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\u7ee7AlphaGo\u4e4b\u540e\uff0c\u667a\u80fd\u6a21\u62df<\/span>\uff08AI Simulation\uff09<\/span>\u6216\u6a21\u62df\u667a\u80fd<\/span>\uff08Simulation Intelligence\uff09<\/span>\u5df2\u7ecf\u6210\u4e3a\u5f53\u4eca\u5b66\u672f\u754c\u548c\u5de5\u4e1a\u754c\u5171\u540c\u5173\u6ce8\u7684\u6700\u4e3a\u706b\u70ed\u7684\u65b9\u5411\u4e4b\u4e00\u3002DeepMind\u5728\u5206\u5b50\u7ed3\u6784\u6a21\u62df\u3001\u6838\u805a\u53d8\u63a7\u5236\u7b49\u9886\u57df\u5728Nature\u4e0a\u9891\u9891\u53d1\u8868\u91cd\u78c5\u7ea7\u6587\u7ae0\uff1b\u4e16\u754c\u4e0a\u6700\u9876\u5c16\u7684\u5341\u5927\u8d85\u7ea7\u8ba1\u7b97\u673a\u4e2d\uff0c\u4e5d\u53f0\u90fd\u5728\u8fd0\u884c\u6a21\u62df\u76f8\u5173\u7684\u4efb\u52a1\u3002\u6765\u81ea\u56fe\u7075\u7814\u7a76\u9662\u3001\u725b\u6d25\u5927\u5b66\u3001\u7ebd\u7ea6\u5927\u5b66\u3001\u82f1\u4f1f\u8fbe\u3001\u82f1\u7279\u5c14\u7b49\u884c\u4e1a\u4e13\u5bb6\u5728\u5176\u8054\u5408\u53d1\u8868\u7684\u6700\u65b0\u5927\u4f5c\u4e2d\u603b\u7ed3\u5230\uff0c\u6a21\u62df\u667a\u80fd\u5df2\u7ecf\u6210\u4e3a\u4fc3\u8fdb\u79d1\u5b66\u548c\u793e\u4f1a\u53d1\u5c55\u7684\u65b0\u4e00\u4ee3\u65b9\u6cd5\u3002<\/span><\/section>\n

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\u7b2c\u4e8c\u5c4aAI\u6a21\u62df\u4e0e\u667a\u80fd\u51b3\u7b56\u8bba\u575b\u5c06\u4e8e7\u67082\u65e5\u4e0a\u5348\u4e3e\u884c<\/strong>\uff0c<\/span>\u8be0\u91ca\u51b3\u7b56\u667a\u80fd\u65b0\u8303\u5f0f\u3002\u4e3a\u4ec0\u4e48AI\u80fd\u8ba9\u51b3\u7b56\u66f4\u667a\u80fd\uff1f\u5982\u4f55\u8ba9AI\u5e2e\u52a9\u6211\u4eec\u5b9e\u73b0\u66f4\u667a\u80fd\u7684\u51b3\u7b56\uff1f\u8fd9\u4e0d\u4ec5\u662f\u73b0\u4ee3\u79d1\u6280\u53d1\u5c55\u7684\u524d\u6cbf\uff0c\u66f4\u662f\u5f53\u4e0b\u793e\u4f1a\u73b0\u4ee3\u5316\u6cbb\u7406\u548c\u4f01\u4e1a\u6570\u667a\u5316\u8f6c\u578b\u8fc7\u7a0b\u7684\u5173\u952e\u4e3e\u63aa\u548c\u5fc5\u7531\u4e4b\u8def\u3002<\/span>
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\u4e3a\u6b64\uff0c\u6e05\u534e\u5927\u5b66\u7535\u5b50\u5de5\u7a0b\u7cfb\u4e3b\u529e\uff0c\u6e05\u534e\u5927\u5b66\u7535\u5b50\u5de5\u7a0b\u7cfb\u57ce\u5e02\u79d1\u5b66\u4e0e\u8ba1\u7b97\u7814\u7a76\u4e2d\u5fc3\u627f\u529e\u3001\u5317\u4eac\u6e05\u9e4f\u667a\u80fd\u79d1\u6280\u6709\u9650\u516c\u53f8\u534f\u529e\uff0c\u591a\u5bb6\u79d1\u6280\u524d\u6cbf\u5a92\u4f53\u652f\u6301\u7684\u7b2c\u4e8c\u5c4a\u201cAI\u6a21\u62df\u4e0e\u667a\u80fd\u51b3\u7b56\u201d\u8bba\u575b\u5c06\u4e8e7\u67082\u65e5\u4e0a\u5348\u4e3e\u884c\u3002\u8bba\u575b\u6c47\u805a\u5168\u7403\u9876\u5c16\u4e13\u5bb6\u5b66\u8005\u3001\u77e5\u540d\u4f01\u4e1a\u4e0e\u521b\u4e1a\u516c\u53f8\u8d1f\u8d23\u4eba\uff0c\u5171\u540c\u63a2\u8ba8\u5982\u4f55\u63a8\u52a8\u667a\u80fd\u6a21\u62df\u4e0e\u51b3\u7b56\u9886\u57df\u7684\u524d\u6cbf\u95ee\u9898\uff0c\u63a8\u52a8\u79d1\u5b66\u4e0e\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u4fc3\u8fdb\u76f8\u5173\u9886\u57df\u7684\u521b\u65b0\u4e0e\u5b9e\u8df5\u843d\u5730\u3002<\/span><\/section>\n<\/section>\n
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4. \u5609\u5bbe\u4ecb\u7ecd\u53ca\u5206\u4eab\u4e3b\u9898<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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 <\/span><\/section>\n<\/h3>\n
\u5206\u4eab\u5609\u5bbe\uff1aCao Zhiguang<\/strong><\/span><\/section>\n
Dr. Zhiguang Cao is currently a Scientist at Singapore Institute of Manufacturing Technology<\/span> (SIMTech)<\/span>, Agency for Science Technology and Research<\/span> (A*STAR)<\/span>. Previously he was a Research Assistant Processor in Department of Industrial Systems Engineering and Management, National University of Singapore<\/span>(NUS)<\/span>. In recent years, his research interests focus on Learning to Optimize, where he exploited deep (reinforcement)<\/span> learning to solve Combinatorial Optimization Problems, such as Vehicle Routing Problem, Job Shop Scheduling Problem, Bin Packing Problem and Integer Programs. It is a hot yet challenging topic in both AI and OR. His works under this topic are published in NeurIPS, ICLR, AAAI, IJCAI and IEEE Trans, and the code is available at:<\/span><\/section>\n
https:\/\/zhiguangcaosg.github.io\/publications\/<\/span><\/section>\n
\u5206\u4eab\u4e3b\u9898: Learning to Solve Vehicle Routing Problems<\/strong><\/span><\/section>\n
Vehicle routing problem <\/span>(VRP) <\/span>is the most widely studied problem in operations research<\/span> (OR)<\/span>, which is always solved using heuristics with hand-crafted rules. In recent years, there is a growing trend towards exploiting deep (reinforcement) learning to automatically discover a heuristic or rule for solving VRPs. In this talk, I will first briefly introduce the construction type of neural methods, followed by the elaboration of improvement type. Then, I will present the challenges in this area and my personal thoughts on them.<\/span><\/section>\n

