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_748 | GraphArch:Hiaml:748 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_417[FLOAT, 64x3x3x3]
%onnx::Conv_418[FLOAT, 64]
%onnx::Conv_420[FLOAT, 64x64x1x3]
%onnx::Conv_423[FLOAT, 64x64x3x1]
%onnx::Conv_426[FLOAT, 64x64x1x3]
%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": 172406272,
"params": 1773386,
"val_accuracy": 92.4
} | {
"arch_str": "748",
"identifier": "Hiaml_748",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "748"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 748 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 172406272,
"val_accuracy": 92.4
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4025 | GraphArch:Hiaml:4025 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_481[FLOAT, 64x3x3x3]
%onnx::Conv_482[FLOAT, 64]
%onnx::Conv_484[FLOAT, 64x64x1x3]
%onnx::Conv_487[FLOAT, 64x64x3x1]
%onnx::Conv_490[FLOAT, 64x64x1x3]
%onnx::Conv_493[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230176256,
"params": 2667850,
"val_accuracy": 92.36
} | {
"arch_str": "4025",
"identifier": "Hiaml_4025",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4025"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4025 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 230176256,
"val_accuracy": 92.36
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3305 | GraphArch:Hiaml:3305 | 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": 181908992,
"params": 1848906,
"val_accuracy": 92.64
} | {
"arch_str": "3305",
"identifier": "Hiaml_3305",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3305"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3305 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181908992,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1489 | GraphArch:Hiaml:1489 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x1x3]
%onnx::Conv_418[FLOAT, 64x64x3x1]
%onnx::Conv_421[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 168932864,
"params": 1896522,
"val_accuracy": 92.2
} | {
"arch_str": "1489",
"identifier": "Hiaml_1489",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1489"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1489 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 168932864,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1758 | GraphArch:Hiaml:1758 | 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": 182629888,
"params": 1683018,
"val_accuracy": 92.68
} | {
"arch_str": "1758",
"identifier": "Hiaml_1758",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1758"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1758 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 182629888,
"val_accuracy": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3847 | GraphArch:Hiaml:3847 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x3x3]
%onnx::Conv_417[FLOAT, 64x64x1x1]
%onnx::Conv_420[FLOAT, 64x64x3x3]
%onnx::Conv_423[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189576704,
"params": 2479946,
"val_accuracy": 92.2
} | {
"arch_str": "3847",
"identifier": "Hiaml_3847",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3847"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3847 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189576704,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1177 | GraphArch:Hiaml:1177 | 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, 64x64x3x3]
%onnx::Conv_382[FLOAT, 64x64x3x3]
%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": 238827008,
"params": 2273098,
"val_accuracy": 93.15
} | {
"arch_str": "1177",
"identifier": "Hiaml_1177",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1177"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1177 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 238827008,
"val_accuracy": 93.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4455 | GraphArch:Hiaml:4455 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[FLOAT, 64x64x1x3]
%onnx::Conv_414[FLOAT, 64x64x3x1]
%onnx::Conv_417[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 161527296,
"params": 2307402,
"val_accuracy": 91.78
} | {
"arch_str": "4455",
"identifier": "Hiaml_4455",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4455"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4455 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 161527296,
"val_accuracy": 91.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2158 | GraphArch:Hiaml:2158 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_323[FLOAT, 64x3x3x3]
%onnx::Conv_324[FLOAT, 64]
%onnx::Conv_326[FLOAT, 64x64x3x3]
%onnx::Conv_329[FLOAT, 64x64x1x1]
%onnx::Conv_332[FLOAT, 64x64x1x1]
%onnx::Conv_335[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222934528,
"params": 2222154,
"val_accuracy": 92.09
} | {
"arch_str": "2158",
"identifier": "Hiaml_2158",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2158"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2158 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222934528,
"val_accuracy": 92.09
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4150 | GraphArch:Hiaml:4150 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_475[FLOAT, 64x3x3x3]
%onnx::Conv_476[FLOAT, 64]
%onnx::Conv_478[FLOAT, 64x64x1x1]
%onnx::Conv_481[FLOAT, 64x64x1x3]
%onnx::Conv_484[FLOAT, 64x64x3x1]
%onnx::Conv_487[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170440192,
"params": 1709898,
"val_accuracy": 91.93
} | {
"arch_str": "4150",
"identifier": "Hiaml_4150",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4150"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4150 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170440192,
"val_accuracy": 91.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4505 | GraphArch:Hiaml:4505 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_572[FLOAT, 64x3x3x3]
%onnx::Conv_573[FLOAT, 64]
%onnx::Conv_575[FLOAT, 64x64x1x3]
%onnx::Conv_578[FLOAT, 64x64x3x1]
%onnx::Conv_581[FLOAT, 64x64x1x1]
%onnx::Conv_584[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219067904,
"params": 2630474,
"val_accuracy": 92.03
} | {
"arch_str": "4505",
"identifier": "Hiaml_4505",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4505"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4505 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219067904,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2729 | GraphArch:Hiaml:2729 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_444[FLOAT, 64x3x3x3]
%onnx::Conv_445[FLOAT, 64]
%onnx::Conv_447[FLOAT, 64x64x3x3]
%onnx::Conv_450[FLOAT, 64x64x1x1]
%onnx::Conv_453[FLOAT, 64x64x3x3]
%onnx::Conv_456[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206616064,
"params": 2610506,
"val_accuracy": 92.56
} | {
"arch_str": "2729",
"identifier": "Hiaml_2729",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2729"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2729 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206616064,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1958 | GraphArch:Hiaml:1958 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_454[FLOAT, 64x3x3x3]
%onnx::Conv_455[FLOAT, 64]
%onnx::Conv_457[FLOAT, 64x64x1x1]
%onnx::Conv_460[FLOAT, 64x64x1x3]
%onnx::Conv_463[FLOAT, 64x64x3x1]
%onnx::Conv_466[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202749440,
"params": 2135882,
"val_accuracy": 92.19
} | {
"arch_str": "1958",
"identifier": "Hiaml_1958",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1958"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1958 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202749440,
