{"id":42504,"date":"2023-05-14T09:03:59","date_gmt":"2023-05-14T01:03:59","guid":{"rendered":"https:\/\/swarma.org\/?p=42504"},"modified":"2023-05-14T09:03:59","modified_gmt":"2023-05-14T01:03:59","slug":"%e7%94%9f%e6%88%90%e6%b5%81%e7%bd%91%e7%bb%9c%ef%bc%9a%e5%9f%ba%e4%ba%8e%e7%bd%91%e7%bb%9c%e9%87%87%e6%a0%b7%e6%89%a9%e5%b1%95%e6%90%9c%e7%b4%a2%e7%a9%ba%e9%97%b4-gflownet-%e6%9c%80%e6%96%b0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=42504","title":{"rendered":"\u751f\u6210\u6d41\u7f51\u7edc\uff1a\u57fa\u4e8e\u7f51\u7edc\u91c7\u6837\u6269\u5c55\u641c\u7d22\u7a7a\u95f4 | GFlowNet \u6700\u65b0\u7814\u7a76\u68b3\u7406"},"content":{"rendered":"
\n
\n
\n
\n

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

\n
\n
\n
\n
\n
\n

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

\u5bfc\u8bed<\/strong><\/p>\n<\/section>\n<\/section>\n

\n

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

\u751f\u6210\u6d41\u7f51\u7edc\uff08GFlowNet\uff09\u662f\u4e00\u79cd\u65b0\u7684\u6982\u7387\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u548c\u5f3a\u5316\u5b66\u4e60\u540c\u5c42\u6b21\uff0c\u53ef\u4ee5\u5e94\u7528\u4e8e\u5b9e\u9a8c\u79d1\u5b66\u5faa\u73af\u7684\u5efa\u6a21\u3001\u5047\u8bbe\u751f\u6210\u548c\u5b9e\u9a8c\u8bbe\u8ba1\u9636\u6bb5\u3002\u5728\u96c6\u667a\u4ff1\u4e50\u90e8\u300c<\/span><\/strong>AI+Science\u300d\u8bfb\u4e66\u4f1a<\/span><\/strong><\/a>\uff0cMila \u7814\u7a76\u6240\u548c\u8499\u7279\u5229\u5c14\u5927\u5b66\u5728\u8bfb\u535a\u58eb\u5f20\u9f0e\u6000\u7efc\u8ff0\u4ecb\u7ecd\u4e86GFlowNet\u6846\u67b6\u3002\u672c\u6587\u4ece\u5f20\u9f0e\u6000\u535a\u58eb\u7684 Github \u63a8\u8350\u4e2d\u7cbe\u9009\u4e86\u90e8\u5206\u8bba\u6587\u548c\u8d44\u6599\uff0c\u4ecb\u7ecd\u8fd1\u671f\u751f\u6210\u6d41\u7f51\u7edc\u7684\u7814\u7a76\u524d\u6cbf\uff0c\u5e0c\u671b\u5e2e\u52a9\u5927\u5bb6\u4e86\u89e3\u8fd9\u4e00\u9886\u57df\u7684\u6700\u65b0\u8fdb\u5c55\u3002<\/span><\/strong><\/strong><\/p>\n


<\/section>\n<\/section>\n
\u5173\u952e\u8bcd\uff1a\u673a\u5668\u5b66\u4e60\uff0c\u751f\u6210\u6d41\u7f51\u7edc\uff0c\u4e3b\u52a8\u5b66\u4e60\uff0c\u56e0\u679c\u63a8\u65ad<\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/section>\n
<\/strong><\/strong><\/strong><\/section>\n
<\/strong><\/strong><\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\n
\n
\n
<\/svg><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
\n
<\/strong><\/section>\n<\/section>\n
\n

\u9773\u79cb\u91ce<\/span> <\/span>| \u4f5c\u8005<\/strong><\/span><\/span><\/p>\n

\u5f20\u9f0e\u6000<\/span> <\/span>| \u5ba1\u6821<\/span><\/strong><\/span><\/p>\n

\u6881\u91d1<\/span> <\/span>| \u7f16\u8f91<\/span><\/span><\/strong><\/span><\/p>\n


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

\u751f\u6210\u6d41\u7f51\u7edc<\/span><\/strong>\uff08GFlowNet\uff0cGFN\uff09\u662f\u7531 Yoshua Bengio \u6559\u6388\u548c\u4ed6\u7684\u56e2\u961f\u4e8e2021\u5e74\u63d0\u51fa\u7684\u4e00\u79cd\u751f\u6210\u6a21\u578b\uff0c\u7528\u4e8e\u4ece\u7ed9\u5b9a\u7684\u5956\u52b1\u51fd\u6570\u4e2d\u91c7\u6837\u591a\u6837\u6027\u9ad8\u7684\u5019\u9009\u5bf9\u8c61\u3002<\/span>\u4f8b\u5982\uff0c\u5728\u8bbe\u8ba1\u65b0\u5206\u5b50\u7684\u65f6\u5019\uff0c\u6211\u4eec\u5e0c\u671b\u80fd\u591f\u751f\u6210\u4e00\u7cfb\u5217\u5177\u6709\u9ad8\u5956\u52b1\uff08\u5982\u7a33\u5b9a\u6027\u3001\u6d3b\u6027\u7b49\uff09\u7684\u5206\u5b50\u7ed3\u6784\u3002<\/span>\u4f20\u7edf\u7684\u6700\u5927\u5316\u56de\u62a5\u7684\u65b9\u6cd5\uff0c\u5982\u5f3a\u5316\u5b66\u4e60<\/strong>\uff08Reinforcement Learning\uff0cRL\uff09\uff0c\u5f80\u5f80\u4f1a\u6536\u655b\u5230\u5355\u4e00\u7684\u6700\u4f18\u89e3\uff0c\u800c\u5ffd\u7565\u4e86\u5176\u4ed6\u53ef\u80fd\u7684\u9ad8\u56de\u62a5\u89e3\uff1b<\/span>\u9a6c\u5c14\u53ef\u592b\u94fe\u8499\u7279\u5361\u7f57\u6cd5<\/span><\/strong>\uff08Markov Chain Monte Carlo\uff0cMCMC\uff09\u867d\u7136\u53ef\u4ee5\u4ece\u4e00\u4e2a\u80fd\u91cf\u51fd\u6570\u4e2d\u751f\u6210\u5206\u5e03\uff0c\u4f46\u662f\u8ba1\u7b97\u4ee3\u4ef7\u9ad8\u6602\u4e14\u53ea\u80fd\u8fdb\u884c\u5c40\u90e8\u63a2\u7d22\u3002<\/span>
<\/p>\n

\u76f8\u8f83\u4e8e RL \u548c MCMC \u65b9\u6cd5\uff0cGFlowNet \u7684\u4f18\u52bf\u5728\u4e8e\u53ef\u4ee5\u751f\u6210\u591a\u6837\u5316\u7684\u9ad8\u56de\u62a5\u89e3<\/strong>\uff1b\u540c\u65f6\uff0cGFlowNet \u5229\u7528\u4e86\u65f6\u5e8f\u5dee\u5206\u5b66\u4e60\u7684\u601d\u60f3\uff0c\u5c06\u751f\u6210\u8fc7\u7a0b\u89c6\u4e3a\u4e00\u4e2a\u6d41\u7f51\u7edc\uff0c\u53ef\u4ee5\u5904\u7406\u4e0d\u540c\u8f68\u8ff9\u4ea7\u751f\u76f8\u540c\u6700\u7ec8\u72b6\u6001\u7684\u60c5\u51b5\u3002\u4f8b\u5982\uff0c\u5728\u751f\u6210\u67d0\u4e2a\u5206\u5b50\u56fe\u65f6\uff0c\u6709\u591a\u79cd\u65b9\u5f0f\u53ef\u4ee5\u9010\u6b65\u6dfb\u52a0\u539f\u5b50\u3002GFlowNet \u5c06\u8f68\u8ff9\u96c6\u5408\u89c6\u4e3a\u4e00\u4e2a\u6d41\uff0c\u5e76\u5c06\u6d41\u4e00\u81f4\u6027\u65b9\u7a0b\u8f6c\u5316\u4e3a\u4e00\u4e2a\u5b66\u4e60\u76ee\u6807\uff0c\u7c7b\u4f3c\u4e8e\u5c06\u8d1d\u5c14\u66fc\u65b9\u7a0b\u8f6c\u5316\u4e3a\u65f6\u5e8f\u5dee\u5206\u65b9\u6cd5\u3002<\/span><\/p>\n

\u7ecf\u8fc7 Bengio \u53ca\u5176\u56e2\u961f\u7684\u4e0d\u61c8\u52aa\u529b\uff0cGFlowNet \u5df2\u5728\u4e0d\u540c\u4efb\u52a1\u4e0a\u5c55\u793a\u4e86\u6709\u6548\u6027\u548c\u4f18\u8d8a\u6027\uff0c\u4f8b\u5982\u53ef\u4ee5\u7528\u4e8e\u4f30\u8ba1\u8054\u5408\u6982\u7387\u5206\u5e03\u3001\u8fb9\u7f18\u6982\u7387\u5206\u5e03\u3001\u71b5\u3001\u4e92\u4fe1\u606f\u3001Pareto\u524d\u6cbf\u3001\u5956\u52b1\u6700\u5927\u5316\u7b56\u7565\u7b49\uff0c\u5c55\u793a\u4e86\u751f\u6210\u6d41\u7f51\u7edc\u7684\u7075\u6d3b\u6027\u548c\u901a\u7528\u6027\u3002<\/span><\/p>\n

\u672c\u6587\u65e8\u5728\u4ecb\u7ecd\u8fd1\u671f\u751f\u6210\u6d41\u7f51\u7edc\u7684\u7814\u7a76\u524d\u6cbf\uff0c\u5176\u4e2d\u7684\u8bba\u6587\u548c\u8d44\u6599\u7b49\u9009\u81eaMila\u7814\u7a76\u6240\u548c\u8499\u7279\u5229\u5c14\u5927\u5b66\u5f20\u9f0e\u6000\u535a\u58eb\u7684 Github \u63a8\u8350\uff0c\u5e0c\u671b\u5e2e\u52a9\u5927\u5bb6\u4e86\u89e3\u8fd9\u4e00\u9886\u57df\u7684\u6700\u65b0\u8fdb\u5c55\u3002<\/span><\/p>\n

\n
\n
\n
\u5f20\u9f0e\u6000\u7684 Github\uff1a<\/span><\/section>\n
https:\/\/github.com\/zdhNarsil\/Awesome-GFlowNets<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n


<\/p>\n

<\/p>\n

\u4f7f\u7528GFlowNets\u4fc3\u8fdb\u79d1\u5b66\u53d1\u73b0\u7684\u8fed\u4ee3\u5b9e\u9a8c\u5faa\u73af\u3002| \u56fe\u7247\u6765\u6e90\uff1aGFlowNets for AI-Driven Scientific Discovery<\/span><\/section>\n
 <\/span><\/section>\n
| DynGFN: \u751f\u6210\u6d41\u7f51\u7edc\u8fdb\u884c\u8d1d\u53f6\u65af\u52a8\u529b\u5b66\u56e0\u679c\u53d1\u73b0<\/strong><\/span><\/section>\n
\n
\n
\n
\u8bba\u6587<\/span>\u9898\u76ee\uff1aDynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks
<\/span><\/span><\/section>\n

\u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/p>\n

https:\/\/arxiv.org\/abs\/2302.04178<\/span><\/p>\n

\u4f5c\u8005\uff1aLazar Atanackovic, Alexander Tong, Jason Hartford, Leo J. Lee, Bo Wang, Yoshua Bengio <\/span>[Mila – The Quebec AI Institute]<\/span><\/span><\/p>\n<\/section>\n<\/section>\n<\/blockquote>\n

\n


<\/p>\n

\u80cc\u666f<\/span><\/strong>\uff1a\u79d1\u5b66\u53d1\u73b0\u7684\u4e00\u4e2a\u6838\u5fc3\u95ee\u9898\uff0c\u662f\u4ece\u53ef\u89c2\u6d4b\u6570\u636e\u4e2d\u5b66\u4e60\u6f5c\u5728\u7684\u56e0\u679c\u7ed3\u6784\u3002\u8d1d\u53f6\u65af\u56e0\u679c\u53d1\u73b0\u65b9\u6cd5\u901a\u8fc7\u5728\u7ed9\u5b9a\u5148\u9a8c\u548c\u89c2\u5bdf\u7684\u53ef\u63a5\u53d7\u56fe\u96c6\u4e0a\u5b66\u4e60\u540e\u9a8c\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/span><\/p>\n


<\/p>\n

\u52a8\u673a<\/span><\/strong>\uff1a\u73b0\u6709\u7684\u65b9\u6cd5\u4e3b\u8981\u8003\u8651\u9759\u6001\u7cfb\u7edf\u7684\u89c2\u6d4b\u6570\u636e\uff0c\u5e76\u5047\u8bbe\u6f5c\u5728\u7684\u56e0\u679c\u7ed3\u6784\u91c7\u7528\u6709\u5411\u65e0\u73af\u56fe\uff08directed acyclic graph, DAG\uff09\u7684\u5f62\u5f0f\u3002\u7136\u800c\uff0c\u5728\u5177\u6709\u52a8\u6001\u53cd\u9988\u673a\u5236\u8c03\u8282\u5355\u4e2a\u53d8\u91cf\u8f68\u8ff9\u7684\u60c5\u51b5\u4e0b\uff0c\u8fd9\u79cd\u65e0\u73af\u6027\u5047\u8bbe\u4f1a\u5931\u6548\uff0c\u9664\u975e\u53ef\u4ee5\u8003\u8651\u65f6\u95f4\u56e0\u7d20\u3002<\/span><\/p>\n


<\/p>\n

\u65b9\u6cd5<\/span><\/strong>\uff1a\u672c\u6587\u63d0\u51fa\u4e00\u79cd\u540d\u4e3a DynGFN \u7684\u8d1d\u53f6\u65af\u52a8\u6001\u56e0\u679c\u53d1\u73b0\u65b9\u6cd5\uff0c\u5b83\u4f7f\u7528\u751f\u6210\u6d41\u7f51\u7edc\u6765\u5904\u7406\u5177\u6709\u52a8\u6001\u53cd\u9988\u673a\u5236\u7684\u7cfb\u7edf\u4e2d\u7684\u56e0\u679c\u7ed3\u6784\u3002DynGFN \u5305\u62ec\u4e09\u4e2a\u5173\u952e\u6a21\u5757\uff1a1\uff09\u56fe\u91c7\u6837\u5668\uff0c\u7528\u4e8e\u5bf9\u7f16\u7801\u89c2\u6d4b\u53d8\u91cf\u4e4b\u95f4\u56e0\u679c\u4f9d\u8d56\u5173\u7cfb\u7684\u56fe\u7ed3\u6784\u8fdb\u884c\u91c7\u6837\uff1b2\uff09\u751f\u6210\u6d41\u7f51\u7edc\uff0c\u7528\u4e8e\u5efa\u6a21\u540e\u9a8c\u5206\u5e03\uff1b3\uff09\u57fa\u4e8e\u8d1d\u53f6\u65af\u63a8\u65ad\u7684\u7b97\u6cd5\uff0c\u7528\u4e8e\u5b66\u4e60\u6f5c\u5728\u7684\u56e0\u679c\u7ed3\u6784\u3002<\/span><\/p>\n


<\/p>\n

\u4f18\u52bf<\/span><\/strong>\uff1a1\uff09\u53ef\u4ee5\u5904\u7406\u5177\u6709\u52a8\u6001\u53cd\u673a\u5236\u7684\u7cfb\u7edf\u4e2d\u7684\u56e0\u679c\u7ed3\u6784\uff0c\u76f8\u6bd4\u73b0\u6709\u65b9\u6cd5\u66f4\u52a0\u5168\u9762\uff1b2\uff09\u53ef\u4ee5\u5904\u7406\u5177\u6709\u73af\u8def\u7684\u56e0\u679c\u7ed3\u6784\uff0c\u76f8\u6bd4\u73b0\u6709\u65b9\u6cd5\u66f4\u52a0\u7075\u6d3b\uff1b3\uff09\u53ef\u4ee5\u5bf9\u53ef\u80fd\u7684\u89e3\u91ca\u7ed3\u6784\u5efa\u7acb\u540e\u9a8c\u5206\u5e03\uff0c\u4ece\u800c\u66f4\u597d\u5730\u6a21\u62df\u53ef\u80fd\u6027\u5206\u5e03\uff1b4\uff09\u4f7f\u7528\u751f\u6210\u6d41\u7f51\u7edc\u6765\u63d0\u9ad8\u6027\u80fd\uff0c\u76f8\u6bd4\u73b0\u6709\u65b9\u6cd5\u66f4\u52a0\u9ad8\u6548\uff1b5\uff09\u5728\u5355\u7ec6\u80de RNA \u901f\u5ea6\u6570\u636e\u4e0a\u8fdb\u884c\u4e86\u5b9e\u8bc1\u8bc4\u4f30\uff0c\u5e76\u5c55\u793a\u4e86\u5728\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u53d1\u73b0\u65b9\u9762\u7684\u5e94\u7528\u3002<\/span><\/p>\n<\/section>\n

