基本信息
浏览量:100
职业迁徙
个人简介
Leveraging the power of logic reasoning in machine learning is my main focus right now. Popular machine learning techniques, such as Deep Neural Network and Statistical Learning, are good at mapping noisy sub-symbolic data (e.g. images) into symbols (e.g., labels, clusters, etc.); While symbolic machine learning techniques, such as Inductive Logic Programming and Statistical Relational Learning, are good at modelling complex (e.g., recursive) relationships in symbolic data. The two sub-areas in AI have been developed separately throughout the most of the history, resulting a huge gap between machine perception and reasoning. I am trying in various aspects to bridge the two islands, aiming at building ultra-strong machine learning systems that are human understandable, sample-efficient and applicaple to physical-world tasks.
研究兴趣
论文共 27 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Machine Learning (2024): 1-21
AAAI 2024no. 15 (2024): 16361-16369
AAAI 2024no. 14 (2024): 15310-15318
AAAI 2024no. 8 (2024): 8409-8416
Science China Information Sciencesno. 2 (2023): 1-13
CoRR (2023)
引用0浏览0EI引用
0
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn