{"id":51113,"date":"2024-06-29T19:55:06","date_gmt":"2024-06-29T11:55:06","guid":{"rendered":"https:\/\/swarma.org\/?p=51113"},"modified":"2024-06-29T19:55:06","modified_gmt":"2024-06-29T11:55:06","slug":"%e8%a5%bf%e6%b9%96%e5%a4%a7%e5%ad%a6%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e5%ae%9e%e9%aa%8c%e5%ae%a4ai4science%e5%b0%8f%e7%bb%84%ef%bc%9a%e6%8b%9b%e6%94%b6%e7%a7%91%e7%a0%94%e5%ae%9e%e4%b9%a0%e7%94%9f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51113","title":{"rendered":"\u897f\u6e56\u5927\u5b66\u4eba\u5de5\u667a\u80fd\u5b9e\u9a8c\u5ba4AI4Science\u5c0f\u7ec4\uff1a\u62db\u6536\u79d1\u7814\u5b9e\u4e60\u751f"},"content":{"rendered":"
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\u897f\u6e56\u5927\u5b66\u4eba\u5de5\u667a\u80fd\u5b9e\u9a8c\u5ba4AI4Science\u5c0f\u7ec4\u8bda\u631a\u9080\u8bf7\u5bf9AI\u79d1\u7814\u6709\u6d53\u539a\u5174\u8da3\u7684\u60a8\u52a0\u5165\u6211\u4eec\u7684\u56e2\u961f\u3002\u5728\u8fd9\u91cc\uff0c\u4f60\u5c06\u6709\u673a\u4f1a\u63a5\u89e6\u5230\u6700\u524d\u6cbf\u7684AI\u79d1\u7814\u9879\u76ee\uff0c\u79ef\u7d2f\u5b9d\u8d35\u7684\u79d1\u7814\u7ecf\u9a8c\uff0c\u5e76\u4e0e\u4f18\u79c0\u7684\u79d1\u7814\u56e2\u961f\u5171\u4e8b\u3002\u6211\u4eec\u671f\u5f85\u4f60\u7684\u52a0\u5165\uff0c\u5171\u540c\u63a8\u52a8\u4eba\u5de5\u667a\u80fd\u5728\u79d1\u7814\u9886\u57df\u7684\u5e94\u7528\u548c\u53d1\u5c55\uff01<\/span><\/section>\n<\/section>\n<\/section>\n
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\u674e\u5b50\u9752\uff08Stan Z. Li\uff09\uff0c\u897f\u6e56\u5927\u5b66\u4eba\u5de5\u667a\u80fd\u8bb2\u5e2d\u6559\u6388\uff0cIEEE Fellow\uff0c\u66fe\u4efb\u5fae\u8f6f\u4e9a\u6d32\u7814\u7a76\u9662lead researcher\u3001\u4e2d\u79d1\u9662\u81ea\u52a8\u5316\u6240\u6a21\u5f0f\u8bc6\u522b\u56fd\u5bb6\u91cd\u70b9\u5b9e\u9a8c\u5ba4\u8d44\u6df1\u7814\u7a76\u5458\u3002\u53d1\u8868\u8bba\u6587400+\u7bc7\uff0c\u8457\u4f5c9\u90e8\uff0cGoogle Scholar\u4ed6\u5f1570000+\u6b21\u3002\u7814\u7a76\u65b9\u5411\uff081\uff09\u673a\u5668\u5b66\u4e60\/\u6df1\u5ea6\u5b66\u4e60\u3001\u6570\u636e\u79d1\u5b66\uff1b\uff082\uff09AI+\u5b66\u79d1\u4ea4\u53c9\u5e94\u7528\uff08\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u751f\u547d\u79d1\u5b66\u3001\u5408\u6210\u751f\u7269\u5b66\u3001\u6750\u6599\u79d1\u5b66\u3001\u73af\u5883\u79d1\u5b66\uff09\u3002<\/span><\/span><\/p>\n


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\u4e2a\u4eba\u4e3b\u9875\uff1a<\/span><\/strong><\/span><\/p>\n

https:\/\/www.westlake.edu.cn\/faculty\/stan-zq-li.html<\/span><\/p>\n

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\u7814\u7a76\u5185\u5bb9<\/span><\/strong><\/span><\/p>\n<\/section>\n

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1. \u86cb\u767d\u8d28\u548cRNA\u5927\u5206\u5b50\u7684AI\u5efa\u6a21\uff0c\u8fd1\u671f\u7814\u7a76\u57fa\u7840\u5982\u4e0b\uff1a<\/span><\/p>\n

[1] PiFold: Toward Effective and Efficient Protein Inverse Folding. ICLR 2023.<\/span><\/section>\n
[2] ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics. NeurIPS 2023.<\/span><\/section>\n
[3] Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer. AAAI 2024.<\/span><\/section>\n
[4] KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement. ICLR 2024.<\/span><\/section>\n
[5] RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design. ICLR 2024.<\/span><\/section>\n
[6] Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective. ICML 2024.<\/span><\/section>\n
[7] A Graph is Worth Words: Euclideanizing Graph using Pure Transformer. ICML 2024.<\/span><\/section>\n

2. \u89c6\u9891\u9884\u6d4b\u53ca\u5176\u4ed6\u65b9\u5411\uff0c\u8fd1\u671f\u7814\u7a76\u57fa\u7840\u5982\u4e0b\uff1a<\/span><\/p>\n

[6] SimVP: Simpler Yet Better Video Prediction. CVPR 2022.<\/span><\/section>\n
[7] Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning. CVPR 2023.<\/span><\/section>\n
[8] OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning. NeurIPS 2023.<\/span><\/section>\n
[9] A Survey on Generative Diffusion Model. TKDE.<\/span><\/section>\n
[10] General point model with autoencoding and autoregressive. CVPR 2024.<\/span><\/section>\n

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2. \u80fd\u529b\u7a81\u51fa\u7684\u53ef\u4ee5\u5b89\u6392\u72ec\u7acb\u8d1f\u8d23\u79d1\u7814\u5b50\u9879\u76ee\u3002<\/span><\/p>\n<\/section>\n

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2. \u6570\u5b66\u57fa\u7840\u597d\u3001\u7f16\u7a0b\u80fd\u529b\u5f3a\u3001\u5177\u5907\u4f18\u826f\u7684\u82f1\u6587\u8bfb\u5199\u80fd\u529b\uff0c\u719f\u6089python\u8bed\u8a00\u548cpytorch\u6846\u67b6\uff1b<\/span><\/p>\n

3. \u719f\u6089diffusion model\u3001\u86cb\u767d\u8d28\u5927\u5206\u5b50\u5efa\u6a21\u7684\u7533\u8bf7\u8005\u4f18\u5148\uff1b<\/span><\/p>\n

4. \u5bf9\u79d1\u7814\u6709\u5f3a\u70c8\u5174\u8da3\uff0c\u5de5\u4f5c\u79ef\u6781\u4e3b\u52a8\uff0c\u5177\u5907\u8bda\u5b9e\u7684\u79d1\u7814\u6001\u5ea6\u548c\u826f\u597d\u7684\u6c9f\u901a\u80fd\u529b\u3001\u56e2\u961f\u534f\u4f5c\u7cbe\u795e\u3002<\/span>
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AI + Science\u7b2c\u4e8c\u5b63\u8bfb\u4e66\u4f1a<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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