{"id":43336,"date":"2023-06-27T19:31:16","date_gmt":"2023-06-27T11:31:16","guid":{"rendered":"https:\/\/swarma.org\/?p=43336"},"modified":"2023-06-27T19:31:16","modified_gmt":"2023-06-27T11:31:16","slug":"%e6%8e%a2%e7%b4%a2ai-%e5%a4%a7%e7%bb%9f%e4%b8%80%e7%90%86%e8%ae%ba%ef%bc%9a%e7%a7%91%e5%ad%a6%e5%90%af%e5%8f%91%e7%9a%84%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%90%86%e8%ae%ba","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=43336","title":{"rendered":"\u63a2\u7d22\u201cAI \u5927\u7edf\u4e00\u7406\u8bba\u201d\uff1a\u79d1\u5b66\u542f\u53d1\u7684\u673a\u5668\u5b66\u4e60\u7406\u8bba"},"content":{"rendered":"
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[1]Platt J R. Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others[J]. science, 1964, 146(3642): 347-353.<\/section>\n<\/section>\n<\/blockquote>\n

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\u56fe4. Foundations of Machine Learning\uff0c\u8fd9\u672c\u4e66\u8be6\u7ec6\u4ecb\u7ecd\u4e86PAC\u5b66\u4e60\u7406\u8bba\u548c\u5176\u4ed6\u673a\u5668\u5b66\u4e60\u7684\u57fa\u672c\u7406\u8bba\u3002<\/span><\/p>\n


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PAC \u5b66\u4e60\u7406\u8bba<\/strong>\u662f\u4e00\u4e2a\u673a\u5668\u5b66\u4e60\u7684\u6570\u5b66\u5206\u6790\u6846\u67b6\uff0c1984\u5e74\u7531 Leslie Valiant \u63d0\u51fa\u3002\u8fd9\u4e2a\u6846\u67b6\u5c06\u5b66\u4e60\u4efb\u52a1\u770b\u4f5c\u662f\u4ece\u4e00\u4e2a\u6f5c\u5728\u6982\u7387\u5206\u5e03\u4e2d\u5b66\u4e60\u4e00\u4e2a\u76ee\u6807\u51fd\u6570\u3002\u6211\u4eec\u5e0c\u671b\u901a\u8fc7\u89c2\u5bdf\u4e00\u4e9b\u8bad\u7ec3\u6837\u672c\u6765\u63a8\u65ad\u51fa\u8fd9\u4e2a\u76ee\u6807\u51fd\u6570\u3002\u7136\u800c\uff0c\u7531\u4e8e\u8bad\u7ec3\u6570\u636e\u53ea\u662f\u6982\u7387\u5206\u5e03\u4e2d\u7684\u6709\u9650\u6837\u672c\uff0c\u6211\u4eec\u4e0d\u80fd\u4fdd\u8bc1\u63a8\u65ad\u662f\u5b8c\u5168\u6b63\u786e\u7684\uff0c\u6240\u4ee5\u6700\u7ec8\u4f1a\u5f97\u5230\u4e00\u4e2a\u8fd1\u4f3c\u89e3\uff0c\u6216\u8005\u8bf4\u4e00\u4e2a\u8fb9\u754c\u89e3\u3002<\/span><\/section>\n