"val_accuracy": 92.19
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1126 | GraphArch:Hiaml:1126 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_463[FLOAT, 64x3x3x3]
%onnx::Conv_464[FLOAT, 64]
%onnx::Conv_466[FLOAT, 64x64x1x3]
%onnx::Conv_469[FLOAT, 64x64x3x1]
%onnx::Conv_472[FLOAT, 64x64x1x1]
%onnx::Conv_475[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 211301888,
"params": 2594122,
"val_accuracy": 92.2
} | {
"arch_str": "1126",
"identifier": "Hiaml_1126",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1126"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1126 | Predict neural architecture validation accuracy and compute cost from 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": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_471 | GraphArch:Hiaml:471 | 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, 64x64x3x3]
%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": 201602560,
"params": 2325066,
"val_accuracy": 92.41
} | {
"arch_str": "471",
"identifier": "Hiaml_471",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "471"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 471 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201602560,
"val_accuracy": 92.41
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3508 | GraphArch:Hiaml:3508 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_371[FLOAT, 64x3x3x3]
%onnx::Conv_372[FLOAT, 64]
%onnx::Conv_374[FLOAT, 64x64x1x3]
%onnx::Conv_377[FLOAT, 64x64x3x1]
%onnx::Conv_380[FLOAT, 64x64x1x3]
%onnx::Conv_383[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206681600,
"params": 2042442,
"val_accuracy": 92.56
} | {
"arch_str": "3508",
"identifier": "Hiaml_3508",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3508"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3508 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206681600,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2455 | GraphArch:Hiaml:2455 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_473[FLOAT, 64x3x3x3]
%onnx::Conv_474[FLOAT, 64]
%onnx::Conv_476[FLOAT, 64x64x1x3]
%onnx::Conv_479[FLOAT, 64x64x3x1]
%onnx::Conv_482[FLOAT, 64x64x1x1]
%onnx::Conv_485[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197834240,
"params": 1971018,
"val_accuracy": 92.54
} | {
"arch_str": "2455",
"identifier": "Hiaml_2455",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2455"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2455 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 197834240,
"val_accuracy": 92.54
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3602 | GraphArch:Hiaml:3602 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_437[FLOAT, 64x3x3x3]
%onnx::Conv_438[FLOAT, 64]
%onnx::Conv_440[FLOAT, 64x64x1x1]
%onnx::Conv_443[FLOAT, 64x64x1x1]
%onnx::Conv_446[FLOAT, 64x64x3x3]
%onnx::Conv_449[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 265401856,
"params": 3021130,
"val_accuracy": 92.13
} | {
"arch_str": "3602",
"identifier": "Hiaml_3602",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3602"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3602 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 265401856,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_489 | GraphArch:Hiaml:489 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206616064,
"params": 2093642,
"val_accuracy": 91.99
} | {
"arch_str": "489",
"identifier": "Hiaml_489",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "489"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 489 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 206616064,
"val_accuracy": 91.99
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4221 | GraphArch:Hiaml:4221 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_343[FLOAT, 64x3x3x3]
%onnx::Conv_344[FLOAT, 64]
%onnx::Conv_346[FLOAT, 64x64x3x3]
%onnx::Conv_349[FLOAT, 64x64x1x3]
%onnx::Conv_352[FLOAT, 64x64x3x1]
%onnx::Conv_355[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227259904,
"params": 2157130,
"val_accuracy": 91.89
} | {
"arch_str": "4221",
"identifier": "Hiaml_4221",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4221"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4221 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 227259904,
"val_accuracy": 91.89
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1679 | GraphArch:Hiaml:1679 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_411[FLOAT, 64x3x3x3]
%onnx::Conv_412[FLOAT, 64]
%onnx::Conv_414[FLOAT, 64x64x3x3]
%onnx::Conv_417[FLOAT, 64x64x1x3]
%onnx::Conv_420[FLOAT, 64x64x3x1]
%onnx::Conv_423[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188790272,
"params": 1844170,
"val_accuracy": 92.56
} | {
"arch_str": "1679",
"identifier": "Hiaml_1679",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1679"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1679 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188790272,
"val_accuracy": 92.56
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_430 | GraphArch:Hiaml:430 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_423[FLOAT, 64x3x3x3]
%onnx::Conv_424[FLOAT, 64]
%onnx::Conv_426[FLOAT, 64x64x1x3]
%onnx::Conv_429[FLOAT, 64x64x3x1]
%onnx::Conv_432[FLOAT, 64x64x1x3]
%onnx::Conv_435[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188888576,
"params": 2299210,
"val_accuracy": 92.76
} | {
"arch_str": "430",
"identifier": "Hiaml_430",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "430"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 430 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188888576,
"val_accuracy": 92.76
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3326 | GraphArch:Hiaml:3326 | 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": 208877056,
"params": 2135626,
"val_accuracy": 92.23
} | {
"arch_str": "3326",
"identifier": "Hiaml_3326",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3326"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3326 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 208877056,
"val_accuracy": 92.23
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2817 | GraphArch:Hiaml:2817 | 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": 168277504,
"params": 1827274,
"val_accuracy": 92.33
} | {
"arch_str": "2817",
"identifier": "Hiaml_2817",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2817"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2817 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 168277504,
"val_accuracy": 92.33
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3777 | GraphArch:Hiaml:3777 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_397[FLOAT, 64x3x3x3]
%onnx::Conv_398[FLOAT, 64]
%onnx::Conv_400[FLOAT, 64x64x1x1]
%onnx::Conv_403[FLOAT, 64x64x1x3]
%onnx::Conv_406[FLOAT, 64x64x3x1]
%onnx::Conv_409[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 139572736,
"params": 1643338,
"val_accuracy": 92.08
} | {
"arch_str": "3777",
"identifier": "Hiaml_3777",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3777"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3777 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 139572736,
"val_accuracy": 92.08
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1317 | GraphArch:Hiaml:1317 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_538[FLOAT, 64x3x3x3]
%onnx::Conv_539[FLOAT, 64]
%onnx::Conv_541[FLOAT, 64x64x1x3]
%onnx::Conv_544[FLOAT, 64x64x3x1]
%onnx::Conv_547[FLOAT, 64x64x1x3]
%onnx::Conv_550[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215594496,
"params": 2629962,
