{"id":1015,"date":"2022-07-19T10:41:28","date_gmt":"2022-07-19T10:41:28","guid":{"rendered":"https:\/\/www.gdsig.cn\/?gdsig=52"},"modified":"2022-07-19T10:41:28","modified_gmt":"2022-07-19T10:41:28","slug":"post-52","status":"publish","type":"post","link":"https:\/\/test.gdsig.cn\/index.php\/2022\/07\/19\/post-52\/","title":{"rendered":"2021\u5e74\u7ca4\u6e2f\u6fb3\u56fe\u8c61\u56fe\u5f62\u5b66\u672f\u4f1a\u8bae\u66a8\u4eba\u5de5\u667a\u80fd\u4ea7\u4e1a\u53d1\u5c55\u8bba\u575b\u5728\u5317\u4eac\u5e08\u8303\u5927\u5b66\u73e0\u6d77\u56ed\u533a\u5706\u6ee1\u843d\u5e55"},"content":{"rendered":"

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\u00a0\u00a0<\/span><\/strong>\u4e2d\u56fd\u5de5\u7a0b\u9662\u9662\u58eb\u3001\u6d6a\u6f6e\u96c6\u56e2\u9996\u5e2d\u79d1\u5b66\u5bb6\u738b\u6069\u4e1c\u4f5c\u201c\u8ba1\u7b97\u529b\uff0c\u667a\u6167\u65f6\u4ee3\u751f\u4ea7\u529b\u201d\u7ebf\u4e0a\u4e3b\u9898\u62a5\u544a\u3002<\/span><\/strong>\u4ed6\u6307\u51fa\uff0c\u672a\u6765\u4eba\u5de5\u667a\u80fd\u5bf9\u8ba1\u7b97\u7684\u9700\u6c42\u5c06\u5360\u5168\u7403\u8ba1\u7b97\u9700\u6c42\u768480%\u4ee5\u4e0a\uff0c\u800c\u627f\u8f7d\u8fd9\u79cd\u9700\u6c42\u7684\u5c31\u662f\u667a\u80fd\u8ba1\u7b97\u4e2d\u5fc3\u3002\u56fd\u5bb6\u67a2\u7ebd\u8282\u70b9\u5efa\u8bbe\u5e94\u505a\u597d\u7f51\u7edc\u3001\u80fd\u6e90\u3001\u7b97\u529b\u3001\u6570\u636e\u3001\u5e94\u7528\u7b49\u4e00\u4f53\u5316\u53d1\u5c55\uff0c\u6700\u7ec8\u6784\u5efa\u6210\u4ee5\u6570\u636e\u6d41\u4e3a\u5bfc\u5411\u7684\u65b0\u578b\u7b97\u529b\u7f51\u7edc\u683c\u5c40\u3002\u7b97\u529b\u4f9b\u7ed9\u6b63\u5448\u73b0\u4f9b\u4e0d\u5e94\u6c42\u4e4b\u52bf\u3002 \u5404\u79cd\u4eba\u5de5\u667a\u80fd\u573a\u666f\u548c\u6570\u5b57\u5316\u4e1a\u52a1\u52a0\u901f\u843d\u5730\uff0c\u5bf9\u8ba1\u7b97\u529b\u9700\u6c42\u65fa\u76db\u3002\u8be5\u5982\u4f55\u5f25\u8865\u4eba\u5de5\u667a\u80fd\u7b97\u529b\u201c\u9e3f\u6c9f\u201d\uff1f\u738b\u6069\u4e1c\u9662\u58eb\u7684\u7b54\u6848\u662f\u8ba1\u7b97\u6280\u672f\u4e0e\u4ea7\u4e1a\u9700\u6784\u5efa\u65b0\u53d1\u5c55\u683c\u5c40\uff0c\u52a0\u901f\u8ba1\u7b97\u5411\u667a\u7b97\u8f6c\u578b\uff0c\u5176\u4e2d\u591a\u5143\u7b97\u529b\u878d\u5408\u662f\u5173\u952e\uff0c\u7b97\u529b\u4f9b\u7ed9\u57fa\u5efa\u5316\u662f\u652f\u6491\u3002<\/span><\/span><\/p>\n

