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She has been particularly acclaimed for her groundbreaking work in computation beyond the Turing limit, and for achieving advanced learning capabilities through a new type of Artificial Intelligence: Lifelong Learning. Siegelmann conducts highly interdisciplinary research in next-generation machine learning, neural networks, intelligent machine-human collaboration, and computational studies of the brain - with application to AI, data science, and high-tech industry. Prof. Siegelmann is a co-inventor of the Support Vector Clustering (SVC) algorithm, which is widely used across industry and government. Among her recent Nature publications is Biological Underpinning of Lifelong Learning AI, a bio-inspired replay algorithm for advanced lifelong learning, dual fractal structure & function of the human brain, and identification of a previously unknown brain connectome mechanism, which enables cognitive abstraction.
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论文共 137 篇作者统计合作学者相似作者
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Andrea Soltoggio,Eseoghene Ben-Iwhiwhu,Vladimir Braverman,Eric Eaton, Benjamin Epstein,Yunhao Ge, Lucy Halperin,Jonathan How,Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le,
Nature Machine Intelligenceno. 3 (2024): 251-264
Giulia Pozzati, Jinrui Zhou,Hananel Hazan,Giannoula Lakka Klement,Hava T. Siegelmann, Edward A. Rietman,Jack A. Tuszynski
biorxiv(2024)
SSRN Electronic Journal (2023)
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Edward Andrew Mead,Yongping Wang, Sunali Patel, Austin P Thekkumthala, Rebecca Kepich, Elizabeth Benn-Hirsch, Victoria Lee, Azra Basaly,Susan Bergeson,Hava T Siegelmann,Andrzej Zbigniew Pietrzykowski
Advances in Drug and Alcohol Research (2023)
IPDPSpp.613-623, (2023)
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IEEE transactions on neural networks and learning systems (2023): 1-12
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