{"id":49384,"date":"2024-04-01T22:05:56","date_gmt":"2024-04-01T14:05:56","guid":{"rendered":"https:\/\/swarma.org\/?p=49384"},"modified":"2024-04-01T22:05:56","modified_gmt":"2024-04-01T14:05:56","slug":"%e7%b1%bb%e8%84%91%e6%99%ba%e8%83%bd%e7%9a%84%e4%b8%89%e7%a7%8d%e8%8c%83%e5%bc%8f%ef%bc%9a%e4%bb%8e%e6%8c%81%e7%bb%ad%e5%ad%a6%e4%b9%a0%e8%b0%88%e8%b5%b7%e4%b8%a8%e8%ae%a1%e7%ae%97%e7%a5%9e%e7%bb%8f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=49384","title":{"rendered":"\u7c7b\u8111\u667a\u80fd\u7684\u4e09\u79cd\u8303\u5f0f\uff1a\u4ece\u6301\u7eed\u5b66\u4e60\u8c08\u8d77\u4e28\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u8bfb\u4e66\u4f1a\u00b7\u5468\u4e8c\u76f4\u64ad"},"content":{"rendered":"
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  \u4e00\u3001\u201c\u6620\u5c04\u5f0f\u201d\u7c7b\u8111\u542f\u53d1 \u4f8b\uff1aSNN\uff0c\u4ee5\u53ca\u57fa\u4e8eSNN\u7684\u7c7b\u8111\u8bbe\u8ba1\uff0c\u5305\u62ec\u795e\u7ecf\u5143\u6784\u5efa\u3001\u7f16\u7801\u65b9\u5f0f\u3001\u7ed3\u6784\u8bbe\u8ba1\u3001\u5b66\u4e60\u7b97\u6cd5\uff08\u53ef\u5851\u6027\uff09\u7b49\u5c42\u9762<\/span><\/section>\n
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2024\u5e744\u67082\u65e5\uff08\u672c\u5468\u4e8c\uff09\u665a\u4e0a19:00-21:00\u3002<\/span><\/p>\n


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\u626b\u7801\u53c2\u4e0e<\/span>\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u8bfb\u4e66\u4f1a<\/a>\uff0c\u52a0\u5165\u7fa4\u804a\uff0c\u83b7\u53d6\u7cfb\u5217\u8bfb\u4e66\u4f1a\u56de\u770b\u6743\u9650\uff0c\u6210\u4e3a\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u793e\u533a\u7684\u79cd\u5b50\u7528\u6237\uff0c\u4e0e\u793e\u533a\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u4e0e\u4f01\u4e1a\u5b9e\u8df5\u8005\u6c9f\u901a\u4ea4\u6d41\uff0c\u5171\u540c\u63a8\u52a8\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u793e\u533a\u7684\u53d1\u5c55\u3002<\/span><\/p>\n


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\u5206\u4eab\u6d89\u53ca\u7684\u4e00\u4e9b\u6301\u7eed\u5b66\u4e60\u5de5\u4f5c\uff1a<\/span><\/section>\n
Liyuan Wang, Xingxing Zhang, Hang Su, Jun Zhu. A Comprehensive Survey of Continual Learning: Theory, Method and Application. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. (\u6301\u7eed\u5b66\u4e60\u9886\u57df\u7684\u5168\u9762\u8c03\u7814\uff0c\u4ece\u673a\u5668\u5b66\u4e60\u7406\u8bba\u7684\u89d2\u5ea6\u68b3\u7406\u4e86\u6301\u7eed\u5b66\u4e60\u7684\u4f18\u5316\u76ee\u6807\u548c\u4ee3\u8868\u6027\u65b9\u6cd5)<\/span><\/section>\n
Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong. Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence. Nature Machine Intelligence, 2023. (\u679c\u8747\u5b66\u4e60\u7cfb\u7edf\u542f\u53d1\u7684\u6301\u7eed\u5b66\u4e60\u65b9\u6cd5)<\/span><\/section>\n

Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, Jun Zhu. Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality. Advances in Neural Information Processing Systems (NeurIPS), Spotlight, 2023. (\u8bb0\u5fc6\u9009\u62e9\u6027\u63d0\u53d6\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u6301\u7eed\u5fae\u8c03\u65b9\u6cd5)<\/span><\/p>\n