"val_accuracy": 91.91
} | {
"arch_str": "1317",
"identifier": "Hiaml_1317",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1317"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1317 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 215594496,
"val_accuracy": 91.91
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1713 | GraphArch:Hiaml:1713 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_355[FLOAT, 64x3x3x3]
%onnx::Conv_356[FLOAT, 64]
%onnx::Conv_358[FLOAT, 64x64x3x3]
%onnx::Conv_361[FLOAT, 64x64x1x3]
%onnx::Conv_364[FLOAT, 64x64x3x1]
%onnx::Conv_367[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 252360192,
"params": 2845770,
"val_accuracy": 93.03
} | {
"arch_str": "1713",
"identifier": "Hiaml_1713",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1713"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1713 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 252360192,
"val_accuracy": 93.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3571 | GraphArch:Hiaml:3571 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_439[FLOAT, 64x3x3x3]
%onnx::Conv_440[FLOAT, 64]
%onnx::Conv_442[FLOAT, 64x64x1x3]
%onnx::Conv_445[FLOAT, 64x64x3x1]
%onnx::Conv_448[FLOAT, 64x64x1x1]
%onnx::Conv_451[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 194295296,
"params": 2438730,
"val_accuracy": 92.68
} | {
"arch_str": "3571",
"identifier": "Hiaml_3571",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3571"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3571 | Predict neural architecture validation accuracy and compute cost from 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": 92.68
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3314 | GraphArch:Hiaml:3314 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x1x1]
%onnx::Conv_375[FLOAT, 64x64x1x1]
%onnx::Conv_378[FLOAT, 64x64x3x3]
%onnx::Conv_381[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239023616,
"params": 2739274,
"val_accuracy": 92.45
} | {
"arch_str": "3314",
"identifier": "Hiaml_3314",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3314"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3314 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239023616,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3635 | GraphArch:Hiaml:3635 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_343[FLOAT, 64x3x3x3]
%onnx::Conv_344[FLOAT, 64]
%onnx::Conv_346[FLOAT, 64x64x3x3]
%onnx::Conv_349[FLOAT, 64x64x1x1]
%onnx::Conv_352[FLOAT, 64x64x1x1]
%onnx::Conv_355[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 162182656,
"params": 1576010,
"val_accuracy": 92.35
} | {
"arch_str": "3635",
"identifier": "Hiaml_3635",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3635"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3635 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 162182656,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2685 | GraphArch:Hiaml:2685 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[FLOAT, 64x64x1x1]
%onnx::Conv_462[FLOAT, 64x64x3x3]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210023936,
"params": 2476746,
"val_accuracy": 93.18
} | {
"arch_str": "2685",
"identifier": "Hiaml_2685",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2685"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2685 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210023936,
"val_accuracy": 93.18
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2946 | GraphArch:Hiaml:2946 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_405[FLOAT, 64x3x3x3]
%onnx::Conv_406[FLOAT, 64]
%onnx::Conv_408[FLOAT, 64x64x1x1]
%onnx::Conv_411[FLOAT, 64x64x1x3]
%onnx::Conv_414[FLOAT, 64x64x3x1]
%onnx::Conv_417[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185939456,
"params": 2297674,
"val_accuracy": 92.45
} | {
"arch_str": "2946",
"identifier": "Hiaml_2946",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2946"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2946 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185939456,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2712 | GraphArch:Hiaml:2712 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_445[FLOAT, 64x3x3x3]
%onnx::Conv_446[FLOAT, 64]
%onnx::Conv_448[FLOAT, 64x64x3x3]
%onnx::Conv_451[FLOAT, 64x64x1x1]
%onnx::Conv_454[FLOAT, 64x64x1x1]
%onnx::Conv_457[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 191051264,
"params": 2487882,
"val_accuracy": 92.26
} | {
"arch_str": "2712",
"identifier": "Hiaml_2712",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2712"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2712 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 191051264,
"val_accuracy": 92.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1918 | GraphArch:Hiaml:1918 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_425[FLOAT, 64x3x3x3]
%onnx::Conv_426[FLOAT, 64]
%onnx::Conv_428[FLOAT, 64x64x3x3]
%onnx::Conv_431[FLOAT, 64x64x1x1]
%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": 186562048,
"params": 2431306,
"val_accuracy": 92.45
} | {
"arch_str": "1918",
"identifier": "Hiaml_1918",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1918"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1918 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 186562048,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_420 | GraphArch:Hiaml:420 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_367[FLOAT, 64x3x3x3]
%onnx::Conv_368[FLOAT, 64]
%onnx::Conv_370[FLOAT, 64x64x1x1]
%onnx::Conv_373[FLOAT, 64x64x3x3]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%onnx::Conv_379[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 247248384,
"params": 2105290,
"val_accuracy": 93.26
} | {
"arch_str": "420",
"identifier": "Hiaml_420",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "420"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 420 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 247248384,
"val_accuracy": 93.26
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2172 | GraphArch:Hiaml:2172 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_465[FLOAT, 64x3x3x3]
%onnx::Conv_466[FLOAT, 64]
%onnx::Conv_468[FLOAT, 64x64x3x3]
%onnx::Conv_471[FLOAT, 64x64x1x3]
%onnx::Conv_474[FLOAT, 64x64x3x1]
%onnx::Conv_477[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 252818944,
"params": 2849866,
"val_accuracy": 91.97
} | {
"arch_str": "2172",
"identifier": "Hiaml_2172",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2172"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2172 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 252818944,
"val_accuracy": 91.97
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3461 | GraphArch:Hiaml:3461 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x3x3]
%onnx::Conv_467[FLOAT, 64x64x1x1]
%onnx::Conv_470[FLOAT, 64x64x1x1]
%onnx::Conv_473[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193213952,
"params": 2111306,
"val_accuracy": 92.14
} | {
"arch_str": "3461",
"identifier": "Hiaml_3461",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3461"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3461 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193213952,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2435 | GraphArch:Hiaml:2435 | 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, 64x64x1x1]
%onnx::Conv_393[FLOAT, 64x64x1x1]
%onnx::Conv_396[FLOAT, 64x64x3x3]
%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": 209892864,
"params": 1981642,
"val_accuracy": 92.77
} | {
"arch_str": "2435",
