{"id":51623,"date":"2024-07-19T21:32:11","date_gmt":"2024-07-19T13:32:11","guid":{"rendered":"https:\/\/swarma.org\/?p=51623"},"modified":"2024-07-19T21:32:11","modified_gmt":"2024-07-19T13:32:11","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8%e7%bd%91%e7%bb%9c%e5%b1%9e%e6%80%a7%e9%a2%84%e6%b5%8b%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e6%80%a7%e8%83%bd-%e5%91%a8%e6%97%a5%e7%9b%b4%e6%92%ad%c2%b7ai-by-compl","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51623","title":{"rendered":"\u5982\u4f55\u5229\u7528\u7f51\u7edc\u5c5e\u6027\u9884\u6d4b\u795e\u7ecf\u7f51\u7edc\u6027\u80fd | \u5468\u65e5\u76f4\u64ad\u00b7AI by Complexity\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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      Jiang, C., Huang, Z., Pedapati, T., Chen, P. Y., Sun, Y., & Gao, J. (2024). Network properties determine neural network performance. Nature Communications<\/em>, 15<\/em>(1), 1-9.<\/span><\/p>\n<\/li>\n

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      Gao, J., Barzel, B. & Barab\u00e1si, A.-L. Universal resilience patterns in complex networks. Nature 530, 307\u2013312 (2016).<\/span><\/p>\n<\/li>\n

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      Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J. & Hinton, G. Backpropagation and the brain. Nat. Rev. Neurosci. 1\u201312 (2020).<\/span><\/p>\n<\/li>\n

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      Chandrashekaran, A. & Lane, I. R. Speeding up hyper-parameter optimization by extrapolation of learning curves using previous builds. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 477\u2013492 (Springer, 2017).<\/span><\/p>\n<\/li>\n

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      Nguyen, C., Hassner, T., Seeger, M. & Archambeau, C. LEEP: A new measure to evaluate transferability of learned representations. In International Conference on Machine Learning, 7294\u20137305 (PMLR, 2020).<\/span><\/p>\n<\/li>\n

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      Mellor, J., Turner, J., Storkey, A. & Crowley, E. J. Neural architecture search without training. In International Conference on Machine Learning, 7588\u20137598 (PMLR, 2021).<\/span><\/p>\n<\/li>\n

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      Dong, X., Liu, L., Musial, K. & Gabrys, B. NATS-Bench: Benchmarking nas algorithms for architecture topology and size. IEEE Transac. Pattern Anal. Machine Intelligence 7, 3634\u20133646 (2021).<\/span><\/section>\n<\/li>\n<\/ol>\n

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      \u626b\u7801\u53c2\u4e0eAI by Complexity \u8bfb\u4e66\u4f1a<\/a><\/span>\u52a0\u5165\u7fa4\u804a\uff0c\u83b7\u53d6\u7cfb\u5217\u8bfb\u4e66\u4f1a\u56de\u770b\u6743\u9650\uff0c\u52a0\u5165AI by Complexity\u793e\u533a\uff0c\u4e0e\u793e\u533a\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u6c9f\u901a\u4ea4\u6d41\uff0c\u5171\u540c\u63a2\u7d22\u590d\u6742\u79d1\u5b66\u4e0eAI\u4ea4\u53c9\u7684\u524d\u6cbf\u9886\u57df\u7684\u53d1\u5c55\u3002<\/span><\/section>\n

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      \u5927\u6a21\u578b\u3001\u591a\u6a21\u6001\u3001\u591a\u667a\u80fd\u4f53\u5c42\u51fa\u4e0d\u7a77\uff0c\u5404\u79cd\u5404\u6837\u7684\u795e\u7ecf\u7f51\u7edc\u53d8\u4f53\u5728AI\u5927\u821e\u53f0\u5404\u663e\u8eab\u624b\u3002\u590d\u6742\u7cfb\u7edf\u9886\u57df\u5bf9\u4e8e\u6d8c\u73b0\u3001\u5c42\u7ea7\u3001\u9c81\u68d2\u6027\u3001\u975e\u7ebf\u6027\u3001\u6f14\u5316\u7b49\u95ee\u9898\u7684\u63a2\u7d22\u4e5f\u5728\u6301\u7eed\u63a8\u8fdb\u3002\u800c\u4f18\u79c0\u7684AI\u7cfb\u7edf\u3001\u521b\u65b0\u6027\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u5f80\u5f80\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u5177\u5907\u4f18\u79c0\u590d\u6742\u7cfb\u7edf\u7684\u7279\u5f81\u3002\u56e0\u6b64\uff0c\u53d1\u5c55\u4e2d\u7684\u590d\u6742\u7cfb\u7edf\u7406\u8bba\u65b9\u6cd5\u5982\u4f55\u6307\u5bfc\u672a\u6765AI\u7684\u8bbe\u8ba1\uff0c\u6b63\u5728\u6210\u4e3a\u5907\u53d7\u5173\u6ce8\u7684\u95ee\u9898\u3002<\/span><\/p>\n


