{"id":52008,"date":"2024-08-11T21:55:50","date_gmt":"2024-08-11T13:55:50","guid":{"rendered":"https:\/\/swarma.org\/?p=52008"},"modified":"2024-08-11T21:55:50","modified_gmt":"2024-08-11T13:55:50","slug":"%e4%bb%8e%e5%a4%8d%e6%9d%82%e7%b3%bb%e7%bb%9f%e5%88%b0%e7%94%9f%e6%88%90%e5%bc%8f%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd-%e5%91%a8%e4%b8%80%e7%9b%b4%e6%92%ad%c2%b7ai-by-complexity%e8%af%bb%e4%b9%a6","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=52008","title":{"rendered":"\u4ece\u590d\u6742\u7cfb\u7edf\u5230\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd | \u5468\u4e00\u76f4\u64ad\u00b7AI by Complexity\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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2024\u5e742\u6708\uff0c\u56fd\u9645\u77e5\u540d\u4eba\u5de5\u667a\u80fd\u516c\u53f8OpenAI\u548cStability AI\u76f8\u7ee7\u53d1\u5e03\u4e86Sora\u548cStable Diffusion 3.0\uff0c\u8fd9\u4e9b\u6280\u672f\u4e0d\u4ec5\u80fd\u591f\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u56fe\u50cf\u548c\u89c6\u9891\uff0c\u8fd8\u80fd\u7406\u89e3\u590d\u6742\u5bf9\u8c61\uff0c\u5e76\u5904\u7406\u591a\u79cd\u751f\u6210\u5f0f\u4efb\u52a1\u3002\u8fd9\u4e9b\u6280\u672f\u80cc\u540e\u4e0e\u590d\u6742\u7cfb\u7edf\u7406\u8bba\u548c\u65b9\u6cd5\u5bc6\u5207\u76f8\u5173\uff0c\u7279\u522b\u662f\u4f9d\u8d56\u4e8e\u5fae\u5206\u65b9\u7a0b\u7684\u6570\u5b66\u7406\u8bba\u3002\u795e\u7ecf\u5fae\u5206\u65b9\u7a0b\u4f5c\u4e3a\u4e00\u79cd\u5178\u578b\u7684\u590d\u6742\u7cfb\u7edf\u9a71\u52a8\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u5df2\u7ecf\u5728\u591a\u4e2a\u9886\u57df\u53d6\u5f97\u4e86\u663e\u8457\u6210\u5c31\uff0c\u5e76\u53d7\u5230\u5b66\u672f\u754c\u548c\u5de5\u4e1a\u754c\u7684\u5e7f\u6cdb\u5173\u6ce8\u548c\u5e94\u7528\u3002<\/span><\/section>\n

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\u5173\u952e\u8bcd\uff1a<\/strong><\/span>\u590d\u6742\u7cfb\u7edf\uff0c\u673a\u5668\u5b66\u4e60\uff0c\u5fae\u5206\u65b9\u7a0b\uff0c\u50a8\u5907\u6c60\u8ba1\u7b97\uff0c\u751f\u6210\u6a21\u578b<\/strong><\/span><\/section>\n

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      Chen R T Q, Rubanova Y, Bettencourt J, et al. Neural ordinary differential equations. Advances in Neural Information Processing Systems, 2018, 31.<\/span><\/p>\n<\/li>\n

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      Dupont E, Doucet A, Teh Y W. Augmented neural odes. Advances in Neural Information Processing Systems, 2019, 32.<\/span><\/p>\n<\/li>\n

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      Qunxi Zhu, Yao Guo, and Wei Lin [2021], Neural delay differential equations, 9th International Conference on Learning Representations (ICLR 2021).<\/span><\/p>\n<\/li>\n

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      Pathak J, Hunt B, Girvan M, et al. Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical Review Letters, 2018, 120(2): 024102.<\/span><\/p>\n<\/li>\n

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      Min Yan, Can Huang, Peter Bienstman, Peter Tino, Wei Lin, and Jie Sun [2024], Emerging opportunities and challenges for the future of reservoir computing, Nature Communications, vol. 15, Art. no. 2056.<\/span><\/p>\n<\/li>\n

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      Xin Li, Qunxi Zhu, Chengli Zhao, Xiaojun Duan, Bolin Zhao, Xue Zhang, Huanfei Ma, Jie Sun, and Wei Lin [2024], Higher-order Granger reservoir computing: Simultaneously achieving scalable complex structures inference and accurate dynamics prediction, Nature Communications, vol. 15, Art. no. 2506.<\/span><\/p>\n<\/li>\n

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      Song Y, Sohl-Dickstein J, Kingma D P, et al. Score-Based Generative Modeling through Stochastic Differential Equations. International Conference on Learning Representations (ICLR 2021).<\/span><\/p>\n<\/li>\n

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      Lipman Y, Chen R T Q, Ben-Hamu H, et al. Flow Matching for Generative Modeling. The Eleventh International Conference on Learning Representations (ICLR 2023).<\/span><\/p>\n<\/li>\n

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      Qunxi Zhu and Wei Lin [2024], Switched flow matching: Eliminating singularities via switching ODEs, 41st International Conference on Machine Learning (ICML 2024).<\/span><\/p>\n<\/li>\n<\/ol>\n


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      \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><\/p>\n


      <\/p>\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><\/p>\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


      <\/section>\n

      \u5f80\u671f\u5206\u4eab\uff1a<\/strong><\/span><\/p>\n

        \n
      1. \n

        \u7b2c\u4e00\u671f \u5f20\u7ae0 \u4e8e\u7389\u56fd \u7530\u6d0b \u725f\u7267\u4e91 \u5218\u5b87 \u6768\u660e\u54f2\uff1a\u590d\u6742\u6027\u600e\u6837\u91cf\u5316\u548c\u9a71\u52a8\u4e0b\u4e00\u4ee3AI\u7cfb\u7edf<\/span><\/a><\/p>\n<\/li>\n

      2. \n

        \u7b2c\u4e8c\u671f \u5f90\u5955\u821f \u7fc1\u5eb7\u5b87\uff1a\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<\/span><\/a><\/p>\n<\/li>\n

      3. \n

        \u7b2c\u4e09\u671f \u5218\u5b87\uff1a\u201c\u538b\u7f29\u5373\u667a\u80fd\u201d\u4e0e\u7b97\u6cd5\u4fe1\u606f\u8bba<\/span><\/a><\/p>\n<\/li>\n

      4. \n

        \u7b2c\u56db\u671f \u7a0b\u5965\u534e \u718a\u5dcd\uff1a\u4ece\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u5230\u795e\u7ecf\u7b97\u5b50\u6a21\u578b\uff1a\u542f\u53d1\u66f4\u597d\u7684AI<\/span><\/a><\/p>\n<\/li>\n

      5. \n

        \u7b2c\u4e94\u671f \u848b\u6625\u6052\uff1a\u7f51\u7edc\u5c5e\u6027\u51b3\u5b9a\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u6027\u80fd<\/span><\/a><\/p>\n<\/li>\n

      6. \n
        \u7b2c\u516d\u671f \u6768\u660e\u54f2\uff1a\u56e0\u679c\u6d8c\u73b0\u542f\u53d1AI\u7684\u201c\u601d\u8003\u201d<\/span><\/a><\/section>\n<\/li>\n<\/ol>\n

        <\/section>\n


        <\/p>\n

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

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

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