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\u57fa\u4e8e\u7edf\u8ba1\u7269\u7406\u7684\u5206\u6790<\/strong>\u5219\u63d0\u4f9b\u4e86\u4e00\u79cd\u4ece\u5b8f\u89c2\u89d2\u5ea6\u7406\u89e3\u5b66\u4e60\u73b0\u8c61\u7684\u65b9\u6cd5\uff0c\u5b83\u5173\u6ce8\u7684\u662f\u5b66\u4e60\u7cfb\u7edf\u7684\u5168\u5c40\u6027\u8d28\u548c\u884c\u4e3a\u3002\u8fd9\u79cd\u65b9\u6cd5\u4ece\u7edf\u8ba1\u7269\u7406\u7684\u601d\u8def\u51fa\u53d1\uff0c\u5c06\u673a\u5668\u5b66\u4e60\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6211\u4eec\u5173\u5fc3\u7684\u6307\u6807\uff0c\u6bd4\u5982\u6cdb\u5316\u8bef\u5dee\u7b49\u770b\u6210\u662f\u7cfb\u7edf\u7684\u5178\u578b\u7edf\u8ba1\u91cf\uff0c\u4e3b\u8981\u505a\u7684\u4e8b\u60c5\uff0c\u662f\u5bf9\u8fd9\u4e9b\u6f5c\u5728\u7684\u76ee\u6807\u51fd\u6570<\/span>\uff08\u6216\u8005\u8bf4\u5047\u8bbe\uff09<\/span>\u8ba1\u7b97\u7edf\u8ba1\u91cf\uff0c\u7136\u540e\u5173\u6ce8\u8fd9\u4e9b\u7edf\u8ba1\u91cf\u5206\u5e03\u7684\u671f\u671b\u503c\u5982\u4f55\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u8fd9\u91cc\u5173\u5fc3\u7684\u662f\u6700\u53ef\u80fd\u51fa\u73b0\u7684\u7ed3\u679c\u3002\u5e76\u4e14\u5728\u4e00\u4e9b\u70ed\u529b\u5b66\u6781\u9650\u6761\u4ef6\u4e0b\uff0c\u8fd9\u4e9b\u8ba1\u7b97\u4f1a\u53d8\u5f97\u5f88\u7b80\u5355\u3002<\/span><\/section>\n

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\u56fe5. Statistical Mechanics of Learning\uff0c\u8fd9\u672c\u4e66\u4ece\u7edf\u8ba1\u7269\u7406\u7684\u89d2\u5ea6\u4ecb\u7ecd\u673a\u5668\u5b66\u4e60\u7406\u8bba\u3002<\/span><\/p>\n


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\u4e0b\u56fe\u5c55\u793a\u4e86\u4e24\u79cd\u65b9\u6cd5\u6c42\u89e3\u7684\u53ef\u89c6\u5316\uff0c\u56fe\u4e2d\u7ea2\u8272\u548c\u84dd\u8272\u70b9\u5206\u522b\u4ee3\u8868\u5206\u5e03\u5728\u8fd9\u4e00\u7a7a\u95f4\u4e2d\u7684\u4e24\u7c7b\u6837\u672c\u89c2\u6d4b\u70b9\uff0c\u6211\u4eec\u7684\u76ee\u6807\u662f\u5e0c\u671b\u627e\u5230\u4e00\u4e2a\u77e9\u5f62\u5212\u5206\u8fd9\u4e24\u7c7b\u6837\u672c\uff0c\u4f7f\u5f97\u5bf9\u4e8e\u4e00\u4e9b\u672a\u77e5\u7684\u6837\u672c\u70b9\u4e5f\u80fd\u5212\u5206\u7684\u6bd4\u8f83\u597d\u3002\u84dd\u8272\u77e9\u5f62\u662f ground truth\u3002\u5de6\u56fe\u9ec4\u8272\u7684\u77e9\u5f62\u662f\u4f7f\u7528PAC\u7406\u8bba\u627e\u5230\u7684\u76ee\u6807\uff0c\u5708\u51fa\u4e86\u5212\u5206\u7684\u8fb9\u754c\u4f4d\u7f6e\u3002\u53f3\u56fe\u662f\u57fa\u4e8e\u7edf\u8ba1\u7269\u7406\u7684\u5206\u6790\u627e\u51fa\u7684\u89e3\uff0c\u7528\u6d45\u84dd\u8272\u77e9\u5f62\u8868\u793a\uff0c\u53ef\u4ee5\u770b\u5230\u8fd9\u4e00\u7406\u8bba\u4f1a\u8ba1\u7b97\u6240\u6709\u7684\u53ef\u80fd\u6027\uff0c\u5e76\u4e14\u6700\u7ec8\u7528\u671f\u671b\u503c\u6765\u8868\u793a\u6700\u540e\u7684\u7ed3\u679c\u3002<\/span><\/section>\n

