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  "documentTitle": "2019 Air Street Capital The State of AI Report 2019",
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  "authorName": "Nathan Benaich and Ian Hogarth",
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  "notes": "The slide compares LEAF against existing benchmarks like CheXNet and Google AutoML.",
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      "text": "Jointly optimising for hyperparameters, maximising network performance while minimising complexity and size.",
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      "text": "An alternative (Learning Evolutionary AI Framework: LEAF) is to use evolutionary algorithms to conduct both hyperparameter and network architecture optimisation, ultimately yielding smaller and more effective networks.",
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      "text": "Prior AutoML work optimize hyperparameters or network architecture individually using RL. Unfortunately, RL systems require a user to define an appropriate search space beforehand for the algorithm to use as a starting point. The number of hyperparameters that can be optimized for each layer is also limited.\nFurthermore, the computations are extremely heavy. To generate the final best network, many thousands of candidate architectures have to be evaluated and trained, which requires >100k GPU hours.\nAn alternative (Learning Evolutionary AI Framework: LEAF) is to use evolutionary algorithms to conduct both hyperparameter and network architecture optimisation, ultimately yielding smaller and more effective networks.\nFor example, LEAF matches the performance of a hand-crafted dataset-specific network (CheXNet) for Chest X-Ray diagnostic classification and outperforms Google’s AutoML.",
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      "text": "Test AUROC (%): 84.3",
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      "text": "Algorithm | Test AUROC (%)\n1. Wang et al. (2017) [48] | 73.8\n2. CheXNet (2017) [39] | 84.4\n3. Google AutoML (2018) [1] | 79.7\n4. LEAF | 84.3",
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