KAN一作刘子鸣回国任教，清华官网盖章认证了

Core Viewpoint - The article discusses the emergence of KAN (Kolmogorov-Arnold Networks) as a significant advancement in neural network architecture, highlighting its advantages over traditional multi-layer perceptrons (MLPs) in terms of accuracy and interpretability [3][4]. Group 1: KAN Development and Impact - KAN's initial paper was published in April 2024 and quickly gained attention for outperforming MLPs, receiving 1.1k stars on GitHub within a few days [3][4]. - The architecture of KAN offers a new opportunity to improve deep learning models that heavily rely on MLPs, positioning it as a strong alternative [4]. - KAN's design allows for the observation of variable interaction paths, providing interpretability and interactivity that MLPs lack [13]. Group 2: Research Background of Liu Ziming - Liu Ziming, the lead author of KAN, is set to join Tsinghua University as an assistant professor in September 2024, with his first batch of PhD students already recruited [1][7]. - Liu has a strong academic background, having been a top physics student and later pursuing a PhD at MIT under Max Tegmark, focusing on the intersection of physics and machine learning [9][19]. - The inspiration for KAN stems from the Kolmogorov-Arnold theorem, which suggests that complex multi-dimensional functions can be represented as a combination of simpler functions [10][11]. Group 3: Research Philosophy and Future Directions - Liu's research philosophy emphasizes curiosity-driven and impact-driven approaches, aiming for both scientific insight and long-term influence [18]. - He advocates for a combination of theoretical and experimental research, focusing on high-quality abstractions that can be applied across various scientific fields [18]. - Liu maintains a blog titled "physics of AI," where he explores AI phenomena through the lens of physics, aiming to uncover insights that could significantly impact the field [20][24].