::Conv_580[FLOAT, 64x64x1x3]
%onnx::Conv_583[FLOAT, 64x64x3x1]
%onnx::Conv_586[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219067904,
"params": 2630474,
"val_accuracy": 92.05
} | {
"arch_str": "3322",
"identifier": "Hiaml_3322",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3322"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3322 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219067904,
"val_accuracy": 92.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_676 | GraphArch:Hiaml:676 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_505[FLOAT, 64x3x3x3]
%onnx::Conv_506[FLOAT, 64]
%onnx::Conv_508[FLOAT, 64x64x1x3]
%onnx::Conv_511[FLOAT, 64x64x3x1]
%onnx::Conv_514[FLOAT, 64x64x1x1]
%onnx::Conv_517[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 216741376,
"params": 2635850,
"val_accuracy": 92.46
} | {
"arch_str": "676",
"identifier": "Hiaml_676",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "676"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 676 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 216741376,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_143 | GraphArch:Hiaml:143 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x3x3]
%onnx::Conv_451[FLOAT, 64x64x1x1]
%onnx::Conv_454[FLOAT, 64x64x1x1]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194164224,
"params": 2094154,
"val_accuracy": 91.75
} | {
"arch_str": "143",
"identifier": "Hiaml_143",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "143"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 143 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194164224,
"val_accuracy": 91.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2532 | GraphArch:Hiaml:2532 | 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": 152319488,
"params": 1741130,
"val_accuracy": 92.52
} | {
"arch_str": "2532",
"identifier": "Hiaml_2532",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2532"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2532 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 152319488,
"val_accuracy": 92.52
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_691 | GraphArch:Hiaml:691 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x1x1]
%onnx::Conv_369[FLOAT, 64x64x3x3]
%onnx::Conv_372[FLOAT, 64x64x3x3]
%onnx::Conv_375[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194819584,
"params": 2297418,
"val_accuracy": 92.64
} | {
"arch_str": "691",
"identifier": "Hiaml_691",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "691"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 691 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194819584,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3738 | GraphArch:Hiaml:3738 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_510[FLOAT, 64x3x3x3]
%onnx::Conv_511[FLOAT, 64]
%onnx::Conv_513[FLOAT, 64x64x1x1]
%onnx::Conv_516[FLOAT, 64x64x3x3]
%onnx::Conv_519[FLOAT, 64x64x3x3]
%onnx::Conv_522[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240498176,
"params": 2654282,
"val_accuracy": 92.5
} | {
"arch_str": "3738",
"identifier": "Hiaml_3738",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3738"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3738 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240498176,
"val_accuracy": 92.5
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2868 | GraphArch:Hiaml:2868 | 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, 64x64x1x3]
%onnx::Conv_478[FLOAT, 64x64x3x1]
%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": 230176256,
"params": 2681290,
"val_accuracy": 92.78
} | {
"arch_str": "2868",
"identifier": "Hiaml_2868",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2868"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2868 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230176256,
"val_accuracy": 92.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2225 | GraphArch:Hiaml:2225 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_425[FLOAT, 64x3x3x3]
%onnx::Conv_426[FLOAT, 64]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[FLOAT, 64x64x1x3]
%onnx::Conv_434[FLOAT, 64x64x3x1]
%onnx::Conv_437[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 171128320,
"params": 2405194,
"val_accuracy": 92.69
} | {
"arch_str": "2225",
"identifier": "Hiaml_2225",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2225"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2225 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 171128320,
"val_accuracy": 92.69
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_223 | GraphArch:Hiaml:223 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_395[FLOAT, 64x3x3x3]
%onnx::Conv_396[FLOAT, 64]
%onnx::Conv_398[FLOAT, 64x64x1x3]
%onnx::Conv_401[FLOAT, 64x64x3x1]
%onnx::Conv_404[FLOAT, 64x64x1x3]
%onnx::Conv_407[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 186856960,
"params": 2404426,
"val_accuracy": 92.03
} | {
"arch_str": "223",
