{"id":51159,"date":"2024-06-30T20:52:17","date_gmt":"2024-06-30T12:52:17","guid":{"rendered":"https:\/\/swarma.org\/?p=51159"},"modified":"2024-06-30T20:52:17","modified_gmt":"2024-06-30T12:52:17","slug":"%e7%bb%9f%e8%ae%a1%e7%89%a9%e7%90%86%e4%b8%8e%e4%bf%a1%e6%81%af%e8%ae%ba%e8%a7%86%e8%a7%92%e4%b8%8b%ef%bc%8c%e7%bb%93%e6%9e%84%e5%8c%96%e5%99%aa%e5%a3%b0%e4%b8%8e%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51159","title":{"rendered":"\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\uff5c\u5468\u4e00\u76f4\u64ad\u00b7AI by Complexity\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u6211\u4eec\u8003\u8651\u4e86\u8d1d\u53f6\u65af\u63a8\u65ad\u4e2d\u7684\u4e00\u4e2a\u5178\u578b\u95ee\u9898\uff1a\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u4e2d\u7684\u4f4e\u79e9\u4fe1\u53f7\u88ab\u52a0\u6027\u7ed3\u6784\u5316\u566a\u58f0\u6240\u5e72\u6270\u3002\u5f53\u566a\u58f0\u662f\u72ec\u7acb\u540c\u5206\u5e03\u7684\u9ad8\u65af\u566a\u58f0\u65f6\uff0c\u65e0\u8bba\u662f\u4fe1\u606f\u8bba\u6781\u9650\u8fd8\u662f\u7b97\u6cd5\u6781\u9650\u90fd\u5df2\u88ab\u5145\u5206\u7406\u89e3\uff0c\u7136\u800c\u66f4\u73b0\u5b9e\u7684\u7ed3\u6784\u5316\u566a\u58f0\u60c5\u51b5\u4ecd\u7136\u5177\u6709\u6311\u6218\u6027\u3002\u4e3a\u4e86\u5728\u6570\u5b66\u53ef\u5904\u7406\u7684\u540c\u65f6\u6355\u6349\u566a\u97f3\u7684\u7ed3\u6784\u7279\u5f81\uff0c\u6211\u4eec\u9996\u6b21\u523b\u753b\u4e86\u4e00\u822c\u65cb\u8f6c\u4e0d\u53d8\u566a\u58f0\u7684\u4fe1\u606f\u8bba\u6781\u9650\uff0c\u5e76\u5229\u7528\u4e86\u7edf\u8ba1\u7269\u7406\u4e2d\u7684\u526f\u672c\u65b9\u6cd5\u548c\u968f\u673a\u77e9\u9635\u7406\u8bba\u4e2d\u7684\u5de5\u5177\uff0c\u8868\u660e\u7531Thouless-Anderson-Palmer\uff08TAP\uff09\u65b9\u7a0b\u7406\u8bba\u542f\u53d1\u7684\u7b97\u6cd5\u53ef\u4ee5\u8fbe\u5230\u8be5\u6781\u9650\u3002\u8fd9\u9879\u7814\u7a76\u53ef\u4ee5\u63a8\u52a8AI\u7b97\u6cd5\u5728\u5904\u7406\u73b0\u5b9e\u6570\u636e\u4e2d\u7684\u80fd\u529b\uff0c\u6784\u5efa\u9ad8\u6548\u7684AI\u7cfb\u7edf\u3002<\/span><\/section>\n

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    \u524d\u7f6e\u77e5\u8bc6<\/p>\n<\/li>\n

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      \u4e3b\u6210\u5206\u5206\u6790\u4ecb\u7ecd<\/p>\n<\/li>\n

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      I-MMSE\u5173\u7cfb\u4e0e\u5df2\u77e5\u7684\u6e10\u8fd1\u884c\u4e3a<\/p>\n<\/li>\n<\/ol>\n

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      \u566a\u58f0\u7ed3\u6784\u4e0e\u6570\u5b66\u5206\u6790<\/p>\n<\/li>\n

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      \u526f\u672c\u65b9\u6cd5\u4e0e\u526f\u672c\u5bf9\u79f0\u81ea\u7531\u71b5<\/p>\n<\/li>\n

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      Nishimori \u6052\u7b49\u5f0f\u548c\u978d\u70b9\u65b9\u7a0b<\/p>\n<\/li>\n

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      \u81ea\u9002\u5e94TAP\u65b9\u6cd5<\/p>\n<\/li>\n