      <\/p>\n

      \u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u52a0\u5229\u798f\u5c3c\u4e9a\u5927\u5b66\u5723\u8fed\u6208\u5206\u6821\u52a9\u7406\u6559\u6388\u5c24\u4ea6\u5e84\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u526f\u6559\u6388\u5218\u5b87\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u5728\u8bfb\u535a\u58eb\u5f20\u7ae0\u3001\u725f\u7267\u4e91\u548c\u5728\u8bfb\u7855\u58eb\u6768\u660e\u54f2\u3001\u6e05\u534e\u5927\u5b66\u5728\u8bfb\u535a\u58eb\u7530\u6d0b\u5171\u540c\u53d1\u8d77\u300cAI By Complexity\u300d\u8bfb\u4e66\u4f1a<\/a>\uff0c\u63a2\u7a76\u5982\u4f55\u5ea6\u91cf\u590d\u6742\u7cfb\u7edf\u7684\u201c\u597d\u574f\u201d\uff1f\u5982\u4f55\u7406\u89e3\u590d\u6742\u7cfb\u7edf\u7684\u673a\u5236\uff1f\u8fd9\u4e9b\u7406\u89e3\u662f\u5426\u53ef\u4ee5\u542f\u53d1\u6211\u4eec\u8bbe\u8ba1\u66f4\u597d\u7684AI\u6a21\u578b\uff1f\u5728\u672c\u8d28\u4e0a\u5e2e\u52a9\u6211\u4eec\u8bbe\u8ba1\u66f4\u597d\u7684AI\u7cfb\u7edf\u3002\u8bfb\u4e66\u4f1a\u4e8e6\u670810\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u4e00\u665a\u4e0a20:00-22:00\u4e3e\u529e\u3002\u6b22\u8fce\u4ece\u4e8b\u76f8\u5173\u9886\u57df\u7814\u7a76\u3001\u5bf9AI+Complexity\u611f\u5174\u8da3\u7684\u670b\u53cb\u4eec\u62a5\u540d\u8bfb\u4e66\u4f1a\u4ea4\u6d41\uff01<\/span><\/p>\n


      <\/span><\/p>\n

      <\/span><\/span><\/a><\/p>\n


      <\/section>\n
      \u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
      AI by Complexity \u8bfb\u4e66\u4f1a\u542f\u52a8\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/span><\/a>
      <\/section>\n

      <\/span><\/p>\n


      <\/strong><\/span><\/section>\n

      \u5f80\u671f\u5206\u4eab\uff1a<\/strong><\/span><\/p>\n

        \n
      1. \n

        \u7b2c\u4e00\u671f \u5f20\u7ae0 \u4e8e\u7389\u56fd \u7530\u6d0b \u725f\u7267\u4e91 \u5218\u5b87 \u6768\u660e\u54f2\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/span><\/a><\/p>\n<\/li>\n

      2. \n

        \u7b2c\u4e8c\u671f \u5f90\u5955\u821f \u7fc1\u5eb7\u5b87\uff1a\u7edf\u8ba1\u7269\u7406\u4e0e\u4fe1\u606f\u8bba\u89c6\u89d2\u4e0b\uff0c\u7ed3\u6784\u5316\u566a\u58f0\u4e0e\u795e\u7ecf\u7f51\u7edc\u521d\u59cb\u5316\u7814\u7a76<\/span><\/a><\/p>\n<\/li>\n

      3. \n

        \u7b2c\u4e09\u671f \u5218\u5b87\uff1a\u201c\u538b\u7f29\u5373\u667a\u80fd\u201d\u4e0e\u7b97\u6cd5\u4fe1\u606f\u8bba<\/span><\/a><\/p>\n<\/li>\n

      4. \n
        \u7b2c\u56db\u671f \u7a0b\u5965\u534e \u718a\u5dcd\uff1a\u4ece\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u5230\u795e\u7ecf\u7b97\u5b50\u6a21\u578b\uff1a\u542f\u53d1\u66f4\u597d\u7684AI<\/span><\/a><\/section>\n<\/li>\n<\/ol>\n

