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_544 | GraphArch:Hiaml:544 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[FLOAT, 64x64x1x3]
%onnx::Conv_462[FLOAT, 64x64x3x1]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180925952,
"params": 1828298,
"val_accuracy": 92.23
} | {
"arch_str": "544",
"identifier": "Hiaml_544",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "544"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 544 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180925952,
"val_accuracy": 92.23
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3858 | GraphArch:Hiaml:3858 | 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, 64x64x1x3]
%onnx::Conv_378[FLOAT, 64x64x3x1]
%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": 193738240,
"params": 2019146,
"val_accuracy": 92.62
} | {
"arch_str": "3858",
"identifier": "Hiaml_3858",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3858"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3858 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193738240,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2564 | GraphArch:Hiaml:2564 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x1x1]
%onnx::Conv_379[FLOAT, 64x64x1x3]
%onnx::Conv_382[FLOAT, 64x64x3x1]
%onnx::Conv_385[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189675008,
"params": 2465738,
"val_accuracy": 93.05
} | {
"arch_str": "2564",
"identifier": "Hiaml_2564",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2564"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2564 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189675008,
"val_accuracy": 93.05
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2839 | GraphArch:Hiaml:2839 | 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, 64x64x3x3]
%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": 178304512,
"params": 1602122,
"val_accuracy": 92.18
} | {
"arch_str": "2839",
"identifier": "Hiaml_2839",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2839"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2839 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178304512,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_631 | GraphArch:Hiaml:631 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[FLOAT, 64x64x3x1]
%onnx::Conv_428[FLOAT, 64x64x1x3]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 246691328,
"params": 2871626,
"val_accuracy": 92.67
} | {
"arch_str": "631",
"identifier": "Hiaml_631",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "631"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 631 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 246691328,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4339 | GraphArch:Hiaml:4339 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x1x3]
%onnx::Conv_443[FLOAT, 64x64x3x1]
%onnx::Conv_446[FLOAT, 64x64x1x1]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 232175104,
"params": 3322442,
"val_accuracy": 92.53
} | {
"arch_str": "4339",
"identifier": "Hiaml_4339",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4339"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4339 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 232175104,
"val_accuracy": 92.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1279 | GraphArch:Hiaml:1279 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_514[FLOAT, 64x3x3x3]
%onnx::Conv_515[FLOAT, 64]
%onnx::Conv_517[FLOAT, 64x64x1x1]
%onnx::Conv_520[FLOAT, 64x64x1x3]
%onnx::Conv_523[FLOAT, 64x64x3x1]
%onnx::Conv_526[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193541632,
"params": 2531146,
"val_accuracy": 91.7
} | {
"arch_str": "1279",
"identifier": "Hiaml_1279",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1279"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1279 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193541632,
"val_accuracy": 91.7
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4574 | GraphArch:Hiaml:4574 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_385[FLOAT, 64x3x3x3]
%onnx::Conv_386[FLOAT, 64]
%onnx::Conv_388[FLOAT, 64x64x1x1]
%onnx::Conv_391[FLOAT, 64x64x1x1]
%onnx::Conv_394[FLOAT, 64x64x3x3]
%onnx::Conv_397[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201405952,
"params": 2444874,
"val_accuracy": 91.84
} | {
"arch_str": "4574",
"identifier": "Hiaml_4574",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4574"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4574 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201405952,
"val_accuracy": 91.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1385 | GraphArch:Hiaml:1385 | 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, 64x64x3x3]
%onnx::Conv_486[FLOAT, 64x64x1x3]
%onnx::Conv_489[FLOAT, 64x64x3x1]
%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": 251835904,
"params": 2841930,
"val_accuracy": 92.31
} | {
"arch_str": "1385",
"identifier": "Hiaml_1385",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1385"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1385 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 251835904,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_377 | GraphArch:Hiaml:377 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_434[FLOAT, 64x3x3x3]
%onnx::Conv_435[FLOAT, 64]
%onnx::Conv_437[FLOAT, 64x64x3x3]
%onnx::Conv_440[FLOAT, 64x64x1x1]
%onnx::Conv_443[FLOAT, 64x64x1x1]
%onnx::Conv_446[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 203503104,
"params": 2585674,
"val_accuracy": 92.17
} | {
"arch_str": "377",
"identifier": "Hiaml_377",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "377"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 377 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 203503104,
"val_accuracy": 92.17
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_692 | GraphArch:Hiaml:692 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_379[FLOAT, 64x3x3x3]
%onnx::Conv_380[FLOAT, 64]
%onnx::Conv_382[FLOAT, 64x64x3x3]
%onnx::Conv_385[FLOAT, 64x64x1x1]
%onnx::Conv_388[FLOAT, 64x64x1x1]
%onnx::Conv_391[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202225152,
"params": 1888074,
"val_accuracy": 91.85
} | {
"arch_str": "692",
"identifier": "Hiaml_692",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "692"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 692 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202225152,
"val_accuracy": 91.85
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2527 | GraphArch:Hiaml:2527 | 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": 199570944,
"params": 2503242,
"val_accuracy": 92.49
} | {
"arch_str": "2527",
"identifier": "Hiaml_2527",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2527"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2527 | Predict neural architecture validation accuracy and compute cost from 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": 92.49
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3499 | GraphArch:Hiaml:3499 | 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, 64x64x1x1]
%onnx::Conv_394[FLOAT, 64x64x3x3]
%onnx::Conv_397[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 217822720,
"params": 2589898,
"val_accuracy": 92.58
} | {
"arch_str": "3499",
"identifier": "Hiaml_3499",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3499"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3499 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 217822720,