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      <\/strong><\/span><\/section>\n
      \u53c2\u8003\u6587\u732e<\/strong><\/span><\/section>\n
        \n
      1. \n

        Fundamental limits in structured principal component analysis and how to reach them, J. Barbier, F.Camilli, M. Mondelli, M. S \u0301aenz, Proceedings of the National Academy of Sciences, 120 (30) e2302028120 (2023)<\/span><\/p>\n<\/li>\n

      2. \n

        Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise, J. Barbier, F. Camilli, M. Mondelli, Y. Xu, arXiv preprint arXiv:2405.20993, 2024.<\/span><\/p>\n<\/li>\n

      3. \n

        Random matrix methods for high-dimensional machine learning models, A. Bodin, PhD Thesis, EPFL, doi.org\/10.5075\/epfl-thesis-10524, 2024<\/span><\/p>\n<\/li>\n

      4. \n

        Adaptive and Self-Averaging Thouless-Anderson-Palmer Mean-Field Theory for Probabilistic Modeling, M. Opper, O. Winther, Physical Review E 64 5 (2001)<\/span><\/p>\n<\/li>\n

      5. \n

        A First Course in Random Matrix Theory: For Physicists, Engineers and Data Scientists, M. Potters, J.-P. Bouchaud, Cambridge University Press, 2020<\/span><\/p>\n<\/li>\n

      6. \n

        Optimality of Approximate Message Passing Algorithms for Spiked Matrix Models with Rotationally Invariant Noise, R. Dudeja, S. Liu, J. Ma, arXiv preprint arXiv:2405.18081, 2024<\/span><\/p>\n<\/li>\n<\/ol>\n


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        \u5728\u7edf\u8ba1\u7269\u7406\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u4ea4\u53c9\u89c6\u89d2\u4e0b\uff0c\u6211\u4eec\u7684\u7814\u7a76\u9996\u5148\u5efa\u7acb\u7528\u4e8e\u8ba1\u7b97\u795e\u7ecf\u7f51\u7edc\u8f93\u5165\u4fe1\u53f7\u548c\u4e2d\u95f4\u5c42\u8868\u5f81\u95f4\u4e92\u4fe1\u606f\u7684\u3001\u57fa\u4e8e\u5e73\u5747\u573a\u5047\u8bbe\u7684\u7406\u8bba\u6846\u67b6\uff0c\u5e76\u5728\u6240\u5efa\u7acb\u7684\u6846\u67b6\u4e2d\u7ed9\u51fa\u4e86\u4e34\u754c\u7ebf\u548c\u52a8\u529b\u7b49\u8ddd\u70b9\u7684\u89e3\u6790\u8868\u8fbe\u5f0f\u3002\u4e0d\u540c\u4e8e\u5176\u4ed6\u5e38\u89c1\u7684\u7406\u8bba\u6a21\u578b\u4e2d\u90fd\u4f1a\u91c7\u7528\u72ec\u7acb\u540c\u5206\u5e03\u5047\u8bbe\uff0c\u7814\u7a76\u8bc1\u660e\u5728\u5c06\u795e\u7ecf\u7f51\u7edc\u770b\u4f5c\u4fe1\u9053\u7684\u60c5\u5f62\u4e0b\uff0c\u72ec\u7acb\u540c\u5206\u5e03\u5047\u8bbe\u5bf9\u4e8e\u8ba1\u7b97\u4e92\u4fe1\u606f\u662f\u65e0\u6548\u7684\u3002\u5229\u7528\u4fe1\u606f\u74f6\u9888\u7406\u8bba\u5206\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u5728\u907f\u514d\u72ec\u7acb\u540c\u5206\u5e03\u5047\u8bbe\u7684\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u8bc1\u660e\u8f93\u5165\u4fe1\u53f7\u4e0e\u9690\u85cf\u5c42\u8868\u5f81\u95f4\u7684\u4e92\u4fe1\u606f\u6700\u5927\u5316\u70b9\u6b63\u662f\u52a8\u529b\u7b49\u8ddd\u70b9\u3002\u901a\u8fc7\u8ba1\u7b97\u673a\u5b9e\u9a8c\uff0c\u8bf4\u660e\u9488\u5bf9\u65e0\u9650\u5bbd\u795e\u7ecf\u7f51\u7edc\u7684\u63a8\u5bfc\u4e5f\u80fd\u7528\u4e8e\u6709\u9650\u5bbd\u795e\u7ecf\u7f51\u7edc\u3002\u8fd9\u4e2a\u7814\u7a76\u5de5\u4f5c\u53ef\u80fd\u4e3a\u63a8\u52a8\u6df1\u5ea6\u5b66\u4e60\u5728\u7f51\u7edc\u521d\u59cb\u5316\u65b9\u9762\u7684\u7814\u7a76\u5960\u5b9a\u57fa\u7840\uff0c\u5e76\u63ed\u793a\u4e86\u591a\u79cd\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u80cc\u540e\u7684\u7edf\u8ba1\u7269\u7406\u673a\u5236\u3002<\/span><\/section>\n