"identifier": "Hiaml_223",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "223"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 223 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 186856960,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_751 | GraphArch:Hiaml:751 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x1x3]
%onnx::Conv_419[FLOAT, 64x64x3x1]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205633024,
"params": 2442442,
"val_accuracy": 92.45
} | {
"arch_str": "751",
"identifier": "Hiaml_751",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "751"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 751 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205633024,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3289 | GraphArch:Hiaml:3289 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_469[FLOAT, 64x3x3x3]
%onnx::Conv_470[FLOAT, 64]
%onnx::Conv_472[FLOAT, 64x64x1x3]
%onnx::Conv_475[FLOAT, 64x64x3x1]
%onnx::Conv_478[FLOAT, 64x64x1x1]
%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": 231290368,
"params": 2650954,
"val_accuracy": 92.23
} | {
"arch_str": "3289",
"identifier": "Hiaml_3289",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3289"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3289 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231290368,
"val_accuracy": 92.23
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3772 | GraphArch:Hiaml:3772 | 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": 188036608,
"params": 2253770,
"val_accuracy": 92.31
} | {
"arch_str": "3772",
"identifier": "Hiaml_3772",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3772"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3772 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188036608,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3369 | GraphArch:Hiaml:3369 | 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": 260257280,
"params": 2732874,
"val_accuracy": 92.57
} | {
"arch_str": "3369",
"identifier": "Hiaml_3369",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3369"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3369 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260257280,
"val_accuracy": 92.57
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_687 | GraphArch:Hiaml:687 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_527[FLOAT, 64x3x3x3]
%onnx::Conv_528[FLOAT, 64]
%onnx::Conv_530[FLOAT, 64x64x3x3]
%onnx::Conv_533[FLOAT, 64x64x1x3]
%onnx::Conv_536[FLOAT, 64x64x3x1]
%onnx::Conv_539[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226932224,
"params": 2646346,
"val_accuracy": 92.44
} | {
"arch_str": "687",
"identifier": "Hiaml_687",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "687"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 687 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226932224,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4106 | GraphArch:Hiaml:4106 | 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, 64x64x1x1]
%onnx::Conv_373[FLOAT, 64x64x1x1]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%onnx::Conv_379[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205469184,
"params": 2477130,
"val_accuracy": 92.55
} | {
"arch_str": "4106",
"identifier": "Hiaml_4106",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4106"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4106 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205469184,
"val_accuracy": 92.55
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1827 | GraphArch:Hiaml:1827 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_532[FLOAT, 64x3x3x3]
%onnx::Conv_533[FLOAT, 64]
%onnx::Conv_535[FLOAT, 64x64x1x1]
%onnx::Conv_538[FLOAT, 64x64x1x3]
%onnx::Conv_541[FLOAT, 64x64x3x1]
%onnx::Conv_544[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194491904,
"params": 2589258,
"val_accuracy": 91.53
} | {
"arch_str": "1827",
"identifier": "Hiaml_1827",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1827"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1827 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194491904,
"val_accuracy": 91.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2240 | GraphArch:Hiaml:2240 | 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, 64x64x1x1]
%onnx::Conv_383[FLOAT, 64x64x3x3]
%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": 247379456,
"params": 2215242,
"val_accuracy": 92.89
} | {
"arch_str": "2240",
"identifier": "Hiaml_2240",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2240"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2240 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 247379456,
"val_accuracy": 92.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3079 | GraphArch:Hiaml:3079 | 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, 64x64x1x1]
%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": 199407104,
"params": 2477642,
"val_accuracy": 92.5
} | {
"arch_str": "3079",
"identifier": "Hiaml_3079",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3079"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3079 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199407104,