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\u00a0\u00a0\u56fd\u5bb6\u6770\u51fa\u9752\u5e74\u57fa\u91d1\uff08\u6d77\u5916\uff09\u83b7\u5f97\u8005\u3001\u9999\u6e2f\u4e2d\u6587\u5927\u5b66\uff08\u6df1\u5733\uff09\u7406\u5de5\u5b66\u9662\u9662\u957f\u9648\u957f\u6c76\u4f5c”<\/span><\/span><\/strong><\/span>Effective\u00a0 Deep Learning\u00a0 via\u00a0 Network Morphism”<\/span>\u7ebf\u4e0a\u4e3b\u9898\u62a5\u544a<\/span>\u3002<\/span><\/strong><\/span><\/strong><\/strong><\/span>Prof. Chen presented that network morphism is an effective learning scheme capable of morphing a well-trained neural network to a new one with the network function preserved. Current network morphism scheme addresses only basic morphing types at the layer level. In this talk, we address the central problem of network morphism at a higher level: how a convolutional layer can be morphed into an arbitrary module of a neural network. In particular, we abstract a module as a graph with blobs as vertices and convolutional layers as edges. Then, the morphing process can be formulated as a graph transformation problem. We introduce two atomic morphing operations to construct the graphs and classify the modules into two families: simple morphable modules and complex modules. We prove that any module can be morphed from a single convolutional layer. Extensive experiments were carried out to verify the effectiveness of the proposed scheme.<\/span><\/span><\/p>\n

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\u00a0\u00a0\u56fd\u5bb6\u7279\u8058\u4e13\u5bb6\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66-\u9999\u6e2f\u6d78\u4f1a\u5927\u5b66\u8054\u5408\u56fd\u9645\u5b66\u9662\u526f\u6821\u957f\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u81ea\u7136\u79d1\u5b66\u9ad8\u7b49\u7814\u7a76\u9662\u4eba\u5de5\u667a\u80fd\u4e0e\u672a\u6765\u7f51\u7edc\u7814\u7a76\u9662\u9662\u957f\u8d3e\u7ef4\u5609\u4f5c\u201c\u4eba\u673a\u7269\u878d\u5408\u53ca\u667a\u6167\u57ce\u5e02\u5e94\u7528\u201d\u73b0\u573a\u4e3b\u9898\u62a5\u544a\u3002<\/strong>\u4ed6\u8ba4\u4e3a\uff0c\u667a\u6167\u57ce\u5e02\u7a7a\u95f4\u5305\u542b\u5404\u7c7b\u5f02\u6784\u7684\u6570\u636e\u7f51\u7edc\uff0c\u793e\u4ea4\u7f51\u7edc\uff0c\u7535\u5b50\u4e0e\u7269\u8054\u7f51\u7a7a\u95f4\uff1b\u8fd9\u4e9b\u7a7a\u95f4\u5bf9\u4fe1\u606f\u8868\u8fbe\u548c\u63d0\u53d6\u5f62\u6210\u4e86\u58c1\u5792\u3002\u7528\u4e8e\u667a\u6167\u57ce\u5e02\u7684\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u5fc5\u987b\u514b\u670d\u8fd9\u4e9b\u58c1\u5792\u3002\u672c\u6f14\u8bb2\u7ed9\u51fa\u6211\u4eec\u6700\u65b0\u7684 AI \u7814\u7a76\uff1a\u5373\u667a\u6167\u57ce\u5e02\u8d85\u6a21\u6001\u7a7a\u95f4\u4fe1\u606f\u63d0\u53d6\u548c\u5173\u8054\u4ee5\u53ca\u7269\u8054\u7f51\u65b9\u9762\u7684\u5e94\u7528\u3002<\/span><\/p>\n

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\u00a0\u00a0\u5357\u975e\u56fd\u5bb6\u79d1\u5b66\u9662\u9662\u58eb\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u81ea\u7136\u79d1\u5b66\u9ad8\u7b49\u7814\u7a76\u9662\u4eba\u5de5\u667a\u80fd\u4e0e\u672a\u6765\u7f51\u7edc\u7814\u7a76\u9662\u6559\u6388\u738b\u5e86\u56fd\u4f5c\u201cMachine learning in Presence of Outliers\u201d\u73b0\u573a\u4e3b\u9898\u62a5\u544a\u3002<\/strong><\/span>Prof. Wang presented that the outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank and sparse matrices, and further recast as a semidefinite programming (SDP) problem. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers, and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered \u201cclean\u201d’ data from the proposed method can give much better parameter estimation compared with that based on the raw data.<\/span><\/p>\n

