
Chaolong Ying YingChinese University of Hong Kong | CUHK
Chaolong Ying Ying
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Publications (5)
Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut...
Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut...
Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing
large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut...
Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut...
Fuzzy cognitive maps (FCMs) are a powerful tool for simulating and analyzing complex systems. Many efficient methods based on evolutionary algorithms have been proposed to learn small-scale FCMs. However, large number of function evaluations of those methods make them difficult to cope with large-scale FCM learning problems. To overcome this issue,...