"val_accuracy": 92.5
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_664 | GraphArch:Hiaml:664 | 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, 64x64x1x1]
%onnx::Conv_373[FLOAT, 64x64x1x1]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%onnx::Conv_379[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178173440,
"params": 1944906,
"val_accuracy": 92.37
} | {
"arch_str": "664",
"identifier": "Hiaml_664",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "664"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 664 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178173440,
"val_accuracy": 92.37
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2520 | GraphArch:Hiaml:2520 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_343[FLOAT, 64x3x3x3]
%onnx::Conv_344[FLOAT, 64]
%onnx::Conv_346[FLOAT, 64x64x3x3]
%onnx::Conv_349[FLOAT, 64x64x1x1]
%onnx::Conv_352[FLOAT, 64x64x1x1]
%onnx::Conv_355[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 162182656,
"params": 1576010,
"val_accuracy": 92.46
} | {
"arch_str": "2520",
"identifier": "Hiaml_2520",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2520"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2520 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 162182656,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4204 | GraphArch:Hiaml:4204 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_525[FLOAT, 64x3x3x3]
%onnx::Conv_526[FLOAT, 64]
%onnx::Conv_528[FLOAT, 64x64x1x1]
%onnx::Conv_531[FLOAT, 64x64x3x3]
%onnx::Conv_534[FLOAT, 64x64x3x3]
%onnx::Conv_537[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 243709440,
"params": 2679114,
"val_accuracy": 92.69
} | {
"arch_str": "4204",
"identifier": "Hiaml_4204",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4204"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4204 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 243709440,
"val_accuracy": 92.69
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1783 | GraphArch:Hiaml:1783 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_351[FLOAT, 64x3x3x3]
%onnx::Conv_352[FLOAT, 64]
%onnx::Conv_354[FLOAT, 64x64x3x3]
%onnx::Conv_357[FLOAT, 64x64x1x1]
%onnx::Conv_360[FLOAT, 64x64x1x1]
%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": 260748800,
"params": 3599434,
"val_accuracy": 92.82
} | {
"arch_str": "1783",
"identifier": "Hiaml_1783",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1783"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1783 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260748800,
"val_accuracy": 92.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3098 | GraphArch:Hiaml:3098 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_359[FLOAT, 64x3x3x3]
%onnx::Conv_360[FLOAT, 64]
%onnx::Conv_362[FLOAT, 64x64x3x3]
%onnx::Conv_365[FLOAT, 64x64x1x3]
%onnx::Conv_368[FLOAT, 64x64x3x1]
%onnx::Conv_371[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180139520,
"params": 1777610,
"val_accuracy": 92.54
} | {
"arch_str": "3098",
"identifier": "Hiaml_3098",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3098"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3098 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180139520,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1565 | GraphArch:Hiaml:1565 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_463[FLOAT, 64x3x3x3]
%onnx::Conv_464[FLOAT, 64]
%onnx::Conv_466[FLOAT, 64x64x1x3]
%onnx::Conv_469[FLOAT, 64x64x3x1]
%onnx::Conv_472[FLOAT, 64x64x1x1]
%onnx::Conv_475[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172504576,
"params": 2144330,
"val_accuracy": 92.22
} | {
"arch_str": "1565",
"identifier": "Hiaml_1565",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1565"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1565 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172504576,
"val_accuracy": 92.22
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1874 | GraphArch:Hiaml:1874 | 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": 220935680,
"params": 2668618,
"val_accuracy": 92.18
} | {
"arch_str": "1874",
"identifier": "Hiaml_1874",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1874"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1874 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 220935680,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_690 | GraphArch:Hiaml:690 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_485[FLOAT, 64x3x3x3]
%onnx::Conv_486[FLOAT, 64]
%onnx::Conv_488[FLOAT, 64x64x1x3]
%onnx::Conv_491[FLOAT, 64x64x3x1]
%onnx::Conv_494[FLOAT, 64x64x1x3]
%onnx::Conv_497[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244889088,
"params": 2832970,
"val_accuracy": 92.35
} | {
"arch_str": "690",
"identifier": "Hiaml_690",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "690"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 690 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244889088,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4375 | GraphArch:Hiaml:4375 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x1x1]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239351296,