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\u56fe6. \u56fe\u4e2d\u5706\u5708\u8868\u793a\u8bad\u7ec3\u6837\u672c\uff0c\u84dd\u8272\u77e9\u5f62\u8868\u793a\u76ee\u6807\u6982\u5ff5\uff0c\u6a59\u8272\u77e9\u5f62\u8868\u793a\u53ef\u80fd\u7684\u731c\u60f3\u3002\u5de6\u56fe\u662fPAC\u7406\u8bba\u7684\u53ef\u89c6\u5316\uff0c\u9ec4\u8272\u662f\u6700\u7ec8\u7684\u89e3\uff0c\u627e\u5230\u4e86\u4e00\u4e2a\u8fb9\u754c\u89e3\u3002\u53f3\u56fe\u662f\u57fa\u4e8e\u7edf\u8ba1\u7269\u7406\u7684\u5206\u6790\u7684\u53ef\u89c6\u5316\uff0c\u6d45\u84dd\u8272\u8868\u793a\u89e3\u7684\u5206\u5e03\u533a\u57df\uff0c\u6700\u7ec8\u8ba1\u7b97\u7cfb\u7efc\u5e73\u5747\u4f5c\u4e3a\u89e3\u3002<\/span><\/p>\n


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\u8fd9\u4e24\u79cd\u7406\u8bba\u6846\u67b6\u90fd\u9762\u4e34\u7740\u6311\u6218\u3002\u4f8b\u5982\uff0cPAC\u5b66\u4e60\u7406\u8bba\u7684\u4e00\u4e9b\u5047\u8bbe\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u53ef\u80fd\u4e0d\u6210\u7acb\uff0c\u800c\u57fa\u4e8e\u7edf\u8ba1\u7269\u7406\u7684\u5206\u6790\u5219\u9700\u8981\u5bf9\u7cfb\u7edf\u7684\u7ec6\u8282\u6709\u6df1\u5165\u7684\u7406\u89e3\u3002\u5982\u4f55\u5c06\u8fd9\u4e9b\u7406\u8bba\u5e94\u7528\u5230\u5b9e\u9645\u7684\u5b66\u4e60\u7cfb\u7edf\u4e2d\uff0c\u4ee5\u53ca\u5982\u4f55\u7528\u8fd9\u4e9b\u7406\u8bba\u6765\u89e3\u91ca\u548c\u9884\u6d4b\u5b9e\u9a8c\u73b0\u8c61\uff0c\u4e5f\u662f\u5f53\u524d\u7684\u7814\u7a76\u70ed\u70b9\u3002<\/span><\/section>\n

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\u6b64\u5916\uff0c\u8fd9\u4e24\u79cd\u7ecf\u5178\u7406\u8bba\u90fd\u9884\u8bbe\u4e86\u4e00\u4e9b\u5047\u8bbe\uff0c\u6bd4\u5982\u4e8c\u8005\u90fd\u5047\u8bbe<\/span><\/section>\n

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1.\u6211\u4eec\u662f\u5728\u4e00\u4e9b\u5df2\u77e5\u7684\u76ee\u6807\u51fd\u6570\u96c6\u4e2d\u8fdb\u884c\u9009\u62e9\u3002\u4f46\u5728\u6df1\u5ea6\u5b66\u4e60\u4e2d\uff0c\u8fd9\u4e00\u5047\u8bbe\u4f9d\u8d56\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u67b6\u6784\uff0c\u4e5f\u5c31\u662f\u8bf4\u8fd9\u4e9b\u7406\u8bba\u90fd\u4e0d\u80fd\u6307\u5bfc\u6211\u4eec\u8fdb\u884c\u67b6\u6784\u7684\u8bbe\u8ba1\uff1b<\/span><\/section>\n

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2.\u6211\u4eec\u77e5\u9053 ground truth \u662f\u4ec0\u4e48\u3002\u4f46\u5b9e\u9645\u60c5\u51b5\u66f4\u591a\u662f\u6570\u636e\u548c\u7b97\u6cd5\u7684\u7ed3\u6784\u662f\u4e0d\u6e05\u695a\u7684\uff0c\u6211\u4eec\u5bf9\u4e8e ground truth \u662f\u4ec0\u4e48\u4e5f\u4e0d\u591f\u6e05\u695a\uff1b<\/span><\/section>\n