\u4e00\u53e5\u8bdd\u603b\u7ed3<\/span><\/strong>\uff1a\u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u79cd\u540d\u4e3a DynGFN \u7684\u8d1d\u53f6\u65af\u52a8\u6001\u56e0\u679c\u53d1\u73b0\u65b9\u6cd5\uff0c\u5b83\u4f7f\u7528\u751f\u6210\u6d41\u7f51\u7edc\u6765\u5904\u7406\u5177\u6709\u52a8\u6001\u53cd\u9988\u673a\u5236\u7684\u7cfb\u7edf\u4e2d\u7684\u56e0\u679c\u7ed3\u6784\u3002<\/span><\/section>\n
<\/section>\n
\u56fe1. \u8d1d\u53f6\u65af\u56e0\u679c\u53d1\u73b0\u52a8\u6001\u7cfb\u7edf\u7684\u7ed3\u6784<\/span><\/section>\n
<\/section>\n
\u56fe2. \u57fa\u4e8e\u56fe\u7ed3\u6784\u7684\u4e0d\u786e\u5b9a\u6027\uff08indeterminacy\uff09\u6a21\u578b<\/span><\/section>\n

<\/section>\n
| GAFlowNet\uff1a\u751f\u6210\u5f0f\u589e\u5f3a\u6d41\u7f51\u7edc<\/strong><\/span><\/section>\n
\n
\n
\n
\u8bba\u6587\u9898\u76ee\uff1aGenerative Au<\/span>gmented Flow Networks
<\/span><\/span><\/section>\n
\u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/2210.03308<\/span><\/section>\n
\u4f5c\u8005\uff1aLing Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio [Mila – Quebec AI Institute & Tsinghua University]<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

ICLR 2023 spotlight<\/span><\/strong><\/p>\n

\u80cc\u666f<\/span><\/strong>\uff1a\u751f\u6210\u6d41\u7f51\u7edc\u662f\u4e00\u4e2a\u6982\u7387\u6846\u67b6\uff0c\u5176\u4e2d\u667a\u80fd\u4f53\u5b66\u4e60\u7528\u4e8e\u5bf9\u8c61\u751f\u6210\u7684\u968f\u673a\u7b56\u7565\uff0c\u4f7f\u5f97\u751f\u6210\u5bf9\u8c61\u7684\u6982\u7387\u4e0e\u7ed9\u5b9a\u7684\u5956\u52b1\u51fd\u6570\u6210\u6b63\u6bd4\u3002\u4e0e\u5956\u52b1\u6700\u5927\u5316\u7684\u5f3a\u5316\u5b66\u4e60\u65b9\u6cd5\u76f8\u6bd4\uff0c\u5176\u6709\u6548\u6027\u5df2\u5728\u53d1\u73b0\u9ad8\u8d28\u91cf\u548c\u591a\u6837\u5316\u7684\u89e3\u51b3\u65b9\u6848\u65b9\u9762\u5f97\u5230\u8bc1\u5b9e\u3002<\/span><\/p>\n

\u52a8\u673a<\/span><\/strong>\uff1a\u7531\u4e8eGFlowNets\u53ea\u80fd\u4ece\u7ec8\u7aef\u72b6\u6001\u7684\u5956\u52b1\u4e2d\u5b66\u4e60\uff0c\u5728\u7a00\u758f\u5956\u52b1\u4efb\u52a1\u4e2d\u5bb9\u6613\u9677\u5165\u5c40\u90e8\u6700\u4f18\u89e3\uff0c\u53ef\u80fd\u9650\u5236\u4e86\u5176\u9002\u7528\u6027\u3002\u5b9e\u9645\u4e0a\uff0c\u4e2d\u95f4\u5956\u52b1\u5728\u5b66\u4e60\u4e2d\u8d77\u7740\u5173\u952e\u4f5c\u7528\uff0c\u4f8b\u5982\u901a\u8fc7\u5185\u5728\u52a8\u673a\u63d0\u4f9b\u4e2d\u95f4\u53cd\u9988\uff0c\u5373\u4f7f\u5728\u7279\u522b\u5177\u6709\u6311\u6218\u6027\u7684\u7a00\u758f\u5956\u52b1\u4efb\u52a1\u4e2d\u4e5f\u662f\u5982\u6b64\u3002<\/span><\/p>\n

\u65b9\u6cd5<\/span><\/strong>\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u5b66\u4e60\u6846\u67b6\uff0c\u79f0\u4e3a\u751f\u6210\u5f0f\u589e\u5f3a\u6d41\u7f51\u7edc\uff08Generative Augmented Flow Networks, GAFlowNet\uff09\u3002\u8be5\u6846\u67b6\u901a\u8fc7\u5f15\u5165\u4e2d\u95f4\u5956\u52b1\u6765\u6307\u5bfc\u667a\u80fd\u4f53\u5728\u72b6\u6001\u7a7a\u95f4\u4e2d\u8fdb\u884c\u63a2\u7d22\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u4f5c\u8005\u4f7f\u7528\u5185\u5728\u52a8\u673a\u6765\u6307\u5b9a\u4e2d\u95f4\u5956\u52b1\uff0c\u4ee5\u89e3\u51b3\u7a00\u758f\u5956\u52b1\u4efb\u52a1\u4e2d\u7684\u63a2\u7d22\u95ee\u9898\u3002<\/span><\/p>\n

\u4f18\u52bf<\/span><\/strong>\uff1a1\uff09\u901a\u8fc7\u5f15\u5165\u4e2d\u95f4\u5956\u52b1\u6765\u6307\u5bfc\u667a\u80fd\u4f53\u5728\u72b6\u6001\u7a7a\u95f4\u4e2d\u8fdb\u884c\u63a2\u7d22\uff0c\u53ef\u4ee5\u89e3\u51b3\u7a00\u758f\u5956\u52b1\u4efb\u52a1\u4e2d\u7684\u63a2\u7d22\u95ee\u9898\uff1b2\uff09\u4f7f\u7528\u5185\u5728\u52a8\u673a\u6765\u6307\u5b9a\u4e2d\u95f4\u5956\u52b1\uff0c\u53ef\u4ee5\u63d0\u9ad8\u667a\u80fd\u4f53\u7684\u591a\u6837\u6027\u548c\u6536\u655b\u6027\uff1b3\uff09\u7406\u8bba\u4e0a\u8bc1\u660e\u4e86\u8be5\u6846\u67b6\u53ef\u4ee5\u4ea7\u751f\u65e0\u504f\u5dee\u7684\u89e3\u51b3\u65b9\u6848\uff1b4\uff09\u5728GridWorld\u548c\u5206\u5b50\u9886\u57df\u4e0a\u8fdb\u884c\u4e86\u5e7f\u6cdb\u5b9e\u9a8c\uff0c\u8bc1\u660e\u4e86\u8be5\u6846\u67b6\u7684\u6709\u6548\u6027\uff0c\u672a\u6765\u8fd8\u53ef\u4ee5\u6269\u5c55\u5230\u590d\u6742\u4efb\u52a1\uff0c\u5982\u5206\u5b50\u56fe\u751f\u6210\u3002<\/span><\/p>\n

\u4e00\u53e5\u8bdd\u603b\u7ed3<\/span><\/strong>\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u5b66\u4e60\u6846\u67b6GAFlowNet\uff0c\u901a\u8fc7\u5f15\u5165\u4e2d\u95f4\u5956\u52b1\u6765\u6307\u5bfc\u667a\u80fd\u4f53\u5728\u72b6\u6001\u7a7a\u95f4\u4e2d\u8fdb\u884c\u63a2\u7d22\uff0c\u89e3\u51b3\u7a00\u758f\u5956\u52b1\u4efb\u52a1\u4e2d\u7684\u63a2\u7d22\u95ee\u9898\uff0c\u5e76\u5728GridWorld\u548c\u5206\u5b50\u9886\u57df\u4e0a\u8fdb\u884c\u4e86\u5e7f\u6cdb\u5b9e\u9a8c\uff0c\u8bc1\u660e\u4e86\u8be5\u6846\u67b6\u7684\u6709\u6548\u6027\u3002<\/span><\/p>\n

<\/span><\/section>\n
\u56fe2. \u751f\u6210\u5f0f\u589e\u5f3a\u6d41\u7f51\u7edcGAFlowNet\u7684\u6574\u4f53\u6846\u67b6<\/span><\/section>\n
<\/section>\n
\u56fe8. \u5206\u5b50\u751f\u6210\u4efb\u52a1\u7684\u6027\u80fd\u5c55\u793a<\/span><\/section>\n


<\/h2>\n

| MOGFNs\uff1a\u591a\u76ee\u6807\u751f\u6210\u6d41\u7f51\u7edc<\/strong><\/p>\n

\n
\n
\n
\u8bba\u6587\u9898\u76ee\uff1aMulti-Objective GFlowNets<\/span><\/section>\n
\u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/span><\/section>\n
<\/span>https:\/\/arxiv.org\/abs\/2210.127\u200d<\/span>65<\/span><\/section>\n
\u4f5c\u8005\uff1aMoksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio<\/span><\/section>\n
[Mila – The Quebec AI Institute]<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
ICML 2023<\/strong>
<\/section>\n