"params": 2421066,
"val_accuracy": 92.03
} | {
"arch_str": "4375",
"identifier": "Hiaml_4375",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4375"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4375 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239351296,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2615 | GraphArch:Hiaml:2615 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x3x3]
%onnx::Conv_451[FLOAT, 64x64x1x1]
%onnx::Conv_454[FLOAT, 64x64x1x1]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184628736,
"params": 2390602,
"val_accuracy": 91.48
} | {
"arch_str": "2615",
"identifier": "Hiaml_2615",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2615"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2615 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184628736,
"val_accuracy": 91.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2738 | GraphArch:Hiaml:2738 | 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, 64x64x1x1]
%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": 219395584,
"params": 2168138,
"val_accuracy": 92.07
} | {
"arch_str": "2738",
"identifier": "Hiaml_2738",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2738"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2738 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219395584,
"val_accuracy": 92.07
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3957 | GraphArch:Hiaml:3957 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x3]
%onnx::Conv_411[FLOAT, 64x64x3x1]
%onnx::Conv_414[FLOAT, 64x64x1x1]
%onnx::Conv_417[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178501120,
"params": 1920842,
"val_accuracy": 92.52
} | {
"arch_str": "3957",
"identifier": "Hiaml_3957",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3957"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3957 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178501120,
"val_accuracy": 92.52
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3812 | GraphArch:Hiaml:3812 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_511[FLOAT, 64x3x3x3]
%onnx::Conv_512[FLOAT, 64]
%onnx::Conv_514[FLOAT, 64x64x1x3]
%onnx::Conv_517[FLOAT, 64x64x3x1]
%onnx::Conv_520[FLOAT, 64x64x1x3]
%onnx::Conv_523[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235582976,
"params": 2649546,
"val_accuracy": 93.3
} | {
"arch_str": "3812",
"identifier": "Hiaml_3812",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3812"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3812 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235582976,
"val_accuracy": 93.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_238 | GraphArch:Hiaml:238 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_468[FLOAT, 64x3x3x3]
%onnx::Conv_469[FLOAT, 64]
%onnx::Conv_471[FLOAT, 64x64x1x3]
%onnx::Conv_474[FLOAT, 64x64x3x1]
%onnx::Conv_477[FLOAT, 64x64x1x1]
%onnx::Conv_480[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199603712,
"params": 2480714,
"val_accuracy": 92.29
} | {
"arch_str": "238",
"identifier": "Hiaml_238",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "238"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 238 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199603712,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_547 | GraphArch:Hiaml:547 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_427[FLOAT, 64x3x3x3]
%onnx::Conv_428[FLOAT, 64]
%onnx::Conv_430[FLOAT, 64x64x3x3]
%onnx::Conv_433[FLOAT, 64x64x1x1]
%onnx::Conv_436[FLOAT, 64x64x1x1]
%onnx::Conv_439[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 187774464,
"params": 2303434,
"val_accuracy": 92.45
} | {
"arch_str": "547",
"identifier": "Hiaml_547",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "547"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 547 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 187774464,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2399 | GraphArch:Hiaml:2399 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_401[FLOAT, 64x3x3x3]
%onnx::Conv_402[FLOAT, 64]
%onnx::Conv_404[FLOAT, 64x64x1x1]
%onnx::Conv_407[FLOAT, 64x64x3x3]
%onnx::Conv_410[FLOAT, 64x64x3x3]
%onnx::Conv_413[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 294565376,
"params": 3665994,
"val_accuracy": 92.81
} | {
"arch_str": "2399",
"identifier": "Hiaml_2399",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2399"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2399 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 294565376,
"val_accuracy": 92.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_449 | GraphArch:Hiaml:449 | 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, 64x64x1x3]
%onnx::Conv_478[FLOAT, 64x64x3x1]
%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": 197604864,
"params": 2439754,
"val_accuracy": 92.68
} | {
"arch_str": "449",