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3.\u6211\u4eec\u5df2\u77e5\u8be5\u5982\u4f55\u8fdb\u884c\u4f18\u5316\u548c\u9009\u62e9\u76ee\u6807\u3002\u4f46\u662f\u5728\u6df1\u5ea6\u5b66\u4e60\u4e2d\uff0c\u57fa\u4e8e\u68af\u5ea6\u4f18\u5316\u7684\u65b9\u5f0f\u662f\u4e0d\u53ef\u63a7\u7684\uff0c\u6211\u4eec\u5f80\u5f80\u4e5f\u4e0d\u6e05\u695a\u5b83\u4e3a\u4ec0\u4e48\u4f1a\u5f80\u67d0\u4e2a\u65b9\u5411\u4f18\u5316\u3002<\/span><\/section>\n

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\u90a3\u4e48\uff0c\u597d\u7684\u673a\u5668\u5b66\u4e60\u7406\u8bba\u5e94\u8be5\u662f\u4ec0\u4e48\u6837\uff1f<\/span><\/section>\n

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3. \u597d\u7684\u673a\u5668\u5b66\u4e60\u7406\u8bba\u5e94\u8be5\u662f\u4ec0\u4e48\u6837<\/strong><\/strong><\/p>\n<\/section>\n

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\u5982\u679c\u628a\u7406\u8bba\u5927\u81f4\u5206\u6210\u201c\u767b\u5c71\u5f0f\u7406\u8bba\u201d\u548c\u201c\u94fa\u8def\u5f0f\u7406\u8bba\u201d\uff0c\u524d\u8005\u6307\u76ee\u6807\u6e05\u6670\uff0c\u53ea\u9700\u8981\u60f3\u65b9\u8bbe\u6cd5\u722c\u5230\u5c71\u9876\uff0c\u8fc7\u7a0b\u4e2d\u95f4\u6211\u4eec\u53ef\u4ee5\u60f3\u529e\u6cd5\u589e\u52a0\u5047\u8bbe\u4f7f\u5f97\u80fd\u591f\u5c3d\u5feb\u8fbe\u5230\u76ee\u6807\u3002\u8fc7\u53bb10\u5e74\u4e2d\uff0c\u7406\u8bba\u5b66\u5bb6\u4e3a\u4e86\u7406\u89e3\u4e00\u4e2a\u6982\u5ff5\u6216\u8005\u7b97\u6cd5\uff0c\u5236\u5b9a\u4e86\u5f88\u591a\u5f88\u9ad8\u7684\u767b\u5c71\u76ee\u6807\uff0c\u4f46\u5f88\u591a\u5047\u8bbe\u7ecf\u4e0d\u8d77\u63a8\u6572\uff0c\u4f7f\u5f97\u5f88\u591a\u7ed3\u8bba\u4e5f\u90fd\u5357\u8f95\u5317\u8f99\uff0c\u65e0\u6cd5\u5f97\u5230\u5708\u5916\u4eba\u8ba4\u53ef\u3002\u800c\u540e\u8005\u5219\u662f\u4e13\u6ce8\u8d70\u811a\u4e0b\u7684\u8def\uff0c\u81f4\u529b\u4e8e\u7406\u89e3\u5f53\u524d\u7684\u95ee\u9898\uff0c\u5e76\u4e0d\u671f\u5f85\u80fd\u591f\u8fbe\u5230\u591a\u8fdc\u7684\u76ee\u6807\u3002<\/span><\/section>\n

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\u8881\u6d0b\uff1aAI \u8fd8\u9700\u8981\u7406\u8bba\u4e48\uff1f<\/span><\/section>\n
https:\/\/zhuanlan.zhihu.com\/p\/634193692<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