"val_accuracy": 92.58
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_612 | GraphArch:Hiaml:612 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_459[FLOAT, 64x3x3x3]
%onnx::Conv_460[FLOAT, 64]
%onnx::Conv_462[FLOAT, 64x64x1x3]
%onnx::Conv_465[FLOAT, 64x64x3x1]
%onnx::Conv_468[FLOAT, 64x64x1x3]
%onnx::Conv_471[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198620672,
"params": 2472010,
"val_accuracy": 92.65
} | {
"arch_str": "612",
"identifier": "Hiaml_612",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "612"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 612 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198620672,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4228 | GraphArch:Hiaml:4228 | 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, 64x64x1x1]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 204486144,
"params": 2456522,
"val_accuracy": 92.56
} | {
"arch_str": "4228",
"identifier": "Hiaml_4228",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4228"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4228 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 204486144,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3518 | GraphArch:Hiaml:3518 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_379[FLOAT, 64x3x3x3]
%onnx::Conv_380[FLOAT, 64]
%onnx::Conv_382[FLOAT, 64x64x1x1]
%onnx::Conv_385[FLOAT, 64x64x1x3]
%onnx::Conv_388[FLOAT, 64x64x3x1]
%onnx::Conv_391[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190789120,
"params": 2559818,
"val_accuracy": 92.44
} | {
"arch_str": "3518",
"identifier": "Hiaml_3518",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3518"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3518 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 190789120,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_811 | GraphArch:Hiaml:811 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_448[FLOAT, 64x3x3x3]
%onnx::Conv_449[FLOAT, 64]
%onnx::Conv_451[FLOAT, 64x64x3x3]
%onnx::Conv_454[FLOAT, 64x64x1x3]
%onnx::Conv_457[FLOAT, 64x64x3x1]
%onnx::Conv_460[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206550528,
"params": 2488906,
"val_accuracy": 92.83
} | {
"arch_str": "811",
"identifier": "Hiaml_811",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "811"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 811 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206550528,
"val_accuracy": 92.83
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1372 | GraphArch:Hiaml:1372 | 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, 64x64x3x3]
%onnx::Conv_356[FLOAT, 64x64x3x3]
%onnx::Conv_359[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234599936,
"params": 2497482,
"val_accuracy": 93.44
} | {
"arch_str": "1372",
"identifier": "Hiaml_1372",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1372"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1372 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234599936,
"val_accuracy": 93.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_35 | GraphArch:Hiaml:35 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_465[FLOAT, 64x3x3x3]
%onnx::Conv_466[FLOAT, 64]
%onnx::Conv_468[FLOAT, 64x64x1x3]
%onnx::Conv_471[FLOAT, 64x64x3x1]
%onnx::Conv_474[FLOAT, 64x64x1x1]
%onnx::Conv_477[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 269858304,
"params": 3029578,
"val_accuracy": 92.24
} | {
"arch_str": "35",
"identifier": "Hiaml_35",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "35"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 35 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 269858304,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3496 | GraphArch:Hiaml:3496 | 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": 239711744,
"params": 3380554,
"val_accuracy": 91.96
} | {
"arch_str": "3496",
"identifier": "Hiaml_3496",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3496"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3496 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239711744,
"val_accuracy": 91.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3797 | GraphArch:Hiaml:3797 | 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, 64x64x1x1]
%onnx::Conv_446[FLOAT, 64x64x1x1]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206714368,
"params": 2610506,
"val_accuracy": 91.65
} | {
"arch_str": "3797",
"identifier": "Hiaml_3797",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3797"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3797 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206714368,
"val_accuracy": 91.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2831 | GraphArch:Hiaml:2831 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_443[FLOAT, 64x3x3x3]
%onnx::Conv_444[FLOAT, 64]
%onnx::Conv_446[FLOAT, 64x64x1x1]
%onnx::Conv_449[FLOAT, 64x64x1x1]
%onnx::Conv_452[FLOAT, 64x64x3x3]
%onnx::Conv_455[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181679616,
"params": 2242378,
"val_accuracy": 92.18
} | {
"arch_str": "2831",
"identifier": "Hiaml_2831",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2831"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2831 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181679616,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1794 | GraphArch:Hiaml:1794 | 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": 241219072,
"params": 1995338,
"val_accuracy": 92.77
} | {
"arch_str": "1794",
"identifier": "Hiaml_1794",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1794"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1794 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 241219072,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2407 | GraphArch:Hiaml:2407 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_414[FLOAT, 64x3x3x3]
%onnx::Conv_415[FLOAT, 64]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[FLOAT, 64x64x1x3]
%onnx::Conv_426[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 168801792,
"params": 2266954,
"val_accuracy": 92.16
} | {
"arch_str": "2407",
"identifier": "Hiaml_2407",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2407"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2407 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 168801792,
"val_accuracy": 92.16
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2425 | GraphArch:Hiaml:2425 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_391[FLOAT, 64x3x3x3]
%onnx::Conv_392[FLOAT, 64]
%onnx::Conv_394[FLOAT, 64x64x1x3]
%onnx::Conv_397[FLOAT, 64x64x3x1]
%onnx::Conv_400[FLOAT, 64x64x1x3]
%onnx::Conv_403[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177321472,
"params": 1838410,
"val_accuracy": 92.02
} | {
"arch_str": "2425",
"identifier": "Hiaml_2425",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2425"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2425 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177321472,
"val_accuracy": 92.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1977 | GraphArch:Hiaml:1977 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_353[FLOAT, 64x3x3x3]
%onnx::Conv_354[FLOAT, 64]
%onnx::Conv_356[FLOAT, 64x64x3x3]
%onnx::Conv_359[FLOAT, 64x64x1x3]
%onnx::Conv_362[FLOAT, 64x64x3x1]
%onnx::Conv_365[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 243971584,
