{"id":46424,"date":"2023-11-10T17:05:09","date_gmt":"2023-11-10T09:05:09","guid":{"rendered":"https:\/\/swarma.org\/?p=46424"},"modified":"2023-11-10T17:05:09","modified_gmt":"2023-11-10T09:05:09","slug":"nat-mach-intell-%e9%80%9f%e9%80%92%ef%bc%9a%e5%9f%ba%e4%ba%8e%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%9a%84%e9%ab%98%e7%bb%b4%e6%95%b0%e6%8d%ae%e5%9b%a0%e6%9e%9c%e7%bb%93%e6%9e%84%e5%ad%a6%e4%b9%a0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=46424","title":{"rendered":"Nat. Mach. Intell.\u901f\u9012\uff1a\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u9ad8\u7ef4\u6570\u636e\u56e0\u679c\u7ed3\u6784\u5b66\u4e60"},"content":{"rendered":"
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\u90ed\u745e\u4e1c<\/span> <\/span>| \u7f16\u8bd1<\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/section>\n
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\u8bba\u6587\u6807\u9898\uff1a<\/span><\/section>\n
Deep learning of causal structures in high dimensions under data limitations<\/span><\/section>\n
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https:\/\/www.nature.com\/articles\/s42256-023-00744-z<\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
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\u8fd1\u65e5Nature Machine Intelligenc<\/em>e<\/em>\u7684\u7814\u7a76\uff0c\u4ee5\u751f\u7269\u533b\u5b66\u95ee\u9898\u4e3a\u6848\u4f8b\uff0c\u63d0\u51fa\u4e00\u79cd\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u67b6\u6784\uff0c\u7528\u4e8e\u4ece\u9ad8\u7ef4\u6570\u636e\u548c\u5148\u9a8c\u56e0\u679c\u77e5\u8bc6\u7684\u7ec4\u5408\u4e2d\u5b66\u4e60\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\u3002\u8be5\u7814\u7a76\u5728\u56e0\u679c\u98ce\u9669\u6846\u67b6\u5185\u7ed3\u5408\u4e86\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u548c\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u63d0\u4f9b\u4e86\u4e00\u79cd\u5728\u9ad8\u7ef4\u3001\u5305\u542b\u566a\u58f0\u548c\u6570\u636e\u9650\u5236\u6761\u4ef6\u4e0b\u660e\u663e\u6709\u6548\u7684\u65b9\u6cd5<\/span>\uff08D2CL\uff09<\/span>\uff0c\u8fd9\u4e9b\u6761\u4ef6\u662f\u5305\u62ec\u5927\u89c4\u6a21\u751f\u7269\u6570\u636e\u5728\u5185\u7684\u8bb8\u591a\u573a\u666f\u4e0b\u6570\u636e\u5171\u6709\u7684\u7279\u5f81\u3002\u8be5\u65b9\u6cd5\u6240\u63d0\u51fa\u7684\u5b66\u4e60\u5668\u80fd\u6709\u6548\u8bc6\u522b\u6570\u5343\u4e2a\u53d8\u91cf\u4e2d\u7684\u672a\u77e5\u56e0\u679c\u5173\u7cfb\u3002\u5b9e\u9a8c\u7ed3\u679c\u5305\u62ec\u5e7f\u6cdb\u7684<\/span>\uff08\u7ebf\u6027\u548c\u975e\u7ebf\u6027\uff09<\/span>\u6a21\u62df<\/span>\uff08\u5176\u4e2d\