<\/section>\n
\u4ece\u4e00\u4e2a\u7269\u7406\u5b66\u5bb6\u7684\u89c6\u89d2\uff0c\u597d\u7684\u7406\u8bba\u5e94\u8be5\u66f4\u50cf\u662f\u94fa\u8def\u5f0f\u7406\u8bba\uff0c\u8fd9\u91cc\u79f0\u4e4b\u4e3a\u201cA physics-like theory\u201d\uff0c\u8868\u8ff0\u4e0d\u540c\uff0c\u4f46\u542b\u4e49\u57fa\u672c\u4e0a\u662f\u76f8\u901a\u7684\u3002\u8fd9\u91cc\u6240\u8981\u5f3a\u8c03\u7684\u662f\uff0c\u6211\u4eec\u5e94\u8be5\u66f4\u52a0\u5173\u6ce8\u7406\u8bba\u6240\u5e26\u6765\u7684\u7269\u7406\u56fe\u666f\u5982\u4f55<\/strong>\uff0c\u800c\u8fd9\u6070\u6070\u4e5f\u662f\u5386\u53f2\u4e0a\u7269\u7406\u5b66\u5bb6\u63a2\u7d22\u771f\u7406\u8fc7\u7a0b\u4e2d\u8d2f\u7a7f\u59cb\u7ec8\u7684\u539f\u5219\u3002<\/span><\/section>\n

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\u90a3\u4e48\uff0c\u673a\u5668\u5b66\u4e60\u7684\u7269\u7406\u56fe\u666f\u662f\u4ec0\u4e48\uff1f\u524d\u6587\u4e5f\u6709\u63d0\u5230\uff0c\u6211\u4eec\u5bf9\u673a\u5668\u5b66\u4e60\u7684\u7814\u7a76\u5c31\u50cf\u662f\u76f2\u4eba\u6478\u8c61\uff0c\u6211\u4eec\u4ece\u5404\u4e2a\u89d2\u5ea6\u5207\u5165\uff0c\u8bd5\u56fe\u5f04\u660e\u767d\u8fd9\u4e2a\u590d\u6742\u7cfb\u7edf\u5230\u5e95\u5982\u4f55\u8fd0\u4f5c\uff0c\u5e76\u4e14\u4e5f\u5f97\u5230\u4e86\u8bb8\u591a\u7ed3\u8bba<\/span>\uff08\u5982\u4e0b\u56fe\u6240\u793a\uff09<\/span>\u3002\u5f53\u7136\u6211\u4eec\u53ef\u4ee5\u7ee7\u7eed\u53bb\u4ece\u65b0\u7684\u89d2\u5ea6\u53bb\u6478\u8fd9\u53ea\u201c\u5927\u8c61\u201d\u7684\u5c40\u90e8\uff0c\u5c1d\u8bd5\u5bf9\u5b83\u6709\u65b0\u7684\u7406\u89e3\uff1b\u6211\u4eec\u4e5f\u53ef\u80fd\u8bd5\u56fe\u4ece\u5f53\u524d\u5df2\u6709\u7684\u7ed3\u8bba\u4e2d\u601d\u8003\u8fd9\u4e2a\u5e9e\u7136\u5927\u7269\u7684\u5168\u8c8c\u7a76\u7adf\u5982\u4f55\u3002<\/span><\/section>\n
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\u56fe6. \u5f53\u524d\u673a\u5668\u5b66\u4e60\u7406\u8bba\u5217\u4e3e<\/span><\/section>\n

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\u6211\u4eec\u5982\u4f55\u5c3d\u53ef\u80fd\u5730\u63a5\u8fd1\u673a\u5668\u5b66\u4e60\u7684\u7269\u7406\u56fe\u666f\u5462\uff1f\u8bb2\u8005\u7ed9\u51fa\u4e86\u7269\u7406\u5b66\u5bb6\u770b\u5f85\u4e16\u754c\u7684\u65b9\u5f0f\uff0c\u6216\u8bb8\u5bf9\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7406\u8bba\u7814\u7a76\u4e5f\u6709\u5e2e\u52a9\uff1a<\/span><\/section>\n