"params": 3370058,
"val_accuracy": 92.43
} | {
"arch_str": "1977",
"identifier": "Hiaml_1977",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1977"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1977 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 243971584,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4586 | GraphArch:Hiaml:4586 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[FLOAT, 64x64x1x3]
%onnx::Conv_462[FLOAT, 64x64x3x1]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180893184,
"params": 1963338,
"val_accuracy": 91.88
} | {
"arch_str": "4586",
"identifier": "Hiaml_4586",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4586"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4586 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180893184,
"val_accuracy": 91.88
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1133 | GraphArch:Hiaml:1133 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_457[FLOAT, 64x3x3x3]
%onnx::Conv_458[FLOAT, 64]
%onnx::Conv_460[FLOAT, 64x64x1x3]
%onnx::Conv_463[FLOAT, 64x64x3x1]
%onnx::Conv_466[FLOAT, 64x64x1x1]
%onnx::Conv_469[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 268776960,
"params": 2996298,
"val_accuracy": 92.54
} | {
"arch_str": "1133",
"identifier": "Hiaml_1133",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1133"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1133 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 268776960,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3422 | GraphArch:Hiaml:3422 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_393[FLOAT, 64x3x3x3]
%onnx::Conv_394[FLOAT, 64]
%onnx::Conv_396[FLOAT, 64x64x1x3]
%onnx::Conv_399[FLOAT, 64x64x3x1]
%onnx::Conv_402[FLOAT, 64x64x1x1]
%onnx::Conv_405[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177452544,
"params": 1813578,
"val_accuracy": 92.73
} | {
"arch_str": "3422",
"identifier": "Hiaml_3422",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3422"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3422 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177452544,
"val_accuracy": 92.73
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2591 | GraphArch:Hiaml:2591 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_381[FLOAT, 64x3x3x3]
%onnx::Conv_382[FLOAT, 64]
%onnx::Conv_384[FLOAT, 64x64x1x1]
%onnx::Conv_387[FLOAT, 64x64x1x3]
%onnx::Conv_390[FLOAT, 64x64x3x1]
%onnx::Conv_393[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159266304,
"params": 1945930,
"val_accuracy": 92.46
} | {
"arch_str": "2591",
"identifier": "Hiaml_2591",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2591"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2591 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159266304,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_699 | GraphArch:Hiaml:699 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_425[FLOAT, 64x3x3x3]
%onnx::Conv_426[FLOAT, 64]
%onnx::Conv_428[FLOAT, 64x64x1x3]
%onnx::Conv_431[FLOAT, 64x64x3x1]
%onnx::Conv_434[FLOAT, 64x64x1x1]
%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": 152417792,
"params": 1619018,
"val_accuracy": 92.29
} | {
"arch_str": "699",
"identifier": "Hiaml_699",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "699"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 699 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 152417792,
"val_accuracy": 92.29
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_396 | GraphArch:Hiaml:396 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_464[FLOAT, 64x3x3x3]
%onnx::Conv_465[FLOAT, 64]
%onnx::Conv_467[FLOAT, 64x64x3x3]
%onnx::Conv_470[FLOAT, 64x64x1x3]
%onnx::Conv_473[FLOAT, 64x64x3x1]
%onnx::Conv_476[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209892864,
"params": 2501578,
"val_accuracy": 92.6
} | {
"arch_str": "396",
"identifier": "Hiaml_396",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "396"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 396 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209892864,
"val_accuracy": 92.6
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4400 | GraphArch:Hiaml:4400 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_531[FLOAT, 64x3x3x3]
%onnx::Conv_532[FLOAT, 64]
%onnx::Conv_534[FLOAT, 64x64x1x1]
%onnx::Conv_537[FLOAT, 64x64x1x1]
%onnx::Conv_540[FLOAT, 64x64x3x3]
%onnx::Conv_543[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194622976,
"params": 2466122,
"val_accuracy": 91.71
} | {
"arch_str": "4400",
"identifier": "Hiaml_4400",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4400"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4400 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194622976,
"val_accuracy": 91.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4330 | GraphArch:Hiaml:4330 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_426[FLOAT, 64x3x3x3]
%onnx::Conv_427[FLOAT, 64]
%onnx::Conv_429[FLOAT, 64x64x3x3]
%onnx::Conv_432[FLOAT, 64x64x1x3]
%onnx::Conv_435[FLOAT, 64x64x3x1]
%onnx::Conv_438[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219002368,
"params": 2586698,
"val_accuracy": 92.44
} | {
"arch_str": "4330",
"identifier": "Hiaml_4330",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4330"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4330 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219002368,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3309 | GraphArch:Hiaml:3309 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_515[FLOAT, 64x3x3x3]
%onnx::Conv_516[FLOAT, 64]
%onnx::Conv_518[FLOAT, 64x64x1x3]
%onnx::Conv_521[FLOAT, 64x64x3x1]
%onnx::Conv_524[FLOAT, 64x64x1x3]
%onnx::Conv_527[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235582976,
"params": 2649546,
"val_accuracy": 92.77
} | {
"arch_str": "3309",
"identifier": "Hiaml_3309",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3309"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3309 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235582976,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3767 | GraphArch:Hiaml:3767 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_489[FLOAT, 64x3x3x3]
%onnx::Conv_490[FLOAT, 64]
%onnx::Conv_492[FLOAT, 64x64x1x3]
%onnx::Conv_495[FLOAT, 64x64x3x1]
%onnx::Conv_498[FLOAT, 64x64x1x1]
%onnx::Conv_501[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240629248,
"params": 2800714,
"val_accuracy": 92.32
} | {
"arch_str": "3767",
"identifier": "Hiaml_3767",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3767"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3767 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240629248,
"val_accuracy": 92.32
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4584 | GraphArch:Hiaml:4584 | 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, 64x64x1x3]
%onnx::Conv_438[FLOAT, 64x64x3x1]
%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": 205534720,
"params": 2356810,
"val_accuracy": 92.64
} | {
"arch_str": "4584",
"identifier": "Hiaml_4584",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4584"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4584 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205534720,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1824 | GraphArch:Hiaml:1824 | 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, 64x64x1x1]
%onnx::Conv_435[FLOAT, 64x64x1x3]