\u80cc\u666f<\/span><\/strong>\uff1a<\/span>\u5728\u673a\u5668\u5b66\u4e60\u7684\u8bb8\u591a\u5e94\u7528\u4e2d\uff0c\u5982\u836f\u7269\u53d1\u73b0\u548c\u6750\u6599\u8bbe\u8ba1\uff0c\u76ee\u6807\u662f\u751f\u6210\u540c\u65f6\u6700\u5927\u5316\u4e00\u7ec4\u76ee\u6807\u7684\u5019\u9009\u9879\u3002\u7531\u4e8e\u8fd9\u4e9b\u76ee\u6807\u7ecf\u5e38\u662f\u76f8\u4e92\u51b2\u7a81\u7684\uff0c\u6240\u4ee5\u6ca1\u6709\u4e00\u4e2a\u5019\u9009\u9879\u53ef\u4ee5\u540c\u65f6\u6700\u5927\u5316\u6240\u6709\u76ee\u6807\uff0c\u65e0\u6cd5\u5728\u4e0d\u635f\u5bb3\u4e00\u4e2a\u76ee\u6807\u7684\u60c5\u51b5\u4e0b\u6539\u5584\u53e6\u4e00\u76ee\u6807\u3002\u6b64\u5916\uff0c\u5728\u5b9e\u8df5\u4e2d\uff0c\u8fd9\u4e9b\u76ee\u6807\u5f80\u5f80\u662f\u4e0d\u660e\u786e\u7684\uff0c\u4f7f\u5f97\u5019\u9009\u9879\u7684\u591a\u6837\u6027\u6210\u4e3a\u5173\u952e\u8003\u8651\u56e0\u7d20\u3002\u73b0\u6709\u7684\u591a\u76ee\u6807\u4f18\u5316\u65b9\u6cd5\u672a\u80fd\u6355\u6349\u5019\u9009\u7a7a\u95f4\u7684\u591a\u6837\u6027\u3002<\/span><\/p>\n

\u52a8\u673a<\/span><\/strong>\uff1a\u53d7\u5230GFlowNets\u5728\u5355\u76ee\u6807\u4f18\u5316\u4e2d\u751f\u6210\u591a\u6837\u6027\u5019\u9009\u9879\u7684\u6210\u529f\u542f\u53d1\uff0cBengio\u7b49\u4eba\u8003\u8651\u5c06\u5176\u6269\u5c55\u5230\u591a\u76ee\u6807\u4f18\u5316\u4e2d\uff0c\u63d0\u51fa\u4e86Multi-Objective GFlowNets (MOGFNs)\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002MOGFNs \u7531\u4e00\u79cd\u65b0\u7684\u6761\u4ef6 GFlowNet \u7ec4\u6210\uff0c\u5176\u5bf9\u591a\u76ee\u6807\u4f18\u5316\u95ee\u9898\u5206\u89e3\u5f97\u5230\u7684\u4e00\u7ec4\u5355\u76ee\u6807\u5b50\u95ee\u9898\u8fdb\u884c\u5efa\u6a21\u3002<\/span><\/section>\n
\u65b9\u6cd5<\/span><\/strong>\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u4e24\u79cdMOGFNs\u65b9\u6cd5\u6765\u89e3\u51b3\u591a\u76ee\u6807\u4f18\u5316\u548c\u5019\u9009\u9879\u751f\u6210\u4e2d\u7684\u6311\u6218\u3002\u7b2c\u4e00\u79cd\u65b9\u6cd5\u662fPreference-Conditional GFlowNets (MOGFN-PC)\uff0c\u5b83\u5c06Reward-Conditional GFlowNets\u4e0eWeighted Sum Scalarization\u76f8\u7ed3\u5408\uff0c\u53ef\u4ee5\u5728\u4e0d\u540c\u7684\u504f\u597d\u6761\u4ef6\u4e0b\u751f\u6210\u591a\u6837\u6027\u7684\u5e15\u7d2f\u6258\u6700\u4f18\u5019\u9009\u9879\u3002\u7b2c\u4e8c\u79cd\u65b9\u6cd5\u662fMOGFN-AL\uff0c\u5b83\u662fGFlowNet-AL\u5728\u591a\u76ee\u6807\u4e3b\u52a8\u5b66\u4e60\u8bbe\u7f6e\u4e0b\u7684\u6269\u5c55\uff0c\u53ef\u4ee5\u5728\u6709\u9650\u7684\u6807\u7b7e\u6570\u636e\u4e0b\u751f\u6210\u5177\u6709\u591a\u6837\u6027\u7684\u5e15\u7d2f\u6258\u6700\u4f18\u5019\u9009\u9879\u3002<\/span><\/section>\n
\u4f18\u52bf<\/span><\/strong>\uff1a1\uff09MOGFNs\u53ef\u4ee5\u540c\u65f6\u6700\u5927\u5316\u591a\u4e2a\u76ee\u6807\uff0c\u5e76\u4e14\u80fd\u591f\u751f\u6210\u4e00\u7ec4\u5177\u6709\u591a\u6837\u6027\u7684\u5e15\u7d2f\u6258\u6700\u4f18\u5019\u9009\u9879\uff1b2\uff09MOGFNs\u53ef\u4ee5\u5728\u4e0d\u540c\u7684\u504f\u597d\u6761\u4ef6\u4e0b\u751f\u6210\u591a\u6837\u6027\u7684\u5e15\u7d2f\u6258\u6700\u4f18\u5019\u9009\u9879\uff1b3\uff09\u5728\u5c0f\u5206\u5b50\u3001DNA Aptamers \u548c\u8367\u5149\u86cb\u767d\u7b49\u6750\u6599\u8bbe\u8ba1\u4efb\u52a1\u4e0a\uff0cMOGFNs \u76f8\u5bf9\u4e8e\u73b0\u6709\u65b9\u6cd5\u5177\u6709\u66f4\u597d\u7684\u6027\u80fd\u3002<\/span><\/section>\n
\u4e00\u53e5\u8bdd\u603b\u7ed3<\/span><\/strong>\uff1a\u672c\u6587\u63d0\u51fa\u4e86Multi-Objective GFlowNets (MOGFNs)\u65b9\u6cd5\uff0c\u53ef\u4ee5\u540c\u65f6\u6700\u5927\u5316\u591a\u4e2a\u76ee\u6807\uff0c\u5e76\u4e14\u80fd\u591f\u751f\u6210\u4e00\u7ec4\u5177\u6709\u591a\u6837\u6027\u7684\u5e15\u7d2f\u6258\u6700\u4f18\u5019\u9009\u9879\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u591a\u76ee\u6807\u4f18\u5316\u548c\u5019\u9009\u9879\u751f\u6210\u4efb\u52a1\u3002<\/span><\/section>\n
<\/section>\n
\u56fe1. \u5206\u5b50\u56fe\u751f\u6210\u548cDNA\u5e8f\u5217\u751f\u6210\u7684\u4efb\u52a1\u8bf4\u660e<\/span><\/section>\n
<\/section>\n
\u56fe2. \u591a\u76ee\u6807\u751f\u6210\u6d41\u7f51\u7edcMOGFN\u7684\u91c7\u6837\u5206\u5e03\u53ef\u89c6\u5316<\/span><\/section>\n