"identifier": "Hiaml_2435",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2435"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2435 | Predict neural architecture validation accuracy and compute cost from 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": 92.77
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1494 | GraphArch:Hiaml:1494 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_409[FLOAT, 64x3x3x3]
%onnx::Conv_410[FLOAT, 64]
%onnx::Conv_412[FLOAT, 64x64x1x1]
%onnx::Conv_415[FLOAT, 64x64x3x3]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223458816,
"params": 2027594,
"val_accuracy": 92.84
} | {
"arch_str": "1494",
"identifier": "Hiaml_1494",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1494"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1494 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223458816,
"val_accuracy": 92.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3135 | GraphArch:Hiaml:3135 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_443[FLOAT, 64x3x3x3]
%onnx::Conv_444[FLOAT, 64]
%onnx::Conv_446[FLOAT, 64x64x1x3]
%onnx::Conv_449[FLOAT, 64x64x3x1]
%onnx::Conv_452[FLOAT, 64x64x1x3]
%onnx::Conv_455[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235353600,
"params": 2683210,
"val_accuracy": 93.1
} | {
"arch_str": "3135",
"identifier": "Hiaml_3135",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3135"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3135 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 235353600,
"val_accuracy": 93.1
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_289 | GraphArch:Hiaml:289 | 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": 176469504,
"params": 1792970,
"val_accuracy": 92.24
} | {
"arch_str": "289",
"identifier": "Hiaml_289",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "289"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 289 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176469504,
"val_accuracy": 92.24
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2011 | GraphArch:Hiaml:2011 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x1x1]
%onnx::Conv_467[FLOAT, 64x64x1x3]
%onnx::Conv_470[FLOAT, 64x64x3x1]
%onnx::Conv_473[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260486656,
"params": 2845130,
"val_accuracy": 92.72
} | {
"arch_str": "2011",
"identifier": "Hiaml_2011",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2011"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2011 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260486656,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1324 | GraphArch:Hiaml:1324 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_369[FLOAT, 64x3x3x3]
%onnx::Conv_370[FLOAT, 64]
%onnx::Conv_372[FLOAT, 64x64x3x3]
%onnx::Conv_375[FLOAT, 64x64x1x1]
%onnx::Conv_378[FLOAT, 64x64x1x1]
%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": 205272576,
"params": 2207562,
"val_accuracy": 92.31
} | {
"arch_str": "1324",
"identifier": "Hiaml_1324",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1324"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1324 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 205272576,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_794 | GraphArch:Hiaml:794 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_465[FLOAT, 64x3x3x3]
%onnx::Conv_466[FLOAT, 64]
%onnx::Conv_468[FLOAT, 64x64x1x1]
%onnx::Conv_471[FLOAT, 64x64x1x3]
%onnx::Conv_474[FLOAT, 64x64x3x1]
%onnx::Conv_477[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180991488,
"params": 1938762,
"val_accuracy": 92.44
} | {
"arch_str": "794",
"identifier": "Hiaml_794",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "794"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 794 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 180991488,
"val_accuracy": 92.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3273 | GraphArch:Hiaml:3273 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_461[FLOAT, 64x3x3x3]
%onnx::Conv_462[FLOAT, 64]
%onnx::Conv_464[FLOAT, 64x64x1x1]
%onnx::Conv_467[FLOAT, 64x64x1x3]
%onnx::Conv_470[FLOAT, 64x64x3x1]
%onnx::Conv_473[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 165983744,
"params": 1701450,
"val_accuracy": 91.44
} | {
"arch_str": "3273",
"identifier": "Hiaml_3273",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3273"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3273 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 165983744,
"val_accuracy": 91.44
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3435 | GraphArch:Hiaml:3435 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_373[FLOAT, 64x3x3x3]
%onnx::Conv_374[FLOAT, 64]
%onnx::Conv_376[FLOAT, 64x64x3x3]
%onnx::Conv_379[FLOAT, 64x64x1x3]
%onnx::Conv_382[FLOAT, 64x64x3x1]
%onnx::Conv_385[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 237843968,
"params": 2252490,
"val_accuracy": 93.15
} | {
"arch_str": "3435",
"identifier": "Hiaml_3435",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3435"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3435 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 237843968,
"val_accuracy": 93.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1063 | GraphArch:Hiaml:1063 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_383[FLOAT, 64x3x3x3]
%onnx::Conv_384[FLOAT, 64]
%onnx::Conv_386[FLOAT, 64x64x1x1]
%onnx::Conv_389[FLOAT, 64x64x1x3]
%onnx::Conv_392[FLOAT, 64x64x3x1]
%onnx::Conv_395[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210810368,
"params": 3280714,
"val_accuracy": 91.71
} | {
"arch_str": "1063",
"identifier": "Hiaml_1063",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1063"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1063 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210810368,
"val_accuracy": 91.71
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2989 | GraphArch:Hiaml:2989 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_427[FLOAT, 64x3x3x3]
%onnx::Conv_428[FLOAT, 64]
%onnx::Conv_430[FLOAT, 64x64x3x3]
%onnx::Conv_433[FLOAT, 64x64x1x3]
%onnx::Conv_436[FLOAT, 64x64x3x1]
%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": 193934848,
"params": 2365514,
"val_accuracy": 92.21
} | {
"arch_str": "2989",
"identifier": "Hiaml_2989",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2989"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2989 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193934848,
"val_accuracy": 92.21
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3121 | GraphArch:Hiaml:3121 | 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": 159659520,
"params": 1922122,
"val_accuracy": 91.53
} | {
"arch_str": "3121",
"identifier": "Hiaml_3121",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3121"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3121 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 159659520,
"val_accuracy": 91.53
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1395 | GraphArch:Hiaml:1395 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_433[FLOAT, 64x3x3x3]
%onnx::Conv_434[FLOAT, 64]
%onnx::Conv_436[FLOAT, 64x64x3x3]
%onnx::Conv_439[FLOAT, 64x64x1x1]
%onnx::Conv_442[FLOAT, 64x64x1x1]
%onnx::Conv_445[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193050112,
"params": 2467786,
"val_accuracy": 92.2
} | {
"arch_str": "1395",
"identifier": "Hiaml_1395",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1395"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1395 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193050112,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1796 | GraphArch:Hiaml:1796 | 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": 219985408,