%onnx::Conv_438[FLOAT, 64x64x3x1]
%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": 173028864,
"params": 2398538,
"val_accuracy": 92.14
} | {
"arch_str": "1824",
"identifier": "Hiaml_1824",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1824"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1824 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 173028864,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4528 | GraphArch:Hiaml:4528 | 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, 64x64x3x3]
%onnx::Conv_365[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172602880,
"params": 1855818,
"val_accuracy": 91.56
} | {
"arch_str": "4528",
"identifier": "Hiaml_4528",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4528"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4528 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172602880,
"val_accuracy": 91.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2062 | GraphArch:Hiaml:2062 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_508[FLOAT, 64x3x3x3]
%onnx::Conv_509[FLOAT, 64]
%onnx::Conv_511[FLOAT, 64x64x3x3]
%onnx::Conv_514[FLOAT, 64x64x1x3]
%onnx::Conv_517[FLOAT, 64x64x3x1]
%onnx::Conv_520[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206943744,
"params": 2490442,
"val_accuracy": 92.57
} | {
"arch_str": "2062",
"identifier": "Hiaml_2062",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2062"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2062 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206943744,
"val_accuracy": 92.57
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4410 | GraphArch:Hiaml:4410 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x3x3]
%onnx::Conv_459[FLOAT, 64x64x1x3]
%onnx::Conv_462[FLOAT, 64x64x3x1]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234993152,
"params": 2709834,
"val_accuracy": 92.54
} | {
"arch_str": "4410",
"identifier": "Hiaml_4410",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4410"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4410 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234993152,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3119 | GraphArch:Hiaml:3119 | 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, 64x64x1x3]
%onnx::Conv_386[FLOAT, 64x64x3x1]
%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": 215856640,
"params": 3346250,
"val_accuracy": 92.38
} | {
"arch_str": "3119",
"identifier": "Hiaml_3119",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3119"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3119 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215856640,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_615 | GraphArch:Hiaml:615 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_393[FLOAT, 64x3x3x3]
%onnx::Conv_394[FLOAT, 64]
%onnx::Conv_396[FLOAT, 64x64x1x3]
%onnx::Conv_399[FLOAT, 64x64x3x1]
%onnx::Conv_402[FLOAT, 64x64x1x3]
%onnx::Conv_405[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177255936,
"params": 1937226,
"val_accuracy": 92.28
} | {
"arch_str": "615",
"identifier": "Hiaml_615",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "615"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 615 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 177255936,
"val_accuracy": 92.28
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4421 | GraphArch:Hiaml:4421 | 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, 64x64x1x3]
%onnx::Conv_533[FLOAT, 64x64x3x1]
%onnx::Conv_536[FLOAT, 64x64x1x3]
%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": 215791104,
"params": 2604618,
"val_accuracy": 92.33
} | {
"arch_str": "4421",
"identifier": "Hiaml_4421",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4421"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4421 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215791104,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4135 | GraphArch:Hiaml:4135 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_393[FLOAT, 64x3x3x3]
%onnx::Conv_394[FLOAT, 64]
%onnx::Conv_396[FLOAT, 64x64x1x3]
%onnx::Conv_399[FLOAT, 64x64x3x1]
%onnx::Conv_402[FLOAT, 64x64x1x1]
%onnx::Conv_405[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 149075456,
"params": 1420874,
"val_accuracy": 92.46
} | {
"arch_str": "4135",
"identifier": "Hiaml_4135",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4135"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4135 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 149075456,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1304 | GraphArch:Hiaml:1304 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_462[FLOAT, 64x3x3x3]
%onnx::Conv_463[FLOAT, 64]
%onnx::Conv_465[FLOAT, 64x64x1x1]
%onnx::Conv_468[FLOAT, 64x64x1x3]
%onnx::Conv_471[FLOAT, 64x64x3x1]
%onnx::Conv_474[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194622976,
"params": 1946186,
"val_accuracy": 92.55
} | {
"arch_str": "1304",
"identifier": "Hiaml_1304",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1304"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1304 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194622976,
"val_accuracy": 92.55
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2619 | GraphArch:Hiaml:2619 | 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": 233977344,
"params": 2797386,
"val_accuracy": 92.47
} | {
"arch_str": "2619",
"identifier": "Hiaml_2619",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2619"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2619 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 233977344,
"val_accuracy": 92.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1248 | GraphArch:Hiaml:1248 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_465[FLOAT, 64x3x3x3]
%onnx::Conv_466[FLOAT, 64]
%onnx::Conv_468[FLOAT, 64x64x1x3]
%onnx::Conv_471[FLOAT, 64x64x3x1]
%onnx::Conv_474[FLOAT, 64x64x1x1]
%onnx::Conv_477[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211301888,
"params": 2594122,
"val_accuracy": 92.3
} | {
"arch_str": "1248",
"identifier": "Hiaml_1248",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1248"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1248 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211301888,
"val_accuracy": 92.3
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3194 | GraphArch:Hiaml:3194 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_341[FLOAT, 64x3x3x3]
%onnx::Conv_342[FLOAT, 64]
%onnx::Conv_344[FLOAT, 64x64x3x3]
%onnx::Conv_347[FLOAT, 64x64x1x1]
%onnx::Conv_350[FLOAT, 64x64x3x3]
%onnx::Conv_353[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 233387520,
"params": 2342346,
"val_accuracy": 92.69
} | {
"arch_str": "3194",
"identifier": "Hiaml_3194",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3194"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3194 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 233387520,
"val_accuracy": 92.69
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3426 | GraphArch:Hiaml:3426 | 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, 64x64x1x3]
%onnx::Conv_374[FLOAT, 64x64x3x1]
%onnx::Conv_377[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 253441536,
"params": 2879050,
"val_accuracy": 93
} | {
"arch_str": "3426",
"identifier": "Hiaml_3426",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3426"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3426 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 253441536,