"identifier": "Hiaml_449",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "449"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 449 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197604864,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2758 | GraphArch:Hiaml:2758 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_474[FLOAT, 64x3x3x3]
%onnx::Conv_475[FLOAT, 64]
%onnx::Conv_477[FLOAT, 64x64x1x3]
%onnx::Conv_480[FLOAT, 64x64x3x1]
%onnx::Conv_483[FLOAT, 64x64x1x1]
%onnx::Conv_486[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260486656,
"params": 2845130,
"val_accuracy": 93.07
} | {
"arch_str": "2758",
"identifier": "Hiaml_2758",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2758"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2758 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260486656,
"val_accuracy": 93.07
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4351 | GraphArch:Hiaml:4351 | 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": 173192704,
"params": 2003274,
"val_accuracy": 91.63
} | {
"arch_str": "4351",
"identifier": "Hiaml_4351",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4351"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4351 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 173192704,
"val_accuracy": 91.63
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1761 | GraphArch:Hiaml:1761 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_441[FLOAT, 64x3x3x3]
%onnx::Conv_442[FLOAT, 64]
%onnx::Conv_444[FLOAT, 64x64x1x1]
%onnx::Conv_447[FLOAT, 64x64x1x1]
%onnx::Conv_450[FLOAT, 64x64x3x3]
%onnx::Conv_453[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 195376640,
"params": 2495818,
"val_accuracy": 91.81
} | {
"arch_str": "1761",
"identifier": "Hiaml_1761",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1761"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1761 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 195376640,
"val_accuracy": 91.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_868 | GraphArch:Hiaml:868 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_375[FLOAT, 64x3x3x3]
%onnx::Conv_376[FLOAT, 64]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[FLOAT, 64x64x1x3]
%onnx::Conv_384[FLOAT, 64x64x3x1]
%onnx::Conv_387[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180041216,
"params": 2257994,
"val_accuracy": 92.42
} | {
"arch_str": "868",
"identifier": "Hiaml_868",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "868"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 868 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180041216,
"val_accuracy": 92.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1639 | GraphArch:Hiaml:1639 | 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, 64x64x1x3]
%onnx::Conv_383[FLOAT, 64x64x3x1]
%onnx::Conv_386[FLOAT, 64x64x1x3]
%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": 206484992,
"params": 2166090,
"val_accuracy": 92.31
} | {
"arch_str": "1639",
"identifier": "Hiaml_1639",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1639"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1639 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206484992,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1626 | GraphArch:Hiaml:1626 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_429[FLOAT, 64x3x3x3]
%onnx::Conv_430[FLOAT, 64]
%onnx::Conv_432[FLOAT, 64x64x3x3]
%onnx::Conv_435[FLOAT, 64x64x1x1]
%onnx::Conv_438[FLOAT, 64x64x3x3]
%onnx::Conv_441[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188659200,
"params": 2460362,
"val_accuracy": 92.13
} | {
"arch_str": "1626",
"identifier": "Hiaml_1626",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1626"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1626 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188659200,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3449 | GraphArch:Hiaml:3449 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x3x3]
%onnx::Conv_443[FLOAT, 64x64x1x3]
%onnx::Conv_446[FLOAT, 64x64x3x1]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215070208,
"params": 2061898,
"val_accuracy": 92.53
} | {
"arch_str": "3449",
"identifier": "Hiaml_3449",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3449"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3449 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215070208,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4305 | GraphArch:Hiaml:4305 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_367[FLOAT, 64x3x3x3]
%onnx::Conv_368[FLOAT, 64]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[FLOAT, 64x64x1x3]
%onnx::Conv_376[FLOAT, 64x64x3x1]
%onnx::Conv_379[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205370880,
"params": 1973706,
"val_accuracy": 92.96
} | {
"arch_str": "4305",
"identifier": "Hiaml_4305",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4305"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4305 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205370880,