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Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, et al. Biological Underpinnings for Lifelong Learning Machines. Nature Machine Intelligence, 2022. (\u68b3\u7406\u4e86\u7ec8\u8eab\u5b66\u4e60\u7684\u4e3b\u8981\u6311\u6218\u4ee5\u53ca\u4e0e\u4e4b\u5bf9\u5e94\u7684\u751f\u7269\u5b66\u673a\u5236)<\/span><\/section>\n
Raia Hadsell, Dushyant Rao, Andrei Rusu, Razvan Pascanu. Embracing Change: Continual Learning in Deep Neural Networks.  Trends in Cognitive Sciences, 2020. (\u68b3\u7406\u4e86\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u7684\u6301\u7eed\u5b66\u4e60\u8fc7\u7a0b\u4e0e\u795e\u7ecf\u79d1\u5b66\u7684\u8054\u7cfb)<\/span><\/section>\n

German Parisi, Ronald Kemker, Jose Part, Christopher Kanan, Stefan Wermter. Continual Lifelong Learning with Neural Networks: A Review. Neural Networks, 2019. (\u68b3\u7406\u4e86\u7a81\u89e6\u53ef\u5851\u6027\u4e0e\u4e92\u8865\u5b66\u4e60\u7cfb\u7edf\u7406\u8bba\u5bf9\u6301\u7eed\u5b66\u4e60\u7684\u4f5c\u7528)<\/span><\/p>\n


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\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u7efc\u8ff0\uff1a<\/span><\/section>\n

Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Georgina Cosma, Liam P. Maguire, T.M. McGinnity. A review of learning in biologically plausible spiking neural networks. Neural Networks, 2020.<\/span><\/p>\n


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Tielin Zhang, Xiang Cheng, Shuncheng Jia, Mu-Ming Poo, Yi Zeng, Bo Xu. Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks. Science advances, 2021.\uff08\u53ef\u5851\u6027\u9006\u5411\u4f20\u64ad\u542f\u53d1\u7684\u9ad8\u80fd\u6548\u4ecb\u89c2\u6743\u91cd\u5b66\u4e60\uff09<\/span><\/section>\n
Tielin Zhang, Xiang Cheng, Shuncheng Jia, Chengyu T Li, Mu-ming Poo, Bo Xu. A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost. Science advances, 2023.\uff08\u795e\u7ecf\u8c03\u5236\u73b0\u8c61\u5728\u7a81\u89e6\u548c\u795e\u7ecf\u5143\u6c34\u5e73\u7684\u5efa\u6a21\uff09<\/span><\/section>\n

Xiang Cheng, Tielin Zhang, Shuncheng Jia, Bo Xu. Meta neurons improve spiking neural networks for efficient spatio-temporal learning. Neurocomputing, 2023.\uff08\u795e\u7ecf\u5143\u53d1\u653e\u6a21\u5f0f\u5f02\u8d28\u5316\u4e0e\u7f51\u7edc\u4fe1\u606f\u5904\u7406\u80fd\u529b\u95f4\u7684\u5173\u7cfb\uff09<\/span><\/p>\n


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Hinton\u5bf9\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5\u751f\u7269\u5408\u7406\u6027\u7684\u8ba8\u8bba\uff1a<\/span><\/section>\n

Timothy P. Lillicrap, Adam Santoro, Luke Marris, Colin J. Akerman, Geoffrey Hinton.<\/em> Backpropagation and the brain. Nature Reviews Neuroscience, 2020.<\/span><\/p>\n


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Kenji Doya\u5229\u7528\u5f3a\u5316\u5b66\u4e60\u7406\u8bba\u5bf9\u751f\u7269\u51b3\u7b56\u8fc7\u7a0b\u7684\u89e3\u91ca\uff1a<\/span><\/section>\n

Kenji Doya. Modulators of decision making. Nature neuroscience, 2008.<\/span><\/p>\n


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\u6574\u5408\u53d1\u653e\u6a21\u578b\u542f\u53d1\u7684CIF\u7b97\u6cd5\uff1a<\/span><\/section>\n

Linhao Dong, Bo Xu. CIF: Continuous integrate-and-fire for end-to-end speech recognition. ICASSP, 2020.<\/span><\/p>\n


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