"val_accuracy": 93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_155 | GraphArch:Hiaml:155 | 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, 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": 194295296,
"params": 2438730,
"val_accuracy": 92.62
} | {
"arch_str": "155",
"identifier": "Hiaml_155",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "155"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 155 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194295296,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_638 | GraphArch:Hiaml:638 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x3x3]
%onnx::Conv_369[FLOAT, 64x64x1x3]
%onnx::Conv_372[FLOAT, 64x64x3x1]
%onnx::Conv_375[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196949504,
"params": 2338122,
"val_accuracy": 92.18
} | {
"arch_str": "638",
"identifier": "Hiaml_638",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "638"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 638 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196949504,
"val_accuracy": 92.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_306 | GraphArch:Hiaml:306 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x3]
%onnx::Conv_451[FLOAT, 64x64x3x1]
%onnx::Conv_454[FLOAT, 64x64x1x3]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209139200,
"params": 2319818,
"val_accuracy": 93.03
} | {
"arch_str": "306",
"identifier": "Hiaml_306",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "306"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 306 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 209139200,
"val_accuracy": 93.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1475 | GraphArch:Hiaml:1475 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_427[FLOAT, 64x3x3x3]
%onnx::Conv_428[FLOAT, 64]
%onnx::Conv_430[FLOAT, 64x64x1x1]
%onnx::Conv_433[FLOAT, 64x64x1x1]
%onnx::Conv_436[FLOAT, 64x64x3x3]
%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": 188986880,
"params": 2028106,
"val_accuracy": 92.24
} | {
"arch_str": "1475",
"identifier": "Hiaml_1475",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1475"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1475 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188986880,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3167 | GraphArch:Hiaml:3167 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[FLOAT, 64x64x1x3]
%onnx::Conv_430[FLOAT, 64x64x3x1]
%onnx::Conv_433[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 204748288,
"params": 2568522,
"val_accuracy": 91.89
} | {
"arch_str": "3167",
"identifier": "Hiaml_3167",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3167"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3167 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 204748288,
"val_accuracy": 91.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4419 | GraphArch:Hiaml:4419 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x3x3]
%onnx::Conv_425[FLOAT, 64x64x1x3]
%onnx::Conv_428[FLOAT, 64x64x3x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206484992,
"params": 2487882,
"val_accuracy": 92.74
} | {
"arch_str": "4419",
"identifier": "Hiaml_4419",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4419"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4419 | Predict neural architecture validation accuracy and compute cost from 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": 92.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_653 | GraphArch:Hiaml:653 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[FLOAT, 64x64x3x3]
%onnx::Conv_454[FLOAT, 64x64x3x3]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 224605696,
"params": 2553418,
"val_accuracy": 92.92
} | {
"arch_str": "653",
"identifier": "Hiaml_653",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "653"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 653 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 224605696,
"val_accuracy": 92.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3199 | GraphArch:Hiaml:3199 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_391[FLOAT, 64x3x3x3]
%onnx::Conv_392[FLOAT, 64]
%onnx::Conv_394[FLOAT, 64x64x3x3]
%onnx::Conv_397[FLOAT, 64x64x1x3]
%onnx::Conv_400[FLOAT, 64x64x3x1]
%onnx::Conv_403[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181286400,
"params": 1921354,
"val_accuracy": 92.38
} | {
"arch_str": "3199",
"identifier": "Hiaml_3199",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3199"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3199 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181286400,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4257 | GraphArch:Hiaml:4257 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_464[FLOAT, 64x3x3x3]
%onnx::Conv_465[FLOAT, 64]
%onnx::Conv_467[FLOAT, 64x64x1x1]
%onnx::Conv_470[FLOAT, 64x64x1x3]
%onnx::Conv_473[FLOAT, 64x64x3x1]
%onnx::Conv_476[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194098688,
"params": 2587722,
"val_accuracy": 92.38
} | {
"arch_str": "4257",
"identifier": "Hiaml_4257",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4257"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4257 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194098688,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2987 | GraphArch:Hiaml:2987 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_417[FLOAT, 64x3x3x3]
%onnx::Conv_418[FLOAT, 64]
%onnx::Conv_420[FLOAT, 64x64x3x3]
%onnx::Conv_423[FLOAT, 64x64x1x3]
%onnx::Conv_426[FLOAT, 64x64x3x1]
%onnx::Conv_429[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219166208,
"params": 2609994,
"val_accuracy": 92
} | {
"arch_str": "2987",
"identifier": "Hiaml_2987",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2987"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2987 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219166208,
"val_accuracy": 92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3231 | GraphArch:Hiaml:3231 | 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, 64x64x1x1]
%onnx::Conv_532[FLOAT, 64x64x1x3]
%onnx::Conv_535[FLOAT, 64x64x3x1]
%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": 198948352,
"params": 2474058,
"val_accuracy": 91.75
} | {
"arch_str": "3231",
"identifier": "Hiaml_3231",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3231"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3231 | Predict neural architecture validation accuracy and compute cost from 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": 91.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3739 | GraphArch:Hiaml:3739 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x1x1]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219231744,
"params": 2634826,
"val_accuracy": 92.63
} | {
"arch_str": "3739",
"identifier": "Hiaml_3739",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3739"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3739 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219231744,
"val_accuracy": 92.63
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1924 | GraphArch:Hiaml:1924 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x1x1]
%onnx::Conv_369[FLOAT, 64x64x1x3]
%onnx::Conv_372[FLOAT, 64x64x3x1]
%onnx::Conv_375[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194950656,