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\u5206\u8bba\u575b\u6d3b\u52a8\u00a0 \u805a\u7126\u4e13\u9898\u00a0 \u6df1\u5165\u4ea4\u6d41\u4e13\u4e1a\u9886\u57df\u70ed\u70b9\u4e0e\u8d8b\u52bf<\/span><\/h2>\n


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17\u65e5\u4e0b\u5348\uff0c\u535a\u58eb\u5b66\u672f\u8bba\u575b\u6d3b\u52a8\u4e3e\u884c\uff0c\u5317\u4eac\u5e08\u8303\u5927\u5b66\u4eba\u5de5\u667a\u80fd\u5b66\u9662\u526f\u9662\u957f\u738b\u5fd7\u6625\u4e3b\u6301\u3002\u5317\u4eac\u5e08\u8303\u5927\u5b66\u4eba\u5de5\u667a\u80fd\u5b66\u9662\u6267\u884c\u9662\u957f\u59da\u529b\u53ca\u5b66\u9662\u7279\u9080\u6559\u6388\u3001\u5b66\u9662\u672c\u7855\u535a\u5b66\u751f\u4ee3\u8868\u53ca\u5927\u4f1a\u5176\u4ed6\u53c2\u4f1a\u5609\u5bbe\u53ca\u90e8\u5206\u9ad8\u6821\u5e08\u751f\u53c2\u52a0\u5206\u8bba\u575b\u6d3b\u52a8\u3002\u5317\u4eac\u5e08\u8303\u5927\u5b66\u4eba\u5de5\u667a\u80fd\u5b66\u9662\u674e\u7428\u3001\u674e\u6674\u3001\u738b\u660a\u7b1b\u3001\u5434\u701a\u9716\u3001\u8f9b\u73b0\u4f1f\u3001\u6768\u7433\u3001\u795d\u5fd7\u8fdc\u7b49\u4e03\u4f4d\u535a\u58eb\u751f\u5206\u522b\u5c31\u65e0\u8d28\u91cf\u9a8c\u8bc1\u7684\u53cd\u5171\u8c0b\u4f17\u5305\u673a\u5236\u3001\u57fa\u4e8e\u6df1\u5c42\u6a21\u578b\u7684\u65f6-\u7a7a\u5171\u53d8\u8111\u7f51\u7edc\u5206\u6790\u7b97\u6cd5\u3001\u57fa\u4e8e\u96f6\u77e5\u8bc6\u6570\u636e\u53ef\u53d6\u56de\u6027\u8bc1\u660e\u7684\u7269\u6d41\u8054\u76df\u94fe\u3001\u57fa\u4e8e\u89c6\u89c9\u6ce8\u610f\u4e0e\u81ea\u9002\u5e94\u5b66\u4e60\u7684\u9065\u611f\u5f71\u50cf\u8d85\u5206\u8fa8\u7387\u3001\u4e0d\u786e\u5b9a\u4fe1\u606f\u4e0b\u7684\u76f4\u89c9\u6a21\u7cca\u4e09\u652f\u51b3\u7b56\u6a21\u578b\u7814\u7a76\u3001\u5929\u6587\u6570\u636e\u5b9a\u6807\u53ca\u6052\u661f\u53c2\u6570\u4f30\u8ba1\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u7814\u7a76\u3001\u57fa\u4e8e\u56fe\u5b66\u4e60\u7684\u8111\u529f\u80fd\u7f51\u7edc\u5efa\u6a21\u7b97\u6cd5\u7814\u7a76\u7b49\u4e13\u9898\u6c47\u62a5\u7814\u7a76\u60c5\u51b5\uff0c\u5206\u8bba\u575b\u5bf9\u4f18\u79c0\u535a\u58eb\u751f\u8fdb\u884c\u4e86\u8868\u5f70\u3002<\/span><\/p>\n

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