"params": 1830474,
"val_accuracy": 92.73
} | {
"arch_str": "1796",
"identifier": "Hiaml_1796",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1796"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1796 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219985408,
"val_accuracy": 92.73
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3219 | GraphArch:Hiaml:3219 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_367[FLOAT, 64x3x3x3]
%onnx::Conv_368[FLOAT, 64]
%onnx::Conv_370[FLOAT, 64x64x3x3]
%onnx::Conv_373[FLOAT, 64x64x1x1]
%onnx::Conv_376[FLOAT, 64x64x1x1]
%onnx::Conv_379[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213693952,
"params": 2039754,
"val_accuracy": 93.06
} | {
"arch_str": "3219",
"identifier": "Hiaml_3219",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3219"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3219 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 213693952,
"val_accuracy": 93.06
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4011 | GraphArch:Hiaml:4011 | 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": 201602560,
"params": 2151498,
"val_accuracy": 92.2
} | {
"arch_str": "4011",
"identifier": "Hiaml_4011",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4011"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4011 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 201602560,
"val_accuracy": 92.2
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1751 | GraphArch:Hiaml:1751 | 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, 64x64x3x3]
%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": 254883328,
"params": 2532810,
"val_accuracy": 92.91
} | {
"arch_str": "1751",
"identifier": "Hiaml_1751",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1751"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1751 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 254883328,
"val_accuracy": 92.91
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_857 | GraphArch:Hiaml:857 | 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, 64x64x1x3]
%onnx::Conv_435[FLOAT, 64x64x3x1]
%onnx::Conv_438[FLOAT, 64x64x1x3]
%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": 176436736,
"params": 1954890,
"val_accuracy": 91.51
} | {
"arch_str": "857",
"identifier": "Hiaml_857",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "857"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 857 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 176436736,
"val_accuracy": 91.51
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_424 | GraphArch:Hiaml:424 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_377[FLOAT, 64x3x3x3]
%onnx::Conv_378[FLOAT, 64]
%onnx::Conv_380[FLOAT, 64x64x1x1]
%onnx::Conv_383[FLOAT, 64x64x3x3]
%onnx::Conv_386[FLOAT, 64x64x3x3]
%onnx::Conv_389[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207468032,
"params": 2002762,
"val_accuracy": 93.02
} | {
"arch_str": "424",
"identifier": "Hiaml_424",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "424"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 424 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207468032,
"val_accuracy": 93.02
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3873 | GraphArch:Hiaml:3873 | 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, 64x64x1x3]
%onnx::Conv_435[FLOAT, 64x64x3x1]
%onnx::Conv_438[FLOAT, 64x64x1x1]
%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": 185906688,
"params": 2373194,
"val_accuracy": 91.81
} | {
"arch_str": "3873",
"identifier": "Hiaml_3873",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3873"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3873 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 185906688,
"val_accuracy": 91.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_366 | GraphArch:Hiaml:366 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_425[FLOAT, 64x3x3x3]
%onnx::Conv_426[FLOAT, 64]
%onnx::Conv_428[FLOAT, 64x64x1x3]
%onnx::Conv_431[FLOAT, 64x64x3x1]
%onnx::Conv_434[FLOAT, 64x64x1x3]
%onnx::Conv_437[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 203470336,
"params": 2537546,
"val_accuracy": 92.48
} | {
"arch_str": "366",
"identifier": "Hiaml_366",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "366"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 366 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 203470336,
"val_accuracy": 92.48
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4426 | GraphArch:Hiaml:4426 | 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": 193508864,
"params": 2510154,
"val_accuracy": 92.06
} | {
"arch_str": "4426",
"identifier": "Hiaml_4426",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4426"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4426 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 193508864,
"val_accuracy": 92.06
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1441 | GraphArch:Hiaml:1441 | 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, 64x64x3x3]
%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": 222410240,
"params": 2118474,
"val_accuracy": 92.13
} | {
"arch_str": "1441",
"identifier": "Hiaml_1441",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1441"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1441 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222410240,
"val_accuracy": 92.13
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4281 | GraphArch:Hiaml:4281 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_551[FLOAT, 64x3x3x3]
%onnx::Conv_552[FLOAT, 64]
%onnx::Conv_554[FLOAT, 64x64x1x3]
%onnx::Conv_557[FLOAT, 64x64x3x1]
%onnx::Conv_560[FLOAT, 64x64x1x1]
%onnx::Conv_563[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214742528,
"params": 2597194,
"val_accuracy": 92.34
} | {
"arch_str": "4281",
"identifier": "Hiaml_4281",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4281"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4281 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214742528,
"val_accuracy": 92.34
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3714 | GraphArch:Hiaml:3714 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_417[FLOAT, 64x3x3x3]
%onnx::Conv_418[FLOAT, 64]
%onnx::Conv_420[FLOAT, 64x64x1x1]
%onnx::Conv_423[FLOAT, 64x64x3x3]
%onnx::Conv_426[FLOAT, 64x64x3x3]
%onnx::Conv_429[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239154688,
"params": 2568522,
"val_accuracy": 92.31
} | {
"arch_str": "3714",
"identifier": "Hiaml_3714",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3714"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3714 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239154688,
"val_accuracy": 92.31
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1032 | GraphArch:Hiaml:1032 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_471[FLOAT, 64x3x3x3]
%onnx::Conv_472[FLOAT, 64]
%onnx::Conv_474[FLOAT, 64x64x1x1]
%onnx::Conv_477[FLOAT, 64x64x1x3]
%onnx::Conv_480[FLOAT, 64x64x3x1]
%onnx::Conv_483[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196687360,
"params": 2320330,
"val_accuracy": 92.58
} | {
"arch_str": "1032",
"identifier": "Hiaml_1032",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1032"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1032 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 196687360,
"val_accuracy": 92.58
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_623 | GraphArch:Hiaml:623 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_407[FLOAT, 64x3x3x3]