u57fa\u672c\u4e8b\u5b9e\u662f\u5df2\u77e5\u7684\uff0c\u53ef\u4ee5\u76f4\u63a5\u4e0e\u4e4b\u6bd4\u8f83\uff09<\/span>\u4ee5\u53ca\u771f\u5b9e\u7684\u751f\u7269\u5b66\u5b9e\u4f8b\uff0c\u5176\u4e2d\u6a21\u578b\u88ab\u5e94\u7528\u4e8e\u9ad8\u7ef4\u5206\u5b50\u6570\u636e\uff0c\u5176\u8f93\u51fa\u7ed3\u679c\u4e0e\u6a21\u578b\u672a\u77e5\u7684\u9a8c\u8bc1\u5b9e\u9a8c\u8fdb\u884c\u6bd4\u8f83\u3002\u8fd9\u4e9b\u7ed3\u679c\u652f\u6301\u4e86\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u53ef\u7528\u4e8e\u5927\u89c4\u6a21\u5b66\u4e60\u56e0\u679c\u7f51\u7edc\u7684\u89c2\u70b9\u3002<\/span><\/section>\n
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\u6a21\u578b\u67b6\u6784\uff1aD2CL\u901a\u8fc7\u7ed3\u5408\u6570\u636e\u548c\u5148\u9a8c\u77e5\u8bc6\u6765\u5b66\u4e60\u56e0\u679c\u7ed3\u6784\uff0c\u4ece\u800c\u751f\u6210\u4e00\u4e2a\u56fe G\uff0c\u7528\u6765\u8868\u793a\u7cfb\u7edf\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u800c\u4e0d\u4ec5\u4ec5\u662f\u76f8\u5173\u5173\u7cfb\u3002\u5728\u5de5\u4f5c\u6d41<\/span>\uff08\u56fe1\u5de6\uff09<\/span>\u56fe\u4e2d\uff0c\u6765\u81ea\u7279\u5b9a\u7cfb\u7edf\u7684\u7ecf\u9a8c\u6570\u636e\u4e0e\u5148\u9a8c\u56e0\u679c\u77e5\u8bc6\u76f8\u7ed3\u5408\uff0c\u4ee5\u4f30\u8ba1\u672a\u77e5\u56e0\u679c\u7ed3\u6784\uff0c\u6700\u7ec8\u5f97\u5230\u5b8c\u6574\u7684\u5305\u542b\u56e0\u679c\u5173\u7cfb\u7684\u7f51\u7edc\u3002\u5728\u751f\u7269\u95ee\u9898\u5de5\u4f5c\u6d41\u7a0b<\/span>\uff08\u56fe1\u53f3\uff09<\/span>\u4e2d\uff0c\u4ece\u7279\u5b9a\u7684\u751f\u7269\u7cfb\u7edf\u4e2d\u6536\u96c6\u6570\u636e\uff0c\u5e76\u4ece\u5bf9\u8be5\u7cfb\u7edf\u7684\u65e2\u5b9a\u79d1\u5b66\u6216\u5e72\u9884\u5b9e\u9a8c\u4e2d\u83b7\u5f97\u56e0\u679c\u5148\u9a8c\u77e5\u8bc6\u3002\u8be5\u6a21\u578b\u8bd5\u56fe\u4ece\u6709\u9650\u7684\u8f93\u5165\u4e2d\u5f52\u7eb3\u51fa\u4e00\u4e2a\u6db5\u76d6\u6240\u6709\u7cfb\u7edf\u53d8\u91cf\u7684\u5168\u5c40\u56fe\u3002<\/span><\/section>\n
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\u56fe1 DCL\u7684\u5de5\u4f5c\u6d41<\/span><\/section>\n
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D2CL \u5c06\u591a\u4e2a\u53d8\u91cf\u7684\u89c2\u6d4b\u6570\u636e\u4e0e\u5148\u9a8c\u56e0\u679c\u77e5\u8bc6\u76f8\u7ed3\u5408\uff0c\u4ee5\u5b66\u4e60\u53d8\u91cf\u4e4b\u95f4\u7684\u56e0\u679c\u5173\u7cfb\u3002\u5bf9\u4e8e\u4efb\u4f55\u4e00\u5bf9\u53d8\u91cf Xi<\/sub><\/span> \u548c Xj<\/sub><\/span><\/span>\uff08\u5bf9\u5e94\u4e8e\u8f93\u5165\u6570\u636e\u77e9\u9635\u7684\u4e24\u5217\uff09<\/span>\uff0cD2CL \u90fd\u529b\u6c42\u4e86\u89e3 Xi<\/sub><\/span> \u662f\u5426\u5bf9 Xj<\/sub><\/span> \u6709\u56e0\u679c\u5f71\u54cd\uff0cXj<\/sub><\/span> \u662f\u5426\u5bf9 Xi<\/sub><\/span> \u6709\u56e0\u679c\u5f71\u54cd\uff0c\u6216\u8005\u4e24\u8005\u90fd\u6ca1\u6709\u3002<\/span><\/section>\n