"params": 2567498,
"val_accuracy": 92.46
} | {
"arch_str": "1924",
"identifier": "Hiaml_1924",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1924"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1924 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194950656,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_624 | GraphArch:Hiaml:624 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_447[FLOAT, 64x3x3x3]
%onnx::Conv_448[FLOAT, 64]
%onnx::Conv_450[FLOAT, 64x64x1x1]
%onnx::Conv_453[FLOAT, 64x64x1x1]
%onnx::Conv_456[FLOAT, 64x64x3x3]
%onnx::Conv_459[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235255296,
"params": 3372106,
"val_accuracy": 91.75
} | {
"arch_str": "624",
"identifier": "Hiaml_624",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "624"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 624 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235255296,
"val_accuracy": 91.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3849 | GraphArch:Hiaml:3849 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x3]
%onnx::Conv_411[FLOAT, 64x64x3x1]
%onnx::Conv_414[FLOAT, 64x64x1x3]
%onnx::Conv_417[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 195212800,
"params": 1977930,
"val_accuracy": 92.77
} | {
"arch_str": "3849",
"identifier": "Hiaml_3849",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3849"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3849 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 195212800,
"val_accuracy": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_872 | GraphArch:Hiaml:872 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_381[FLOAT, 64x3x3x3]
%onnx::Conv_382[FLOAT, 64]
%onnx::Conv_384[FLOAT, 64x64x3x3]
%onnx::Conv_387[FLOAT, 64x64x1x1]
%onnx::Conv_390[FLOAT, 64x64x1x1]
%onnx::Conv_393[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 179123712,
"params": 2396234,
"val_accuracy": 92.26
} | {
"arch_str": "872",
"identifier": "Hiaml_872",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "872"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 872 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 179123712,
"val_accuracy": 92.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4052 | GraphArch:Hiaml:4052 | 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, 64x64x1x1]
%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": 170243584,
"params": 2374986,
"val_accuracy": 91.81
} | {
"arch_str": "4052",
"identifier": "Hiaml_4052",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4052"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4052 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170243584,
"val_accuracy": 91.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2143 | GraphArch:Hiaml:2143 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_361[FLOAT, 64x3x3x3]
%onnx::Conv_362[FLOAT, 64]
%onnx::Conv_364[FLOAT, 64x64x1x1]
%onnx::Conv_367[FLOAT, 64x64x3x3]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213726720,
"params": 2370890,
"val_accuracy": 92.42
} | {
"arch_str": "2143",
"identifier": "Hiaml_2143",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2143"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2143 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213726720,
"val_accuracy": 92.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_808 | GraphArch:Hiaml:808 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_399[FLOAT, 64x3x3x3]
%onnx::Conv_400[FLOAT, 64]
%onnx::Conv_402[FLOAT, 64x64x1x3]
%onnx::Conv_405[FLOAT, 64x64x3x1]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236107264,
"params": 3354186,
"val_accuracy": 92.67
} | {
"arch_str": "808",
"identifier": "Hiaml_808",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "808"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 808 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 236107264,
"val_accuracy": 92.67
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3607 | GraphArch:Hiaml:3607 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[FLOAT, 64x64x1x3]
%onnx::Conv_462[FLOAT, 64x64x3x1]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198620672,
"params": 2472010,
"val_accuracy": 92.57
} | {
"arch_str": "3607",
"identifier": "Hiaml_3607",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3607"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3607 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 198620672,
"val_accuracy": 92.57
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3256 | GraphArch:Hiaml:3256 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_395[FLOAT, 64x3x3x3]
%onnx::Conv_396[FLOAT, 64]
%onnx::Conv_398[FLOAT, 64x64x1x1]
%onnx::Conv_401[FLOAT, 64x64x1x3]
%onnx::Conv_404[FLOAT, 64x64x3x1]
%onnx::Conv_407[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 165590528,
"params": 2363466,
"val_accuracy": 91.99
} | {
"arch_str": "3256",
"identifier": "Hiaml_3256",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3256"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3256 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 165590528,
"val_accuracy": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_922 | GraphArch:Hiaml:922 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x3]
%onnx::Conv_415[FLOAT, 64x64x3x1]
%onnx::Conv_418[FLOAT, 64x64x1x3]
%onnx::Conv_421[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244594176,
"params": 2744266,
"val_accuracy": 93.16
} | {
"arch_str": "922",
"identifier": "Hiaml_922",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "922"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 922 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244594176,
"val_accuracy": 93.16
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2444 | GraphArch:Hiaml:2444 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_341[FLOAT, 64x3x3x3]
%onnx::Conv_342[FLOAT, 64]
%onnx::Conv_344[FLOAT, 64x64x3x3]
%onnx::Conv_347[FLOAT, 64x64x1x3]
%onnx::Conv_350[FLOAT, 64x64x3x1]
%onnx::Conv_353[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193574400,
"params": 1896010,
"val_accuracy": 92.58
} | {
"arch_str": "2444",
"identifier": "Hiaml_2444",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2444"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2444 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193574400,
"val_accuracy": 92.58
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_66 | GraphArch:Hiaml:66 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_470[FLOAT, 64x3x3x3]
%onnx::Conv_471[FLOAT, 64]
%onnx::Conv_473[FLOAT, 64x64x1x3]
%onnx::Conv_476[FLOAT, 64x64x3x1]
%onnx::Conv_479[FLOAT, 64x64x1x3]
%onnx::Conv_482[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244692480,
"params": 2832970,
"val_accuracy": 92.96
} | {
"arch_str": "66",
"identifier": "Hiaml_66",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "66"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 66 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244692480,
"val_accuracy": 92.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2201 | GraphArch:Hiaml:2201 | 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": 199833088,
"params": 2504266,
"val_accuracy": 92.8
} | {
"arch_str": "2201",
"identifier": "Hiaml_2201",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2201"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2201 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199833088,