        <\/strong><\/span><\/section>\n
        \u5173\u952e\u8bcd\uff1a<\/strong><\/span>\u795e\u7ecf\u7f51\u7edc\uff0c\u521d\u59cb\u5316\uff0c\u968f\u673a\u4fe1\u9053\uff0c\u4e92\u4fe1\u606f\uff0c\u5e73\u5747\u573a<\/strong><\/span><\/section>\n

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

        \u5206\u4eab\u5927\u7eb2<\/strong><\/span><\/p>\n

        1. \u57fa\u7840\u77e5\u8bc6<\/span><\/p>\n

        a. \u968f\u673a\u521d\u59cb\u5316\u795e\u7ecf\u7f51\u7edc\uff1a\u51e0\u4f55\u89d2\u5ea6\u7684\u7406\u8bba\u4e0e\u5b9e\u9a8c\u7ed3\u679c<\/span><\/p>\n

        b. \u4e92\u4fe1\u606f\uff1a\u5728\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u5e94\u7528\u4e0e\u6d4b\u91cf<\/span><\/p>\n

        2. \u8bba\u6587\u89e3\u8bfb<\/span><\/section>\n
        a. \u7fc1\u5eb7\u5b87\u4e00\u4f5c\u9876\u520a\u8bba\u6587\u89e3\u8bfb\uff1aStatistical physics of deep neural networks: Initialization toward optimal channels<\/span><\/section>\n

        <\/strong><\/span><\/section>\n
        \u53c2\u8003\u6587\u732e<\/strong><\/span><\/section>\n

        1. Weng, Kangyu, et al. "Statistical <\/span>physics of deep neural networks: Initialization toward optimal channels." Physical Review Research 5.2<\/span> (2023): 023023.<\/span><\/span><\/span><\/p>\n


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        \u65f6\u95f4\uff1a<\/strong>2024\u5e747\u67081\u65e5\uff08\u5468\u4e00\uff09\u665a20:00-22:00<\/span><\/span><\/p>\n

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        \u6591\u56fe\u94fe\u63a5\uff1ahttps:\/\/pattern.swarma.org\/mobile\/study_group_issue\/706?from=wechat<\/span><\/section>\n


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

        \u626b\u7801\u53c2\u4e0e<\/span><\/span><\/a>AI by Complexity \u8bfb\u4e66\u4f1a<\/a>\uff0c\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
        <\/span>
        <\/section>\n


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

        \u62a5\u540d\u6210\u4e3a\u4e3b\u8bb2\u4eba<\/strong><\/span><\/p>\n

        \u8bfb\u4e66\u4f1a\u6210\u5458\u5747\u53ef\u4ee5\u5728\u8bfb\u4e66\u4f1a\u671f\u95f4\u7533\u8bf7\u6210\u4e3a\u4e3b\u8bb2\u4eba\u3002\u4e3b\u8bb2\u4eba\u4f5c\u4e3a\u8bfb\u4e66\u4f1a\u6210\u5458\uff0c\u5747\u9075\u5faa\u5185\u5bb9\u5171\u521b\u5171\u4eab\u673a\u5236\uff0c\u53ef\u4ee5\u83b7\u5f97\u62a5\u540d\u8d39\u9000\u6b3e\uff0c\u5e76\u5171\u4eab\u672c\u8bfb\u4e66\u4f1a\u4ea7\u751f\u7684\u6240\u6709\u5185\u5bb9\u8d44\u6e90\u3002\u8be6\u60c5\u8bf7\u89c1\uff1aAI by Complexity \u8bfb\u4e66\u4f1a\u542f\u52a8\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/a><\/span><\/p>\n


        <\/span><\/h2>\n


        <\/span><\/h2>\n
        \n
        \n
        \n
        \n

        AI By Complexity\u8bfb\u4e66\u4f1a\u62db\u52df\u4e2d<\/strong>
        <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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

        <\/section>\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><\/section>\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


        <\/p>\n

        \n


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

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

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