Zobrazeno 1 - 10
of 258
pro vyhledávání: '"Liang, X. San"'
It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non
Externí odkaz:
http://arxiv.org/abs/2402.13427
Autor:
Liang, X. San
The information flow-based quantitative causality analysis has been widely applied in different disciplines because of its origin from first principles, its concise form, and its computational efficiency. So far the algorithm for its estimation is ba
Externí odkaz:
http://arxiv.org/abs/2303.03113
Autor:
Liang, X. San
Inference of causal relations from data now has become an important field in artificial intelligence. During the past 16 years, causality analysis (in a quantitative sense) has been developed independently in physics from first principles. This short
Externí odkaz:
http://arxiv.org/abs/2112.14839
Autor:
Liang, X. San
Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a lar
Externí odkaz:
http://arxiv.org/abs/2112.14160
Autor:
Liang, X. San
Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as real physical notion so as to formulate it from
Externí odkaz:
http://arxiv.org/abs/2104.11360
The 2014-2015 "Monster"/"Super" El Ni\~no failed to be predicted one year earlier due to the growing importance of a new type of El Ni\~no, El Ni\~no Modoki, which reportedly has much lower forecast skill with the classical models. In this study, we
Externí odkaz:
http://arxiv.org/abs/2104.05540
Autor:
Liang, X. San
A quantitative evaluation of the contribution of individual units in producing the collective behavior of a complex network can allow us to understand the potential damage to the structure integrity due to the failure of local nodes. Given time serie
Externí odkaz:
http://arxiv.org/abs/2104.09290
Autor:
Liang, X. San, Yang, Xiuqun
Recently, it has been shown that the causality and information flow between two time series can be inferred in a rigorous and quantitative sense, and, besides, the resulting causality can be normalized. A corollary that follows is, in the linear limi
Externí odkaz:
http://arxiv.org/abs/2001.10823
Publikováno v:
In Deep-Sea Research Part I July 2023 197
Publikováno v:
In Deep-Sea Research Part II June 2023 209