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D2CL \u91c7\u7528\u7684\u795e\u7ecf\u67b6\u6784\u7531\u4e24\u90e8\u5206\u7ec4\u6210\uff1a\u4e00\u4e2a\u662f\u65e8\u5728\u5b66\u4e60\u5206\u5e03\u7279\u5f81\u7684 CNN \uff0c\u53e6\u4e00\u4e2a\u662f\u68c0\u6d4b\u7ed3\u6784\u89c4\u5f8b\u6027\u7684 GNN \u3002\u5bf9\u4e8e\u6709\u5e8f\u5bf9\uff08Xi<\/sub><\/span>\uff0cXj<\/sub><\/span>\uff09\uff0cCNN \u901a\u8fc7\u904d\u5386\u6838\u5bc6\u5ea6\u4f30\u8ba1<\/span>\uff08kernel density estimate\uff0cKDE\uff09<\/span>\u5f62\u6210\u5d4c\u5165\u7684\u5bc6\u5ea6\u4f30\u8ba1\u503c\u6765\u6355\u6349\u5206\u5e03\u4fe1\u606f\u3002GNN \u4ece\u521d\u59cb\u56fe\u4e2d\u63d0\u53d6\u4e00\u4e2a\u5b50\u56fe\uff0c\u5e76\u8ba1\u7b97\u5305\u542b\u8282\u70b9\u90bb\u57df\u7ed3\u6784\u4fe1\u606f\u7684\u5d4c\u5165\u3002\u7136\u540e\uff0cCNN \u548c GNN \u5d4c\u5165\u901a\u8fc7\u591a\u5c42\u5408\u5e76\uff0c\u6700\u7ec8\u8f93\u51fa\u6709\u5411\u56e0\u679c\u5173\u7cfb\u7684\u6982\u7387\u3002\u8f93\u5165\u7684\u56e0\u679c\u4fe1\u606f\u7528\u4e8e\u63d0\u4f9b\u6709\u76d1\u7763\u5b66\u4e60\u8bad\u7ec3\u7684\u6807\u7b7e\u3002\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\uff0c\u7f51\u7edc\u5c06\u8d85\u8d8a\u521d\u59cb\u8f93\u5165\uff0c\u63d0\u4f9b\u8de8\u8d8a\u6240\u6709\u76f8\u5173\u53d8\u91cf\u7684\u5168\u5c40\u56fe\u7684\u4f30\u8ba1\u503c\u3002<\/span><\/section>\n
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\u56fe2 D2CL\u7684\u67b6\u6784<\/span><\/section>\n
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D2CL\u5904\u7406\u5305\u542b\u6270\u52a8\u7684\u9ad8\u7ef4\u6570\u636e\uff0c\u53ef\u901a\u8fc7\u65b0\u7684\u3001\u53ef\u6269\u5c55\u7684\u65b9\u6cd5\u4ee5\u7279\u5b9a\u7684\u65b9\u5f0f\u67e5\u8be2\u5206\u5b50\u7f51\u7edc\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4e0e\u53ef\u6269\u5c55\u7684\u56e0\u679c\u5173\u7cfb\u5b66\u4e60\u5de5\u5177\u76f8\u7ed3\u5408\uff0c\u6709\u53ef\u80fd\u5bf9\u75be\u75c5\u751f\u7269\u5b66\u4ea7\u751f\u91cd\u5927\u5f71\u54cd\uff0c\u4f7f\u8de8\u8d8a\u6570\u5343\u6216\u6570\u4e07\u4e2a\u53d8\u91cf\u7684\u5168\u5c40\u7f51\u7edc\u80fd\u591f\u5728\u8bb8\u591a\u4e0d\u540c\u7684\u73af\u5883\u4e2d\u5f97\u5230\u7814\u7a76\u3002\u5176\u5b66\u4e60\u5230\u7684\u7f51\u7edc\u5c06\u6765\u53ef\u7528\u4e8e\u9884\u6d4b\u65b0\u6270\u52a8\u4e0b\u7684\u75be\u75c5\u8868\u578b\u6216\u836f\u7269\u53cd\u5e94<\/span>\uff08\u8fd9\u662f\u4e00\u9879\u4e0d\u