"val_accuracy": 92.8
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2209 | GraphArch:Hiaml:2209 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_379[FLOAT, 64x3x3x3]
%onnx::Conv_380[FLOAT, 64]
%onnx::Conv_382[FLOAT, 64x64x1x1]
%onnx::Conv_385[FLOAT, 64x64x1x3]
%onnx::Conv_388[FLOAT, 64x64x3x1]
%onnx::Conv_391[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223360512,
"params": 2703050,
"val_accuracy": 93.04
} | {
"arch_str": "2209",
"identifier": "Hiaml_2209",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2209"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2209 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223360512,
"val_accuracy": 93.04
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_237 | GraphArch:Hiaml:237 | 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": 255604224,
"params": 2749002,
"val_accuracy": 92.45
} | {
"arch_str": "237",
"identifier": "Hiaml_237",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "237"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 237 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 255604224,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1206 | GraphArch:Hiaml:1206 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_357[FLOAT, 64x3x3x3]
%onnx::Conv_358[FLOAT, 64]
%onnx::Conv_360[FLOAT, 64x64x1x1]
%onnx::Conv_363[FLOAT, 64x64x1x3]
%onnx::Conv_366[FLOAT, 64x64x3x1]
%onnx::Conv_369[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 187708928,
"params": 2423754,
"val_accuracy": 92.23
} | {
"arch_str": "1206",
"identifier": "Hiaml_1206",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1206"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1206 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 187708928,
"val_accuracy": 92.23
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4463 | GraphArch:Hiaml:4463 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x3x3]
%onnx::Conv_425[FLOAT, 64x64x1x3]
%onnx::Conv_428[FLOAT, 64x64x3x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202421760,
"params": 2060874,
"val_accuracy": 92.17
} | {
"arch_str": "4463",
"identifier": "Hiaml_4463",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4463"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4463 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202421760,
"val_accuracy": 92.17
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_970 | GraphArch:Hiaml:970 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_345[FLOAT, 64x3x3x3]
%onnx::Conv_346[FLOAT, 64]
%onnx::Conv_348[FLOAT, 64x64x1x1]
%onnx::Conv_351[FLOAT, 64x64x1x3]
%onnx::Conv_354[FLOAT, 64x64x3x1]
%onnx::Conv_357[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 173716992,
"params": 1764426,
"val_accuracy": 92.38
} | {
"arch_str": "970",
"identifier": "Hiaml_970",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "970"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 970 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 173716992,
"val_accuracy": 92.38
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1641 | GraphArch:Hiaml:1641 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_381[FLOAT, 64x3x3x3]
%onnx::Conv_382[FLOAT, 64]
%onnx::Conv_384[FLOAT, 64x64x3x3]
%onnx::Conv_387[FLOAT, 64x64x1x3]
%onnx::Conv_390[FLOAT, 64x64x3x1]
%onnx::Conv_393[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197047808,
"params": 1920330,
"val_accuracy": 92.19
} | {
"arch_str": "1641",
"identifier": "Hiaml_1641",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1641"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1641 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197047808,
"val_accuracy": 92.19
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1553 | GraphArch:Hiaml:1553 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_349[FLOAT, 64x3x3x3]
%onnx::Conv_350[FLOAT, 64]
%onnx::Conv_352[FLOAT, 64x64x3x3]
%onnx::Conv_355[FLOAT, 64x64x1x3]
%onnx::Conv_358[FLOAT, 64x64x3x1]
%onnx::Conv_361[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185251328,
"params": 2321482,
"val_accuracy": 92.64
} | {
"arch_str": "1553",
"identifier": "Hiaml_1553",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1553"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1553 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185251328,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1379 | GraphArch:Hiaml:1379 | 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": 240563712,
"params": 2825034,
"val_accuracy": 92.42
} | {
"arch_str": "1379",
"identifier": "Hiaml_1379",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1379"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1379 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240563712,
"val_accuracy": 92.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_359 | GraphArch:Hiaml:359 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_523[FLOAT, 64x3x3x3]
%onnx::Conv_524[FLOAT, 64]
%onnx::Conv_526[FLOAT, 64x64x1x1]
%onnx::Conv_529[FLOAT, 64x64x1x1]
%onnx::Conv_532[FLOAT, 64x64x3x3]
%onnx::Conv_535[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210286080,
"params": 2588746,
"val_accuracy": 92.19
} | {
"arch_str": "359",
"identifier": "Hiaml_359",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "359"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 359 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210286080,
"val_accuracy": 92.19
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_487 | GraphArch:Hiaml:487 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x1x1]
%onnx::Conv_379[FLOAT, 64x64x1x3]
%onnx::Conv_382[FLOAT, 64x64x3x1]
%onnx::Conv_385[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199046656,
"params": 2650186,
"val_accuracy": 91.42
} | {
"arch_str": "487",
"identifier": "Hiaml_487",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "487"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 487 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199046656,
"val_accuracy": 91.42
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1951 | GraphArch:Hiaml:1951 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x1x1]
%onnx::Conv_375[FLOAT, 64x64x3x3]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194885120,
"params": 1805386,
"val_accuracy": 92.43
} | {
"arch_str": "1951",
"identifier": "Hiaml_1951",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1951"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1951 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194885120,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_880 | GraphArch:Hiaml:880 | 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": 91.92
} | {
"arch_str": "880",
"identifier": "Hiaml_880",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "880"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 880 | Predict neural architecture validation accuracy and compute cost from 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": 91.92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_40 | GraphArch:Hiaml:40 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_421[FLOAT, 64x3x3x3]
%onnx::Conv_422[FLOAT, 64]
%onnx::Conv_424[FLOAT, 64x64x1x1]
%onnx::Conv_427[FLOAT, 64x64x1x3]
%onnx::Conv_430[FLOAT, 64x64x3x1]
%onnx::Conv_433[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205764096,
"params": 2257482,
"val_accuracy": 92.37
} | {
"arch_str": "40",