<\/h2>\n
| \u5e7f\u4e49\u751f\u6210\u6d41\u7f51\u7edc\u7406\u8bba<\/strong><\/section>\n
\n
\n
\n
\u8bba\u6587\u9898\u76ee\uff1aA theory of continuous generative flow networks<\/span><\/section>\n
\u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/2301.12594<\/span><\/section>\n
\u4f5c\u8005\uff1aSalem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hern\u00e1ndez-Garc\u00eda, L\u00e9na N\u00e9hale Ezzine, Yoshua Bengio, Nikolay Malkin <\/span>[Mila – The Quebec AI Institute]<\/span><\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
ICML 2023<\/strong><\/span><\/section>\n
\u80cc\u666f<\/span><\/strong>\uff1a\u751f\u6210\u6d41\u7f51\u7edc\u662f\u4e00\u79cd\u5e73\u644a\u53d8\u5206\u63a8\u7406\u7b97\u6cd5\uff0c\u5b83\u88ab\u8bad\u7ec3\u4ece\u7ec4\u5408\u5bf9\u8c61\u4e0a\u7684\u975e\u5f52\u4e00\u5316\u76ee\u6807\u5206\u5e03\u4e2d\u91c7\u6837\u3002\u5b83\u7684\u4e00\u4e2a\u5173\u952e\u9650\u5236\u662f\u4ec5\u9650\u4e8e\u79bb\u6563\u7a7a\u95f4\u3002<\/span><\/section>\n
\u52a8\u673a<\/span><\/strong>\uff1a\u6269\u5c55 GFlowNets \u7684\u5e94\u7528\u8303\u56f4\uff0c\u4f7f\u5176\u80fd\u591f\u5904\u7406\u8fde\u7eed\u6216\u6df7\u5408\u72b6\u6001\u7a7a\u95f4\uff0c\u800c\u4e0d\u4ec5\u4ec5\u5c40\u9650\u4e8e\u79bb\u6563\u7a7a\u95f4\uff1b\u6b64\u5916\uff0c\u672c\u6587\u8fd8\u65e8\u5728\u63a2\u7d22GFlowNets\u7684\u7406\u8bba\u57fa\u7840\uff0c\u5e76\u901a\u8fc7\u5b9e\u9a8c\u9a8c\u8bc1\u5176\u5728\u6982\u7387\u63a8\u65ad\u548c\u5404\u79cd\u5efa\u6a21\u8bbe\u7f6e\u4e2d\u7684\u6027\u80fd\u3002<\/span><\/section>\n
\u7406\u8bba<\/span><\/strong>\uff1a\u63d0\u51fa\u4e86\u4e00\u79cd\u5e7f\u4e49 GFlowNets \u7406\u8bba\uff0c\u8be5\u7406\u8bba\u5305\u62ec\u79bb\u6563 GFlowNets \u548c\u5177\u6709\u8fde\u7eed\u6216\u6df7\u5408\u72b6\u6001\u7a7a\u95f4\u7684GFlowNets\uff1b\u5e7f\u4e49 GFlowNets \u7406\u8bba\u8981\u6c42\u72b6\u6001\u7a7a\u95f4\u7ed3\u6784\u5fc5\u987b\u6ee1\u8db3\u4e00\u5b9a\u6761\u4ef6\uff0c\u5305\u62ec\u6240\u6709\u72b6\u6001\u90fd\u53ef\u4ee5\u4ece\u6e90\u72b6\u6001\u5230\u8fbe\u3001\u4ece\u6e90\u72b6\u6001\u5230\u8fbe\u4efb\u4f55\u72b6\u6001\u6240\u9700\u7684\u6b65\u6570\u6709\u9650\u7b49\uff1b\u5e7f\u4e49 GFlowNets \u7406\u8bba\u8981\u6c42\u5b66\u4e60\u5230\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u5fc5\u987b\u662f\u57fa\u4e8e\u72b6\u6001\u800c\u975e\u57fa\u4e8e\u52a8\u4f5c\u3002<\/span><\/section>\n
\u65b9\u6cd5<\/span><\/strong>\uff1a\u4f7f\u7528\u4e86\u4e24\u4e2a\u5b9e\u9a8c\u6765\u9a8c\u8bc1\u5e7f\u4e49 GFlowNets \u7684\u6027\u80fd\uff1a\u4e00\u4e2a\u662f\u5728\u79bb\u6563\u7a7a\u95f4\u4e2d\u8fdb\u884c\u7684\u5b9e\u9a8c\uff0c\u53e6\u4e00\u4e2a\u662f\u5728\u8fde\u7eed\u7a7a\u95f4\u4e2d\u8fdb\u884c\u7684\u5b9e\u9a8c\u3002\u901a\u8fc7\u8fd9\u4e9b\u5b9e\u9a8c\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u4e86\u89e3\u5e7f\u4e49 GFlowNets \u5728\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u8868\u73b0\uff0c\u5e76\u4e3a\u672a\u6765\u7814\u7a76\u63d0\u4f9b\u65b0\u601d\u8def\u548c\u65b9\u5411\u3002<\/span><\/section>\n
\u4f18\u52bf<\/span><\/strong>\uff1a\u5728\u8fde\u7eed\u7a7a\u95f4\u4e2d\uff0c\u5e7f\u4e49GFlowNets\u76f8\u6bd4\u4e8e\u79bb\u6563GFlowNets\u548c\u975eGFlowNet\u57fa\u7ebf\u5177\u6709\u66f4\u597d\u7684\u6027\u80fd\u8868\u73b0\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5e7f\u4e49GFlowNets\u5728\u591a\u4e2a\u6570\u636e\u96c6\u4e0a\u90fd\u53d6\u5f97\u4e86\u66f4\u597d\u7684\u91c7\u6837\u6548\u679c\u548c\u5bf9\u6570\u4f3c\u7136\u503c\uff0c\u5e76\u4e14\u53ef\u4ee5\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u5206\u5e03\u3002\u8fd9\u4e9b\u7ed3\u679c\u8868\u660e\uff0c\u5e7f\u4e49GFlowNets\u662f\u4e00\u79cd\u6709\u6548\u7684\u751f\u6210\u6a21\u578b\uff0c\u5728\u5904\u7406\u8fde\u7eed\u6216\u6df7\u5408\u72b6\u6001\u7a7a\u95f4\u65f6\u5177\u6709\u5f88\u5f3a\u7684\u4f18\u52bf\u3002<\/span><\/section>\n

\u4e00\u53e5\u8bdd\u603b\u7ed3<\/span><\/strong>\uff1a<\/span>\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u5e7f\u4e49\u7684\u751f\u6210\u6d41\u7f51\u7edc\uff08GFlowNets\uff09\u7406\u8bba\uff0c\u53ef\u4ee5\u5904\u7406\u8fde\u7eed\u6216\u6df7\u5408\u72b6\u6001\u7a7a\u95f4\uff0c\u5e76\u5728\u5b9e\u9a8c\u4e2d\u8bc1\u660e\u4e86\u5176\u5728\u8fde\u7eed\u7a7a\u95f4\u4e2d\u5177\u6709\u66f4\u597d\u7684\u6027\u80fd\u8868\u73b0\u3002<\/span><\/p>\n

<\/span><\/section>\n
\u56fe1. \u5e7f\u4e49\u751f\u6210\u6d41\u7f51\u7edc\u7684\u8bad\u7ec3\u548c\u5b66\u4e60\u8fc7\u7a0b\u5c55\u793a<\/span><\/section>\n
<\/span><\/section>\n
\u56fe2. \u5e7f\u4e49\u751f\u6210\u6d41\u7f51\u7edc\u7684\u91c7\u6837\u5206\u5e03\u53ef\u89c6\u5316<\/span><\/section>\n

<\/span><\/section>\n
| GFlowOut\uff1a\u57fa\u4e8e\u751f\u6210\u6d41\u7f51\u7edc\u7684 Dropout<\/strong><\/span><\/section>\n
\n
\n
\n
\u8bba\u6587\u9898\u76ee\uff1aGFlowOut: Dropout with Generative Flow Networks<\/span><\/section>\n
\u200d<\/span>\u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/arxiv.org\/abs\/2210.12928\u200d<\/span><\/span><\/section>\n
\u4f5c\u8005\uff1aDianbo Liu, …, Yoshua Bengio <\/span>[Mila – The Quebec AI Institute & NUS, Singapore & DeepMind & Facebook AI Research]<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
ICML 2023<\/strong><\/span><\/section>\n

\u80cc\u666f<\/span><\/strong>\uff1a\u8d1d\u53f6\u65af\u63a8\u65ad\u4f5c\u4e3a\u4e00\u79cd\u57fa\u7840\u5de5\u5177\u53ef\u4ee5\u89e3\u51b3\u73b0\u4ee3\u795e\u7ecf\u7f51\u7edc\u7684\u8bb8\u591a\u5173\u952e\u95ee\u9898\uff0c\u5982\u6821\u51c6\u4e0d\u826f\u3001\u6cdb\u5316\u80fd\u529b\u5dee\u548c\u6570\u636e\u6548\u7387\u4f4e\u4e0b\u3002\u7136\u800c\uff0c\u5c06\u8d1d\u53f6\u65af\u63a8\u65ad\u6269\u5c55\u5230\u5927\u578b\u7f51\u7edc\u67b6\u6784\u662f\u5177\u6709\u6311\u6218\u6027\u7684\uff0c\u5e76\u4e14\u9700\u8981\u9650\u5236\u6027\u7684\u8fd1\u4f3c\u3002
<\/span><\/p>\n