%onnx::Conv_408[FLOAT, 64]
%onnx::Conv_410[FLOAT, 64x64x1x1]
%onnx::Conv_413[FLOAT, 64x64x1x1]
%onnx::Conv_416[FLOAT, 64x64x3x3]
%onnx::Conv_419[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188888576,
"params": 2052682,
"val_accuracy": 91.95
} | {
"arch_str": "623",
"identifier": "Hiaml_623",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "623"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 623 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188888576,
"val_accuracy": 91.95
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_144 | GraphArch:Hiaml:144 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_323[FLOAT, 64x3x3x3]
%onnx::Conv_324[FLOAT, 64]
%onnx::Conv_326[FLOAT, 64x64x1x1]
%onnx::Conv_329[FLOAT, 64x64x3x3]
%onnx::Conv_332[FLOAT, 64x64x3x3]
%onnx::Conv_335[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214545920,
"params": 1960010,
"val_accuracy": 92.75
} | {
"arch_str": "144",
"identifier": "Hiaml_144",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "144"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 144 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 214545920,
"val_accuracy": 92.75
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_850 | GraphArch:Hiaml:850 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_463[FLOAT, 64x3x3x3]
%onnx::Conv_464[FLOAT, 64]
%onnx::Conv_466[FLOAT, 64x64x1x3]
%onnx::Conv_469[FLOAT, 64x64x3x1]
%onnx::Conv_472[FLOAT, 64x64x1x3]
%onnx::Conv_475[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260486656,
"params": 2845130,
"val_accuracy": 92.88
} | {
"arch_str": "850",
"identifier": "Hiaml_850",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "850"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 850 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 260486656,
"val_accuracy": 92.88
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_157 | GraphArch:Hiaml:157 | 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": 202487296,
"params": 2653258,
"val_accuracy": 91.5
} | {
"arch_str": "157",
"identifier": "Hiaml_157",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "157"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 157 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202487296,
"val_accuracy": 91.5
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_311 | GraphArch:Hiaml:311 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_563[FLOAT, 64x3x3x3]
%onnx::Conv_564[FLOAT, 64]
%onnx::Conv_566[FLOAT, 64x64x1x3]
%onnx::Conv_569[FLOAT, 64x64x3x1]
%onnx::Conv_572[FLOAT, 64x64x1x1]
%onnx::Conv_575[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199079424,
"params": 2474570,
"val_accuracy": 92.1
} | {
"arch_str": "311",
"identifier": "Hiaml_311",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "311"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 311 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 199079424,
"val_accuracy": 92.1
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4223 | GraphArch:Hiaml:4223 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_351[FLOAT, 64x3x3x3]
%onnx::Conv_352[FLOAT, 64]
%onnx::Conv_354[FLOAT, 64x64x3x3]
%onnx::Conv_357[FLOAT, 64x64x1x3]
%onnx::Conv_360[FLOAT, 64x64x3x1]
%onnx::Conv_363[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207271424,
"params": 1829962,
"val_accuracy": 92.84
} | {
"arch_str": "4223",
"identifier": "Hiaml_4223",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4223"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4223 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207271424,
"val_accuracy": 92.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1093 | GraphArch:Hiaml:1093 | 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, 64x64x3x3]
%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": 234501632,
"params": 2584650,
"val_accuracy": 92.47
} | {
"arch_str": "1093",
"identifier": "Hiaml_1093",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1093"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1093 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 234501632,
"val_accuracy": 92.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_191 | GraphArch:Hiaml:191 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_427[FLOAT, 64x3x3x3]
%onnx::Conv_428[FLOAT, 64]
%onnx::Conv_430[FLOAT, 64x64x1x3]
%onnx::Conv_433[FLOAT, 64x64x3x1]
%onnx::Conv_436[FLOAT, 64x64x1x3]
%onnx::Conv_439[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188986880,
"params": 2421322,
"val_accuracy": 92.37
} | {
"arch_str": "191",
"identifier": "Hiaml_191",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "191"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 191 | Predict neural architecture validation accuracy and compute cost from 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": 92.37
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2208 | GraphArch:Hiaml:2208 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_458[FLOAT, 64x3x3x3]
%onnx::Conv_459[FLOAT, 64]
%onnx::Conv_461[FLOAT, 64x64x1x1]
%onnx::Conv_464[FLOAT, 64x64x1x3]
%onnx::Conv_467[FLOAT, 64x64x3x1]
%onnx::Conv_470[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210122240,
"params": 2315850,
"val_accuracy": 91.78
} | {
"arch_str": "2208",
"identifier": "Hiaml_2208",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2208"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2208 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 210122240,
"val_accuracy": 91.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2683 | GraphArch:Hiaml:2683 | 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": 167622144,
"params": 2368458,
"val_accuracy": 92.16
} | {
"arch_str": "2683",
"identifier": "Hiaml_2683",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2683"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2683 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 167622144,
"val_accuracy": 92.16
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_717 | GraphArch:Hiaml:717 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_433[FLOAT, 64x3x3x3]
%onnx::Conv_434[FLOAT, 64]
%onnx::Conv_436[FLOAT, 64x64x1x1]
%onnx::Conv_439[FLOAT, 64x64x1x3]
%onnx::Conv_442[FLOAT, 64x64x3x1]
%onnx::Conv_445[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223622656,
"params": 2815050,
"val_accuracy": 92.46
} | {
"arch_str": "717",
"identifier": "Hiaml_717",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "717"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 717 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223622656,
"val_accuracy": 92.46
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_499 | GraphArch:Hiaml:499 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_453[FLOAT, 64x3x3x3]
%onnx::Conv_454[FLOAT, 64]
%onnx::Conv_456[FLOAT, 64x64x1x1]
%onnx::Conv_459[FLOAT, 64x64x3x3]
%onnx::Conv_462[FLOAT, 64x64x3x3]
%onnx::Conv_465[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245577216,
"params": 2717258,
"val_accuracy": 92.43
} | {
"arch_str": "499",
"identifier": "Hiaml_499",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "499"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 499 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 245577216,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3261 | GraphArch:Hiaml:3261 | 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": 181417472,
"params": 2093130,
"val_accuracy": 92.45
} | {
"arch_str": "3261",