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\u5206\u4eab\u5609\u5bbe\uff1a\u9ece\u73cd\u8f89\uff08Jessie\uff09<\/strong><\/span><\/section>\n
Dr. Zhenhui (Jessie)<\/span> Li is the chief scientist at Yunqi Academy of Engineering, a non-profit organization for technological innovation. Her research has been dedicated to innovating spatial-temporal data mining techniques to address real-world problems in cities, environment, and society. Dr. Li obtained her PhD in computer science from University of Illinois at Urbana-Champaign and was a tenured associate professor at the Pennsylvania State University.<\/span><\/section>\n
\u5206\u4eab\u4e3b\u9898\uff1aOpen data research toward a science of cities<\/strong><\/span><\/section>\n
Research paradigm is shifting. We are now at the exciting beginning of data-driven research paradigm. However, we often over-emphasize the methodologies and theories but data itself is understated. In this talk, I revisit some classical city science theories with open data. I would like to elaborate how open data play a critical role in defining science.<\/span><\/section>\n

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\u5206\u4eab\u5609\u5bbe\uff1a\u5370\u5367\u6d9b<\/span><\/strong><\/span><\/section>\n
Dr. Yin received his Ph.D. in OR from Columbia University in 2006. Before joining Alibaba US and starting to lead the Decision Intelligence Lab in 2019, he was a Professor in the Department of Mathematics at University of California, Los Angeles. Dr. Yin won the NSF CAREER award in 2008, an Alfred P. Sloan Research Fellowship in 2009, a Morningside Gold Medal in 2016, and the Damo Award and Egon Balas Award in 2021. Dr. Yin\u2019s research interests include computational optimization and its applications in signal processing, machine learning, and other data science problems. Since 2018, he has been among the top 1% cited researchers by Clarivate Analytics.<\/span><\/section>\n
\u5206\u4eab\u4e3b\u9898\uff1aLearning to Optimize: Algorithmic Unfolding<\/strong><\/span><\/section>\n
Learning to optimize, or L2O, is a method of developing optimization algorithms and improving their performance through offline training. It has achieved significant success in deep learning acceleration, signal processing, inverse problems, and SAT and MIP problems. In this talk, after giving a short introduction to L2O methods, we will focus on Algorithmic Unrolling or AU, an L2O methodology that has evolved quickly over the past 10 years. Early AUs imitate DNNs, so they have a large number of free parameters, which were expensive to train and hard to generalize. Recent AU methods have significantly reduced the number of parameters, in some cases to only a few. By examing the algorithms generated by L2O, we can obtain extremely high-performance, almost universal algorithms that no longer need training. This lecture will overview the sparse coding problem and its LISTA-series methods.<\/span><\/section>\n

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\u5206\u4eab\u5609\u5bbe\uff1a\u90dd\u5efa\u4e1a<\/span><\/strong><\/span><\/section>\n
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\u5206\u4eab\u4e3b\u9898\uff1a\u81ea\u76d1\u7763\u5f3a\u5316\u5b66\u4e60<\/strong><\/span><\/section>\n
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