\u52a8\u673a<\/span><\/strong>\uff1a\u4f20\u7edf\u7684\u8d1d\u53f6\u65af\u63a8\u65ad\u65b9\u6cd5dropout\u5728\u5904\u7406\u73b0\u5b9e\u4e16\u754c\u4e2d\u7684\u4efb\u52a1\u65f6\u5b58\u5728\u4e00\u4e9b\u6311\u6218\uff0c\u5982\u4fe1\u606f\u6709\u9650\u3001\u6570\u636e\u5206\u5e03\u4e0d\u5e73\u8861\u3001\u8bad\u7ec3\u6570\u636e\u6709\u9650\u7b49\uff0c\u672c\u6587\u65e8\u5728\u5229\u7528\u751f\u6210\u6d41\u7f51\u7edc\u7684\u6982\u7387\u6846\u67b6\u6765\u5b66\u4e60\u540e\u9a8c\u5206\u5e03\uff0c\u4ee5\u671f\u89e3\u51b3\u73b0\u4ee3\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u5173\u952e\u95ee\u9898\uff0c\u5982\u4e0d\u786e\u5b9a\u6027\u6821\u51c6\uff08calibration\uff09\u3001\u63d0\u5347\u7f51\u7edc\u6cdb\u5316\u80fd\u529b\u4ee5\u53ca\u6570\u636e\u6548\u7387\u7b49\u3002<\/span><\/section>\n
\u65b9\u6cd5<\/span><\/strong>\uff1a\u63d0\u51fa\u7684\u65b9\u6cd5\u662fGFlowOut\uff0c\u5b83\u4f7f\u7528\u751f\u6210\u6d41\u7f51\u7edc\u7684\u6982\u7387\u6846\u67b6\u6765\u5b66\u4e60dropout\u63a9\u7801\u4e0a\u7684\u540e\u9a8c\u5206\u5e03\u3002\u5177\u4f53\u6765\u8bf4\uff0cGFlowOut\u901a\u8fc7\u5c06dropout\u63a9\u7801\u89c6\u4e3a\u4e8c\u8fdb\u5236\u968f\u673a\u53d8\u91cf\uff0c\u5e76\u4f7f\u7528\u751f\u6210\u6d41\u7f51\u7edc\u6765\u5efa\u6a21\u8fd9\u4e9b\u968f\u673a\u53d8\u91cf\u7684\u540e\u9a8c\u5206\u5e03\u3002\u672c\u6587\u901a\u8fc7\u5728\u591a\u4e2a\u6570\u636e\u96c6\u548c\u6a21\u578b\u4e0a\u8fdb\u884c\u5b9e\u9a8c\u8bc1\u660e\u4e86GFlowOut\u65b9\u6cd5\u7684\u6709\u6548\u6027\u3002<\/span><\/section>\n
\u4f18\u52bf<\/span><\/strong>\uff1a1\uff09\u66f4\u597d\u7684\u6cdb\u5316\u80fd\u529b\uff1aGFlowOut\u53ef\u4ee5\u66f4\u597d\u5730\u6cdb\u5316\u5230\u5206\u5e03\u4e0d\u540c\u7684\u6570\u636e\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u548c\u9c81\u68d2\u6027\uff1b2\uff09\u66f4\u51c6\u786e\u7684\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\uff1aGFlowOut\u53ef\u4ee5\u63d0\u4f9b\u66f4\u51c6\u786e\u7684\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\uff0c\u4ece\u800c\u4f7f\u6a21\u578b\u66f4\u52a0\u53ef\u9760\u548c\u9c81\u68d2\uff1b3\uff09\u5b9e\u7528\u6027\u5f3a\uff1aGFlowOut\u65b9\u6cd5\u6613\u4e8e\u5b9e\u73b0\uff0c\u5e76\u4e14\u5728\u591a\u4e2a\u6570\u636e\u96c6\u548c\u6a21\u578b\u4e0a\u8fdb\u884c\u4e86\u5b9e\u9a8c\u8bc1\u660e\u4e86\u5176\u4f18\u8d8a\u6027\u3002<\/span><\/section>\n
\u4e00\u53e5\u8bdd\u603b\u7ed3<\/span><\/strong>\uff1aGFlowOut\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3001\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\u548c\u6821\u51c6\u80fd\u529b\uff0c\u5177\u6709\u5b9e\u7528\u6027\u5f3a\u7684\u4f18\u70b9\u3002<\/span><\/section>\n
<\/span><\/section>\n
\u56fe1. GFlowOut\u7684\u7406\u8bba\u6846\u67b6\u5c55\u793a<\/span><\/section>\n
<\/span><\/section>\n
\u88682. \u5728\u5206\u5e03\u5916\u68c0\u6d4b\u8bd5\u9a8c\u4e0a\u7684\u6027\u80fd<\/span><\/section>\n


<\/h2>\n
| \u5173\u4e8e\u751f\u6210\u6d41\u7f51\u7edc\u5728\u836f\u7269\u8bbe\u8ba1\u4efb\u52a1\u4e2d\u7684\u6cdb\u5316\u6027\u8bc4\u4f30<\/strong><\/section>\n
\n
\n
\n
\u8bba\u6587\u9898\u76ee\uff1aEvaluating Generalization in GFlowNets for Molecule Design<\/span><\/section>\n
\u8bba\u6587\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/openreview.net\/forum?id=JFSaHKNZ35b<\/span><\/section>\n
\u4f5c\u8005\uff1aAndrei Cristian Nica, Moksh Jain, Emmanuel Bengio, Cheng-Hao Liu, Maksym Korablyov, Michael M. Bronstein, Yoshua Bengio <\/span>[Mila – The Quebec AI Institute & University of Oxford & Politehnica University of Bucharest]<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
ICLR 2023 MLDD workshop<\/strong><\/span><\/section>\n

\u80cc\u666f<\/span><\/strong>\uff1a\u6df1\u5ea6\u5b66\u4e60\u5bf9\u4e8e\u836f\u7269\u53d1\u73b0\u95ee\u9898\uff0c\u5982\u4ece\u5934\u8bbe\u8ba1\u5206\u5b50\uff0c\u5177\u6709\u5f88\u5927\u7684\u6f5c\u529b\u3002\u4f46\u662f\uff0c\u751f\u6210\u7528\u4e8e\u8bad\u7ec3\u8fd9\u7c7b\u6a21\u578b\u7684\u6570\u636e\u662f\u4e00\u4e2a\u6602\u8d35\u548c\u8017\u65f6\u7684\u8fc7\u7a0b\uff0c\u56e0\u4e3a\u9700\u8981\u8fdb\u884c\u5b9e\u9a8c\u5ba4\u5b9e\u9a8c\u6216\u6602\u8d35\u7684\u6a21\u62df\u3002\u8fd9\u4e2a\u95ee\u9898\u8fd8\u56e0\u4e3a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u6570\u636e\u9700\u6c42\u91cf\u5927\u800c\u52a0\u5267\u3002<\/span><\/p>\n

\u52a8\u673a<\/span><\/strong>\uff1a\u63a2\u7d22\u5982\u4f55\u8bc4\u4f30GFlowNet\u65b9\u6cd5\u5728\u5c0f\u5206\u5b50\u751f\u6210\u4e2d\u7684\u6cdb\u5316\u80fd\u529b\u3002GFlowNet\u65b9\u6cd5\u5df2\u5728\u751f\u6210\u9ad8\u5f97\u5206\u591a\u6837\u5316\u5019\u9009\u7269\u65b9\u9762\u8868\u73b0\u51fa\u826f\u597d\u7684\u6027\u80fd\uff0c\u5e76\u5177\u6709\u79bb\u7ebf\u975e\u7b56\u7565\u65b9\u6cd5\u7684\u4f18\u52bf\u3002\u7136\u800c\uff0c\u4e3a\u8fd9\u6837\u7684\u6a21\u578b\u627e\u5230\u9002\u5f53\u7684\u6cdb\u5316\u8bc4\u4f30\u6307\u6807\uff0c\u5373\u627e\u5230\u9ad8\u5f97\u5206\u591a\u6837\u5316\u5019\u9009\u7269\u7684\u6307\u6807\uff0c\u5c06\u6709\u52a9\u4e8e\u6307\u5bfc\u5728\u7ebf\u6570\u636e\u6536\u96c6\u3002\u56e0\u6b64\uff0c\u672c\u6587\u65e8\u5728\u5f00\u53d1\u8bc4\u4f30GFlowNet\u6027\u80fd\u7684\u6280\u672f\uff0c\u5e76\u786e\u5b9a\u6700\u6709\u524d\u9014\u7684\u5ea6\u91cf\u6807\u51c6\u4ee5\u9884\u6d4b\u6cdb\u5316\u3002<\/span><\/p>\n

\u65b9\u6cd5<\/span><\/strong>\uff1a\u9488\u5bf9GFlowNet\u65b9\u6cd5\u7684\u6cdb\u5316\u80fd\u529b\u8bc4\u4f30\u6307\u6807\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u540d\u4e3aGFNEval\u7684\u8bc4\u4f30\u6307\u6807\uff0c\u7528\u4e8e\u8ddf\u8e2a\u8bad\u7ec3\u548c\u8bc4\u4f30GFlowNets\u7684\u6cdb\u5316\u80fd\u529b\u3002\u8be5\u6307\u6807\u57fa\u4e8e\u751f\u6210\u6a21\u578b\u7684\u6982\u7387\u5206\u5e03\u548c\u771f\u5b9e\u6570\u636e\u5206\u5e03\u4e4b\u95f4\u7684KL\u6563\u5ea6\uff0c\u53ef\u4ee5\u7528\u4e8e\u8861\u91cf\u6a21\u578b\u5728\u672a\u89c1\u8fc7\u7684\u6570\u636e\u4e0a\u7684\u6027\u80fd\u3002<\/span><\/p>\n

