{
    "author": null,
    "date_published": "2016-11-10T01:26:00.000Z",
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    "domain": "arxiv.org",
    "excerpt": "Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small…",
    "lead_image_url": "https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png",
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    "title": "Understanding deep learning requires rethinking generalization",
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    "url": "https://arxiv.org/abs/1611.03530v2",
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