"identifier": "Hiaml_40",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "40"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 40 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205764096,
"val_accuracy": 92.37
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_412 | GraphArch:Hiaml:412 | 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, 64x64x3x3]
%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": 244332032,
"params": 2707530,
"val_accuracy": 92.44
} | {
"arch_str": "412",
"identifier": "Hiaml_412",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "412"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 412 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244332032,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2483 | GraphArch:Hiaml:2483 | 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, 64x64x1x1]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 204420608,
"params": 2063818,
"val_accuracy": 92.82
} | {
"arch_str": "2483",
"identifier": "Hiaml_2483",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2483"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2483 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 204420608,
"val_accuracy": 92.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3590 | GraphArch:Hiaml:3590 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x1x3]
%onnx::Conv_443[FLOAT, 64x64x3x1]
%onnx::Conv_446[FLOAT, 64x64x1x3]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199439872,
"params": 2528074,
"val_accuracy": 92.27
} | {
"arch_str": "3590",
"identifier": "Hiaml_3590",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3590"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3590 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199439872,
"val_accuracy": 92.27
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1950 | GraphArch:Hiaml:1950 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_327[FLOAT, 64x3x3x3]
%onnx::Conv_328[FLOAT, 64]
%onnx::Conv_330[FLOAT, 64x64x3x3]
%onnx::Conv_333[FLOAT, 64x64x1x3]
%onnx::Conv_336[FLOAT, 64x64x3x1]
%onnx::Conv_339[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214480384,
"params": 2550346,
"val_accuracy": 92.84
} | {
"arch_str": "1950",
"identifier": "Hiaml_1950",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1950"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1950 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214480384,
"val_accuracy": 92.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1388 | GraphArch:Hiaml:1388 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_489[FLOAT, 64x3x3x3]
%onnx::Conv_490[FLOAT, 64]
%onnx::Conv_492[FLOAT, 64x64x1x3]
%onnx::Conv_495[FLOAT, 64x64x3x1]
%onnx::Conv_498[FLOAT, 64x64x1x1]
%onnx::Conv_501[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211334656,
"params": 2570826,
"val_accuracy": 92
} | {
"arch_str": "1388",
"identifier": "Hiaml_1388",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1388"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1388 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211334656,
"val_accuracy": 92
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1710 | GraphArch:Hiaml:1710 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_466[FLOAT, 64x3x3x3]
%onnx::Conv_467[FLOAT, 64]
%onnx::Conv_469[FLOAT, 64x64x1x1]
%onnx::Conv_472[FLOAT, 64x64x1x3]
%onnx::Conv_475[FLOAT, 64x64x3x1]
%onnx::Conv_478[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184858112,
"params": 2489418,
"val_accuracy": 91.96
} | {
"arch_str": "1710",
"identifier": "Hiaml_1710",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1710"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1710 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 184858112,
"val_accuracy": 91.96
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1195 | GraphArch:Hiaml:1195 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_363[FLOAT, 64x3x3x3]
%onnx::Conv_364[FLOAT, 64]
%onnx::Conv_366[FLOAT, 64x64x3x3]
%onnx::Conv_369[FLOAT, 64x64x1x3]
%onnx::Conv_372[FLOAT, 64x64x3x1]
%onnx::Conv_375[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 269235712,
"params": 2903114,
"val_accuracy": 92.86
} | {
"arch_str": "1195",
"identifier": "Hiaml_1195",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1195"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1195 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 269235712,
"val_accuracy": 92.86
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_669 | GraphArch:Hiaml:669 | 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, 64x64x1x1]
%onnx::Conv_384[FLOAT, 64x64x3x3]
%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": 178992640,
"params": 2027850,
"val_accuracy": 92.21
} | {
"arch_str": "669",
"identifier": "Hiaml_669",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "669"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 669 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178992640,
"val_accuracy": 92.21
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4258 | GraphArch:Hiaml:4258 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x1x3]
%onnx::Conv_379[FLOAT, 64x64x3x1]
%onnx::Conv_382[FLOAT, 64x64x1x1]
%onnx::Conv_385[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 248559104,
"params": 3451978,
"val_accuracy": 92.49
} | {
"arch_str": "4258",
"identifier": "Hiaml_4258",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4258"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4258 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 248559104,
"val_accuracy": 92.49
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2419 | GraphArch:Hiaml:2419 | 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": 231716352,
"params": 2783818,
"val_accuracy": 91.86
} | {
"arch_str": "2419",
"identifier": "Hiaml_2419",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2419"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2419 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231716352,
"val_accuracy": 91.86
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2741 | GraphArch:Hiaml:2741 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_548[FLOAT, 64x3x3x3]
%onnx::Conv_549[FLOAT, 64]
%onnx::Conv_551[FLOAT, 64x64x1x3]
%onnx::Conv_554[FLOAT, 64x64x3x1]
%onnx::Conv_557[FLOAT, 64x64x1x3]
%onnx::Conv_560[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214644224,
"params": 2597194,
"val_accuracy": 92.09
} | {
"arch_str": "2741",
"identifier": "Hiaml_2741",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2741"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2741 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214644224,
"val_accuracy": 92.09
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3512 | GraphArch:Hiaml:3512 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_389[FLOAT, 64x3x3x3]
%onnx::Conv_390[FLOAT, 64]
%onnx::Conv_392[FLOAT, 64x64x1x1]
%onnx::Conv_395[FLOAT, 64x64x1x3]
%onnx::Conv_398[FLOAT, 64x64x3x1]
%onnx::Conv_401[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 150943232,
"params": 2200906,
"val_accuracy": 91.95
} | {
"arch_str": "3512",
"identifier": "Hiaml_3512",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3512"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3512 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 150943232,
"val_accuracy": 91.95
} | full |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.