        <\/strong><\/span><\/section>\n
        \u7f51\u7edc\u79d1\u5b66\u4e3b\u9898\u76f8\u5173\u8bfe\u7a0b\u63a8\u8350\uff1a<\/strong><\/span><\/section>\n
        \u7f51\u7edc\u79d1\u5b661\uff1a\u5df4\u62c9\u5df4\u897f\u7f51\u7edc\u79d1\u5b66<\/strong><\/span><\/section>\n
        \u4e3b\u8bb2\u4eba\uff1a<\/strong>\u6c6a\u5c0f\u5e06\u3001\u8d75\u6d77\u5174\u3001\u8bb8\u5c0f\u53ef\u3001\u53f2\u5b9a\u534e\u3001\u9648\u6e05\u534e\u3001\u5f20\u6c5f\u3001\u72c4\u589e\u5982\u3001\u9648\u5173\u8363\u3001\u6a0a\u745b\u3001\u5218\u5b97\u534e<\/span><\/section>\n

        \u8bfe\u7a0b\u94fe\u63a5\uff1a<\/strong>https:\/\/campus.swarma.org\/course\/1754<\/span><\/p>\n

        \u7f51\u7edc\u79d1\u5b662\uff1a\u7f51\u7edc\u79d1\u5b66\u5bfc\u8bba<\/strong>
        <\/section>\n
        \u4e3b\u8bb2\u4eba\uff1a<\/strong>\u9648\u5173\u8363\u3001\u9879\u6797\u82f1\u3001\u6a0a\u745b\u3001\u5ba3\u7426\u3001\u674e\u7fd4\u3001\u53f2\u5b9a\u534e\u3001\u674e\u806a\u3001\u8363\u667a\u6d77\u3001\u5468\u8fdb\u3001\u738b\u7433<\/span><\/section>\n

        \u8bfe\u7a0b\u94fe\u63a5\uff1a<\/strong>https:\/\/campus.swarma.org\/course\/2328<\/span><\/p>\n

        \u7f51\u7edc\u79d1\u5b663\uff1a\u590d\u6742\u7f51\u7edc\u7684\u6570\u5b66\u5efa\u6a21\u4e0e\u5e94\u7528<\/strong><\/span><\/section>\n
        \u4e3b\u8bb2\u4eba\uff1a<\/strong>\u9648\u5173\u8363\u3001\u6a0a\u745b\u3001\u5468\u8fdb\u3001\u674e\u7fd4\u3001\u5f20\u6c5f\u3001\u95eb\u5c0f\u52c7\u3001\u5218\u5b97\u534e\u3001\u77f3\u5ddd\u3001\u865e\u6587\u6b66\u3001\u8d75\u6d77\u5174\u3001\u53f2\u5b9a\u534e<\/span><\/section>\n
        \u8bfe\u7a0b\u94fe\u63a5\uff1a<\/strong>https:\/\/campus.swarma.org\/course\/3533<\/span><\/section>\n

        <\/span><\/section>\n


        <\/p>\n

        \u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u62a5\u540d\u8bfb\u4e66\u4f1a<\/span><\/p>\n

        <\/mp-style-type><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

        \u5bfc\u8bed \u5bf9\u7ed3\u6784\u4e0e\u529f\u80fd\u5173\u7cfb\uff0c\u751f\u957f\u4e0e\u6f14\u5316\u89c4\u5f8b\u7684\u7814\u7a76\uff0c\u5bf9\u4e8e\u7406\u89e3\u548c\u8bbe\u8ba1\u590d\u6742\u7cfb\u7edf\u81f3\u5173\u91cd\u8981\u3002\u795e\u7ecf\u7f51\u7edc\u4f5c\u4e3a\u4e00\u4e2a\u590d\u6742\u7684\u4fe1\u606f\u5904\u7406\u7cfb\u7edf\uff0c\u5176\u7ed3\u6784\u51b3\u5b9a\u4e86\u5176\u529f\u80fd\u4e0e\u6548\u7387\u3002\u4eca\u5e747\u6708\u5728 Nature Communications\u4e0a\u53d1\u8868\u7684\u4e00\u7bc7\u6587\u7ae0\u201cNetwork properties determine neural net…<\/p>\n","protected":false},"author":0,"featured_media":51450,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/51623"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=51623"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/51623\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/51450"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=51623"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=51623"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=51623"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=51623"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}