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0. \u7b2c\u4e00\u4e2a\u539f\u5219\u662f\u4ece\u73b0\u5b9e\u5b9e\u9a8c\u548c\u89c2\u6d4b\u51fa\u53d1\u6784\u5efa\u7406\u8bba\uff0c\u800c\u4e0d\u662f\u51ed\u7a7a\u505a\u51fa\u5047\u8bbe\uff1b<\/span><\/p>\n

1. \u9700\u8981\u80fd\u591f\u627e\u51fa\u7cfb\u7edf\u6700\u5173\u952e\u7684\u81ea\u7531\u5ea6\uff1b<\/span><\/p>\n

2. \u9700\u8981\u7528\u52a8\u6001\u7684\u89c6\u89d2\u770b\u5f85\u7cfb\u7edf\uff1b<\/span><\/p>\n

3. \u66f4\u504f\u597d\u76f4\u89c2\u56fe\u50cf<\/span>\uff08mental pictures\uff09<\/span>\u751a\u4e8e\u4e25\u683c\u7684\u6570\u5b66\u3002\u4e5f\u5c31\u662f\u8bf4\u5f53\u76f4\u89c2\u56fe\u50cf\u548c\u6570\u5b66\u4e25\u683c\u6027\u53d1\u751f\u77db\u76fe\u65f6\uff0c\u503e\u5411\u4e8e\u66f4\u63a5\u8fd1\u7269\u7406\u73b0\u5b9e\u7684\u76f4\u89c2\u56fe\u50cf\u3002<\/span><\/section>\n

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\u4ee5 grokking \u8fd9\u4e00\u73b0\u8c61\u4e3a\u4f8b\u3002\u673a\u5668\u5b66\u4e60\u4e2d\u7684 grokking \u73b0\u8c61\u662f\u6307\uff0c\u8bad\u7ec3\u96c6\u7684\u6536\u655b\u5f80\u5f80\u5feb\u4e8e\u6d4b\u8bd5\u96c6\u7684\u6536\u655b\uff0c\u4e5f\u5c31\u662f\u4e8c\u8005\u7684\u6536\u655b\u901f\u5ea6\u4f1a\u6709\u4e00\u4e2a\u65f6\u95f4\u5dee\u3002\u5728\u8fd9\u4e2a\u65f6\u95f4\u5dee\u671f\u95f4\uff0c\u7b97\u6cd5\u66f4\u50cf\u662f\u5728\u8fc7\u62df\u5408\u7684\u7a7a\u95f4\u4e2d\u8fd0\u4f5c\uff0c\u7136\u540e\u968f\u7740\u8fed\u4ee3\u6b21\u6570\u589e\u52a0\u624d\u4f1a\u5411\u6cdb\u5316\u6027\u4e0a\u4f18\u5316\uff0c\u5373\u6d4b\u8bd5\u96c6\u51c6\u786e\u5ea6\u5347\u9ad8\uff0c\u5982\u56fe7\u6240\u793a[2]\u3002<\/span><\/section>\n

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[2] Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets\u201d by Power et al. https:\/\/mathai-iclr.github.io\/papers\/papers\/MATHAI_29_paper.pdf<\/span><\/section>\n
[3] \u201cOmnigrok: Grokking Beyond Algorithmic Data\u201d Liu et al.<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

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\u56fe8. \u5de6\u56fe\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684 grokking \u73b0\u8c61\u3002\u53f3\u56fe\u662f\u53d1\u73b0 grokking \u73b0\u8c61\u548c weight norm \u6709\u76f8\u5173\u6027<\/span><\/p>\n


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\u53c2\u770b\u5b50\u9e23\u7684\u535a\u5ba2\u6587\u7ae0\uff0c\u4e86\u89e3\u5bf9 Grokking \u73b0\u8c61\u7684\u6df1\u5165\u5206\u6790<\/span><\/p>\n

\u6587\u7ae0\u9898\u76ee\uff1a<\/span><\/p>\n

A Good ML theory is Like Physics: A Physicist\u2019s Analysis of Grokking<\/span><\/p>\n

\u6587\u7ae0\u94fe\u63a5\uff1a<\/span><\/p>\n

https:\/\/zimingliublog.wordpress.com\/2023\/06\/16\/a-good-ml-theory-is-like-physics-a-physicists-analysis-of-grokking\/<\/span><\/p>\n<\/section>\n<\/section>\n<\/blockquote>\n