\u53d1\u73b0<\/span><\/strong>\uff1a\u672c\u6587\u4f7f\u7528GFNEval\u6307\u6807\u6765\u7814\u7a76GFlowNet\u65b9\u6cd5\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5b66\u4e60\u52a8\u6001\u3002\u901a\u8fc7\u5bf9GFNEval\u503c\u968f\u65f6\u95f4\u53d8\u5316\u7684\u89c2\u5bdf\uff0c\u672c\u6587\u53d1\u73b0\u4e86\u4e00\u4e9b\u6709\u8da3\u7684\u73b0\u8c61\uff0c\u4f8b\u5982\u6a21\u578b\u5728\u8bad\u7ec3\u65e9\u671f\u4f1a\u751f\u6210\u8f83\u4e3a\u76f8\u4f3c\u4e14\u4f4e\u5f97\u5206\u7684\u5206\u5b50\uff0c\u800c\u5728\u540e\u671f\u5219\u4f1a\u751f\u6210\u66f4\u591a\u591a\u6837\u5316\u4e14\u9ad8\u5f97\u5206\u7684\u5206\u5b50\u3002\u8fd9\u4e9b\u53d1\u73b0\u6709\u52a9\u4e8e\u6df1\u5165\u7406\u89e3GFlowNet\u65b9\u6cd5\u5728\u5c0f\u5206\u5b50\u751f\u6210\u4e2d\u7684\u5de5\u4f5c\u539f\u7406\u3002<\/span><\/p>\n

\u8d21\u732e<\/span><\/strong>\uff1a\u901a\u8fc7\u5b9e\u9a8c\u5c06GFNEval\u6307\u6807\u4e0e\u5176\u4ed6\u5e38\u7528\u6307\u6807\u8fdb\u884c\u4e86\u6bd4\u8f83\uff0c\u5305\u62ecSpearman\u76f8\u5173\u7cfb\u6570\u3001\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08MAE\uff09\u548c\u5e73\u5747\u76f8\u5bf9\u8bef\u5dee\uff08MRE\uff09\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0cGFNEval\u6307\u6807\u5728\u4e0d\u540c\u7684\u5c0f\u5206\u5b50\u751f\u6210\u4efb\u52a1\u4e2d\u90fd\u80fd\u591f\u5f88\u597d\u5730\u9884\u6d4b\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5e76\u4e14\u4e0e\u5176\u4ed6\u6307\u6807\u76f8\u6bd4\u5177\u6709\u66f4\u9ad8\u7684\u51c6\u786e\u6027\u548c\u7a33\u5b9a\u6027\u3002\u56e0\u6b64\uff0cGFNEval\u6307\u6807\u662f\u6700\u5177\u524d\u666f\u7684\u6cdb\u5316\u8bc4\u4f30\u6307\u6807\u4e4b\u4e00\u3002<\/span><\/section>\n
\u4e00\u53e5\u8bdd\u603b\u7ed3<\/span><\/strong>\uff1a\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u8bc4\u4f30\u6307\u6807GFNEval\uff0c\u7528\u4e8e\u8bc4\u4f30GFlowNet\u65b9\u6cd5\u5728\u5c0f\u5206\u5b50\u751f\u6210\u4e2d\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5e76\u901a\u8fc7\u5b9e\u9a8c\u8bc1\u660e\u5176\u4f18\u8d8a\u6027\u3002\u540c\u65f6\uff0c\u672c\u6587\u8fd8\u5bf9GFlowNet\u65b9\u6cd5\u8fdb\u884c\u4e86\u6df1\u5165\u7814\u7a76\u548c\u5206\u6790\uff0c\u4e3a\u5c0f\u5206\u5b50\u751f\u6210\u9886\u57df\u63d0\u4f9b\u4e86\u6709\u4ef7\u503c\u7684\u53c2\u8003\u3002<\/span><\/section>\n
<\/span><\/section>\n
\u56fe1. \u6240\u63d0\u6307\u6807GFNEvalS\u548c\u5df2\u6709\u6307\u6807Top1000Diversive\u4e4b\u95f4\u7684\u5173\u8054\u6027\u5206\u6790<\/span><\/section>\n
<\/span><\/section>\n
\u88681. \u6240\u63d0\u6307\u6807\u548c\u5df2\u6709\u6307\u6807\u4e4b\u95f4\u7684\u65af\u76ae\u5c14\u66fc\u76f8\u5173\u7cfb\u6570<\/span><\/section>\n


<\/h2>\n

\n
\n
\n
\n
\n

<\/section>\n

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

\u7ed3\u8bba<\/span><\/strong><\/p>\n<\/section>\n

\n

<\/section>\n

<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n

<\/section>\n
\u672c\u6587\u4ecb\u7ecd\u4e866\u7bc7\u5173\u4e8eGFlowNet\u7684\u6700\u65b0\u7814\u7a76\u5de5\u4f5c\uff0c\u8fd9\u4e9b\u7814\u7a76\u5de5\u4f5c\u4e3aGFlowNet\u7684\u53d1\u5c55\u63d0\u4f9b\u4e86\u6709\u529b\u7684\u652f\u6301\uff0c\u4f7fGFlowNet\u53ef\u4ee5\u5e94\u7528\u4e8e\u66f4\u5e7f\u6cdb\u7684\u9886\u57df\u4e2d\u3002<\/span><\/section>\n
\u76ee\u524d\uff0cGFlowNet\u7684\u7814\u7a76\u5de5\u4f5c\u4e3b\u8981\u96c6\u4e2d\u5728\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a\u4e00\u662f\u8fdb\u4e00\u6b65\u62d3\u5c55GFlowNet\u7684\u5e94\u7528\u8303\u56f4\uff0c\u5305\u62ec\u5728\u5904\u7406\u66f4\u591a\u6837\u5316\u3001\u66f4\u590d\u6742\u7684\u6570\u636e\u5206\u5e03\u65f6\u7684\u6027\u80fd\u8868\u73b0\uff1b\u4e8c\u662f\u8fdb\u4e00\u6b65\u63d0\u5347GFlowNet\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5305\u62ec\u5728\u4e0d\u540c\u4efb\u52a1\u4e2d\u7684\u6027\u80fd\u8868\u73b0\u3001\u5728\u6269\u5c55\u6570\u636e\u96c6\u4e0a\u7684\u6cdb\u5316\u80fd\u529b\u7b49\uff1b\u4e09\u662f\u6df1\u5165\u7814\u7a76GFlowNet\u7684\u7406\u8bba\u57fa\u7840\uff0c\u5305\u62ec\u7406\u8bba\u63a8\u5bfc\u3001\u6570\u5b66\u8bc1\u660e\u7b49\u65b9\u9762\u7684\u5de5\u4f5c\u3002<\/span><\/section>\n
\u672a\u6765\u7684\u5de5\u4f5c\u53ef\u4ee5\u63a2\u7d22GFlowNet\u5728\u5176\u4ed6\u4ea4\u53c9\u9886\u57df\u7684\u5e94\u7528\uff0c\u4f8b\u5982\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u8ba1\u7b97\u673a\u8f85\u52a9\u836f\u7269\u8bbe\u8ba1\u7b49\u9886\u57df\u3002\u901a\u8fc7\u591a\u5b66\u79d1\u4ea4\u53c9\u548c\u534f\u4f5c\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u53d1\u6398\u548c\u5229\u7528GFlowNet\u7684\u6f5c\u529b\uff0c\u63a8\u52a8\u5176\u5728\u4e0b\u6e38\u4efb\u52a1\u4e2d\u53d1\u6325\u4f5c\u7528\u3002<\/span><\/section>\n

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

\n
\n
\n
\n
\n

<\/section>\n

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

\u8d44\u6e90\u5206\u4eab<\/span><\/strong><\/p>\n<\/section>\n

\n

<\/section>\n

<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n

<\/section>\n
\u6559\u7a0b<\/span><\/strong>\uff1aYoshua Bengio\u6559\u6388\u64b0\u5199\u4e86\u4e00\u7bc7\u8be6\u7ec6\u7684\u6559\u7a0b\uff0c\u4ecb\u7ecd\u4e86\u751f\u6210\u6d41\u7f51\u7edc\u7684\u57fa\u672c\u6982\u5ff5\u3001\u539f\u7406\u548c\u5e94\u7528\uff0c\u4ee5\u53ca\u4e00\u4e9b\u672a\u6765\u7684\u7814\u7a76\u65b9\u5411\u548c\u6311\u6218\u3002<\/span><\/section>\n
\n
\u76f8\u5173\u4fe1\u606f\uff1aYoshua Bengio. Generative Flow Networks. 2022.<\/span><\/section>\n
\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/yoshuabengio.org\/2022\/03\/05\/generative-flow-networks<\/span><\/section>\n<\/blockquote>\n