"val_accuracy": 92.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4409 | GraphArch:Hiaml:4409 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_359[FLOAT, 64x3x3x3]
%onnx::Conv_360[FLOAT, 64]
%onnx::Conv_362[FLOAT, 64x64x3x3]
%onnx::Conv_365[FLOAT, 64x64x1x3]
%onnx::Conv_368[FLOAT, 64x64x3x1]
%onnx::Conv_371[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225162752,
"params": 1969482,
"val_accuracy": 92.65
} | {
"arch_str": "4409",
"identifier": "Hiaml_4409",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4409"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4409 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 225162752,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3615 | GraphArch:Hiaml:3615 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_463[FLOAT, 64x3x3x3]
%onnx::Conv_464[FLOAT, 64]
%onnx::Conv_466[FLOAT, 64x64x1x1]
%onnx::Conv_469[FLOAT, 64x64x3x3]
%onnx::Conv_472[FLOAT, 64x64x3x3]
%onnx::Conv_475[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244430336,
"params": 2686026,
"val_accuracy": 92.68
} | {
"arch_str": "3615",
"identifier": "Hiaml_3615",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3615"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3615 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244430336,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_57 | GraphArch:Hiaml:57 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_399[FLOAT, 64x3x3x3]
%onnx::Conv_400[FLOAT, 64]
%onnx::Conv_402[FLOAT, 64x64x1x1]
%onnx::Conv_405[FLOAT, 64x64x1x3]
%onnx::Conv_408[FLOAT, 64x64x3x1]
%onnx::Conv_411[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172832256,
"params": 2397770,
"val_accuracy": 92.26
} | {
"arch_str": "57",
"identifier": "Hiaml_57",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "57"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 57 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172832256,
"val_accuracy": 92.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3664 | GraphArch:Hiaml:3664 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_492[FLOAT, 64x3x3x3]
%onnx::Conv_493[FLOAT, 64]
%onnx::Conv_495[FLOAT, 64x64x1x1]
%onnx::Conv_498[FLOAT, 64x64x3x3]
%onnx::Conv_501[FLOAT, 64x64x3x3]
%onnx::Conv_504[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 252950016,
"params": 2752074,
"val_accuracy": 92.64
} | {
"arch_str": "3664",
"identifier": "Hiaml_3664",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3664"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3664 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 252950016,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4573 | GraphArch:Hiaml:4573 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x3x3]
%onnx::Conv_375[FLOAT, 64x64x1x1]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 237778432,
"params": 2670026,
"val_accuracy": 92.94
} | {
"arch_str": "4573",
"identifier": "Hiaml_4573",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4573"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4573 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 237778432,
"val_accuracy": 92.94
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_667 | GraphArch:Hiaml:667 | 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, 64x64x1x3]
%onnx::Conv_360[FLOAT, 64x64x3x1]
%onnx::Conv_363[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159004160,
"params": 2191434,
"val_accuracy": 92.45
} | {
"arch_str": "667",
"identifier": "Hiaml_667",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "667"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 667 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159004160,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1290 | GraphArch:Hiaml:1290 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_482[FLOAT, 64x3x3x3]
%onnx::Conv_483[FLOAT, 64]
%onnx::Conv_485[FLOAT, 64x64x1x1]
%onnx::Conv_488[FLOAT, 64x64x1x3]
%onnx::Conv_491[FLOAT, 64x64x3x1]
%onnx::Conv_494[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198948352,
"params": 1979466,
"val_accuracy": 92.46
} | {
"arch_str": "1290",
"identifier": "Hiaml_1290",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1290"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1290 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198948352,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3841 | GraphArch:Hiaml:3841 | 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, 64x64x3x3]
%onnx::Conv_447[FLOAT, 64x64x1x1]
%onnx::Conv_450[FLOAT, 64x64x1x1]
%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": 184628736,
"params": 2390602,
"val_accuracy": 91.75
} | {
"arch_str": "3841",
"identifier": "Hiaml_3841",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3841"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3841 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184628736,
"val_accuracy": 91.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2890 | GraphArch:Hiaml:2890 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_475[FLOAT, 64x3x3x3]