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\u6591\u56fe\u8def\u5f84\uff1ahttps:\/\/pattern.swarma.org\/article\/239<\/span><\/section>\n

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\u5218\u5b50\u9e23\uff0c\u9ebb\u7701\u7406\u5de5\u5b66\u9662\uff08MIT\uff09\u7269\u7406\u7cfb\u535a\u58eb\u751f\uff0c\u5bfc\u5e08\u662f Max Tegmark\u30022020\u5e74\u4ece\u5317\u4eac\u5927\u5b66\u83b7\u5f97\u7269\u7406\u5b66\u58eb\u5b66\u4f4d\u3002\u4ed6\u7684\u7814\u7a76\u5174\u8da3\u5728AI\u548c\u7269\u7406\u7684\u4ea4\u53c9\uff1a\u4e00\u65b9\u9762AI for Physics\uff0c\u5229\u7528AI\u5de5\u5177\u81ea\u52a8\u5316\u7269\u7406\u89c4\u5f8b\u548c\u6982\u5ff5\u7684\u53d1\u73b0\uff1b\u53e6\u4e00\u65b9\u9762Physics for AI\uff0c\u5229\u7528\u7269\u7406\u542f\u53d1\u6784\u5efaAI\u7406\u8bba\u548c\u66f4\u5177\u53ef\u89e3\u91ca\u6027\u7684\u6a21\u578b\u3002<\/span><\/section>\n
\u4e2a\u4eba\u4e3b\u9875\uff1ahttps:\/\/kindxiaoming.github.io\/<\/span><\/section>\n

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AI+Science \u662f\u8fd1\u5e74\u5174\u8d77\u7684\u5c06\u4eba\u5de5\u667a\u80fd\u548c\u79d1\u5b66\u76f8\u7ed3\u5408\u7684\u4e00\u79cd\u8d8b\u52bf\u3002\u4e00\u65b9\u9762\u662f AI for Science\uff0c\u673a\u5668\u5b66\u4e60\u548c\u5176\u4ed6 AI \u6280\u672f\u53ef\u4ee5\u7528\u6765\u89e3\u51b3\u79d1\u5b66\u7814\u7a76\u4e2d\u7684\u95ee\u9898\uff0c\u4ece\u9884\u6d4b\u5929\u6c14\u548c\u86cb\u767d\u8d28\u7ed3\u6784\uff0c\u5230\u6a21\u62df\u661f\u7cfb\u78b0\u649e\u3001\u8bbe\u8ba1\u4f18\u5316\u6838\u805a\u53d8\u53cd\u5e94\u5806\uff0c\u751a\u81f3\u50cf\u79d1\u5b66\u5bb6\u4e00\u6837\u8fdb\u884c\u79d1\u5b66\u53d1\u73b0\uff0c\u88ab\u79f0\u4e3a\u79d1\u5b66\u53d1\u73b0\u7684\u201c\u7b2c\u4e94\u8303\u5f0f\u201d\u3002\u53e6\u4e00\u65b9\u9762\u662f Science for AI\uff0c\u79d1\u5b66\u5c24\u5176\u662f\u7269\u7406\u5b66\u4e2d\u7684\u89c4\u5f8b\u548c\u601d\u60f3\u542f\u53d1\u673a\u5668\u5b66\u4e60\u7406\u8bba\uff0c\u4e3a\u4eba\u5de5\u667a\u80fd\u7684\u53d1\u5c55\u63d0\u4f9b\u5168\u65b0\u7684\u89c6\u89d2\u548c\u65b9\u6cd5\u3002<\/span><\/section>\n