<\/p>\n

\u4e66\u7c4d<\/span><\/strong>\uff1a\u76ee\u524d\u8fd8\u6ca1\u6709\u4e13\u95e8\u4ecb\u7ecd\u751f\u6210\u6d41\u7f51\u7edc\u7684\u4e66\u7c4d\uff0c\u4f46\u53ef\u4ee5\u53c2\u8003\u4e00\u4e9b\u6d89\u53ca\u751f\u6210\u6a21\u578b\u3001\u53d8\u5206\u63a8\u7406\u3001\u80fd\u91cf\u6a21\u578b\u7b49\u76f8\u5173\u4e3b\u9898\u7684\u4e66\u7c4d\uff0c\u5982\u300a\u6df1\u5ea6\u5b66\u4e60\u300b\u3001\u300a\u673a\u5668\u5b66\u4e60\u300b\u7b49\uff1b\u6b64\u5916\uff0cBengio\u4ecb\u7ecd\u4e86GFlowNet\u5728\u79d1\u5b66\u53d1\u73b0\u4e2d\u7684\u5e94\u7528\u548c\u524d\u666f\u3002<\/span><\/section>\n
\n
\u76f8\u5173\u4fe1\u606f\uff1aYoshua Bengio et al. GFlowNets for AI-Driven Scientific Discovery.<\/span><\/section>\n
\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2302.00615<\/span><\/section>\n<\/blockquote>\n

<\/strong><\/section>\n
\u9879\u76ee<\/span><\/strong>\uff1a\u6709\u4e00\u4e9b\u5f00\u6e90\u7684\u4ee3\u7801\u5e93\u548c\u9879\u76ee\uff0c\u5b9e\u73b0\u4e86\u751f\u6210\u6d41\u7f51\u7edc\u7684\u4e0d\u540c\u7248\u672c\u548c\u5e94\u7528\u3002\u53ef\u4ee5\u5728GitHub\u5f00\u6e90\u5e93\u4e0a\u627e\u5230\u6709\u5173GFlowNets\u7684\u6587\u7ae0\u548c\u9879\u76ee\uff0c\u5e76\u6839\u636e\u81ea\u5df1\u7684\u9700\u8981\u8fdb\u884c\u4fee\u6539\u6216\u6269\u5c55\u3002<\/span><\/section>\n
\n
\u76f8\u5173\u4fe1\u606f\uff1aGitHub – zdhNarsil\/Awesome-GFlowNets: A curated list of resources about generative flow networks (GFlowNets).<\/span><\/section>\n
\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/github.com\/zdhNarsil\/Awesome-GFlowNets<\/span><\/section>\n<\/blockquote>\n
<\/span><\/strong><\/section>\n
\u8fd8\u6709 Emmanuel Bengio \u7684\u5e2e\u52a9\u5927\u5bb6\u5feb\u901f\u4e0a\u624b\u5199\u4ee3\u7801\u7684 colab\uff0c\u975e\u5e38\u63a8\u8350\uff1a<\/span><\/section>\n
\n
\n
\u76f8\u5173\u4fe1\u606f\uff1aGFlowNet Tutorial [PRACTICAL colab notebook] <\/section>\n
\u94fe\u63a5\uff1a<\/section>\n
https:\/\/colab.research.google.com\/drive\/1fUMwgu2OhYpQagpzU5mhe9_Esib3Q2VR<\/span><\/section>\n<\/section>\n<\/blockquote>\n
\n
\n
\n
\n


<\/p>\n

AI+Science \u8bfb\u4e66\u4f1a\u542f\u52a8<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

\n


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

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

<\/section>\n
\u96c6\u667a\u4ff1\u4e50\u90e8\u8054\u5408\u65af\u5766\u798f\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u7cfb\u535a\u58eb\u540e\u7814\u7a76\u5458\u5434\u6cf0\u9716\uff08Jure Leskovec \u6559\u6388\u6307\u5bfc\uff09\u3001\u54c8\u4f5b\u91cf\u5b50\u8ba1\u5212\u7814\u7a76\u5458\u6248\u9e3f\u4e1a\u3001\u9ebb\u7701\u7406\u5de5\u5b66\u9662\u7269\u7406\u7cfb\u535a\u58eb\u751f\u5218\u5b50\u9e23\uff08Max Tegmark \u6559\u6388\u6307\u5bfc\uff09\uff0c\u5171\u540c\u53d1\u8d77\u4ee5\u201cAI+Science<\/a>\u201d\u4e3a\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a\uff0c\u63a2\u8ba8\u8be5\u9886\u57df\u7684\u91cd\u8981\u95ee\u9898\uff0c\u5171\u5b66\u5171\u7814\u76f8\u5173\u6587\u732e\u3002\u8bfb\u4e66\u4f1a\u4ece2023\u5e743\u670826\u65e5\u5f00\u59cb\uff0c\u6bcf\u5468\u65e5\u65e9\u4e0a 9:00-11:00 \u7ebf\u4e0a\u4e3e\u884c\uff0c\u6301\u7eed\u65f6\u95f4\u9884\u8ba110\u5468\u3002\u6b22\u8fce\u5bf9\u63a2\u7d22\u8fd9\u4e2a\u6fc0\u52a8\u4eba\u5fc3\u7684\u524d\u6cbf\u9886\u57df\u6709\u5174\u8da3\u7684\u670b\u53cb\u62a5\u540d\u53c2\u4e0e\u3002<\/span><\/section>\n

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

<\/section>\n
\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/section>\n
\u4eba\u5de5\u667a\u80fd\u548c\u79d1\u5b66\u53d1\u73b0\u76f8\u4e92\u8d4b\u80fd\u7684\u65b0\u8303\u5f0f\uff1aAI+Science \u8bfb\u4e66\u4f1a\u542f\u52a8<\/a><\/span><\/section>\n

<\/section>\n

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

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

1. <\/strong>\u751f\u6210\u6d41\u7f51\u7edc\uff08GFlowNet\uff09\u5165\u95e8\u8def\u5f84<\/a><\/strong><\/span><\/section>\n
2.<\/strong> AI\u751f\u6210\u827a\u672f\u7684\u5e95\u5c42\u539f\u7406\uff1a\u975e\u5e73\u8861\u7269\u7406\u7684\u6269\u6563\u6a21\u578b<\/a><\/strong><\/strong><\/span><\/section>\n
<\/strong><\/section>\n
3.<\/strong><\/strong> \u4eba\u8138\u548c\u5b87\u5b99\u662f\u5565\u5173\u7cfb\uff1f\u770b\u7269\u7406\u5b66\u5bb6\u600e\u6837\u7528\u91cd\u6574\u5316\u7fa4\u6d41\u6a21\u578b\u91cd\u65b0\u7406\u89e3\u89c6\u89c9<\/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
<\/strong><\/a><\/strong><\/strong><\/strong><\/section>\n
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><\/strong><\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/a><\/strong><\/strong><\/strong><\/span><\/section>\n


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


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

\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":"

\u5bfc\u8bed \u751f\u6210\u6d41\u7f51\u7edc\uff08GFlowNet\uff09\u662f\u4e00\u79cd\u65b0\u7684\u6982\u7387\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u548c\u5f3a\u5316\u5b66\u4e60\u540c\u5c42\u6b21\uff0c\u53ef\u4ee5\u5e94\u7528\u4e8e\u5b9e\u9a8c\u79d1\u5b66\u5faa\u73af\u7684\u5efa\u6a21\u3001\u5047\u8bbe\u751f\u6210\u548c\u5b9e\u9a8c\u8bbe\u8ba1\u9636\u6bb5\u3002\u5728\u96c6\u667a\u4ff1\u4e50\u90e8\u300cAI+Science\u300d\u8bfb\u4e66\u4f1a\uff0cMila \u7814\u7a76\u6240\u548c\u8499\u7279\u5229\u5c14\u5927\u5b66\u5728\u8bfb\u535a\u58eb\u5f20\u9f0e\u6000\u7efc\u8ff0\u4ecb\u7ecd\u4e86GFlowNet\u6846\u67b6\u3002\u672c\u6587\u4ece\u5f20\u9f0e\u6000\u535a\u58eb\u7684 Github \u63a8\u8350\u4e2d\u7cbe\u9009…<\/p>\n","protected":false},"author":0,"featured_media":42489,"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\/42504"}],"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=42504"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/42504\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/42489"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=42504"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=42504"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=42504"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=42504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}