%onnx::Conv_476[FLOAT, 64]
%onnx::Conv_478[FLOAT, 64x64x3x3]
%onnx::Conv_481[FLOAT, 64x64x1x1]
%onnx::Conv_484[FLOAT, 64x64x1x1]
%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": 212153856,
"params": 2652234,
"val_accuracy": 91.79
} | {
"arch_str": "2890",
"identifier": "Hiaml_2890",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2890"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2890 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 212153856,
"val_accuracy": 91.79
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_803 | GraphArch:Hiaml:803 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_385[FLOAT, 64x3x3x3]
%onnx::Conv_386[FLOAT, 64]
%onnx::Conv_388[FLOAT, 64x64x3x3]
%onnx::Conv_391[FLOAT, 64x64x1x3]
%onnx::Conv_394[FLOAT, 64x64x3x1]
%onnx::Conv_397[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 229455360,
"params": 2433482,
"val_accuracy": 93.04
} | {
"arch_str": "803",
"identifier": "Hiaml_803",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "803"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 803 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 229455360,
"val_accuracy": 93.04
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3271 | GraphArch:Hiaml:3271 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x1x1]
%onnx::Conv_419[FLOAT, 64x64x1x3]
%onnx::Conv_422[FLOAT, 64x64x3x1]
%onnx::Conv_425[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244364800,
"params": 3568202,
"val_accuracy": 92.13
} | {
"arch_str": "3271",
"identifier": "Hiaml_3271",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3271"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3271 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244364800,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1563 | GraphArch:Hiaml:1563 | 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": 186955264,
"params": 1716298,
"val_accuracy": 92.56
} | {
"arch_str": "1563",
"identifier": "Hiaml_1563",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1563"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1563 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 186955264,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3919 | GraphArch:Hiaml:3919 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_514[FLOAT, 64x3x3x3]
%onnx::Conv_515[FLOAT, 64]
%onnx::Conv_517[FLOAT, 64x64x1x3]
%onnx::Conv_520[FLOAT, 64x64x3x1]
%onnx::Conv_523[FLOAT, 64x64x1x1]
%onnx::Conv_526[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244954624,
"params": 2833994,
"val_accuracy": 92.36
} | {
"arch_str": "3919",
"identifier": "Hiaml_3919",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3919"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3919 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244954624,
"val_accuracy": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3949 | GraphArch:Hiaml:3949 | 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": 248821248,
"params": 3453002,
"val_accuracy": 91.84
} | {
"arch_str": "3949",
"identifier": "Hiaml_3949",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3949"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3949 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 248821248,
"val_accuracy": 91.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3224 | GraphArch:Hiaml:3224 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_361[FLOAT, 64x3x3x3]
%onnx::Conv_362[FLOAT, 64]
%onnx::Conv_364[FLOAT, 64x64x1x1]
%onnx::Conv_367[FLOAT, 64x64x1x3]
%onnx::Conv_370[FLOAT, 64x64x3x1]
%onnx::Conv_373[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176961024,
"params": 2084682,
"val_accuracy": 91.55
} | {
"arch_str": "3224",
"identifier": "Hiaml_3224",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3224"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3224 | Predict neural architecture validation accuracy and compute cost from 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": 91.55
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3629 | GraphArch:Hiaml:3629 | 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": 207468032,
"params": 2398282,
"val_accuracy": 92.74
} | {
"arch_str": "3629",
"identifier": "Hiaml_3629",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3629"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3629 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207468032,
"val_accuracy": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_769 | GraphArch:Hiaml:769 | 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, 64x64x3x3]
%onnx::Conv_505[FLOAT, 64x64x1x3]
%onnx::Conv_508[FLOAT, 64x64x3x1]
%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": 236172800,
"params": 2719306,
"val_accuracy": 92.4
} | {
"arch_str": "769",
"identifier": "Hiaml_769",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "769"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 769 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236172800,
"val_accuracy": 92.4
} | full |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.