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\u4e3a\u4e86\u63a2\u8ba8\u56fe\u795e\u7ecf\u7f51\u7edc\u5728\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u7684\u53d1\u5c55\u4e0e\u73b0\u5b9e\u5e94\u7528\uff0c\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u56fd\u9632\u79d1\u6280\u5927\u5b66\u7cfb\u7edf\u5de5\u7a0b\u5b66\u9662\u526f\u6559\u6388\u8303\u957f\u4fca\u3001\u4e2d\u56fd\u4eba\u6c11\u5927\u5b66\u9ad8\u74f4\u4eba\u5de5\u667a\u80fd\u5b66\u9662\u52a9\u7406\u6559\u6388\u9ec4\u6587\u70b3\uff0c\u5171\u540c\u53d1\u8d77\u300c\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7ec4\u5408\u4f18\u5316\u300d\u8bfb\u4e66\u4f1a\u3002\u8bfb\u4e66\u4f1a\u5c06\u805a\u7126\u4e8e\u56fe\u795e\u7ecf\u7f51\u7edc\u4e0e\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u7684\u76f8\u5173\u9886\u57df\uff0c\u5305\u62ec\u795e\u7ecf\u7b97\u6cd5\u63a8\u7406\u3001\u7ec4\u5408\u4f18\u5316\u95ee\u9898\u6c42\u89e3\u3001\u51e0\u4f55\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u4ee5\u53ca\u7b97\u6cd5\u795e\u7ecf\u5316\u6c42\u89e3\u5728 AI for Science \u4e2d\u7684\u5e94\u7528\u7b49\u65b9\u9762\uff0c\u5e0c\u671b\u4e3a\u53c2\u4e0e\u8005\u63d0\u4f9b\u4e00\u4e2a\u5b66\u672f\u4ea4\u6d41\u5e73\u53f0\uff0c\u6fc0\u53d1\u53c2\u4e0e\u8005\u7684\u5b66\u672f\u5174\u8da3\uff0c\u8fdb\u4e00\u6b65\u63a8\u52a8\u76f8\u5173\u9886\u57df\u7684\u7814\u7a76\u548c\u5e94\u7528\u53d1\u5c55\u3002\u8bfb\u4e66\u4f1a\u4ece2023\u5e746\u670814\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u4e09\u665a 19:00-21:00 \u4e3e\u884c\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba18\u5468\u3002\u6b22\u8fce\u611f\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\uff01<\/span><\/section>\n

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\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/p>\n

1. <\/strong>\u5f53\u7269\u7406\u5b66\u9047\u5230\u673a\u5668\u5b66\u4e60\uff1a\u57fa\u4e8e\u7269\u7406\u77e5\u8bc6\u7684\u673a\u5668\u5b66\u4e60\u7efc\u8ff0<\/a><\/strong><\/span><\/strong><\/strong><\/section>\n
2.<\/strong> \u7269\u7406\u7f51\u7edc\u4e2d\u7684\u76d1\u7763\u5b66\u4e60\uff1a\u4ece\u673a\u5668\u5b66\u4e60\u5230\u5b66\u4e60\u673a\u5668<\/a><\/a><\/a><\/strong><\/span><\/section>\n
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3.<\/strong><\/strong> Nat. Mach. Intell\uff1a\u4f18\u5316\u7269\u7406\u5b66\u548c\u673a\u5668\u5b66\u4e60\u4e4b\u95f4\u7684\u534f\u540c\u4f5c\u7528<\/a><\/a><\/strong><\/span><\/section>\n
4. \u300a\u5f20\u6c5f\u00b7\u590d\u6742\u79d1\u5b66\u524d\u6cbf27\u8bb2\u300b\u5b8c\u6574\u4e0a\u7ebf\uff01<\/strong><\/a><\/strong><\/strong><\/strong><\/section>\n
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5. \u6210\u4e3a\u96c6\u667aVIP\uff0c\u89e3\u9501\u5168\u7ad9\u8bfe\u7a0b\uff0f\u8bfb\u4e66\u4f1a<\/span><\/strong><\/a><\/strong><\/strong><\/strong><\/strong><\/section>\n
6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/a><\/strong><\/strong><\/strong><\/span><\/section>\n


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

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

\u70b9\u51fb\u201c\u9605\u8bfb\u539f\u6587\u201d\uff0c\u89c2\u770b\u8bfb\u4e66\u4f1a\u89c6\u9891<\/span><\/strong><\/p>\n

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

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