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Research
Machine learning is aimed at developing a computer that learns like humans. State-of-the-art machine learning technologies, which are based on statistical processing of big data by powerful computers, are highly successful in various real-world problems such as speech recognition, image understanding, and natural language translation. However, humans do not require big data or an enormous computational power to acquire intelligence and thus there is still a significant gap between machine learning and human learning. The goal of my research is to construct neuro-inspired machine learning paradigms that can fill the gap between artificial intelligence and human intelligence and establish a foundation of next-generation intelligent data processing technologies.
Machine learning is aimed at developing a computer that learns like humans. State-of-the-art machine learning technologies, which are based on statistical processing of big data by powerful computers, are highly successful in various real-world problems such as speech recognition, image understanding, and natural language translation. However, humans do not require big data or an enormous computational power to acquire intelligence and thus there is still a significant gap between machine learning and human learning. The goal of my research is to construct neuro-inspired machine learning paradigms that can fill the gap between artificial intelligence and human intelligence and establish a foundation of next-generation intelligent data processing technologies.
研究兴趣
论文共 939 篇作者统计合作学者相似作者
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CoRR (2024)
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arxiv(2024)
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Abudukelimu Wuerkaixi,Sen Cui,Jingfeng Zhang, Kunda Yan, Bo Han,Gang Niu, Lei Fang,Changshui Zhang,Masashi Sugiyama
ICLR 2024 (2024)
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AAAI 2024no. 13 (2024): 14414-14421
CoRR (2024)
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2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)pp.2768-2777, (2024)
CoRRno. 99 (2024): 1-12
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