540c\u4e8e\u6807\u51c6\u76d1\u7763\u5b66\u4e60\u7684\u4efb\u52a1\uff0c\u56e0\u4e3a\u6d4b\u8bd5\u6848\u4f8b\u6d89\u53ca\u5bf9\u7cfb\u7edf\u7684\u672a\u77e5\u6270\u52a8\uff09<\/span>\u3002\u6b64\u5916\uff0c\u5728\u4e2a\u6027\u5316\u533b\u7597\u9886\u57df\uff0c\u8fd9\u79cd\u65b9\u6cd5\u751a\u81f3\u53ef\u4ee5\u5bf9\u6f5c\u5728\u7684\u6cbb\u7597\u7b56\u7565\u8fdb\u884c\u5408\u7406\u4f18\u5316\uff0c\u56e0\u4e3a\u540e\u8005\u901a\u5e38\u662f\u9488\u5bf9\u5206\u5b50\u8282\u70b9\u7684\u5e72\u9884\u63aa\u65bd\u3002<\/span><\/section>\n
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1. <\/strong>\u524d\u6cbf\u7b97\u6cd5\uff1a\u5982\u4f55\u5229\u7528\u7fa4\u8bba\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u4e0b\u7684\u56e0\u679c\u7279\u5f81\u89e3\u8026<\/a><\/strong><\/span><\/section>\n
2.<\/strong> \u56e0\u679c\u5b66\u4e60\u65b0\u8fdb\u5c55\uff1a\u6df1\u5ea6\u7a33\u5b9a\u5b66\u4e60<\/a><\/strong><\/span><\/strong><\/section>\n
3.<\/strong> \u9762\u5411\u56e0\u679c\u89c4\u5f8b\u7684\u8868\u793a\u5b66\u4e60\u65b0\u65b9\u6cd5\u2014\u2014\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u6700\u65b0\u653b\u7565<\/a><\/a><\/strong><\/strong><\/span><\/section>\n
4. <\/strong><\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
5. \u6210\u4e3a\u96c6\u667aVIP\uff0c\u89e3\u9501\u5168\u7ad9\u8bfe\u7a0b\uff0f\u8bfb\u4e66\u4f1a<\/a><\/span><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/span><\/section>\n
6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/section>\n


<\/p>\n


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

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

\u7814\u7a76\u9886\u57df\uff1a\u6df1\u5ea6\u5b66\u4e60\uff0c\u56e0\u679c\u79d1\u5b66\uff0c\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u751f\u7269\u533b\u5b66 \u90ed\u745e\u4e1c | \u7f16\u8bd1 \u8bba\u6587\u6807\u9898\uff1a Deep learning of causal structures in high dimensions under data limitations \u8bba\u6587\u94fe\u63a5\uff1a https:\/\/www.nature.co…<\/p>\n","protected":false},"author":0,"featured_media":46415,"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\/46424"}],"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=46424"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/46424\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/46415"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=46424"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=46424"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=46424"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=46424"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}