"identifier": "Hiaml_3261",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3261"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3261 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 181417472,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3740 | GraphArch:Hiaml:3740 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_522[FLOAT, 64x3x3x3]
%onnx::Conv_523[FLOAT, 64]
%onnx::Conv_525[FLOAT, 64x64x1x1]
%onnx::Conv_528[FLOAT, 64x64x1x3]
%onnx::Conv_531[FLOAT, 64x64x3x1]
%onnx::Conv_534[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244004352,
"params": 2801226,
"val_accuracy": 92.27
} | {
"arch_str": "3740",
"identifier": "Hiaml_3740",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3740"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3740 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 244004352,
"val_accuracy": 92.27
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3933 | GraphArch:Hiaml:3933 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_371[FLOAT, 64x3x3x3]
%onnx::Conv_372[FLOAT, 64]
%onnx::Conv_374[FLOAT, 64x64x3x3]
%onnx::Conv_377[FLOAT, 64x64x1x3]
%onnx::Conv_380[FLOAT, 64x64x3x1]
%onnx::Conv_383[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178042368,
"params": 1871434,
"val_accuracy": 92.64
} | {
"arch_str": "3933",
"identifier": "Hiaml_3933",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3933"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3933 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 178042368,
"val_accuracy": 92.64
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4548 | GraphArch:Hiaml:4548 | 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": 251573760,
"params": 2729930,
"val_accuracy": 92.78
} | {
"arch_str": "4548",
"identifier": "Hiaml_4548",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4548"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4548 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 251573760,
"val_accuracy": 92.78
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_762 | GraphArch:Hiaml:762 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_458[FLOAT, 64x3x3x3]
%onnx::Conv_459[FLOAT, 64]
%onnx::Conv_461[FLOAT, 64x64x3x3]
%onnx::Conv_464[FLOAT, 64x64x1x1]
%onnx::Conv_467[FLOAT, 64x64x1x1]
%onnx::Conv_470[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207828480,
"params": 2618954,
"val_accuracy": 92.62
} | {
"arch_str": "762",
"identifier": "Hiaml_762",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "762"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 762 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 207828480,
"val_accuracy": 92.62
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3155 | GraphArch:Hiaml:3155 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_479[FLOAT, 64x3x3x3]
%onnx::Conv_480[FLOAT, 64]
%onnx::Conv_482[FLOAT, 64x64x1x3]
%onnx::Conv_485[FLOAT, 64x64x3x1]
%onnx::Conv_488[FLOAT, 64x64x1x1]
%onnx::Conv_491[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239580672,
"params": 2791754,
"val_accuracy": 92.14
} | {
"arch_str": "3155",
"identifier": "Hiaml_3155",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3155"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3155 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 239580672,
"val_accuracy": 92.14
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3503 | GraphArch:Hiaml:3503 | 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, 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": 188593664,
"params": 2039242,
"val_accuracy": 92.93
} | {
"arch_str": "3503",
"identifier": "Hiaml_3503",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3503"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3503 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 188593664,
"val_accuracy": 92.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1767 | GraphArch:Hiaml:1767 | 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": 156513792,
"params": 1897546,
"val_accuracy": 92.35
} | {
"arch_str": "1767",
"identifier": "Hiaml_1767",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1767"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1767 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 156513792,
"val_accuracy": 92.35
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2528 | GraphArch:Hiaml:2528 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_371[FLOAT, 64x3x3x3]
%onnx::Conv_372[FLOAT, 64]
%onnx::Conv_374[FLOAT, 64x64x1x1]
%onnx::Conv_377[FLOAT, 64x64x3x3]
%onnx::Conv_380[FLOAT, 64x64x3x3]
%onnx::Conv_383[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 254686720,
"params": 2677962,
"val_accuracy": 92.81
} | {
"arch_str": "2528",
"identifier": "Hiaml_2528",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2528"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2528 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 254686720,
"val_accuracy": 92.81
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3877 | GraphArch:Hiaml:3877 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_433[FLOAT, 64x3x3x3]
%onnx::Conv_434[FLOAT, 64]
%onnx::Conv_436[FLOAT, 64x64x1x1]
%onnx::Conv_439[FLOAT, 64x64x1x3]
%onnx::Conv_442[FLOAT, 64x64x3x1]
%onnx::Conv_445[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226899456,
"params": 2323274,
"val_accuracy": 92.03
} | {
"arch_str": "3877",
"identifier": "Hiaml_3877",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3877"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3877 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 226899456,
"val_accuracy": 92.03
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2309 | GraphArch:Hiaml:2309 | 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": 136099328,
"params": 1470026,
"val_accuracy": 91.47
} | {
"arch_str": "2309",
"identifier": "Hiaml_2309",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2309"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2309 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 136099328,
"val_accuracy": 91.47
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_4254 | GraphArch:Hiaml:4254 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[FLOAT, 64x64x3x1]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240432640,
"params": 2797642,
"val_accuracy": 92.15
} | {
"arch_str": "4254",
"identifier": "Hiaml_4254",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "4254"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 4254 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 240432640,
"val_accuracy": 92.15
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1714 | GraphArch:Hiaml:1714 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_446[FLOAT, 64x3x3x3]
%onnx::Conv_447[FLOAT, 64]
%onnx::Conv_449[FLOAT, 64x64x1x3]
%onnx::Conv_452[FLOAT, 64x64x3x1]
%onnx::Conv_455[FLOAT, 64x64x1x3]
%onnx::Conv_458[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222475776,
"params": 2574538,
"val_accuracy": 92.43
} | {
"arch_str": "1714",
"identifier": "Hiaml_1714",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1714"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1714 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222475776,
"val_accuracy": 92.43
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_562 | GraphArch:Hiaml:562 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_413[FLOAT, 64x3x3x3]
%onnx::Conv_414[FLOAT, 64]
%onnx::Conv_416[FLOAT, 64x64x3x3]
%onnx::Conv_419[FLOAT, 64x64x1x1]
%onnx::Conv_422[FLOAT, 64x64x1x1]
%onnx::Conv_425[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 212940288,
"params": 2155210,
"val_accuracy": 92.84
} | {
"arch_str": "562",
"identifier": "Hiaml_562",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "562"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 562 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 212940288,
"val_accuracy": 92.84
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3539 | GraphArch:Hiaml:3539 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_371[FLOAT, 64x3x3x3]
%onnx::Conv_372[FLOAT, 64]
%onnx::Conv_374[FLOAT, 64x64x3x3]
%onnx::Conv_377[FLOAT, 64x64x1x1]
%onnx::Conv_380[FLOAT, 64x64x1x1]
%onnx::Conv_383[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231519744,
"params": 2780746,
"val_accuracy": 91.82
} | {
"arch_str": "3539",
"identifier": "Hiaml_3539",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3539"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3539 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 231519744,
"val_accuracy": 91.82
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3088 | GraphArch:Hiaml:3088 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_419[FLOAT, 64x3x3x3]
%onnx::Conv_420[FLOAT, 64]
%onnx::Conv_422[FLOAT, 64x64x1x3]
%onnx::Conv_425[FLOAT, 64x64x3x1]
%onnx::Conv_428[FLOAT, 64x64x1x1]
%onnx::Conv_431[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223655424,
"params": 3256394,
"val_accuracy": 92.65
} | {
"arch_str": "3088",
"identifier": "Hiaml_3088",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3088"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3088 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 223655424,
"val_accuracy": 92.65
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_1428 | GraphArch:Hiaml:1428 | 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, 64x64x3x3]
%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": 272381440,
"params": 2781770,
"val_accuracy": 92.45
} | {
"arch_str": "1428",
"identifier": "Hiaml_1428",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "1428"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 1428 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 272381440,
"val_accuracy": 92.45
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2523 | GraphArch:Hiaml:2523 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_544[FLOAT, 64x3x3x3]
%onnx::Conv_545[FLOAT, 64]
%onnx::Conv_547[FLOAT, 64x64x3x3]
%onnx::Conv_550[FLOAT, 64x64x1x3]
%onnx::Conv_553[FLOAT, 64x64x3x1]
%onnx::Conv_556[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 228046336,
"params": 2654794,
"val_accuracy": 92.52
} | {
"arch_str": "2523",
"identifier": "Hiaml_2523",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2523"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2523 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 228046336,
"val_accuracy": 92.52
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3730 | GraphArch:Hiaml:3730 | 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, 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": 174011904,
"params": 2428746,
"val_accuracy": 92.19
} | {
"arch_str": "3730",
"identifier": "Hiaml_3730",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3730"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3730 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 174011904,
"val_accuracy": 92.19
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_97 | GraphArch:Hiaml:97 | 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": 202618368,
"params": 2135370,
"val_accuracy": 91.74
} | {
"arch_str": "97",
"identifier": "Hiaml_97",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "97"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 97 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 202618368,
"val_accuracy": 91.74
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_2636 | GraphArch:Hiaml:2636 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x3x3]
%onnx::Conv_421[FLOAT, 64x64x1x3]
%onnx::Conv_424[FLOAT, 64x64x3x1]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222344704,
"params": 2008010,
"val_accuracy": 92.93
} | {
"arch_str": "2636",
"identifier": "Hiaml_2636",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "2636"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 2636 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222344704,
"val_accuracy": 92.93
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_3493 | GraphArch:Hiaml:3493 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_435[FLOAT, 64x3x3x3]
%onnx::Conv_436[FLOAT, 64]
%onnx::Conv_438[FLOAT, 64x64x1x1]
%onnx::Conv_441[FLOAT, 64x64x3x3]
%onnx::Conv_444[FLOAT, 64x64x3x3]
%onnx::Conv_447[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222410240,
"params": 2118474,
"val_accuracy": 92.72
} | {
"arch_str": "3493",
"identifier": "Hiaml_3493",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "3493"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 3493 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 222410240,
"val_accuracy": 92.72
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_680 | GraphArch:Hiaml:680 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_415[FLOAT, 64x3x3x3]
%onnx::Conv_416[FLOAT, 64]
%onnx::Conv_418[FLOAT, 64x64x1x1]
%onnx::Conv_421[FLOAT, 64x64x3x3]
%onnx::Conv_424[FLOAT, 64x64x3x3]
%onnx::Conv_427[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 219002368,
"params": 2488394,
"val_accuracy": 92.9
} | {
"arch_str": "680",
"identifier": "Hiaml_680",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "680"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 680 | Predict neural architecture validation accuracy and compute cost from 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": 92.9
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_993 | GraphArch:Hiaml:993 | graph torch_jit (
%input.1[FLOAT, 1x3x32x32]
%out_net.classifier.weight[FLOAT, 10x256]
%out_net.classifier.bias[FLOAT, 10]
%onnx::Conv_417[FLOAT, 64x3x3x3]
%onnx::Conv_418[FLOAT, 64]
%onnx::Conv_420[FLOAT, 64x64x1x3]
%onnx::Conv_423[FLOAT, 64x64x3x1]
%onnx::Conv_426[FLOAT, 64x64x1x1]
%onnx::Conv_429[F... | graph | {
"flops": "Floating-point operation count for the architecture.",
"params": "Number of trainable parameters in the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170178048,
"params": 1585226,
"val_accuracy": 92.39
} | {
"arch_str": "993",
"identifier": "Hiaml_993",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "993"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 993 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 170178048,
"val_accuracy": 92.39
} | full |
GraphArch | architecture_regression | GraphArch:Hiaml_558 | GraphArch:Hiaml:558 | 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, 64x64x1x3]
%onnx::Conv_435[FLOAT, 64x64x3x1]
%onnx::Conv_438[FLOAT, 64x64x1x1]
%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": 189117952,
"params": 2396490,
"val_accuracy": 92.2
} | {
"arch_str": "558",
"identifier": "Hiaml_558",
"params_retained_not_default_target": true,
"search_space": "Hiaml",
"source_dataset": "grapharch_regression",
"source_target_metric": "val_accuracy",
"uid": "558"
} | {
"input_format": "serialized neural-network graph text",
"input_to_evaluate_column": "input_text",
"problem_context": "Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text.",
"search_space": "Hiaml"
} | [] | 558 | Predict neural architecture validation accuracy and compute cost from serialized neural-network graph text. | train | {
"flops": "Floating-point operation count for the architecture.",
"val_accuracy": "Validation accuracy reported for the neural architecture."
} | {
"flops": 189117952,
"val_accuracy": 92.2
} | full |
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